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arxiv:1709.09018 | paper | corpus_metadata | null | AutoEncoder by Forest | Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder. We present a procedure for enabling forests to do backward reconstruction by ut... | https://arxiv.org/abs/1709.09018 | 2017-09-26T13:54:34Z | 1709.09018 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1709.09018 | c84daacc8573f1fc5c5ef14430d49fcebd774dff7c0c960c73c8623f3da3975d | 0.730483 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.01241 | paper | corpus_metadata | null | Semantic Segmentation of Underwater Imagery: Dataset and Benchmark | In this paper, we present the first large-scale dataset for semantic Segmentation of Underwater IMagery (SUIM). It contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images have b... | https://arxiv.org/abs/2004.01241 | 2020-04-02T19:53:14Z | 2004.01241 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2004.01241 | 8fcbd62041c33fe2359a92a6f94686cca9e1cd3c4f3476de47982a988fac4991 | 0.761012 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.15218 | paper | corpus_metadata | null | Traditional and accelerated gradient descent for neural architecture search | In this paper we introduce two algorithms for neural architecture search (NASGD and NASAGD) following the theoretical work by two of the authors [5] which used the geometric structure of optimal transport to introduce the conceptual basis for new notions of traditional and accelerated gradient descent algorithms for th... | https://arxiv.org/abs/2006.15218 | 2020-06-26T21:28:35Z | 2006.15218 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2006.15218 | b570ffcffb97062c6983583e44f0621e7bf59df2690298d6f2c9220061f12860 | 0.586606 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1505.07293 | paper | corpus_metadata | null | SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling | We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label predictions, (ii) with increasing depth, a larger context is considered for pixel labelling which imp... | https://arxiv.org/abs/1505.07293 | 2015-05-27T12:54:17Z | 1505.07293 | 10.48550/arxiv.1505.07293 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1505.07293 | 7324efb1991968f1db516d219d898673795c8e5e603643335dc3f27f2504443d | 0.764365 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1604.05085 | paper | corpus_metadata | null | Mastering 2048 with Delayed Temporal Coherence Learning, Multi-Stage Weight Promotion, Redundant Encoding and Carousel Shaping | 2048 is an engaging single-player, nondeterministic video puzzle game, which, thanks to the simple rules and hard-to-master gameplay, has gained massive popularity in recent years. As 2048 can be conveniently embedded into the discrete-state Markov decision processes framework, we treat it as a testbed for evaluating e... | https://arxiv.org/abs/1604.05085 | 2016-04-18T11:06:32Z | 1604.05085 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1604.05085 | a42cd39c64c668feb4285132433f670201b4e5dbe8688935a9dbc87601f25e04 | 0.597495 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.02972 | paper | corpus_metadata | null | Blockwise Self-Attention for Long Document Understanding | We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short- ... | https://arxiv.org/abs/1911.02972 | 2019-11-07T16:35:53Z | 1911.02972 | 10.48550/arxiv.1911.02972 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1911.02972 | cf7bb2448ddd749d86bc7e77af333b7052f963b8988351003484c4f8997f9bba | 0.763379 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.08604 | paper | corpus_metadata | null | Deep Energy-Based Modeling of Discrete-Time Physics | Physical phenomena in the real world are often described by energy-based modeling theories, such as Hamiltonian mechanics or the Landau theory, which yield various physical laws. Recent developments in neural networks have enabled the mimicking of the energy conservation law by learning the underlying continuous-time d... | https://arxiv.org/abs/1905.08604 | 2019-05-21T13:21:46Z | 1905.08604 | 10.48550/arxiv.1905.08604 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.08604 | adc080ad5cf8de4cd46fcbaf0ef0506c9c824c8cbf33debd4c98db45125d027e | 0.514635 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1807.07543 | paper | corpus_metadata | null | Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer | Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can "interpolate": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce ... | https://arxiv.org/abs/1807.07543 | 2018-07-19T17:17:23Z | 1807.07543 | 10.48550/arxiv.1807.07543 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1807.07543 | c1a0db36a7b50b4c7421400305cac526fc148b145ac1892e64ea91ab0e1ef184 | 0.709169 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1804.02767 | paper | corpus_metadata | YOLOv3 | YOLOv3: An Incremental Improvement | We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times f... | https://arxiv.org/abs/1804.02767 | 2018-04-08T22:27:57Z | 1804.02767 | 10.48550/arxiv.1804.02767 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1804.02767 | 6f210ec1255f6e7b8d8c63e5c069e5aa4406dc8b610bf1467087ee8fa4af9f08 | 0.534729 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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arxiv:1812.11941 | paper | corpus_metadata | null | High Quality Monocular Depth Estimation via Transfer Learning | Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction. Existing solutions for depth estimation often produce blurry approximations of low resolution. This paper presents a convolutional neural network for computing a high-resolution depth map g... | https://arxiv.org/abs/1812.11941 | 2018-12-31T18:25:21Z | 1812.11941 | 10.48550/arxiv.1812.11941 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1812.11941 | c4117f347fa6e57ce62a8ef35d3775a7e231ee82339b456063c03025ca3ff1de | 0.868479 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1705.09792 | paper | corpus_metadata | null | Deep Complex Networks | At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and representations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capac... | https://arxiv.org/abs/1705.09792 | 2017-05-27T09:04:55Z | 1705.09792 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1705.09792 | 6c1f5808a803dd5473dab85f8c62185d21a548c492f8ad34fa0dea3ab0ecf09e | 0.809956 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1602.09046 | paper | corpus_metadata | null | On Complex Valued Convolutional Neural Networks | Convolutional neural networks (CNNs) are the cutting edge model for supervised machine learning in computer vision. In recent years CNNs have outperformed traditional approaches in many computer vision tasks such as object detection, image classification and face recognition. CNNs are vulnerable to overfitting, and a l... | https://arxiv.org/abs/1602.09046 | 2016-02-29T17:13:47Z | 1602.09046 | 10.48550/arxiv.1602.09046 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1602.09046 | f6a01185596534648bb4b203f3666973b06998d1a02b05590dbe925e1721063e | 0.780284 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.08026 | paper | corpus_metadata | null | Complex-valued Neural Networks with Non-parametric Activation Functions | Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers. However, several analytical properties of the complex domain (e.g., holomorphicity) make the design of CVNNs a more challenging task than their real counterpart. In this p... | https://arxiv.org/abs/1802.08026 | 2018-02-22T13:11:22Z | 1802.08026 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1802.08026 | 046225c29e4a506883bd9887dc37910349ee7f27983c50e5de47f4c329f4c0a2 | 0.586098 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1709.04905 | paper | corpus_metadata | null | One-Shot Visual Imitation Learning via Meta-Learning | In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity models such as deep neural networks can enable a robot to represent complex skills, but learning each skill from ... | https://arxiv.org/abs/1709.04905 | 2017-09-14T17:50:18Z | 1709.04905 | 10.48550/arxiv.1709.04905 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1709.04905 | 268b393dc58b0722bb6cec29b61a2184bac6b085d1d17512ae729f6e9c60180a | 0.760244 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.04247 | paper | corpus_metadata | null | DeepFM: A Factorization-Machine based Neural Network for CTR Prediction | Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to de... | https://arxiv.org/abs/1703.04247 | 2017-03-13T04:55:19Z | 1703.04247 | 10.48550/arxiv.1703.04247 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.04247 | e815a4461bac143aa3c7512cc6f40c91b51d681e83b52d7b88ff31bec482c4e0 | 0.758228 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1511.06434 | paper | corpus_metadata | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks | In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised lea... | https://arxiv.org/abs/1511.06434 | 2015-11-19T22:50:32Z | 1511.06434 | 10.48550/arxiv.1511.06434 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1511.06434 | d379f34812f5dc41e7069231541080d90ed353d4caa3cabfbbb5b0015d0b5bcd | 0.806821 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"iclr-test-of-time"
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"iclr-test-of-time:award:2026:5"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.02553 | paper | corpus_metadata | null | Oriented Point Sampling for Plane Detection in Unorganized Point Clouds | Plane detection in 3D point clouds is a crucial pre-processing step for applications such as point cloud segmentation, semantic mapping and SLAM. In contrast to many recent plane detection methods that are only applicable on organized point clouds, our work is targeted to unorganized point clouds that do not permit a 2... | https://arxiv.org/abs/1905.02553 | 2019-05-04T01:02:57Z | 1905.02553 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1905.02553 | b7cb4fd6749c1fc8af72bef95344036a5b78988a43c614ecdcb4360493b8a5c5 | 0.515266 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2403.11940 | paper | corpus_metadata | null | Multistep Inverse Is Not All You Need | In real-world control settings, the observation space is often unnecessarily high-dimensional and subject to time-correlated noise. However, the controllable dynamics of the system are often far simpler than the dynamics of the raw observations. It is therefore desirable to learn an encoder to map the observation space... | https://arxiv.org/abs/2403.11940 | 2024-03-18T16:36:01Z | 2403.11940 | 10.48550/arxiv.2403.11940 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2403.11940 | 6a50713962781b59e98fb48760fae290956c706ec2f29d13b719e8d7dc24480c | 0.670845 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.01852 | paper | corpus_metadata | null | Language-agnostic BERT Sentence Embedding | While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embedd... | https://arxiv.org/abs/2007.01852 | 2020-07-03T17:58:42Z | 2007.01852 | 10.48550/arxiv.2007.01852 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.01852 | ca2d74c34e684ac91175093b247ba23001b9c83f2b9ff847753fa59810fc74ef | 0.828282 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1701.04143 | paper | corpus_metadata | null | Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks | Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transfer... | https://arxiv.org/abs/1701.04143 | 2017-01-16T02:39:01Z | 1701.04143 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1701.04143 | b02ad1fce88c9fbd356924f7c761cb4878818c953b45cda7fdcad18a7026f684 | 0.677184 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.14288 | paper | corpus_metadata | null | Model-free bounds for multi-asset options using option-implied information and their exact computation | We consider derivatives written on multiple underlyings in a one-period financial market, and we are interested in the computation of model-free upper and lower bounds for their arbitrage-free prices. We work in a completely realistic setting, in that we only assume the knowledge of traded prices for other single- and ... | https://arxiv.org/abs/2006.14288 | 2020-06-25T10:14:12Z | 2006.14288 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2006.14288 | 534d327c36229a1cee7fd370d9ca15c196e6a0254294a78cd3e71b3a65cffa02 | 0.500028 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1610.09585 | paper | corpus_metadata | null | Conditional Image Synthesis With Auxiliary Classifier GANs | Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 r... | https://arxiv.org/abs/1610.09585 | 2016-10-30T00:29:31Z | 1610.09585 | 10.48550/arxiv.1610.09585 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1610.09585 | 7db294db6e11f040d73f96d4357dbab782861d658d6c914ac38c6a1ad70af163 | 0.660904 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.10683 | paper | corpus_metadata | null | Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data | This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples. However, directly employing adversarial learning and cycle-consistency constraints is insufficient to learn the underlying relationship between the shadow and shado... | https://arxiv.org/abs/1903.10683 | 2019-03-26T05:30:55Z | 1903.10683 | 10.1109/iccv.2019.00256 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/iccv.2019.00256 | 8365ee97f9db055e71c413e857cf2871ecc9573a337b9be97a967ea27362e35f | 0.674356 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.06412 | paper | corpus_metadata | null | TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network | In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions. Former approaches have tried to condition the generative process on the textual data; but allying it to ... | https://arxiv.org/abs/1703.06412 | 2017-03-19T10:07:58Z | 1703.06412 | 10.48550/arxiv.1703.06412 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.06412 | 26ddc65bfe077eba6c41ac317efeaf4e3c3f3c5f61e2aa19fa34b41e273b001a | 0.747897 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.08718 | paper | corpus_metadata | null | Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN | Shadow removal is an essential task for scene understanding. Many studies consider only matching the image contents, which often causes two types of ghosts: color in-consistencies in shadow regions or artifacts on shadow boundaries. In this paper, we tackle these issues in two ways. First, to carefully learn the border... | https://arxiv.org/abs/1911.08718 | 2019-11-20T05:52:14Z | 1911.08718 | 10.48550/arxiv.1911.08718 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1911.08718 | c62732aab9caf0e630542169af9c0529d4e7732e8d425f7cdbba6aa3d0e4e345 | 0.748879 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1806.05376 | paper | corpus_metadata | null | Single Image Reflection Separation with Perceptual Losses | We present an approach to separating reflection from a single image. The approach uses a fully convolutional network trained end-to-end with losses that exploit low-level and high-level image information. Our loss function includes two perceptual losses: a feature loss from a visual perception network, and an adversari... | https://arxiv.org/abs/1806.05376 | 2018-06-14T06:03:03Z | 1806.05376 | 10.48550/arxiv.1806.05376 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1806.05376 | 55d5ac39f03ddb3987fc5c08af8f178c9f265f5b75ce9db3a988aa12a28825ea | 0.786606 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1308.3432 | paper | corpus_metadata | null | Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation | Stochastic neurons and hard non-linearities can be useful for a number of reasons in deep learning models, but in many cases they pose a challenging problem: how to estimate the gradient of a loss function with respect to the input of such stochastic or non-smooth neurons? I.e., can we "back-propagate" through these st... | https://arxiv.org/abs/1308.3432 | 2013-08-15T15:19:34Z | 1308.3432 | 10.48550/arxiv.1308.3432 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1308.3432 | 003f7c9c6e50b9c4e011d84873efa0e8166381ae591a21f50fa1ebae32c1e418 | 0.582031 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.09531 | paper | corpus_metadata | null | Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting | A learned generative model often produces biased statistics relative to the underlying data distribution. A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions. When the likelihood ratio is unknown, it can b... | https://arxiv.org/abs/1906.09531 | 2019-06-23T01:57:29Z | 1906.09531 | 10.48550/arxiv.1906.09531 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.09531 | 944e8a1f148aa28595e2c08862bc3af7bb4bde557e9a1b87b1e7e83d2b619d20 | 0.775101 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1910.02600 | paper | corpus_metadata | null | Deep Evidential Regression | Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this paper, we propose a novel method for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order ... | https://arxiv.org/abs/1910.02600 | 2019-10-07T04:11:34Z | 1910.02600 | 10.48550/arxiv.1910.02600 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1910.02600 | 866b4f7cc53cbfda30a368371f54f8d738cdb178f1f020579ead7ae5b7c45ac9 | 0.696473 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.08095 | paper | corpus_metadata | null | Automatic Fault Detection for Deep Learning Programs Using Graph Transformations | Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting Deep Learning (DL) to solve complex real-world problems. A DL program encodes the network structure of a desirable DL model and the process by which the model learns from the training dataset. Like any software, a DL program... | https://arxiv.org/abs/2105.08095 | 2021-05-17T18:06:11Z | 2105.08095 | 10.48550/arxiv.2105.08095 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2105.08095 | cba4fa268416ff7876d04ad43affce72eff24ce651babf0b9834376f08d5baf4 | 0.745175 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.08024 | paper | corpus_metadata | null | EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces | Brain computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct ch... | https://arxiv.org/abs/1611.08024 | 2016-11-23T22:36:58Z | 1611.08024 | 10.1088/1741-2552/aace8c | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1088/1741-2552/aace8c | cc86647830ec87d87e32c690ed2220f7b90baaf3cd50d076dcbfaca7f3f8f8fb | 0.726145 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.08206 | paper | corpus_metadata | null | LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer | Many types of text style transfer can be achieved with only small, precise edits (e.g. sentiment transfer from I had a terrible time... to I had a great time...). We propose a coarse-to-fine editor for style transfer that transforms text using Levenshtein edit operations (e.g. insert, replace, delete). Unlike prior sin... | https://arxiv.org/abs/2105.08206 | 2021-05-18T00:08:30Z | 2105.08206 | 10.48550/arxiv.2105.08206 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2105.08206 | b412144662b589e5afc66f2e427a170ece42f26c6da1c2bf21e585bfe1e72fa9 | 0.767357 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1807.01613 | paper | corpus_metadata | null | Conditional Neural Processes | Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPs are computationally expensive, and it... | https://arxiv.org/abs/1807.01613 | 2018-07-04T14:36:15Z | 1807.01613 | 10.48550/arxiv.1807.01613 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1807.01613 | 126e1ecfd011ae1619f3394e8ac415fbdc50a2e0056463f34cd1f7e823374562 | 0.757796 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.02950 | paper | corpus_metadata | null | Supervised Multimodal Bitransformers for Classifying Images and Text | Self-supervised bidirectional transformer models such as BERT have led to dramatic improvements in a wide variety of textual classification tasks. The modern digital world is increasingly multimodal, however, and textual information is often accompanied by other modalities such as images. We introduce a supervised mult... | https://arxiv.org/abs/1909.02950 | 2019-09-06T14:59:18Z | 1909.02950 | 10.48550/arxiv.1909.02950 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1909.02950 | 18a96449e7e5460b096b63e20432d8a8b7fb895a6314acae0b37587430b54d95 | 0.823593 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.04208 | paper | corpus_metadata | null | Adversarial Audio Synthesis | Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to aud... | https://arxiv.org/abs/1802.04208 | 2018-02-12T17:50:43Z | 1802.04208 | 10.48550/arxiv.1802.04208 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1802.04208 | 253b358e63a64be2c355d4f021ac8146031613ec50d6345c0b66ff0a5067139f | 0.703367 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.14378 | paper | corpus_metadata | null | Towards Faster Reasoners By Using Transparent Huge Pages | Various state-of-the-art automated reasoning (AR) tools are widely used as backend tools in research of knowledge representation and reasoning as well as in industrial applications. In testing and verification, those tools often run continuously or nightly. In this work, we present an approach to reduce the runtime of ... | https://arxiv.org/abs/2004.14378 | 2020-04-29T17:57:19Z | 2004.14378 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2004.14378 | 0319ad23673f0da88dd27d63ad7b299e9674fb6e7cdfacb9ef3c00c1c55bc8b8 | 0.592952 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.05018 | paper | corpus_metadata | null | TedNet: A Pytorch Toolkit for Tensor Decomposition Networks | Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures. To give more researchers a flexible way to exploit TDNs, we present a Pytorch toolkit named TedNet. TedNet implements 5 kinds of tensor decomposition(i.e., CANDECOMP/PARAFAC (CP), Block-Term Tucker (BTT), Tucker-2, Tensor Train (TT) ... | https://arxiv.org/abs/2104.05018 | 2021-04-11T14:04:06Z | 2104.05018 | 10.1016/j.neucom.2021.10.064 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1016/j.neucom.2021.10.064 | 07ee0291cac271a13b9885a57475e15b16f02f7125e12b80773102fee48de501 | 0.516375 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.13917 | paper | corpus_metadata | null | Task Programming: Learning Data Efficient Behavior Representations | Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video track... | https://arxiv.org/abs/2011.13917 | 2020-11-27T18:58:32Z | 2011.13917 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2011.13917 | 62133493e4d9191b09fd36f7a271e7ab3830a67c2bd695dd1dce764ee4a3cd04 | 0.727044 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.12832 | paper | corpus_metadata | null | General audio tagging with ensembling convolutional neural network and statistical features | Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general audio tagging challenge. The contributions of our solution include: We investigated a variety of convo... | https://arxiv.org/abs/1810.12832 | 2018-10-30T15:59:28Z | 1810.12832 | 10.1121/1.5111059 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1121/1.5111059 | 807115b49e5226aa5d4e61d2ba0a69d1e028c4ef4000e794b49e59589819ca0e | 0.761578 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.07192 | paper | corpus_metadata | null | Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data | We address the problem of learning to benchmark the best achievable classifier performance. In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a Bayes-optimal classifier. Our learning to benchmark framework improves on previ... | https://arxiv.org/abs/1909.07192 | 2019-09-16T13:37:59Z | 1909.07192 | 10.48550/arxiv.1909.07192 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1909.07192 | 19f1b2e50bbb216ac688dd3b24571e457dc493c09165e597f823f4c37fe9b301 | 0.769629 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.04094 | paper | corpus_metadata | null | BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model | We show that BERT (Devlin et al., 2018) is a Markov random field language model. This formulation gives way to a natural procedure to sample sentences from BERT. We generate from BERT and find that it can produce high-quality, fluent generations. Compared to the generations of a traditional left-to-right language model... | https://arxiv.org/abs/1902.04094 | 2019-02-11T19:02:27Z | 1902.04094 | 10.48550/arxiv.1902.04094 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1902.04094 | 989c0fef4846850f1fdbd4e5ee83de57cfa2114c7c76319cd1bfd3a8547b67c3 | 0.589776 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1506.04972 | paper | corpus_metadata | null | A Unified Successive Pseudo-Convex Approximation Framework | In this paper, we propose a successive pseudo-convex approximation algorithm to efficiently compute stationary points for a large class of possibly nonconvex optimization problems. The stationary points are obtained by solving a sequence of successively refined approximate problems, each of which is much easier to solv... | https://arxiv.org/abs/1506.04972 | 2015-06-16T13:54:00Z | 1506.04972 | 10.1109/tsp.2017.2684748 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tsp.2017.2684748 | 02733d2849439519604a452d76058056bd22b35257bd8fc8d89bd819a66dee89 | 0.606079 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.04899 | paper | corpus_metadata | null | CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features | Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers. They have proved to be effective for guiding the model to attend on less discriminative parts of objects (e.g. leg as opposed to head of a person), thereby letting the network generalize better and hav... | https://arxiv.org/abs/1905.04899 | 2019-05-13T08:10:22Z | 1905.04899 | 10.48550/arxiv.1905.04899 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.04899 | f272a1ef902578024bb6683b237db05cc90cc46f3355b7011896e798f2cc380d | 0.852505 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.04052 | paper | corpus_metadata | null | On the Texture Bias for Few-Shot CNN Segmentation | Despite the initial belief that Convolutional Neural Networks (CNNs) are driven by shapes to perform visual recognition tasks, recent evidence suggests that texture bias in CNNs provides higher performing models when learning on large labeled training datasets. This contrasts with the perceptual bias in the human visua... | https://arxiv.org/abs/2003.04052 | 2020-03-09T11:55:47Z | 2003.04052 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2003.04052 | d7355dd4b664eacb051dbe5069d3fbbc1c1138ad3d613cd80df726d369ee648e | 0.874941 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1602.07261 | paper | corpus_metadata | The impact of residual connections in image recognition was once again showed in this paper by Google: , Szegedy et al, 2017, AAAI, **864 citations** | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning | Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunct... | https://arxiv.org/abs/1602.07261 | 2016-02-23T18:44:39Z | 1602.07261 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1602.07261 | 6c7c45b2ce2137c7494cbebb99c9acc7acd00e384877fdb9d1fcdce818af57e9 | 0.706036 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:1806.05620 | paper | corpus_metadata | null | DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes | The assumption of scene rigidity is typical in SLAM algorithms. Such a strong assumption limits the use of most visual SLAM systems in populated real-world environments, which are the target of several relevant applications like service robotics or autonomous vehicles. In this paper we present DynaSLAM, a visual SLAM s... | https://arxiv.org/abs/1806.05620 | 2018-06-14T15:52:07Z | 1806.05620 | 10.1109/lra.2018.2860039 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/lra.2018.2860039 | ec354cec4da64945fd207ec5bcf71c3ea68e2aa5a9f016e3e386c56326e61065 | 0.59309 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1612.07695 | paper | corpus_metadata | null | MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving | While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving. Towards this goal, we present an approach to joint classification, detection and semantic segmentati... | https://arxiv.org/abs/1612.07695 | 2016-12-22T16:55:02Z | 1612.07695 | 10.17863/cam.26778 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.17863/cam.26778 | 071606d52a52278801bc26eb5028d4d19e08070f3ed184f4e55088d672eca495 | 0.791912 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1704.04861 | paper | corpus_metadata | MobileNet | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications | We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off betw... | https://arxiv.org/abs/1704.04861 | 2017-04-17T03:57:34Z | 1704.04861 | 10.48550/arxiv.1704.04861 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1704.04861 | 59fac1aabfae3a105806539a7d57665e4b6672fb4570a64cf16a4e49a9102180 | 0.747989 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:1612.00563 | paper | corpus_metadata | null | Self-critical Sequence Training for Image Captioning | Recently it has been shown that policy-gradient methods for reinforcement learning can be utilized to train deep end-to-end systems directly on non-differentiable metrics for the task at hand. In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by car... | https://arxiv.org/abs/1612.00563 | 2016-12-02T04:37:22Z | 1612.00563 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1612.00563 | fde29bf037724261481c2b8b4e284e39d7c4bb023e7f24726a250f06441f748a | 0.755161 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2204.07188 | paper | corpus_metadata | null | Flexible Marginal Models for Dependent Data | Models for dependent data are distinguished by their targets of inference. Marginal models are useful when interest lies in quantifying associations averaged across a population of clusters. When the functional form of a covariate-outcome association is unknown, flexible regression methods are needed to allow for poten... | https://arxiv.org/abs/2204.07188 | 2022-04-14T18:48:32Z | 2204.07188 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2204.07188 | b8d3d21a49f080700e898f573dc345c0621c973d8ec9fad8d7f949432b836f27 | 0.548267 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1506.02640 | paper | corpus_metadata | YOLO | You Only Look Once: Unified, Real-Time Object Detection | We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class ... | https://arxiv.org/abs/1506.02640 | 2015-06-08T19:52:52Z | 1506.02640 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1506.02640 | 5399e3421a595e31b98e50ff28553d99e84466c2e491c4ee7487a797e8c1bde8 | 0.74657 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:1506.07285 | paper | corpus_metadata | DMN | Ask Me Anything: Dynamic Memory Networks for Natural Language Processing | Most tasks in natural language processing can be cast into question answering (QA) problems over language input. We introduce the dynamic memory network (DMN), a neural network architecture which processes input sequences and questions, forms episodic memories, and generates relevant answers. Questions trigger an itera... | https://arxiv.org/abs/1506.07285 | 2015-06-24T08:27:02Z | 1506.07285 | 10.48550/arxiv.1506.07285 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1506.07285 | 19c909966c1932078a1484214f81ad18cf00439f0c3453de9bf31c0fed27d0fb | 0.825089 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:1808.06601 | paper | corpus_metadata | null | Video-to-Video Synthesis | We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image synthesis pro... | https://arxiv.org/abs/1808.06601 | 2018-08-20T17:58:42Z | 1808.06601 | 10.48550/arxiv.1808.06601 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1808.06601 | 82f0b0a6c3245bba872c8af8b5bc76d317a62aaf98570847f2dcf3f84317d576 | 0.796582 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1607.01759 | paper | corpus_metadata | fastText | Bag of Tricks for Efficient Text Classification | This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion wo... | https://arxiv.org/abs/1607.01759 | 2016-07-06T19:40:15Z | 1607.01759 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1607.01759 | 75acc043bb827501d8b52134d3aed0b0440c615d86d9821c697cc38d3e6dd0d6 | 0.68715 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:1904.01355 | paper | corpus_metadata | null | FCOS: Fully Convolutional One-Stage Object Detection | We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed dete... | https://arxiv.org/abs/1904.01355 | 2019-04-02T11:56:36Z | 1904.01355 | 10.48550/arxiv.1904.01355 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.1904.01355 | a32170d87b35ce3bbcc33633515400f208af1ba44c6567f95108321a2c8f553e | 0.736422 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.03353 | paper | corpus_metadata | null | RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free | Recently two-stage detectors have surged ahead of single-shot detectors in the accuracy-vs-speed trade-off. Nevertheless single-shot detectors are immensely popular in embedded vision applications. This paper brings single-shot detectors up to the same level as current two-stage techniques. We do this by improving trai... | https://arxiv.org/abs/1901.03353 | 2019-01-10T19:35:28Z | 1901.03353 | 10.48550/arxiv.1901.03353 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1901.03353 | b547aefe78e78ba7ef61f923a9574bc47da28cf66aaa3f136205ed91b4d8ac36 | 0.696316 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2211.14058 | paper | corpus_metadata | null | Cross-Domain Ensemble Distillation for Domain Generalization | Domain generalization is the task of learning models that generalize to unseen target domains. We propose a simple yet effective method for domain generalization, named cross-domain ensemble distillation (XDED), that learns domain-invariant features while encouraging the model to converge to flat minima, which recently... | https://arxiv.org/abs/2211.14058 | 2022-11-25T12:32:36Z | 2211.14058 | 10.48550/arxiv.2211.14058 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2211.14058 | 7dcc6c05969ad254fbf1e113204a1cd601334d0cddf7163747d9b119c64e83b2 | 0.857926 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.00722 | paper | corpus_metadata | null | Topological Autoencoders | We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders. Using persistent homology, a technique from topological data analysis, we calculate topological signatures of both the input and latent space to derive a topological loss term. Under weak theo... | https://arxiv.org/abs/1906.00722 | 2019-06-03T11:41:47Z | 1906.00722 | 10.3929/ethz-b-000462046 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.3929/ethz-b-000462046 | 0df8b285b2832508f1861c587d93b0559715c42446409d2f7e24040e33a36431 | 0.589764 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1804.09541 | paper | corpus_metadata | null | QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension | Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\&A architecture called QANet, which ... | https://arxiv.org/abs/1804.09541 | 2018-04-23T11:33:43Z | 1804.09541 | 10.48550/arxiv.1804.09541 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1804.09541 | 5be83d7fcbd5466298157f3b613c967bdf587d54b0ddfdd0e969033e19cc2e38 | 0.802552 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.01393 | paper | corpus_metadata | null | On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR | We propose an on-the-fly data augmentation method for automatic speech recognition (ASR) that uses alignment information to generate effective training samples. Our method, called Aligned Data Augmentation (ADA) for ASR, replaces transcribed tokens and the speech representations in an aligned manner to generate previou... | https://arxiv.org/abs/2104.01393 | 2021-04-03T13:00:00Z | 2104.01393 | 10.21437/interspeech.2021-1679 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.21437/interspeech.2021-1679 | 6d6dcb5be771b9e7abc12d7f7e14df6ee87ed8c10a72d97d1737565bf25fad88 | 0.753526 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1708.05509 | paper | corpus_metadata | null | Towards the Automatic Anime Characters Creation with Generative Adversarial Networks | Automatic generation of facial images has been well studied after the Generative Adversarial Network (GAN) came out. There exists some attempts applying the GAN model to the problem of generating facial images of anime characters, but none of the existing work gives a promising result. In this work, we explore the trai... | https://arxiv.org/abs/1708.05509 | 2017-08-18T04:57:28Z | 1708.05509 | 10.48550/arxiv.1708.05509 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1708.05509 | 179ea635791fea9c7434d9ea6177ccd76ea4d3ae9eeb42b45fb0be463ae9a9b3 | 0.728187 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1512.02949 | paper | corpus_metadata | null | Video captioning with recurrent networks based on frame- and video-level features and visual content classification | In this paper, we describe the system for generating textual descriptions of short video clips using recurrent neural networks (RNN), which we used while participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015. Our work builds on static image captioning systems with RNN based language models and ... | https://arxiv.org/abs/1512.02949 | 2015-12-09T17:17:29Z | 1512.02949 | 10.48550/arxiv.1512.02949 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1512.02949 | 052b8159bdf00ab3eb8dae2b4bba9e9b6c45abed64b6fbfe0ff9c22d163f8309 | 0.739342 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.06837 | paper | corpus_metadata | null | FastTrack: an open-source software for tracking varying numbers of deformable objects | Analyzing the dynamical properties of mobile objects requires to extract trajectories from recordings, which is often done by tracking movies. We compiled a database of two-dimensional movies for very different biological and physical systems spanning a wide range of length scales and developed a general-purpose, optim... | https://arxiv.org/abs/2011.06837 | 2020-11-13T09:52:58Z | 2011.06837 | 10.1371/journal.pcbi.1008697 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1371/journal.pcbi.1008697 | e90d7eb67501228c29711b187b7e5067510c16bd327949b00be1793d1871cf17 | 0.651964 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.13457 | paper | corpus_metadata | null | Learning Semantically Enhanced Feature for Fine-Grained Image Classification | We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter. Unlike previous methods that rely on complex part localization modules, our approach learns fine-grained features by enhancing the semantics of sub-features of a global feature. Specifically, we... | https://arxiv.org/abs/2006.13457 | 2020-06-24T03:41:12Z | 2006.13457 | 10.1109/lsp.2020.3020227 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/lsp.2020.3020227 | 5b6abbd4e8fd4385449b6967caa4285260f5f20f98933e5b70b8b57ecca0f2ee | 0.806994 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.08080 | paper | corpus_metadata | null | Jointly De-biasing Face Recognition and Demographic Attribute Estimation | We address the problem of bias in automated face recognition and demographic attribute estimation algorithms, where errors are lower on certain cohorts belonging to specific demographic groups. We present a novel de-biasing adversarial network (DebFace) that learns to extract disentangled feature representations for bo... | https://arxiv.org/abs/1911.08080 | 2019-11-19T03:44:34Z | 1911.08080 | 10.48550/arxiv.1911.08080 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1911.08080 | 3dfe55f3514820c504d8495efb24229f45997f15050f54e4cef373ec07accc55 | 0.648971 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.02407 | paper | corpus_metadata | null | Explaining Anomalies Detected by Autoencoders Using SHAP | Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts. In Contrast, deep learning algorithms for anomaly detection, such as autoencoders, point out the outliers, saving experts the time-consuming task of examining normal ... | https://arxiv.org/abs/1903.02407 | 2019-03-06T14:29:27Z | 1903.02407 | 10.48550/arxiv.1903.02407 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1903.02407 | 6e4a4c02dbf93a4d8f9d59741ec96ad0bbf310edd0be105e9513decad92e449d | 0.60685 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.12173 | paper | corpus_metadata | null | Painting with baryons: augmenting N-body simulations with gas using deep generative models | Running hydrodynamical simulations to produce mock data of large-scale structure and baryonic probes, such as the thermal Sunyaev-Zeldovich (tSZ) effect, at cosmological scales is computationally challenging. We propose to leverage the expressive power of deep generative models to find an effective description of the l... | https://arxiv.org/abs/1903.12173 | 2019-03-28T17:59:16Z | 1903.12173 | 10.1093/mnrasl/slz075 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1093/mnrasl/slz075 | eac6e8939a6a05292fdde725921216e6c6396393e2c291eeb03cbe64e507dbdf | 0.543909 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.04218 | paper | corpus_metadata | null | Teaching Temporal Logics to Neural Networks | We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics. In this work we focus on linear-time temporal logic (LTL), as it is widely used in verification. We train a Transformer on the proble... | https://arxiv.org/abs/2003.04218 | 2020-03-06T14:46:49Z | 2003.04218 | 10.48550/arxiv.2003.04218 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.04218 | 77efa1c76550e5cc4361d3a571827607f20985f9401f71138947671ba26c2f01 | 0.713117 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1512.02325 | paper | corpus_metadata | SSD | SSD: Single Shot MultiBox Detector | We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence ... | https://arxiv.org/abs/1512.02325 | 2015-12-08T04:46:38Z | 1512.02325 | 10.1007/978-3-319-46448-0_2 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/978-3-319-46448-0_2 | 60cdd5c4d88a155eea111471ee3c913de34dacf13662456435b681da9fcf9588 | 0.82055 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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"epoch-all-ai-models:row:3232"
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arxiv:1812.00058 | paper | corpus_metadata | null | Corresponding Projections for Orphan Screening | We propose a novel transfer learning approach for orphan screening called corresponding projections. In orphan screening the learning task is to predict the binding affinities of compounds to an orphan protein, i.e., one for which no training data is available. The identification of compounds with high affinity is a ce... | https://arxiv.org/abs/1812.00058 | 2018-11-30T21:07:33Z | 1812.00058 | 10.24406/publica-fhg-407873 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.24406/publica-fhg-407873 | 443a7bfbc90bc261fc0860ea4b59a207808ab1fd7ee6afcc5a13ad7c77fbd5c4 | 0.678936 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.11876 | paper | corpus_metadata | null | Adversarial Robustness Guarantees for Classification with Gaussian Processes | We investigate adversarial robustness of Gaussian Process Classification (GPC) models. Given a compact subset of the input space $T\subseteq \mathbb{R}^d$ enclosing a test point $x^*$ and a GPC trained on a dataset $\mathcal{D}$, we aim to compute the minimum and the maximum classification probability for the GPC over ... | https://arxiv.org/abs/1905.11876 | 2019-05-28T15:18:13Z | 1905.11876 | 10.48550/arxiv.1905.11876 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.11876 | dc12a3a84a50f4c815a3517088434d70990e1bd70b613c6aab6361d960360c06 | 0.572779 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.14529 | paper | corpus_metadata | null | Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation | Video object detection is a fundamental problem in computer vision and has a wide spectrum of applications. Based on deep networks, video object detection is actively studied for pushing the limits of detection speed and accuracy. To reduce the computation cost, we sparsely sample key frames in video and treat the rest... | https://arxiv.org/abs/2103.14529 | 2021-03-25T01:38:31Z | 2103.14529 | 10.1016/j.cviu.2021.103188 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1016/j.cviu.2021.103188 | 255d97f6d619613ed671c3d74ed6506d4bd5f1b86c6a18ddd31698b68607e7a2 | 0.758753 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2210.09013 | paper | corpus_metadata | null | Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization | Knowledge Tracing (KT), which aims to model student knowledge level and predict their performance, is one of the most important applications of user modeling. Modern KT approaches model and maintain an up-to-date state of student knowledge over a set of course concepts according to students' historical performance in a... | https://arxiv.org/abs/2210.09013 | 2022-10-06T06:22:46Z | 2210.09013 | 10.48550/arxiv.2210.09013 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2210.09013 | e9d161c29795129f041e7d699678a5d637b065c4d19db021c3fa20b82334ad7b | 0.632178 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1904.05049 | paper | corpus_metadata | null | Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution | In natural images, information is conveyed at different frequencies where higher frequencies are usually encoded with fine details and lower frequencies are usually encoded with global structures. Similarly, the output feature maps of a convolution layer can also be seen as a mixture of information at different frequen... | https://arxiv.org/abs/1904.05049 | 2019-04-10T08:15:00Z | 1904.05049 | 10.48550/arxiv.1904.05049 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1904.05049 | 576663ca6ab32c97e59d53e1adbe0b7959d90c06321e4c01b594bac39d3887be | 0.642233 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1806.01260 | paper | corpus_metadata | null | Digging Into Self-Supervised Monocular Depth Estimation | Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform monocular depth estimation. In this paper, we propose a set of improvements, which together result in both quantitatively and ... | https://arxiv.org/abs/1806.01260 | 2018-06-04T17:58:05Z | 1806.01260 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1806.01260 | 86c7214a100f35670b91c059c167c198584b63908b88653fb3637280c3719719 | 0.752642 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.06022 | paper | corpus_metadata | null | A Fully Differentiable Beam Search Decoder | We introduce a new beam search decoder that is fully differentiable, making it possible to optimize at training time through the inference procedure. Our decoder allows us to combine models which operate at different granularities (e.g. acoustic and language models). It can be used when target sequences are not aligned... | https://arxiv.org/abs/1902.06022 | 2019-02-16T01:28:12Z | 1902.06022 | 10.48550/arxiv.1902.06022 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1902.06022 | f29bbf328a7218afd51577229cbcb87900cc0c707b1bdc4d872d1fa567100056 | 0.768063 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.03783 | paper | corpus_metadata | null | Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions | Frequentist statistical methods, such as hypothesis testing, are standard practice in papers that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their statistical test assumptions or without controlling for family-wise errors in multiple group comparisons,... | https://arxiv.org/abs/2010.03783 | 2020-10-08T06:01:29Z | 2010.03783 | 10.1109/tevc.2021.3081167 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tevc.2021.3081167 | de8fb7c1873b688a0e78e7829bc72b02a202d1968a0745622688134b5501634e | 0.557296 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1808.03818 | paper | corpus_metadata | null | Automatically designing CNN architectures using genetic algorithm for image classification | Convolutional Neural Networks (CNNs) have gained a remarkable success on many image classification tasks in recent years. However, the performance of CNNs highly relies upon their architectures. For most state-of-the-art CNNs, their architectures are often manually-designed with expertise in both CNNs and the investiga... | https://arxiv.org/abs/1808.03818 | 2018-08-11T15:32:06Z | 1808.03818 | 10.1109/tcyb.2020.2983860 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tcyb.2020.2983860 | 54d00d877cecbd5596534a1acd3924fa44ac155ac61fe743aa2912eaf882a491 | 0.792821 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.02976 | paper | corpus_metadata | null | Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling Denotation and Connotation in Vector Spaces | In politics, neologisms are frequently invented for partisan objectives. For example, "undocumented workers" and "illegal aliens" refer to the same group of people (i.e., they have the same denotation), but they carry clearly different connotations. Examples like these have traditionally posed a challenge to reference-... | https://arxiv.org/abs/2010.02976 | 2020-10-06T19:09:03Z | 2010.02976 | 10.48550/arxiv.2010.02976 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2010.02976 | 2702fec45ccb03dea1884768f320ac839a45cfbddf1fbc0de1af98feb7d2794d | 0.562587 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.01224 | paper | corpus_metadata | null | Sample Efficient Actor-Critic with Experience Replay | This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems. To achieve this, the paper introduces several inno... | https://arxiv.org/abs/1611.01224 | 2016-11-03T23:21:32Z | 1611.01224 | 10.48550/arxiv.1611.01224 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1611.01224 | e5454d15bcda2ee16d01e87d8d2200a2375b397313542dc9e523c6a37726224b | 0.622136 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.03713 | paper | corpus_metadata | null | Naturalization of Text by the Insertion of Pauses and Filler Words | In this article, we introduce a set of methods to naturalize text based on natural human speech. Voice-based interactions provide a natural way of interfacing with electronic systems and are seeing a widespread adaptation of late. These computerized voices can be naturalized to some degree by inserting pauses and fille... | https://arxiv.org/abs/2011.03713 | 2020-11-07T06:56:54Z | 2011.03713 | 10.48550/arxiv.2011.03713 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2011.03713 | 776cfd40d6be1d07e1dacdd757b3c3da992dfd2558939d54fc6a9ec9395b0cfd | 0.619011 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1207.3270 | paper | corpus_metadata | null | Probabilistic Event Calculus for Event Recognition | Symbolic event recognition systems have been successfully applied to a variety of application domains, extracting useful information in the form of events, allowing experts or other systems to monitor and respond when significant events are recognised. In a typical event recognition application, however, these systems ... | https://arxiv.org/abs/1207.3270 | 2012-07-13T14:57:35Z | 1207.3270 | 10.48550/arxiv.1207.3270 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1207.3270 | 8d8472265c1257f997f0e0904bd8554101ca78c6f8ea07ec6c6927f4ce40953f | 0.585866 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2001.03024 | paper | corpus_metadata | null | DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection | We present our on-going effort of constructing a large-scale benchmark for face forgery detection. The first version of this benchmark, DeeperForensics-1.0, represents the largest face forgery detection dataset by far, with 60,000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datas... | https://arxiv.org/abs/2001.03024 | 2020-01-09T14:37:17Z | 2001.03024 | 10.48550/arxiv.2001.03024 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2001.03024 | 9ac6ca7a097dedc21976a2a901619c1f150111b9c56497898f9afc0330418c3c | 0.795332 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1704.00529 | paper | corpus_metadata | null | 3D Object Reconstruction from Hand-Object Interactions | Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera. Although these approaches are successful for a wide range of object classes, they rely on stable and distinctive geometric or texture features. Many objects like mechanical parts, toys, household or decorative ar... | https://arxiv.org/abs/1704.00529 | 2017-04-03T11:19:03Z | 1704.00529 | 10.1109/iccv.2015.90 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/iccv.2015.90 | eae5136ee5e95ab6223be55d42dc31e183dfc3f3d821db882ae20cd36839b629 | 0.546695 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.13202 | paper | corpus_metadata | null | Simple and Effective VAE Training with Calibrated Decoders | Variational autoencoders (VAEs) provide an effective and simple method for modeling complex distributions. However, training VAEs often requires considerable hyperparameter tuning to determine the optimal amount of information retained by the latent variable. We study the impact of calibrated decoders, which learn the ... | https://arxiv.org/abs/2006.13202 | 2020-06-23T17:57:47Z | 2006.13202 | 10.48550/arxiv.2006.13202 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2006.13202 | c9cf8d239c249a79513df9ad16d5135127677987d8b0de7f644bd0548da79049 | 0.699836 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1807.09936 | paper | corpus_metadata | null | Multi-Agent Generative Adversarial Imitation Learning | Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. We propose a new framework for multi-... | https://arxiv.org/abs/1807.09936 | 2018-07-26T03:21:49Z | 1807.09936 | 10.48550/arxiv.1807.09936 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1807.09936 | b407138f7d76fbb8c925576cd91f349f0be157c704ba5975c2b58c0edbf5d976 | 0.713852 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2009.04274 | paper | corpus_metadata | null | Where's Crypto?: Automated Identification and Classification of Proprietary Cryptographic Primitives in Binary Code | The continuing use of proprietary cryptography in embedded systems across many industry verticals, from physical access control systems and telecommunications to machine-to-machine authentication, presents a significant obstacle to black-box security-evaluation efforts. In-depth security analysis requires locating and ... | https://arxiv.org/abs/2009.04274 | 2020-09-09T12:55:04Z | 2009.04274 | 10.48550/arxiv.2009.04274 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2009.04274 | 8c77e24f98bc6fbfe04d29360edac7957885de4f4b94d169ca224b0677c181db | 0.575804 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2206.08687 | paper | corpus_metadata | null | You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks | Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value. In particular, the so-called sensitivity value measures the quantity of interest's partial derivative with respect to the networ... | https://arxiv.org/abs/2206.08687 | 2022-06-17T11:11:19Z | 2206.08687 | 10.48550/arxiv.2206.08687 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2206.08687 | 463771663a04e9e458be9d0c6f0aa5b587d20c33516bae6cedf71e5c65478c93 | 0.593515 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.05259 | paper | corpus_metadata | null | MRI to CT Translation with GANs | We present a detailed description and reference implementation of preprocessing steps necessary to prepare the public Retrospective Image Registration Evaluation (RIRE) dataset for the task of magnetic resonance imaging (MRI) to X-ray computed tomography (CT) translation. Furthermore we describe and implement three sta... | https://arxiv.org/abs/1901.05259 | 2019-01-16T12:31:08Z | 1901.05259 | 10.48550/arxiv.1901.05259 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1901.05259 | 8290b856748c57a26f9b6f65ca806ac503760edcd983e3fd3285ac31c78d846a | 0.622592 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1804.01149 | paper | corpus_metadata | null | Music Genre Classification using Machine Learning Techniques | Categorizing music files according to their genre is a challenging task in the area of music information retrieval (MIR). In this study, we compare the performance of two classes of models. The first is a deep learning approach wherein a CNN model is trained end-to-end, to predict the genre label of an audio signal, so... | https://arxiv.org/abs/1804.01149 | 2018-04-03T20:04:04Z | 1804.01149 | 10.48550/arxiv.1804.01149 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1804.01149 | f2a0b2c542090a9d0593b6fff4349791fe654248650be7a9287f486201d5f180 | 0.781671 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.07921 | paper | corpus_metadata | null | Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network | Long Short-Term Memory Networks (LSTMs) have been applied to daily discharge prediction with remarkable success. Many practical scenarios, however, require predictions at more granular timescales. For instance, accurate prediction of short but extreme flood peaks can make a life-saving difference, yet such peaks may es... | https://arxiv.org/abs/2010.07921 | 2020-10-15T17:52:16Z | 2010.07921 | 10.5194/hess-25-2045-2021 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.5194/hess-25-2045-2021 | 6ff8797a95860c29f3507817e886f5b2c28e516e0e06f628bec44297a564cc88 | 0.690948 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.07646 | paper | corpus_metadata | null | Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM | In this paper we present a data-driven approach to obtain the static image of a scene, eliminating dynamic objects that might have been present at the time of traversing the scene with a camera. The general objective is to improve vision-based localization and mapping tasks in dynamic environments, where the presence (... | https://arxiv.org/abs/2010.07646 | 2020-10-15T10:31:12Z | 2010.07646 | 10.1109/tro.2020.3031267 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tro.2020.3031267 | 0e3450b643db2deb7dce1ede67199b43bda76ba76041d14f3c2f9cf82c87ce60 | 0.760911 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.00468 | paper | corpus_metadata | null | New Evolutionary Computation Models and their Applications to Machine Learning | Automatic Programming is one of the most important areas of computer science research today. Hardware speed and capability have increased exponentially, but the software is years behind. The demand for software has also increased significantly, but it is still written in old fashion: by using humans. There are multiple... | https://arxiv.org/abs/2110.00468 | 2021-10-01T15:03:03Z | 2110.00468 | 10.48550/arxiv.2110.00468 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2110.00468 | 494e42bcc67b3cf54eaa83e4c9bfdb0d08d0e392f80e82c14bfca5f6e4d11209 | 0.524503 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1708.00961 | paper | corpus_metadata | null | Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss | In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transform theory, and promises to improve the performance of the GAN. The perceptual loss compares t... | https://arxiv.org/abs/1708.00961 | 2017-08-03T00:37:11Z | 1708.00961 | 10.1109/tmi.2018.2827462 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1109/tmi.2018.2827462 | 179f1d7adaa61447ac997cd67703660c406c3b5b37c7135381fce72751c2114f | 0.651407 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.00650 | paper | corpus_metadata | null | Automatic Detection of Generated Text is Easiest when Humans are Fooled | Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. The capabilities of humans and automatic discriminators to detect machine-generated text have been a large source of research interest, but humans and machines rely on different cues to make their ... | https://arxiv.org/abs/1911.00650 | 2019-11-02T04:52:00Z | 1911.00650 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1911.00650 | 0cd817752f9b03e5717e23cddb7a909f9e77b8c78c3665b6547ca4f8c88ca74e | 0.620219 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.07349 | paper | corpus_metadata | null | Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection | Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applications. However, most existing methods neglect the complex correlation among data samples, which is important for capturing normal patterns fr... | https://arxiv.org/abs/2002.07349 | 2020-02-18T03:32:06Z | 2002.07349 | 10.48550/arxiv.2002.07349 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2002.07349 | ebb9687a1a4ac6d058ef05c6bfd8ec77bbbb633609cc73459ab4ffb0c2c18bad | 0.690493 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1502.05698 | paper | corpus_metadata | null | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question a... | https://arxiv.org/abs/1502.05698 | 2015-02-19T20:46:10Z | 1502.05698 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1502.05698 | f88d8c6247849dca2febbbd46f51a3de289f42409a7275e82876759399347158 | 0.707448 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1710.01330 | paper | corpus_metadata | null | Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching | This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve thi... | https://arxiv.org/abs/1710.01330 | 2017-10-03T18:16:09Z | 1710.01330 | 10.48550/arxiv.1710.01330 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1710.01330 | 827b63ae60d30c5d558d541ca167e9aada7a42f369b5e413ca62d0524ce08eed | 0.69218 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.10832 | paper | corpus_metadata | null | Sensing Ambiguity in Henry James' "The Turn of the Screw" | Fields such as the philosophy of language, continental philosophy, and literary studies have long established that human language is, at its essence, ambiguous and that this quality, although challenging to communication, enriches language and points to the complexity of human thought. On the other hand, in the NLP fie... | https://arxiv.org/abs/2011.10832 | 2020-11-21T17:53:41Z | 2011.10832 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2011.10832 | 26c6838eebe281f33d702099dab3262b1f0cb7c245b56dc34fed98ccb1567174 | 0.520391 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.10103 | paper | corpus_metadata | null | Two-stage generative adversarial networks for document image binarization with color noise and background removal | Document image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition. Traditional non-machine-learning methods are constructed on low-level features in an unsupervised manner but have difficulty with binarization on documents... | https://arxiv.org/abs/2010.10103 | 2020-10-20T07:51:50Z | 2010.10103 | 10.48550/arxiv.2010.10103 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2010.10103 | 6b2fbdfc5cd2bb7fe7eefdbce99f40e2842371393c6ec637b23cceccc6d5214b | 0.823073 | 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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