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arxiv:1907.07487 | paper | corpus_metadata | null | Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems | Recently, reconstruction-based anomaly detection was proposed as an effective technique to detect attacks in dynamic industrial control networks. Unlike classical network anomaly detectors that observe the network traffic, reconstruction-based detectors operate on the measured sensor data, leveraging physical process m... | https://arxiv.org/abs/1907.07487 | 2019-07-17T12:54:48Z | 1907.07487 | 10.1145/3427228.3427660 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3427228.3427660 | e22be44e46531804984b477b078a068784e237e672a74bd79c53394ba85f2fd7 | 0.68428 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.00981 | paper | corpus_metadata | null | Contextual Bandit with Adaptive Feature Extraction | We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature extraction (representation learning) based on online clustering. Our approach starts with an off-line pre-training on unlabeled history of co... | https://arxiv.org/abs/1802.00981 | 2018-02-03T14:44:51Z | 1802.00981 | 10.48550/arxiv.1802.00981 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1802.00981 | e2e0fdf1b867f268d12b4796fe2d8cbb03e29d3d7f109b9baddbdbf005c8ba68 | 0.695379 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.04286 | paper | corpus_metadata | null | Manifold Regularization for Locally Stable Deep Neural Networks | We apply concepts from manifold regularization to develop new regularization techniques for training locally stable deep neural networks. Our regularizers are based on a sparsification of the graph Laplacian which holds with high probability when the data is sparse in high dimensions, as is common in deep learning. Emp... | https://arxiv.org/abs/2003.04286 | 2020-03-09T17:45:44Z | 2003.04286 | 10.48550/arxiv.2003.04286 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.04286 | 1281c4b271f5bec9fcbdfb0067a1207cbea09122082de58edc62cda02f7223f4 | 0.732771 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.02337 | paper | corpus_metadata | null | MNIST-C: A Robustness Benchmark for Computer Vision | We introduce the MNIST-C dataset, a comprehensive suite of 15 corruptions applied to the MNIST test set, for benchmarking out-of-distribution robustness in computer vision. Through several experiments and visualizations we demonstrate that our corruptions significantly degrade performance of state-of-the-art computer v... | https://arxiv.org/abs/1906.02337 | 2019-06-05T22:23:43Z | 1906.02337 | 10.48550/arxiv.1906.02337 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.02337 | e1434afd1937ae89c6ea592b47bb4556340ffb48945fb6a9a2fe673f337b4e47 | 0.786404 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.03035 | paper | corpus_metadata | null | Self-Monitoring Navigation Agent via Auxiliary Progress Estimation | The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the ... | https://arxiv.org/abs/1901.03035 | 2019-01-10T06:46:50Z | 1901.03035 | 10.48550/arxiv.1901.03035 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1901.03035 | 218980aa19c92a9aab2d9a90300a4e49526a1f9254b932b55c14ce9e2583018b | 0.680439 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.10004 | paper | corpus_metadata | null | Deep Image Clustering with Category-Style Representation | Deep clustering which adopts deep neural networks to obtain optimal representations for clustering has been widely studied recently. In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and ca... | https://arxiv.org/abs/2007.10004 | 2020-07-20T11:20:35Z | 2007.10004 | 10.48550/arxiv.2007.10004 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.10004 | d5c72b09c70d63d01efb29b3fc21a75835ffdb5967cf45f3f70cf43592b562b7 | 0.734828 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.13561 | paper | corpus_metadata | null | Non-Gaussian Gaussian Processes for Few-Shot Regression | Gaussian Processes (GPs) have been widely used in machine learning to model distributions over functions, with applications including multi-modal regression, time-series prediction, and few-shot learning. GPs are particularly useful in the last application since they rely on Normal distributions and enable closed-form ... | https://arxiv.org/abs/2110.13561 | 2021-10-26T10:45:25Z | 2110.13561 | 10.48550/arxiv.2110.13561 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2110.13561 | af54a267e64e0dee0065e96acbac914870c9579c20df536caeefaa5606a93609 | 0.753727 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1511.05939 | paper | corpus_metadata | null | Metric Learning with Adaptive Density Discrimination | Distance metric learning (DML) approaches learn a transformation to a representation space where distance is in correspondence with a predefined notion of similarity. While such models offer a number of compelling benefits, it has been difficult for these to compete with modern classification algorithms in performance ... | https://arxiv.org/abs/1511.05939 | 2015-11-18T20:41:05Z | 1511.05939 | 10.48550/arxiv.1511.05939 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1511.05939 | 3bbb0e718243f84bdafff6e3025a2fa0681b6a0dfb4f93d10428e0d4ccd442a4 | 0.805286 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2001.05311 | paper | corpus_metadata | null | ABCNet: An attention-based method for particle tagging | In high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments. To expand on this concept, we propose a graph neural network enhanced by attention mechanisms called ABCNet. To exemplify the advantages and flexibility ... | https://arxiv.org/abs/2001.05311 | 2020-01-13T16:07:24Z | 2001.05311 | 10.1140/epjp/s13360-020-00497-3 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1140/epjp/s13360-020-00497-3 | 40ed839a7570a76b0c0fb7ac822060444110d1a50d4a5c5b00eb1382df69626f | 0.573773 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.08705 | paper | corpus_metadata | null | GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud | Exploiting fine-grained semantic features on point cloud is still challenging due to its irregular and sparse structure in a non-Euclidean space. Among existing studies, PointNet provides an efficient and promising approach to learn shape features directly on unordered 3D point cloud and has achieved competitive perfor... | https://arxiv.org/abs/1905.08705 | 2019-05-21T15:44:31Z | 1905.08705 | 10.48550/arxiv.1905.08705 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.08705 | eec0b6da639f58e26148283854a04110c0c1764714596d26c077392aee31776e | 0.753203 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.04268 | paper | corpus_metadata | null | Solving Inverse Problems With Deep Neural Networks -- Robustness Included? | In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems. Before such approaches can find application in safety-critical fields, a verification of their reliability appears mandatory. Recent works have pointed out instabilities of deep neural networks for several im... | https://arxiv.org/abs/2011.04268 | 2020-11-09T09:33:07Z | 2011.04268 | 10.1109/tpami.2022.3148324 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tpami.2022.3148324 | 19a7dd7ca9babe0a357cc1f5e4ad9772428ff6ace6ba1f93d6e8b24662735d11 | 0.741612 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.13690 | paper | corpus_metadata | null | Style Transfer by Rigid Alignment in Neural Net Feature Space | Arbitrary style transfer is an important problem in computer vision that aims to transfer style patterns from an arbitrary style image to a given content image. However, current methods either rely on slow iterative optimization or fast pre-determined feature transformation, but at the cost of compromised visual qualit... | https://arxiv.org/abs/1909.13690 | 2019-09-27T02:54:00Z | 1909.13690 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1909.13690 | 8dd627f55fa2594e35d0ec11a49533e67931617827203410c5f0b0cc9e37ea75 | 0.709676 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.14676 | paper | corpus_metadata | null | NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels | Adversarial training (AT) formulated as the minimax optimization problem can effectively enhance the model's robustness against adversarial attacks. The existing AT methods mainly focused on manipulating the inner maximization for generating quality adversarial variants or manipulating the outer minimization for design... | https://arxiv.org/abs/2105.14676 | 2021-05-31T02:17:51Z | 2105.14676 | 10.48550/arxiv.2105.14676 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2105.14676 | 1148feb78a05da42580aa37869c0376a2e80a123e3e60f7c67550b07f3a0f013 | 0.694261 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.05068 | paper | corpus_metadata | null | Uncrowded Hypervolume-based Multi-objective Optimization with Gene-pool Optimal Mixing | Domination-based multi-objective (MO) evolutionary algorithms (EAs) are today arguably the most frequently used type of MOEA. These methods however stagnate when the majority of the population becomes non-dominated, preventing convergence to the Pareto set. Hypervolume-based MO optimization has shown promising results ... | https://arxiv.org/abs/2004.05068 | 2020-04-10T15:14:54Z | 2004.05068 | 10.48550/arxiv.2004.05068 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2004.05068 | bf217f382ea607d76fe80444ed084f3d85c2cffed2ada18cd5b02933261e9721 | 0.533514 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1907.05047 | paper | corpus_metadata | null | BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs | We present BlazeFace, a lightweight and well-performing face detector tailored for mobile GPU inference. It runs at a speed of 200-1000+ FPS on flagship devices. This super-realtime performance enables it to be applied to any augmented reality pipeline that requires an accurate facial region of interest as an input for... | https://arxiv.org/abs/1907.05047 | 2019-07-11T08:40:08Z | 1907.05047 | 10.48550/arxiv.1907.05047 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1907.05047 | 7fff1ab951b196f6b569c9591188af417df1a32812248267075a1ca03f672ab1 | 0.560779 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.03598 | paper | corpus_metadata | null | Interactive Classification by Asking Informative Questions | We study the potential for interaction in natural language classification. We add a limited form of interaction for intent classification, where users provide an initial query using natural language, and the system asks for additional information using binary or multi-choice questions. At each turn, our system decides ... | https://arxiv.org/abs/1911.03598 | 2019-11-09T03:05:50Z | 1911.03598 | 10.48550/arxiv.1911.03598 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1911.03598 | 2ff2fe32be8d45780948aa9c900b572391efb4d1038afe7bda1c64b0d481cf2a | 0.62347 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1912.13321 | paper | corpus_metadata | null | OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network | To transcribe spoken language to written medium, most alphabets enable an unambiguous sound-to-letter rule. However, some writing systems have distanced themselves from this simple concept and little work exists in Natural Language Processing (NLP) on measuring such distance. In this study, we use an Artificial Neural ... | https://arxiv.org/abs/1912.13321 | 2019-12-31T14:36:45Z | 1912.13321 | 10.18653/v1/2021.sigtyp-1.1 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.18653/v1/2021.sigtyp-1.1 | d2bd2f605a9169eda40b59c43c1485f4378885750b0ffd3fdcd48ada6465176d | 0.598634 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.06749 | paper | corpus_metadata | null | Learning hierarchical relationships for object-goal navigation | Direct search for objects as part of navigation poses a challenge for small items. Utilizing context in the form of object-object relationships enable hierarchical search for targets efficiently. Most of the current approaches tend to directly incorporate sensory input into a reward-based learning approach, without lea... | https://arxiv.org/abs/2003.06749 | 2020-03-15T04:01:09Z | 2003.06749 | 10.48550/arxiv.2003.06749 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.06749 | 58cea9de6af94382bc61e2d22258becf30d689414a3669c53f18ee1252064587 | 0.693368 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1212.5668 | paper | corpus_metadata | null | Initial Semantics for Reduction Rules | We give an algebraic characterization of the syntax and operational semantics of a class of simply-typed languages, such as the language PCF: we characterize simply-typed syntax with variable binding and equipped with reduction rules via a universal property, namely as the initial object of some category of models. For... | https://arxiv.org/abs/1212.5668 | 2012-12-22T08:48:22Z | 1212.5668 | 10.23638/lmcs-15(1:28)2019 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.23638/lmcs-15(1:28)2019 | 44216dcceea9dfb7712af9b3b7fdb9018a6ed2af8251f5fd577a3215291e446e | 0.541618 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1808.02578 | paper | corpus_metadata | null | Deep learning of dynamics and signal-noise decomposition with time-stepping constraints | A critical challenge in the data-driven modeling of dynamical systems is producing methods robust to measurement error, particularly when data is limited. Many leading methods either rely on denoising prior to learning or on access to large volumes of data to average over the effect of noise. We propose a novel paradig... | https://arxiv.org/abs/1808.02578 | 2018-08-07T23:05:13Z | 1808.02578 | 10.1016/j.jcp.2019.06.056 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1016/j.jcp.2019.06.056 | aeb48e5acc8ae9e7abd6cd66091b908a11359bb28c39a1252b6d06fa4ac095e9 | 0.704328 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.10867 | paper | corpus_metadata | null | A Neuro-Inspired Autoencoding Defense Against Adversarial Perturbations | Deep Neural Networks (DNNs) are vulnerable to adversarial attacks: carefully constructed perturbations to an image can seriously impair classification accuracy, while being imperceptible to humans. While there has been a significant amount of research on defending against such attacks, most defenses based on systematic... | https://arxiv.org/abs/2011.10867 | 2020-11-21T21:03:08Z | 2011.10867 | 10.48550/arxiv.2011.10867 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2011.10867 | a1124123af3976addc4a46ebe2a3f99f4870f1c51e741c3af7ce2791155e10c3 | 0.752946 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.00461 | paper | corpus_metadata | null | Multilingual Email Zoning | The segmentation of emails into functional zones (also dubbed email zoning) is a relevant preprocessing step for most NLP tasks that deal with emails. However, despite the multilingual character of emails and their applications, previous literature regarding email zoning corpora and systems was developed essentially fo... | https://arxiv.org/abs/2102.00461 | 2021-01-31T14:32:20Z | 2102.00461 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/publicdomain/zero/1.0 | arxiv:2102.00461 | 44979bd10dfabfb4b7b233f40a571e492d1fcb1e2df9535d35c17d61d2fd3d39 | 0.787649 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.03039 | paper | corpus_metadata | null | Coherent Point Drift Networks: Unsupervised Learning of Non-Rigid Point Set Registration | Given new pairs of source and target point sets, standard point set registration methods often repeatedly conduct the independent iterative search of desired geometric transformation to align the source point set with the target one. This limits their use in applications to handle the real-time point set registration w... | https://arxiv.org/abs/1906.03039 | 2019-06-07T12:12:22Z | 1906.03039 | 10.48550/arxiv.1906.03039 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.03039 | 9f5743ae4f35b4f6bd4999cd22f304f42e3013bbb232ecee9bed09333819723a | 0.727674 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2504.06327 | paper | corpus_metadata | null | Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometries | Kolmogorov-Arnold Networks (KANs) have gained attention as an alternative to traditional multilayer perceptrons (MLPs) for deep learning applications in computational physics, particularly for solving inverse problems with sparse data, as exemplified by the physics-informed Kolmogorov-Arnold network (PIKAN). However, t... | https://arxiv.org/abs/2504.06327 | 2025-04-08T12:31:57Z | 2504.06327 | 10.48550/arxiv.2504.06327 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2504.06327 | c17bc7a0f7a26b471db78860c51ce7c444b93f7708d235bf7ae33d05316f297c | 0.638478 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.14662 | paper | corpus_metadata | null | Large-scale neural recordings call for new insights to link brain and behavior | Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. Here, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to character... | https://arxiv.org/abs/2103.14662 | 2021-03-26T18:02:50Z | 2103.14662 | 10.1038/s41593-021-00980-9 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1038/s41593-021-00980-9 | 0868d7e22051547b9b42df378d82a7a541731e3cd30706942be62ead961e686b | 0.52705 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.01067 | paper | corpus_metadata | null | Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities | Visual-Inertial Odometry (VIO) algorithms typically rely on a point cloud representation of the scene that does not model the topology of the environment. A 3D mesh instead offers a richer, yet lightweight, model. Nevertheless, building a 3D mesh out of the sparse and noisy 3D landmarks triangulated by a VIO algorithm ... | https://arxiv.org/abs/1903.01067 | 2019-03-04T04:24:50Z | 1903.01067 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1903.01067 | 057487574ab1a781b60adb83f6b7051fda2dee44061afafc2afeb9e1868e32bb | 0.573344 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.09144 | paper | corpus_metadata | null | PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data | In natural language processing (NLP), there is a need for more resources in Portuguese, since much of the data used in the state-of-the-art research is in other languages. In this paper, we pretrain a T5 model on the BrWac corpus, an extensive collection of web pages in Portuguese, and evaluate its performance against ... | https://arxiv.org/abs/2008.09144 | 2020-08-20T18:10:13Z | 2008.09144 | 10.48550/arxiv.2008.09144 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2008.09144 | afcfb98c14c9482e3527d7a420c04c778426123447698c076a9b807cc2b1f24e | 0.851909 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.06169 | paper | corpus_metadata | null | Offline Reinforcement Learning with Implicit Q-Learning | Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This trade-off is critical, because m... | https://arxiv.org/abs/2110.06169 | 2021-10-12T17:05:05Z | 2110.06169 | 10.48550/arxiv.2110.06169 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2110.06169 | 3065a618e6f8b5d61305afc70f25237319a662851eb16c9b283ffd26b674a023 | 0.742228 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1707.04993 | paper | corpus_metadata | null | MoCoGAN: Decomposing Motion and Content for Video Generation | Visual signals in a video can be divided into content and motion. While content specifies which objects are in the video, motion describes their dynamics. Based on this prior, we propose the Motion and Content decomposed Generative Adversarial Network (MoCoGAN) framework for video generation. The proposed framework gen... | https://arxiv.org/abs/1707.04993 | 2017-07-17T03:42:17Z | 1707.04993 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1707.04993 | aa541402a6723c397a69a678142c5126000f5b6acc92171e2b9f1a5508ac9cfc | 0.765292 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.03412 | paper | corpus_metadata | null | Machine Learning in Precision Medicine to Preserve Privacy via Encryption | Precision medicine is an emerging approach for disease treatment and prevention that delivers personalized care to individual patients by considering their genetic makeups, medical histories, environments, and lifestyles. Despite the rapid advancement of precision medicine and its considerable promise, several underlyi... | https://arxiv.org/abs/2102.03412 | 2021-02-05T20:22:15Z | 2102.03412 | 10.1016/j.patrec.2021.07.004 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1016/j.patrec.2021.07.004 | 88bad2c25c9bfece707a1bca0c1be22a35136cb3bd13d54c2bb009e08148f737 | 0.571028 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.13100 | paper | corpus_metadata | null | Shellcode_IA32: A Dataset for Automatic Shellcode Generation | We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode_IA32), consisting of challenging but common assembl... | https://arxiv.org/abs/2104.13100 | 2021-04-27T10:50:47Z | 2104.13100 | 10.18653/v1/2021.nlp4prog-1.7 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.18653/v1/2021.nlp4prog-1.7 | 5544f430044cc57eff965105f7239f52008a909f671a63d992ffabc0e93fa149 | 0.711609 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1706.03461 | paper | corpus_metadata | null | Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning | There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of meta-algorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the Conditional Average Treatmen... | https://arxiv.org/abs/1706.03461 | 2017-06-12T04:10:09Z | 1706.03461 | 10.1073/pnas.1804597116 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1073/pnas.1804597116 | 7379c817d373ef4cfee801f70ebb9064670516829159baae5d013ce6abd1f6a7 | 0.526712 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2112.09653 | paper | corpus_metadata | null | Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN | Conditional generation is a subclass of generative problems where the output of the generation is conditioned by the attribute information. In this paper, we present a stochastic contrastive conditional generative adversarial network (InfoSCC-GAN) with an explorable latent space. The InfoSCC-GAN architecture is based o... | https://arxiv.org/abs/2112.09653 | 2021-12-17T17:56:30Z | 2112.09653 | 10.48550/arxiv.2112.09653 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2112.09653 | 73bd1ba9c873ff1aa7160974d2408fb9a35689879332ec180872b587ac20c1a8 | 0.760536 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.10107 | paper | corpus_metadata | null | Utterance-level Aggregation For Speaker Recognition In The Wild | The objective of this paper is speaker recognition "in the wild"-where utterances may be of variable length and also contain irrelevant signals. Crucial elements in the design of deep networks for this task are the type of trunk (frame level) network, and the method of temporal aggregation. We propose a powerful speake... | https://arxiv.org/abs/1902.10107 | 2019-02-26T18:34:05Z | 1902.10107 | 10.48550/arxiv.1902.10107 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1902.10107 | cb0b6337048c02436437adb5703ba2c7d7a82609fefb4cf4874ac616a60c3d3d | 0.652588 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.01219 | paper | corpus_metadata | null | Exactly Computing the Local Lipschitz Constant of ReLU Networks | The local Lipschitz constant of a neural network is a useful metric with applications in robustness, generalization, and fairness evaluation. We provide novel analytic results relating the local Lipschitz constant of nonsmooth vector-valued functions to a maximization over the norm of the generalized Jacobian. We prese... | https://arxiv.org/abs/2003.01219 | 2020-03-02T22:15:54Z | 2003.01219 | 10.48550/arxiv.2003.01219 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.01219 | 5201fd5e5139ed0a4c1823a1505255d86d6279abe22dddecc5895471f51ddcaf | 0.529095 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.01388 | paper | corpus_metadata | null | Neural Network Verification in Control | Learning-based methods could provide solutions to many of the long-standing challenges in control. However, the neural networks (NNs) commonly used in modern learning approaches present substantial challenges for analyzing the resulting control systems' safety properties. Fortunately, a new body of literature could pro... | https://arxiv.org/abs/2110.01388 | 2021-09-30T22:28:44Z | 2110.01388 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2110.01388 | eb3af1b18ff6400c4c2e686199b1f16008bc6cd3402288f145319bf33a8f2057 | 0.6624 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2307.11137 | paper | corpus_metadata | null | Of Models and Tin Men: A Behavioural Economics Study of Principal-Agent Problems in AI Alignment using Large-Language Models | AI Alignment is often presented as an interaction between a single designer and an artificial agent in which the designer attempts to ensure the agent's behavior is consistent with its purpose, and risks arise solely because of conflicts caused by inadvertent misalignment between the utility function intended by the de... | https://arxiv.org/abs/2307.11137 | 2023-07-20T17:19:15Z | 2307.11137 | 10.48550/arxiv.2307.11137 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2307.11137 | 3e72a100531a81cace62fa974d8c65a6ca725dadd182e4e3c2fc1c6c6db42db3 | 0.597133 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.08785 | paper | corpus_metadata | null | PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization | We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding, and of multivariate Gaussian distributions to get a probabilistic representa... | https://arxiv.org/abs/2011.08785 | 2020-11-17T17:29:18Z | 2011.08785 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2011.08785 | fda512336ab53b1295e81773fb6aa3560bd988d616f7188ac69a576026aa984f | 0.762976 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2403.14960 | paper | corpus_metadata | null | Model Construction for Convex-Constrained Derivative-Free Optimization | We develop a new approximation theory for linear and quadratic interpolation models, suitable for use in convex-constrained derivative-free optimization (DFO). Most existing model-based DFO methods for constrained problems assume the ability to construct sufficiently accurate approximations via interpolation, but the s... | https://arxiv.org/abs/2403.14960 | 2024-03-22T05:28:21Z | 2403.14960 | 10.48550/arxiv.2403.14960 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2403.14960 | 3b7f9886b4e9866e1d9e1ea043373e76c5378748131278f635a7e747eec09634 | 0.553142 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.02249 | paper | corpus_metadata | null | MixMatch: A Holistic Approach to Semi-Supervised Learning | Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that works by guessing low-entropy labels for data-aug... | https://arxiv.org/abs/1905.02249 | 2019-05-06T19:56:03Z | 1905.02249 | 10.48550/arxiv.1905.02249 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.02249 | 0dc8949b677f2a68fc1900ca3b1f75cdaee32e36c126b36fb8e6a074fa0ff114 | 0.725231 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.06107 | paper | corpus_metadata | null | Aspect Sentiment Triplet Extraction Using Reinforcement Learning | Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting triplets of aspect terms, their associated sentiments, and the opinion terms that provide evidence for the expressed sentiments. Previous approaches to ASTE usually simultaneously extract all three components or first identify the aspect and opinion t... | https://arxiv.org/abs/2108.06107 | 2021-08-13T07:38:48Z | 2108.06107 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | arxiv:2108.06107 | 919d5c9d8c34a191307d82a1ca2025768562e7fb6dd92370f3d340250c3d7002 | 0.744352 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2203.03952 | paper | corpus_metadata | null | ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer | Recently, vision transformers started to show impressive results which outperform large convolution based models significantly. However, in the area of small models for mobile or resource constrained devices, ConvNet still has its own advantages in both performance and model complexity. We propose ParC-Net, a pure Conv... | https://arxiv.org/abs/2203.03952 | 2022-03-08T09:25:17Z | 2203.03952 | 10.48550/arxiv.2203.03952 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2203.03952 | 56a06a4bb9153388237ea16b188d0b99309d2a0711811c0c17e8109970b6aff4 | 0.785366 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.09117 | paper | corpus_metadata | null | DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting | Analyzing the worst-case performance of deep neural networks against input perturbations amounts to solving a large-scale non-convex optimization problem, for which several past works have proposed convex relaxations as a promising alternative. However, even for reasonably-sized neural networks, these relaxations are n... | https://arxiv.org/abs/2106.09117 | 2021-06-16T20:43:49Z | 2106.09117 | 10.48550/arxiv.2106.09117 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | doi:10.48550/arxiv.2106.09117 | bea81503824147559946eda3f4ad54728f8549172482543f96767fc6fd4b54a3 | 0.725857 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1602.07358 | paper | corpus_metadata | null | Post-selection inference for L1-penalized likelihood models | We present a new method for post-selection inference for L1 (lasso)-penalized likelihood models, including generalized regression models. Our approach generalizes the post-selection framework presented in Lee et al (2014). The method provides p-values and confidence intervals that are asymptotically valid, conditional ... | https://arxiv.org/abs/1602.07358 | 2016-02-24T00:06:43Z | 1602.07358 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1602.07358 | 47758e844783254dab3793f0d47f76386f01af742397fa4224ac5ecfebefc7f3 | 0.524627 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2210.16587 | paper | corpus_metadata | null | Relating Human Perception of Musicality to Prediction in a Predictive Coding Model | We explore the use of a neural network inspired by predictive coding for modeling human music perception. This network was developed based on the computational neuroscience theory of recurrent interactions in the hierarchical visual cortex. When trained with video data using self-supervised learning, the model manifest... | https://arxiv.org/abs/2210.16587 | 2022-10-29T12:20:01Z | 2210.16587 | 10.48550/arxiv.2210.16587 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2210.16587 | 95ba61133655c678d53659bca446118477b8790be7da0777539caf94b4473cfc | 0.613247 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1602.07360 | paper | corpus_metadata | SqueezeNet | SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size | Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require... | https://arxiv.org/abs/1602.07360 | 2016-02-24T00:09:45Z | 1602.07360 | 10.48550/arxiv.1602.07360 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1602.07360 | c978d3d3ac900e59770303dbf10e7c3d99e6ac26de123c78bc58c4247eb183b4 | 0.686243 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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"epoch-all-ai-models:row:3217"
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arxiv:1706.02263 | paper | corpus_metadata | null | Graph Convolutional Matrix Completion | We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we prop... | https://arxiv.org/abs/1706.02263 | 2017-06-07T17:05:19Z | 1706.02263 | 10.48550/arxiv.1706.02263 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1706.02263 | eb6321fc350e3c291c5142597a7eaabd7486129f34f934fa67234a5c19453178 | 0.767641 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1711.00350 | paper | corpus_metadata | null | Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks | Humans can understand and produce new utterances effortlessly, thanks to their compositional skills. Once a person learns the meaning of a new verb "dax," he or she can immediately understand the meaning of "dax twice" or "sing and dax." In this paper, we introduce the SCAN domain, consisting of a set of simple composi... | https://arxiv.org/abs/1711.00350 | 2017-10-31T01:50:02Z | 1711.00350 | 10.48550/arxiv.1711.00350 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1711.00350 | a30ce31c9897b1aed8be4e41529d2d1907b14469ba1dbf968087c3e7fb21565c | 0.685831 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.05196 | paper | corpus_metadata | null | StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer | Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e.g. positive to negative), which enable controllability at a... | https://arxiv.org/abs/2104.05196 | 2021-04-12T04:25:09Z | 2104.05196 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2104.05196 | 77a0f58eb79ba6684ab8d622e14ae0720e4bfe50ef5aad90dacb7915aabc0a21 | 0.752269 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.03666 | paper | corpus_metadata | null | Joint inference of multiple graphs with hidden variables from stationary graph signals | Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference. However, current approaches are limited by typically focusing on inferring single networks, and they assume that observations from all nodes are available. First, many contemporary setups involve m... | https://arxiv.org/abs/2110.03666 | 2021-10-05T21:31:36Z | 2110.03666 | 10.48550/arxiv.2110.03666 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2110.03666 | 6370977394760f5fed78c4e1509c0787b631bd4b7839dd0562cb28b0173f8158 | 0.588075 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.03032 | paper | corpus_metadata | null | Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss | Forecasting the particulate matter (PM) concentration in South Korea has become urgently necessary owing to its strong negative impact on human life. In most statistical or machine learning methods, independent and identically distributed data, for example, a Gaussian distribution, are assumed; however, time series suc... | https://arxiv.org/abs/2106.03032 | 2021-06-06T05:17:24Z | 2106.03032 | 10.1007/s12206-021-0817-4 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | doi:10.1007/s12206-021-0817-4 | ca06d87ab01572c4ff796662b6f44fcb0d339177aa22b1b7d4c3c5529ddc4aaa | 0.511993 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.07015 | paper | corpus_metadata | null | Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks | Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, f... | https://arxiv.org/abs/1703.07015 | 2017-03-21T00:33:36Z | 1703.07015 | 10.48550/arxiv.1703.07015 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.07015 | d5689f344e99edc14ef6d81d37e46aebc0de3e66a3938c751fd7552abb2d23c9 | 0.82228 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2302.13520 | paper | corpus_metadata | null | Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural Networks | Bit-flip attacks (BFAs) have attracted substantial attention recently, in which an adversary could tamper with a small number of model parameter bits to break the integrity of DNNs. To mitigate such threats, a batch of defense methods are proposed, focusing on the untargeted scenarios. Unfortunately, they either requir... | https://arxiv.org/abs/2302.13520 | 2023-02-27T05:15:02Z | 2302.13520 | 10.48550/arxiv.2302.13520 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2302.13520 | b1181b2501cd8d09a1e0dec5f42c08801bc55ef8383589d42452008f6ac6ebd3 | 0.680131 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.06802 | paper | corpus_metadata | null | Deep Analysis on Subgraph Isomorphism | Subgraph isomorphism is a well-known NP-hard problem which is widely used in many applications, such as social network analysis and knowledge graph query. Its performance is often limited by the inherent hardness. Several insightful works have been done since 2012, mainly optimizing pruning rules and matching orders to... | https://arxiv.org/abs/2012.06802 | 2020-12-12T12:29:38Z | 2012.06802 | 10.48550/arxiv.2012.06802 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2012.06802 | 44fbca7a7dcef35f9210c800ab53f99e350645deea8fb62b619e6a688c3f3fba | 0.614175 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.08013 | paper | corpus_metadata | null | Optimal monomial quadratization for ODE systems | Quadratization problem is, given a system of ODEs with polynomial right-hand side, transform the system to a system with quadratic right-hand side by introducing new variables. Such transformations have been used, for example, as a preprocessing step by model order reduction methods and for transforming chemical reacti... | https://arxiv.org/abs/2103.08013 | 2021-03-14T19:49:05Z | 2103.08013 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2103.08013 | 2a8921ed591d7d679d0932918fa58041693c23aa6090ce99fd84e071e61ede34 | 0.539579 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2312.01966 | paper | corpus_metadata | null | Spike distance function as a learning objective for spike prediction | Approaches to predicting neuronal spike responses commonly use a Poisson learning objective. This objective quantizes responses into spike counts within a fixed summation interval, typically on the order of 10 to 100 milliseconds in duration; however, neuronal responses are often time accurate down to a few millisecond... | https://arxiv.org/abs/2312.01966 | 2023-12-04T15:24:48Z | 2312.01966 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2312.01966 | 08cb95f7f6843782811446bb64df8e699f78282fb3f33f3e3df7ead21a248db2 | 0.675108 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2201.07620 | paper | corpus_metadata | null | Validating Simulations of User Query Variants | System-oriented IR evaluations are limited to rather abstract understandings of real user behavior. As a solution, simulating user interactions provides a cost-efficient way to support system-oriented experiments with more realistic directives when no interaction logs are available. While there are several user models ... | https://arxiv.org/abs/2201.07620 | 2022-01-19T14:25:55Z | 2201.07620 | 10.48550/arxiv.2201.07620 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2201.07620 | 1f9b019fb9aa51dbdbf5308c453658adcc49159a21db787d6a313813eb811a37 | 0.582417 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2303.14076 | paper | corpus_metadata | null | Interferometer response characterization algorithm for multi-aperture Fabry-Perot imaging spectrometers | In recent years, the demand for hyperspectral imaging devices has grown significantly, driven by their ability of capturing high-resolution spectral information. Among the several possible optical designs for acquiring hyperspectral images, there is a growing interest in interferometric spectral imaging systems based o... | https://arxiv.org/abs/2303.14076 | 2023-03-24T15:41:39Z | 2303.14076 | 10.1364/oe.491698 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1364/oe.491698 | 7c71c7ce81500ec726fc7a6085bbc2d7bee9239bb88dc69e0ec86536f858fc43 | 0.582413 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.06072 | paper | corpus_metadata | null | Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders | Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware image restoration (CARE) approaches are now used to improve the interpretability of acquired data. Naturally, there are limitations to what... | https://arxiv.org/abs/2006.06072 | 2020-06-10T21:28:13Z | 2006.06072 | 10.48550/arxiv.2006.06072 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2006.06072 | e70541c4f3eefdaa476df6a5a36027c72e47d683f7fcf260e2ebdf494e09bbd3 | 0.749399 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.08023 | paper | corpus_metadata | null | Joint self-supervised blind denoising and noise estimation | We propose a novel self-supervised image blind denoising approach in which two neural networks jointly predict the clean signal and infer the noise distribution. Assuming that the noisy observations are independent conditionally to the signal, the networks can be jointly trained without clean training data. Therefore, ... | https://arxiv.org/abs/2102.08023 | 2021-02-16T08:37:47Z | 2102.08023 | 10.48550/arxiv.2102.08023 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2102.08023 | 0e81ef4f3f28ebb80cb173a37cc7b84a7fe117455a8c577cb893fa4d9e05c3b0 | 0.758393 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.06022 | paper | corpus_metadata | null | Lessons on Parameter Sharing across Layers in Transformers | We propose a parameter sharing method for Transformers (Vaswani et al., 2017). The proposed approach relaxes a widely used technique, which shares parameters for one layer with all layers such as Universal Transformers (Dehghani et al., 2019), to increase the efficiency in the computational time. We propose three strat... | https://arxiv.org/abs/2104.06022 | 2021-04-13T08:41:07Z | 2104.06022 | 10.48550/arxiv.2104.06022 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2104.06022 | f6bcc06b36c603de892ba305b4928045b57a05e86c09ddff6b2d7fe013a8117d | 0.616257 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.08414 | paper | corpus_metadata | null | Improving Blind Spot Denoising for Microscopy | Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervise... | https://arxiv.org/abs/2008.08414 | 2020-08-19T13:06:24Z | 2008.08414 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2008.08414 | f51e12ddc3e7cc22910cec6d3bd0d56bad947c0a9513b3149f01be22495cc426 | 0.666951 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2202.08973 | paper | corpus_metadata | null | Energy-Efficient Parking Analytics System using Deep Reinforcement Learning | Advances in deep vision techniques and ubiquity of smart cameras will drive the next generation of video analytics. However, video analytics applications consume vast amounts of energy as both deep learning techniques and cameras are power-hungry. In this paper, we focus on a parking video analytics platform and propos... | https://arxiv.org/abs/2202.08973 | 2022-02-15T18:27:50Z | 2202.08973 | 10.1145/3486611.3486660 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3486611.3486660 | 637d774fa6740a6345f791bae3ca52b62766990c0140fa6d3143322102d778f9 | 0.677878 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.01705 | paper | corpus_metadata | null | Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning | Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning. This behaviour is quite common in the state-of-the-art captioning models which is not desirable by humans. To decrease the object hallucination in captioning, we propose three simple yet efficient train... | https://arxiv.org/abs/2110.01705 | 2021-10-04T20:25:22Z | 2110.01705 | 10.48550/arxiv.2110.01705 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2110.01705 | 5c79b18affe7a2a67cad48e48c5f98f103c79ef25aa8fc029e119e5068f65fe9 | 0.74053 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.00731 | paper | corpus_metadata | null | Order Optimal One-Shot Distributed Learning | We consider distributed statistical optimization in one-shot setting, where there are $m$ machines each observing $n$ i.i.d. samples. Based on its observed samples, each machine then sends an $O(\log(mn))$-length message to a server, at which a parameter minimizing an expected loss is to be estimated. We propose an alg... | https://arxiv.org/abs/1911.00731 | 2019-11-02T15:14:40Z | 1911.00731 | 10.48550/arxiv.1911.00731 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1911.00731 | c050c65b7ea82cb9a6c76df0341adb12c6f3f12b1458df85b93a840a154cdbd5 | 0.560058 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.05654 | paper | corpus_metadata | null | Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras | Reliable detection of human occupancy in indoor environments is critical for various energy efficiency, security, and safety applications. We consider this challenge of occupancy detection using extremely low-quality, privacy-preserving images from low power image sensors. We propose a combined few shot learning and cl... | https://arxiv.org/abs/2008.05654 | 2020-08-13T02:47:01Z | 2008.05654 | 10.48550/arxiv.2008.05654 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2008.05654 | 31ebf10bd0b413e72f16d7528a80d685e49be64d02bd9aeb8604be83cfc8236a | 0.640752 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1806.09055 | paper | corpus_metadata | DARTS | DARTS: Differentiable Architecture Search | This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architectu... | https://arxiv.org/abs/1806.09055 | 2018-06-24T00:06:13Z | 1806.09055 | 10.48550/arxiv.1806.09055 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1806.09055 | 573c5ed7c043259bab842e43296ce4c3517c32e6257b8f1f07a1c0a3049c9cb0 | 0.775784 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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"epoch-all-ai-models:row:2993",
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arxiv:1908.00722 | paper | corpus_metadata | null | Learning to combine primitive skills: A step towards versatile robotic manipulation | Manipulation tasks such as preparing a meal or assembling furniture remain highly challenging for robotics and vision. Traditional task and motion planning (TAMP) methods can solve complex tasks but require full state observability and are not adapted to dynamic scene changes. Recent learning methods can operate direct... | https://arxiv.org/abs/1908.00722 | 2019-08-02T07:04:17Z | 1908.00722 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1908.00722 | db79b844ded30dcffbc34113d560497a7acdeaece193ef529e426b328b9772d0 | 0.728763 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.07740 | paper | corpus_metadata | null | Learning to Augment Synthetic Images for Sim2Real Policy Transfer | Vision and learning have made significant progress that could improve robotics policies for complex tasks and environments. Learning deep neural networks for image understanding, however, requires large amounts of domain-specific visual data. While collecting such data from real robots is possible, such an approach lim... | https://arxiv.org/abs/1903.07740 | 2019-03-18T22:01:57Z | 1903.07740 | 10.48550/arxiv.1903.07740 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1903.07740 | 64abb38a42543aa4a4c7effa302bc4be08deb9a1e9b18f4dde5cb8b244ff7bc6 | 0.751204 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2303.15298 | paper | corpus_metadata | null | The percolating cluster is invisible to image recognition with deep learning | We study the two-dimensional site-percolation model on a square lattice. In this paradigmatic model, sites are randomly occupied with probability $p$; a second-order phase transition from a non-percolating to a fully percolating phase appears at occupation density $p_c$, called percolation threshold. Through supervised... | https://arxiv.org/abs/2303.15298 | 2023-03-27T15:14:47Z | 2303.15298 | 10.1088/1367-2630/ad0525 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1088/1367-2630/ad0525 | 21c54e1306b24472cc61af8459a0b06b4c842b75db338d5a38957d97dc770968 | 0.544241 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.05045 | paper | corpus_metadata | null | Semi-Supervised Domain Generalizable Person Re-Identification | Existing person re-identification (re-id) methods are stuck when deployed to a new unseen scenario despite the success in cross-camera person matching. Recent efforts have been substantially devoted to domain adaptive person re-id where extensive unlabeled data in the new scenario are utilized in a transductive learnin... | https://arxiv.org/abs/2108.05045 | 2021-08-11T06:08:25Z | 2108.05045 | 10.48550/arxiv.2108.05045 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2108.05045 | 8b4c2fd34d1563a94060821f87c6a4662b2a2db61bc53b1f670e78adb8e890e0 | 0.797848 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1603.09048 | paper | corpus_metadata | null | Cost-Sensitive Label Embedding for Multi-Label Classification | Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of interest. Current LE algorithms often aim to optimize one specific cost function, b... | https://arxiv.org/abs/1603.09048 | 2016-03-30T06:19:02Z | 1603.09048 | 10.1007/s10994-017-5659-z | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/s10994-017-5659-z | 69ed7474b43167a2c581f9df418c5f47790b74ba834148488dd3bae26f7a69f6 | 0.63087 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.06196 | paper | corpus_metadata | null | GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding | Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present G... | https://arxiv.org/abs/2110.06196 | 2021-10-12T17:49:46Z | 2110.06196 | 10.48550/arxiv.2110.06196 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2110.06196 | 133602ce7413ec1a653579b154077856b26e17f9a9b9eeda124ad33d0bdbff15 | 0.683149 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1812.02956 | paper | corpus_metadata | null | LNEMLC: Label Network Embeddings for Multi-Label Classification | Multi-label classification aims to classify instances with discrete non-exclusive labels. Most approaches on multi-label classification focus on effective adaptation or transformation of existing binary and multi-class learning approaches but fail in modelling the joint probability of labels or do not preserve generali... | https://arxiv.org/abs/1812.02956 | 2018-12-07T09:30:18Z | 1812.02956 | 10.48550/arxiv.1812.02956 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | doi:10.48550/arxiv.1812.02956 | 6a1f197766febcf8660a85b630bd8533d425f99b3da7b3ddad24b3411a613b9c | 0.726511 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2005.11909 | paper | corpus_metadata | null | Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method | Despite various methods are proposed to make progress in pedestrian attribute recognition, a crucial problem on existing datasets is often neglected, namely, a large number of identical pedestrian identities in train and test set, which is not consistent with practical application. Thus, images of the same pedestrian i... | https://arxiv.org/abs/2005.11909 | 2020-05-25T03:30:15Z | 2005.11909 | 10.48550/arxiv.2005.11909 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2005.11909 | 571163baa8117d2063d36ed7f774a9ce3ee5878efd9547843375ae213f74983a | 0.795766 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1807.03903 | paper | corpus_metadata | null | Deep Imbalanced Attribute Classification using Visual Attention Aggregation | For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem. Its challenges originate from its multi-label nature, the large underlying class imbalance and the lack of spatial annotations. Existing methods... | https://arxiv.org/abs/1807.03903 | 2018-07-10T23:49:03Z | 1807.03903 | 10.48550/arxiv.1807.03903 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1807.03903 | 972ba2f3564c9b611643a9bf0b09942f4f5880783010b99a1ed007daad1a3b7c | 0.736681 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2209.10091 | paper | corpus_metadata | null | Variational Inference for Infinitely Deep Neural Networks | We introduce the unbounded depth neural network (UDN), an infinitely deep probabilistic model that adapts its complexity to the training data. The UDN contains an infinite sequence of hidden layers and places an unbounded prior on a truncation L, the layer from which it produces its data. Given a dataset of observation... | https://arxiv.org/abs/2209.10091 | 2022-09-21T03:54:34Z | 2209.10091 | 10.48550/arxiv.2209.10091 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2209.10091 | faa914ae88fca95c617cfa8d8004e44f30356c1d5f5762264c661582bee8dc04 | 0.740876 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2206.13044 | paper | corpus_metadata | null | Extended U-Net for Speaker Verification in Noisy Environments | Background noise is a well-known factor that deteriorates the accuracy and reliability of speaker verification (SV) systems by blurring speech intelligibility. Various studies have used separate pretrained enhancement models as the front-end module of the SV system in noisy environments, and these methods effectively r... | https://arxiv.org/abs/2206.13044 | 2022-06-27T05:01:09Z | 2206.13044 | 10.48550/arxiv.2206.13044 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2206.13044 | 9f0182cd3da14a7704a831e60bac50d0dcffaed3fe8495692c3ff5119bd267be | 0.70154 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2109.03795 | paper | corpus_metadata | null | Desiderata for Representation Learning: A Causal Perspective | Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data. This learning problem is often approached by describing various desiderata associated with learned representations; e.g., that they be non-spurious, efficient, or disentangled. It can be challeng... | https://arxiv.org/abs/2109.03795 | 2021-09-08T17:33:54Z | 2109.03795 | 10.48550/arxiv.2109.03795 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2109.03795 | 48322bf0ac884e23d6d962bfdac7e323ddce3c88441c6fe475b19b8ae8ab5aab | 0.590713 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.06426 | paper | corpus_metadata | null | Catch-A-Waveform: Learning to Generate Audio from a Single Short Example | Models for audio generation are typically trained on hours of recordings. Here, we illustrate that capturing the essence of an audio source is typically possible from as little as a few tens of seconds from a single training signal. Specifically, we present a GAN-based generative model that can be trained on one short ... | https://arxiv.org/abs/2106.06426 | 2021-06-11T14:35:11Z | 2106.06426 | 10.48550/arxiv.2106.06426 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2106.06426 | 41242af634c3df5072cd9c8fdc0f88da88322df7566f09faa3da6999ab3db765 | 0.721478 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.08265 | paper | corpus_metadata | MuZero | Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model | Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics gove... | https://arxiv.org/abs/1911.08265 | 2019-11-19T13:58:52Z | 1911.08265 | 10.1038/s41586-020-03051-4 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1038/s41586-020-03051-4 | 4625b08d30f2de4bdb8bda3879617fbc4a7125a437fc203f1e7b31e06f71f6f5 | 0.708463 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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"epoch-all-ai-models:row:2794"
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arxiv:2406.17484 | paper | corpus_metadata | null | MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation | Large language models (LLMs) have shown substantial progress in natural language understanding and generation, proving valuable especially in the medical field. Despite advancements, challenges persist due to the complexity and diversity inherent in medical tasks, which can be categorized as knowledge-intensive tasks a... | https://arxiv.org/abs/2406.17484 | 2024-06-25T12:05:56Z | 2406.17484 | 10.48550/arxiv.2406.17484 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2406.17484 | 1a05fac59aa17fd855b8fe56ea90466d1f68841aa3775364eafe6794176fa93f | 0.775175 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.07725 | paper | corpus_metadata | null | iCaRL: Incremental Classifier and Representation Learning | A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training dat... | https://arxiv.org/abs/1611.07725 | 2016-11-23T10:24:11Z | 1611.07725 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1611.07725 | 669317b5364179f0f4a043267b00c185f8ac3e307feb13e74ee5c2b760b34d7e | 0.676685 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.06713 | paper | corpus_metadata | null | Deep learning approach for identification of HII regions during reionization in 21-cm observations | The upcoming Square Kilometre Array (SKA-Low) will map the distribution of neutral hydrogen during reionization, and produce a tremendous amount of 3D tomographic data. These images cubes will be subject to instrumental limitations, such as noise and limited resolution. Here we present SegU-Net, a stable and reliable m... | https://arxiv.org/abs/2102.06713 | 2021-02-12T19:00:02Z | 2102.06713 | 10.1093/mnras/stab1518 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.1093/mnras/stab1518 | c01f0fa620f45579c5f1f562f9cad3d9bee8a6a202cd3290e9bc506a549a5868 | 0.552069 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2406.09801 | paper | corpus_metadata | null | RaNeuS: Ray-adaptive Neural Surface Reconstruction | Our objective is to leverage a differentiable radiance field \eg NeRF to reconstruct detailed 3D surfaces in addition to producing the standard novel view renderings. There have been related methods that perform such tasks, usually by utilizing a signed distance field (SDF). However, the state-of-the-art approaches sti... | https://arxiv.org/abs/2406.09801 | 2024-06-14T07:54:25Z | 2406.09801 | 10.48550/arxiv.2406.09801 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2406.09801 | b9570fb52da0de9da6f911b8f9d0be9b15caf95bf0b8ee8fe735122e6af6876b | 0.618034 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.00820 | paper | corpus_metadata | null | LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions | Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs). These directions enable controllable image generation and support a wide range of semantic editing operations, such as zoom or rotation. The discovery of such direct... | https://arxiv.org/abs/2104.00820 | 2021-04-02T00:11:22Z | 2104.00820 | 10.48550/arxiv.2104.00820 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2104.00820 | d7b3e356c6377b14ee88e52c2f6e3fd2b3dd8f23e49af4db7b367aa10145ab67 | 0.72077 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.04906 | paper | corpus_metadata | null | Dense Passage Retrieval for Open-Domain Question Answering | Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are le... | https://arxiv.org/abs/2004.04906 | 2020-04-10T04:53:17Z | 2004.04906 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2004.04906 | 8e56eaa67ca0e90aa1243b239e59b41c0d04d848a5d6ae0d04ea60f604fa70e5 | 0.776963 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2301.04195 | paper | corpus_metadata | null | Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments | We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes and high-fidelity rigid and deformable body simulation. With Orbit, we provide a suite of benchmark tasks of vary... | https://arxiv.org/abs/2301.04195 | 2023-01-10T20:19:17Z | 2301.04195 | 10.1109/lra.2023.3270034 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | doi:10.1109/lra.2023.3270034 | 93afdb092ba1f88190866ba9897b6bfd80478b193ec2359f167a62cf0d0a60e6 | 0.694862 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.06515 | paper | corpus_metadata | null | A Real-Time Deep Network for Crowd Counting | Automatic analysis of highly crowded people has attracted extensive attention from computer vision research. Previous approaches for crowd counting have already achieved promising performance across various benchmarks. However, to deal with the real situation, we hope the model run as fast as possible while keeping acc... | https://arxiv.org/abs/2002.06515 | 2020-02-16T06:09:22Z | 2002.06515 | 10.48550/arxiv.2002.06515 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2002.06515 | 38abe70218415aa0952515318cc0526a084be9849f047d93112c1397bbbc537d | 0.829236 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2409.09661 | paper | corpus_metadata | null | ContractTinker: LLM-Empowered Vulnerability Repair for Real-World Smart Contracts | Smart contracts are susceptible to being exploited by attackers, especially when facing real-world vulnerabilities. To mitigate this risk, developers often rely on third-party audit services to identify potential vulnerabilities before project deployment. Nevertheless, repairing the identified vulnerabilities is still ... | https://arxiv.org/abs/2409.09661 | 2024-09-15T08:24:01Z | 2409.09661 | 10.1145/3691620.3695349 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3691620.3695349 | 0056101de7003abe405afed17fabb034e8990d7b2f0afb3d172e93c676eea7df | 0.664779 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.15000 | paper | corpus_metadata | null | DeepSphere: a graph-based spherical CNN | Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance. DeepSphere, a method based on a graph representation of the sampled sphere, strikes a controllable balance between these two desiderata. This contribution is twofold. First, we study both th... | https://arxiv.org/abs/2012.15000 | 2020-12-30T01:35:27Z | 2012.15000 | 10.48550/arxiv.2012.15000 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2012.15000 | a2014602a18d99400a5b99ee247ccaa4e87e9fa9624b759c1fb0b2484e1f233e | 0.637562 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.02005 | paper | corpus_metadata | null | Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning | The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics. However, teasing out this effect poses a significant challe... | https://arxiv.org/abs/1909.02005 | 2019-09-04T18:00:00Z | 1909.02005 | 10.3847/1538-4357/ab4c41 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.3847/1538-4357/ab4c41 | 70a749914a51f75bc30546dad4cb9b5b0e05b1f43284e6f9a6da3067420a1df7 | 0.576149 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.10566 | paper | corpus_metadata | null | Exploring Simple Siamese Representation Learning | Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results th... | https://arxiv.org/abs/2011.10566 | 2020-11-20T18:59:33Z | 2011.10566 | 10.48550/arxiv.2011.10566 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2011.10566 | 21a4002d19b1ea07bce65176a88d720db0ed176ad9bac752924a66e4a6b6624a | 0.730741 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1801.05599 | paper | corpus_metadata | null | Additive Margin Softmax for Face Verification | In this paper, we propose a conceptually simple and geometrically interpretable objective function, i.e. additive margin Softmax (AM-Softmax), for deep face verification. In general, the face verification task can be viewed as a metric learning problem, so learning large-margin face features whose intra-class variation... | https://arxiv.org/abs/1801.05599 | 2018-01-17T09:13:05Z | 1801.05599 | 10.1109/lsp.2018.2822810 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | doi:10.1109/lsp.2018.2822810 | d61a157e41b0e08f39b8d7bfa6ab47b7c9e543b8d06bb5ad084fda994b735070 | 0.781186 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.04815 | paper | corpus_metadata | null | A data-centric framework for crystal structure identification in atomistic simulations using machine learning | Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are requ... | https://arxiv.org/abs/2010.04815 | 2020-10-09T21:29:52Z | 2010.04815 | 10.48550/arxiv.2010.04815 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2010.04815 | 2ead5359f1f45fcddb1ed6997034ab5b52272d732f87fcf84af4a76509d561cc | 0.658609 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.06846 | paper | corpus_metadata | null | A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes | Skew-Gaussian processes (SkewGPs) extend the multivariate Unified Skew-Normal distributions over finite dimensional vectors to distribution over functions. SkewGPs are more general and flexible than Gaussian processes, as SkewGPs may also represent asymmetric distributions. In a recent contribution we showed that SkewG... | https://arxiv.org/abs/2012.06846 | 2020-12-12T15:58:16Z | 2012.06846 | 10.1007/s10994-021-06039-x | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/s10994-021-06039-x | 340ad34130194b8501ade6789a32bff08a6271c95040768516b174a8586d9bc6 | 0.554761 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.00726 | paper | corpus_metadata | null | Domain-Specific Bias Filtering for Single Labeled Domain Generalization | Conventional Domain Generalization (CDG) utilizes multiple labeled source datasets to train a generalizable model for unseen target domains. However, due to expensive annotation costs, the requirements of labeling all the source data are hard to be met in real-world applications. In this paper, we investigate a Single ... | https://arxiv.org/abs/2110.00726 | 2021-10-02T05:08:01Z | 2110.00726 | 10.48550/arxiv.2110.00726 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2110.00726 | 20563a09006d5726f228b544e80c377d43b006aa6b2419bf4150b43f4954732d | 0.799489 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2112.15093 | paper | corpus_metadata | null | Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study | The flourishing blossom of deep learning has witnessed the rapid development of text recognition in recent years. However, the existing text recognition methods are mainly proposed for English texts. As another widely-spoken language, Chinese text recognition (CTR) in all ways has extensive application markets. Based o... | https://arxiv.org/abs/2112.15093 | 2021-12-30T15:30:52Z | 2112.15093 | 10.48550/arxiv.2112.15093 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2112.15093 | b5f69cb3b35390f8d35bcf130bd5faf3405d817813287bf21fce2452a12f78ad | 0.787921 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2205.12465 | paper | corpus_metadata | null | FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation | Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks constructed from fMRI data for clinical predictions. Traditional functional brain networks, however, are noisy ... | https://arxiv.org/abs/2205.12465 | 2022-05-25T03:26:50Z | 2205.12465 | 10.48550/arxiv.2205.12465 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2205.12465 | eddc6f4a5d275d4f84c39560532d1a39443bfa178d0ce09881821f22dad01ec7 | 0.687554 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.06541 | paper | corpus_metadata | null | Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality | We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use the ideas from emotion causation theories to computationally model and determine... | https://arxiv.org/abs/2103.06541 | 2021-03-11T09:07:25Z | 2103.06541 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2103.06541 | fabb2b68071ec008884cc3bbc4535037eb1d5874214c2bac4f1678136c8b247f | 0.621529 | 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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