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arxiv:2108.04175 | paper | corpus_metadata | null | Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI | The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and robustness. In fetal brain MRI, abnormalities exacerbate the variability of the developing brain anatomy compared to non-pathological cases. A... | https://arxiv.org/abs/2108.04175 | 2021-08-09T17:00:21Z | 2108.04175 | 10.1007/978-3-030-87735-4_25 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/978-3-030-87735-4_25 | 0d3735b01b138afcb1044852353b6b2a57cae3ad5d94754807c217baebb15e63 | 0.733513 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.03019 | paper | corpus_metadata | null | Deep learning Local Reduced Density Matrices for Many-body Hamiltonian Estimation | Human experts cannot efficiently access the physical information of quantum many-body states by simply "reading" the coefficients, but have to reply on the previous knowledge such as order parameters and quantum measurements. In this work, we demonstrate that convolutional neural network (CNN) can learn from the coeffi... | https://arxiv.org/abs/2012.03019 | 2020-12-05T12:22:12Z | 2012.03019 | 10.1088/0256-307x/38/11/110301 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1088/0256-307x/38/11/110301 | 4879b6b456df224f57ff6a2e22954d443eca5632e4c9c34dfa1188cdbda50063 | 0.606018 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.02899 | paper | corpus_metadata | null | Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents | Document digitization is essential for the digital transformation of our societies, yet a crucial step in the process, Optical Character Recognition (OCR), is still not perfect. Even commercial OCR systems can produce questionable output depending on the fidelity of the scanned documents. In this paper, we demonstrate ... | https://arxiv.org/abs/2108.02899 | 2021-08-06T00:32:54Z | 2108.02899 | 10.48550/arxiv.2108.02899 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | doi:10.48550/arxiv.2108.02899 | b24791692236e00d526cfc974fc3fd4541d681c3537bdf81ac37f55e938509c1 | 0.700242 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1704.01745 | paper | corpus_metadata | null | How to Make an Image More Memorable? A Deep Style Transfer Approach | Recent works have shown that it is possible to automatically predict intrinsic image properties like memorability. In this paper, we take a step forward addressing the question: "Can we make an image more memorable?". Methods for automatically increasing image memorability would have an impact in many application field... | https://arxiv.org/abs/1704.01745 | 2017-04-06T08:25:19Z | 1704.01745 | 10.48550/arxiv.1704.01745 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1704.01745 | 04677731424607665d927c140a0b77049089a972b600e7cce9b6a113df0f5134 | 0.754126 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2411.04188 | paper | corpus_metadata | null | Astronomaly Protege: Discovery Through Human-Machine Collaboration | Modern telescopes generate catalogs of millions of objects with the potential for new scientific discoveries, but this is beyond what can be examined visually. Here we introduce Astronomaly: Protege, an extension of the general purpose machine learning-based active anomaly detection framework Astronomaly. Protege is de... | https://arxiv.org/abs/2411.04188 | 2024-11-06T19:00:03Z | 2411.04188 | 10.48550/arxiv.2411.04188 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2411.04188 | af54f1fe2b79475068993767c4d17145a33a39b7aadb203d3f5c50dfc04b9356 | 0.637602 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2111.05990 | paper | corpus_metadata | null | Traffic4cast -- Large-scale Traffic Prediction using 3DResNet and Sparse-UNet | The IARAI competition Traffic4cast 2021 aims to predict short-term city-wide high-resolution traffic states given the static and dynamic traffic information obtained previously. The aim is to build a machine learning model for predicting the normalized average traffic speed and flow of the subregions of multiple large-... | https://arxiv.org/abs/2111.05990 | 2021-11-10T23:40:52Z | 2111.05990 | 10.48550/arxiv.2111.05990 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2111.05990 | cd6759ad4732dff5ee078fb2241708331fdab1f6f5c1b31815245ef1d792c04e | 0.790892 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1709.05074 | paper | corpus_metadata | null | A Deep Generative Framework for Paraphrase Generation | Paraphrase generation is an important problem in NLP, especially in question answering, information retrieval, information extraction, conversation systems, to name a few. In this paper, we address the problem of generating paraphrases automatically. Our proposed method is based on a combination of deep generative mode... | https://arxiv.org/abs/1709.05074 | 2017-09-15T06:58:13Z | 1709.05074 | 10.48550/arxiv.1709.05074 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1709.05074 | dce2737cc09522fdfe547270db3ca3e9c3e1ee0a542308cbb022b1fc07460dcd | 0.846108 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.15361 | paper | corpus_metadata | null | Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning | The Frank-Wolfe (FW) method is a popular algorithm for solving large-scale convex optimization problems appearing in structured statistical learning. However, the traditional Frank-Wolfe method can only be applied when the feasible region is bounded, which limits its applicability in practice. Motivated by two applicat... | https://arxiv.org/abs/2012.15361 | 2020-12-30T23:16:34Z | 2012.15361 | 10.48550/arxiv.2012.15361 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2012.15361 | 1ed354508f20814a85516004f2e0d6fe8d34c88ad86549634734d7017b9aa405 | 0.543204 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1509.06461 | paper | corpus_metadata | null | Deep Reinforcement Learning with Double Q-learning | The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In ... | https://arxiv.org/abs/1509.06461 | 2015-09-22T04:40:22Z | 1509.06461 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1509.06461 | 73d2005e60fc886806cef9707c8df9a8ee74c7d614bc7eac646e82703e1b6153 | 0.665623 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.09661 | paper | corpus_metadata | null | Implicit Neural Representations with Periodic Activation Functions | Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals... | https://arxiv.org/abs/2006.09661 | 2020-06-17T05:13:33Z | 2006.09661 | 10.48550/arxiv.2006.09661 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2006.09661 | 623385c694175a1fa421a405da5805f85daa5c6e16d538b4e08eb77e3f5c34d3 | 0.542404 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.05884 | paper | corpus_metadata | null | Need for Speed: A Benchmark for Higher Frame Rate Object Tracking | In this paper, we propose the first higher frame rate video dataset (called Need for Speed - NfS) and benchmark for visual object tracking. The dataset consists of 100 videos (380K frames) captured with now commonly available higher frame rate (240 FPS) cameras from real world scenarios. All frames are annotated with a... | https://arxiv.org/abs/1703.05884 | 2017-03-17T04:18:25Z | 1703.05884 | 10.48550/arxiv.1703.05884 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.05884 | b447c7f0b14ac7a1b5349b786cab6a16f4a472b3a59e7a2d973cf828a114f9d0 | 0.733214 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2107.14185 | paper | corpus_metadata | null | Feature Importance-aware Transferable Adversarial Attacks | Transferability of adversarial examples is of central importance for attacking an unknown model, which facilitates adversarial attacks in more practical scenarios, e.g., black-box attacks. Existing transferable attacks tend to craft adversarial examples by indiscriminately distorting features to degrade prediction accu... | https://arxiv.org/abs/2107.14185 | 2021-07-29T17:13:29Z | 2107.14185 | 10.48550/arxiv.2107.14185 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2107.14185 | 928e6ca0b531e291072ab28f85dac722fa5dcbbee67c3a74c3cc9a27c68202f5 | 0.775418 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.06332 | paper | corpus_metadata | null | FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning | Most previous methods for text data augmentation are limited to simple tasks and weak baselines. We explore data augmentation on hard tasks (i.e., few-shot natural language understanding) and strong baselines (i.e., pretrained models with over one billion parameters). Under this setting, we reproduced a large number of... | https://arxiv.org/abs/2108.06332 | 2021-08-13T17:51:31Z | 2108.06332 | 10.48550/arxiv.2108.06332 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2108.06332 | e5fc499791b5830bc80de4a5e5467cf879095525da343fb34eb5949a445c9135 | 0.813872 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.07469 | paper | corpus_metadata | null | RobustFill: Neural Program Learning under Noisy I/O | The problem of automatically generating a computer program from some specification has been studied since the early days of AI. Recently, two competing approaches for automatic program learning have received significant attention: (1) neural program synthesis, where a neural network is conditioned on input/output (I/O)... | https://arxiv.org/abs/1703.07469 | 2017-03-21T23:29:47Z | 1703.07469 | 10.48550/arxiv.1703.07469 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.07469 | a94d23634106a5cd34548f97b67193cc068a9c0254ee401ab199c693df277c2f | 0.782016 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.08202 | paper | corpus_metadata | null | Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation | Internet video delivery has undergone a tremendous explosion of growth over the past few years. However, the quality of video delivery system greatly depends on the Internet bandwidth. Deep Neural Networks (DNNs) are utilized to improve the quality of video delivery recently. These methods divide a video into chunks, a... | https://arxiv.org/abs/2108.08202 | 2021-08-18T15:34:11Z | 2108.08202 | 10.48550/arxiv.2108.08202 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2108.08202 | 1a254ceb3211011a905c3ee054090abfbe9178c116f8c5ba3ab46dfe614b9a77 | 0.761861 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.08218 | paper | corpus_metadata | null | Out-of-Distribution Detection Using Outlier Detection Methods | Out-of-distribution detection (OOD) deals with anomalous input to neural networks. In the past, specialized methods have been proposed to reject predictions on anomalous input. Similarly, it was shown that feature extraction models in combination with outlier detection algorithms are well suited to detect anomalous inp... | https://arxiv.org/abs/2108.08218 | 2021-08-18T16:05:53Z | 2108.08218 | 10.48550/arxiv.2108.08218 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2108.08218 | ca9d4d6ea109bd4c5923cee18698ffe74fd0f59f7080b65bd6e76c108374e297 | 0.63702 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2107.03974 | paper | corpus_metadata | null | Offline Meta-Reinforcement Learning with Online Self-Supervision | Meta-reinforcement learning (RL) methods can meta-train policies that adapt to new tasks with orders of magnitude less data than standard RL, but meta-training itself is costly and time-consuming. If we can meta-train on offline data, then we can reuse the same static dataset, labeled once with rewards for different ta... | https://arxiv.org/abs/2107.03974 | 2021-07-08T17:01:32Z | 2107.03974 | 10.48550/arxiv.2107.03974 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2107.03974 | acee281d71654c906d4a8be171cc9689f0fb2578b42c6501cf3703c9a2e24130 | 0.808198 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.07739 | paper | corpus_metadata | null | Model-Change Active Learning in Graph-Based Semi-Supervised Learning | Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify which points to label to best improve performance while limiting the number of new labels. "Model Change" active learning quantifi... | https://arxiv.org/abs/2110.07739 | 2021-10-14T21:47:10Z | 2110.07739 | 10.1007/s42967-023-00328-z | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1007/s42967-023-00328-z | 50803f9203a9ded275aaf9b820bbb889920a079b39d29fdb3d8c0f8359eed587 | 0.676443 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1712.05382 | paper | corpus_metadata | null | Monotonic Chunkwise Attention | Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction. To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptiv... | https://arxiv.org/abs/1712.05382 | 2017-12-14T18:29:42Z | 1712.05382 | 10.48550/arxiv.1712.05382 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1712.05382 | fa1af7e94294ba168213721a64ff837e067f24d5ee671984e79089c6a1c1869d | 0.696368 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.00671 | paper | corpus_metadata | null | What Can I Do Here? Learning New Skills by Imagining Visual Affordances | A generalist robot equipped with learned skills must be able to perform many tasks in many different environments. However, zero-shot generalization to new settings is not always possible. When the robot encounters a new environment or object, it may need to finetune some of its previously learned skills to accommodate... | https://arxiv.org/abs/2106.00671 | 2021-06-01T17:58:02Z | 2106.00671 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2106.00671 | ec56f7d1cbcfbdeaaad3d7c362be17eac8811ff80a643e83642e993cec1a3e85 | 0.696124 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1904.00889 | paper | corpus_metadata | null | Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters | We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide anchor structures for learned filters, which localize, score and rank repeatable features. Scale-space representation is used within the netw... | https://arxiv.org/abs/1904.00889 | 2019-04-01T14:47:24Z | 1904.00889 | 10.48550/arxiv.1904.00889 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1904.00889 | daa96151b03a8f317a68b033e34e3defa1128f6e0b395686d905d21d8bc3749a | 0.752606 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1706.05087 | paper | corpus_metadata | null | Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder | We investigate the integration of a planning mechanism into an encoder-decoder architecture with an explicit alignment for character-level machine translation. We develop a model that plans ahead when it computes alignments between the source and target sequences, constructing a matrix of proposed future alignments and... | https://arxiv.org/abs/1706.05087 | 2017-06-13T23:11:04Z | 1706.05087 | 10.48550/arxiv.1706.05087 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1706.05087 | 3c7d388dc9df30177419c2b627cba363f7182481bdd063e322e7307087cf3365 | 0.692259 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1711.08477 | paper | corpus_metadata | null | Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining | Modern biomedical data mining requires feature selection methods that can (1) be applied to large scale feature spaces (e.g. `omics' data), (2) function in noisy problems, (3) detect complex patterns of association (e.g. gene-gene interactions), (4) be flexibly adapted to various problem domains and data types (e.g. ge... | https://arxiv.org/abs/1711.08477 | 2017-11-22T19:18:47Z | 1711.08477 | 10.48550/arxiv.1711.08477 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.1711.08477 | 48d0e48f7fd6709ceec15d0877d48f9f7ffa8949f45bd0a736a3c01fef82d602 | 0.579703 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.03136 | paper | corpus_metadata | null | Automating the search for a patent's prior art with a full text similarity search | More than ever, technical inventions are the symbol of our society's advance. Patents guarantee their creators protection against infringement. For an invention being patentable, its novelty and inventiveness have to be assessed. Therefore, a search for published work that describes similar inventions to a given patent... | https://arxiv.org/abs/1901.03136 | 2019-01-10T13:04:25Z | 1901.03136 | 10.1371/journal.pone.0212103 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1371/journal.pone.0212103 | d697e18df6471562f7fcdf5ab014e2decd9ae11cde7de7467e27d83514887de0 | 0.634902 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2204.09122 | paper | corpus_metadata | null | Continuous cutting plane algorithms in integer programming | Cutting planes for mixed-integer linear programs (MILPs) are typically computed in rounds by iteratively solving optimization problems, the so-called separation. Instead, we reframe the problem of finding good cutting planes as a continuous optimization problem over weights parametrizing families of valid inequalities.... | https://arxiv.org/abs/2204.09122 | 2022-04-19T20:36:30Z | 2204.09122 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2204.09122 | fcfa27e56e5a5a313b0a9982344ff609fc0df2c59eff1ac8fcb3175ee16dd47e | 0.567823 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2009.06385 | paper | corpus_metadata | null | Adaptive Convolution Kernel for Artificial Neural Networks | Many deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3$\times$3) kernels. This paper describes a method for training the size of convolutional kernels to provide varying size kernels in a single layer. The method utilizes a differentiable, and therefore backpropagati... | https://arxiv.org/abs/2009.06385 | 2020-09-14T12:36:50Z | 2009.06385 | 10.1016/j.jvcir.2020.103015 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1016/j.jvcir.2020.103015 | 97b11532ee100f1b4c6b9c5971a64daf833fb127544624820d7c50f59fde8d79 | 0.703415 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.06816 | paper | corpus_metadata | null | Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression | The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images. This underscores the urgent need for practical defense that can be readily deployed to combat attacks in real-time. Observing that many attack s... | https://arxiv.org/abs/1802.06816 | 2018-02-19T19:13:42Z | 1802.06816 | 10.48550/arxiv.1802.06816 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1802.06816 | c0d94662e73cc0f2050372519d439a173322a2ea7bd1dcd8f560936883a14e7d | 0.72359 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.09576 | paper | corpus_metadata | null | UnMask: Adversarial Detection and Defense Through Robust Feature Alignment | Deep learning models are being integrated into a wide range of high-impact, security-critical systems, from self-driving cars to medical diagnosis. However, recent research has demonstrated that many of these deep learning architectures are vulnerable to adversarial attacks--highlighting the vital need for defensive te... | https://arxiv.org/abs/2002.09576 | 2020-02-21T23:20:23Z | 2002.09576 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2002.09576 | 01a58d61302a101f61aeb1f7200a755510296fa9525151a10e1fb35c97bc361f | 0.725551 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1705.10461 | paper | corpus_metadata | null | The Numerics of GANs | In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives. Our findings suggest that the convergence of current algorithms suffers due to two f... | https://arxiv.org/abs/1705.10461 | 2017-05-30T05:54:59Z | 1705.10461 | 10.48550/arxiv.1705.10461 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1705.10461 | 130dbeb76a703b6ea3182b203483a035b26d91b499d76725a65d2cc7938bc6ce | 0.526283 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2302.11346 | paper | corpus_metadata | null | Task-Aware Information Routing from Common Representation Space in Lifelong Learning | Intelligent systems deployed in the real world suffer from catastrophic forgetting when exposed to a sequence of tasks. Humans, on the other hand, acquire, consolidate, and transfer knowledge between tasks that rarely interfere with the consolidated knowledge. Accompanied by self-regulated neurogenesis, continual learn... | https://arxiv.org/abs/2302.11346 | 2023-02-14T16:25:46Z | 2302.11346 | 10.48550/arxiv.2302.11346 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | doi:10.48550/arxiv.2302.11346 | 65751c4113875878aeb50e1d26b5b1aa158978e4a1026d713f7eb30b981a07e3 | 0.722082 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2009.05584 | paper | corpus_metadata | null | Developing a unified pipeline for large-scale structure data analysis with angular power spectra -- III. Implementing the multi-tracer technique to constrain neutrino masses | In this paper, we apply the multi-tracer technique to harmonic-space (i.e.\ angular) power spectra with a likelihood-based approach. This goes beyond the usual Fisher matrix formalism hitherto implemented in forecasts with angular statistics, opening up a window for future developments and direct application to availab... | https://arxiv.org/abs/2009.05584 | 2020-09-11T18:00:14Z | 2009.05584 | 10.1093/mnras/staa3536 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1093/mnras/staa3536 | 5d593f171ade1281ab9960585ffbabaac2a68831e4b85381f5ca77198eeb4b89 | 0.521603 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.11726 | paper | corpus_metadata | null | Learning to Diversify for Single Domain Generalization | Domain generalization (DG) aims to generalize a model trained on multiple source (i.e., training) domains to a distributionally different target (i.e., test) domain. In contrast to the conventional DG that strictly requires the availability of multiple source domains, this paper considers a more realistic yet challengi... | https://arxiv.org/abs/2108.11726 | 2021-08-26T12:04:32Z | 2108.11726 | 10.48550/arxiv.2108.11726 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2108.11726 | 24ec4acbe1d8f035e94d6e99866b5bf70ac976eac89660c1c2224743cda0544b | 0.810577 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.12144 | paper | corpus_metadata | null | Lyra: A Benchmark for Turducken-Style Code Generation | Recently, neural techniques have been used to generate source code automatically. While promising for declarative languages, these approaches achieve much poorer performance on datasets for imperative languages. Since a declarative language is typically embedded in an imperative language (i.e., the turducken-style prog... | https://arxiv.org/abs/2108.12144 | 2021-08-27T07:22:55Z | 2108.12144 | 10.24963/ijcai.2022/588 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.24963/ijcai.2022/588 | 93be1d41629a876e67574917337f1e9a0125718c013d87899713d56d6f730e1f | 0.81729 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2302.12417 | paper | corpus_metadata | null | Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction | Emotion-cause pair extraction (ECPE) task aims to extract all the pairs of emotions and their causes from an unannotated emotion text. The previous works usually extract the emotion-cause pairs from two perspectives of emotion and cause. However, emotion extraction is more crucial to the ECPE task than cause extraction... | https://arxiv.org/abs/2302.12417 | 2023-02-24T02:45:49Z | 2302.12417 | 10.48550/arxiv.2302.12417 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2302.12417 | 70e561f46529fc30d09c694cda7a81616d8591fcbd13b82b384887fe89c425ca | 0.684462 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2203.15348 | paper | corpus_metadata | null | SIGLE: a valid procedure for Selective Inference with the Generalized Linear Lasso | This article investigates uncertainty quantification of the generalized linear lasso~(GLL), a popular variable selection method in high-dimensional regression settings. In many fields of study, researchers use data-driven methods to select a subset of variables that are most likely to be associated with a response vari... | https://arxiv.org/abs/2203.15348 | 2022-03-29T08:42:38Z | 2203.15348 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2203.15348 | 7386c3c69ee19cc8e232bd9cb836a0a888c1ad688e62ea4d112beffd03aee225 | 0.52776 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.10323 | paper | corpus_metadata | null | Topic-Guided Abstractive Text Summarization: a Joint Learning Approach | We introduce a new approach for abstractive text summarization, Topic-Guided Abstractive Summarization, which calibrates long-range dependencies from topic-level features with globally salient content. The idea is to incorporate neural topic modeling with a Transformer-based sequence-to-sequence (seq2seq) model in a jo... | https://arxiv.org/abs/2010.10323 | 2020-10-20T14:45:25Z | 2010.10323 | 10.48550/arxiv.2010.10323 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2010.10323 | 11aadeb1ef64b62af24e4756ed60bfe21b34c8a351b4fd171052674dadd31acf | 0.779453 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2109.00150 | paper | corpus_metadata | null | Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning | We describe federated reconnaissance, a class of learning problems in which distributed clients learn new concepts independently and communicate that knowledge efficiently. In particular, we propose an evaluation framework and methodological baseline for a system in which each client is expected to learn a growing set ... | https://arxiv.org/abs/2109.00150 | 2021-09-01T01:51:30Z | 2109.00150 | 10.48550/arxiv.2109.00150 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2109.00150 | 505d1722ab310a8d9d3c6875d0ee768b0a4644e5cb391c68fee0e6cd6cb7be67 | 0.702482 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2109.00412 | paper | corpus_metadata | null | Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis | In multimodal sentiment analysis (MSA), the performance of a model highly depends on the quality of synthesized embeddings. These embeddings are generated from the upstream process called multimodal fusion, which aims to extract and combine the input unimodal raw data to produce a richer multimodal representation. Prev... | https://arxiv.org/abs/2109.00412 | 2021-09-01T14:45:16Z | 2109.00412 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | arxiv:2109.00412 | 36bd606dfcddbb62a27b26c63874810d7b72d04fac7a18a002198567e1c69d44 | 0.723557 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.15018 | paper | corpus_metadata | null | TorchAudio: Building Blocks for Audio and Speech Processing | This document describes version 0.10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain. The objective of TorchAudio is to accelerate the development and deployment of machine learning applications for researchers and engineers by providing off-the-shelf building ... | https://arxiv.org/abs/2110.15018 | 2021-10-28T10:58:22Z | 2110.15018 | 10.48550/arxiv.2110.15018 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2110.15018 | b4bc2c8087db58ba47302dfdac51043a34927399b48fcccbc9407b4fcbbad023 | 0.688461 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2303.13419 | paper | corpus_metadata | null | Modular Retrieval for Generalization and Interpretation | New retrieval tasks have always been emerging, thus urging the development of new retrieval models. However, instantiating a retrieval model for each new retrieval task is resource-intensive and time-consuming, especially for a retrieval model that employs a large-scale pre-trained language model. To address this issue... | https://arxiv.org/abs/2303.13419 | 2023-03-23T16:40:00Z | 2303.13419 | 10.48550/arxiv.2303.13419 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2303.13419 | 4ae9df5695fdb68defef89c32e06fb1304a3d4fb27a6a376cd5d37e9290f9b68 | 0.861666 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2201.00764 | paper | corpus_metadata | null | Have I done enough planning or should I plan more? | People's decisions about how to allocate their limited computational resources are essential to human intelligence. An important component of this metacognitive ability is deciding whether to continue thinking about what to do and move on to the next decision. Here, we show that people acquire this ability through lear... | https://arxiv.org/abs/2201.00764 | 2022-01-03T17:11:07Z | 2201.00764 | 10.48550/arxiv.2201.00764 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2201.00764 | 4e41897ccffdd56086450e4e07cb0eafacdad72cde71836ba1938732879bb31a | 0.562608 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2411.04822 | paper | corpus_metadata | null | Shared Heritage, Distinct Writing: Rethinking Resource Selection for East Asian Historical Documents | Historical documents in the Sinosphere are known to share common formats and practices, particularly in veritable records compiled by court historians. This shared linguistic heritage has led researchers to use Classical Chinese resources for cross-lingual transfer when processing historical documents from Korea and Ja... | https://arxiv.org/abs/2411.04822 | 2024-11-07T15:59:54Z | 2411.04822 | 10.18653/v1/2025.findings-ijcnlp.98 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | doi:10.18653/v1/2025.findings-ijcnlp.98 | c0db9554b6a2e637ae11fc1f4dee3cda76cc6fb5ae9363b3b190869450603da2 | 0.641661 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2005.01583 | paper | corpus_metadata | null | The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America | Chronicling America is a product of the National Digital Newspaper Program, a partnership between the Library of Congress and the National Endowment for the Humanities to digitize historic newspapers. Over 16 million pages of historic American newspapers have been digitized for Chronicling America to date, complete wit... | https://arxiv.org/abs/2005.01583 | 2020-05-04T15:51:13Z | 2005.01583 | 10.48550/arxiv.2005.01583 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/publicdomain/zero/1.0 | doi:10.48550/arxiv.2005.01583 | 30677db9d9a68ad248a4b94f6177c7611fa3d6a7c782c7416a784fa80081fb6f | 0.594339 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2502.00751 | paper | corpus_metadata | null | Online Generalized Method of Moments for Time Series | Online learning has gained popularity in recent years due to the urgent need to analyse large-scale streaming data, which can be collected in perpetuity and serially dependent. This motivates us to develop the online generalized method of moments (OGMM), an explicitly updated estimation and inference framework in the t... | https://arxiv.org/abs/2502.00751 | 2025-02-02T11:16:20Z | 2502.00751 | 10.48550/arxiv.2502.00751 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2502.00751 | c441ad3e8c80b512e77968f699dca2da8db5858801b138eb2d79ec5348937712 | 0.603534 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2204.00916 | paper | corpus_metadata | null | BERT-Assisted Semantic Annotation Correction for Emotion-Related Questions | Annotated data have traditionally been used to provide the input for training a supervised machine learning (ML) model. However, current pre-trained ML models for natural language processing (NLP) contain embedded linguistic information that can be used to inform the annotation process. We use the BERT neural language ... | https://arxiv.org/abs/2204.00916 | 2022-04-02T18:00:49Z | 2204.00916 | 10.48550/arxiv.2204.00916 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2204.00916 | 8298e2f60b202c9637966a79c172b3375050eda353d16ca4b85bc5101d3aa0ac | 0.731914 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2101.02035 | paper | corpus_metadata | null | Scalable Feature Matching Across Large Data Collections | This paper is concerned with matching feature vectors in a one-to-one fashion across large collections of datasets. Formulating this task as a multidimensional assignment problem with decomposable costs (MDADC), we develop extremely fast algorithms with time complexity linear in the number $n$ of datasets and space com... | https://arxiv.org/abs/2101.02035 | 2021-01-06T13:52:30Z | 2101.02035 | 10.48550/arxiv.2101.02035 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2101.02035 | 00d66316ef1a5b680199630a85d5c87ebf1624ae2a205e3ef47102a96afd0a25 | 0.667497 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.01547 | paper | corpus_metadata | null | WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit | In this paper, we propose an open source, production first, and production ready speech recognition toolkit called WeNet in which a new two-pass approach is implemented to unify streaming and non-streaming end-to-end (E2E) speech recognition in a single model. The main motivation of WeNet is to close the gap between th... | https://arxiv.org/abs/2102.01547 | 2021-02-02T15:19:41Z | 2102.01547 | 10.48550/arxiv.2102.01547 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2102.01547 | 6c9d05fc49413d740900809ce8078846b1ac9b76ccdb662bb758b4e9b5def303 | 0.730603 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2109.04101 | paper | corpus_metadata | null | TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting | Temporal knowledge graph (TKG) reasoning is a crucial task that has gained increasing research interest in recent years. Most existing methods focus on reasoning at past timestamps to complete the missing facts, and there are only a few works of reasoning on known TKGs to forecast future facts. Compared with the comple... | https://arxiv.org/abs/2109.04101 | 2021-09-09T08:41:01Z | 2109.04101 | 10.48550/arxiv.2109.04101 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2109.04101 | 4738f560d75c748a1c703a5f3ef7d5022699c526c589a3ef0887949c44ca7388 | 0.830838 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2202.13312 | paper | corpus_metadata | null | Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data | Practical tools for clustering streaming data must be fast enough to handle the arrival rate of the observations. Typically, they also must adapt on the fly to possible lack of stationarity; i.e., the data statistics may be time-dependent due to various forms of drifts, changes in the number of clusters, etc. The Diric... | https://arxiv.org/abs/2202.13312 | 2022-02-27T08:51:50Z | 2202.13312 | 10.48550/arxiv.2202.13312 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2202.13312 | 22cf32e51ece3b24bd6c2023b57a0b5347f1a02b2efbd03e9cbbd92e0fecbf40 | 0.586367 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2202.13140 | paper | corpus_metadata | null | Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering | Over the past decades, for One-Class Collaborative Filtering (OCCF), many learning objectives have been researched based on a variety of underlying probabilistic models. From our analysis, we observe that models trained with different OCCF objectives capture distinct aspects of user-item relationships, which in turn pr... | https://arxiv.org/abs/2202.13140 | 2022-02-26T13:34:29Z | 2202.13140 | 10.48550/arxiv.2202.13140 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2202.13140 | ee2403247861f95d4eae622f50b434a03e6b754e7a63fe1f62822b4633b91dae | 0.69576 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2109.02497 | paper | corpus_metadata | null | Voxel Transformer for 3D Object Detection | We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds. Conventional 3D convolutional backbones in voxel-based 3D detectors cannot efficiently capture large context information, which is crucial for object recognition and localization, owing... | https://arxiv.org/abs/2109.02497 | 2021-09-06T14:10:22Z | 2109.02497 | 10.48550/arxiv.2109.02497 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | doi:10.48550/arxiv.2109.02497 | 52e45940a816f95b14f099dd242dadd6ab6c00ced0d42766bceb3a3f913f3ed1 | 0.666422 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.04378 | paper | corpus_metadata | null | TransReID: Transformer-based Object Re-Identification | Extracting robust feature representation is one of the key challenges in object re-identification (ReID). Although convolution neural network (CNN)-based methods have achieved great success, they only process one local neighborhood at a time and suffer from information loss on details caused by convolution and downsamp... | https://arxiv.org/abs/2102.04378 | 2021-02-08T17:33:59Z | 2102.04378 | 10.48550/arxiv.2102.04378 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2102.04378 | 320f755bb376b634c870ca73b426f1e0dde05d3f44ac526333c7d3194715e30b | 0.819876 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2109.05112 | paper | corpus_metadata | null | Improved Latent Tree Induction with Distant Supervision via Span Constraints | For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to their supervised counterparts to have any practical use as structural annotation of text. In this w... | https://arxiv.org/abs/2109.05112 | 2021-09-10T21:22:09Z | 2109.05112 | 10.48550/arxiv.2109.05112 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2109.05112 | 985d2afb9926d3d0aaea521078d1865cfd64e907abf7d413a39ca453c0d5cd7b | 0.62243 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.01477 | paper | corpus_metadata | null | Exploring the Role of BERT Token Representations to Explain Sentence Probing Results | Several studies have been carried out on revealing linguistic features captured by BERT. This is usually achieved by training a diagnostic classifier on the representations obtained from different layers of BERT. The subsequent classification accuracy is then interpreted as the ability of the model in encoding the corr... | https://arxiv.org/abs/2104.01477 | 2021-04-03T20:40:42Z | 2104.01477 | 10.48550/arxiv.2104.01477 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2104.01477 | 38c68419200b3d511a6d3b4fcc14cc6af339a44360193c9ec7714ef755780afd | 0.582428 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2109.05186 | paper | corpus_metadata | null | Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers | This paper investigates continual learning for semantic parsing. In this setting, a neural semantic parser learns tasks sequentially without accessing full training data from previous tasks. Direct application of the SOTA continual learning algorithms to this problem fails to achieve comparable performance with re-trai... | https://arxiv.org/abs/2109.05186 | 2021-09-11T04:33:28Z | 2109.05186 | 10.48550/arxiv.2109.05186 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2109.05186 | 8e49d269f9110588930df5159fe2a1ba2c2d4d3797ccee06a0edfbed0ffca048 | 0.83507 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1901.09821 | paper | corpus_metadata | null | Squeezed Very Deep Convolutional Neural Networks for Text Classification | Most of the research in convolutional neural networks has focused on increasing network depth to improve accuracy, resulting in a massive number of parameters which restricts the trained network to platforms with memory and processing constraints. We propose to modify the structure of the Very Deep Convolutional Neural... | https://arxiv.org/abs/1901.09821 | 2019-01-28T17:14:12Z | 1901.09821 | 10.1007/978-3-030-30487-4_16 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/978-3-030-30487-4_16 | e5e361f0da31aab4a14f32e4fcc27b0d299222283b0bb79b611463cad692acd5 | 0.694117 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1806.00701 | paper | corpus_metadata | null | On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks | Parsimonious representations are ubiquitous in modeling and processing information. Motivated by the recent Multi-Layer Convolutional Sparse Coding (ML-CSC) model, we herein generalize the traditional Basis Pursuit problem to a multi-layer setting, introducing similar sparse enforcing penalties at different representat... | https://arxiv.org/abs/1806.00701 | 2018-06-02T20:31:30Z | 1806.00701 | 10.1109/tpami.2019.2904255 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/tpami.2019.2904255 | bc862704cbfbee495de60c136e610deb16ec4b9b9f5284df8cdeae4bc9a09599 | 0.717304 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2010.14159 | paper | corpus_metadata | null | Federated Learning From Big Data Over Networks | This paper formulates and studies a novel algorithm for federated learning from large collections of local datasets. This algorithm capitalizes on an intrinsic network structure that relates the local datasets via an undirected "empirical" graph. We model such big data over networks using a networked linear regression ... | https://arxiv.org/abs/2010.14159 | 2020-10-27T09:39:46Z | 2010.14159 | 10.48550/arxiv.2010.14159 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2010.14159 | 500e819d2fb15fbcb955ff60fba16ab5ce4bd8a8dcbd9ede7f2ad341f7880b11 | 0.620374 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1805.10267 | paper | corpus_metadata | null | Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling | This paper describes the Duluth UROP systems that participated in SemEval--2018 Task 2, Multilingual Emoji Prediction. We relied on a variety of ensembles made up of classifiers using Naive Bayes, Logistic Regression, and Random Forests. We used unigram and bigram features and tried to offset the skewness of the data t... | https://arxiv.org/abs/1805.10267 | 2018-05-25T17:36:51Z | 1805.10267 | 10.48550/arxiv.1805.10267 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.1805.10267 | b4ae7f54a9831931c54168a130c79192b1fc5cfb6b3fa2fd12deed08f0a89d72 | 0.562763 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1710.07480 | paper | corpus_metadata | null | HDR image reconstruction from a single exposure using deep CNNs | Camera sensors can only capture a limited range of luminance simultaneously, and in order to create high dynamic range (HDR) images a set of different exposures are typically combined. In this paper we address the problem of predicting information that have been lost in saturated image areas, in order to enable HDR rec... | https://arxiv.org/abs/1710.07480 | 2017-10-20T10:48:22Z | 1710.07480 | 10.1145/3130800.3130816 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3130800.3130816 | 757dff14544f4e11a524426cd518f4014879adf21d09fbc7fe6a7410959d9a38 | 0.713102 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1604.06815 | paper | corpus_metadata | null | Non-convex Global Minimization and False Discovery Rate Control for the TREX | The TREX is a recently introduced method for performing sparse high-dimensional regression. Despite its statistical promise as an alternative to the lasso, square-root lasso, and scaled lasso, the TREX is computationally challenging in that it requires solving a non-convex optimization problem. This paper shows a remar... | https://arxiv.org/abs/1604.06815 | 2016-04-22T20:28:55Z | 1604.06815 | 10.1080/10618600.2017.1341414 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1080/10618600.2017.1341414 | 805c6772e1af860a8f606b8dec58564efdcf96ce195ca08997ce2aaa1b3a4a82 | 0.505568 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1611.06440 | paper | corpus_metadata | null | Pruning Convolutional Neural Networks for Resource Efficient Inference | We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion ba... | https://arxiv.org/abs/1611.06440 | 2016-11-19T22:48:30Z | 1611.06440 | 10.48550/arxiv.1611.06440 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1611.06440 | 3d2797e82fba2cd86a59a6b6e639b5e94be264b5bad2bc03c4dcf9b218f03fc5 | 0.805415 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.03632 | paper | corpus_metadata | null | Detecting Gang-Involved Escalation on Social Media Using Context | Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience the loss of a loved one, their online expression of emotion may evolve into aggression towards rival gangs and ultimately into real-world vi... | https://arxiv.org/abs/1809.03632 | 2018-09-10T23:20:00Z | 1809.03632 | 10.48550/arxiv.1809.03632 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1809.03632 | 5d545e72ed7ed878de7e01ecd93190016f0c1d9d8a3221849632ee995ed55692 | 0.552578 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1806.02449 | paper | corpus_metadata | null | Joint Power Allocation in Interference-Limited Networks via Distributed Coordinated Learning | The deployment of ultra-dense networks is one of the main methods to meet the 5G data rate requirements. However, high density of independent small base stations (SBSs) will increase the interference within the network. To circumvent this interference, there is a need to develop self-organizing methods to manage the re... | https://arxiv.org/abs/1806.02449 | 2018-06-06T22:41:52Z | 1806.02449 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1806.02449 | 5d1d46ba5d118492b6fca9165a0b265068c1b085844f894bb1de0ba825fef82e | 0.542649 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1703.01910 | paper | corpus_metadata | null | DIMSpan - Transactional Frequent Subgraph Mining with Distributed In-Memory Dataflow Systems | Transactional frequent subgraph mining identifies frequent subgraphs in a collection of graphs. This research problem has wide applicability and increasingly requires higher scalability over single machine solutions to address the needs of Big Data use cases. We introduce DIMSpan, an advanced approach to frequent subgr... | https://arxiv.org/abs/1703.01910 | 2017-03-06T14:57:03Z | 1703.01910 | 10.48550/arxiv.1703.01910 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1703.01910 | 7faeb3248e422a126385bff476b26063d8edd5be84fc82f9bec399be91d26e03 | 0.617916 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1502.03520 | paper | corpus_metadata | null | A Latent Variable Model Approach to PMI-based Word Embeddings | Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. This paper proposes a new generative model, a dynamic version of the log-linear topic model o... | https://arxiv.org/abs/1502.03520 | 2015-02-12T02:50:08Z | 1502.03520 | 10.48550/arxiv.1502.03520 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1502.03520 | 43871421c0cd4b631cf64051daa39b710eb3048958d6c38ff53684d3b2d9f94f | 0.542722 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.10804 | paper | corpus_metadata | null | Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells | Automated design of neural network architectures tailored for a specific task is an extremely promising, albeit inherently difficult, avenue to explore. While most results in this domain have been achieved on image classification and language modelling problems, here we concentrate on dense per-pixel tasks, in particul... | https://arxiv.org/abs/1810.10804 | 2018-10-25T09:27:23Z | 1810.10804 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1810.10804 | 684d9e6d27f351ed333ce1f3eb08f6a797f5f38444a8589ddad6b28815e25864 | 0.834888 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1704.03155 | paper | corpus_metadata | null | EAST: An Efficient and Accurate Scene Text Detector | Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple st... | https://arxiv.org/abs/1704.03155 | 2017-04-11T06:04:12Z | 1704.03155 | 10.48550/arxiv.1704.03155 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1704.03155 | 9adeaa21fb61b452ca9ea96c502f72e06f7d3d118f4d03db1b5c3c01d62ec3ea | 0.799044 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.00614 | paper | corpus_metadata | null | Visual Interpretability for Deep Learning: a Survey | This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, the interpretability is always the Achilles' heel of deep neura... | https://arxiv.org/abs/1802.00614 | 2018-02-02T09:39:40Z | 1802.00614 | 10.48550/arxiv.1802.00614 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1802.00614 | 619f1ad7f08d31dda2e2bad23653894c8fe86acc8854f4eff979ef9fb8719e54 | 0.736165 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.01514 | paper | corpus_metadata | null | Perturbative GAN: GAN with Perturbation Layers | Perturbative GAN, which replaces convolution layers of existing convolutional GANs (DCGAN, WGAN-GP, BIGGAN, etc.) with perturbation layers that adds a fixed noise mask, is proposed. Compared with the convolu-tional GANs, the number of parameters to be trained is smaller, the convergence of training is faster, the incep... | https://arxiv.org/abs/1902.01514 | 2019-02-05T01:43:35Z | 1902.01514 | 10.48550/arxiv.1902.01514 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1902.01514 | f2d6e4e11c07929640d85acaf1fe2760cb7c2158bfe1eb08879be3dc7ffbb8ab | 0.569349 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1704.07434 | paper | corpus_metadata | null | Paying Attention to Descriptions Generated by Image Captioning Models | To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene. In this paper, we study the agreement between bottom-up saliency-based visual attention and object referrals in scene description constructs. We investigate the proper... | https://arxiv.org/abs/1704.07434 | 2017-04-24T19:51:16Z | 1704.07434 | 10.48550/arxiv.1704.07434 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1704.07434 | e8f0a70888b0af52d1622a875a43e817ac982e9c10143dd57ce0a2281fa1bd2a | 0.643753 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.03194 | paper | corpus_metadata | null | Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge | Building systems that can communicate with humans is a core problem in Artificial Intelligence. This work proposes a novel neural network architecture for response selection in an end-to-end multi-turn conversational dialogue setting. The architecture applies context level attention and incorporates additional external... | https://arxiv.org/abs/1809.03194 | 2018-09-10T09:10:25Z | 1809.03194 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1809.03194 | da19b48b034c50dff0490754fe706e2bbde95550e951d4bd72cd8b8eca41fa87 | 0.743449 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1609.04309 | paper | corpus_metadata | null | Efficient softmax approximation for GPUs | We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies. Our approach, called adaptive softmax, circumvents the linear dependency on the vocabulary size by exploiting the unbalanced word distribution to form clusters that explicitly minimize the expectat... | https://arxiv.org/abs/1609.04309 | 2016-09-14T15:15:08Z | 1609.04309 | 10.48550/arxiv.1609.04309 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1609.04309 | 5275d3d4e5a53ddf85a9c9b473b905429eace74fbf9618b514a613ea68e5f435 | 0.770604 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1912.02315 | paper | corpus_metadata | null | 12-in-1: Multi-Task Vision and Language Representation Learning | Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually-grounded language understanding skills required for success at these tasks overlap significantly. In this work, we investigate these relationships betwee... | https://arxiv.org/abs/1912.02315 | 2019-12-05T00:07:35Z | 1912.02315 | 10.48550/arxiv.1912.02315 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1912.02315 | 452178051ab4fb29257a90077134a6fe3b86a0313c249a6a42f464ff6f1b1af1 | 0.83443 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1812.09747 | paper | corpus_metadata | null | Enhancing Discrete Choice Models with Representation Learning | In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven... | https://arxiv.org/abs/1812.09747 | 2018-12-23T17:59:51Z | 1812.09747 | 10.1016/j.trb.2020.08.006 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1016/j.trb.2020.08.006 | b94caac67c88a5056f5f4b64aa82e70d4a3bfd20a7a92ad85ad050a5e0a57ff4 | 0.768035 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.05971 | paper | corpus_metadata | null | Synthesizing Number Generators for Stochastic Computing using Mixed Integer Programming | Stochastic computing (SC) is a high density, low-power computation technique which encodes values as unary bitstreams instead of binary-encoded (BE) values. Practical SC implementations require deterministic or pseudo-random number sequences which are optimally correlated to generate bitstreams and achieve accurate res... | https://arxiv.org/abs/1902.05971 | 2019-02-15T19:23:30Z | 1902.05971 | 10.48550/arxiv.1902.05971 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1902.05971 | 9574ce742b857ca90a951c22166441ac318e00a51ab14004a27a5870cdf8e3a9 | 0.515015 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.01759 | paper | corpus_metadata | null | Sparse Perturbations for Improved Convergence in Stochastic Zeroth-Order Optimization | Interest in stochastic zeroth-order (SZO) methods has recently been revived in black-box optimization scenarios such as adversarial black-box attacks to deep neural networks. SZO methods only require the ability to evaluate the objective function at random input points, however, their weakness is the dependency of thei... | https://arxiv.org/abs/2006.01759 | 2020-06-02T16:39:37Z | 2006.01759 | 10.48550/arxiv.2006.01759 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2006.01759 | 0e001bf692d57b31d381cf277018941eb107231484285f3efc28a98634e73beb | 0.592388 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1411.2003 | paper | corpus_metadata | null | Efficient Estimation of Mutual Information for Strongly Dependent Variables | We demonstrate that a popular class of nonparametric mutual information (MI) estimators based on k-nearest-neighbor graphs requires number of samples that scales exponentially with the true MI. Consequently, accurate estimation of MI between two strongly dependent variables is possible only for prohibitively large samp... | https://arxiv.org/abs/1411.2003 | 2014-11-07T19:00:57Z | 1411.2003 | 10.48550/arxiv.1411.2003 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1411.2003 | a95becabf1a9f717fd687145c8afcfe680af3a2db212bb8d1445cbd2330307d1 | 0.548464 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.10257 | paper | corpus_metadata | null | SwinIR: Image Restoration Using Swin Transformer | Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which... | https://arxiv.org/abs/2108.10257 | 2021-08-23T15:55:32Z | 2108.10257 | 10.48550/arxiv.2108.10257 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | doi:10.48550/arxiv.2108.10257 | 0cd4b9abb37d8e6f77d68eff7841e6f1e44caa498057eac3a2fa1556f7e2491e | 0.791032 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.00453 | paper | corpus_metadata | null | Topics to Avoid: Demoting Latent Confounds in Text Classification | Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. In this work, we observe this limitation with respect to the task of native language identification. We find that s... | https://arxiv.org/abs/1909.00453 | 2019-09-01T19:18:44Z | 1909.00453 | 10.48550/arxiv.1909.00453 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1909.00453 | 740c02ea01aa362f63137c71b27081788d887aaf23717256842de635ce628c0f | 0.818961 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.03112 | paper | corpus_metadata | null | Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction | There have been several recent attempts to improve the accuracy of grammar induction systems by bounding the recursive complexity of the induction model (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016; Jin et al., 2018). Modern depth-bounded grammar inducers have been shown to be more accurate than ea... | https://arxiv.org/abs/1809.03112 | 2018-09-10T03:02:46Z | 1809.03112 | 10.48550/arxiv.1809.03112 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1809.03112 | dbd9787ea1dd81086ef6a1b473d7dd8ae6b0f2e5056cec9bf32d0c6b115f7430 | 0.58424 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.01993 | paper | corpus_metadata | null | Exascale Deep Learning for Climate Analytics | We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipeline, and the network training algorithms necessary to efficiently scale deep learning on the Piz Daint and Summit systems. The Tiramisu netw... | https://arxiv.org/abs/1810.01993 | 2018-10-03T22:45:53Z | 1810.01993 | 10.5555/3291656.3291724 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.5555/3291656.3291724 | 109223c14e958d1a572e85e31bd31c63c9019bb06cf978cec80bd2acf86aa5f6 | 0.573606 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2203.05167 | paper | corpus_metadata | null | TiSAT: Time Series Anomaly Transformer | While anomaly detection in time series has been an active area of research for several years, most recent approaches employ an inadequate evaluation criterion leading to an inflated F1 score. We show that a rudimentary Random Guess method can outperform state-of-the-art detectors in terms of this popular but faulty eva... | https://arxiv.org/abs/2203.05167 | 2022-03-10T05:46:58Z | 2203.05167 | 10.48550/arxiv.2203.05167 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2203.05167 | 3352f29b521ac5571c3607dd6c52adfc4cbc794529f7ca553b0fa13ea78f9344 | 0.738675 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1910.03835 | paper | corpus_metadata | null | Interpreting Deep Learning-Based Networking Systems | While many deep learning (DL)-based networking systems have demonstrated superior performance, the underlying Deep Neural Networks (DNNs) remain blackboxes and stay uninterpretable for network operators. The lack of interpretability makes DL-based networking systems prohibitive to deploy in practice. In this paper, we ... | https://arxiv.org/abs/1910.03835 | 2019-10-09T08:19:57Z | 1910.03835 | 10.1145/3387514.3405859 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3387514.3405859 | 09a24f4479f1bc8d445a941b34b32315e52af7c946ea7c687956efa8876a0d1a | 0.674996 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2404.17793 | paper | corpus_metadata | null | CLFT: Camera-LiDAR Fusion Transformer for Semantic Segmentation in Autonomous Driving | Critical research about camera-and-LiDAR-based semantic object segmentation for autonomous driving significantly benefited from the recent development of deep learning. Specifically, the vision transformer is the novel ground-breaker that successfully brought the multi-head-attention mechanism to computer vision applic... | https://arxiv.org/abs/2404.17793 | 2024-04-27T06:18:23Z | 2404.17793 | 10.1109/tiv.2024.3454971 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1109/tiv.2024.3454971 | 75fee3181d4b849d38d10ab94d5664894f7a3bdaa92cee26d813388941ca5ea8 | 0.73557 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1704.01085 | paper | corpus_metadata | null | Deep Depth From Focus | Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhibits maximal sharpness. Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas. In this pape... | https://arxiv.org/abs/1704.01085 | 2017-04-04T16:15:54Z | 1704.01085 | 10.48550/arxiv.1704.01085 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1704.01085 | 10a91bd91fddd83e027ceaf2efd12176b04206ed577cbcf88fb9073f92942c59 | 0.780904 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.10607 | paper | corpus_metadata | null | Average Individual Fairness: Algorithms, Generalization and Experiments | We propose a new family of fairness definitions for classification problems that combine some of the best properties of both statistical and individual notions of fairness. We posit not only a distribution over individuals, but also a distribution over (or collection of) classification tasks. We then ask that standard ... | https://arxiv.org/abs/1905.10607 | 2019-05-25T14:45:56Z | 1905.10607 | 10.48550/arxiv.1905.10607 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1905.10607 | 6a8a956523067f977fe20ec0ce8ea2246d80606bc40826cdc8c971d62f0c0d1b | 0.601773 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1608.07253 | paper | corpus_metadata | null | Learning Latent Vector Spaces for Product Search | We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between products and a particular ... | https://arxiv.org/abs/1608.07253 | 2016-08-25T18:57:50Z | 1608.07253 | 10.1145/2983323.2983702 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/2983323.2983702 | eff096060cc9182b4c5b4c61ba524596238c522b4473f4dcb35511952f13464b | 0.695253 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2002.00367 | paper | corpus_metadata | null | Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks | A number of techniques for interpretability have been presented for deep learning in computer vision, typically with the goal of understanding what the networks have based their classification on. However, interpretability for deep video architectures is still in its infancy and we do not yet have a clear concept of ho... | https://arxiv.org/abs/2002.00367 | 2020-02-02T11:27:07Z | 2002.00367 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2002.00367 | cd80b9e90688613842fb4fa9824c85d51986a5ed0206bef640072170c1a5d57d | 0.658356 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2208.04589 | paper | corpus_metadata | null | Long-term Causal Effects Estimation via Latent Surrogates Representation Learning | Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e.g., marketing and medicine. Despite its success in certain domains, most existing methods estimate causal effects in an idealistic and simplistic way - ignoring the causal struc... | https://arxiv.org/abs/2208.04589 | 2022-08-09T08:07:21Z | 2208.04589 | 10.48550/arxiv.2208.04589 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2208.04589 | 3824e5cc3fec6d91071179fc3087d8afce7cbab714cd1ed0505a6608a7b7ba6e | 0.588881 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.01092 | paper | corpus_metadata | null | NUBES: A Corpus of Negation and Uncertainty in Spanish Clinical Texts | This paper introduces the first version of the NUBes corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish). The corpus is part of an on-going research and currently consists of 29,682 sentences obtained from anonymised health records annotated with negation and uncertainty. The article includes a... | https://arxiv.org/abs/2004.01092 | 2020-04-02T15:51:31Z | 2004.01092 | 10.48550/arxiv.2004.01092 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2004.01092 | 7afc6ee6b5a50f4cf0d2ee35c6ce0057010ba80042176661fd2ff6941784ec7c | 0.509188 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1909.08128 | paper | corpus_metadata | null | The Explanation Game: Explaining Machine Learning Models Using Shapley Values | A number of techniques have been proposed to explain a machine learning model's prediction by attributing it to the corresponding input features. Popular among these are techniques that apply the Shapley value method from cooperative game theory. While existing papers focus on the axiomatic motivation of Shapley values... | https://arxiv.org/abs/1909.08128 | 2019-09-17T22:15:09Z | 1909.08128 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1909.08128 | 93a9e336c3a43e5bea2c26476476dd34e8bb6e6f8aefe4f411491af906271aa4 | 0.689304 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1506.05869 | paper | corpus_metadata | Towards conversation with a deep learning agent: , Vinyals et al., 2015, ICML, **491 citations** | A Neural Conversational Model | Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., booking an airline ticket) and require hand-crafted rules. In this paper, we present a simple approach for this task which use... | https://arxiv.org/abs/1506.05869 | 2015-06-19T02:52:23Z | 1506.05869 | 10.48550/arxiv.1506.05869 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1506.05869 | 82b794fac62d42c242c487082bea5570ad6a7ed28b9ec4362e6a7a626d44ed1a | 0.800258 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
"spdin-deep-learning-chronology"
] | [
"spdin-deep-learning-chronology:line:153"
] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1503.03832 | paper | corpus_metadata | null | FaceNet: A Unified Embedding for Face Recognition and Clustering | Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space whe... | https://arxiv.org/abs/1503.03832 | 2015-03-12T18:10:53Z | 1503.03832 | 10.1109/cvpr.2015.7298682 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/cvpr.2015.7298682 | 2b5a0ec8cc943d02ccdbd1c723c2ffa8ccfbec3e72213b50e4b1fc74851fe4e3 | 0.774518 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2304.06397 | paper | corpus_metadata | null | A Declarative Validator for GSOS Languages | Rule formats can quickly establish meta-theoretic properties of process algebras. It is then desirable to identify domain-specific languages (DSLs) that can easily express rule formats. In prior work, we have developed Lang-n-Change, a DSL that includes convenient features for browsing language definitions and retrievi... | https://arxiv.org/abs/2304.06397 | 2023-04-13T11:02:36Z | 2304.06397 | 10.4204/eptcs.378.2 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.4204/eptcs.378.2 | d23a62e4620cdd84af7081b607b815a69cf7763be96df12340d3e6ceab1ce1d7 | 0.519829 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1911.06784 | paper | corpus_metadata | null | Learning an Optimally Reduced Formulation of OPF through Meta-optimization | With increasing share of renewables in power generation mix, system operators would need to run Optimal Power Flow (OPF) problems closer to real-time to better manage uncertainty. Given that OPF is an expensive optimization problem to solve, shifting computational effort away from real-time to offline training by machi... | https://arxiv.org/abs/1911.06784 | 2019-11-15T18:00:14Z | 1911.06784 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:1911.06784 | e4154051f0b066c2be2cd56a7217c5a0e521d034cba266aa441f7697708eff61 | 0.651874 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2004.04523 | paper | corpus_metadata | null | k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples) | Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier -- classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of parti... | https://arxiv.org/abs/2004.04523 | 2020-04-09T13:00:05Z | 2004.04523 | 10.1145/3459665 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3459665 | 9792ef7a9d419f1ed4fc97a724b614cec32da6e28db8fcc30a215ec2c0928038 | 0.537983 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.00962 | paper | corpus_metadata | null | Off The Beaten Sidewalk: Pedestrian Prediction In Shared Spaces For Autonomous Vehicles | Pedestrians and drivers interact closely in a wide range of environments. Autonomous vehicles (AVs) correspondingly face the need to predict pedestrians' future trajectories in these same environments. Traditional model-based prediction methods have been limited to making predictions in highly structured scenes with si... | https://arxiv.org/abs/2006.00962 | 2020-06-01T14:20:04Z | 2006.00962 | 10.1109/lra.2020.3023713 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/lra.2020.3023713 | 989b5e7001fe0f49c3c3b14ffb11916c828a79d51f784a5907a6e909a96ed026 | 0.657163 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.08147 | paper | corpus_metadata | null | LARNet: Lie Algebra Residual Network for Face Recognition | Face recognition is an important yet challenging problem in computer vision. A major challenge in practical face recognition applications lies in significant variations between profile and frontal faces. Traditional techniques address this challenge either by synthesizing frontal faces or by pose invariant learning. In... | https://arxiv.org/abs/2103.08147 | 2021-03-15T05:44:54Z | 2103.08147 | 10.48550/arxiv.2103.08147 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2103.08147 | a83cedf16454c7d7531fe1ec9856bf798332ef5bdb4a9473060dc32f9ecd6697 | 0.753545 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2203.15250 | paper | corpus_metadata | null | Analysis of EEG frequency bands for Envisioned Speech Recognition | The use of Automatic speech recognition (ASR) interfaces have become increasingly popular in daily life for use in interaction and control of electronic devices. The interfaces currently being used are not feasible for a variety of users such as those suffering from a speech disorder, locked-in syndrome, paralysis or p... | https://arxiv.org/abs/2203.15250 | 2022-03-29T05:50:09Z | 2203.15250 | 10.48550/arxiv.2203.15250 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | doi:10.48550/arxiv.2203.15250 | 86302e4be38686432133ee4aca329625aa037a19ef54b296adfd99ae886e3995 | 0.630498 | 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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