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arxiv:2203.10473 | paper | corpus_metadata | null | ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis | In recent years, neural network based methods for multi-speaker text-to-speech synthesis (TTS) have made significant progress. However, the current speaker encoder models used in these methods still cannot capture enough speaker information. In this paper, we focus on accurate speaker encoder modeling and propose an en... | https://arxiv.org/abs/2203.10473 | 2022-03-20T07:04:26Z | 2203.10473 | 10.48550/arxiv.2203.10473 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2203.10473 | 198201b0448624a38216ce3b7228c7663578105bcf67d487f0b7c361c00f1c72 | 0.81757 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.10349 | paper | corpus_metadata | null | Learning to Adapt Structured Output Space for Semantic Segmentation | Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to the target domain is ... | https://arxiv.org/abs/1802.10349 | 2018-02-28T10:39:26Z | 1802.10349 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1802.10349 | bd89ba6166e572f0e7e65de509aa1e9eb14b28f660219f1dd79d422e6a4ed354 | 0.823194 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.09791 | paper | corpus_metadata | null | Identity-Aware Multi-Sentence Video Description | Standard video and movie description tasks abstract away from person identities, thus failing to link identities across sentences. We propose a multi-sentence Identity-Aware Video Description task, which overcomes this limitation and requires to re-identify persons locally within a set of consecutive clips. We introduc... | https://arxiv.org/abs/2008.09791 | 2020-08-22T09:50:43Z | 2008.09791 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2008.09791 | 8b5c9bfbbfef690f840a8095e26b0911a6f4ff511e6eb592bd1b54c73d136e9d | 0.720865 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.10858 | paper | corpus_metadata | null | LowFER: Low-rank Bilinear Pooling for Link Prediction | Knowledge graphs are incomplete by nature, with only a limited number of observed facts from the world knowledge being represented as structured relations between entities. To partly address this issue, an important task in statistical relational learning is that of link prediction or knowledge graph completion. Both l... | https://arxiv.org/abs/2008.10858 | 2020-08-25T07:33:52Z | 2008.10858 | 10.48550/arxiv.2008.10858 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2008.10858 | 38af61007be40ee1901acd624e440b3ebbab01cb4406aa70c3e1fbc71265a786 | 0.789142 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.06999 | paper | corpus_metadata | null | RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation | Quantifying segmentation uncertainty has become an important issue in medical image analysis due to the inherent ambiguity of anatomical structures and its pathologies. Recently, neural network-based uncertainty quantification methods have been successfully applied to various problems. One of the main limitations of th... | https://arxiv.org/abs/2008.06999 | 2020-08-16T21:16:19Z | 2008.06999 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2008.06999 | edac7ca933f1f3ef7aeeb2573ffd087a655687b9cbcfd3e7b0cbf950d6107939 | 0.665046 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.05131 | paper | corpus_metadata | null | Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters | Sequential reasoning is a complex human ability, with extensive previous research focusing on gaming AI in a single continuous game, round-based decision makings extending to a sequence of games remain less explored. Counter-Strike: Global Offensive (CS:GO), as a round-based game with abundant expert demonstrations, pr... | https://arxiv.org/abs/2008.05131 | 2020-08-12T06:29:26Z | 2008.05131 | 10.48550/arxiv.2008.05131 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2008.05131 | 31ac93fed01250b0761b4ffc33bad3341f6150c6fc4d6aefe593b1999b495b0c | 0.683182 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.14487 | paper | corpus_metadata | null | Unsupervised Learning of Particle Image Velocimetry | Particle Image Velocimetry (PIV) is a classical flow estimation problem which is widely considered and utilised, especially as a diagnostic tool in experimental fluid dynamics and the remote sensing of environmental flows. Recently, the development of deep learning based methods has inspired new approaches to tackle th... | https://arxiv.org/abs/2007.14487 | 2020-07-28T21:08:37Z | 2007.14487 | 10.48550/arxiv.2007.14487 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.14487 | 999299ab39c86639776843791a37e91121683cddf23cc0817d66a7dee06a3ea8 | 0.789142 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.13993 | paper | corpus_metadata | null | Robust Ego and Object 6-DoF Motion Estimation and Tracking | The problem of tracking self-motion as well as motion of objects in the scene using information from a camera is known as multi-body visual odometry and is a challenging task. This paper proposes a robust solution to achieve accurate estimation and consistent track-ability for dynamic multi-body visual odometry. A comp... | https://arxiv.org/abs/2007.13993 | 2020-07-28T05:12:56Z | 2007.13993 | 10.48550/arxiv.2007.13993 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.13993 | da7e32ff4317e0d373bc518bdcd3237702d34fddc83562f6e9567918b8b51755 | 0.68479 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.09278 | paper | corpus_metadata | null | XingGAN for Person Image Generation | We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i.e., translating the pose of a given person to a desired one. The proposed Xing generator consists of two generation branches that model the person's appearance and shape information, respectively. Moreover, w... | https://arxiv.org/abs/2007.09278 | 2020-07-17T23:40:22Z | 2007.09278 | 10.48550/arxiv.2007.09278 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.09278 | c4874321830c3ca51effa2e1519ca042a0a129ffbc23ff2c06944bc29f97d2f4 | 0.844111 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.10791 | paper | corpus_metadata | null | Learning to Match Distributions for Domain Adaptation | When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is difficult to directly match the cross-domain joint distributions, existing methods tend to reduce the marginal or conditional distribution diver... | https://arxiv.org/abs/2007.10791 | 2020-07-17T03:26:13Z | 2007.10791 | 10.48550/arxiv.2007.10791 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.10791 | 3c83fed8ff9368787ae2a7b8dfa4b468e408dd568a32f345462709695e58603b | 0.76936 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.08454 | paper | corpus_metadata | null | Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation | We present a novel learning approach to recover the 6D poses and sizes of unseen object instances from an RGB-D image. To handle the intra-class shape variation, we propose a deep network to reconstruct the 3D object model by explicitly modeling the deformation from a pre-learned categorical shape prior. Additionally, ... | https://arxiv.org/abs/2007.08454 | 2020-07-16T16:45:05Z | 2007.08454 | 10.48550/arxiv.2007.08454 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.08454 | fff7174231a174572234616105e9da55f5f48603c16e3cba05aaee104d37e4dd | 0.8134 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.08398 | paper | corpus_metadata | null | Semi-Siamese Training for Shallow Face Learning | Most existing public face datasets, such as MS-Celeb-1M and VGGFace2, provide abundant information in both breadth (large number of IDs) and depth (sufficient number of samples) for training. However, in many real-world scenarios of face recognition, the training dataset is limited in depth, i.e. only two face images a... | https://arxiv.org/abs/2007.08398 | 2020-07-16T15:20:04Z | 2007.08398 | 10.48550/arxiv.2007.08398 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.08398 | 18bbb9539721f50b4b9cc5599fc7947760ccf92d98e3552912dc04bea2cc9edc | 0.813932 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.06765 | paper | corpus_metadata | null | Patch-wise Attack for Fooling Deep Neural Network | By adding human-imperceptible noise to clean images, the resultant adversarial examples can fool other unknown models. Features of a pixel extracted by deep neural networks (DNNs) are influenced by its surrounding regions, and different DNNs generally focus on different discriminative regions in recognition. Motivated ... | https://arxiv.org/abs/2007.06765 | 2020-07-14T01:50:22Z | 2007.06765 | 10.48550/arxiv.2007.06765 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.06765 | 38f281fd3079acb41241f6e308c7695ee4677799d6e88457f5f04229df72819e | 0.78448 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.11892 | paper | corpus_metadata | null | Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering | Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has been to fine-tune a pretrained language model on a task-specific labeled dataset. This paradigm, however, relies on scarce, and costly to obtai... | https://arxiv.org/abs/2004.11892 | 2020-04-24T17:57:45Z | 2004.11892 | 10.48550/arxiv.2004.11892 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2004.11892 | c7322ee310ecb44bb2b1414e17aa17cec2a9cc1be69c15a5479a3678194f03a4 | 0.848515 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.10250 | paper | corpus_metadata | null | Certifying Joint Adversarial Robustness for Model Ensembles | Deep Neural Networks (DNNs) are often vulnerable to adversarial examples.Several proposed defenses deploy an ensemble of models with the hope that, although the individual models may be vulnerable, an adversary will not be able to find an adversarial example that succeeds against the ensemble. Depending on how the ense... | https://arxiv.org/abs/2004.10250 | 2020-04-21T19:38:31Z | 2004.10250 | 10.48550/arxiv.2004.10250 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2004.10250 | 7ad6b99b0413d8b269027df02d703437b457f8f921e0de653092b4b3754feec5 | 0.623111 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.09845 | paper | corpus_metadata | null | LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow Recognition | Automatic surgical workflow recognition in video is an essentially fundamental yet challenging problem for developing computer-assisted and robotic-assisted surgery. Existing approaches with deep learning have achieved remarkable performance on analysis of surgical videos, however, heavily relying on large-scale labell... | https://arxiv.org/abs/2004.09845 | 2020-04-21T09:21:22Z | 2004.09845 | 10.48550/arxiv.2004.09845 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2004.09845 | cabde8409cfb6c9d8d204c15213f7d9ff5617f07c68429b07fcf79d0c897afec | 0.78241 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.08728 | paper | corpus_metadata | null | SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings | Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data, and quality decreas... | https://arxiv.org/abs/2004.08728 | 2020-04-18T23:10:36Z | 2004.08728 | 10.48550/arxiv.2004.08728 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2004.08728 | 95f7088636dad519d291a2d75f21f87db24815389cb74dbafeb2333685bd8cfa | 0.746071 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.07124 | paper | corpus_metadata | null | A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box | Currently, reliable and accurate ship detection in optical remote sensing images is still challenging. Even the state-of-the-art convolutional neural network (CNN) based methods cannot obtain very satisfactory results. To more accurately locate the ships in diverse orientations, some recent methods conduct the detectio... | https://arxiv.org/abs/2004.07124 | 2020-04-15T14:48:46Z | 2004.07124 | 10.1109/tgrs.2020.2995477 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tgrs.2020.2995477 | d6ef4022a355b62c7a16c39b15ac1192d8d0fb3aa6671e740c9c807be20195d7 | 0.671787 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.04667 | paper | corpus_metadata | null | Geomstats: A Python Package for Riemannian Geometry in Machine Learning | We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, man... | https://arxiv.org/abs/2004.04667 | 2020-04-07T20:41:50Z | 2004.04667 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2004.04667 | c3c7e8d4f2750e75bc82d09bd764d5137eddb805818726e05dbfe9ebc3eb5fb6 | 0.502258 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.03032 | paper | corpus_metadata | null | A Systematic Analysis of Morphological Content in BERT Models for Multiple Languages | This work describes experiments which probe the hidden representations of several BERT-style models for morphological content. The goal is to examine the extent to which discrete linguistic structure, in the form of morphological features and feature values, presents itself in the vector representations and attention d... | https://arxiv.org/abs/2004.03032 | 2020-04-06T22:50:27Z | 2004.03032 | 10.48550/arxiv.2004.03032 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2004.03032 | a6c4d3546ebfad9aff6a9984fbcaf040957c105f2e5c21d5a3c228c5a974ad91 | 0.610846 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.01793 | paper | corpus_metadata | null | Self-Supervised Viewpoint Learning From Image Collections | Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone, and time-consuming. On the other hand, it is relatively easy to mine many unlabelled images of an object category from the internet, e.g., ... | https://arxiv.org/abs/2004.01793 | 2020-04-03T22:01:41Z | 2004.01793 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2004.01793 | dad19e962fcb0e082bc64447cd4552fedfb2fedb721fc92c20d36513e273b7c5 | 0.811503 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.14111 | paper | corpus_metadata | null | Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition | Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics. To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal dependency modeling are critical aspects of a powerful feature extracto... | https://arxiv.org/abs/2003.14111 | 2020-03-31T11:28:25Z | 2003.14111 | 10.48550/arxiv.2003.14111 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.14111 | f30ae5b92d43fa4721cfdbb2937bd541ee4f9fb06f6f67e01946f9f54dff0c62 | 0.657182 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.12805 | paper | corpus_metadata | null | Real-Time Detection of Dictionary DGA Network Traffic using Deep Learning | Botnets and malware continue to avoid detection by static rules engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closel... | https://arxiv.org/abs/2003.12805 | 2020-03-28T14:57:22Z | 2003.12805 | 10.1007/s42979-021-00507-w | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/s42979-021-00507-w | 42f92caaa20201e28e5a64d949d8eaf630b53cd31b5ffcac048ac0d56df7e4a0 | 0.753434 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.13328 | paper | corpus_metadata | null | Strip Pooling: Rethinking Spatial Pooling for Scene Parsing | Spatial pooling has been proven highly effective in capturing long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling s... | https://arxiv.org/abs/2003.13328 | 2020-03-30T10:40:11Z | 2003.13328 | 10.48550/arxiv.2003.13328 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.13328 | 1275a85061978c1eb790a0c9762f9e015c61580b6ab26a488cf7e44ea79f016e | 0.732485 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.09093 | paper | corpus_metadata | null | Masked Face Recognition Dataset and Application | In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train statio... | https://arxiv.org/abs/2003.09093 | 2020-03-20T04:15:19Z | 2003.09093 | 10.48550/arxiv.2003.09093 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.09093 | 2cb8e785651100f503f4e365ae246d23be115e110c2131e8b71573891fe8fe79 | 0.724505 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.08400 | paper | corpus_metadata | null | Adversarial Texture Optimization from RGB-D Scans | Realistic color texture generation is an important step in RGB-D surface reconstruction, but remains challenging in practice due to inaccuracies in reconstructed geometry, misaligned camera poses, and view-dependent imaging artifacts. In this work, we present a novel approach for color texture generation using a condit... | https://arxiv.org/abs/2003.08400 | 2020-03-18T18:00:05Z | 2003.08400 | 10.48550/arxiv.2003.08400 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.08400 | 4739118076b03e222df9db8712602581ba3cbd52bb3d3738d37539746432c49e | 0.711565 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.06561 | paper | corpus_metadata | null | Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online | Many geoportals such as ArcGIS Online are established with the goal of improving geospatial data reusability and achieving intelligent knowledge discovery. However, according to previous research, most of the existing geoportals adopt Lucene-based techniques to achieve their core search functionality, which has a limit... | https://arxiv.org/abs/2003.06561 | 2020-03-14T06:16:30Z | 2003.06561 | 10.48550/arxiv.2003.06561 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.06561 | 723eae0d2a94be5431a602a1bf782ffdff70d3a980c9607f0e52c2ec214e73e2 | 0.625506 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.04453 | paper | corpus_metadata | null | Select and Attend: Towards Controllable Content Selection in Text Generation | Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box architecture. As a result, the content to be described in the text cannot be explicitly controlled. This paper tackles this problem by decoupling ... | https://arxiv.org/abs/1909.04453 | 2019-09-10T12:59:10Z | 1909.04453 | 10.48550/arxiv.1909.04453 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1909.04453 | 083742242ad867f78e8bc5d4b0a2bfad53fc828dd519f5f682626a4b580653b5 | 0.721235 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.01098 | paper | corpus_metadata | null | 3DSiameseNet to Analyze Brain MRI | Prediction of the cognitive evolution of a person susceptible to develop a neurodegenerative disorder is crucial to provide an appropriate treatment as soon as possible. In this paper we propose a 3D siamese network designed to extract features from whole-brain 3D MRI images. We show that it is possible to extract mean... | https://arxiv.org/abs/1909.01098 | 2019-09-03T11:52:25Z | 1909.01098 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1909.01098 | 52bff9f12c45ef0545cc406def0160a661984bb916aee0dd94899fb7a37b6a03 | 0.530087 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.08210 | paper | corpus_metadata | null | Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs | Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between enti... | https://arxiv.org/abs/1908.08210 | 2019-08-22T05:45:30Z | 1908.08210 | 10.24963/ijcai.2019/733 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.24963/ijcai.2019/733 | 8b084bc4218f03725fcaa08034348a968dfc94323ca2d56eb605db441b863b03 | 0.789344 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.07836 | paper | corpus_metadata | null | PubLayNet: largest dataset ever for document layout analysis | Recognizing the layout of unstructured digital documents is an important step when parsing the documents into structured machine-readable format for downstream applications. Deep neural networks that are developed for computer vision have been proven to be an effective method to analyze layout of document images. Howev... | https://arxiv.org/abs/1908.07836 | 2019-08-16T00:40:08Z | 1908.07836 | 10.48550/arxiv.1908.07836 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1908.07836 | 68afcc107b65053275fb78d3c0d9232a8187ecf0d1cda763b278b23088b0f4b8 | 0.81109 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.07226 | paper | corpus_metadata | null | Prosodic Phrase Alignment for Machine Dubbing | Dubbing is a type of audiovisual translation where dialogues are translated and enacted so that they give the impression that the media is in the target language. It requires a careful alignment of dubbed recordings with the lip movements of performers in order to achieve visual coherence. In this paper, we deal with t... | https://arxiv.org/abs/1908.07226 | 2019-08-20T08:52:52Z | 1908.07226 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1908.07226 | 8b3f43813dd2ce4044fd7fbba66c97a5e8cc0493258d0b4982917feca09a0063 | 0.540623 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.04950 | paper | corpus_metadata | null | VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering | Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language underst... | https://arxiv.org/abs/1908.04950 | 2019-08-14T04:44:26Z | 1908.04950 | 10.48550/arxiv.1908.04950 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1908.04950 | 539d4879ba4b9ce73f95325ddba2fe6291f0bd7de1c9b693cdda7d7cd201cd14 | 0.743361 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.04174 | paper | corpus_metadata | null | Domain-Specific Embedding Network for Zero-Shot Recognition | Zero-Shot Learning (ZSL) seeks to recognize a sample from either seen or unseen domain by projecting the image data and semantic labels into a joint embedding space. However, most existing methods directly adapt a well-trained projection from one domain to another, thereby ignoring the serious bias problem caused by do... | https://arxiv.org/abs/1908.04174 | 2019-08-12T14:32:50Z | 1908.04174 | 10.48550/arxiv.1908.04174 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1908.04174 | fa5c8bbf13bbe1edc5b5f1c5ecce3dd908e526af761e57b439b8ea960f07dbdf | 0.749173 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.02021 | paper | corpus_metadata | null | Consensus Maximization Tree Search Revisited | Consensus maximization is widely used for robust fitting in computer vision. However, solving it exactly, i.e., finding the globally optimal solution, is intractable. A* tree search, which has been shown to be fixed-parameter tractable, is one of the most efficient exact methods, though it is still limited to small inp... | https://arxiv.org/abs/1908.02021 | 2019-08-06T08:54:04Z | 1908.02021 | 10.48550/arxiv.1908.02021 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1908.02021 | af4c070e27ec0e00aec8a4007528adb20ae5dfc04aaf5a0bbc5be62510bb0025 | 0.570975 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.10505 | paper | corpus_metadata | null | Viable Dependency Parsing as Sequence Labeling | We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BiLSTM-based ... | https://arxiv.org/abs/1902.10505 | 2019-02-27T13:08:27Z | 1902.10505 | 10.48550/arxiv.1902.10505 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1902.10505 | 3f832ce877c84519d8d4fbc355c77b8155eb9bb987970ad3f1e968621126afe9 | 0.538266 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.09574 | paper | corpus_metadata | null | The State of Sparsity in Deep Neural Networks | We rigorously evaluate three state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageNet. Across thousands of experiments, we demonstrate that complex techniques (Molchanov et al., 2017;... | https://arxiv.org/abs/1902.09574 | 2019-02-25T19:18:40Z | 1902.09574 | 10.48550/arxiv.1902.09574 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1902.09574 | 5735198a3e01df533377d720125a50e58973034f86cd6a0d9fb9c5195b30077c | 0.854392 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.05965 | paper | corpus_metadata | null | From Dark Matter to Galaxies with Convolutional Networks | Cosmological surveys aim at answering fundamental questions about our Universe, including the nature of dark matter or the reason of unexpected accelerated expansion of the Universe. In order to answer these questions, two important ingredients are needed: 1) data from observations and 2) a theoretical model that allow... | https://arxiv.org/abs/1902.05965 | 2019-02-15T19:01:20Z | 1902.05965 | 10.48550/arxiv.1902.05965 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1902.05965 | b9035a79e095c8ebeaf07a8a68d3911e541e5314d6c4a120ea4b89e392ecc814 | 0.617079 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.09557 | paper | corpus_metadata | null | Out-of-Sample Testing for GANs | We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a test set to directly measure the reconstruction quality in the original sample space (no auxiliary networks are necessary), and it also computes the (log)likelihood for the reconstructed samples in the test set. Further, EvalGAN is agnostic t... | https://arxiv.org/abs/1901.09557 | 2019-01-28T09:04:32Z | 1901.09557 | 10.48550/arxiv.1901.09557 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1901.09557 | a94ea6c1560d11e132ee35c8dd48316b0409e7767bc8b3fab6fb3780662326ef | 0.64723 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.06803 | paper | corpus_metadata | null | Active Learning with Gaussian Processes for High Throughput Phenotyping | A looming question that must be solved before robotic plant phenotyping capabilities can have significant impact to crop improvement programs is scalability. High Throughput Phenotyping (HTP) uses robotic technologies to analyze crops in order to determine species with favorable traits, however, the current practices r... | https://arxiv.org/abs/1901.06803 | 2019-01-21T06:35:02Z | 1901.06803 | 10.48550/arxiv.1901.06803 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1901.06803 | ed17d323d115e00b7bd2f8c9fa6bce919f14c732e0ce8dc917590ea3f3e7d592 | 0.567608 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.06958 | paper | corpus_metadata | null | Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks | Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approxima... | https://arxiv.org/abs/1901.06958 | 2019-01-21T15:19:01Z | 1901.06958 | 10.1109/ijcnn.2019.8852018 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/ijcnn.2019.8852018 | 8f31d695ed598a75474964dc6c7a70d1e753bfefd7166e7b348410c398c16471 | 0.663803 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1507.08439 | paper | corpus_metadata | null | Metadata Embeddings for User and Item Cold-start Recommendations | I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features' latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as ... | https://arxiv.org/abs/1507.08439 | 2015-07-30T10:08:14Z | 1507.08439 | 10.48550/arxiv.1507.08439 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1507.08439 | ced439494aac4461469babc3eff3e71467f6cd8ad7ce4c8a19c75470aa4cccc1 | 0.595291 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1807.02758 | paper | corpus_metadata | null | Image Super-Resolution Using Very Deep Residual Channel Attention Networks | Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hinderi... | https://arxiv.org/abs/1807.02758 | 2018-07-08T05:45:45Z | 1807.02758 | 10.48550/arxiv.1807.02758 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1807.02758 | 815cf894aa255e7df049372f688debf89209854d271d6545b37040cbcac2941d | 0.747839 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1605.07946 | paper | corpus_metadata | null | Steganalysis via a Convolutional Neural Network using Large Convolution Filters for Embedding Process with Same Stego Key | For the past few years, in the race between image steganography and steganalysis, deep learning has emerged as a very promising alternative to steganalyzer approaches based on rich image models combined with ensemble classifiers. A key knowledge of image steganalyzer, which combines relevant image features and innovati... | https://arxiv.org/abs/1605.07946 | 2016-05-25T15:58:57Z | 1605.07946 | 10.48550/arxiv.1605.07946 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1605.07946 | 6eb4a019ab15b38f94dec1d013ee3a5e743df4b52f3c1d593b6e6991251f95e4 | 0.761411 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1301.4083 | paper | corpus_metadata | null | Knowledge Matters: Importance of Prior Information for Optimization | We explore the effect of introducing prior information into the intermediate level of neural networks for a learning task on which all the state-of-the-art machine learning algorithms tested failed to learn. We motivate our work from the hypothesis that humans learn such intermediate concepts from other individuals via... | https://arxiv.org/abs/1301.4083 | 2013-01-17T13:06:52Z | 1301.4083 | 10.48550/arxiv.1301.4083 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/3.0 | doi:10.48550/arxiv.1301.4083 | 1883b5c6e186362d49e1e929d399fd0db3b8bf552534cf7d7c43be90477e1cee | 0.76713 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1402.4525 | paper | corpus_metadata | null | Off-Policy General Value Functions to Represent Dynamic Role Assignments in RoboCup 3D Soccer Simulation | Collecting and maintaining accurate world knowledge in a dynamic, complex, adversarial, and stochastic environment such as the RoboCup 3D Soccer Simulation is a challenging task. Knowledge should be learned in real-time with time constraints. We use recently introduced Off-Policy Gradient Descent algorithms within Rein... | https://arxiv.org/abs/1402.4525 | 2014-02-18T23:01:13Z | 1402.4525 | 10.48550/arxiv.1402.4525 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/3.0 | doi:10.48550/arxiv.1402.4525 | 1ce890d052cbd3cce63812ac6e75badda8515a2aa97bbc41d1c5e1aeb3d06e41 | 0.565398 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1604.01692 | paper | corpus_metadata | null | An Ensemble Method to Produce High-Quality Word Embeddings (2016) | A currently successful approach to computational semantics is to represent words as embeddings in a machine-learned vector space. We present an ensemble method that combines embeddings produced by GloVe (Pennington et al., 2014) and word2vec (Mikolov et al., 2013) with structured knowledge from the semantic networks Co... | https://arxiv.org/abs/1604.01692 | 2016-04-06T16:58:35Z | 1604.01692 | 10.48550/arxiv.1604.01692 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1604.01692 | 1dd70be1ca9ca9fdf22da33882285d74d6750f213d63e1e6296553116aae44e9 | 0.67937 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1605.02464 | paper | corpus_metadata | null | Orientation Driven Bag of Appearances for Person Re-identification | Person re-identification (re-id) consists of associating individual across camera network, which is valuable for intelligent video surveillance and has drawn wide attention. Although person re-identification research is making progress, it still faces some challenges such as varying poses, illumination and viewpoints. ... | https://arxiv.org/abs/1605.02464 | 2016-05-09T08:25:33Z | 1605.02464 | 10.48550/arxiv.1605.02464 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1605.02464 | 81d16e5c46bf43b25da1019edc2e50b1407aed2b277fa77f68df24587b3684c7 | 0.71652 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1606.07792 | paper | corpus_metadata | Wide & Deep | Wide & Deep Learning for Recommender Systems | Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more... | https://arxiv.org/abs/1606.07792 | 2016-06-24T19:07:02Z | 1606.07792 | 10.48550/arxiv.1606.07792 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1606.07792 | a44252ed2bf73b0f3936cc3633ef41d1eaedf1a38bbc4086e645a590364d62b0 | 0.746674 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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"epoch-all-ai-models:row:3200"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.10827 | paper | corpus_metadata | null | Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks | Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates an intraoperative setting for breast cancer lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art methods (Weight Decay an... | https://arxiv.org/abs/1703.10827 | 2017-03-31T10:05:00Z | 1703.10827 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1703.10827 | 62ef73789da55979eea402c680b7cd1a440358c8a2bb195ecc56e2e92e3bfcd3 | 0.608342 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2112.13513 | paper | corpus_metadata | null | MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer | Pancreatic cancer is one of the most malignant cancers in the world, which deteriorates rapidly with very high mortality. The rapid on-site evaluation (ROSE) technique innovates the workflow by immediately analyzing the fast stained cytopathological images with on-site pathologists, which enables faster diagnosis in th... | https://arxiv.org/abs/2112.13513 | 2021-12-27T05:04:11Z | 2112.13513 | 10.48550/arxiv.2112.13513 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | doi:10.48550/arxiv.2112.13513 | 056eb049af15ecdbf8850826b4cb8a42e9774339f7ed0c32e70295dbcc3f6609 | 0.719957 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.07371 | paper | corpus_metadata | null | Expressive Explanations of DNNs by Combining Concept Analysis with ILP | Explainable AI has emerged to be a key component for black-box machine learning approaches in domains with a high demand for reliability or transparency. Examples are medical assistant systems, and applications concerned with the General Data Protection Regulation of the European Union, which features transparency as a... | https://arxiv.org/abs/2105.07371 | 2021-05-16T07:00:27Z | 2105.07371 | 10.1007/978-3-030-58285-2_11 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1007/978-3-030-58285-2_11 | 372a775aedfc4971551df004b26e5f67ce7bd8d060dc21475087175e74fbf832 | 0.710745 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1805.04398 | paper | corpus_metadata | null | Iteratively Trained Interactive Segmentation | Deep learning requires large amounts of training data to be effective. For the task of object segmentation, manually labeling data is very expensive, and hence interactive methods are needed. Following recent approaches, we develop an interactive object segmentation system which uses user input in the form of clicks as... | https://arxiv.org/abs/1805.04398 | 2018-05-11T13:43:27Z | 1805.04398 | 10.48550/arxiv.1805.04398 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1805.04398 | c7f2920a189559f38efaa57e9346a3914f2e256bc58dc0efeed67255dfe94da1 | 0.737569 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1707.07631 | paper | corpus_metadata | null | Deep Architectures for Neural Machine Translation | It has been shown that increasing model depth improves the quality of neural machine translation. However, different architectural variants to increase model depth have been proposed, and so far, there has been no thorough comparative study. In this work, we describe and evaluate several existing approaches to introduc... | https://arxiv.org/abs/1707.07631 | 2017-07-24T16:19:59Z | 1707.07631 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:1707.07631 | c4808b61f60e24ec68b540ab05dde2a983cd87132e4e689ebf2fdf7862f067e2 | 0.754322 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.00573 | paper | corpus_metadata | null | Generalization and Equilibrium in Generative Adversarial Nets (GANs) | We show that training of generative adversarial network (GAN) may not have good generalization properties; e.g., training may appear successful but the trained distribution may be far from target distribution in standard metrics. However, generalization does occur for a weaker metric called neural net distance. It is a... | https://arxiv.org/abs/1703.00573 | 2017-03-02T01:14:03Z | 1703.00573 | 10.48550/arxiv.1703.00573 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.00573 | ed6fabaa619d2b02632ac681522327ab7da59e7334aa256e5b604f943630b600 | 0.591724 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1708.05027 | paper | corpus_metadata | null | Neural Factorization Machines for Sparse Predictive Analytics | Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these categorical predictors are always converted to a set of binary features via one-hot encoding, making the resultant feature v... | https://arxiv.org/abs/1708.05027 | 2017-08-16T18:24:16Z | 1708.05027 | 10.48550/arxiv.1708.05027 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1708.05027 | df1f0a565438baea529aec28fb50c05e2551e69d0fb5b92338ab613523015b56 | 0.745255 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1709.06126 | paper | corpus_metadata | null | How intelligent are convolutional neural networks? | Motivated by the Gestalt pattern theory, and the Winograd Challenge for language understanding, we design synthetic experiments to investigate a deep learning algorithm's ability to infer simple (at least for human) visual concepts, such as symmetry, from examples. A visual concept is represented by randomly generated,... | https://arxiv.org/abs/1709.06126 | 2017-09-18T19:04:36Z | 1709.06126 | 10.48550/arxiv.1709.06126 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1709.06126 | c652038a3e40de349d1673916a9bd13d32bab8c4be732681df27024705888a8b | 0.684999 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1711.09405 | paper | corpus_metadata | null | Learning a Rotation Invariant Detector with Rotatable Bounding Box | Detection of arbitrarily rotated objects is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. The existing methods are not robust to angle varies of the objects because of the use of traditional bounding box, which is a rotation variant s... | https://arxiv.org/abs/1711.09405 | 2017-11-26T15:20:52Z | 1711.09405 | 10.48550/arxiv.1711.09405 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1711.09405 | 9a64239a87ae209ac2d61099af0ac8a8ebdccc69770f1533a4fd66a010756b65 | 0.719042 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.01220 | paper | corpus_metadata | null | Denoising Adversarial Autoencoders | Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space. More robust r... | https://arxiv.org/abs/1703.01220 | 2017-03-03T15:59:16Z | 1703.01220 | 10.1109/tnnls.2018.2852738 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tnnls.2018.2852738 | 1e0e40acc3b87abb82a3d55b2e644056453ae53a7378a2d582f4fb5d12bf2f48 | 0.735316 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1712.08838 | paper | corpus_metadata | null | Texture Synthesis with Recurrent Variational Auto-Encoder | We propose a recurrent variational auto-encoder for texture synthesis. A novel loss function, FLTBNK, is used for training the texture synthesizer. It is rotational and partially color invariant loss function. Unlike L2 loss, FLTBNK explicitly models the correlation of color intensity between pixels. Our texture synthe... | https://arxiv.org/abs/1712.08838 | 2017-12-23T20:38:57Z | 1712.08838 | 10.48550/arxiv.1712.08838 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1712.08838 | e4f69855a11778b8b105fa0e02ecb17cbe3f49442cf07d76b5459de3c820cd89 | 0.595123 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.00955 | paper | corpus_metadata | null | Toward Controlled Generation of Text | Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated... | https://arxiv.org/abs/1703.00955 | 2017-03-02T21:23:47Z | 1703.00955 | 10.48550/arxiv.1703.00955 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.00955 | 36017c3c0dccd4aaee5daf4638016db079dd82ac17d0d4be5c785327bb030ca3 | 0.744221 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.00850 | paper | corpus_metadata | null | Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification | State-of-the-art computer codes for simulating real physical systems are often characterized by a vast number of input parameters. Performing uncertainty quantification (UQ) tasks with Monte Carlo (MC) methods is almost always infeasible because of the need to perform hundreds of thousands or even millions of forward m... | https://arxiv.org/abs/1802.00850 | 2018-02-02T21:20:17Z | 1802.00850 | 10.1016/j.jcp.2018.08.036 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1016/j.jcp.2018.08.036 | ac04a6e415c22f09b94a9c8f4c545a7db79875be6be9ca9a8047236429d28b77 | 0.671453 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.02989 | paper | corpus_metadata | null | TensorFlow Quantum: A Software Framework for Quantum Machine Learning | We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-perf... | https://arxiv.org/abs/2003.02989 | 2020-03-06T01:31:43Z | 2003.02989 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2003.02989 | aaa704a864fabced31db99a458c6c51119a025e16276c2026b3431d29ee9b0fc | 0.66064 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.02824 | paper | corpus_metadata | null | Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation | Despite the recent progress of fully-supervised action segmentation techniques, the performance is still not fully satisfactory. One main challenge is the problem of spatiotemporal variations (e.g. different people may perform the same activity in various ways). Therefore, we exploit unlabeled videos to address this pr... | https://arxiv.org/abs/2003.02824 | 2020-03-05T18:52:33Z | 2003.02824 | 10.48550/arxiv.2003.02824 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.02824 | f13c89abcc1995e75df98cac14fded4e7f0e8727a41411bb09d3bab051bf4cd4 | 0.801153 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.02001 | paper | corpus_metadata | null | Dancing to Music | Dancing to music is an instinctive move by humans. Learning to model the music-to-dance generation process is, however, a challenging problem. It requires significant efforts to measure the correlation between music and dance as one needs to simultaneously consider multiple aspects, such as style and beat of both music... | https://arxiv.org/abs/1911.02001 | 2019-11-05T18:56:15Z | 1911.02001 | 10.48550/arxiv.1911.02001 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1911.02001 | 2b0517507420dd09e6c6241150862bf61c8e303dc1302ce46d59bb3801834a7c | 0.617532 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1910.09200 | paper | corpus_metadata | null | Deep Reinforcement Learning Control of Quantum Cartpoles | We generalize a standard benchmark of reinforcement learning, the classical cartpole balancing problem, to the quantum regime by stabilizing a particle in an unstable potential through measurement and feedback. We use state-of-the-art deep reinforcement learning to stabilize a quantum cartpole and find that our deep le... | https://arxiv.org/abs/1910.09200 | 2019-10-21T08:24:24Z | 1910.09200 | 10.1103/physrevlett.125.100401 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1103/physrevlett.125.100401 | d984b3613e40c99cf290e666c47aca10f052bc9e2a010e448499448e86f1aeab | 0.632105 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.11436 | paper | corpus_metadata | null | Nonlinear classifiers for ranking problems based on kernelized SVM | Many classification problems focus on maximizing the performance only on the samples with the highest relevance instead of all samples. As an example, we can mention ranking problems, accuracy at the top or search engines where only the top few queries matter. In our previous work, we derived a general framework includ... | https://arxiv.org/abs/2002.11436 | 2020-02-26T12:37:11Z | 2002.11436 | 10.48550/arxiv.2002.11436 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2002.11436 | e9b7bd37d3f4c2be7236316a8faf8914377dbb4ad39ff75eff0a68f54b2d6926 | 0.583328 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1808.03575 | paper | corpus_metadata | null | Weakly- and Semi-Supervised Panoptic Segmentation | We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks. In contrast to many popular instance segmentation approaches based on object detectors, our method does... | https://arxiv.org/abs/1808.03575 | 2018-08-10T15:04:14Z | 1808.03575 | 10.48550/arxiv.1808.03575 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1808.03575 | 4d679565c326034994fa4610f6c9fb3c19d2fb631d90bd44b3c5a5410c83c88a | 0.703834 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2001.09239 | paper | corpus_metadata | null | Multi-task self-supervised learning for Robust Speech Recognition | Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called work... | https://arxiv.org/abs/2001.09239 | 2020-01-25T00:24:45Z | 2001.09239 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2001.09239 | bc9fc53840a236d799237bc39b75cb755f80e936af15e3a2a10b6c602ee86fdd | 0.751019 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.05221 | paper | corpus_metadata | null | A Model to Search for Synthesizable Molecules | Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate molecules with desirable properties, they give no guarantees that the molecules can actually be synthesized in practice. We propose a new molecu... | https://arxiv.org/abs/1906.05221 | 2019-06-12T15:53:47Z | 1906.05221 | 10.48550/arxiv.1906.05221 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.05221 | 6bf8829de4cae239bb506f4663435e080142104eb9313fc6c5dbd8cd44db4939 | 0.60769 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.04108 | paper | corpus_metadata | null | Adversarial Policy Gradient for Deep Learning Image Augmentation | The use of semantic segmentation for masking and cropping input images has proven to be a significant aid in medical imaging classification tasks by decreasing the noise and variance of the training dataset. However, implementing this approach with classical methods is challenging: the cost of obtaining a dense segment... | https://arxiv.org/abs/1909.04108 | 2019-09-09T19:04:21Z | 1909.04108 | 10.48550/arxiv.1909.04108 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1909.04108 | 23d0d7ae71c4f1964dfa67d949fc374b650900d6b163cdfaf8aaf07f24222289 | 0.798173 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1708.04552 | paper | corpus_metadata | Cutout-regularized net | Improved Regularization of Convolutional Neural Networks with Cutout | Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to ... | https://arxiv.org/abs/1708.04552 | 2017-08-15T15:21:53Z | 1708.04552 | 10.48550/arxiv.1708.04552 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1708.04552 | 30aaa3fd0b83c7329e9969b88922a22e07049059992fb84205276caea4fd27c8 | 0.860836 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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arxiv:1907.07210 | paper | corpus_metadata | null | Real-time Vision-based Depth Reconstruction with NVidia Jetson | Vision-based depth reconstruction is a challenging problem extensively studied in computer vision but still lacking universal solution. Reconstructing depth from single image is particularly valuable to mobile robotics as it can be embedded to the modern vision-based simultaneous localization and mapping (vSLAM) method... | https://arxiv.org/abs/1907.07210 | 2019-07-16T18:33:20Z | 1907.07210 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1907.07210 | fda8c405b6353ab1c76263d3109f60825540e453aa4f09b42336e54a4e8434e4 | 0.761436 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.00363 | paper | corpus_metadata | null | A Kernel Perspective for Regularizing Deep Neural Networks | We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm cannot be computed, it admits upper and lower approximations leading to various practical strategies. Specifically, this perspective (i) provides a common umbrella f... | https://arxiv.org/abs/1810.00363 | 2018-09-30T11:40:59Z | 1810.00363 | 10.48550/arxiv.1810.00363 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1810.00363 | 756e35b497bee93c41e3b2f3eda97db5cfe86326f6a28a7f49e7ab3ff9f03da0 | 0.656163 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.01203 | paper | corpus_metadata | null | Query-guided End-to-End Person Search | Person search has recently gained attention as the novel task of finding a person, provided as a cropped sample, from a gallery of non-cropped images, whereby several other people are also visible. We believe that i. person detection and re-identification should be pursued in a joint optimization framework and that ii.... | https://arxiv.org/abs/1905.01203 | 2019-05-03T14:31:23Z | 1905.01203 | 10.48550/arxiv.1905.01203 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.01203 | 0c5c28c939a0cb07e22080cee22219e65d0b7784b0a172b5f46150e0235d19f3 | 0.720487 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.07126 | paper | corpus_metadata | null | SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects | Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direction, and dense distribution. Apart from natural images, such issues are especially pronounced for aerial images of great importance. This p... | https://arxiv.org/abs/1811.07126 | 2018-11-17T08:24:25Z | 1811.07126 | 10.48550/arxiv.1811.07126 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1811.07126 | 477de1c307e9b4862d34e62a91ccbc98069ac9b4a7eb7553e76b524ff161ebd6 | 0.73828 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.02918 | paper | corpus_metadata | null | Towards Query Efficient Black-box Attacks: An Input-free Perspective | Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial attacks, even in a black-box scenario. However, most of the existing black-box attack algorithms need to make a huge amount of queries to perform attacks, which is not practical in the real world. We note one of the main reas... | https://arxiv.org/abs/1809.02918 | 2018-09-09T03:49:06Z | 1809.02918 | 10.48550/arxiv.1809.02918 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1809.02918 | 94f0d3c0bc6b50ebbda19b8a506bf4f905f65394290b3f48766b89d3f79a80e1 | 0.66523 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.00794 | paper | corpus_metadata | null | Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation | We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar ... | https://arxiv.org/abs/1809.00794 | 2018-09-04T04:40:34Z | 1809.00794 | 10.48550/arxiv.1809.00794 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1809.00794 | 1bc0683789683643e28ea69a1ab032de08c119bbfc42c98f189a6430b760a07e | 0.728557 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.04682 | paper | corpus_metadata | null | Automatic Program Synthesis of Long Programs with a Learned Garbage Collector | We consider the problem of generating automatic code given sample input-output pairs. We train a neural network to map from the current state and the outputs to the program's next statement. The neural network optimizes multiple tasks concurrently: the next operation out of a set of high level commands, the operands of... | https://arxiv.org/abs/1809.04682 | 2018-09-12T21:25:28Z | 1809.04682 | 10.48550/arxiv.1809.04682 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1809.04682 | 2341389b92dbb9790491e8c99b258393fc9f3ef1ccaa93e58aaa5c673144bf9d | 0.766156 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1807.11590 | paper | corpus_metadata | null | Acquisition of Localization Confidence for Accurate Object Detection | Modern CNN-based object detectors rely on bounding box regression and non-maximum suppression to localize objects. While the probabilities for class labels naturally reflect classification confidence, localization confidence is absent. This makes properly localized bounding boxes degenerate during iterative regression ... | https://arxiv.org/abs/1807.11590 | 2018-07-30T21:36:20Z | 1807.11590 | 10.48550/arxiv.1807.11590 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1807.11590 | 0aca9efc9917fcd474048f4307df37d7e50957449857c02e273ea15ab304d7bc | 0.616578 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1907.12374 | paper | corpus_metadata | null | Topic Modeling with Wasserstein Autoencoders | We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean D... | https://arxiv.org/abs/1907.12374 | 2019-07-24T14:08:23Z | 1907.12374 | 10.48550/arxiv.1907.12374 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1907.12374 | 91c094f826352f667186cf3cf28442b746feb5575642d70d854fd1240ff2d840 | 0.741233 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.05594 | paper | corpus_metadata | null | Neural Duplicate Question Detection without Labeled Training Data | Supervised training of neural models to duplicate question detection in community Question Answering (cQA) requires large amounts of labeled question pairs, which are costly to obtain. To minimize this cost, recent works thus often used alternative methods, e.g., adversarial domain adaptation. In this work, we propose ... | https://arxiv.org/abs/1911.05594 | 2019-11-13T16:38:30Z | 1911.05594 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1911.05594 | 6fea08d10b4e3ecd39b5565575b953947bd75e26434fe42e7710b8fd7a5a74b0 | 0.782641 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.10186 | paper | corpus_metadata | null | Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding | We present a model of pragmatic referring expression interpretation in a grounded communication task (identifying colors from descriptions) that draws upon predictions from two recurrent neural network classifiers, a speaker and a listener, unified by a recursive pragmatic reasoning framework. Experiments show that thi... | https://arxiv.org/abs/1703.10186 | 2017-03-29T18:15:06Z | 1703.10186 | 10.48550/arxiv.1703.10186 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1703.10186 | 917684154e7eb45b28aac50edfaf892054fd267a1d6a33d76df78ac3a36ac6c1 | 0.608621 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1502.04623 | paper | corpus_metadata | Draw | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of com... | https://arxiv.org/abs/1502.04623 | 2015-02-16T16:48:56Z | 1502.04623 | 10.48550/arxiv.1502.04623 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1502.04623 | 962d259d85daa72736a95578b11d6066d375806322327f0d139a7a4fb06e485a | 0.740418 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1612.03975 | paper | corpus_metadata | null | ConceptNet 5.5: An Open Multilingual Graph of General Knowledge | Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge... | https://arxiv.org/abs/1612.03975 | 2016-12-12T23:54:52Z | 1612.03975 | 10.48550/arxiv.1612.03975 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1612.03975 | 1bba2f0273579c49937fd3d569249e97bc780424b920c56ea25018c019dcc9b3 | 0.710329 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1704.03560 | paper | corpus_metadata | null | ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge | This paper describes Luminoso's participation in SemEval 2017 Task 2, "Multilingual and Cross-lingual Semantic Word Similarity", with a system based on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses on general knowledge that relates the meanings of words and phrases. Our submission to SemE... | https://arxiv.org/abs/1704.03560 | 2017-04-11T22:44:35Z | 1704.03560 | 10.18653/v1/s17-2008 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.18653/v1/s17-2008 | 4e970865666c56dacab9c47d6924488fba30def2481ea7bf1debe3727e64407f | 0.576936 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1603.06393 | paper | corpus_metadata | null | Incorporating Copying Mechanism in Sequence-to-Sequence Learning | We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which certain segments in the input sequence are selectively replicated in the output sequence. A similar phenomenon is observable in human language communication. For example, humans tend to repeat entity names or eve... | https://arxiv.org/abs/1603.06393 | 2016-03-21T11:35:08Z | 1603.06393 | 10.48550/arxiv.1603.06393 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1603.06393 | a8a19ca21b2e2b6ff4db93f86d7f602c7362347a3b9788a24d65dd8bc1dd0f23 | 0.732835 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1505.08075 | paper | corpus_metadata | null | Transition-Based Dependency Parsing with Stack Long Short-Term Memory | We propose a technique for learning representations of parser states in transition-based dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networks---the stack LSTM. Like the conventional stack data structures used in transition-based parsing, elements can be pushed t... | https://arxiv.org/abs/1505.08075 | 2015-05-29T14:58:12Z | 1505.08075 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1505.08075 | 5cb49f32519025f874e178f4d513c5853e74aadc8bead14d57e2e38554e95fcc | 0.662564 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1708.04729 | paper | corpus_metadata | null | Deconvolutional Paragraph Representation Learning | Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a cornerstone for this challenging task. However, the quality of sentences during RNN-based decoding (reconstruction) decreases with the leng... | https://arxiv.org/abs/1708.04729 | 2017-08-16T00:52:32Z | 1708.04729 | 10.48550/arxiv.1708.04729 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1708.04729 | 4b415d43b208cedbaf0b360b2bdc3e4d731335d48b6ec57a80751506923ec1ea | 0.791364 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1506.01057 | paper | corpus_metadata | null | A Hierarchical Neural Autoencoder for Paragraphs and Documents | Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models. In this paper, we explore an important step toward this generation task: training an LSTM (Long-short term memory) auto-encoder to preserve and reconstruct multi-sentence paragr... | https://arxiv.org/abs/1506.01057 | 2015-06-02T20:53:53Z | 1506.01057 | 10.48550/arxiv.1506.01057 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1506.01057 | f8ec9e132465213e7e4ee25aa26a170062e45e68a8eeb040cdc61a2722d1b63f | 0.757607 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1509.01626 | paper | corpus_metadata | null | Character-level Convolutional Networks for Text Classification | This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against tra... | https://arxiv.org/abs/1509.01626 | 2015-09-04T22:31:53Z | 1509.01626 | 10.48550/arxiv.1509.01626 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1509.01626 | 8cde3040fde74e4206a62afce579a1c5e12e8821470439ef5bab2fcba190fd1a | 0.743661 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1912.02288 | paper | corpus_metadata | null | Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning | In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, ... | https://arxiv.org/abs/1912.02288 | 2019-12-04T22:34:54Z | 1912.02288 | 10.48550/arxiv.1912.02288 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1912.02288 | 709d0f1ed740609f60ab2c67f31752f7e710dea5d6afc1405c6ac18c4d6d2539 | 0.745744 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1804.10660 | paper | corpus_metadata | null | Large-Scale Visual Relationship Understanding | Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of <subject, relation, object> triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relation... | https://arxiv.org/abs/1804.10660 | 2018-04-27T19:41:39Z | 1804.10660 | 10.48550/arxiv.1804.10660 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1804.10660 | acc34fe9a1a5d10c974f4611cde54e20d1aa17124bb4852add5714c0bd1ef48e | 0.76023 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.08782 | paper | corpus_metadata | null | Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning | In this work we apply the Deep Galerkin Method (DGM) described in Sirignano and Spiliopoulos (2018) to solve a number of partial differential equations that arise in quantitative finance applications including option pricing, optimal execution, mean field games, etc. The main idea behind DGM is to represent the unknown... | https://arxiv.org/abs/1811.08782 | 2018-11-21T15:34:05Z | 1811.08782 | 10.48550/arxiv.1811.08782 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1811.08782 | f2c0b137d20e66dfd3138618fab6dd0dce0c79c09ca48907bb1edebb3e674699 | 0.531414 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.01753 | paper | corpus_metadata | null | Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions | While the history of machine learning so far largely encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is whether the problems themselves can be generated by the algorithm at the same time as they are being solved. Such a process would in effect build... | https://arxiv.org/abs/1901.01753 | 2019-01-07T11:28:36Z | 1901.01753 | 10.48550/arxiv.1901.01753 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1901.01753 | a349e46eecae0a7a6c214308739167d43d548467a57408dab31b63b391d511bb | 0.608385 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1605.03170 | paper | corpus_metadata | null | DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model | The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allo... | https://arxiv.org/abs/1605.03170 | 2016-05-10T19:49:40Z | 1605.03170 | 10.48550/arxiv.1605.03170 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1605.03170 | 549ed5a38e8c19a295442a3804691da8a41ab364c8b0e50efaaa727f484eda75 | 0.747796 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.01314 | paper | corpus_metadata | null | 'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images | Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this shortcoming by learning a new class from a few annotated support examples. We introduce, a novel few-shot framework, for the segmentation o... | https://arxiv.org/abs/1902.01314 | 2019-02-04T17:11:32Z | 1902.01314 | 10.48550/arxiv.1902.01314 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1902.01314 | 739417b727a8a203222707d401e724e41ae3b201afac7b048226701a7e3b2332 | 0.846866 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.10778 | paper | corpus_metadata | null | PanNuke Dataset Extension, Insights and Baselines | The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides. However, it is imperative for the DL algorithms relying on nuclei-level details to be abl... | https://arxiv.org/abs/2003.10778 | 2020-03-24T11:25:12Z | 2003.10778 | 10.48550/arxiv.2003.10778 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.10778 | dda54477e3b68006de4237fde1b4055a0d52d6f0200baf18f2a07dd4502f6850 | 0.662597 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1607.00133 | paper | corpus_metadata | null | Deep Learning with Differential Privacy | Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addr... | https://arxiv.org/abs/1607.00133 | 2016-07-01T07:29:10Z | 1607.00133 | 10.1145/2976749.2978318 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/2976749.2978318 | b4863c36772bbd1f0f80a11428879545c2f0e22ec51506c0da3941e28e508a1e | 0.79482 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.01046 | paper | corpus_metadata | null | Learning to Pivot with Adversarial Networks | Several techniques for domain adaptation have been proposed to account for differences in the distribution of the data used for training and testing. The majority of this work focuses on a binary domain label. Similar problems occur in a scientific context where there may be a continuous family of plausible data genera... | https://arxiv.org/abs/1611.01046 | 2016-11-03T14:41:40Z | 1611.01046 | 10.48550/arxiv.1611.01046 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1611.01046 | f8602df5797633f8e7cb6c24c11ebfe885d2dc852f9b6a6c035df6e7e891fe32 | 0.651629 | 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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