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arxiv:1712.02225 | paper | corpus_metadata | null | Pose-Normalized Image Generation for Person Re-identification | Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. In this work, we address both problems by proposing a novel deep person image generation model for... | https://arxiv.org/abs/1712.02225 | 2017-12-06T15:18:53Z | 1712.02225 | 10.48550/arxiv.1712.02225 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1712.02225 | 383330438bf3259d928790ec7f272313d744d1a4c4d9ea1a157a1f1bdf673b77 | 0.786871 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1912.08855 | paper | corpus_metadata | null | Simulating Content Consistent Vehicle Datasets with Attribute Descent | This paper uses a graphic engine to simulate a large amount of training data with free annotations. Between synthetic and real data, there is a two-level domain gap, i.e., content level and appearance level. While the latter has been widely studied, we focus on reducing the content gap in attributes like illumination a... | https://arxiv.org/abs/1912.08855 | 2019-12-18T19:40:31Z | 1912.08855 | 10.48550/arxiv.1912.08855 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1912.08855 | cb7816fbec881c52ba0fff624d816688618e153a47f1f3377c4f6803fb676818 | 0.725599 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1912.11347 | paper | corpus_metadata | null | Robust Group Synchronization via Cycle-Edge Message Passing | We propose a general framework for solving the group synchronization problem, where we focus on the setting of adversarial or uniform corruption and sufficiently small noise. Specifically, we apply a novel message passing procedure that uses cycle consistency information in order to estimate the corruption levels of gr... | https://arxiv.org/abs/1912.11347 | 2019-12-24T13:41:00Z | 1912.11347 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1912.11347 | 00bac90f1b7e1a4ba69264f11f5aac78b3ebe365861a9e07b208ac45df446966 | 0.513607 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.05222 | paper | corpus_metadata | null | Partial FC: Training 10 Million Identities on a Single Machine | Training face recognition models with millions of identities is challenging because classifier storage, logit memory, and computation grow linearly with the number of classes, eventually making full softmax impractical even when the backbone itself fits comfortably in memory. We present Partial FC (PFC), a scalable app... | https://arxiv.org/abs/2010.05222 | 2020-10-11T11:15:26Z | 2010.05222 | 10.48550/arxiv.2010.05222 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2010.05222 | 699e795c8dd5e8bb86593bef1f46a0850a8914c9086d81de14aedcf0c3cea6d1 | 0.687771 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2107.13306 | paper | corpus_metadata | null | Tab2Know: Building a Knowledge Base from Tables in Scientific Papers | Tables in scientific papers contain a wealth of valuable knowledge for the scientific enterprise. To help the many of us who frequently consult this type of knowledge, we present Tab2Know, a new end-to-end system to build a Knowledge Base (KB) from tables in scientific papers. Tab2Know addresses the challenge of automa... | https://arxiv.org/abs/2107.13306 | 2021-07-28T11:56:53Z | 2107.13306 | 10.1007/978-3-030-62419-4_20 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/978-3-030-62419-4_20 | 495c70bc646dc1daca636fb5950c707b2b3c02841860d9f7cf1587b59381ad52 | 0.634536 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2403.10943 | paper | corpus_metadata | null | MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations | Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. Existing benchmark datasets are limited in scale and suffer from difficulties in handling out-of-scope samples that arise in multi-t... | https://arxiv.org/abs/2403.10943 | 2024-03-16T15:14:15Z | 2403.10943 | 10.48550/arxiv.2403.10943 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2403.10943 | 70bf9427f51c3beabbef74c5ac7b8f6b1867a82cee0c45100d6ab8923ac46d19 | 0.843066 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.02146 | paper | corpus_metadata | null | FaultNet: A Deep Convolutional Neural Network for bearing fault classification | The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal data can provide us a greater understanding of different faults occurring in mechan... | https://arxiv.org/abs/2010.02146 | 2020-10-05T16:50:08Z | 2010.02146 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2010.02146 | 293a8d934f8e703f2adf5ca9171e7fc399b6ad375f5924857222c3ffbe473f67 | 0.615232 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.03737 | paper | corpus_metadata | null | Physics-Informed Neural Networks for Power Systems | This paper introduces for the first time, to our knowledge, a framework for physics-informed neural networks in power system applications. Exploiting the underlying physical laws governing power systems, and inspired by recent developments in the field of machine learning, this paper proposes a neural network training ... | https://arxiv.org/abs/1911.03737 | 2019-11-09T17:03:08Z | 1911.03737 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1911.03737 | 7d936b8f34b75dd8ca31054ba3a606db04ad8f349b72321bd1d25e498bb8a4ba | 0.661042 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1707.06347 | paper | corpus_metadata | null | Proximal Policy Optimization Algorithms | We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data s... | https://arxiv.org/abs/1707.06347 | 2017-07-20T02:32:33Z | 1707.06347 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1707.06347 | 9927b1fd39622aaaa15ab2999e5921e2096543a6313efa55fbc101aa5e4c5c53 | 0.613479 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.10964 | paper | corpus_metadata | null | Don't Stop Pretraining: Adapt Language Models to Domains and Tasks | Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and com... | https://arxiv.org/abs/2004.10964 | 2020-04-23T04:21:19Z | 2004.10964 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2004.10964 | 69636ef06e2da46c92049937e688d84031735440e665e0b2772220fdb742afac | 0.775584 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.09820 | paper | corpus_metadata | null | HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection | Sentiment analysis for code-mixed social media text continues to be an under-explored area. This work adds two common approaches: fine-tuning large transformer models and sample efficient methods like ULMFiT. Prior work demonstrates the efficacy of classical ML methods for polarity detection. Fine-tuned general-purpose... | https://arxiv.org/abs/2008.09820 | 2020-08-22T12:01:44Z | 2008.09820 | 10.48550/arxiv.2008.09820 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2008.09820 | fed0717599a3a1ef1133f0224f36f82ad6f940efce3d7751e8fddac0d69ad68b | 0.784314 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.10176 | paper | corpus_metadata | null | Deep Soft Procrustes for Markerless Volumetric Sensor Alignment | With the advent of consumer grade depth sensors, low-cost volumetric capture systems are easier to deploy. Their wider adoption though depends on their usability and by extension on the practicality of spatially aligning multiple sensors. Most existing alignment approaches employ visual patterns, e.g. checkerboards, or... | https://arxiv.org/abs/2003.10176 | 2020-03-23T10:51:32Z | 2003.10176 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2003.10176 | 2b102173802471f02c420adcd09bbc4788ff7c6d7fc41525f7aff6dea85b38a9 | 0.659925 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.14162 | paper | corpus_metadata | null | Conversations with Search Engines: SERP-based Conversational Response Generation | In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner. Recently, there have... | https://arxiv.org/abs/2004.14162 | 2020-04-29T13:07:53Z | 2004.14162 | 10.48550/arxiv.2004.14162 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2004.14162 | 91f6e8892f1d909f9aaa32faa8a156e89022d8496b3978182f1fcee3d2935546 | 0.662387 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.14294 | paper | corpus_metadata | null | Boilerplate Removal using a Neural Sequence Labeling Model | The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, to information retrieval or natural language processing. Existing approaches are lacking as they rely on large amounts of hand-crafted featur... | https://arxiv.org/abs/2004.14294 | 2020-04-22T08:06:59Z | 2004.14294 | 10.1145/3366424.3383547 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3366424.3383547 | ad7573257bf6346a2a894e5515e17a32c856241bac446a860cca2ddf429dab2e | 0.754076 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2112.10558 | paper | corpus_metadata | null | Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes | Lifelong graph learning deals with the problem of continually adapting graph neural network (GNN) models to changes in evolving graphs. We address two critical challenges of lifelong graph learning in this work: dealing with new classes and tackling imbalanced class distributions. The combination of these two challenge... | https://arxiv.org/abs/2112.10558 | 2021-12-20T14:29:39Z | 2112.10558 | 10.1016/j.neunet.2023.04.022 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | doi:10.1016/j.neunet.2023.04.022 | b49e04dbe3b28bfae574b7abcc6486abb1df650a1a74055fa8879a7718453d11 | 0.732791 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.12537 | paper | corpus_metadata | null | Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation | Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fig... | https://arxiv.org/abs/2004.12537 | 2020-04-27T01:31:48Z | 2004.12537 | 10.1002/mp.14676 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.1002/mp.14676 | 4249bfd842435bc45a7cc2a185806cb853e61a3e4933eb15f17f3e1b85c8cbf5 | 0.764816 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1806.08727 | paper | corpus_metadata | null | Jack the Reader - A Machine Reading Framework | Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as question... | https://arxiv.org/abs/1806.08727 | 2018-06-20T00:30:29Z | 1806.08727 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1806.08727 | 1e12e257f0392d1b2b3f0194778a81f6d2c505e15e1eec606293d7350c83bca1 | 0.696883 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.04816 | paper | corpus_metadata | null | Making Neural QA as Simple as Possible but not Simpler | Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose ... | https://arxiv.org/abs/1703.04816 | 2017-03-14T23:09:45Z | 1703.04816 | 10.48550/arxiv.1703.04816 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.04816 | 18df594333c1672919bf7036446afff33729d38dcfec0d508106f00498ddd279 | 0.701717 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.13166 | paper | corpus_metadata | null | A Disentangling Invertible Interpretation Network for Explaining Latent Representations | Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layer... | https://arxiv.org/abs/2004.13166 | 2020-04-27T20:43:20Z | 2004.13166 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2004.13166 | 4167342c857b2b3c97e963daac7ba5af03aaa5c18fb0a0d87f94c271add9a043 | 0.764636 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.02537 | paper | corpus_metadata | null | Weakly-Supervised Open-Retrieval Conversational Question Answering | Recent studies on Question Answering (QA) and Conversational QA (ConvQA) emphasize the role of retrieval: a system first retrieves evidence from a large collection and then extracts answers. This open-retrieval ConvQA setting typically assumes that each question is answerable by a single span of text within a particula... | https://arxiv.org/abs/2103.02537 | 2021-03-03T17:23:30Z | 2103.02537 | 10.48550/arxiv.2103.02537 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2103.02537 | b965ab8b403273d0bd343660a321a507f958e93bf74506075ba625b83430a8f1 | 0.619597 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1411.1784 | paper | corpus_metadata | null | Conditional Generative Adversarial Nets | Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this... | https://arxiv.org/abs/1411.1784 | 2014-11-06T22:33:22Z | 1411.1784 | 10.48550/arxiv.1411.1784 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1411.1784 | 7e650fed450d35c2c97d354144668e931ca708b249aede416829e49e24f01192 | 0.701818 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.09318 | paper | corpus_metadata | null | Inverse Kinematics for Serial Kinematic Chains via Sum of Squares Optimization | Inverse kinematics is a fundamental problem for articulated robots: fast and accurate algorithms are needed for translating task-related workspace constraints and goals into feasible joint configurations. In general, inverse kinematics for serial kinematic chains is a difficult nonlinear problem, for which closed form ... | https://arxiv.org/abs/1909.09318 | 2019-09-20T04:39:57Z | 1909.09318 | 10.1109/icra40945.2020.9196704 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/icra40945.2020.9196704 | 9f8e26c386c93ee9cd0c41b5c2876677085904c2da86a2e0ff1351dd7769b729 | 0.511945 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.14515 | paper | corpus_metadata | null | How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages | Despite the recent advancements of attention-based deep learning architectures across a majority of Natural Language Processing tasks, their application remains limited in a low-resource setting because of a lack of pre-trained models for such languages. In this study, we make the first attempt to investigate the chall... | https://arxiv.org/abs/2105.14515 | 2021-05-30T12:09:59Z | 2105.14515 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2105.14515 | df7a0f0264f9f3741949f1958632c99958c992939c2bb872023e23768aedddba | 0.823569 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.12998 | paper | corpus_metadata | null | VoxLingua107: a Dataset for Spoken Language Recognition | This paper investigates the use of automatically collected web audio data for the task of spoken language recognition. We generate semi-random search phrases from language-specific Wikipedia data that are then used to retrieve videos from YouTube for 107 languages. Speech activity detection and speaker diarization are ... | https://arxiv.org/abs/2011.12998 | 2020-11-25T19:47:38Z | 2011.12998 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2011.12998 | 2f353a10f6657a109a02ac880d9d692575f788e0379f7e60a5fbde11cee14b17 | 0.737842 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1811.10100 | paper | corpus_metadata | null | WarpGAN: Automatic Caricature Generation | We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo. Besides transferring rich texture styles, WarpGAN learns to automatically predict a set of control points that can warp the photo into a caricature, while preserving identity. We introduce an identity-preserving adve... | https://arxiv.org/abs/1811.10100 | 2018-11-25T21:36:01Z | 1811.10100 | 10.48550/arxiv.1811.10100 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1811.10100 | 3e9d1acc14cee744806a2f5ef51e9963e2bac535211d2a6a0304ebf8ca6d7e7e | 0.610549 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.05008 | paper | corpus_metadata | null | AdvFaces: Adversarial Face Synthesis | Face recognition systems have been shown to be vulnerable to adversarial examples resulting from adding small perturbations to probe images. Such adversarial images can lead state-of-the-art face recognition systems to falsely reject a genuine subject (obfuscation attack) or falsely match to an impostor (impersonation ... | https://arxiv.org/abs/1908.05008 | 2019-08-14T07:58:00Z | 1908.05008 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1908.05008 | 7cdb198a6690650f10fbe1facb338502b6a4713949bb1b8fd3aa31c3bd6da81d | 0.611196 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.09452 | paper | corpus_metadata | null | SEGAN: Speech Enhancement Generative Adversarial Network | Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn comp... | https://arxiv.org/abs/1703.09452 | 2017-03-28T08:39:06Z | 1703.09452 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1703.09452 | 8e6b32553582054f5783cce476b16ad17a21a0c2a898e662c1638843da50f706 | 0.741703 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2005.08047 | paper | corpus_metadata | null | Simple, Scalable, and Stable Variational Deep Clustering | Deep clustering (DC) has become the state-of-the-art for unsupervised clustering. In principle, DC represents a variety of unsupervised methods that jointly learn the underlying clusters and the latent representation directly from unstructured datasets. However, DC methods are generally poorly applied due to high opera... | https://arxiv.org/abs/2005.08047 | 2020-05-16T17:24:01Z | 2005.08047 | 10.48550/arxiv.2005.08047 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2005.08047 | 6e8220347ae32af5d627504f68f037425ce9c070118c81003ecf822ba9536b41 | 0.757492 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1601.06116 | paper | corpus_metadata | null | A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler | Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, inspired by the neocortex, currently does not have a comprehensive mathematical framework. This work brings together all aspects of the spatia... | https://arxiv.org/abs/1601.06116 | 2016-01-22T19:26:16Z | 1601.06116 | 10.3389/frobt.2016.00081 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.3389/frobt.2016.00081 | c978e9549bc207384de5524f84b487e965df4ad8439e4e2f0460cb3f6b834ac3 | 0.563078 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.09153 | paper | corpus_metadata | null | Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks | The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view. In this article, we focus on resource-efficient randomly connected neural networks known as Random Vector Functional Link (RVFL) networks since their simple desi... | https://arxiv.org/abs/1909.09153 | 2019-09-19T10:57:34Z | 1909.09153 | 10.1109/tnnls.2020.3015971 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1109/tnnls.2020.3015971 | 99a4de91456fe029ff49cbbb8dad253c6b2f0b65c4a8ca9d3a6c6615fe51d437 | 0.699186 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2009.03979 | paper | corpus_metadata | null | A Distance-preserving Matrix Sketch | Visualizing very large matrices involves many formidable problems. Various popular solutions to these problems involve sampling, clustering, projection, or feature selection to reduce the size and complexity of the original task. An important aspect of these methods is how to preserve relative distances between points ... | https://arxiv.org/abs/2009.03979 | 2020-09-08T20:15:14Z | 2009.03979 | 10.1080/10618600.2022.2050246 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1080/10618600.2022.2050246 | b2809d9f16908a600c74a8b39d36d3ec0dc8fe0e623de50d9b2b75fc125e6533 | 0.513299 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1708.05031 | paper | corpus_metadata | NeuMF (Pinterest) | Neural Collaborative Filtering | In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks... | https://arxiv.org/abs/1708.05031 | 2017-08-16T18:30:09Z | 1708.05031 | 10.48550/arxiv.1708.05031 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1708.05031 | 9b1648c8ab258eefdf84b44117866e61031f19dcd0732b111a77f135a139ab0f | 0.815061 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:2402.03242 | paper | corpus_metadata | null | JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching | Recent approaches in skill matching, employing synthetic training data for classification or similarity model training, have shown promising results, reducing the need for time-consuming and expensive annotations. However, previous synthetic datasets have limitations, such as featuring only one skill per sentence and g... | https://arxiv.org/abs/2402.03242 | 2024-02-05T17:57:26Z | 2402.03242 | 10.48550/arxiv.2402.03242 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2402.03242 | 0bb5172445992d9094592166df572a0887b38fa46725224dc2ae534514979edc | 0.839155 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1503.03578 | paper | corpus_metadata | null | LINE: Large-scale Information Network Embedding | This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain mill... | https://arxiv.org/abs/1503.03578 | 2015-03-12T04:07:32Z | 1503.03578 | 10.1145/2736277.2741093 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/3.0 | doi:10.1145/2736277.2741093 | cfc35a9115cdf2b73af74fc5555d69f7e4cb2e07982492792883eeb1420ae1cc | 0.761848 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2009.13211 | paper | corpus_metadata | null | Instance-based Counterfactual Explanations for Time Series Classification | In recent years, there has been a rapidly expanding focus on explaining the predictions made by black-box AI systems that handle image and tabular data. However, considerably less attention has been paid to explaining the predictions of opaque AI systems handling time series data. In this paper, we advance a novel mode... | https://arxiv.org/abs/2009.13211 | 2020-09-28T10:52:48Z | 2009.13211 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2009.13211 | 319146a521a600931b091c36a01ce65e890f76af7366c94a74681f223c238c71 | 0.595406 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.04805 | paper | corpus_metadata | BERT-Large | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right contex... | https://arxiv.org/abs/1810.04805 | 2018-10-11T00:50:01Z | 1810.04805 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1810.04805 | 67a031d901474502ddf9c09eb12edb9af1c9daea271eebf001f8a721d5d75be7 | 0.812679 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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arxiv:1910.04732 | paper | corpus_metadata | null | Structured Pruning of Large Language Models | Large language models have recently achieved state of the art performance across a wide variety of natural language tasks. Meanwhile, the size of these models and their latency have significantly increased, which makes their usage costly, and raises an interesting question: do language models need to be large? We study... | https://arxiv.org/abs/1910.04732 | 2019-10-10T17:44:18Z | 1910.04732 | 10.18653/v1/2020.emnlp-main.496 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.18653/v1/2020.emnlp-main.496 | 57a360aa60fbf8ae80666ccb91b5e84bb44462b0604f37ad65ad1bc3726f276e | 0.860248 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.09243 | paper | corpus_metadata | null | Pretraining-Based Natural Language Generation for Text Summarization | In this paper, we propose a novel pretraining-based encoder-decoder framework, which can generate the output sequence based on the input sequence in a two-stage manner. For the encoder of our model, we encode the input sequence into context representations using BERT. For the decoder, there are two stages in our model,... | https://arxiv.org/abs/1902.09243 | 2019-02-25T13:07:32Z | 1902.09243 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1902.09243 | 5a330030ba291f99ce4fa69ca3834e47096e20ae72053aa93644873853a4cc53 | 0.851388 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.10505 | paper | corpus_metadata | null | SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images | Dense 3D object reconstruction from a single image has recently witnessed remarkable advances, but supervising neural networks with ground-truth 3D shapes is impractical due to the laborious process of creating paired image-shape datasets. Recent efforts have turned to learning 3D reconstruction without 3D supervision ... | https://arxiv.org/abs/2010.10505 | 2020-10-20T17:59:47Z | 2010.10505 | 10.48550/arxiv.2010.10505 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2010.10505 | cc3c9ecc16a3da67aa4c1acb2b96812ef19e4c1ec28966eba17f726dfc9a68a8 | 0.789515 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.04761 | paper | corpus_metadata | null | MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data. However, training existing models requires huge amounts of compute, while pretrained cross-lingual models often underperform on low-resource languages. We propose Multi-lingual language model Fine-Tuni... | https://arxiv.org/abs/1909.04761 | 2019-09-10T21:30:54Z | 1909.04761 | 10.48550/arxiv.1909.04761 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1909.04761 | 62e4da1885eee902187377c246fb81bf73979d8c90f8eb057079e2f768f5d2f9 | 0.879938 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1910.08210 | paper | corpus_metadata | null | RTFM: Generalising to Novel Environment Dynamics via Reading | Obtaining policies that can generalise to new environments in reinforcement learning is challenging. In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments. We propose a grounded policy learning problem, Read to Fight Monsters ... | https://arxiv.org/abs/1910.08210 | 2019-10-18T00:49:15Z | 1910.08210 | 10.48550/arxiv.1910.08210 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1910.08210 | c9b30c8454a5564465e6b0ea5e07436db74c7b57092b20f2356492fe80149719 | 0.739652 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2005.12522 | paper | corpus_metadata | null | What Are People Asking About COVID-19? A Question Classification Dataset | We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question clusters. The most common questions in our dataset asked about transmission, prevention, and societal effects of COVID, and we found that many questions that appeared in multiple s... | https://arxiv.org/abs/2005.12522 | 2020-05-26T05:41:58Z | 2005.12522 | 10.48550/arxiv.2005.12522 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2005.12522 | ecd8e633fbc99655840cad839b3d82f90766d3119feaff1cb78391fa276ff98b | 0.65811 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.09297 | paper | corpus_metadata | null | MPNet: Masked and Permuted Pre-training for Language Understanding | BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models. Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for pre-training to address this problem. However, XLNet does not leverage the full position informa... | https://arxiv.org/abs/2004.09297 | 2020-04-20T13:54:12Z | 2004.09297 | 10.48550/arxiv.2004.09297 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2004.09297 | dffede74529e97b7ebf166a3e44bc8ddfee8714858b718de66111ff07cc08044 | 0.866826 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2204.07803 | paper | corpus_metadata | null | Logical Inference for Counting on Semi-structured Tables | Recently, the Natural Language Inference (NLI) task has been studied for semi-structured tables that do not have a strict format. Although neural approaches have achieved high performance in various types of NLI, including NLI between semi-structured tables and texts, they still have difficulty in performing a numerica... | https://arxiv.org/abs/2204.07803 | 2022-04-16T14:08:43Z | 2204.07803 | 10.48550/arxiv.2204.07803 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2204.07803 | e0c1f2bc069470e06eb56be0f014ed5d0d286fcc205c0217b012f175d5107e8a | 0.585832 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2204.07789 | paper | corpus_metadata | null | Contrastive Learning with Hard Negative Entities for Entity Set Expansion | Entity Set Expansion (ESE) is a promising task which aims to expand entities of the target semantic class described by a small seed entity set. Various NLP and IR applications will benefit from ESE due to its ability to discover knowledge. Although previous ESE methods have achieved great progress, most of them still l... | https://arxiv.org/abs/2204.07789 | 2022-04-16T12:26:42Z | 2204.07789 | 10.48550/arxiv.2204.07789 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2204.07789 | c4a9165fc5b14d5803ccec7ae9f59a030e1ef06b549874263fd2a2c86e07f673 | 0.773574 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.01751 | paper | corpus_metadata | null | Reordering Examples Helps during Priming-based Few-Shot Learning | The ability to learn from limited data, or few-shot learning, is a desirable and often critical requirement for NLP systems. While many existing methods do poorly at learning from a handful of examples, large pretrained language models have recently been shown to be efficient few-shot learners. One approach to few-shot... | https://arxiv.org/abs/2106.01751 | 2021-06-03T11:02:36Z | 2106.01751 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2106.01751 | 28a919ec3a57cc1d5fe3fff91960d397b6e7e0a4488695712616aa4d919ab7f1 | 0.861168 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.10022 | paper | corpus_metadata | null | Conversational Neuro-Symbolic Commonsense Reasoning | In order for conversational AI systems to hold more natural and broad-ranging conversations, they will require much more commonsense, including the ability to identify unstated presumptions of their conversational partners. For example, in the command "If it snows at night then wake me up early because I don't want to ... | https://arxiv.org/abs/2006.10022 | 2020-06-17T17:28:38Z | 2006.10022 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2006.10022 | 11f214d8245448454a8fb118191c054c7936e979590b0b1f1a27a278a276449f | 0.636338 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1904.00830 | paper | corpus_metadata | null | Depth-Aware Video Frame Interpolation | Video frame interpolation aims to synthesize nonexistent frames in-between the original frames. While significant advances have been made from the recent deep convolutional neural networks, the quality of interpolation is often reduced due to large object motion or occlusion. In this work, we propose a video frame inte... | https://arxiv.org/abs/1904.00830 | 2019-04-01T13:19:59Z | 1904.00830 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1904.00830 | 671ecda8145aa94f4bd811d42ad223809f368efaffc8caa6c4bff64e9c0243b0 | 0.694808 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2005.05960 | paper | corpus_metadata | null | Planning to Explore via Self-Supervised World Models | Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge. We present Plan2Explore, a self-supervised reinforcement learning agent that tackles both these challenges through a new approach to self-supervised exploration and fast ad... | https://arxiv.org/abs/2005.05960 | 2020-05-12T17:59:45Z | 2005.05960 | 10.48550/arxiv.2005.05960 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2005.05960 | 09cec61b33e69fd53bc3a291b5a8caa17d3d8a9f31c28537fd09ad25bb6c1253 | 0.792835 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.09070 | paper | corpus_metadata | EfficientDet | EfficientDet: Scalable and Efficient Object Detection | Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which al... | https://arxiv.org/abs/1911.09070 | 2019-11-20T18:16:09Z | 1911.09070 | 10.48550/arxiv.1911.09070 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1911.09070 | 3cd19535f777c9c3a105156bb1e2cb6770f46486ec4723b6204b2f9ecd35914b | 0.788349 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:1906.07625 | paper | corpus_metadata | null | Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data | The collection of large, complex datasets has become common across a wide variety of domains. Visual analytics tools increasingly play a key role in exploring and answering complex questions about these large datasets. However, many visualizations are not designed to concurrently visualize the large number of dimension... | https://arxiv.org/abs/1906.07625 | 2019-06-18T14:58:52Z | 1906.07625 | 10.1109/tvcg.2019.2934209 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tvcg.2019.2934209 | 9937f72a87be81278ffd2f2c7b1c0d675e2eb08c625757efcb471dcaa77862e8 | 0.508486 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.07402 | paper | corpus_metadata | null | Simple Online and Realtime Tracking with a Deep Association Metric | Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT. Due to this extension we are able to track objects through longer periods of occlusions, eff... | https://arxiv.org/abs/1703.07402 | 2017-03-21T19:40:25Z | 1703.07402 | 10.48550/arxiv.1703.07402 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.07402 | 54a5d460f177dd57363cf0b94e1548d865345507f3ce42bfd9949ab0c4059b8f | 0.629885 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.02983 | paper | corpus_metadata | null | Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning | Semi-supervised learning, i.e. jointly learning from labeled and unlabeled samples, is an active research topic due to its key role on relaxing human supervision. In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encoura... | https://arxiv.org/abs/1908.02983 | 2019-08-08T09:17:54Z | 1908.02983 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1908.02983 | 15c7c6a0ae74e9cbd3d17e9716a1d060e198f6d7fa36fb57aa07011c45782aa1 | 0.755248 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.06586 | paper | corpus_metadata | null | Selective Kernel Networks | In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in const... | https://arxiv.org/abs/1903.06586 | 2019-03-15T15:04:22Z | 1903.06586 | 10.48550/arxiv.1903.06586 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1903.06586 | 2529ab5ed49588eb7f52df73d711d1a510afd0a55acb811779e3c296cc8907ed | 0.733775 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.08564 | paper | corpus_metadata | null | Intra-Processing Methods for Debiasing Neural Networks | As deep learning models become tasked with more and more decisions that impact human lives, such as criminal recidivism, loan repayment, and face recognition for law enforcement, bias is becoming a growing concern. Debiasing algorithms are typically split into three paradigms: pre-processing, in-processing, and post-pr... | https://arxiv.org/abs/2006.08564 | 2020-06-15T17:30:57Z | 2006.08564 | 10.48550/arxiv.2006.08564 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2006.08564 | db9008de8805df1c2c857ab6239ea9f94d674e313f897b975d8a6a758a4f08e1 | 0.83179 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1709.02656 | paper | corpus_metadata | null | Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning | Internet traffic classification has become more important with rapid growth of current Internet network and online applications. There have been numerous studies on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify networ... | https://arxiv.org/abs/1709.02656 | 2017-09-08T11:40:37Z | 1709.02656 | 10.1007/s00500-019-04030-2 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/s00500-019-04030-2 | 18725ca41cef1516f32e33fba4ece593f5996c445b0483c5ec32aa0af9a72523 | 0.676015 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.10919 | paper | corpus_metadata | null | On the effect of normalization layers on Differentially Private training of deep Neural networks | Differentially private stochastic gradient descent (DPSGD) is a variation of stochastic gradient descent based on the Differential Privacy (DP) paradigm, which can mitigate privacy threats that arise from the presence of sensitive information in training data. However, one major drawback of training deep neural network... | https://arxiv.org/abs/2006.10919 | 2020-06-19T01:43:52Z | 2006.10919 | 10.48550/arxiv.2006.10919 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2006.10919 | ed39bb288ef4804bd92ce52dc16e29854924ff491016037eabad3df44038ad24 | 0.680082 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.09940 | paper | corpus_metadata | null | Neural Combinatorial Optimization with Reinforcement Learning | This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent network that, given a set of city coordinates, predicts a distribution over different city permutations. Using negative t... | https://arxiv.org/abs/1611.09940 | 2016-11-29T23:22:39Z | 1611.09940 | 10.48550/arxiv.1611.09940 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1611.09940 | 68bb49dc058cfe4032f2890181cd2124035ef8034c3d7b22d1821a5135cdefc9 | 0.683395 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1506.03134 | paper | corpus_metadata | null | Pointer Networks | We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence and Neural Turing Machines, because th... | https://arxiv.org/abs/1506.03134 | 2015-06-09T23:38:16Z | 1506.03134 | 10.48550/arxiv.1506.03134 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1506.03134 | 130ea277200c13ca728e36f946f80309f9138e459cf59b6c5b04bed908ad9974 | 0.758732 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.11206 | paper | corpus_metadata | null | Oops! Predicting Unintentional Action in Video | From just a short glance at a video, we can often tell whether a person's action is intentional or not. Can we train a model to recognize this? We introduce a dataset of in-the-wild videos of unintentional action, as well as a suite of tasks for recognizing, localizing, and anticipating its onset. We train a supervised... | https://arxiv.org/abs/1911.11206 | 2019-11-25T20:15:31Z | 1911.11206 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1911.11206 | 636c178345faf82b47579d2cc460b0e453e4928f4d472a233e765d6f851c7363 | 0.769397 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.15410 | paper | corpus_metadata | null | Mining Persistent Activity in Continually Evolving Networks | Frequent pattern mining is a key area of study that gives insights into the structure and dynamics of evolving networks, such as social or road networks. However, not only does a network evolve, but often the way that it evolves, itself evolves. Thus, knowing, in addition to patterns' frequencies, for how long and how ... | https://arxiv.org/abs/2006.15410 | 2020-06-27T17:29:45Z | 2006.15410 | 10.1145/3394486.3403136 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3394486.3403136 | 80b29114ea66a649b1b7b0fa2a7a0bd4c8849ae44672410da69280323cad912e | 0.544333 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.06103 | paper | corpus_metadata | null | Modeling Relational Data with Graph Convolutional Networks | Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. We introduce Relational Graph Convolutional Networks (R-GCNs) and app... | https://arxiv.org/abs/1703.06103 | 2017-03-17T17:09:14Z | 1703.06103 | 10.48550/arxiv.1703.06103 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.06103 | 832f0eb9daaf04efd084f64297fcd16b472a63c22cdbff57eedf1c36ebb46e50 | 0.680004 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.02151 | paper | corpus_metadata | null | KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning | Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform exp... | https://arxiv.org/abs/1909.02151 | 2019-09-04T23:37:25Z | 1909.02151 | 10.48550/arxiv.1909.02151 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1909.02151 | a9072f710e01cdb75011662153d1861e9c7ab45f4f0c31c02914fa37c13de4b7 | 0.781452 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.02702 | paper | corpus_metadata | null | Port-Hamiltonian Approach to Neural Network Training | Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations. Recent work proposes networks with layer outputs which are no longer quantized but are solutions of an ordinary differential equation (ODE); however, these network... | https://arxiv.org/abs/1909.02702 | 2019-09-06T03:31:40Z | 1909.02702 | 10.48550/arxiv.1909.02702 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1909.02702 | e4fdf044972e415a261b6727a98d2aa62355d2c6fb6d0bfe69e2fe27e6016e75 | 0.604784 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.13342 | paper | corpus_metadata | null | The Shapley Value of coalition of variables provides better explanations | While Shapley Values (SV) are one of the gold standard for interpreting machine learning models, we show that they are still poorly understood, in particular in the presence of categorical variables or of variables of low importance. For instance, we show that the popular practice that consists in summing the SV of dum... | https://arxiv.org/abs/2103.13342 | 2021-03-24T17:02:57Z | 2103.13342 | 10.48550/arxiv.2103.13342 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2103.13342 | 34a580509bba31db8400bc5bb5603fd16e27911072e6f37a8295c96f4a051402 | 0.629617 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1812.03320 | paper | corpus_metadata | null | GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud | We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data. Instead of treating object proposal as a direct bounding box regression problem, we take an analysis-by-synthesis strategy and generate proposals by reconstructing shapes from n... | https://arxiv.org/abs/1812.03320 | 2018-12-08T13:41:05Z | 1812.03320 | 10.48550/arxiv.1812.03320 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1812.03320 | db535c56b48582d11a8bc9bcb118d88daf93a2219a3d4a6a641a3556265b75df | 0.644249 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1612.03144 | paper | corpus_metadata | null | Feature Pyramid Networks for Object Detection | Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning object detectors have avoided pyramid representations, in part because they are compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep ... | https://arxiv.org/abs/1612.03144 | 2016-12-09T19:55:54Z | 1612.03144 | 10.48550/arxiv.1612.03144 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1612.03144 | eb50d9f47ddbe5e7b509f4c2229b990dad900f8bb4a03bc82be51231b1a3db1c | 0.755622 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.00025 | paper | corpus_metadata | null | PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning | Sample efficiency and scalability to a large number of agents are two important goals for multi-agent reinforcement learning systems. Recent works got us closer to those goals, addressing non-stationarity of the environment from a single agent's perspective by utilizing a deep net critic which depends on all observatio... | https://arxiv.org/abs/1911.00025 | 2019-10-31T18:04:42Z | 1911.00025 | 10.48550/arxiv.1911.00025 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1911.00025 | b3b1d4ec2eddfa4670ab9347f80963bef097bd3447d885604f829e4929afebe8 | 0.638022 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2406.14929 | paper | corpus_metadata | null | Efficient Graph Similarity Computation with Alignment Regularization | We consider the graph similarity computation (GSC) task based on graph edit distance (GED) estimation. State-of-the-art methods treat GSC as a learning-based prediction task using Graph Neural Networks (GNNs). To capture fine-grained interactions between pair-wise graphs, these methods mostly contain a node-level match... | https://arxiv.org/abs/2406.14929 | 2024-06-21T07:37:28Z | 2406.14929 | 10.48550/arxiv.2406.14929 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2406.14929 | 2f4577a7625ae4bd3fe05c365f6bbe26c45f5fe940c527e9f793a23a4133e0a9 | 0.828786 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.00807 | paper | corpus_metadata | null | Fake Review Detection Using Behavioral and Contextual Features | User reviews reflect significant value of product in the world of e-market. Many firms or product providers hire spammers for misleading new customers by posting spam reviews. There are three types of fake reviews, untruthful reviews, brand reviews and non-reviews. All three types mislead the new customers. A multinomi... | https://arxiv.org/abs/2003.00807 | 2020-02-26T10:36:40Z | 2003.00807 | 10.48550/arxiv.2003.00807 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.00807 | eb6404a67cdf959e0b9bd197d36a8f96ad9f5d94d94263ecb8d4072461c553ac | 0.55319 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.12456 | paper | corpus_metadata | null | ASSD: Attentive Single Shot Multibox Detector | This paper proposes a new deep neural network for object detection. The proposed network, termed ASSD, builds feature relations in the spatial space of the feature map. With the global relation information, ASSD learns to highlight useful regions on the feature maps while suppressing the irrelevant information, thereby... | https://arxiv.org/abs/1909.12456 | 2019-09-27T01:36:41Z | 1909.12456 | 10.48550/arxiv.1909.12456 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1909.12456 | 125d4ab8bfa245989b44f46030e5e7eeeade42602c6b36f8b3fc4517f93d842d | 0.616031 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1312.6034 | paper | corpus_metadata | null | Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps | This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [Erh... | https://arxiv.org/abs/1312.6034 | 2013-12-20T16:45:54Z | 1312.6034 | 10.48550/arxiv.1312.6034 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1312.6034 | bffac5abd15d1c99cb661a0a1019b293c80eee18a382d92b380a7db4479dc69c | 0.655351 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.09314 | paper | corpus_metadata | null | Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification | Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Due to the large intra-class variations and cross-modality discrepancy with large amount of sample noise, it is difficult to learn discriminative part features. Existing VI-ReID methods instead tend to lear... | https://arxiv.org/abs/2007.09314 | 2020-07-18T03:08:13Z | 2007.09314 | 10.48550/arxiv.2007.09314 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.09314 | 738b6039d063cb670f93af381a6e2129f3a7a89c656d6efd1592a64330a753c2 | 0.745977 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.02130 | paper | corpus_metadata | null | Playing Chess with Limited Look Ahead | We have seen numerous machine learning methods tackle the game of chess over the years. However, one common element in these works is the necessity of a finely optimized look ahead algorithm. The particular interest of this research lies with creating a chess engine that is highly capable, but restricted in its look ah... | https://arxiv.org/abs/2007.02130 | 2020-07-04T16:02:43Z | 2007.02130 | 10.48550/arxiv.2007.02130 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.02130 | f457aa59d4fdb7ae7b229bef430f0722599fb61db1a297c5e3a4a324c5a11e46 | 0.52851 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2101.11174 | paper | corpus_metadata | null | Graph Neural Network for Traffic Forecasting: A Survey | Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph... | https://arxiv.org/abs/2101.11174 | 2021-01-27T02:35:41Z | 2101.11174 | 10.1016/j.eswa.2022.117921 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1016/j.eswa.2022.117921 | 45c6ac36ff4bd573a1b3a4e940df32bd0092e9c7bf71a0020edc1f2e081ab665 | 0.681049 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.10452 | paper | corpus_metadata | null | Additive Noise Annealing and Approximation Properties of Quantized Neural Networks | We present a theoretical and experimental investigation of the quantization problem for artificial neural networks. We provide a mathematical definition of quantized neural networks and analyze their approximation capabilities, showing in particular that any Lipschitz-continuous map defined on a hypercube can be unifor... | https://arxiv.org/abs/1905.10452 | 2019-05-24T21:30:54Z | 1905.10452 | 10.48550/arxiv.1905.10452 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.10452 | f8544570c9401efffd4567c34bbee32231dc8e542b75e735bcb3936457b20358 | 0.693557 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.06877 | paper | corpus_metadata | null | Fast and Accurate Model Scaling | In this work we analyze strategies for convolutional neural network scaling; that is, the process of scaling a base convolutional network to endow it with greater computational complexity and consequently representational power. Example scaling strategies may include increasing model width, depth, resolution, etc. Whil... | https://arxiv.org/abs/2103.06877 | 2021-03-11T18:59:14Z | 2103.06877 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2103.06877 | b5ad820bd39ef2edae2f2f6ac4069b1a1387b7394e1b81770edc151f4f6222bd | 0.611791 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.06319 | paper | corpus_metadata | null | Exact Hard Monotonic Attention for Character-Level Transduction | Many common character-level, string-to string transduction tasks, e.g., grapheme-tophoneme conversion and morphological inflection, consist almost exclusively of monotonic transductions. However, neural sequence-to sequence models that use non-monotonic soft attention often outperform popular monotonic models. In this ... | https://arxiv.org/abs/1905.06319 | 2019-05-15T17:51:09Z | 1905.06319 | 10.48550/arxiv.1905.06319 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.06319 | 456f1182c2e49b0d8c607c64cab2bd5f1ced7a688ce7c939679d3294baafd039 | 0.726529 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1301.0322 | paper | corpus_metadata | null | Solving Large Scale Structure in Ten Easy Steps with COLA | We present the COmoving Lagrangian Acceleration (COLA) method: an N-body method for solving for Large Scale Structure (LSS) in a frame that is comoving with observers following trajectories calculated in Lagrangian Perturbation Theory (LPT). Unlike standard N-body methods, the COLA method can straightforwardly trade ac... | https://arxiv.org/abs/1301.0322 | 2013-01-02T21:00:02Z | 1301.0322 | 10.1088/1475-7516/2013/06/036 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1088/1475-7516/2013/06/036 | b29fd8df28f760b815d05b27effedbec1417e4007ddf9038af5c77c43aea4b14 | 0.508861 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.02680 | paper | corpus_metadata | null | Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection | The widespread of fake news and misinformation in various domains ranging from politics, economics to public health has posed an urgent need to automatically fact-check information. A recent trend in fake news detection is to utilize evidence from external sources. However, existing evidence-aware fake news detection m... | https://arxiv.org/abs/2102.02680 | 2021-02-04T15:18:44Z | 2102.02680 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2102.02680 | 8d2bcc63c251adb0a9c63932ea200c56da105c4a0ab970593e02f76af7162c6c | 0.757185 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1904.07850 | paper | corpus_metadata | null | Objects as Points | Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as ... | https://arxiv.org/abs/1904.07850 | 2019-04-16T17:54:26Z | 1904.07850 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1904.07850 | 241005015e6f9c2fd48ff0e82b87629eb79677c10febaf2d504e7c2f4bfcc127 | 0.635243 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.00948 | paper | corpus_metadata | null | HarDNet: A Low Memory Traffic Network | State-of-the-art neural network architectures such as ResNet, MobileNet, and DenseNet have achieved outstanding accuracy over low MACs and small model size counterparts. However, these metrics might not be accurate for predicting the inference time. We suggest that memory traffic for accessing intermediate feature maps... | https://arxiv.org/abs/1909.00948 | 2019-09-03T04:34:18Z | 1909.00948 | 10.48550/arxiv.1909.00948 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1909.00948 | f8c70a2f16603ce248433aa681f154d4ef47ba31bff18815fd1ee39788827f3c | 0.674319 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.00545 | paper | corpus_metadata | null | Cross-Domain Adaptation of Spoken Language Identification for Related Languages: The Curious Case of Slavic Languages | State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between distant languages but also between closely-related languages or even different spoken varieties of the same language. However, it is still u... | https://arxiv.org/abs/2008.00545 | 2020-08-02T19:30:39Z | 2008.00545 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2008.00545 | eda930371e8f0afe0837e55db53dafe510e200fa9021e3464f3d49263d688282 | 0.781389 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.09019 | paper | corpus_metadata | null | Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate | In design, fabrication, and control problems, we are often faced with the task of synthesis, in which we must generate an object or configuration that satisfies a set of constraints while maximizing one or more objective functions. The synthesis problem is typically characterized by a physical process in which many dif... | https://arxiv.org/abs/2106.09019 | 2021-06-16T17:59:45Z | 2106.09019 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2106.09019 | 019f513541159366bae786a018eb5b83c5ef0aa67974e252437896ce9edd0838 | 0.652223 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.05770 | paper | corpus_metadata | null | Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses | 3D human pose estimation from a single image is an inverse problem due to the inherent ambiguity of the missing depth. Several previous works addressed the inverse problem by generating multiple hypotheses. However, these works are strongly supervised and require ground truth 2D-to-3D correspondences which can be diffi... | https://arxiv.org/abs/2008.05770 | 2020-08-13T09:26:01Z | 2008.05770 | 10.48550/arxiv.2008.05770 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2008.05770 | d3201639e7d892755e56680ade31d407580a429fec55e121b88f179ade5b5d6d | 0.801407 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.05730 | paper | corpus_metadata | null | Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks | We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems. This algorithm is based on deep neural networks and its key feature is the iterative selection of training data through a feed... | https://arxiv.org/abs/2008.05730 | 2020-08-13T07:31:07Z | 2008.05730 | 10.1016/j.cma.2020.113575 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1016/j.cma.2020.113575 | 82ee21c6a65e733a13f0f1c25320a48b584777b993075062c5fce4ea926de64a | 0.666665 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.05646 | paper | corpus_metadata | null | LAC : LSTM AUTOENCODER with Community for Insider Threat Detection | The employees of any organization, institute, or industry, spend a significant amount of time on a computer network, where they develop their own routine of activities in the form of network transactions over a time period. Insider threat detection involves identifying deviations in the routines or anomalies which may ... | https://arxiv.org/abs/2008.05646 | 2020-08-13T02:08:39Z | 2008.05646 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2008.05646 | 42906f3e1efb1ada34dc5b5880eb9600af9ca4b04deaa26d6cae5659fab651e1 | 0.553319 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.08230 | paper | corpus_metadata | TAPE Transformer | Evaluating Protein Transfer Learning with TAPE | Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation tec... | https://arxiv.org/abs/1906.08230 | 2019-06-19T17:19:31Z | 1906.08230 | 10.48550/arxiv.1906.08230 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1906.08230 | 0a209fcd563ed33045b2615a20b3285e46acd8a3628d15b2d39c8c7878482dd4 | 0.850074 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
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] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.08866 | paper | corpus_metadata | null | Contextual Lensing of Universal Sentence Representations | What makes a universal sentence encoder universal? The notion of a generic encoder of text appears to be at odds with the inherent contextualization and non-permanence of language use in a dynamic world. However, mapping sentences into generic fixed-length vectors for downstream similarity and retrieval tasks has been ... | https://arxiv.org/abs/2002.08866 | 2020-02-20T17:06:27Z | 2002.08866 | 10.48550/arxiv.2002.08866 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2002.08866 | a7e6f35354a281360148e3089e8a5ee38f1981e1bfcf7dd568f89739cb6a3927 | 0.645786 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.15065 | paper | corpus_metadata | null | Fixed-Length Protein Embeddings using Contextual Lenses | The Basic Local Alignment Search Tool (BLAST) is currently the most popular method for searching databases of biological sequences. BLAST compares sequences via similarity defined by a weighted edit distance, which results in it being computationally expensive. As opposed to working with edit distance, a vector similar... | https://arxiv.org/abs/2010.15065 | 2020-10-15T14:54:55Z | 2010.15065 | 10.48550/arxiv.2010.15065 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2010.15065 | 0d867a7031165e606ac4b3c4b3b8740a783aaacdcf26ad7fa09d2255af5f7736 | 0.719033 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1910.08787 | paper | corpus_metadata | null | SpatialFlow: Bridging All Tasks for Panoptic Segmentation | Object location is fundamental to panoptic segmentation as it is related to all things and stuff in the image scene. Knowing the locations of objects in the image provides clues for segmenting and helps the network better understand the scene. How to integrate object location in both thing and stuff segmentation is a c... | https://arxiv.org/abs/1910.08787 | 2019-10-19T15:16:27Z | 1910.08787 | 10.48550/arxiv.1910.08787 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1910.08787 | b22d4a1a33e99f8194c489a7404d9eb1f4de3bfdcd8fcc992205fa6b9d3b0391 | 0.727733 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.02287 | paper | corpus_metadata | null | Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography | In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements. However, in emergencies or point-of-care situations, acquiring an ECG is often not an option, hence motivating the need for alternative temporal synchronization ... | https://arxiv.org/abs/2102.02287 | 2021-02-03T20:48:16Z | 2102.02287 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | arxiv:2102.02287 | f3f8c0469f84cd5759beb9bff2166e9778e56bc0b7c72f0af0a79cae10339450 | 0.640551 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2008.03596 | paper | corpus_metadata | null | TriFinger: An Open-Source Robot for Learning Dexterity | Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade. We argue that a hindrance is the high cost of experimentation on real systems, in terms of both time and money. We address this problem by proposing an open-source robotic platform w... | https://arxiv.org/abs/2008.03596 | 2020-08-08T20:54:30Z | 2008.03596 | 10.48550/arxiv.2008.03596 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2008.03596 | 92657f56a294b99f081eaa8aede493b517e718188abbca0f899daa9f328c031a | 0.636907 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1805.05081 | paper | corpus_metadata | null | Constructing Narrative Event Evolutionary Graph for Script Event Prediction | Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. To remedy this, we propose constructing an event graph to ... | https://arxiv.org/abs/1805.05081 | 2018-05-14T09:23:26Z | 1805.05081 | 10.48550/arxiv.1805.05081 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1805.05081 | 5b3ed812aecf63c46e960b6180469fe6fe795f5241a777d1369188d4e16be3ba | 0.749583 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.14966 | paper | corpus_metadata | null | Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps | Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps. However, choosing which of the myriad structured transformations to use (and... | https://arxiv.org/abs/2012.14966 | 2020-12-29T22:51:29Z | 2012.14966 | 10.48550/arxiv.2012.14966 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2012.14966 | 9f73384b70498d38c87e0118bffd6629b479b31aa58d3be07d28edfd0ceeb6b0 | 0.778339 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1906.03533 | paper | corpus_metadata | null | Proposed Guidelines for the Responsible Use of Explainable Machine Learning | Explainable machine learning (ML) enables human learning from ML, human appeal of automated model decisions, regulatory compliance, and security audits of ML models. Explainable ML (i.e. explainable artificial intelligence or XAI) has been implemented in numerous open source and commercial packages and explainable ML i... | https://arxiv.org/abs/1906.03533 | 2019-06-08T22:12:11Z | 1906.03533 | 10.48550/arxiv.1906.03533 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1906.03533 | 43c5af13786adf717edd6f4b7ca69a8c5bf808a34c9858ca8b8d4a34d03602e7 | 0.550129 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1801.08839 | paper | corpus_metadata | null | SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation | Instance segmentation is a problem of significance in computer vision. However, preparing annotated data for this task is extremely time-consuming and costly. By combining the advantages of 3D scanning, reasoning, and GAN-based domain adaptation techniques, we introduce a novel pipeline named SRDA to obtain large quant... | https://arxiv.org/abs/1801.08839 | 2018-01-26T15:10:15Z | 1801.08839 | 10.48550/arxiv.1801.08839 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1801.08839 | 06c95c1bfc725e25a5e79755b89e1cfa796204a58717d3a1e26f42303dddb1fe | 0.782998 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1707.07998 | paper | corpus_metadata | null | Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering | Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables att... | https://arxiv.org/abs/1707.07998 | 2017-07-25T13:50:17Z | 1707.07998 | 10.48550/arxiv.1707.07998 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1707.07998 | 843dc92ea2c0c4bf4c706d2c74574634449894c3b58df30350a11a216adcaaf8 | 0.627535 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.11946 | paper | corpus_metadata | EfficientNet-L2 | EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks | Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performan... | https://arxiv.org/abs/1905.11946 | 2019-05-28T17:05:32Z | 1905.11946 | 10.48550/arxiv.1905.11946 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.11946 | d6cc02a005a30293d0b53be42988c7c4538c2c3d228440802f847d3a3399aa94 | 0.847178 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"epoch-all-ai-models"
] | [
"epoch-all-ai-models:row:2884",
"epoch-all-ai-models:row:2886"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.14571 | paper | corpus_metadata | null | Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus | Large text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets in a multilingual context. LangID is largely treated as solved in the literature, with models reported that ach... | https://arxiv.org/abs/2010.14571 | 2020-10-27T19:29:17Z | 2010.14571 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2010.14571 | da24eafa54c8d01d489167c258cfeae5c7acdf2f4d7f6b7c71a8be33bc142022 | 0.792057 | 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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