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arxiv:2010.10054 | paper | corpus_metadata | null | Teacher-Student Consistency For Multi-Source Domain Adaptation | In Multi-Source Domain Adaptation (MSDA), models are trained on samples from multiple source domains and used for inference on a different, target, domain. Mainstream domain adaptation approaches learn a joint representation of source and target domains. Unfortunately, a joint representation may emphasize features that... | https://arxiv.org/abs/2010.10054 | 2020-10-20T06:17:40Z | 2010.10054 | 10.48550/arxiv.2010.10054 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2010.10054 | a6ae8fdea5117fc3659cfe736eb7a49767cd7f06c5774c3dfdfe1cd13a37640f | 0.735674 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.05163 | paper | corpus_metadata | null | ConfNet2Seq: Full Length Answer Generation from Spoken Questions | Conversational and task-oriented dialogue systems aim to interact with the user using natural responses through multi-modal interfaces, such as text or speech. These desired responses are in the form of full-length natural answers generated over facts retrieved from a knowledge source. While the task of generating natu... | https://arxiv.org/abs/2006.05163 | 2020-06-09T10:04:49Z | 2006.05163 | 10.1007/978-3-030-58323-1_56 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/978-3-030-58323-1_56 | 41a9243ee1f714e064ecbdb26004e58760834f44431ad55546a4f104b1ebeec0 | 0.7345 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.07155 | paper | corpus_metadata | null | MMDetection: Open MMLab Detection Toolbox and Benchmark | We present MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules. The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. It gradually evolves into a unified platform... | https://arxiv.org/abs/1906.07155 | 2019-06-17T17:58:12Z | 1906.07155 | 10.48550/arxiv.1906.07155 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.07155 | 26441403b3d34e31241a63b5f0d2c3a4fa06b3b0f3c8821959d69797bffd1129 | 0.742576 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1505.00353 | paper | corpus_metadata | null | Joint Multi-Leaf Segmentation, Alignment and Tracking from Fluorescence Plant Videos | This paper proposes a novel framework for fluorescence plant video processing. The plant research community is interested in the leaf-level photosynthetic analysis within a plant. A prerequisite for such analysis is to segment all leaves, estimate their structures, and track them over time. We identify this as a joint ... | https://arxiv.org/abs/1505.00353 | 2015-05-02T16:37:57Z | 1505.00353 | 10.48550/arxiv.1505.00353 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1505.00353 | 3654441d0051732f09e2799fa1df21b813735597ee15db50a12fbe05ac15a862 | 0.576958 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.08834 | paper | corpus_metadata | null | Meta-Reinforcement Learning by Tracking Task Non-stationarity | Many real-world domains are subject to a structured non-stationarity which affects the agent's goals and the environmental dynamics. Meta-reinforcement learning (RL) has been shown successful for training agents that quickly adapt to related tasks. However, most of the existing meta-RL algorithms for non-stationary dom... | https://arxiv.org/abs/2105.08834 | 2021-05-18T21:19:41Z | 2105.08834 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2105.08834 | 7ed2ab02a9c1893dd8ce5bdadbd782735e02a0cfd7741b2afa1d5908982852c1 | 0.713093 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.03045 | paper | corpus_metadata | null | Rotate to Attend: Convolutional Triplet Attention Module | Benefiting from the capability of building inter-dependencies among channels or spatial locations, attention mechanisms have been extensively studied and broadly used in a variety of computer vision tasks recently. In this paper, we investigate light-weight but effective attention mechanisms and present triplet attenti... | https://arxiv.org/abs/2010.03045 | 2020-10-06T21:31:00Z | 2010.03045 | 10.48550/arxiv.2010.03045 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2010.03045 | 0440440d9395653e086b8dad964ff2b732e1f5ce79a490d70a2d266d396095aa | 0.704193 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1805.08403 | paper | corpus_metadata | null | Autofocus Layer for Semantic Segmentation | We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus layers adaptively change the size of the effective receptive field based on the processed context to generate more powerful features. This is a... | https://arxiv.org/abs/1805.08403 | 2018-05-22T05:35:20Z | 1805.08403 | 10.48550/arxiv.1805.08403 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1805.08403 | 0ab25f5f7f9fe8b04243151204e9f90096984cb62419d47fcdee6a4e5691649c | 0.747042 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.01747 | paper | corpus_metadata | null | Bengali Abstractive News Summarization(BANS): A Neural Attention Approach | Abstractive summarization is the process of generating novel sentences based on the information extracted from the original text document while retaining the context. Due to abstractive summarization's underlying complexities, most of the past research work has been done on the extractive summarization approach. Nevert... | https://arxiv.org/abs/2012.01747 | 2020-12-03T08:17:31Z | 2012.01747 | 10.1007/978-981-33-4673-4_4 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/978-981-33-4673-4_4 | 08602fcd76299931c00e54abe4cca708c0918950f5a32501b9402ee40d6fdaed | 0.704918 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.10680 | paper | corpus_metadata | null | Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning | This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis. Medical imaging--focusing on particular parts of the body for defined clinical purposes--generates images of great similarity in anatomy across patients ... | https://arxiv.org/abs/2102.10680 | 2021-02-21T20:44:55Z | 2102.10680 | 10.48550/arxiv.2102.10680 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2102.10680 | 3c270e8052053472549e93c031d28a664611f1bac9418c5d353320696293590f | 0.757804 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1705.06848 | paper | corpus_metadata | null | SPRINT: Ultrafast protein-protein interaction prediction of the entire human interactome | Proteins perform their functions usually by interacting with other proteins. Predicting which proteins interact is a fundamental problem. Experimental methods are slow, expensive, and have a high rate of error. Many computational methods have been proposed among which sequence-based ones are very promising. However, so... | https://arxiv.org/abs/1705.06848 | 2017-05-19T02:08:58Z | 1705.06848 | 10.48550/arxiv.1705.06848 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1705.06848 | 16a2d44c3845c2c20621b188268113c3a8c850386192d0f3acb07a5182dbcabd | 0.688243 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2009.03115 | paper | corpus_metadata | null | Githru: Visual Analytics for Understanding Software Development History Through Git Metadata Analysis | Git metadata contains rich information for developers to understand the overall context of a large software development project. Thus it can help new developers, managers, and testers understand the history of development without needing to dig into a large pile of unfamiliar source code. However, the current tools for... | https://arxiv.org/abs/2009.03115 | 2020-09-07T14:06:59Z | 2009.03115 | 10.1109/tvcg.2020.3030414 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tvcg.2020.3030414 | 9e78f08475a32a19d98678916ebea171af3c143ab9a5f1ec5fc81d9689954b7a | 0.514935 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1701.03304 | paper | corpus_metadata | null | Client-Based Control Channel Analysis for Connectivity Estimation in LTE Networks | Advanced Cyber-Physical Systems aim for the balancing of restricted local resources of deeply embedded systems with cloud-based resources depending on the availability of network connectivity: in case of excellent connectivity, the offloading of large amounts of data can be more efficient than the local processing on a... | https://arxiv.org/abs/1701.03304 | 2017-01-12T11:09:00Z | 1701.03304 | 10.1109/vtcfall.2016.7880932 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/vtcfall.2016.7880932 | 654666d54b88990d58698497a21da09839d32649875c5a143fe7acfd9615c42c | 0.553173 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1711.06820 | paper | corpus_metadata | null | Discover Your Competition in LTE: Client-Based Passive Data Rate Prediction by Machine Learning | To receive the highest possible data rate or/and the most reliable connection, the User Equipment (UE) may want to choose between different networks. However, current LTE and LTE-Advanced mobile networks do not supply the UE with an explicit indicator about the currently achievable data rate. For this reason, the mobil... | https://arxiv.org/abs/1711.06820 | 2017-11-18T06:45:04Z | 1711.06820 | 10.1109/glocom.2017.8254567 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/glocom.2017.8254567 | 45993273563d6e53f3fb44a964c257e4202bd4a4e55a7bd0a70dc60b67f6f621 | 0.55813 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.00733 | paper | corpus_metadata | null | Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN | Text-to-speech (TTS) acoustic models map linguistic features into an acoustic representation out of which an audible waveform is generated. The latest and most natural TTS systems build a direct mapping between linguistic and waveform domains, like SampleRNN. This way, possible signal naturalness losses are avoided as ... | https://arxiv.org/abs/1906.00733 | 2019-06-03T12:15:56Z | 1906.00733 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:1906.00733 | 47fd3715b30d7748a25838ec8ed99cea7539db1ea83d8777d44bfa1490ad9080 | 0.638985 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.06728 | paper | corpus_metadata | null | Implementing perceptron models with qubits | We propose a method for learning a quantum probabilistic model of a perceptron. By considering a cross entropy between two density matrices we can learn a model that takes noisy output labels into account while learning. A multitude of proposals already exist that aim to utilize the curious properties of quantum system... | https://arxiv.org/abs/1905.06728 | 2019-05-16T13:21:25Z | 1905.06728 | 10.1103/physreva.100.020301 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1103/physreva.100.020301 | d6057b719c8473bcd17acc9a5a6ab65ba9f7a2cda76db591c8f22ed14266a501 | 0.551731 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.02024 | paper | corpus_metadata | null | Aerial Imagery Pixel-level Segmentation | Aerial imagery can be used for important work on a global scale. Nevertheless, the analysis of this data using neural network architectures lags behind the current state-of-the-art on popular datasets such as PASCAL VOC, CityScapes and Camvid. In this paper we bridge the performance-gap between these popular datasets a... | https://arxiv.org/abs/2012.02024 | 2020-12-03T16:09:09Z | 2012.02024 | 10.48550/arxiv.2012.02024 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2012.02024 | 986d5cd0ebdac51b59c266f2035a735596ba84a711020ccca80c93e96c013681 | 0.806047 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.08411 | paper | corpus_metadata | null | Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples | Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive eno... | https://arxiv.org/abs/1905.08411 | 2019-05-21T02:54:06Z | 1905.08411 | 10.48550/arxiv.1905.08411 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.08411 | 88b76e5cc46f63f75d915d97746159a1013c296bb0b7130da3218eec010920d1 | 0.584188 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1712.07804 | paper | corpus_metadata | null | ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming | Recent empirical studies show that the performance of GenProg is not satisfactory, particularly for Java. In this paper, we propose ARJA, a new GP based repair approach for automated repair of Java programs. To be specific, we present a novel lower-granularity patch representation that properly decouples the search sub... | https://arxiv.org/abs/1712.07804 | 2017-12-21T06:06:07Z | 1712.07804 | 10.48550/arxiv.1712.07804 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1712.07804 | 1a11f225d96a6e8a69fc44b6ba62f6ddc2415f0d5f76bceb755b41fe0a57229e | 0.657299 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1706.07136 | paper | corpus_metadata | null | Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes | Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and p... | https://arxiv.org/abs/1706.07136 | 2017-06-21T23:02:11Z | 1706.07136 | 10.3390/e19080408 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.3390/e19080408 | 6c430022511599b2642e0e5cd79c5420679ca76592a50c15185ca7b8a8595973 | 0.521048 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.07503 | paper | corpus_metadata | null | Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition | Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequen... | https://arxiv.org/abs/1811.07503 | 2018-11-19T05:10:14Z | 1811.07503 | 10.48550/arxiv.1811.07503 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1811.07503 | 43953be6ff391efb3c620f8e05543808cae5908293cb90cd50181301593fcc1d | 0.802237 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.11582 | paper | corpus_metadata | null | EncryptGAN: Image Steganography with Domain Transform | We propose an image steganographic algorithm called EncryptGAN, which disguises private image communication in an open communication channel. The insight is that content transform between two very different domains (e.g., face to flower) allows one to hide image messages in one domain (face) and communicate using its c... | https://arxiv.org/abs/1905.11582 | 2019-05-28T02:57:55Z | 1905.11582 | 10.48550/arxiv.1905.11582 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.11582 | 1dd5a41a4a0d2be538b18f538137757985d8b57e27c7801d2c87d15cda5ed566 | 0.609392 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1709.02341 | paper | corpus_metadata | null | Deep Active Inference | This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference in deep generative models, and evolution strategies to introduce the "deep active inference" agent. This agent minimises a variational free energy bound on the... | https://arxiv.org/abs/1709.02341 | 2017-09-07T16:36:52Z | 1709.02341 | 10.1007/s00422-018-0785-7 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/s00422-018-0785-7 | bb1337ea341e9ce4b0c9f06610fce86852cc8076a4449ed92cb9bcb7c067021a | 0.638456 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.12892 | paper | corpus_metadata | null | Model selection for estimation of causal parameters | A popular technique for selecting and tuning machine learning estimators is cross-validation. Cross-validation evaluates overall model fit, usually in terms of predictive accuracy. In causal inference, the optimal choice of estimator depends not only on the fitted models but also on assumptions the statistician is will... | https://arxiv.org/abs/2008.12892 | 2020-08-29T02:08:11Z | 2008.12892 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2008.12892 | 5f223be4bcd36f02c6f8bc4b3f6c93638aa1e5478b3446385a0b1127f8e4bf2f | 0.606599 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.05953 | paper | corpus_metadata | null | On Bayesian inference for the Extended Plackett-Luce model | The analysis of rank ordered data has a long history in the statistical literature across a diverse range of applications. In this paper we consider the Extended Plackett-Luce model that induces a flexible (discrete) distribution over permutations. The parameter space of this distribution is a combination of potentiall... | https://arxiv.org/abs/2002.05953 | 2020-02-14T10:19:20Z | 2002.05953 | 10.1214/21-ba1258 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1214/21-ba1258 | 7a077ff40e7912a7de8e2d4bab2855a265de1b2b890443c843c393b3c8db5ab7 | 0.543695 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1806.05507 | paper | corpus_metadata | null | Cold-Start Aware User and Product Attention for Sentiment Classification | The use of user/product information in sentiment analysis is important, especially for cold-start users/products, whose number of reviews are very limited. However, current models do not deal with the cold-start problem which is typical in review websites. In this paper, we present Hybrid Contextualized Sentiment Class... | https://arxiv.org/abs/1806.05507 | 2018-06-14T12:48:31Z | 1806.05507 | 10.48550/arxiv.1806.05507 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1806.05507 | 91ecce121248e79e4f88d36ae425076b9aa43f24df1701284cbc41f0e9ca94c6 | 0.623052 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.02458 | paper | corpus_metadata | null | A data-set of piercing needle through deformable objects for Deep Learning from Demonstrations | Many robotic tasks are still teleoperated since automating them is very time consuming and expensive. Robot Learning from Demonstrations (RLfD) can reduce programming time and cost. However, conventional RLfD approaches are not directly applicable to many robotic tasks, e.g. robotic suturing with minimally invasive rob... | https://arxiv.org/abs/2012.02458 | 2020-12-04T08:27:06Z | 2012.02458 | 10.48550/arxiv.2012.02458 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2012.02458 | cb251885387fda4d93b51984c8d65fa81308d1ba5937f6c4b4ed701e47b5390e | 0.760146 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2009.09094 | paper | corpus_metadata | null | FlexWatts: A Power- and Workload-Aware Hybrid Power Delivery Network for Energy-Efficient Microprocessors | Modern client processors typically use one of three commonly-used power delivery network (PDN): 1) motherboard voltage regulators (MBVR), 2) integrated voltage regulators (IVR), and 3) low dropout voltage regulators (LDO). We observe that the energy-efficiency of each of these PDNs varies with the processor power (e.g.... | https://arxiv.org/abs/2009.09094 | 2020-09-18T21:33:36Z | 2009.09094 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2009.09094 | 382bc30516bd0157d5d7fec90fab06efadff3053b6bf85c5b818ddaf70a62049 | 0.514353 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.03357 | paper | corpus_metadata | null | Rethinking FUN: Frequency-Domain Utilization Networks | The search for efficient neural network architectures has gained much focus in recent years, where modern architectures focus not only on accuracy but also on inference time and model size. Here, we present FUN, a family of novel Frequency-domain Utilization Networks. These networks utilize the inherent efficiency of t... | https://arxiv.org/abs/2012.03357 | 2020-12-06T19:16:37Z | 2012.03357 | 10.48550/arxiv.2012.03357 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2012.03357 | 6047745136e21967f3d23b85309682e989fcff6beb9c0aff12e55eb5ec1526c3 | 0.73065 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.01519 | paper | corpus_metadata | null | SelfPose: 3D Egocentric Pose Estimation from a Headset Mounted Camera | We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device. This unusual viewpoint leads to images with unique visual appearance, with severe self-occlusions and perspective distortions that result in... | https://arxiv.org/abs/2011.01519 | 2020-11-02T16:18:06Z | 2011.01519 | 10.1109/tpami.2020.3029700 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.1109/tpami.2020.3029700 | 5e0505d4690b4baa0a92893f1b30328431b4781ff707824940246efd087a1556 | 0.776677 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.12307 | paper | corpus_metadata | null | Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments | Recent reinforcement learning studies extensively explore the interplay between cooperative and competitive behaviour in mixed environments. Unlike cooperative environments where agents strive towards a common goal, mixed environments are notorious for the conflicts of selfish and social interests. As a consequence, pu... | https://arxiv.org/abs/2102.12307 | 2021-02-24T14:35:32Z | 2102.12307 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2102.12307 | 575001315caacd0ede357784d5cb56edbd2bb1ed1193d4abe26a3f6dd6a9b05b | 0.615053 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.04110 | paper | corpus_metadata | null | Reducing Network Agnostophobia | Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real-world applications. Unfortunately, network behavior is not well defined for inputs far from a networks training set. In an uncontrolled environment, networks face many instances that are not of in... | https://arxiv.org/abs/1811.04110 | 2018-11-09T19:29:58Z | 1811.04110 | 10.48550/arxiv.1811.04110 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1811.04110 | 414d9caa2e748c0fdb0f995e8d72a6005479c785247d09648b1a014a1445bc41 | 0.666235 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.05321 | paper | corpus_metadata | null | Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters | Deep Learning (DL) algorithms have gained popularity owing to their practical problem-solving capacity. However, they suffer from a serious integrity threat, i.e., their vulnerability to adversarial attacks. In the quest for DL trustworthiness, recent works claimed the inherent robustness of Spiking Neural Networks (SN... | https://arxiv.org/abs/2012.05321 | 2020-12-09T21:09:03Z | 2012.05321 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2012.05321 | 92822988798f8688f4690764da5de4d3a52d905be09edc6cac803dcb5348712c | 0.622676 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.14866 | paper | corpus_metadata | null | Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits | We introduce the "inverse bandit" problem of estimating the rewards of a multi-armed bandit instance from observing the learning process of a low-regret demonstrator. Existing approaches to the related problem of inverse reinforcement learning assume the execution of an optimal policy, and thereby suffer from an identi... | https://arxiv.org/abs/2106.14866 | 2021-06-28T17:37:49Z | 2106.14866 | 10.48550/arxiv.2106.14866 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2106.14866 | 5286af17ec904796302f147110c9fa7a0d71c1272f12b5d4538d6e1b7d603a06 | 0.580897 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.04668 | paper | corpus_metadata | null | MALI: A memory efficient and reverse accurate integrator for Neural ODEs | Neural ordinary differential equations (Neural ODEs) are a new family of deep-learning models with continuous depth. However, the numerical estimation of the gradient in the continuous case is not well solved: existing implementations of the adjoint method suffer from inaccuracy in reverse-time trajectory, while the na... | https://arxiv.org/abs/2102.04668 | 2021-02-09T06:33:47Z | 2102.04668 | 10.48550/arxiv.2102.04668 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2102.04668 | a4434fe07a7fc619cfed88cf3cfa0f3742a60fe70ea17081420fe1ecdb1992b0 | 0.786419 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.03691 | paper | corpus_metadata | null | Direct design of biquad filter cascades with deep learning by sampling random polynomials | Designing infinite impulse response filters to match an arbitrary magnitude response requires specialized techniques. Methods like modified Yule-Walker are relatively efficient, but may not be sufficiently accurate in matching high order responses. On the other hand, iterative optimization techniques often enable super... | https://arxiv.org/abs/2110.03691 | 2021-10-07T17:58:08Z | 2110.03691 | 10.48550/arxiv.2110.03691 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2110.03691 | fe3eff16ee72a8eaf17c71076b54825c4674d3ef2034667d013762595c56aa99 | 0.700884 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.12229 | paper | corpus_metadata | null | Woodbury Transformations for Deep Generative Flows | Normalizing flows are deep generative models that allow efficient likelihood calculation and sampling. The core requirement for this advantage is that they are constructed using functions that can be efficiently inverted and for which the determinant of the function's Jacobian can be efficiently computed. Researchers h... | https://arxiv.org/abs/2002.12229 | 2020-02-27T16:21:43Z | 2002.12229 | 10.48550/arxiv.2002.12229 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2002.12229 | 62653dbcdd318286f977f9891c8b5f70055cb0487e65b68f8e55acea867eb814 | 0.674587 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.12415 | paper | corpus_metadata | null | Using Inverse Optimization to Learn Cost Functions in Generalized Nash Games | As demonstrated by Ratliff et al. (2014), inverse optimization can be used to recover the objective function parameters of players in multi-player Nash games. These games involve the optimization problems of multiple players in which the players can affect each other in their objective functions. In generalized Nash eq... | https://arxiv.org/abs/2102.12415 | 2021-02-24T17:13:27Z | 2102.12415 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2102.12415 | b07bfaaa36624991bf7392fd89f8e00149914342d6c6faab6feb149f5e4399ba | 0.521161 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.06108 | paper | corpus_metadata | null | SWAGAN: A Style-based Wavelet-driven Generative Model | In recent years, considerable progress has been made in the visual quality of Generative Adversarial Networks (GANs). Even so, these networks still suffer from degradation in quality for high-frequency content, stemming from a spectrally biased architecture, and similarly unfavorable loss functions. To address this iss... | https://arxiv.org/abs/2102.06108 | 2021-02-11T16:43:10Z | 2102.06108 | 10.48550/arxiv.2102.06108 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2102.06108 | 4edaaf17a77e09ac1a2c13198bf8edb7901ecabb56fd5aa1149d21275ea6b2f2 | 0.732546 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.09609 | paper | corpus_metadata | null | Draw your Neural Networks | Deep Neural Networks are the basic building blocks of modern Artificial Intelligence. They are increasingly replacing or augmenting existing software systems due to their ability to learn directly from the data and superior accuracy on variety of tasks. Existing Software Development Life Cycle (SDLC) methodologies fall... | https://arxiv.org/abs/2012.09609 | 2020-12-12T09:44:03Z | 2012.09609 | 10.48550/arxiv.2012.09609 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | doi:10.48550/arxiv.2012.09609 | aac4236e70ed98c4f7157d5182ccd5d3aa202909b08826028b062542469a72a0 | 0.688333 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.02524 | paper | corpus_metadata | null | Impact of non-normal error distributions on the benchmarking and ranking of Quantum Machine Learning models | Quantum machine learning models have been gaining significant traction within atomistic simulation communities. Conventionally, relative model performances are being assessed and compared using learning curves (prediction error vs. training set size). This article illustrates the limitations of using the Mean Absolute ... | https://arxiv.org/abs/2004.02524 | 2020-04-06T09:55:09Z | 2004.02524 | 10.1088/2632-2153/aba184 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1088/2632-2153/aba184 | e42688d66f2e6ce41fa8bee1a8a12d83fe413dca983fb9682a1df960ff2084c2 | 0.58485 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1912.04958 | paper | corpus_metadata | StyleGAN2 | Analyzing and Improving the Image Quality of StyleGAN | The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator... | https://arxiv.org/abs/1912.04958 | 2019-12-03T11:44:01Z | 1912.04958 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1912.04958 | 7e9b34cba566eacc2a3b9771fbeba57123d1fc0f3fdab2a0fb30120cd294b654 | 0.734072 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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"epoch-all-ai-models:row:2786"
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arxiv:1909.03500 | paper | corpus_metadata | null | Self-paced Ensemble for Highly Imbalanced Massive Data Classification | Many real-world applications reveal difficulties in learning classifiers from imbalanced data. The rising big data era has been witnessing more classification tasks with large-scale but extremely imbalance and low-quality datasets. Most of existing learning methods suffer from poor performance or low computation effici... | https://arxiv.org/abs/1909.03500 | 2019-09-08T16:32:47Z | 1909.03500 | 10.1109/icde48307.2020.00078 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/icde48307.2020.00078 | e4201ec70d1ffff6241bf2b7b9f9935fe50efb6f72d7c06a497c5eb98a26bc37 | 0.767204 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2111.00064 | paper | corpus_metadata | null | Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction | Learning on graphs has attracted significant attention in the learning community due to numerous real-world applications. In particular, graph neural networks (GNNs), which take numerical node features and graph structure as inputs, have been shown to achieve state-of-the-art performance on various graph-related learni... | https://arxiv.org/abs/2111.00064 | 2021-10-29T19:55:12Z | 2111.00064 | 10.48550/arxiv.2111.00064 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2111.00064 | cdf58c36393ade268d17804225c3dea86171f77b91fd995e3ad07230b8cc63f9 | 0.815537 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.08526 | paper | corpus_metadata | null | The Weighted Kendall and High-order Kernels for Permutations | We propose new positive definite kernels for permutations. First we introduce a weighted version of the Kendall kernel, which allows to weight unequally the contributions of different item pairs in the permutations depending on their ranks. Like the Kendall kernel, we show that the weighted version is invariant to rela... | https://arxiv.org/abs/1802.08526 | 2018-02-23T13:50:08Z | 1802.08526 | 10.48550/arxiv.1802.08526 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1802.08526 | a36c654bdf5fb7328b8ec4da900cc69e262acaed02d87255fe5f332cbcce0f63 | 0.558939 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2404.05242 | paper | corpus_metadata | null | Collision-Free Trajectory Optimization in Cluttered Environments Using Sums-of-Squares Programming | In this work, we propose a trajectory optimization approach for robot navigation in cluttered 3D environments. We represent the robot's geometry as a semialgebraic set defined by polynomial inequalities such that robots with general shapes can be suitably characterized. To address the robot navigation task in obstacle-... | https://arxiv.org/abs/2404.05242 | 2024-04-08T07:11:43Z | 2404.05242 | 10.48550/arxiv.2404.05242 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2404.05242 | 60bd913b6b02a9f44d1cc72e4f083f7b6b439a3362719c3bea720527a1762de0 | 0.610594 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1805.09512 | paper | corpus_metadata | null | You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery | Detection of small objects in large swaths of imagery is one of the primary problems in satellite imagery analytics. While object detection in ground-based imagery has benefited from research into new deep learning approaches, transitioning such technology to overhead imagery is nontrivial. Among the challenges is the ... | https://arxiv.org/abs/1805.09512 | 2018-05-24T05:17:51Z | 1805.09512 | 10.48550/arxiv.1805.09512 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1805.09512 | e0829d8488317dc5489f4a74887445830ed1100c8acf6a043ac189032185a046 | 0.659256 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2307.10195 | paper | corpus_metadata | null | ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown | The disruptive application of ChatGPT (GPT-3.5, GPT-4) to a variety of domains has become a topic of much discussion in the scientific community and society at large. Large Language Models (LLMs), e.g., BERT, Bard, Generative Pre-trained Transformers (GPTs), LLaMA, etc., have the ability to take instructions, or prompt... | https://arxiv.org/abs/2307.10195 | 2023-07-10T20:07:30Z | 2307.10195 | 10.48550/arxiv.2307.10195 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2307.10195 | d0a6bf6bef0d4464e2a79793a298b3f79064b76927922784109b2793920bd515 | 0.575708 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.07499 | paper | corpus_metadata | null | Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks | The task of Language-Based Image Editing (LBIE) aims at generating a target image by editing the source image based on the given language description. The main challenge of LBIE is to disentangle the semantics in image and text and then combine them to generate realistic images. Therefore, the editing performance is he... | https://arxiv.org/abs/1903.07499 | 2019-03-18T15:13:55Z | 1903.07499 | 10.1109/icassp.2019.8683008 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/icassp.2019.8683008 | d5aadfbe2c7ef8f89236def701b083d6d5f9b7b45912f44d820c4ee25a7b57b1 | 0.780756 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.10185 | paper | corpus_metadata | null | ConvTransformer: A Convolutional Transformer Network for Video Frame Synthesis | Deep Convolutional Neural Networks (CNNs) are powerful models that have achieved excellent performance on difficult computer vision tasks. Although CNNs perform well whenever large labeled training samples are available, they work badly on video frame synthesis due to objects deforming and moving, scene lighting change... | https://arxiv.org/abs/2011.10185 | 2020-11-20T02:52:53Z | 2011.10185 | 10.48550/arxiv.2011.10185 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2011.10185 | 8cb30806f1df3e198eb17b55e4c1cb80734b7de80acc06780519665a92f44d32 | 0.80192 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.07901 | paper | corpus_metadata | null | Decoupling Semantic Context and Color Correlation with multi-class cross branch regularization | This paper presents a novel design methodology for architecting a light-weight and faster DNN architecture for vision applications. The effectiveness of the architecture is demonstrated on Color-Constancy use case an inherent block in camera and imaging pipelines. Specifically, we present a multi-branch architecture th... | https://arxiv.org/abs/1810.07901 | 2018-10-18T05:25:13Z | 1810.07901 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1810.07901 | 30988f1543e9048f9b8c40fae44d9e2e06f5ecd4c56f7c2897b51a82e572ee4f | 0.675324 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.03677 | paper | corpus_metadata | null | Visual Transformers: Token-based Image Representation and Processing for Computer Vision | Computer vision has achieved remarkable success by (a) representing images as uniformly-arranged pixel arrays and (b) convolving highly-localized features. However, convolutions treat all image pixels equally regardless of importance; explicitly model all concepts across all images, regardless of content; and struggle ... | https://arxiv.org/abs/2006.03677 | 2020-06-05T20:49:49Z | 2006.03677 | 10.48550/arxiv.2006.03677 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2006.03677 | fd915d0a6fac886b563c9a8235d3668e567b7e2c652bc62450c4b781e2c65894 | 0.73549 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.10582 | paper | corpus_metadata | null | Learning by Fixing: Solving Math Word Problems with Weak Supervision | Previous neural solvers of math word problems (MWPs) are learned with full supervision and fail to generate diverse solutions. In this paper, we address this issue by introducing a \textit{weakly-supervised} paradigm for learning MWPs. Our method only requires the annotations of the final answers and can generate vario... | https://arxiv.org/abs/2012.10582 | 2020-12-19T03:10:21Z | 2012.10582 | 10.48550/arxiv.2012.10582 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2012.10582 | b1512e47f5d3ce26fe0487134fccbd9295d029f1d591ffbd36e906efa3b76bc2 | 0.80018 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.07751 | paper | corpus_metadata | null | Deep Co-Attention Network for Multi-View Subspace Learning | Many real-world applications involve data from multiple modalities and thus exhibit the view heterogeneity. For example, user modeling on social media might leverage both the topology of the underlying social network and the content of the users' posts; in the medical domain, multiple views could be X-ray images taken ... | https://arxiv.org/abs/2102.07751 | 2021-02-15T18:46:44Z | 2102.07751 | 10.48550/arxiv.2102.07751 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2102.07751 | 9e002a669d0508bbdcfec45fddd66dcb9eb424dd7dbd58f912ffd40ea1e8ba24 | 0.776785 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.12627 | paper | corpus_metadata | null | Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing | We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing. BRIDGE represents the question and DB schema in a tagged sequence where a subset of the fields are augmented with cell values mentioned in the questio... | https://arxiv.org/abs/2012.12627 | 2020-12-23T12:33:52Z | 2012.12627 | 10.48550/arxiv.2012.12627 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2012.12627 | 9f74bab18633e297ff37aba9d2334b9882846faee6439853ffa789509e47781c | 0.761256 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.13809 | paper | corpus_metadata | null | AutoProf -- I. An automated non-parametric light profile pipeline for modern galaxy surveys | We present an automated non-parametric light profile extraction pipeline called AutoProf. All steps for extracting surface brightness (SB) profiles are included in AutoProf, allowing streamlined analyses of galaxy images. AutoProf improves upon previous non-parametric ellipse fitting implementations with fit-stabilizat... | https://arxiv.org/abs/2106.13809 | 2021-06-25T18:00:00Z | 2106.13809 | 10.1093/mnras/stab2709 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1093/mnras/stab2709 | 41c2ccbd4368cdcdc4bec6cf09aaacdc3bb0e7ff342d0e85bae8c2219a10f82a | 0.508347 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.02502 | paper | corpus_metadata | null | 3D Surface Reconstruction From Multi-Date Satellite Images | The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point c... | https://arxiv.org/abs/2102.02502 | 2021-02-04T09:23:21Z | 2102.02502 | 10.5194/isprs-archives-xliii-b2-2021-313-2021 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.5194/isprs-archives-xliii-b2-2021-313-2021 | 2368f38119c8a9ea4a3a47c14fa3913d41bd255da9fd2319b9074edf1e0d26a3 | 0.709984 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2009.11166 | paper | corpus_metadata | null | Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer | Foreseeing the brain evolution as a complex highly inter-connected system, widely modeled as a graph, is crucial for mapping dynamic interactions between different anatomical regions of interest (ROIs) in health and disease. Interestingly, brain graph evolution models remain almost absent in the literature. Here we des... | https://arxiv.org/abs/2009.11166 | 2020-09-23T14:25:40Z | 2009.11166 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2009.11166 | 2eabdfe3bb1d40f95ddf1819a562aabd03675857aa7998ce4050a98cf7370765 | 0.681907 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.03133 | paper | corpus_metadata | null | DeFINE: Delayed Feedback based Immersive Navigation Environment for Studying Goal-Directed Human Navigation | With the advent of consumer-grade products for presenting an immersive virtual environment (VE), there is a growing interest in utilizing VEs for testing human navigation behavior. However, preparing a VE still requires a high level of technical expertise in computer graphics and virtual reality, posing a significant h... | https://arxiv.org/abs/2003.03133 | 2020-03-06T11:00:12Z | 2003.03133 | 10.3758/s13428-021-01586-6 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.3758/s13428-021-01586-6 | 1785b52f9937a4119e48ee9f5e2356a724a3e410fe57c33c4585cf79390e1659 | 0.509036 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.00298 | paper | corpus_metadata | EfficientNetV2-XL | EfficientNetV2: Smaller Models and Faster Training | This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop this family of models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and param... | https://arxiv.org/abs/2104.00298 | 2021-04-01T07:08:36Z | 2104.00298 | 10.48550/arxiv.2104.00298 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2104.00298 | 4d4ce0a246bf3dc73ae90c052a681ad0ef1578eb651130b092df23fec88a5298 | 0.867286 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
] | [
"epoch-all-ai-models:row:2548"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2005.01996 | paper | corpus_metadata | null | NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results | This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided... | https://arxiv.org/abs/2005.01996 | 2020-05-05T08:17:04Z | 2005.01996 | 10.48550/arxiv.2005.01996 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2005.01996 | d0820a07b0ffed399896bc89f35bd8f924ccbe49c83cac446fb9069c69757b66 | 0.723662 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.00089 | paper | corpus_metadata | null | Tensor-Train Density Estimation | Estimation of probability density function from samples is one of the central problems in statistics and machine learning. Modern neural network-based models can learn high dimensional distributions but have problems with hyperparameter selection and are often prone to instabilities during training and inference. We pr... | https://arxiv.org/abs/2108.00089 | 2021-07-30T21:51:12Z | 2108.00089 | 10.48550/arxiv.2108.00089 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2108.00089 | 772efa97ff21c985d3589431daa2b11d831cd5e5178063aa0cd54ee4212acc26 | 0.713845 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2005.13771 | paper | corpus_metadata | null | Sparse SVM for Sufficient Data Reduction | Kernel-based methods for support vector machines (SVM) have shown highly advantageous performance in various applications. However, they may incur prohibitive computational costs for large-scale sample datasets. Therefore, data reduction (reducing the number of support vectors) appears to be necessary, which gives rise... | https://arxiv.org/abs/2005.13771 | 2020-05-28T04:06:16Z | 2005.13771 | 10.1109/tpami.2021.3075339 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tpami.2021.3075339 | 3bed42d34c32b2429412e534bf200a7a778414db734cd4287489d7e33312dd46 | 0.509148 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.00708 | paper | corpus_metadata | null | Group Fisher Pruning for Practical Network Compression | Network compression has been widely studied since it is able to reduce the memory and computation cost during inference. However, previous methods seldom deal with complicated structures like residual connections, group/depth-wise convolution and feature pyramid network, where channels of multiple layers are coupled an... | https://arxiv.org/abs/2108.00708 | 2021-08-02T08:21:44Z | 2108.00708 | 10.48550/arxiv.2108.00708 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2108.00708 | f183d53eed718db4bc41169b9a785a5358169dca11b633a460f45b787498c3bc | 0.636558 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.11531 | paper | corpus_metadata | null | A general framework for ensemble distribution distillation | Ensembles of neural networks have been shown to give better performance than single networks, both in terms of predictions and uncertainty estimation. Additionally, ensembles allow the uncertainty to be decomposed into aleatoric (data) and epistemic (model) components, giving a more complete picture of the predictive u... | https://arxiv.org/abs/2002.11531 | 2020-02-26T14:34:43Z | 2002.11531 | 10.1109/mlsp49062.2020.9231703 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/mlsp49062.2020.9231703 | 52703d0993c5640488e1d673503204b8fd98ab67e756e4af0b05a5f07faee2a5 | 0.612296 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2101.03606 | paper | corpus_metadata | null | The Gaussian Neural Process | Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes. We provide a rigorous analysis of the standard maximum-likelihood objective used to train conditional NPs. Moreover, we propose a new member to the Neural Process ... | https://arxiv.org/abs/2101.03606 | 2021-01-10T19:15:27Z | 2101.03606 | 10.48550/arxiv.2101.03606 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2101.03606 | 3c4dd69bd1ed154cd8f23b51a87283457701b444456015e3bfa009b2fde6b768 | 0.59975 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.00552 | paper | corpus_metadata | null | Multiple-Attribute Text Style Transfer | The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style". In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training tha... | https://arxiv.org/abs/1811.00552 | 2018-11-01T18:00:00Z | 1811.00552 | 10.48550/arxiv.1811.00552 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1811.00552 | 88c5892cb515134e694494449a40f79235873beb8fdfcf648bcbd36a46a813f3 | 0.759714 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.00577 | paper | corpus_metadata | null | Logic-Consistency Text Generation from Semantic Parses | Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries. This is challenging due to two reasons: (1) the complex and intensive inner logic with the data scarcity constraint, (2) the lack of automatic evaluation metrics for logic consi... | https://arxiv.org/abs/2108.00577 | 2021-08-02T01:12:18Z | 2108.00577 | 10.48550/arxiv.2108.00577 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2108.00577 | 6e0caf54d1c95a0b4d844c518d0f3dc41ae064757a143fb35d679d4952ec0410 | 0.796696 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.09198 | paper | corpus_metadata | null | A unified framework for spectral clustering in sparse graphs | This article considers spectral community detection in the regime of sparse networks with heterogeneous degree distributions, for which we devise an algorithm to efficiently retrieve communities. Specifically, we demonstrate that a conveniently parametrized form of regularized Laplacian matrix can be used to perform sp... | https://arxiv.org/abs/2003.09198 | 2020-03-20T10:58:37Z | 2003.09198 | 10.48550/arxiv.2003.09198 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2003.09198 | c73f0a09bd556a8eec7796549df9c030614332aa15793549840d973d35840273 | 0.575543 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.13372 | paper | corpus_metadata | null | What is the best data augmentation for 3D brain tumor segmentation? | Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain. Despite this fact, data augmentation has in our opinion not been fully explored for brain tumor segmentation. In this project we apply different types of data augmentation (flipping, rotation, scaling, brig... | https://arxiv.org/abs/2010.13372 | 2020-10-26T07:00:16Z | 2010.13372 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2010.13372 | e405c86cde26367b86c53d7118895a11d85fd82c392f80900eaec282fd10038a | 0.72086 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.12990 | paper | corpus_metadata | null | Distributed Multi-object Tracking under Limited Field of View Sensors | We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, we develop a novel distributed multi-object tracking algorithm. To accomplish this, we first formali... | https://arxiv.org/abs/2012.12990 | 2020-12-23T21:34:28Z | 2012.12990 | 10.1109/tsp.2021.3103125 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tsp.2021.3103125 | 39148834d7fc2d6a79e5e732bdf28d06c2cf0db9d349475ffea045ddf0c69be4 | 0.66885 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.09871 | paper | corpus_metadata | null | Automating Involutive MCMC using Probabilistic and Differentiable Programming | Involutive MCMC is a unifying mathematical construction for MCMC kernels that generalizes many classic and state-of-the-art MCMC algorithms, from reversible jump MCMC to kernels based on deep neural networks. But as with MCMC samplers more generally, implementing involutive MCMC kernels is often tedious and error-prone... | https://arxiv.org/abs/2007.09871 | 2020-07-20T03:46:36Z | 2007.09871 | 10.48550/arxiv.2007.09871 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.09871 | 0d4518de21e30223fda909cb13aa8d6f37422416fd311bc6f7c49cbb58ee8bd0 | 0.576164 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2310.18562 | paper | corpus_metadata | null | Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition | Human Activity Recognition (HAR) models often suffer from performance degradation in real-world applications due to distribution shifts in activity patterns across individuals. Test-Time Adaptation (TTA) is an emerging learning paradigm that aims to utilize the test stream to adjust predictions in real-time inference, ... | https://arxiv.org/abs/2310.18562 | 2023-10-28T02:20:33Z | 2310.18562 | 10.48550/arxiv.2310.18562 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2310.18562 | 5b1572984d2452f086b77739e8aebfe49a5a73149bad56e29c2cb6063ec64b9d | 0.777291 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2410.13651 | paper | corpus_metadata | null | Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts? | An ability to learn about new objects from a small amount of visual data and produce convincing linguistic justification about the presence/absence of certain concepts (that collectively compose the object) in novel scenarios is an important characteristic of human cognition. This is possible due to abstraction of attr... | https://arxiv.org/abs/2410.13651 | 2024-10-17T15:16:10Z | 2410.13651 | 10.48550/arxiv.2410.13651 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2410.13651 | e04ac670ca7efb074a3c2d3c16db5ce42ed199b0fd6aabb2b077dc3cd567bad5 | 0.738874 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2410.13662 | paper | corpus_metadata | null | ActionCOMET: A Zero-shot Approach to Learn Image-specific Commonsense Concepts about Actions | Humans observe various actions being performed by other humans (physically or in videos/images) and can draw a wide range of inferences about it beyond what they can visually perceive. Such inferences include determining the aspects of the world that make action execution possible (e.g. liquid objects can undergo pouri... | https://arxiv.org/abs/2410.13662 | 2024-10-17T15:22:57Z | 2410.13662 | 10.48550/arxiv.2410.13662 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2410.13662 | 83ec8fa1adf43dc995b12872032ed4ac9b766f8637e5b950ac5a1f4d8d7f0287 | 0.7254 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.01170 | paper | corpus_metadata | null | LEAF: Simulating Large Energy-Aware Fog Computing Environments | Despite constant improvements in efficiency, today's data centers and networks consume enormous amounts of energy and this demand is expected to rise even further. An important research question is whether and how fog computing can curb this trend. As real-life deployments of fog infrastructure are still rare, a signif... | https://arxiv.org/abs/2103.01170 | 2021-03-01T18:08:39Z | 2103.01170 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2103.01170 | 5d2a53ff0a8d99299eaaf290fc72336fe2821bb46153a2a1551d2fe64bec5284 | 0.578082 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1704.07576 | paper | corpus_metadata | null | Towards a quality metric for dense light fields | Light fields become a popular representation of three dimensional scenes, and there is interest in their processing, resampling, and compression. As those operations often result in loss of quality, there is a need to quantify it. In this work, we collect a new dataset of dense reference and distorted light fields as w... | https://arxiv.org/abs/1704.07576 | 2017-04-25T08:21:47Z | 1704.07576 | 10.1109/cvpr.2017.396 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/cvpr.2017.396 | db56b17ac174dbc9849ae125bcb4e91928273f9394f597869ba6cf19f6ab13c2 | 0.571079 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.13719 | paper | corpus_metadata | null | RESA: Recurrent Feature-Shift Aggregator for Lane Detection | Lane detection is one of the most important tasks in self-driving. Due to various complex scenarios (e.g., severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals inherent in lane annotations, lane detection task is still challenging. Thus, it is difficult for the ordinary convolutional neural netwo... | https://arxiv.org/abs/2008.13719 | 2020-08-31T16:37:30Z | 2008.13719 | 10.48550/arxiv.2008.13719 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2008.13719 | 85a364de82e0901b26bd8f7e45e7599f880abde8535a9b29571aa58cbdaa8945 | 0.749523 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.01728 | paper | corpus_metadata | null | Mining Dual Emotion for Fake News Detection | Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotio... | https://arxiv.org/abs/1903.01728 | 2019-03-05T08:52:33Z | 1903.01728 | 10.1145/3442381.3450004 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3442381.3450004 | 3053968a3f3ab95d4ba9e9f7cd1e778a34802d30f87297f3096a4b9e93a0b317 | 0.606396 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1904.11547 | paper | corpus_metadata | null | Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings | Click-through rate (CTR) prediction has been one of the most central problems in computational advertising. Lately, embedding techniques that produce low-dimensional representations of ad IDs drastically improve CTR prediction accuracies. However, such learning techniques are data demanding and work poorly on new ads w... | https://arxiv.org/abs/1904.11547 | 2019-04-25T19:26:42Z | 1904.11547 | 10.48550/arxiv.1904.11547 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1904.11547 | db9ffd300626d112dc53958a63f9ae7baf6d5e5f941abc2adb44e0471608bfdd | 0.768749 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.02074 | paper | corpus_metadata | null | Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition | Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade. However, precision-recall performance of these algorithms dramatically degrades when searching on short temporal window (TW) lengths, while demandi... | https://arxiv.org/abs/2103.02074 | 2021-03-02T22:57:43Z | 2103.02074 | 10.48550/arxiv.2103.02074 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2103.02074 | 8df628a326c07254541238989dabe6ad1ae5fb2c34a65e16fb1c2084f8d19e89 | 0.785368 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2202.05122 | paper | corpus_metadata | null | Ensemble Kalman method for learning turbulence models from indirect observation data | In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven turbulence models have emerged as a promising alternative to traditional models for providing closure mapping from the mean velocities to Reyn... | https://arxiv.org/abs/2202.05122 | 2022-02-10T16:16:23Z | 2202.05122 | 10.1017/jfm.2022.744 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1017/jfm.2022.744 | 88e974ee8d6737377f04c63521d6a79c2a8b7b4b810d132cf856f0939cc6f065 | 0.713113 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.00406 | paper | corpus_metadata | null | Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex Optimization | Momentum methods are now used pervasively within the machine learning community for training non-convex models such as deep neural networks. Empirically, they out perform traditional stochastic gradient descent (SGD) approaches. In this work we develop a Lyapunov analysis of SGD with momentum (SGD+M), by utilizing a eq... | https://arxiv.org/abs/2010.00406 | 2020-10-01T13:46:32Z | 2010.00406 | 10.48550/arxiv.2010.00406 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | doi:10.48550/arxiv.2010.00406 | ca0283a9b8466aa24611714c07e8baad24586e6c50ae351b85eac90f5eef77f2 | 0.601148 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.05424 | paper | corpus_metadata | null | Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots | This paper presents a framework that leverages both control theory and machine learning to obtain stable and robust bipedal locomotion without the need for manual parameter tuning. Traditionally, gaits are generated through trajectory optimization methods and then realized experimentally -- a process that often require... | https://arxiv.org/abs/2011.05424 | 2020-11-10T22:15:56Z | 2011.05424 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2011.05424 | b11dcca8bfdd2349284ace81fbd878305fbb313a46c48dfc217846065af71444 | 0.643097 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2101.11075 | paper | corpus_metadata | null | Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization | We introduce MADGRAD, a novel optimization method in the family of AdaGrad adaptive gradient methods. MADGRAD shows excellent performance on deep learning optimization problems from multiple fields, including classification and image-to-image tasks in vision, and recurrent and bidirectionally-masked models in natural l... | https://arxiv.org/abs/2101.11075 | 2021-01-26T20:38:26Z | 2101.11075 | 10.48550/arxiv.2101.11075 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2101.11075 | fd3354ed41e2c07687a3ca13b26d2ec4669cc96e9f42a3493a53c5211059bf18 | 0.759047 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.05947 | paper | corpus_metadata | null | Room Classification on Floor Plan Graphs using Graph Neural Networks | We present our approach to improve room classification task on floor plan maps of buildings by representing floor plans as undirected graphs and leveraging graph neural networks to predict the room categories. Rooms in the floor plans are represented as nodes in the graph with edges representing their adjacency in the ... | https://arxiv.org/abs/2108.05947 | 2021-08-12T19:59:22Z | 2108.05947 | 10.48550/arxiv.2108.05947 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2108.05947 | d5ab66436c20c0e81ff0cc0e138f8225e281dca53ddea329399a3377e548b94a | 0.665165 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.12839 | paper | corpus_metadata | null | A deep perceptual metric for 3D point clouds | Point clouds are essential for storage and transmission of 3D content. As they can entail significant volumes of data, point cloud compression is crucial for practical usage. Recently, point cloud geometry compression approaches based on deep neural networks have been explored. In this paper, we evaluate the ability to... | https://arxiv.org/abs/2102.12839 | 2021-02-25T13:24:59Z | 2102.12839 | 10.48550/arxiv.2102.12839 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2102.12839 | 29d4fe84d7118a5b6709efcf666f5b901a2b6d45f3fd4aaa9a7f9e95351d8ab3 | 0.676461 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.04910 | paper | corpus_metadata | null | A Crash Course on Reinforcement Learning | The emerging field of Reinforcement Learning (RL) has led to impressive results in varied domains like strategy games, robotics, etc. This handout aims to give a simple introduction to RL from control perspective and discuss three possible approaches to solve an RL problem: Policy Gradient, Policy Iteration, and Model-... | https://arxiv.org/abs/2103.04910 | 2021-03-08T17:15:34Z | 2103.04910 | 10.48550/arxiv.2103.04910 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2103.04910 | ef8d9ba8befb14f1ef0325b49266b5dfbb82eb2519b04dee4504d4e6f6c0accd | 0.500132 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.04503 | paper | corpus_metadata | null | End-to-End Human Object Interaction Detection with HOI Transformer | We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner. Current approaches either decouple HOI task into separated stages of object detection and interaction classification or introduce surrogate interaction problem. In contrast, our method, named HOI Transformer, streamli... | https://arxiv.org/abs/2103.04503 | 2021-03-08T01:31:19Z | 2103.04503 | 10.48550/arxiv.2103.04503 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2103.04503 | ea8bcbeb0411c950ca3e665d6f6f04db6fa52d3d6f1c6d2e256c0c5c5ae795b7 | 0.755339 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.00825 | paper | corpus_metadata | null | Towards High Fidelity Face Relighting with Realistic Shadows | Existing face relighting methods often struggle with two problems: maintaining the local facial details of the subject and accurately removing and synthesizing shadows in the relit image, especially hard shadows. We propose a novel deep face relighting method that addresses both problems. Our method learns to predict t... | https://arxiv.org/abs/2104.00825 | 2021-04-02T00:28:40Z | 2104.00825 | 10.48550/arxiv.2104.00825 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2104.00825 | 62d0a849deb3fff542aa9a4b60631f6907d40e68b4fbfd60bd87f3061acb1983 | 0.741924 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.00848 | paper | corpus_metadata | null | SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom | Deep Neural Network (DNN) based super-resolution algorithms have greatly improved the quality of the generated images. However, these algorithms often yield significant artifacts when dealing with real-world super-resolution problems due to the difficulty in learning misaligned optical zoom. In this paper, we introduce... | https://arxiv.org/abs/2104.00848 | 2021-04-02T01:58:00Z | 2104.00848 | 10.48550/arxiv.2104.00848 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2104.00848 | 0bdf83962609ce42431d14a402ec4a3bcd6b830e77fa2f6a307ccb6bc3c048e4 | 0.800414 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.08784 | paper | corpus_metadata | null | LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval | Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computation cost mainly from cross-modal attention in Transformer architecture. When applied to real-life applica... | https://arxiv.org/abs/2103.08784 | 2021-03-16T00:35:28Z | 2103.08784 | 10.48550/arxiv.2103.08784 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2103.08784 | 8614a5fd09843da2fc63c2e71f913d4fbd02d83ca413209b1d2119f0c453dd60 | 0.869542 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1507.05699 | paper | corpus_metadata | null | Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians | Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a unidirectional bottom-up feed-forward fashion. However, practical experience and biological evidence tells us that feedback plays a crucial role, particularly for detailed spatial understanding... | https://arxiv.org/abs/1507.05699 | 2015-07-21T04:00:44Z | 1507.05699 | 10.48550/arxiv.1507.05699 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1507.05699 | 2a34bece95f41c0cc455fe4bb4aa74ba07ca41fdab36052a0d11ca0972cfdd78 | 0.659673 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2101.11739 | paper | corpus_metadata | null | Compositionality Through Language Transmission, using Artificial Neural Networks | We propose an architecture and process for using the Iterated Learning Model ("ILM") for artificial neural networks. We show that ILM does not lead to the same clear compositionality as observed using DCGs, but does lead to a modest improvement in compositionality, as measured by holdout accuracy and topologic similari... | https://arxiv.org/abs/2101.11739 | 2021-01-27T23:08:16Z | 2101.11739 | 10.48550/arxiv.2101.11739 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2101.11739 | 8d2500b8b25f4aec1d74749d311fc2f00b9d60fa20bbe434b0b121fcd6805e31 | 0.588623 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.10214 | paper | corpus_metadata | null | MediaPipe Hands: On-device Real-time Hand Tracking | We present a real-time on-device hand tracking pipeline that predicts hand skeleton from single RGB camera for AR/VR applications. The pipeline consists of two models: 1) a palm detector, 2) a hand landmark model. It's implemented via MediaPipe, a framework for building cross-platform ML solutions. The proposed model a... | https://arxiv.org/abs/2006.10214 | 2020-06-18T00:19:13Z | 2006.10214 | 10.48550/arxiv.2006.10214 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2006.10214 | c5ccf67642cd2064d6be1dc763867d9c778cefea0cf53d7f5e4fd2760d4f2a98 | 0.579335 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.06057 | paper | corpus_metadata | null | Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models | Emotion is fundamental to humanity. The ability to perceive, understand and respond to social interactions in a human-like manner is one of the most desired capabilities in artificial agents, particularly in social-media bots. Over the past few years, computational understanding and detection of emotional aspects in la... | https://arxiv.org/abs/2103.06057 | 2021-03-10T14:00:54Z | 2103.06057 | 10.48550/arxiv.2103.06057 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2103.06057 | 376ac282f24a6139db9076f93adaebd8fb7200dc0e74484b8445cf38f63b3cbf | 0.687402 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.16591 | paper | corpus_metadata | null | Continuous Weight Balancing | We propose a simple method by which to choose sample weights for problems with highly imbalanced or skewed traits. Rather than naively discretizing regression labels to find binned weights, we take a more principled approach -- we derive sample weights from the transfer function between an estimated source and specifie... | https://arxiv.org/abs/2103.16591 | 2021-03-30T18:03:12Z | 2103.16591 | 10.48550/arxiv.2103.16591 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | doi:10.48550/arxiv.2103.16591 | 0785617fd619b262e01ba32c99969141b1c158c454619dab60abdd9ac7868ac2 | 0.695712 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.11174 | paper | corpus_metadata | null | TanksWorld: A Multi-Agent Environment for AI Safety Research | The ability to create artificial intelligence (AI) capable of performing complex tasks is rapidly outpacing our ability to ensure the safe and assured operation of AI-enabled systems. Fortunately, a landscape of AI safety research is emerging in response to this asymmetry and yet there is a long way to go. In particula... | https://arxiv.org/abs/2002.11174 | 2020-02-25T21:00:52Z | 2002.11174 | 10.48550/arxiv.2002.11174 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2002.11174 | fa96ff6a64079d363df26a36ef34ae64d25936a86c0febb87e3ab98500ff1508 | 0.61034 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.04799 | paper | corpus_metadata | null | Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images | An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. T... | https://arxiv.org/abs/2102.04799 | 2021-02-09T12:38:42Z | 2102.04799 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2102.04799 | 20b2bf456d28fbd429245e72442da551b7bbccebdb018fac3f1d9a8702fabc40 | 0.630453 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.08971 | paper | corpus_metadata | null | TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation | Recently, deep neural networks are widely applied in recommender systems for their effectiveness in capturing/modeling users' preferences. Especially, the attention mechanism in deep learning enables recommender systems to incorporate various features in an adaptive way. Specifically, as for the next item recommendatio... | https://arxiv.org/abs/2103.08971 | 2021-03-16T10:51:57Z | 2103.08971 | 10.1016/j.neucom.2021.02.015 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | doi:10.1016/j.neucom.2021.02.015 | b62675bdae970b75b661ee34924d33171ac457b3c1af86ce7adf2f37c89db3b1 | 0.687403 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2210.16775 | paper | corpus_metadata | null | Nonlinear Causal Discovery via Kernel Anchor Regression | Learning causal relationships is a fundamental problem in science. Anchor regression has been developed to address this problem for a large class of causal graphical models, though the relationships between the variables are assumed to be linear. In this work, we tackle the nonlinear setting by proposing kernel anchor ... | https://arxiv.org/abs/2210.16775 | 2022-10-30T08:46:36Z | 2210.16775 | 10.48550/arxiv.2210.16775 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2210.16775 | 28728d502ebc9eccf07aa012dd2d240dc4632bfd3c1fc76bf9477711c3c9da2a | 0.563535 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
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