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arxiv:1707.01836 | paper | corpus_metadata | null | Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks | We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this d... | https://arxiv.org/abs/1707.01836 | 2017-07-06T15:42:46Z | 1707.01836 | 10.48550/arxiv.1707.01836 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1707.01836 | 0d361afa6f4bced765363d8f432d17d3d7044e6351eb9d2e632db142f4a82d5d | 0.585066 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.05485 | paper | corpus_metadata | null | Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention | Predicting vulnerable road user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be recognized in real-time, especially for urban driving. Recent works have shown the potential of using vision-based deep neural network models for... | https://arxiv.org/abs/2104.05485 | 2021-04-12T14:10:25Z | 2104.05485 | 10.48550/arxiv.2104.05485 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2104.05485 | 37ebcad49982077cb8443c42d0fc7c467fb41a6364fd0b669c04f461e0650028 | 0.738728 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2304.12583 | paper | corpus_metadata | null | Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques | Real-time safety assessment (RTSA) of dynamic systems is a critical task that has significant implications for various fields such as industrial and transportation applications, especially in non-stationary environments. However, the absence of a comprehensive review of real-time safety assessment methods in non-statio... | https://arxiv.org/abs/2304.12583 | 2023-04-25T05:33:39Z | 2304.12583 | 10.48550/arxiv.2304.12583 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2304.12583 | 27003cf9a5400a941bef6dca3554eb734132c9fd60d146e0c56280ceaa22f454 | 0.515578 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2103.09527 | paper | corpus_metadata | null | Implicit Normalizing Flows | Normalizing flows define a probability distribution by an explicit invertible transformation $\boldsymbol{\mathbf{z}}=f(\boldsymbol{\mathbf{x}})$. In this work, we present implicit normalizing flows (ImpFlows), which generalize normalizing flows by allowing the mapping to be implicitly defined by the roots of an equati... | https://arxiv.org/abs/2103.09527 | 2021-03-17T09:24:04Z | 2103.09527 | 10.48550/arxiv.2103.09527 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2103.09527 | 679f54d0d159d31d8b91bd563d2d3c538c453b6693e89a55e69b61a1afe7ce52 | 0.574818 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1702.01992 | paper | corpus_metadata | null | Gated Multimodal Units for Information Fusion | This paper presents a novel model for multimodal learning based on gated neural networks. The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. Th... | https://arxiv.org/abs/1702.01992 | 2017-02-07T13:05:19Z | 1702.01992 | 10.48550/arxiv.1702.01992 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1702.01992 | 24987c16f32f923d29669ed277259c55c5ca25e4b27b7e3ff2805e0518093f6a | 0.755184 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.10121 | paper | corpus_metadata | null | Safely Learning to Control the Constrained Linear Quadratic Regulator | We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques. We present a framework which allows for system identification through persistent excitation, while maintaining safety by guaranteeing the satisfaction of st... | https://arxiv.org/abs/1809.10121 | 2018-09-26T17:04:59Z | 1809.10121 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1809.10121 | 499c991369b5735c4f811c499e4ffd4e96afb999fc9479a5d06c4ffb619277c6 | 0.503079 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.01804 | paper | corpus_metadata | null | Discovering Influential Factors in Variational Autoencoders | In the field of machine learning, it is still a critical issue to identify and supervise the learned representation without manually intervening or intuition assistance to extract useful knowledge or serve for the downstream tasks. In this work, we focus on supervising the influential factors extracted by the variation... | https://arxiv.org/abs/1809.01804 | 2018-09-06T03:33:06Z | 1809.01804 | 10.48550/arxiv.1809.01804 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1809.01804 | 71e231f255a88fb7598f82e11a1773123731adbaa5aa35b374edb8deedfcc6a3 | 0.599879 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.02867 | paper | corpus_metadata | null | Affordance Transfer Learning for Human-Object Interaction Detection | Reasoning the human-object interactions (HOI) is essential for deeper scene understanding, while object affordances (or functionalities) are of great importance for human to discover unseen HOIs with novel objects. Inspired by this, we introduce an affordance transfer learning approach to jointly detect HOIs with novel... | https://arxiv.org/abs/2104.02867 | 2021-04-07T02:37:04Z | 2104.02867 | 10.48550/arxiv.2104.02867 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2104.02867 | d1a5a59fb1eb4e9d3c0defc2ab9b5a4962b75734b80cb22f0f38201fa8b38bd4 | 0.762517 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.12407 | paper | corpus_metadata | null | Visual Compositional Learning for Human-Object Interaction Detection | Human-Object interaction (HOI) detection aims to localize and infer relationships between human and objects in an image. It is challenging because an enormous number of possible combinations of objects and verbs types forms a long-tail distribution. We devise a deep Visual Compositional Learning (VCL) framework, which ... | https://arxiv.org/abs/2007.12407 | 2020-07-24T08:37:40Z | 2007.12407 | 10.48550/arxiv.2007.12407 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2007.12407 | ae0f0882a58b499d480418c241ffe4b3af312e3740006273b0039d96704b6dec | 0.77342 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2107.02463 | paper | corpus_metadata | null | EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data | Time series forecasting is a growing domain with diverse applications. However, changes of the system behavior over time due to internal or external influences are challenging. Therefore, predictions of a previously learned fore-casting model might not be useful anymore. In this paper, we present EVent-triggered Augmen... | https://arxiv.org/abs/2107.02463 | 2021-07-06T08:20:28Z | 2107.02463 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2107.02463 | a414cbe029722188e4c483ff6a672f1a91ec9124f69d38daea5af8e7bbb93bb0 | 0.682456 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.13449 | paper | corpus_metadata | null | Relational Gating for "What If" Reasoning | This paper addresses the challenge of learning to do procedural reasoning over text to answer "What if..." questions. We propose a novel relational gating network that learns to filter the key entities and relationships and learns contextual and cross representations of both procedure and question for finding the answe... | https://arxiv.org/abs/2105.13449 | 2021-05-27T21:07:30Z | 2105.13449 | 10.48550/arxiv.2105.13449 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2105.13449 | b949efbb5133e42a4453eca1fb24d89ffe0c14930fe03a927f28110039dc63d8 | 0.719298 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.03006 | paper | corpus_metadata | null | Unsupervised Selective Labeling for More Effective Semi-Supervised Learning | Given an unlabeled dataset and an annotation budget, we study how to selectively label a fixed number of instances so that semi-supervised learning (SSL) on such a partially labeled dataset is most effective. We focus on selecting the right data to label, in addition to usual SSL's propagating labels from labeled data ... | https://arxiv.org/abs/2110.03006 | 2021-10-06T18:25:50Z | 2110.03006 | 10.48550/arxiv.2110.03006 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2110.03006 | 26313f3725541f41dff33fc1978b8e00a70504c2158878f909b2caa3e6317422 | 0.743892 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.13994 | paper | corpus_metadata | null | Linguistic Structures as Weak Supervision for Visual Scene Graph Generation | Prior work in scene graph generation requires categorical supervision at the level of triplets - subjects and objects, and predicates that relate them, either with or without bounding box information. However, scene graph generation is a holistic task: thus holistic, contextual supervision should intuitively improve pe... | https://arxiv.org/abs/2105.13994 | 2021-05-28T17:20:27Z | 2105.13994 | 10.48550/arxiv.2105.13994 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2105.13994 | e04f52c862f2a9ac8933eea4976b30ac2294732d15106c798355b9fd8703e4c3 | 0.667867 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.03571 | paper | corpus_metadata | null | Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking? | Dialogue state tracking (DST) plays a key role in task-oriented dialogue systems to monitor the user's goal. In general, there are two strategies to track a dialogue state: predicting it from scratch and updating it from previous state. The scratch-based strategy obtains each slot value by inquiring all the dialogue hi... | https://arxiv.org/abs/2105.03571 | 2021-05-08T03:18:13Z | 2105.03571 | 10.48550/arxiv.2105.03571 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2105.03571 | b7c2b72b5b75fa74821f017152ce6b05d45e25cf3f5c7a60cf7c7d1c7f35ea83 | 0.512399 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.01220 | paper | corpus_metadata | null | OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems | Deep learning methods can be used to produce control policies, but certifying their safety is challenging. The resulting networks are nonlinear and often very large. In response to this challenge, we present OVERT: a sound algorithm for safety verification of nonlinear discrete-time closed loop dynamical systems with n... | https://arxiv.org/abs/2108.01220 | 2021-08-03T00:41:27Z | 2108.01220 | 10.48550/arxiv.2108.01220 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2108.01220 | 556048fce35cb2a3ca36a87ec4880ddb96262d5362abc5a07a646d7517f172af | 0.602092 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.01499 | paper | corpus_metadata | null | Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters | Weakly-supervised object detection (WSOD) has emerged as an inspiring recent topic to avoid expensive instance-level object annotations. However, the bounding boxes of most existing WSOD methods are mainly determined by precomputed proposals, thereby being limited in precise object localization. In this paper, we defen... | https://arxiv.org/abs/2108.01499 | 2021-08-03T13:38:20Z | 2108.01499 | 10.48550/arxiv.2108.01499 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2108.01499 | 05260b5441fb273398f429da811998777c19d86ac58a629720cc0f267fb7b05f | 0.790566 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2112.02628 | paper | corpus_metadata | null | Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radars | In the present work, a reinforcement learning (RL) based adaptive algorithm to optimise the transmit beampattern for a colocated massive MIMO radar is presented. Under the massive MIMO regime, a robust Wald type detector, able to guarantee certain detection performances under a wide range of practical disturbance model... | https://arxiv.org/abs/2112.02628 | 2021-12-05T17:19:26Z | 2112.02628 | 10.1109/taes.2022.3168033 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/taes.2022.3168033 | 1d9a144836a5b6d53d488c031e7c9c299911c704056a7e3c95968bbd2f6403a7 | 0.613943 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.01268 | paper | corpus_metadata | null | Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization | Dialogue summarization aims to generate a summary that indicates the key points of a given dialogue. In this work, we propose an end-to-end neural model for dialogue summarization with two novel modules, namely, the \emph{supporting utterance flow modeling module} and the \emph{fact regularization module}. The supporti... | https://arxiv.org/abs/2108.01268 | 2021-08-03T03:09:25Z | 2108.01268 | 10.48550/arxiv.2108.01268 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2108.01268 | e0cc5d80001c9106277db276d865c154d44d77bafdaeec9e152dfc0158672dae | 0.679994 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.04317 | paper | corpus_metadata | null | Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud | Point cloud upsampling is vital for the quality of the mesh in three-dimensional reconstruction. Recent research on point cloud upsampling has achieved great success due to the development of deep learning. However, the existing methods regard point cloud upsampling of different scale factors as independent tasks. Thus... | https://arxiv.org/abs/2102.04317 | 2021-02-08T16:21:48Z | 2102.04317 | 10.48550/arxiv.2102.04317 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2102.04317 | bfc2951f4ba7bdf61b8706c7bb319510bd433b5d539a61a711d44c05b22a536d | 0.685207 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2110.08760 | paper | corpus_metadata | null | Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications | Graph Neural Networks (GNNs) are widely adopted to analyse non-Euclidean data, such as chemical networks, brain networks, and social networks, modelling complex relationships and interdependency between objects. Recently, Membership Inference Attack (MIA) against GNNs raises severe privacy concerns, where training data... | https://arxiv.org/abs/2110.08760 | 2021-10-17T08:41:21Z | 2110.08760 | 10.48550/arxiv.2110.08760 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2110.08760 | 334ed7001070d85b7299acc54c507cdd10f45e917afa93003cf38c4f41bbbc63 | 0.720937 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1603.06147 | paper | corpus_metadata | null | A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation | The existing machine translation systems, whether phrase-based or neural, have relied almost exclusively on word-level modelling with explicit segmentation. In this paper, we ask a fundamental question: can neural machine translation generate a character sequence without any explicit segmentation? To answer this questi... | https://arxiv.org/abs/1603.06147 | 2016-03-19T21:35:04Z | 1603.06147 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1603.06147 | aff3ee5e730597527e90ba5a184caeaa37e4b8b09a692e8629591461c93a9199 | 0.756316 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2204.05841 | paper | corpus_metadata | null | VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration | Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on a single type of distortion, such as speech denoising or dereverberation. However, speech signals can be degraded by several different distortions simultaneously in the real world. It is thus important to extend speech restor... | https://arxiv.org/abs/2204.05841 | 2022-04-12T14:37:39Z | 2204.05841 | 10.21437/interspeech.2022-11026 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.21437/interspeech.2022-11026 | cbf73b0adb4172d84c6ca072dbe76671225c37dec3593bcc59c4b3cb76a4ce20 | 0.626965 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2005.03922 | paper | corpus_metadata | null | Learning Generalized Spoof Cues for Face Anti-spoofing | Many existing face anti-spoofing (FAS) methods focus on modeling the decision boundaries for some predefined spoof types. However, the diversity of the spoof samples including the unknown ones hinders the effective decision boundary modeling and leads to weak generalization capability. In this paper, we reformulate FAS... | https://arxiv.org/abs/2005.03922 | 2020-05-08T09:22:13Z | 2005.03922 | 10.48550/arxiv.2005.03922 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2005.03922 | bb5bc28c79e3ee408798785df7b79b0d992b545f85eb8f7846fb10e3326f44a1 | 0.765489 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.10972 | paper | corpus_metadata | null | ImageNet-21K Pretraining for the Masses | ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which is bigger and more diverse, is used less frequently for pretraining, mainly due to its complexity, low accessibility, and underestimation of its added value. This paper aims to close thi... | https://arxiv.org/abs/2104.10972 | 2021-04-22T10:10:14Z | 2104.10972 | 10.48550/arxiv.2104.10972 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2104.10972 | 786fe8663d06af3b9f59e21ed42c26eeda837974f5bfa6c6d2658ee8cadb8983 | 0.834408 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.10609 | paper | corpus_metadata | null | Lifting Monocular Events to 3D Human Poses | This paper presents a novel 3D human pose estimation approach using a single stream of asynchronous events as input. Most of the state-of-the-art approaches solve this task with RGB cameras, however struggling when subjects are moving fast. On the other hand, event-based 3D pose estimation benefits from the advantages ... | https://arxiv.org/abs/2104.10609 | 2021-04-21T16:07:12Z | 2104.10609 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2104.10609 | bdb27f09d0774bb80d012fe8999dc2b396a518cf0cd0c330971e8e71e8426ff6 | 0.725623 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.10895 | paper | corpus_metadata | null | On convergence rates of adaptive ensemble Kalman inversion for linear ill-posed problems | In this paper we discuss a deterministic form of ensemble Kalman inversion as a regularization method for linear inverse problems. By interpreting ensemble Kalman inversion as a low-rank approximation of Tikhonov regularization, we are able to introduce a new sampling scheme based on the Nystr\"om method that improves ... | https://arxiv.org/abs/2104.10895 | 2021-04-22T06:54:33Z | 2104.10895 | 10.1007/s00211-022-01314-y | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1007/s00211-022-01314-y | 61513d59a647b7dad32462bcb67ecd92f51fab805d405d1d8eed3068c2fa2c90 | 0.509555 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2305.16935 | paper | corpus_metadata | null | Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation | Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation b... | https://arxiv.org/abs/2305.16935 | 2023-05-26T13:51:50Z | 2305.16935 | 10.48550/arxiv.2305.16935 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2305.16935 | bf3d8683f0fd287433937e52038de9e7968e446abef11c1b98b4ba91f4cb62f5 | 0.678798 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2310.17760 | paper | corpus_metadata | null | Novel Models for Multiple Dependent Heteroskedastic Time Series | Functional magnetic resonance imaging or functional MRI (fMRI) is a very popular tool used for differing brain regions by measuring brain activity. It is affected by physiological noise, such as head and brain movement in the scanner from breathing, heart beats, or the subject fidgeting. The purpose of this paper is to... | https://arxiv.org/abs/2310.17760 | 2023-10-26T20:07:25Z | 2310.17760 | 10.48550/arxiv.2310.17760 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2310.17760 | beb99b111ca2e55d97aac0fbdae6bb0f919e1478c61e3ce2b7df941d05b024f1 | 0.585095 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1610.02424 | paper | corpus_metadata | null | Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models | Neural sequence models are widely used to model time-series data. Equally ubiquitous is the usage of beam search (BS) as an approximate inference algorithm to decode output sequences from these models. BS explores the search space in a greedy left-right fashion retaining only the top-B candidates - resulting in sequenc... | https://arxiv.org/abs/1610.02424 | 2016-10-07T20:56:47Z | 1610.02424 | 10.48550/arxiv.1610.02424 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1610.02424 | bd10db91caf126545e8860d6888aaf6aec2dbd13391b2aac695786ab02908ce0 | 0.74172 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2203.02181 | paper | corpus_metadata | null | MANNER: Multi-view Attention Network for Noise Erasure | In the field of speech enhancement, time domain methods have difficulties in achieving both high performance and efficiency. Recently, dual-path models have been adopted to represent long sequential features, but they still have limited representations and poor memory efficiency. In this study, we propose Multi-view At... | https://arxiv.org/abs/2203.02181 | 2022-03-04T08:27:35Z | 2203.02181 | 10.48550/arxiv.2203.02181 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2203.02181 | 5f0accb9b759e8dab4ffc1384d9cc9ae15f4271030d3266e77b2278418ce5c04 | 0.747182 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2211.00325 | paper | corpus_metadata | null | Speech-text based multi-modal training with bidirectional attention for improved speech recognition | To let the state-of-the-art end-to-end ASR model enjoy data efficiency, as well as much more unpaired text data by multi-modal training, one needs to address two problems: 1) the synchronicity of feature sampling rates between speech and language (aka text data); 2) the homogeneity of the learned representations from t... | https://arxiv.org/abs/2211.00325 | 2022-11-01T08:25:11Z | 2211.00325 | 10.48550/arxiv.2211.00325 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2211.00325 | 5f97385fcd5a33957d3fc8ddd6fbe3ff526ffd3bdbb7772475e614581320f9fa | 0.811731 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.09212 | paper | corpus_metadata | null | Deep High-Resolution Representation Learning for Human Pose Estimation | This is an official pytorch implementation of Deep High-Resolution Representation Learning for Human Pose Estimation. In this work, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resolution representations from... | https://arxiv.org/abs/1902.09212 | 2019-02-25T11:55:28Z | 1902.09212 | 10.48550/arxiv.1902.09212 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1902.09212 | 1364d189a732c943b849b097d44783fe7b72df67ff665147634301f7f5c0a7d3 | 0.792331 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1908.10357 | paper | corpus_metadata | null | HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation | Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids. Equipped... | https://arxiv.org/abs/1908.10357 | 2019-08-27T17:54:08Z | 1908.10357 | 10.48550/arxiv.1908.10357 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1908.10357 | 9e5f7b4946ce2eb23866409123ebf4ce6a51be803519bdf574ccdd5f5d12a1fd | 0.788378 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1904.04514 | paper | corpus_metadata | null | High-Resolution Representations for Labeling Pixels and Regions | High-resolution representation learning plays an essential role in many vision problems, e.g., pose estimation and semantic segmentation. The high-resolution network (HRNet)~\cite{SunXLW19}, recently developed for human pose estimation, maintains high-resolution representations through the whole process by connecting h... | https://arxiv.org/abs/1904.04514 | 2019-04-09T08:08:16Z | 1904.04514 | 10.48550/arxiv.1904.04514 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1904.04514 | e345ed0e4078c7b8670149177c33677eff4e3a7b4d1d4072cfd8d50d884179db | 0.741952 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.13715 | paper | corpus_metadata | null | PyTorchDIA: A flexible, GPU-accelerated numerical approach to Difference Image Analysis | We present a GPU-accelerated numerical approach for fast kernel and differential background solutions. The model image proposed in the Bramich (2008) difference image analysis algorithm is analogous to a very simple Convolutional Neural Network (CNN), with a single convolutional filter (i.e. the kernel) and an added sc... | https://arxiv.org/abs/2104.13715 | 2021-04-28T11:40:38Z | 2104.13715 | 10.1093/mnras/stab1114 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1093/mnras/stab1114 | df24daa8524ceffd5bafb5b84039087af9f9bf376d83b994a7765df6c17fecbe | 0.537267 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.12484 | paper | corpus_metadata | null | Constructing long-short stock portfolio with a new listwise learn-to-rank algorithm | Factor strategies have gained growing popularity in industry with the fast development of machine learning. Usually, multi-factors are fed to an algorithm for some cross-sectional return predictions, which are further used to construct a long-short portfolio. Instead of predicting the value of the stock return, emergin... | https://arxiv.org/abs/2104.12484 | 2021-04-26T11:33:52Z | 2104.12484 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2104.12484 | 81dd69e1e7a25cb8cb7b72b1ffb31403a2c2d3d2cb829659307d96b4f65de995 | 0.563864 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2102.07954 | paper | corpus_metadata | null | AlphaNet: Improved Training of Supernets with Alpha-Divergence | Weight-sharing neural architecture search (NAS) is an effective technique for automating efficient neural architecture design. Weight-sharing NAS builds a supernet that assembles all the architectures as its sub-networks and jointly trains the supernet with the sub-networks. The success of weight-sharing NAS heavily re... | https://arxiv.org/abs/2102.07954 | 2021-02-16T04:23:55Z | 2102.07954 | 10.48550/arxiv.2102.07954 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2102.07954 | 1cfe24edaf732ae274ce6bcab715fdbb6b99ebe69aefec93d45f9aac495661a4 | 0.794903 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1903.11921 | paper | corpus_metadata | null | Transfer learning for radio galaxy classification | In the context of radio galaxy classification, most state-of-the-art neural network algorithms have been focused on single survey data. The question of whether these trained algorithms have cross-survey identification ability or can be adapted to develop classification networks for future surveys is still unclear. One ... | https://arxiv.org/abs/1903.11921 | 2019-03-28T12:43:09Z | 1903.11921 | 10.1093/mnras/stz1883 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1093/mnras/stz1883 | b029a01e0a007a37c3ae75a1f5332dc6b225e231ba87c079a749cc75652f6cd2 | 0.811928 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.02484 | paper | corpus_metadata | null | Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization | Adversarial examples cause neural networks to produce incorrect outputs with high confidence. Although adversarial training is one of the most effective forms of defense against adversarial examples, unfortunately, a large gap exists between test accuracy and training accuracy in adversarial training. In this paper, we... | https://arxiv.org/abs/2003.02484 | 2020-03-05T08:47:46Z | 2003.02484 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2003.02484 | abe16cfe37d5c29f62317cdafdd595a04980b538c135c246bfefe3b8a135be16 | 0.720014 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.01900 | paper | corpus_metadata | null | Lachesis: Scalable Asynchronous BFT on DAG Streams | This paper consolidates the core technologies and key concepts of our novel Lachesis consensus protocol and Fantom Opera platform, which is permissionless, leaderless and EVM compatible. We introduce our new protocol, so-called Lachesis, for distributed networks achieving Byzantine fault tolerance (BFT)~\cite{lachesis0... | https://arxiv.org/abs/2108.01900 | 2021-08-04T08:21:28Z | 2108.01900 | 10.48550/arxiv.2108.01900 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2108.01900 | 0aeea6d55c27c3937b0c13017821c91d95b39f758fb1155a10ee5cc2bdc0945b | 0.503476 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.03485 | paper | corpus_metadata | null | Neural Operator: Graph Kernel Network for Partial Differential Equations | The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinite-dimensional space... | https://arxiv.org/abs/2003.03485 | 2020-03-07T01:56:20Z | 2003.03485 | 10.48550/arxiv.2003.03485 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2003.03485 | 772fdb338e7ff00d763d455152a05b3b8cf0f62f2de00e9f17b455391f3655c4 | 0.586039 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2203.17145 | paper | corpus_metadata | null | Convex Parameterization of Stabilizing Controllers and its LMI-based Computation via Filtering | Various new implicit parameterizations for stabilizing controllers that allow one to impose structural constraints on the controller have been proposed lately. They are convex but infinite-dimensional, formulated in the frequency domain with no available efficient methods for computation. In this paper, we introduce a ... | https://arxiv.org/abs/2203.17145 | 2022-03-31T16:14:09Z | 2203.17145 | 10.48550/arxiv.2203.17145 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2203.17145 | a1b1ed61c02ce5f7e63cffe2bc50e91f88d7dadb902c24a01f1f92331101e7bb | 0.546759 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1802.06225 | paper | corpus_metadata | null | A Deep Q-Learning Agent for the L-Game with Variable Batch Training | We employ the Deep Q-Learning algorithm with Experience Replay to train an agent capable of achieving a high-level of play in the L-Game while self-learning from low-dimensional states. We also employ variable batch size for training in order to mitigate the loss of the rare reward signal and significantly accelerate t... | https://arxiv.org/abs/1802.06225 | 2018-02-17T11:45:09Z | 1802.06225 | 10.48550/arxiv.1802.06225 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1802.06225 | 69e06b8c411d50f7966d6cf0d7f44c0436efa23314c17ce9e062f54d2f4f1122 | 0.625302 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1705.04932 | paper | corpus_metadata | null | GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data | Object Transfiguration replaces an object in an image with another object from a second image. For example it can perform tasks like "putting exactly those eyeglasses from image A on the nose of the person in image B". Usage of exemplar images allows more precise specification of desired modifications and improves the ... | https://arxiv.org/abs/1705.04932 | 2017-05-14T08:59:36Z | 1705.04932 | 10.48550/arxiv.1705.04932 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1705.04932 | 0e14cca72426d7fc8b2dfc08a409e67c827e516829045ff1df8416396b58d9f2 | 0.781967 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2003.13402 | paper | corpus_metadata | null | Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks | Autonomous vehicles commonly rely on highly detailed birds-eye-view maps of their environment, which capture both static elements of the scene such as road layout as well as dynamic elements such as other cars and pedestrians. Generating these map representations on the fly is a complex multi-stage process which incorp... | https://arxiv.org/abs/2003.13402 | 2020-03-30T12:39:44Z | 2003.13402 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2003.13402 | 54f653dee35d942a1adc3ff7a7e60a653de084fc13b756c4fcea8b759e07deb1 | 0.727251 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1902.06634 | paper | corpus_metadata | null | Contextual Encoder-Decoder Network for Visual Saliency Prediction | Predicting salient regions in natural images requires the detection of objects that are present in a scene. To develop robust representations for this challenging task, high-level visual features at multiple spatial scales must be extracted and augmented with contextual information. However, existing models aimed at ex... | https://arxiv.org/abs/1902.06634 | 2019-02-18T16:15:25Z | 1902.06634 | 10.1016/j.neunet.2020.05.004 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1016/j.neunet.2020.05.004 | 8feed8c6c1b7c39daf504051dab6839ab8f59f4f61e5a570e3563ddf6f8bd0e7 | 0.842384 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.04309 | paper | corpus_metadata | null | Multi-modal Conditional Bounding Box Regression for Music Score Following | This paper addresses the problem of sheet-image-based on-line audio-to-score alignment also known as score following. Drawing inspiration from object detection, a conditional neural network architecture is proposed that directly predicts x,y coordinates of the matching positions in a complete score sheet image at each ... | https://arxiv.org/abs/2105.04309 | 2021-05-10T12:43:35Z | 2105.04309 | 10.48550/arxiv.2105.04309 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2105.04309 | 97651a2ef52fa12c773fbe7185c3b2068fcd6c4037aaaa8072ba007b48045ecc | 0.76524 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.04488 | paper | corpus_metadata | null | A Deep Reinforcement Learning Approach to Audio-Based Navigation in a Multi-Speaker Environment | In this work we use deep reinforcement learning to create an autonomous agent that can navigate in a two-dimensional space using only raw auditory sensory information from the environment, a problem that has received very little attention in the reinforcement learning literature. Our experiments show that the agent can... | https://arxiv.org/abs/2105.04488 | 2021-05-10T16:26:47Z | 2105.04488 | 10.1109/icassp39728.2021.9415013 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1109/icassp39728.2021.9415013 | 52aa08b0f15980a4f842183980b34fa76c37b684394c5b12123d93a6c997ceb6 | 0.63888 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.03579 | paper | corpus_metadata | null | Unsupervised Remote Sensing Super-Resolution via Migration Image Prior | Recently, satellites with high temporal resolution have fostered wide attention in various practical applications. Due to limitations of bandwidth and hardware cost, however, the spatial resolution of such satellites is considerably low, largely limiting their potentials in scenarios that require spatially explicit inf... | https://arxiv.org/abs/2105.03579 | 2021-05-08T03:29:35Z | 2105.03579 | 10.48550/arxiv.2105.03579 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2105.03579 | b7d96af727bdaa8b05b0ea2e9585c5db3ead9a92e2da67922a7b0e7db64b6234 | 0.80754 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.04158 | paper | corpus_metadata | null | CREPO: An Open Repository to Benchmark Credal Network Algorithms | Credal networks are a popular class of imprecise probabilistic graphical models obtained as a Bayesian network generalization based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to model, process and query credal networks. Despite the NP-hardness of the... | https://arxiv.org/abs/2105.04158 | 2021-05-10T07:31:59Z | 2105.04158 | 10.48550/arxiv.2105.04158 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2105.04158 | 5d47ff735b452c963a768e3627f1dc7ddc25ce4c6db1c8901054f60aaa9469a0 | 0.680566 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.03611 | paper | corpus_metadata | null | 360NorVic: 360-Degree Video Classification from Mobile Encrypted Video Traffic | Streaming 360{\deg} video demands high bandwidth and low latency, and poses significant challenges to Internet Service Providers (ISPs) and Mobile Network Operators (MNOs). The identification of 360{\deg} video traffic can therefore benefits fixed and mobile carriers to optimize their network and provide better Quality... | https://arxiv.org/abs/2105.03611 | 2021-05-08T06:36:01Z | 2105.03611 | 10.1145/3458306.3460998 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-nd/4.0 | doi:10.1145/3458306.3460998 | 4b62d53f704b3cea2f9a3d02e12e02758182645a0fdb7152940174ea96668adb | 0.578998 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.03710 | paper | corpus_metadata | null | Falling Through the Gaps: Neural Architectures as Models of Morphological Rule Learning | Recent advances in neural architectures have revived the problem of morphological rule learning. We evaluate the Transformer as a model of morphological rule learning and compare it with Recurrent Neural Networks (RNN) on English, German, and Russian. We bring to the fore a hitherto overlooked problem, the morphologica... | https://arxiv.org/abs/2105.03710 | 2021-05-08T14:48:29Z | 2105.03710 | 10.48550/arxiv.2105.03710 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2105.03710 | 32454cf27ad86dcf8da1b9662542c466c223bfc58ba49d4d43ce056874e5b34c | 0.628547 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.00976 | paper | corpus_metadata | null | Exploring Discourse Structures for Argument Impact Classification | Discourse relations among arguments reveal logical structures of a debate conversation. However, no prior work has explicitly studied how the sequence of discourse relations influence a claim's impact. This paper empirically shows that the discourse relations between two arguments along the context path are essential f... | https://arxiv.org/abs/2106.00976 | 2021-06-02T06:49:19Z | 2106.00976 | 10.48550/arxiv.2106.00976 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2106.00976 | 3dc50fe01f9674bd1ca9a7e3b93f0ff8f085ccd0029b383492075565e87e6902 | 0.527522 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.13086 | paper | corpus_metadata | null | Anonymizing Machine Learning Models | There is a known tension between the need to analyze personal data to drive business and privacy concerns. Many data protection regulations, including the EU General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA), set out strict restrictions and obligations on the collection and pro... | https://arxiv.org/abs/2007.13086 | 2020-07-26T09:29:03Z | 2007.13086 | 10.1007/978-3-030-93944-1_8 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/978-3-030-93944-1_8 | 0832f8dfec6396bd50cff7ad1f483c4d043f89edca9a83ffb43ac424573c3509 | 0.722854 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.03356 | paper | corpus_metadata | null | DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction | In E-commerce, vouchers are important marketing tools to enhance users' engagement and boost sales and revenue. The likelihood that a user redeems a voucher is a key factor in voucher distribution decision. User-item Click-Through-Rate (CTR) models are often applied to predict the user-voucher redemption rate. However,... | https://arxiv.org/abs/2106.03356 | 2021-06-07T06:16:49Z | 2106.03356 | 10.1145/3447548.3467191 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3447548.3467191 | 3341f00d13145e5f1f3a11dcf78cb8c2b83a425a1bcb7ddc10a4c039dbdc9e9c | 0.665593 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2005.10008 | paper | corpus_metadata | null | Batch Decorrelation for Active Metric Learning | We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$. In contrast to prior work on class-based learning, where the fundamental goal is classification and any implicit or explicit ... | https://arxiv.org/abs/2005.10008 | 2020-05-20T12:47:48Z | 2005.10008 | 10.48550/arxiv.2005.10008 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2005.10008 | 2a06b7869ddf389a778f0752834d932a15f2d37881b40462959da6757adb8dbb | 0.690499 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2211.16550 | paper | corpus_metadata | null | Soft Alignment Objectives for Robust Adaptation of Language Generation | Domain adaptation allows generative language models to address specific flaws caused by the domain shift of their application. However, the traditional adaptation by further training on in-domain data rapidly weakens the model's ability to generalize to other domains, making the open-ended deployments of the adapted mo... | https://arxiv.org/abs/2211.16550 | 2022-11-29T19:23:04Z | 2211.16550 | 10.48550/arxiv.2211.16550 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2211.16550 | 3ee011ea6741d13c0d16ed25c2cf6e05ec6cf2c7a28a9cb1e92604c89aee18ff | 0.733894 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.04378 | paper | corpus_metadata | null | Adaptive Machine Unlearning | Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees only for sequences that are chosen independently of th... | https://arxiv.org/abs/2106.04378 | 2021-06-08T14:11:53Z | 2106.04378 | 10.48550/arxiv.2106.04378 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2106.04378 | ab4d7cfd2214cd4cb2d47314c462ce4c6151b51a9f76f43ee569b8da564d7f07 | 0.684396 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.16054 | paper | corpus_metadata | null | Predicting bulge to total luminosity ratio of galaxies using deep learning | We present a deep learning model to predict the r-band bulge-to-total light ratio (B/T) of nearby galaxies using their multi-band JPEG images alone. Our Convolutional Neural Network (CNN) based regression model is trained on a large sample of galaxies with reliable decomposition into the bulge and disk components. The ... | https://arxiv.org/abs/2106.16054 | 2021-06-30T13:28:12Z | 2106.16054 | 10.1093/mnras/stab1935 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1093/mnras/stab1935 | 1c1fa3b75b9eff10224d6b0258d58e365706453a01203018484b1915eed125e1 | 0.649604 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1511.06939 | paper | corpus_metadata | null | Session-based Recommendations with Recurrent Neural Networks | We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation th... | https://arxiv.org/abs/1511.06939 | 2015-11-21T23:42:59Z | 1511.06939 | 10.48550/arxiv.1511.06939 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1511.06939 | c05f9fe7ce7237132ba7fc925634423e72a08ce96c8efec9a74407d189776668 | 0.644512 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2112.02268 | paper | corpus_metadata | null | Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding | With the great success of pre-trained models, the pretrain-then-finetune paradigm has been widely adopted on downstream tasks for source code understanding. However, compared to costly training a large-scale model from scratch, how to effectively adapt pre-trained models to a new task has not been fully explored. In th... | https://arxiv.org/abs/2112.02268 | 2021-12-04T07:21:28Z | 2112.02268 | 10.48550/arxiv.2112.02268 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2112.02268 | 19f4ddcba728e7f259afe66225e6f5d1f65ad06f672e31a5e109af6f5c0eff5a | 0.902136 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.05526 | paper | corpus_metadata | null | Simplifying Deep Reinforcement Learning via Self-Supervision | Supervised regression to demonstrations has been demonstrated to be a stable way to train deep policy networks. We are motivated to study how we can take full advantage of supervised loss functions for stably training deep reinforcement learning agents. This is a challenging task because it is unclear how the training ... | https://arxiv.org/abs/2106.05526 | 2021-06-10T06:29:59Z | 2106.05526 | 10.48550/arxiv.2106.05526 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2106.05526 | 362761fce6a3ced9b736cbc01c5edce9d416176cd0e04a9eddbc23f793a07339 | 0.806445 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2011.12912 | paper | corpus_metadata | null | DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects | We present DRACO, a method for Dense Reconstruction And Canonicalization of Object shape from one or more RGB images. Canonical shape reconstruction, estimating 3D object shape in a coordinate space canonicalized for scale, rotation, and translation parameters, is an emerging paradigm that holds promise for a multitude... | https://arxiv.org/abs/2011.12912 | 2020-11-25T17:50:56Z | 2011.12912 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2011.12912 | ace32001b6fe254bd262278af7e591fac8d227211e1b78d09278f2ba38302aa0 | 0.650003 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.05624 | paper | corpus_metadata | null | RetinaNet Object Detector based on Analog-to-Spiking Neural Network Conversion | The paper proposes a method to convert a deep learning object detector into an equivalent spiking neural network. The aim is to provide a conversion framework that is not constrained to shallow network structures and classification problems as in state-of-the-art conversion libraries. The results show that models of hi... | https://arxiv.org/abs/2106.05624 | 2021-06-10T10:06:52Z | 2106.05624 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | arxiv:2106.05624 | 3a05c1b1995a2793d651795eb96e8d308cda34d3bf5e18138fdffe64d3c253a9 | 0.703918 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1710.09829 | paper | corpus_metadata | CapsNet (MultiMNIST) | Dynamic Routing Between Capsules | A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Acti... | https://arxiv.org/abs/1710.09829 | 2017-10-26T17:49:04Z | 1710.09829 | 10.48550/arxiv.1710.09829 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1710.09829 | 7d42251c461932226b2b9d9ba066e76b0b1409d21ea1612a36548a63ff9e0a43 | 0.567269 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:2104.11530 | paper | corpus_metadata | null | Supervised Video Summarization via Multiple Feature Sets with Parallel Attention | The assignment of importance scores to particular frames or (short) segments in a video is crucial for summarization, but also a difficult task. Previous work utilizes only one source of visual features. In this paper, we suggest a novel model architecture that combines three feature sets for visual content and motion ... | https://arxiv.org/abs/2104.11530 | 2021-04-23T10:46:35Z | 2104.11530 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2104.11530 | 14b5a9942c1d9e9b0222f23da07551ce17fb40bcfac8a2fc1f602cf03dd350f6 | 0.777089 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.12877 | paper | corpus_metadata | DeiT-B | Training data-efficient image transformers & distillation through attention | Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competit... | https://arxiv.org/abs/2012.12877 | 2020-12-23T18:42:10Z | 2012.12877 | 10.48550/arxiv.2012.12877 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2012.12877 | 2afb43211dcc8d1ac7cd38393fe65aedfa2c0f50a61e54805a77f253c99c44b6 | 0.816867 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [
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arxiv:1611.01734 | paper | corpus_metadata | null | Deep Biaffine Attention for Neural Dependency Parsing | This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art ... | https://arxiv.org/abs/1611.01734 | 2016-11-06T07:26:38Z | 1611.01734 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1611.01734 | 14a4ad610de375953ae962fd2985ff596fd26d4f2f78e9e687e6895c5bbdd8b3 | 0.701502 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.06672 | paper | corpus_metadata | null | Structure-Regularized Attention for Deformable Object Representation | Capturing contextual dependencies has proven useful to improve the representational power of deep neural networks. Recent approaches that focus on modeling global context, such as self-attention and non-local operation, achieve this goal by enabling unconstrained pairwise interactions between elements. In this work, we... | https://arxiv.org/abs/2106.06672 | 2021-06-12T03:10:17Z | 2106.06672 | 10.48550/arxiv.2106.06672 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2106.06672 | 0e7c20b536ba98bf169e6402a6f4fb6347fbd2e5dbfc0cd140f2acf1ab96464b | 0.729512 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2311.07484 | paper | corpus_metadata | null | Psychometric Predictive Power of Large Language Models | Instruction tuning aligns the response of large language models (LLMs) with human preferences. Despite such efforts in human--LLM alignment, we find that instruction tuning does not always make LLMs human-like from a cognitive modeling perspective. More specifically, next-word probabilities estimated by instruction-tun... | https://arxiv.org/abs/2311.07484 | 2023-11-13T17:19:14Z | 2311.07484 | 10.48550/arxiv.2311.07484 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2311.07484 | 1a4db4615c18c392d360594bcea5333e061a621fbdf1eb21f2883a971e054127 | 0.68455 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2106.07462 | paper | corpus_metadata | null | Improving Bridge estimators via $f$-GAN | Bridge sampling is a powerful Monte Carlo method for estimating ratios of normalizing constants. Various methods have been introduced to improve its efficiency. These methods aim to increase the overlap between the densities by applying appropriate transformations to them without changing their normalizing constants. I... | https://arxiv.org/abs/2106.07462 | 2021-06-14T14:40:29Z | 2106.07462 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | arxiv:2106.07462 | b55c8248abbcd3a79dfeae41d093e12ac8ae1d4fd3d4aa9fb3440f85967183aa | 0.56501 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2205.00293 | paper | corpus_metadata | null | TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning | We present a novel procedure for optimization based on the combination of efficient quantized tensor train representation and a generalized maximum matrix volume principle. We demonstrate the applicability of the new Tensor Train Optimizer (TTOpt) method for various tasks, ranging from minimization of multidimensional ... | https://arxiv.org/abs/2205.00293 | 2022-04-30T15:41:13Z | 2205.00293 | 10.48550/arxiv.2205.00293 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2205.00293 | 3bc05c82805a2c6f25fde157e36a2136c563ab9876337131db56f25cf64dc07f | 0.62294 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2007.13203 | paper | corpus_metadata | null | A containerized proof-of-concept implementation of LightChain system | LightChain is the first Distributed Hash Table (DHT)-based blockchain with a logarithmic asymptotic message and memory complexity. In this demo paper, we present the software architecture of our open-source implementation of LightChain, as well as a novel deployment scenario of the entire LightChain system on a single ... | https://arxiv.org/abs/2007.13203 | 2020-07-26T19:36:39Z | 2007.13203 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2007.13203 | d5cb99b1c43327d0c55b1db997e0e774852688cfa136c9b2aef1d951ecbf1f1f | 0.50416 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2411.05752 | paper | corpus_metadata | null | FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher Information | Deep learning (DL) models are popular across various domains due to their remarkable performance and efficiency. However, their effectiveness relies heavily on large amounts of labeled data, which are often time-consuming and labor-intensive to generate manually. To overcome this challenge, it is essential to develop s... | https://arxiv.org/abs/2411.05752 | 2024-11-08T18:10:46Z | 2411.05752 | 10.48550/arxiv.2411.05752 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2411.05752 | ac916f2a5adec3b52638f6f04be7463882b07972a09f6b02af79f0f1b9dbc98e | 0.843608 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1803.10417 | paper | corpus_metadata | null | The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions | Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic image... | https://arxiv.org/abs/1803.10417 | 2018-03-28T05:18:15Z | 1803.10417 | 10.1038/sdata.2018.161 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.1038/sdata.2018.161 | 5d70773b673133c2e48c135d1ff495825cb1e3ffc97180d452eb64f3955f5861 | 0.652735 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2109.13089 | paper | corpus_metadata | null | Automated Mining of Leaderboards for Empirical AI Research | With the rapid growth of research publications, empowering scientists to keep oversight over the scientific progress is of paramount importance. In this regard, the Leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressin... | https://arxiv.org/abs/2109.13089 | 2021-08-31T10:00:52Z | 2109.13089 | 10.48550/arxiv.2109.13089 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2109.13089 | 52931e0104a980fe4e356e0a8f021c566613be2ccf0609b4e7dc3c46c0bddaee | 0.639732 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1602.02282 | paper | corpus_metadata | null | Ladder Variational Autoencoders | Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models. We propose a new inference model, the Ladder Variational Autoencoder, that... | https://arxiv.org/abs/1602.02282 | 2016-02-06T17:32:48Z | 1602.02282 | 10.48550/arxiv.1602.02282 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1602.02282 | 596e4a5d16782dfa6e4f4e914fba02870fa30e954fa8bd78e0df5b8eee23f1e5 | 0.720236 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1103.4601 | paper | corpus_metadata | null | Doubly Robust Policy Evaluation and Learning | We study decision making in environments where the reward is only partially observed, but can be modeled as a function of an action and an observed context. This setting, known as contextual bandits, encompasses a wide variety of applications including health-care policy and Internet advertising. A central task is eval... | https://arxiv.org/abs/1103.4601 | 2011-03-23T19:37:45Z | 1103.4601 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1103.4601 | ebb3b510c5933d57c2c21521e0cca48d0fcbdf68aca3d9273ddb341ccd1f09bd | 0.670024 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1905.08108 | paper | corpus_metadata | null | Neural Graph Collaborative Filtering | Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that des... | https://arxiv.org/abs/1905.08108 | 2019-05-20T13:41:16Z | 1905.08108 | 10.1145/3331184.3331267 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3331184.3331267 | af0bbc1ea83d32e2fa70779b8a4587f7dbeb61393bd536e00291946eb292b46c | 0.761062 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2111.11636 | paper | corpus_metadata | null | Music Classification: Beyond Supervised Learning, Towards Real-world Applications | Music classification is a music information retrieval (MIR) task to classify music items to labels such as genre, mood, and instruments. It is also closely related to other concepts such as music similarity and musical preference. In this tutorial, we put our focus on two directions - the recent training schemes beyond... | https://arxiv.org/abs/2111.11636 | 2021-11-23T03:44:39Z | 2111.11636 | 10.5281/zenodo.5703779 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.5281/zenodo.5703779 | de65d5081192446190a0f0957753c9ed658d185b7ee1cc521f4560318aba99b4 | 0.734166 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2109.03890 | paper | corpus_metadata | null | Model Explanations via the Axiomatic Causal Lens | Explaining the decisions of black-box models is a central theme in the study of trustworthy ML. Numerous measures have been proposed in the literature; however, none of them take an axiomatic approach to causal explainability. In this work, we propose three explanation measures which aggregate the set of all but-for ca... | https://arxiv.org/abs/2109.03890 | 2021-09-08T19:33:52Z | 2109.03890 | 10.48550/arxiv.2109.03890 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2109.03890 | 696b156bbdfb43fccb27ed2e044be0f05bf2cdfbb4d8128be547c8dd3c6e3f27 | 0.557439 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1808.03737 | paper | corpus_metadata | null | Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising | In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to any final sales conversion and transaction. For both campaigners and publishers, it is fundamentally critical to estimate the contribution fr... | https://arxiv.org/abs/1808.03737 | 2018-08-11T01:58:19Z | 1808.03737 | 10.1145/3269206.3271677 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1145/3269206.3271677 | 3bb88ecbcaf713b51b0fc3ead3271dcf50f0be704bbb3ac2b391613980642019 | 0.648106 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2112.13054 | paper | corpus_metadata | null | Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge | Brain tumor segmentation from multiple Magnetic Resonance Imaging (MRI) modalities is a challenging task in medical image computation. The main challenges lie in the generalizability to a variety of scanners and imaging protocols. In this paper, we explore strategies to increase model robustness without increasing infe... | https://arxiv.org/abs/2112.13054 | 2021-12-24T13:01:44Z | 2112.13054 | 10.1007/978-3-031-09002-8_17 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/978-3-031-09002-8_17 | f1eb5329bf188d2895a2feb8a29daa70b378b4debc7591bef44b7bfbb08000fa | 0.776546 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.00677 | paper | corpus_metadata | null | Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis | We present DietNeRF, a 3D neural scene representation estimated from a few images. Neural Radiance Fields (NeRF) learn a continuous volumetric representation of a scene through multi-view consistency, and can be rendered from novel viewpoints by ray casting. While NeRF has an impressive ability to reconstruct geometry ... | https://arxiv.org/abs/2104.00677 | 2021-04-01T17:59:31Z | 2104.00677 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-sa/4.0 | arxiv:2104.00677 | 433b31fab81ef7d3264ad1bba9650fe7fd4056cb108d6ae0d60de8652b451731 | 0.7678 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2006.14422 | paper | corpus_metadata | null | Lifelong Learning of Graph Neural Networks for Open-World Node Classification | Graph neural networks (GNNs) have emerged as the standard method for numerous tasks on graph-structured data such as node classification. However, real-world graphs are often evolving over time and even new classes may arise. We model these challenges as an instance of lifelong learning, in which a learner faces a sequ... | https://arxiv.org/abs/2006.14422 | 2020-06-25T14:03:31Z | 2006.14422 | 10.1109/ijcnn52387.2021.9533412 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1109/ijcnn52387.2021.9533412 | 91064367ad0272e1a86ecfd3f08f360a34c046ec032e3bf7b09dee3df9233faa | 0.801782 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2107.08720 | paper | corpus_metadata | null | Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech | Undermining the impact of hateful content with informed and non-aggressive responses, called counter narratives, has emerged as a possible solution for having healthier online communities. Thus, some NLP studies have started addressing the task of counter narrative generation. Although such studies have made an effort ... | https://arxiv.org/abs/2107.08720 | 2021-07-19T09:45:54Z | 2107.08720 | 10.18653/v1/2021.acl-long.250 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.18653/v1/2021.acl-long.250 | 0de9ee7a23fffd49fba0f00210e1f3c6b2851242733d872dc350c3761bd040f5 | 0.666763 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2112.11282 | paper | corpus_metadata | null | VW-SDK: Efficient Convolutional Weight Mapping Using Variable Windows for Processing-In-Memory Architectures | With their high energy efficiency, processing-in-memory (PIM) arrays are increasingly used for convolutional neural network (CNN) inference. In PIM-based CNN inference, the computational latency and energy are dependent on how the CNN weights are mapped to the PIM array. A recent study proposed shifted and duplicated k... | https://arxiv.org/abs/2112.11282 | 2021-12-21T15:10:30Z | 2112.11282 | 10.48550/arxiv.2112.11282 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2112.11282 | f133996443ebeb0a6e0c96ee8424ee714e38ff9eb9f389761c618dd40d9cba82 | 0.567797 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2210.07811 | paper | corpus_metadata | null | SAILOR: Scaling Anchors via Insights into Latent Object Representation | LiDAR 3D object detection models are inevitably biased towards their training dataset. The detector clearly exhibits this bias when employed on a target dataset, particularly towards object sizes. However, object sizes vary heavily between domains due to, for instance, different labeling policies or geographical locati... | https://arxiv.org/abs/2210.07811 | 2022-10-14T13:40:46Z | 2210.07811 | 10.48550/arxiv.2210.07811 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2210.07811 | de191dbdef2ec192db17528384510119047c3d8775053f70848102117e4bd622 | 0.786733 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2207.09373 | paper | corpus_metadata | null | Multi-Task Learning Framework for Emotion Recognition in-the-wild | This paper presents our system for the Multi-Task Learning (MTL) Challenge in the 4th Affective Behavior Analysis in-the-wild (ABAW) competition. We explore the research problems of this challenge from three aspects: 1) For obtaining efficient and robust visual feature representations, we propose MAE-based unsupervised... | https://arxiv.org/abs/2207.09373 | 2022-07-19T16:18:53Z | 2207.09373 | 10.48550/arxiv.2207.09373 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2207.09373 | 7052525755690fdf4ca46e3124248394f117b86f1322858fb8b95c284a1987d2 | 0.81871 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2308.06926 | paper | corpus_metadata | null | OpenGCD: Assisting Open World Recognition with Generalized Category Discovery | A desirable open world recognition (OWR) system requires performing three tasks: (1) Open set recognition (OSR), i.e., classifying the known (classes seen during training) and rejecting the unknown (unseen$/$novel classes) online; (2) Grouping and labeling these unknown as novel known classes; (3) Incremental learning ... | https://arxiv.org/abs/2308.06926 | 2023-08-14T04:10:45Z | 2308.06926 | 10.48550/arxiv.2308.06926 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by/4.0 | doi:10.48550/arxiv.2308.06926 | eeeddab91cb142cd6e00f2d496340c6dbe223f1e4c8f66b1643123cb9d77e66f | 0.79243 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2405.05957 | paper | corpus_metadata | null | OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning | Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities.However, their massive number of parameters leads to high deployment requirements and incurs significant inference costs, which impedes their practical applications. Training smaller models is an effective way t... | https://arxiv.org/abs/2405.05957 | 2024-05-09T17:53:28Z | 2405.05957 | 10.48550/arxiv.2405.05957 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2405.05957 | d8086fefa98e7f41de98cca1491820756fc57c7fa515ba973cb20bde0502620b | 0.769758 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2104.12525 | paper | corpus_metadata | null | An open-source Bayesian atmospheric radiative transfer (BART) code: III. Initialization, atmospheric profile generator, post-processing routines, and application to exoplanet WASP-43b | This and companion papers by Harrington et al. 2021, submitted and Cubillos et al. 2021, submitted describe an open-source retrieval framework, Bayesian Atmospheric Radiative Transfer (BART), available to the community under the reproducible-research license via https://github.com/exosports/BART . BART is a radiative-t... | https://arxiv.org/abs/2104.12525 | 2021-04-26T12:48:14Z | 2104.12525 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:2104.12525 | e7078697bad2d26f532fdbe7c0a3f541654a9cce37c816734f4ff31c1d992df4 | 0.520314 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2107.02399 | paper | corpus_metadata | null | SOCluster- Towards Intent-based Clustering of Stack Overflow Questions using Graph-Based Approach | Stack Overflow (SO) platform has a huge dataset of questions and answers driven by interactions between users. But the count of unanswered questions is continuously rising. This issue is common across various community Question & Answering platforms (Q&A) such as Yahoo, Quora and so on. Clustering is one of the approac... | https://arxiv.org/abs/2107.02399 | 2021-07-06T05:30:01Z | 2107.02399 | 10.48550/arxiv.2107.02399 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.48550/arxiv.2107.02399 | d6364076706f08f4f7ce0b528714b297c8339a105da1f16ea86b80ec520df01c | 0.623763 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2105.15098 | paper | corpus_metadata | null | Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT | Abnormality detection is essential to the performance of safety-critical and latency-constrained systems. However, as systems are becoming increasingly complicated with a large quantity of heterogeneous data, conventional statistical change point detection methods are becoming less effective and efficient. Although Dee... | https://arxiv.org/abs/2105.15098 | 2021-04-08T03:31:50Z | 2105.15098 | 10.48550/arxiv.2105.15098 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.2105.15098 | 29a6091767bbece21ecef3c4f55f00212e71826f4fd33bdb6534ba971715dcc8 | 0.671993 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2012.14516 | paper | corpus_metadata | null | Synergy between Observation Systems Oceanic in Turbulent Regions | Ocean dynamics constitute a source of incertitude in determining the ocean's role in complex climatic phenomena. Current observation systems have limitations in achieving sufficiently statistical precision for three-dimensional oceanic data. It is crucial knowledge to describe the behavior of internal ocean structures.... | https://arxiv.org/abs/2012.14516 | 2020-12-28T22:52:57Z | 2012.14516 | 10.13140/rg.2.2.29817.90721 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.13140/rg.2.2.29817.90721 | 4f00446d4c27388a92b207b15a2688e90369fe82f53cb5476b6c9f94c59cb134 | 0.562206 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1809.08214 | paper | corpus_metadata | null | RKappa: Software for Analyzing Rule-Based Models | RKappa is a framework for the development, simulation and analysis of rule-base models within the mature statistically empowered R environment. It is designed for model editing, parameter identification, simulation, sensitivity analysis and visualisation. The framework is optimised for high-performance computing platfo... | https://arxiv.org/abs/1809.08214 | 2018-09-21T17:15:53Z | 1809.08214 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1809.08214 | ba276f31382c30c7ebfaf8647fe657c00285799bc104589da1bf48a99e83c22c | 0.564252 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1907.08487 | paper | corpus_metadata | null | A Graph Neural Network Approach for Scalable Wireless Power Control | Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural network structures, e.g., multi-layer perceptron (MLP) and convolutional neural network (CNN), are inherited from deep learning for image proces... | https://arxiv.org/abs/1907.08487 | 2019-07-19T12:38:20Z | 1907.08487 | null | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | arxiv:1907.08487 | 58fc3c85aed2a48481c9b2f8d3af4930f27514afa584ca647c40e82ed6e39983 | 0.788055 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1605.09085 | paper | corpus_metadata | null | Stochastic Function Norm Regularization of Deep Networks | Deep neural networks have had an enormous impact on image analysis. State-of-the-art training methods, based on weight decay and DropOut, result in impressive performance when a very large training set is available. However, they tend to have large problems overfitting to small data sets. Indeed, the available regulari... | https://arxiv.org/abs/1605.09085 | 2016-05-30T01:49:18Z | 1605.09085 | 10.48550/arxiv.1605.09085 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.48550/arxiv.1605.09085 | 89fe061244e82c3e1f146abadc120c6e2b63f0435a6066feeeedc2e20db498e4 | 0.784683 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:1810.03422 | paper | corpus_metadata | null | MRI Super-Resolution using Multi-Channel Total Variation | This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast. The model recasts the recovery of high resolution images as an inverse problem, in which a forward model simulates the slice-select profile of the MR scanner. The paper... | https://arxiv.org/abs/1810.03422 | 2018-10-08T13:14:28Z | 1810.03422 | 10.1007/978-3-030-00928-1_97 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://arxiv.org/licenses/nonexclusive-distrib/1.0 | doi:10.1007/978-3-030-00928-1_97 | e8c03a9a693be34280df4d456638936c340d9654f011ccb6fd2b025445f850e9 | 0.587411 | scored | title_abstract | true | true | en | 1 | eligible | classifier | false | [] | [] | [] | ml-methods-neural-apps-v1 | paper-ml-tfidf-logreg-v1 | df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0 |
arxiv:2108.03618 | paper | corpus_metadata | null | MPI: Multi-receptive and Parallel Integration for Salient Object Detection | The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection. In this paper, a novel method called MPI is proposed for salient object detec... | https://arxiv.org/abs/2108.03618 | 2021-08-08T12:01:44Z | 2108.03618 | 10.1049/ipr2.12324 | arxiv-complete-snapshot | cee894837962fede5612cccf2a4c7cacf49b4c3a | CC0-1.0 | http://creativecommons.org/licenses/by-nc-sa/4.0 | doi:10.1049/ipr2.12324 | 62fef1c3744eb3075fa7c2649cabc86a489e5afef0b86fffdf4c7dc39a0df515 | 0.740576 | 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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