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c1ff2e24-c307-40d3-9fae-68339268180f | ia-gm-a-deep-bidirectional-learning-method | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/16461 | https://ojs.aaai.org/index.php/AAAI/article/view/16461/16268 | IA-GM: A Deep Bidirectional Learning Method for Graph Matching | Existing deep learning methods for graph matching(GM) problems usually considered affinity learningto assist combinatorial optimization in a feedforward pipeline, and parameter learning is executed by back-propagating the gradients of the matching loss. Such a pipeline pays little attention to the possible complementar... | ['Lei Xu', 'Shikui Tu', 'Kaixuan Zhao'] | 2021-05-18 | null | null | null | aaai-2021-5 | ['graph-matching'] | ['graphs'] | [ 1.75092034e-02 3.71657908e-01 -4.40431803e-01 -5.36870480e-01
-6.48044109e-01 -3.17206502e-01 6.21979535e-01 6.33999825e-01
-5.62752843e-01 4.81310219e-01 1.77595362e-01 1.04937829e-01
-2.62057394e-01 -1.02028453e+00 -9.09861684e-01 -8.28302085e-01
5.19347563e-02 3.60229343e-01 4.04609442e-02 -6.15139157... | [7.19616174697876, 6.256736755371094] |
8425dd6f-d6d2-4893-b88d-8688dbae27f3 | end-to-end-detection-and-re-identification | 1804.00376 | null | http://arxiv.org/abs/1804.00376v1 | http://arxiv.org/pdf/1804.00376v1.pdf | End-to-End Detection and Re-identification Integrated Net for Person Search | This paper proposes a pedestrian detection and re-identification (re-id)
integration net (I-Net) in an end-to-end learning framework. The I-Net is used
in real-world video surveillance scenarios, where the target person needs to be
searched in the whole scene videos, while the annotations of pedestrian
bounding boxes a... | ['Wei Jia', 'Zhenwei He', 'Lei Zhang'] | 2018-04-02 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.84651300e-01 -3.71785849e-01 -8.68262649e-02 -4.09521252e-01
-5.82356155e-01 -2.95784861e-01 4.57686245e-01 -1.12788351e-02
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3.53685260e-01 -6.19468451e-01 -6.71854496e-01 -5.33017039e-01
-1.38987884e-01 2.61609942e-01 6.29622042e-01 1.01644255... | [14.792764663696289, 0.8504324555397034] |
e552c6f6-76f6-4031-a2f4-05fddfa9880b | cross-referencing-self-training-network-for | 2105.13392 | null | https://arxiv.org/abs/2105.13392v1 | https://arxiv.org/pdf/2105.13392v1.pdf | Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures | Sound event detection is an important facet of audio tagging that aims to identify sounds of interest and define both the sound category and time boundaries for each sound event in a continuous recording. With advances in deep neural networks, there has been tremendous improvement in the performance of sound event dete... | ['Mounya Elhilali', 'David K. Han', 'Sangwook Park'] | 2021-05-27 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 3.58679801e-01 -1.61901470e-02 2.37473205e-01 -5.44986010e-01
-1.11811578e+00 -5.35746872e-01 2.95777291e-01 6.18832588e-01
-3.86550874e-01 4.94461656e-01 2.92909145e-01 -7.31154680e-02
-8.38870481e-02 -6.82749987e-01 -3.83622408e-01 -3.44103068e-01
-3.37145060e-01 4.24767584e-02 5.36084235e-01 1.99087694... | [15.219409942626953, 5.16130256652832] |
a467b341-9442-4395-a9de-bb3d22d925ee | tridonet-a-triple-domain-model-driven-network | 2211.07190 | null | https://arxiv.org/abs/2211.07190v1 | https://arxiv.org/pdf/2211.07190v1.pdf | TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction | Recent deep learning-based methods have achieved promising performance for computed tomography metal artifact reduction (CTMAR). However, most of them suffer from two limitations: (i) the domain knowledge is not fully embedded into the network training; (ii) metal artifacts lack effective representation models. The afo... | ['Yanwei Qin', 'Qiusheng Lian', 'Shaolei Zhang', 'Ke Jiang', 'Baoshun Shi'] | 2022-11-14 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 3.69504213e-01 -2.04978332e-01 -5.68182170e-02 -2.25867584e-01
-9.90033984e-01 1.35023475e-01 2.18613461e-01 -3.24453920e-01
-1.28210217e-01 7.33974457e-01 5.53377509e-01 -4.04996499e-02
-2.86025405e-01 -6.83461666e-01 -6.62327886e-01 -8.40652943e-01
9.38251913e-02 9.80634764e-02 4.23683286e-01 5.84539063... | [13.526820182800293, -2.5396037101745605] |
f7122521-3bd4-4144-960c-ce52869bb4d7 | improving-accuracy-and-speeding-up-document | 2006.09141 | null | https://arxiv.org/abs/2006.09141v1 | https://arxiv.org/pdf/2006.09141v1.pdf | Improving accuracy and speeding up Document Image Classification through parallel systems | This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions. We show in the RVL-CDIP dataset that we can improve previous results with a much li... | ['Mateo Valero', 'Jordi Cortada', 'Daniel Garrido', 'Juan Luis Dominguez', 'Jordi Torres', 'Javier Ferrando', 'Raul Garcia', 'David Garcia'] | 2020-06-16 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 4.40823659e-02 6.68678358e-02 9.57142711e-02 -2.57326663e-01
-9.51948240e-02 -6.60823107e-01 8.15847456e-01 4.81320322e-02
-7.42229760e-01 4.43926305e-01 4.17916551e-02 -7.31756628e-01
-1.24192499e-01 -9.09910858e-01 -7.39709496e-01 -3.58043343e-01
2.54722506e-01 4.23815310e-01 -1.11944780e-01 -1.31748840... | [11.446207046508789, 2.6088814735412598] |
9d2f7922-020f-497f-9e8d-8100de4f20d1 | cascaded-continuous-regression-for-real-time | 1608.01137 | null | http://arxiv.org/abs/1608.01137v2 | http://arxiv.org/pdf/1608.01137v2.pdf | Cascaded Continuous Regression for Real-time Incremental Face Tracking | This paper introduces a novel real-time algorithm for facial landmark
tracking. Compared to detection, tracking has both additional challenges and
opportunities. Arguably the most important aspect in this domain is updating a
tracker's models as tracking progresses, also known as incremental (face)
tracking. While this... | ['Enrique Sánchez-Lozano', 'Michel Valstar', 'Georgios Tzimiropoulos', 'Brais Martinez'] | 2016-08-03 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [-1.11451685e-01 -2.19015270e-01 -2.54819721e-01 -1.16878174e-01
-1.01355696e+00 -5.43466270e-01 5.76422572e-01 -6.55219033e-02
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-3.29640031e-01 2.86176980e-01 3.28640968e-01 -7.89146870... | [13.417744636535645, 0.25206828117370605] |
8a9cf26a-c30a-40a5-88fa-6a6a4639ec46 | opinion-based-relational-pivoting-for-cross-1 | null | null | https://aclanthology.org/2022.wassa-1.11 | https://aclanthology.org/2022.wassa-1.11.pdf | Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction | Domain adaptation methods often exploit domain-transferable input features, a.k.a. pivots. The task of Aspect and Opinion Term Extraction presents a special challenge for domain transfer: while opinion terms largely transfer across domains, aspects change drastically from one domain to another (e.g. from restaurants to... | ['Ido Dagan', 'Moshe Wasserblat', 'Vasudev Lal', 'Daniel Korat', 'Oren Pereg', 'Ayal Klein'] | null | null | null | null | wassa-acl-2022-5 | ['term-extraction'] | ['natural-language-processing'] | [ 2.64352739e-01 3.06569457e-01 -5.98660111e-01 -6.71736836e-01
-1.10537195e+00 -1.03968179e+00 1.12108982e+00 4.88159657e-01
-3.57780874e-01 9.32079315e-01 4.56147015e-01 -4.50826973e-01
-2.90909231e-01 -6.82173133e-01 -6.98325217e-01 -3.88323724e-01
-8.45941529e-02 8.36763322e-01 5.22542857e-02 -7.02280104... | [11.334986686706543, 6.872641086578369] |
cd5c82a1-5b94-4070-92b0-bf2a8cf39915 | adversarial-normalization-i-can-visualize | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Choi_Adversarial_Normalization_I_Can_Visualize_Everything_ICE_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Choi_Adversarial_Normalization_I_Can_Visualize_Everything_ICE_CVPR_2023_paper.pdf | Adversarial Normalization: I Can Visualize Everything (ICE) | Vision transformers use [CLS] tokens to predict image classes. Their explainability visualization has been studied using relevant information from [CLS] tokens or focusing on attention scores during self-attention. Such visualization, however, is challenging because of the dependence of the structure of a vision tr... | ['Kyungsik Han', 'Seungwan Jin', 'Hoyoung Choi'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['object-discovery', 'object-localization', 'weakly-supervised-object-localization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.53474951e-01 4.95967776e-01 8.54956731e-02 -2.09906742e-01
-2.17055753e-01 -5.40181935e-01 6.36520803e-01 2.51804888e-02
-1.45190760e-01 3.58803511e-01 2.10083071e-02 -5.60696006e-01
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6.70998469e-02 4.52642202e-01 3.61319929e-01 9.94222164... | [9.916299819946289, 1.8866589069366455] |
1e56a6e6-459e-4836-a734-a31a04897505 | an-empirical-study-on-end-to-end-singing | 2108.03008 | null | https://arxiv.org/abs/2108.03008v1 | https://arxiv.org/pdf/2108.03008v1.pdf | An Empirical Study on End-to-End Singing Voice Synthesis with Encoder-Decoder Architectures | With the rapid development of neural network architectures and speech processing models, singing voice synthesis with neural networks is becoming the cutting-edge technique of digital music production. In this work, in order to explore how to improve the quality and efficiency of singing voice synthesis, in this work, ... | ['Cheng-Hao Cai', 'Jing Sun', 'Yanyan Xu', 'Xudong Liu', 'Yuxing Lu', 'Dengfeng Ke'] | 2021-08-06 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-1.71045721e-01 -1.39406309e-01 -1.62046760e-01 1.50260292e-02
-3.01347554e-01 -4.92724806e-01 -1.29252234e-02 -6.65901601e-01
2.57849216e-01 3.32019150e-01 5.34751892e-01 -1.39745891e-01
2.70992756e-01 -5.07461071e-01 -3.57017130e-01 -2.98355401e-01
8.61124992e-02 -3.25696506e-02 -1.00279510e-01 -4.54217821... | [15.53236198425293, 6.182980537414551] |
7eae277a-df5a-4028-8127-0722c297428a | unsupervised-multilingual-sentence-embeddings-1 | 2105.10419 | null | https://arxiv.org/abs/2105.10419v1 | https://arxiv.org/pdf/2105.10419v1.pdf | Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining | Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages. We propose a novel unsupervised method to derive multilingual sentence embeddings relying only on monolingual data. We first produce a synthetic parallel corpus using unsupervise... | ['Ondřej Bojar', 'Eneko Agirre', 'Gorka Labaka', 'Mikel Artetxe', 'Ivana Kvapilikova'] | 2021-05-21 | unsupervised-multilingual-sentence-embeddings | https://aclanthology.org/2020.acl-srw.34 | https://aclanthology.org/2020.acl-srw.34.pdf | acl-2020-6 | ['unsupervised-machine-translation', 'parallel-corpus-mining'] | ['natural-language-processing', 'natural-language-processing'] | [-1.73467025e-01 -4.57308814e-02 -3.79743695e-01 -4.35711205e-01
-1.35426795e+00 -7.39110112e-01 8.54482353e-01 4.79178876e-01
-9.53307092e-01 9.59406137e-01 4.93610352e-01 -5.83973944e-01
5.24079382e-01 -5.33625543e-01 -8.49290252e-01 -1.48315653e-01
6.57992810e-02 6.26653492e-01 -2.76837349e-01 -5.91694653... | [11.141098022460938, 10.04642391204834] |
c275a4df-a0d5-4530-ab03-6e104ce9eb63 | facescape-3d-facial-dataset-and-benchmark-for | 2111.01082 | null | https://arxiv.org/abs/2111.01082v1 | https://arxiv.org/pdf/2111.01082v1.pdf | FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction | In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 1... | ['Xun Cao', 'Ruigang Yang', 'Qiu Shen', 'Mingkai Huang', 'Yanru Wang', 'Yidi Zhang', 'Longwei Guo', 'Haotian Yang', 'Hao Zhu'] | 2021-11-01 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-2.38357782e-01 1.89551592e-01 1.82505608e-01 -9.01062608e-01
-4.55651760e-01 -3.53723466e-01 3.89730126e-01 -8.39950442e-01
3.85241956e-01 2.67138839e-01 5.70964031e-02 3.97049308e-01
2.13605210e-01 -6.42617643e-01 -8.70062053e-01 -4.66932029e-01
-8.10296908e-02 7.98341274e-01 -2.65928835e-01 -4.05294299... | [13.110299110412598, -0.014931575395166874] |
519afb1b-8ff4-445d-a5a1-86a12673d5cf | deepsolo-let-transformer-decoder-with | 2211.10772 | null | https://arxiv.org/abs/2211.10772v4 | https://arxiv.org/pdf/2211.10772v4.pdf | DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting | End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the s... | ['DaCheng Tao', 'Bo Du', 'Tongliang Liu', 'Juhua Liu', 'Shanshan Zhao', 'Jing Zhang', 'Maoyuan Ye'] | 2022-11-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ye_DeepSolo_Let_Transformer_Decoder_With_Explicit_Points_Solo_for_Text_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ye_DeepSolo_Let_Transformer_Decoder_With_Explicit_Points_Solo_for_Text_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-spotting', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [ 3.53775561e-01 -1.74774572e-01 -2.22487330e-01 -3.37741882e-01
-1.14910376e+00 -5.79289973e-01 4.37185079e-01 1.41663253e-01
-2.94875801e-01 3.04903209e-01 8.11822305e-04 -2.94114053e-01
3.51358503e-01 -6.65596962e-01 -7.88282096e-01 -6.53465271e-01
7.00229764e-01 7.84022689e-01 5.86481988e-01 -2.35419013... | [11.966286659240723, 2.2420661449432373] |
782bd794-5f81-4e15-a71d-179c46d63590 | disentangling-semantics-in-language-throughs | 2012.13031 | null | https://arxiv.org/abs/2012.13031v2 | https://arxiv.org/pdf/2012.13031v2.pdf | Disentangling semantics in language through VAEs and a certain architectural choice | We present an unsupervised method to obtain disentangled representations of sentences that single out semantic content. Using modified Transformers as building blocks, we train a Variational Autoencoder to translate the sentence to a fixed number of hierarchically structured latent variables. We study the influence of ... | ['Djamé Seddah', 'Joseph Le Roux', 'Ghazi Felhi'] | 2020-12-24 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 8.57524425e-02 8.24447930e-01 -2.73457140e-01 -4.01217252e-01
-5.71120441e-01 -9.83555913e-01 8.32135618e-01 -3.78369391e-02
-1.47128180e-01 8.81771863e-01 9.67797101e-01 -2.03859672e-01
1.87746003e-01 -1.14206684e+00 -7.33391941e-01 -6.76359773e-01
1.45988435e-01 7.37094522e-01 -1.00459829e-01 -1.64518312... | [10.855985641479492, 8.956674575805664] |
23c6fd15-a8ce-4d4b-9b3a-7eb71a289849 | restricted-boltzmann-machines-for-galaxy | 1911.06259 | null | https://arxiv.org/abs/1911.06259v2 | https://arxiv.org/pdf/1911.06259v2.pdf | Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer | We present the application of Restricted Boltzmann Machines (RBMs) to the task of astronomical image classification using a quantum annealer built by D-Wave Systems. Morphological analysis of galaxies provides critical information for studying their formation and evolution across cosmic time scales. We compress galaxy ... | ['João Caldeira', 'Steven H. Adachi', 'Brian Nord', 'Joshua Job', 'Gabriel N. Perdue'] | 2019-11-14 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [ 1.99524343e-01 -4.04423982e-01 3.23706180e-01 -2.49426544e-01
-8.25352788e-01 -7.56173909e-01 1.04847312e+00 -1.62030742e-01
-8.36553216e-01 4.70124006e-01 -9.89050344e-02 -6.91345215e-01
-1.32288292e-01 -1.00961900e+00 -3.87359798e-01 -1.21126962e+00
-4.92875576e-02 1.02947640e+00 2.98507631e-01 -1.93071499... | [5.577632427215576, 4.8739495277404785] |
4e03f5a2-13af-4523-a9f5-7bd05f3d75c9 | toward-macro-insights-for-suicide-prevention | null | null | https://aclanthology.org/W14-3213 | https://aclanthology.org/W14-3213.pdf | Toward Macro-Insights for Suicide Prevention: Analyzing Fine-Grained Distress at Scale | null | ['Tong Liu', 'Megan Lytle', 'Vincent Silenzio', 'Christopher Homan', 'Ravdeep Johar', 'Cecilia Ovesdotter Alm'] | 2014-06-01 | null | null | null | ws-2014-6 | ['lexical-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.260907173156738, 3.8529069423675537] |
1af8339c-c731-49d5-94d8-12bbb8147ecd | challenges-and-frontiers-in-abusive-content | null | null | https://aclanthology.org/W19-3509 | https://aclanthology.org/W19-3509.pdf | Challenges and frontiers in abusive content detection | Online abusive content detection is an inherently difficult task. It has received considerable attention from academia, particularly within the computational linguistics community, and performance appears to have improved as the field has matured. However, considerable challenges and unaddressed frontiers remain, spann... | ['Helen Margetts', 'Scott Hale', 'Rebekah Tromble', 'Dong Nguyen', 'Alex Harris', 'Bertie Vidgen'] | 2019-08-01 | null | null | null | ws-2019-8 | ['abuse-detection'] | ['natural-language-processing'] | [ 6.80707172e-02 1.63923576e-01 -4.15723920e-01 -3.18023562e-01
-4.65915293e-01 -7.46197164e-01 1.57280043e-01 7.28467941e-01
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-8.32378119e-03 -2.08819270e-01 -7.54944980e-02 -1.66202322e-01
2.34466955e-01 -1.41310781e-01 6.59236237e-02 -2.73022145... | [8.638519287109375, 10.37199592590332] |
cca6d083-46e7-4e40-ba60-4a5362c01b0c | nodule2vec-a-3d-deep-learning-system-for | 2007.07081 | null | https://arxiv.org/abs/2007.07081v1 | https://arxiv.org/pdf/2007.07081v1.pdf | Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation | Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history. We present a deep learning system that transforms a 3D image of a pulmonary nodule from a CT scan into a ... | ['Ilia Kravets', 'Tal Heletz', 'Hayit Greenspan'] | 2020-07-11 | null | null | null | null | ['content-based-image-retrieval', 'lung-nodule-detection'] | ['computer-vision', 'medical'] | [-2.18689777e-02 3.33716005e-01 -2.10917443e-01 -3.28181326e-01
-1.50232553e+00 -4.84379083e-01 4.41871464e-01 5.30331492e-01
-6.37716293e-01 1.07651643e-01 4.14066732e-01 -2.92976439e-01
-5.76085448e-01 -6.04973435e-01 -3.38109910e-01 -7.93983161e-01
8.59511569e-02 1.11832583e+00 3.70361745e-01 1.83886290... | [14.705159187316895, -1.7364832162857056] |
78a141a3-dc79-4f1b-8352-62b1825f2bfd | short-text-topic-modeling-application-to | 2203.11152 | null | https://arxiv.org/abs/2203.11152v1 | https://arxiv.org/pdf/2203.11152v1.pdf | Short Text Topic Modeling: Application to tweets about Bitcoin | Understanding the semantic of a collection of texts is a challenging task. Topic models are probabilistic models that aims at extracting "topics" from a corpus of documents. This task is particularly difficult when the corpus is composed of short texts, such as posts on social networks. Following several previous resea... | ['Hugo Schnoering'] | 2022-03-17 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-7.22127706e-02 3.60332459e-01 -4.94856536e-01 -4.88131046e-01
-6.49837315e-01 -5.47459006e-01 1.32302129e+00 6.05392992e-01
-3.12410116e-01 6.74329877e-01 6.44180000e-01 -1.53829068e-01
1.61175311e-01 -9.64631915e-01 -4.78307933e-01 -2.37685338e-01
-1.31970063e-01 7.43379474e-01 4.89800781e-01 -2.20665902... | [10.372836112976074, 7.186705589294434] |
f2fc2ce7-c581-48e3-b64f-5d5f8557cfef | phatyp-predicting-the-lifestyle-for | 2206.09693 | null | https://arxiv.org/abs/2206.09693v1 | https://arxiv.org/pdf/2206.09693v1.pdf | PhaTYP: Predicting the lifestyle for bacteriophages using BERT | Bacteriophages (or phages), which infect bacteria, have two distinct lifestyles: virulent and temperate. Predicting the lifestyle of phages helps decipher their interactions with their bacterial hosts, aiding phages' applications in fields such as phage therapy. Because experimental methods for annotating the lifestyle... | ['Yanni Sun', 'Xubo Tang', 'Jiayu Shang'] | 2022-06-20 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 2.08469525e-01 -4.04168993e-01 1.32125691e-01 3.32623683e-02
8.15983787e-02 -8.25060844e-01 2.10052237e-01 4.17545080e-01
-4.21408355e-01 1.05101168e+00 8.37828144e-02 -5.60847342e-01
-3.38257700e-02 -7.20557451e-01 -7.43135273e-01 -1.15746891e+00
-1.87618241e-01 7.10334837e-01 5.80915868e-01 -3.49004753... | [4.822412967681885, 5.352592468261719] |
c3d9644b-a4e8-4886-adfe-72aa5e8f7a67 | learning-a-representation-with-the-block | 1911.10301 | null | https://arxiv.org/abs/1911.10301v1 | https://arxiv.org/pdf/1911.10301v1.pdf | Learning a Representation with the Block-Diagonal Structure for Pattern Classification | Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns R... | ['Zhen-Hua Feng', 'Xiao-Jun Wu', 'He-Feng Yin', 'Josef Kittler'] | 2019-11-23 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 4.51196939e-01 -3.88677359e-01 -1.11813404e-01 -3.09767157e-01
-9.30216014e-01 -5.40209897e-02 2.70290405e-01 -1.40824825e-01
-6.89293221e-02 6.50584579e-01 1.74236372e-01 1.41069323e-01
-2.77510613e-01 -3.78605634e-01 -5.28993547e-01 -1.07139552e+00
7.24570453e-02 -2.72170514e-01 -1.43068403e-01 4.03772332... | [12.44161319732666, 0.4167676270008087] |
83ded141-04d3-40a2-8023-5bf1359560b9 | deep-convolutional-forest-a-dynamic-deep | 2110.15718 | null | https://arxiv.org/abs/2110.15718v3 | https://arxiv.org/pdf/2110.15718v3.pdf | Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text | The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medica... | ['Shawkat K. Guirguis', 'Yasser F. Hassan', 'Mai A. Shaaban'] | 2021-10-10 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-8.33577663e-02 -2.05139473e-01 -5.65766282e-02 -2.53979594e-01
-5.65834753e-02 -3.93285453e-01 6.21650815e-01 3.44967932e-01
-5.09518445e-01 7.41545677e-01 4.34079655e-02 -7.05254674e-01
2.09831893e-01 -1.16644681e+00 -3.26034278e-02 -6.40510619e-01
1.39148414e-01 1.25086457e-01 4.78542268e-01 -4.64911550... | [7.823044300079346, 9.997925758361816] |
bf8a7c39-f3a1-437d-a3cc-3ec801945070 | causal-lifting-and-link-prediction | 2302.01198 | null | https://arxiv.org/abs/2302.01198v1 | https://arxiv.org/pdf/2302.01198v1.pdf | Causal Lifting and Link Prediction | Current state-of-the-art causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph. In some causal tasks, however, link formation is path-dependent, i.e., the outcome of link inte... | ['Bruno Ribeiro', 'Nesreen Ahmed', 'Beatrice Bevilacqua', 'Leonardo Cotta'] | 2023-02-02 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 2.21493974e-01 6.08910203e-01 -1.02586257e+00 -1.71902508e-01
1.38792634e-01 -6.75061285e-01 6.92443728e-01 4.94402379e-01
2.18120053e-01 6.42670691e-01 6.91188514e-01 -8.41460407e-01
-1.03750658e+00 -1.21593297e+00 -1.06476855e+00 -4.29797947e-01
-5.66486061e-01 5.45184255e-01 -1.70636550e-01 -2.98107266... | [7.8410258293151855, 5.485200881958008] |
55ec2d87-00a2-4d9d-8007-4e46d1b6a052 | a-dynamic-feature-interaction-framework-for | 2306.05061 | null | https://arxiv.org/abs/2306.05061v1 | https://arxiv.org/pdf/2306.05061v1.pdf | A Dynamic Feature Interaction Framework for Multi-task Visual Perception | Multi-task visual perception has a wide range of applications in scene understanding such as autonomous driving. In this work, we devise an efficient unified framework to solve multiple common perception tasks, including instance segmentation, semantic segmentation, monocular 3D detection, and depth estimation. Simply ... | ['Yifan Liu', 'Chunhua Shen', 'Yanning Zhang', 'Peng Wang', 'Ning Wang', 'Hao Chen', 'Yuling Xi'] | 2023-06-08 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 3.60744834e-01 -4.97476719e-02 -2.43611202e-01 -5.48377812e-01
-7.22364962e-01 -5.78550339e-01 4.04678285e-01 7.82113075e-02
-5.00971317e-01 3.77888352e-01 -2.32631713e-01 -2.46602327e-01
9.76834912e-03 -5.69857001e-01 -8.01948905e-01 -7.75550961e-01
2.13005364e-01 2.85149217e-01 7.39347816e-01 4.39661182... | [8.320440292358398, -2.0204203128814697] |
0ea88c03-6084-4920-bc52-be175a44580c | action-keypoint-network-for-efficient-video | 2201.06304 | null | https://arxiv.org/abs/2201.06304v1 | https://arxiv.org/pdf/2201.06304v1.pdf | Action Keypoint Network for Efficient Video Recognition | Reducing redundancy is crucial for improving the efficiency of video recognition models. An effective approach is to select informative content from the holistic video, yielding a popular family of dynamic video recognition methods. However, existing dynamic methods focus on either temporal or spatial selection indepen... | ['Yi Yang', 'Yifan Sun', 'Xiaohan Wang', 'Yahong Han', 'Xu Chen'] | 2022-01-17 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 1.00767121e-01 -6.89110518e-01 -4.66837585e-01 -1.92062303e-01
-6.28183544e-01 -3.73581797e-01 5.20294964e-01 -2.08993495e-01
-3.63690436e-01 2.39412993e-01 2.38047034e-01 1.75163761e-01
-2.27435067e-01 -7.54884124e-01 -8.69061470e-01 -9.83148813e-01
4.09111306e-02 8.92653912e-02 6.65483415e-01 1.04355603... | [9.03457260131836, 0.3352111876010895] |
d3a7bd25-6e8f-4ada-9b9d-05e6cc34b164 | about-explicit-variance-minimization-training | 2105.14117 | null | https://arxiv.org/abs/2105.14117v4 | https://arxiv.org/pdf/2105.14117v4.pdf | About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations | Self-supervised learning methods for computer vision have demonstrated the effectiveness of pre-training feature representations, resulting in well-generalizing Deep Neural Networks, even if the annotated data are limited. However, representation learning techniques require a significant amount of time for model traini... | ['Sebastian D. Goodfellow', 'Danny Eytan', 'Dmitrii Shubin'] | 2021-05-28 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 4.99577105e-01 2.10341513e-01 -2.78530866e-01 -4.94209617e-01
-7.59067774e-01 -3.30498576e-01 3.02099645e-01 3.00003141e-01
-6.09642684e-01 6.13676250e-01 -1.44061923e-01 -3.74732822e-01
-2.64149547e-01 -5.60706198e-01 -7.99100161e-01 -9.84331489e-01
-1.39158309e-01 6.25596344e-01 1.48470374e-02 2.07438126... | [14.601045608520508, -2.396942615509033] |
64ff6a0b-9c36-4463-a72c-def4bdc37617 | single-model-attribution-via-final-layer | 2306.06210 | null | https://arxiv.org/abs/2306.06210v2 | https://arxiv.org/pdf/2306.06210v2.pdf | Single-Model Attribution of Generative Models Through Final-Layer Inversion | Recent groundbreaking developments on generative modeling have sparked interest in practical single-model attribution. Such methods predict whether a sample was generated by a specific generator or not, for instance, to prove intellectual property theft. However, previous works are either limited to the closed-world se... | ['Asja Fischer', 'Johannes Lederer', 'Jonas Ricker', 'Mike Laszkiewicz'] | 2023-05-26 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 3.95560771e-01 2.64488131e-01 -4.47649598e-01 -2.35672653e-01
-7.51354933e-01 -5.24349809e-01 7.58101702e-01 -8.70293081e-02
-3.19295451e-02 8.38543832e-01 -4.97287624e-02 -3.88646781e-01
-2.05830500e-01 -6.37645543e-01 -8.71466994e-01 -6.53114498e-01
1.63798794e-01 6.56104445e-01 -5.39873242e-01 3.83365929... | [8.052557945251465, 4.455775260925293] |
91d54406-4e6b-49c6-a929-b24552766ae9 | diacorrect-end-to-end-error-correction-for | 2210.17189 | null | https://arxiv.org/abs/2210.17189v1 | https://arxiv.org/pdf/2210.17189v1.pdf | DiaCorrect: End-to-end error correction for speaker diarization | In recent years, speaker diarization has attracted widespread attention. To achieve better performance, some studies propose to diarize speech in multiple stages. Although these methods might bring additional benefits, most of them are quite complex. Motivated by spelling correction in automatic speech recognition (ASR... | ['Yanhua Long', 'Heng Lu', 'Yuhang Cao', 'Jiangyu Han'] | 2022-10-31 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [-2.64017330e-03 1.07327580e-01 2.56763488e-01 -5.57917178e-01
-1.12796414e+00 -4.39788818e-01 4.01126951e-01 -1.91068277e-01
-4.40726101e-01 2.99361229e-01 4.12529767e-01 -2.66030997e-01
2.14480519e-01 -1.58303782e-01 -4.70697433e-01 -6.43371582e-01
2.16453686e-01 1.44684121e-01 9.14229751e-02 -9.33378786... | [14.714470863342285, 6.226296424865723] |
b24d1a66-afb4-453f-850e-4237659bbcf8 | why-using-either-aggregated-features-or | 2306.08274 | null | https://arxiv.org/abs/2306.08274v1 | https://arxiv.org/pdf/2306.08274v1.pdf | Why Using Either Aggregated Features or Adjacency Lists in Directed or Undirected Graph? Empirical Study and Simple Classification Method | Node classification is one of the hottest tasks in graph analysis. In this paper, we focus on the choices of node representations (aggregated features vs. adjacency lists) and the edge direction of an input graph (directed vs. undirected), which have a large influence on classification results. We address the first emp... | ['Makoto Onizuka', 'Yuya Sasaki', 'Seiji Maekawa'] | 2023-06-14 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 1.40646100e-01 -2.02304885e-01 -5.41367531e-01 -1.18079871e-01
1.02437902e-02 -8.14178765e-01 9.94707763e-01 4.87267464e-01
-1.58899695e-01 4.69195545e-01 7.01970384e-02 -6.71171665e-01
-6.42034173e-01 -1.14581037e+00 -1.39190450e-01 -5.98127127e-01
-4.42455530e-01 3.11156511e-01 2.78545409e-01 -4.27360684... | [6.968825340270996, 6.099739074707031] |
252eeb7f-45e7-445e-a025-d92d457c819a | templates-for-3d-object-pose-estimation | 2203.17234 | null | https://arxiv.org/abs/2203.17234v1 | https://arxiv.org/pdf/2203.17234v1.pdf | Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions | We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these objects nor real images depicting them, only their CAD models. It relies on a small set of training objects to learn local object representatio... | ['Vincent Lepetit', 'Mathieu Salzmann', 'Yang Xiao', 'Yinlin Hu', 'Van Nguyen Nguyen'] | 2022-03-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Nguyen_Templates_for_3D_Object_Pose_Estimation_Revisited_Generalization_to_New_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Nguyen_Templates_for_3D_Object_Pose_Estimation_Revisited_Generalization_to_New_CVPR_2022_paper.pdf | cvpr-2022-1 | ['template-matching', '6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.76765579e-01 -3.17073194e-03 -5.68535067e-02 -5.33313096e-01
-8.27637792e-01 -8.08618844e-01 6.06862605e-01 -2.68233925e-01
-1.50414005e-01 3.45274448e-01 -3.54991615e-01 1.08153142e-01
-6.50584698e-02 -6.94272876e-01 -9.57284927e-01 -5.22302747e-01
1.65533677e-01 9.60878491e-01 6.46261215e-01 -1.52743712... | [7.578765392303467, -2.64186429977417] |
7af42749-1be4-4b75-9ae8-87373ec67381 | easy-adaptation-to-mitigate-gender-bias-in | 2204.05459 | null | https://arxiv.org/abs/2204.05459v1 | https://arxiv.org/pdf/2204.05459v1.pdf | Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification | Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined. In this work, we treat the gender as domains (e.g., male vs. female) and present a standard domain adaptation model to reduce the gender bias and improve performance of text classifiers und... | ['Xiaolei Huang'] | 2022-04-12 | null | https://aclanthology.org/2022.naacl-main.52 | https://aclanthology.org/2022.naacl-main.52.pdf | naacl-2022-7 | ['multilingual-text-classification'] | ['miscellaneous'] | [-2.85733283e-01 -1.36875406e-01 -4.64682549e-01 -6.45230830e-01
-7.36609876e-01 -6.33047819e-01 8.46096098e-01 2.32589647e-01
-7.76233017e-01 9.50327516e-01 5.29496968e-01 -4.19858158e-01
6.46802068e-01 -4.10995036e-01 -3.91700596e-01 -2.96821713e-01
5.85299253e-01 3.69635552e-01 1.95734948e-02 -3.33705485... | [9.080235481262207, 10.471344947814941] |
d252a221-01a1-40ac-8e3a-682197ae7438 | guided-filter-based-edge-preserving-image-non | 1609.01839 | null | http://arxiv.org/abs/1609.01839v1 | http://arxiv.org/pdf/1609.01839v1.pdf | Guided Filter based Edge-preserving Image Non-blind Deconvolution | In this work, we propose a new approach for efficient edge-preserving image
deconvolution. Our algorithm is based on a novel type of explicit image filter
- guided filter. The guided filter can be used as an edge-preserving smoothing
operator like the popular bilateral filter, but has better behaviors near
edges. We pr... | ['He-Yan Huang', 'Hang Yang', 'Ming Zhu', 'Zhongbo Zhang'] | 2016-09-07 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 1.85385287e-01 -3.42617542e-01 5.08739889e-01 -1.45205125e-01
-2.85973489e-01 -3.87023687e-01 2.62442201e-01 -4.56372976e-01
-6.56233549e-01 6.16324365e-01 4.90023673e-01 -2.55310237e-02
-1.21586874e-01 -7.15476036e-01 -4.68152642e-01 -9.99404073e-01
3.27358902e-01 -1.12812474e-01 5.73474705e-01 -1.29030511... | [11.56205940246582, -2.6783578395843506] |
643adc8e-c8bb-4120-8094-495c886550f2 | go-explore-a-new-approach-for-hard | 1901.10995 | null | https://arxiv.org/abs/1901.10995v4 | https://arxiv.org/pdf/1901.10995v4.pdf | Go-Explore: a New Approach for Hard-Exploration Problems | A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, whic... | ['Kenneth O. Stanley', 'Joel Lehman', 'Adrien Ecoffet', 'Joost Huizinga', 'Jeff Clune'] | 2019-01-30 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-1.52377337e-01 4.48025674e-01 -4.32701856e-01 2.44003296e-01
-7.69936442e-01 -9.14040387e-01 7.57972240e-01 -4.48497593e-01
-7.51246333e-01 1.35480654e+00 -7.59364441e-02 -4.75983977e-01
-2.80708641e-01 -4.93557423e-01 -9.16363180e-01 -6.50090575e-01
-5.09214759e-01 6.06764853e-01 -3.32181789e-02 -6.82674348... | [4.009409427642822, 1.5951828956604004] |
3fa0c009-77d1-474c-9ecc-e3a44ca67544 | dual-adaptive-representation-alignment-for | 2306.10511 | null | https://arxiv.org/abs/2306.10511v1 | https://arxiv.org/pdf/2306.10511v1.pdf | Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning | Few-shot learning aims to recognize novel queries with limited support samples by learning from base knowledge. Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains, which are usually infeasible for realistic applications. Toward this issue, we prop... | ['Yonghong Tian', 'Jia Li', 'Tong Zhang', 'Yifan Zhao'] | 2023-06-18 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning', 'meta-learning', 'few-shot-learning'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 6.60541356e-02 -3.04410487e-01 -6.44111097e-01 -6.42067373e-01
-1.15041053e+00 -3.16732198e-01 6.30828977e-01 1.33649901e-01
-3.39751840e-01 7.77565300e-01 -1.77659497e-01 3.30403715e-01
-4.67862010e-01 -9.90492761e-01 -7.83907413e-01 -5.99288225e-01
8.28186199e-02 6.44404233e-01 5.41827381e-01 -3.61909986... | [10.038135528564453, 3.0571975708007812] |
bcb45e6e-4f10-44ae-9ba2-13aeb5c0b641 | consistency-of-spectral-clustering-for | 2109.10319 | null | https://arxiv.org/abs/2109.10319v4 | https://arxiv.org/pdf/2109.10319v4.pdf | Community detection for weighted bipartite networks | The bipartite network appears in various areas, such as biology, sociology, physiology, and computer science. \cite{rohe2016co} proposed Stochastic co-Blockmodel (ScBM) as a tool for detecting community structure of binary bipartite graph data in network studies. However, ScBM completely ignores edge weight and is unab... | ['Jingli Wang', 'Huan Qing'] | 2021-09-21 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.31519184e-01 3.50222766e-01 -2.41407588e-01 2.13964097e-02
3.68900329e-01 -6.26718223e-01 1.58221796e-01 6.46762401e-02
7.94264898e-02 8.44289184e-01 -1.19010046e-01 -6.79980218e-01
-7.36090839e-01 -9.43185270e-01 -4.50574040e-01 -7.19472647e-01
-5.01622558e-01 2.87394881e-01 5.02088010e-01 -1.62409797... | [6.922760486602783, 5.187610626220703] |
fee9332c-5783-49b3-9dde-aa347ff51e8f | distributed-representations-of-atoms-and | 2107.14664 | null | https://arxiv.org/abs/2107.14664v1 | https://arxiv.org/pdf/2107.14664v1.pdf | Distributed Representations of Atoms and Materials for Machine Learning | The use of machine learning is becoming increasingly common in computational materials science. To build effective models of the chemistry of materials, useful machine-based representations of atoms and their compounds are required. We derive distributed representations of compounds from their chemical formulas only, v... | ['Keith T. Butler', 'Ricardo Grau-Crespo', 'Luis M. Antunes'] | 2021-07-30 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 1.63646743e-01 -3.85779887e-01 -4.44092453e-01 -1.46414638e-01
-8.11480045e-01 -4.09949183e-01 7.92548537e-01 7.57276893e-01
-2.44140565e-01 1.22403061e+00 2.23645791e-01 -2.97395408e-01
3.36234719e-02 -1.27972984e+00 -9.09797370e-01 -1.27047896e+00
-3.52044106e-02 6.03994012e-01 2.20991626e-01 -1.37518138... | [5.183877944946289, 5.509886741638184] |
3a0bb82f-38b2-4932-8a89-a0df683bcabe | deep-rotation-equivariant-network | 1705.08623 | null | http://arxiv.org/abs/1705.08623v2 | http://arxiv.org/pdf/1705.08623v2.pdf | Deep Rotation Equivariant Network | Recently, learning equivariant representations has attracted considerable
research attention. Dieleman et al. introduce four operations which can be
inserted into convolutional neural network to learn deep representations
equivariant to rotation. However, feature maps should be copied and rotated
four times in each lay... | ['Haifeng Liu', 'Junying Li', 'Deng Cai', 'Zichen Yang'] | 2017-05-24 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [-1.49238333e-01 6.57444820e-02 4.66141440e-02 -5.15687346e-01
-1.54053316e-01 -6.26957953e-01 7.02127516e-01 -5.18182158e-01
-7.22264171e-01 3.34447056e-01 2.15051651e-01 -3.47791702e-01
2.42581993e-01 -8.49348366e-01 -8.82479489e-01 -4.39985096e-01
2.02509865e-01 -9.09061059e-02 2.95200586e-01 -3.78710717... | [8.943256378173828, 2.3149805068969727] |
aebbfdd4-9c54-4006-b9ad-bd04bc2b11cb | a-corpus-to-learn-refer-to-as-relations-for | null | null | https://aclanthology.org/L18-1062 | https://aclanthology.org/L18-1062.pdf | A Corpus to Learn Refer-to-as Relations for Nominals | null | ['Kai-Wei Chang', 'Wasi Ahmad'] | 2018-05-01 | a-corpus-to-learn-refer-to-as-relations-for-1 | https://aclanthology.org/L18-1062 | https://aclanthology.org/L18-1062.pdf | lrec-2018-5 | ['learning-semantic-representations'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.436799049377441, 3.6749534606933594] |
81637978-a9db-4d5d-bee2-aff6c89a7f77 | an-empirical-study-of-training-self | 2104.02057 | null | https://arxiv.org/abs/2104.02057v4 | https://arxiv.org/pdf/2104.02057v4.pdf | An Empirical Study of Training Self-Supervised Vision Transformers | This paper does not describe a novel method. Instead, it studies a straightforward, incremental, yet must-know baseline given the recent progress in computer vision: self-supervised learning for Vision Transformers (ViT). While the training recipes for standard convolutional networks have been highly mature and robust,... | ['Kaiming He', 'Saining Xie', 'Xinlei Chen'] | 2021-04-05 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_An_Empirical_Study_of_Training_Self-Supervised_Vision_Transformers_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_An_Empirical_Study_of_Training_Self-Supervised_Vision_Transformers_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 2.46994808e-01 8.47470164e-02 -2.24396974e-01 -4.90290940e-01
-4.22594547e-01 -7.01066315e-01 8.18301141e-01 -2.72353649e-01
-3.44248831e-01 5.14410496e-01 6.07930981e-02 -2.27031946e-01
-5.56621738e-02 -2.59349495e-01 -6.87653184e-01 -8.68663907e-01
-4.37538736e-02 1.43209606e-01 5.24380207e-01 -4.14266974... | [9.660088539123535, 2.1822853088378906] |
531ef108-8941-433e-ba23-df0dc76ca2fd | hybrid-symbiotic-organisms-search-feedforward | 1906.10121 | null | https://arxiv.org/abs/1906.10121v3 | https://arxiv.org/pdf/1906.10121v3.pdf | Metaheuristics optimized feedforward neural networks for efficient stock price prediction | The prediction of stock prices is an important task in economics, investment and making financial decisions. This has, for decades, spurred the interest of many researchers to make focused contributions to the design of accurate stock price predictive models; of which some have been utilized to predict the next day ope... | ['Bradley J. Pillay', 'Absalom E. Ezugwu'] | 2019-06-23 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-3.04973423e-01 -6.08235061e-01 -1.24301098e-01 8.72033685e-02
3.61401170e-01 -3.65671247e-01 5.43037534e-01 -7.88419098e-02
-3.69586676e-01 1.07253563e+00 -2.59046406e-01 -3.37148666e-01
-7.33195603e-01 -1.08036554e+00 -2.75117487e-01 -1.10856128e+00
-4.17787582e-01 6.08108938e-01 2.97515579e-02 -5.56410432... | [4.810681343078613, 4.015768527984619] |
3a0ff4bc-e7f8-4fa6-83aa-924c7484c010 | scalenet-guiding-object-proposal-generation | 1704.06752 | null | http://arxiv.org/abs/1704.06752v1 | http://arxiv.org/pdf/1704.06752v1.pdf | ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond | Motivated by product detection in supermarkets, this paper studies the
problem of object proposal generation in supermarket images and other natural
images. We argue that estimation of object scales in images is helpful for
generating object proposals, especially for supermarket images where object
scales are usually w... | ['Wei Shen', 'Weichao Qiu', 'Alan Yuille', 'Siyuan Qiao', 'Chenxi Liu'] | 2017-04-22 | scalenet-guiding-object-proposal-generation-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Qiao_ScaleNet_Guiding_Object_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Qiao_ScaleNet_Guiding_Object_ICCV_2017_paper.pdf | iccv-2017-10 | ['object-proposal-generation'] | ['computer-vision'] | [-3.17662060e-02 1.57435000e-01 -1.28005460e-01 -4.11952317e-01
-5.77052474e-01 -4.75074083e-01 4.57228422e-01 1.79776624e-01
-3.54461670e-01 3.40261638e-01 -3.35578889e-01 2.16873363e-01
7.13084787e-02 -9.81895983e-01 -9.22672153e-01 -3.88198853e-01
-2.08951727e-01 6.63722217e-01 1.07906938e+00 -4.43612665... | [9.192612648010254, 0.8143323063850403] |
9506af0e-33df-4edf-810a-324341107934 | hopeedi-a-multilingual-hope-speech-detection | null | null | https://aclanthology.org/2020.peoples-1.5 | https://aclanthology.org/2020.peoples-1.5.pdf | HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion | Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this form of censorship to obstruct the democratic right of freedom of speech. Therefore, it is imperative that research should take a positiv... | ['Bharathi Raja Chakravarthi'] | 2020-12-01 | null | null | null | null | ['hope-speech-detection-for-tamil', 'hope-speech-detection-for-malayalam', 'hope-speech-detection', 'hope-speech-detection-for-english'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.91200063e-01 5.86053491e-01 -4.43204969e-01 -3.35544407e-01
-5.89077771e-01 -8.95794928e-01 1.09631288e+00 2.00696081e-01
-6.20117188e-01 8.32789302e-01 1.08869565e+00 -4.55072016e-01
7.65103623e-02 -3.20325881e-01 -2.39862561e-01 -2.24752977e-01
5.47075987e-01 2.92985104e-02 -3.77036817e-02 -6.06810868... | [8.891242027282715, 10.581218719482422] |
6b9c3f6a-edb9-4647-9623-8a577204146b | amritacen-at-semeval-2016-task-11-complex | null | null | https://aclanthology.org/S16-1159 | https://aclanthology.org/S16-1159.pdf | AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding | null | ['Soman K. P', '', 'Sanjay S.P', 'An Kumar M'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.406706809997559, 3.7817747592926025] |
6841aee4-6716-4985-a6f7-1c1ba2689c01 | who-are-you-referring-to-weakly-supervised | 2211.14563 | null | https://arxiv.org/abs/2211.14563v2 | https://arxiv.org/pdf/2211.14563v2.pdf | Who are you referring to? Coreference resolution in image narrations | Coreference resolution aims to identify words and phrases which refer to same entity in a text, a core task in natural language processing. In this paper, we extend this task to resolving coreferences in long-form narrations of visual scenes. First we introduce a new dataset with annotated coreference chains and their ... | ['Hakan Bilen', 'Frank Keller', 'Basura Fernando', 'Arushi Goel'] | 2022-11-26 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 5.73264420e-01 4.87677395e-01 -5.34250259e-01 -5.61055183e-01
-1.21414971e+00 -8.79237771e-01 8.08512688e-01 5.90937361e-02
-5.43242157e-01 6.98106587e-01 9.75786269e-01 7.50530064e-02
4.42100614e-02 -2.41186857e-01 -7.09926784e-01 -5.32903016e-01
2.21942663e-01 8.26370120e-01 2.47879028e-01 -2.98298448... | [9.291836738586426, 9.507184028625488] |
071246cb-e41a-40a4-b303-c147742e401a | sievenet-a-unified-framework-for-robust-image | 2001.06265 | null | https://arxiv.org/abs/2001.06265v1 | https://arxiv.org/pdf/2001.06265v1.pdf | SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On | Image-based virtual try-on for fashion has gained considerable attention recently. The task requires trying on a clothing item on a target model image. An efficient framework for this is composed of two stages: (1) warping (transforming) the try-on cloth to align with the pose and shape of the target model, and (2) a t... | ['Kumar Ayush', 'Abhijeet Kumar', 'Balaji Krishnamurthy', 'Surgan Jandial', 'Mayur Hemani', 'Ayush Chopra'] | 2020-01-17 | null | null | null | null | ['geometric-matching'] | ['computer-vision'] | [ 6.12867594e-01 -2.35060453e-02 1.03659347e-01 -4.16875422e-01
-8.65667939e-01 -6.08979881e-01 2.65162706e-01 -2.88109541e-01
5.06103039e-03 1.42626375e-01 -1.29454225e-01 4.57129516e-02
2.49175578e-01 -7.44966388e-01 -1.10217166e+00 -5.51726878e-01
5.14659643e-01 4.80664432e-01 4.87002283e-01 -3.44042063... | [11.93808364868164, -0.882952094078064] |
84beab00-0a1c-43a1-a2f8-a393c2d239e2 | ielm-an-open-information-extraction-benchmark | 2210.14128 | null | https://arxiv.org/abs/2210.14128v1 | https://arxiv.org/pdf/2210.14128v1.pdf | IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models | We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, may store linguistic and relational knowledge. In particular, LMs are able to answer ``fill-in-the-blank'' questions when given a pre-defined ... | ['Dawn Song', 'Xiao Liu', 'Chenguang Wang'] | 2022-10-25 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [-1.98557362e-01 9.06569123e-01 -5.38808048e-01 -7.95873180e-02
-8.68315339e-01 -4.40527380e-01 8.61472666e-01 4.37077075e-01
-6.36589885e-01 8.24929178e-01 1.30834833e-01 -4.05915052e-01
-4.60844755e-01 -1.08192670e+00 -9.27672744e-01 1.69328123e-01
-1.17014222e-01 9.23356712e-01 4.72803652e-01 -5.55576265... | [9.65295696258545, 8.550941467285156] |
53f301f3-efdf-46ac-a79a-8291857bfb94 | improving-language-plasticity-via-pretraining | 2307.01163 | null | https://arxiv.org/abs/2307.01163v2 | https://arxiv.org/pdf/2307.01163v2.pdf | Improving Language Plasticity via Pretraining with Active Forgetting | Pretrained language models (PLMs) are today the primary model for natural language processing. Despite their impressive downstream performance, it can be difficult to apply PLMs to new languages, a barrier to making their capabilities universally accessible. While prior work has shown it possible to address this issue ... | ['Mikel Artetxe', 'Pontus Stenetorp', 'Sebastian Riedel', 'David Ifeoluwa Adelani', 'Roberta Raileanu', 'Kelly Marchisio', 'Yihong Chen'] | 2023-07-03 | null | null | null | null | ['meta-learning'] | ['methodology'] | [-4.68662046e-02 3.88345532e-02 -2.37059563e-01 -4.40761536e-01
-3.65582526e-01 -6.36025250e-01 7.18054473e-01 4.01366323e-01
-1.33223796e+00 5.52777171e-01 3.71749550e-01 -4.66493756e-01
3.58780861e-01 -7.57664084e-01 -7.93109119e-01 -2.56076366e-01
-7.28541687e-02 5.72292566e-01 4.36244190e-01 -4.12493140... | [10.59633731842041, 8.5772123336792] |
20d02ba6-dc78-4306-966b-2a15d53fbdd8 | exposing-flaws-of-generative-model-evaluation | 2306.04675 | null | https://arxiv.org/abs/2306.04675v1 | https://arxiv.org/pdf/2306.04675v1.pdf | Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models | We systematically study a wide variety of image-based generative models spanning semantically-diverse datasets to understand and improve the feature extractors and metrics used to evaluate them. Using best practices in psychophysics, we measure human perception of image realism for generated samples by conducting the l... | ['Gabriel Loaiza-Ganem', 'J. Eric T. Taylor', 'Anthony L. Caterini', 'Zhaoyan Liu', 'Valentin Villecroze', 'Brendan Leigh Ross', 'Yi Sui', 'Rasa Hosseinzadeh', 'Jesse C. Cresswell', 'George Stein'] | 2023-06-07 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 5.61723858e-02 -4.67352644e-02 2.47966334e-01 -5.44821441e-01
-6.02391720e-01 -6.72936380e-01 9.01010394e-01 -8.14969987e-02
-5.09215355e-01 5.98271668e-01 4.11101490e-01 -1.81286409e-01
-1.91319734e-01 -6.94183826e-01 -7.48752296e-01 -5.87315261e-01
6.05424345e-02 3.86158884e-01 -1.39416918e-01 -1.15838371... | [10.33255386352539, 2.105342149734497] |
85e2e5d8-f65c-46f0-be69-14697df33fa1 | tensor-completion-via-tensor-networks-with-a | 2010.15819 | null | https://arxiv.org/abs/2010.15819v1 | https://arxiv.org/pdf/2010.15819v1.pdf | Tensor Completion via Tensor Networks with a Tucker Wrapper | In recent years, low-rank tensor completion (LRTC) has received considerable attention due to its applications in image/video inpainting, hyperspectral data recovery, etc. With different notions of tensor rank (e.g., CP, Tucker, tensor train/ring, etc.), various optimization based numerical methods are proposed to LRTC... | ['Ping Li', 'Yunfeng Cai'] | 2020-10-29 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 2.49252155e-01 -3.21532995e-01 2.70472020e-02 1.79350480e-01
-5.68895757e-01 -4.60212708e-01 1.54108152e-01 -2.48449877e-01
-3.26829970e-01 6.37235999e-01 5.71906157e-02 -2.87990242e-01
-6.38212144e-01 -1.86401114e-01 -6.82894051e-01 -1.06408668e+00
-1.13340162e-01 1.59911841e-01 -3.44603896e-01 -2.23032832... | [7.3836846351623535, 4.460415363311768] |
8f3a8d60-bfcd-4bf9-8dca-2f3b80c28b95 | magnification-independent-histopathological | 2107.01063 | null | https://arxiv.org/abs/2107.01063v2 | https://arxiv.org/pdf/2107.01063v2.pdf | Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings | The classification of histopathological images is of great value in both cancer diagnosis and pathological studies. However, multiple reasons, such as variations caused by magnification factors and class imbalance, make it a challenging task where conventional methods that learn from image-label datasets perform unsati... | ['Qianni Zhang', 'Huiyu Zhou', 'Yaqi Wang', 'Xingru Huang', 'Yibao Sun'] | 2021-07-02 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 3.32182080e-01 2.87721828e-02 -2.84360588e-01 -4.79978651e-01
-6.68978155e-01 -2.41589949e-01 4.70064312e-01 7.97812343e-01
-7.32283235e-01 3.56672674e-01 -9.19837505e-02 -1.01836696e-01
-4.40839201e-01 -6.67707086e-01 -6.28116667e-01 -1.20962930e+00
-8.08296949e-02 2.30790496e-01 -5.33520579e-02 -7.48771206... | [15.03271198272705, -2.683673620223999] |
c3a31e87-9458-4698-b752-0eabd7f41e65 | from-visual-to-acoustic-question-answering | 1902.11280 | null | http://arxiv.org/abs/1902.11280v1 | http://arxiv.org/pdf/1902.11280v1.pdf | From Visual to Acoustic Question Answering | We introduce the new task of Acoustic Question Answering (AQA) to promote
research in acoustic reasoning. The AQA task consists of analyzing an acoustic
scene composed by a combination of elementary sounds and answering questions
that relate the position and properties of these sounds. The kind of relational
questions ... | ['Jerome Abdelnour', 'Jean Rouat', 'Giampiero Salvi'] | 2019-02-28 | null | null | null | null | ['acoustic-question-answering'] | ['speech'] | [ 4.74350631e-01 2.03165457e-01 1.20130742e+00 -4.43269670e-01
-1.00438106e+00 -6.06860399e-01 5.73677838e-01 2.19622836e-01
-1.47418067e-01 8.08495879e-02 1.92928582e-01 -5.44467330e-01
-4.50338930e-01 -9.49788749e-01 -5.88127077e-01 -2.82196671e-01
2.96168290e-02 5.77057898e-01 9.56235766e-01 -5.30484438... | [15.296625137329102, 5.17949104309082] |
f4b29bb9-2f09-4248-8c35-23dc9ebc0462 | detection-of-rem-sleep-behaviour-disorder-by | 1811.04662 | null | http://arxiv.org/abs/1811.04662v1 | http://arxiv.org/pdf/1811.04662v1.pdf | Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis | Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is
an early predictor of Parkinson's disease. This study proposes a
fully-automated framework for RBD detection consisting of automated sleep
staging followed by RBD identification. Analysis was assessed using a limited
polysomnography montage fr... | ['Mkael Symmonds', 'Navin Cooray', 'Fernando Andreotti', 'Michele T. M. Hu', 'Christine Lo', 'Maarten De Vos'] | 2018-11-12 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 2.86903292e-01 -2.70168573e-01 -3.94318521e-01 -2.92113364e-01
-6.58357739e-01 -2.54183680e-01 2.01998115e-01 -1.41143173e-01
-8.33494604e-01 9.48422730e-01 4.57943976e-01 -3.03687513e-01
-3.36656332e-01 2.62885150e-02 5.63853264e-01 -6.14572346e-01
-3.28103483e-01 4.83791292e-01 1.22332700e-01 6.08195141... | [13.547517776489258, 3.390143394470215] |
6fcfbb64-2f3b-426d-b2d9-f1c81e861e1b | mydigitalfootprint-an-extensive-context | 2306.15990 | null | https://arxiv.org/abs/2306.15990v1 | https://arxiv.org/pdf/2306.15990v1.pdf | MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edge | The widespread diffusion of connected smart devices has contributed to the rapid expansion and evolution of the Internet at its edge. Personal mobile devices interact with other smart objects in their surroundings, adapting behavior based on rapidly changing user context. The ability of mobile devices to process this d... | ['Franca Delmastro', 'Mattia Giovanni Campana'] | 2023-06-28 | null | null | null | null | ['activity-recognition', 'link-prediction', 'edge-computing'] | ['computer-vision', 'graphs', 'time-series'] | [ 3.84233564e-01 -4.75652069e-01 -5.90356588e-01 -3.23474050e-01
-3.78894478e-01 -5.03260672e-01 5.49046099e-01 1.88722491e-01
-1.69077232e-01 5.93808651e-01 6.03974044e-01 -4.44140196e-01
-2.81809390e-01 -9.17821646e-01 -2.82553643e-01 -3.29696655e-01
-1.18547946e-01 -5.53983562e-02 2.32091889e-01 -2.60614574... | [7.475320339202881, 1.2474594116210938] |
b5265bac-45ab-404c-926c-b0be3c2f3329 | finegan-unsupervised-hierarchical | 1811.11155 | null | http://arxiv.org/abs/1811.11155v2 | http://arxiv.org/pdf/1811.11155v2.pdf | FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery | We propose FineGAN, a novel unsupervised GAN framework, which disentangles
the background, object shape, and object appearance to hierarchically generate
images of fine-grained object categories. To disentangle the factors without
supervision, our key idea is to use information theory to associate each factor
to a late... | ['Yong Jae Lee', 'Krishna Kumar Singh', 'Utkarsh Ojha'] | 2018-11-27 | finegan-unsupervised-hierarchical-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Singh_FineGAN_Unsupervised_Hierarchical_Disentanglement_for_Fine-Grained_Object_Generation_and_Discovery_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Singh_FineGAN_Unsupervised_Hierarchical_Disentanglement_for_Fine-Grained_Object_Generation_and_Discovery_CVPR_2019_paper.pdf | cvpr-2019-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 1.77110985e-01 1.44550696e-01 -9.87499058e-02 -3.95189226e-01
-4.34898406e-01 -8.69087994e-01 7.90190816e-01 -4.20886308e-01
3.75165910e-01 4.46812302e-01 3.55715811e-01 7.19893053e-02
2.39787996e-02 -8.40919137e-01 -7.74459839e-01 -6.34052992e-01
1.71412438e-01 7.39656031e-01 -1.31311342e-01 1.18967809... | [11.668497085571289, -0.3561124801635742] |
05c66244-6cc8-4c17-ab5c-8468ebdbb3e7 | 3d-reconstruction-of-multiple-objects-by | 2211.02150 | null | https://arxiv.org/abs/2211.02150v1 | https://arxiv.org/pdf/2211.02150v1.pdf | 3D Reconstruction of Multiple Objects by mmWave Radar on UAV | In this paper, we explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space. The UAV hovers at various locations in the space, and its onboard radar senor collects raw radar data via scanning the space with Synthetic Aperture Radar (SAR) op... | ['Xiaohui Liang', 'Honggang Zhang', 'Zhuoming Huang', 'Yue Sun'] | 2022-11-03 | null | null | null | null | ['point-cloud-reconstruction', '3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.00413215e-01 -1.35880768e-01 5.93559921e-01 -2.05255076e-01
-6.56052589e-01 -5.55199683e-01 3.77453804e-01 -5.43958247e-01
-1.02300934e-01 3.63012969e-01 -2.27813706e-01 -3.79114449e-01
-4.66658711e-01 -9.86566484e-01 -6.35274947e-01 -5.60922027e-01
-1.52411968e-01 7.92641878e-01 2.28276495e-02 -2.52754778... | [9.799431800842285, -2.2252862453460693] |
9ef50cac-4173-462f-8fac-94566a45f370 | hierarchical-interactive-reconstruction | 2304.07473 | null | https://arxiv.org/abs/2304.07473v1 | https://arxiv.org/pdf/2304.07473v1.pdf | Hierarchical Interactive Reconstruction Network For Video Compressive Sensing | Deep network-based image and video Compressive Sensing(CS) has attracted increasing attentions in recent years. However, in the existing deep network-based CS methods, a simple stacked convolutional network is usually adopted, which not only weakens the perception of rich contextual prior knowledge, but also limits the... | ['Feng Jiang', 'Chen Hui', 'Wenxue Cui', 'Tong Zhang'] | 2023-04-15 | null | null | null | null | ['video-compressive-sensing', 'compressive-sensing'] | ['computer-vision', 'computer-vision'] | [ 3.84906754e-02 -4.58210111e-01 -4.96801026e-02 -1.05814651e-01
-3.15723330e-01 1.41016999e-03 2.66253620e-01 -2.77538240e-01
-2.09465191e-01 2.91065156e-01 5.40142715e-01 4.59317155e-02
-2.73325801e-01 -6.00064456e-01 -6.74928129e-01 -9.38421130e-01
-1.14768885e-01 -5.59008360e-01 6.54030859e-01 -1.38653502... | [11.116991996765137, -1.8912874460220337] |
392f59d3-72ae-447c-ba38-a5441997dca1 | alphastock-a-buying-winners-and-selling | 1908.02646 | null | https://arxiv.org/abs/1908.02646v1 | https://arxiv.org/pdf/1908.02646v1.pdf | AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks | Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT). Despite great research efforts devoted to leveraging deep learning (DL) methods for building better QT strategies, existing studies still face serious challen... | ['Yang Zhang', 'Ke Tang', 'Junjie Wu', 'Jingyuan Wang', 'Zhang Xiong'] | 2019-07-24 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-9.21655536e-01 -1.13664746e-01 -2.81210423e-01 -1.92569986e-01
-2.88404197e-01 -4.52328116e-01 4.36687350e-01 -2.24920303e-01
-2.97325701e-01 8.62987339e-01 1.39989227e-01 -4.91342038e-01
-3.79222125e-01 -1.08899009e+00 -4.20741618e-01 -4.85530943e-01
-1.33140236e-01 6.17598355e-01 -4.63309325e-02 -5.89427531... | [4.462952613830566, 4.031271934509277] |
39c58029-50e8-47db-8fe2-7e6c86254b96 | understand-customer-behavior-and-complaints | null | null | http://web.tecnico.ulisboa.pt/~mcasquilho/CD_Casquilho/PRINT/qp0103goodman.pdf | http://web.tecnico.ulisboa.pt/~mcasquilho/CD_Casquilho/PRINT/qp0103goodman.pdf | Understand Customer Behavior And Complaints Eight areas of quantifiable data can be integrated into quality assurance decisions | USTOMER COMPLAINTS PROVIDE
valuable quality assurance, service and
marketing data. But the challenge is to
use the data to make decisions that
result in substantive action.
To use complaint data to solve problems in design, marketing, installation, distribution
and after sale use and maintenance, you
should have... | ['Steve Newman', 'John Goodman'] | 2023-01-01 | null | null | null | qual-ity-progress-2023-1 | ['marketing'] | ['miscellaneous'] | [ 2.42493048e-01 -2.15279952e-01 -4.35804337e-01 -5.69745481e-01
-9.37034070e-01 -7.72120774e-01 -1.71543851e-01 9.51268077e-01
-3.81446272e-01 6.00342095e-01 4.64868486e-01 -1.01633239e+00
-5.16875386e-01 -8.04216385e-01 -2.06386015e-01 -2.68517256e-01
6.92820370e-01 4.18856263e-01 -2.32993156e-01 -3.20673436... | [9.259124755859375, 5.895352363586426] |
ea7a3af7-d7f6-4fec-8de1-f988f950b4fa | uthealth-at-semeval-2016-task-12-an-end-to | null | null | https://aclanthology.org/S16-1201 | https://aclanthology.org/S16-1201.pdf | UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes | null | ['Sungrim Moon', 'Hee-Jin Lee', 'Yaoyun Zhang', 'Jun Xu', 'Hua Xu', 'Yonghui Wu', 'Jingqi Wang'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.154355525970459, 3.720409631729126] |
06b5c400-198f-47c3-b07d-7b13247513c4 | document-classification-with-word-sense | null | null | https://openreview.net/forum?id=FnXMKuW3fx | https://openreview.net/pdf?id=FnXMKuW3fx | Document Classification with Word Sense Knowledge | The performance of Word Sense Disambiguation (WSD) on a standard evaluation framework has reached an estimated upper bound. However, there is limited research on the application of WSD to relevant NLP tasks due to the high computational cost of supervised systems. In this paper, we propose a partial WSD method with sen... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 2.06470266e-01 -1.18544795e-01 -3.91338140e-01 -3.59561801e-01
-9.47984040e-01 -6.05199635e-01 9.64900315e-01 4.89819199e-01
-8.65013957e-01 8.36666942e-01 4.06319708e-01 -3.02864105e-01
-2.39773672e-02 -5.99415720e-01 7.55172148e-02 -5.17947435e-01
3.16099793e-01 4.46359217e-01 4.36220556e-01 -6.22060180... | [10.26850700378418, 9.035025596618652] |
b364e6e6-07da-4cb5-8484-eca5e0ad535e | deep-reinforcement-learning-assisted-1 | 2206.11715 | null | https://arxiv.org/abs/2206.11715v1 | https://arxiv.org/pdf/2206.11715v1.pdf | Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets | Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federated learning (FL) is increasingly adopted to train STLF models for utility companies (UCs) in recent research. Inspiringly, in wholesale markets, as it is n... | ['Yanru Zhang', 'Yingjie Zhou', 'Changkun Jiang', 'Shengrong Bu', 'Yuxi Chen', 'Shunji Yang', 'Feng Hong', 'Xiaoyi Wang', 'Weilong Chen', 'Chenghao Huang'] | 2022-06-23 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-4.26574469e-01 8.40211883e-02 1.68230534e-02 -3.63621622e-01
-5.19388080e-01 -4.21791017e-01 4.40040588e-01 1.22424819e-01
-1.62641227e-01 1.02202344e+00 -4.38820347e-02 -5.08178353e-01
-4.36013550e-01 -9.93208587e-01 -7.09545195e-01 -9.11229610e-01
-4.05097187e-01 4.80847359e-01 -5.46088874e-01 5.64218871... | [5.872704982757568, 2.728205919265747] |
1400ab46-c47f-4bc6-aafd-fd4d711b9a23 | streaming-end-to-end-target-speaker-asr | 2209.04175 | null | https://arxiv.org/abs/2209.04175v2 | https://arxiv.org/pdf/2209.04175v2.pdf | Streaming Target-Speaker ASR with Neural Transducer | Although recent advances in deep learning technology have boosted automatic speech recognition (ASR) performance in the single-talker case, it remains difficult to recognize multi-talker speech in which many voices overlap. One conventional approach to tackle this problem is to use a cascade of a speech separation or t... | ['Takahiro Shinozaki', 'Marc Delcroix', 'Tsubasa Ochiai', 'Hiroshi Sato', 'Takafumi Moriya'] | 2022-09-09 | null | null | null | null | ['speech-separation', 'speech-extraction'] | ['speech', 'speech'] | [ 2.82427251e-01 2.22490728e-01 2.20389441e-01 -2.69653052e-01
-1.36817801e+00 -7.15638161e-01 4.41077113e-01 -3.34278822e-01
-4.77126241e-01 7.64814615e-02 3.45575720e-01 -7.69235253e-01
1.18979432e-01 -2.32075438e-01 -4.45323169e-01 -5.42704642e-01
2.50839502e-01 2.56745666e-01 9.15201157e-02 -3.16042602... | [14.57767391204834, 6.4388275146484375] |
7323590c-2d46-481a-b743-3bca75d9e4c6 | learn-to-cluster-faces-via-pairwise-1 | 2205.13117 | null | https://arxiv.org/abs/2205.13117v1 | https://arxiv.org/pdf/2205.13117v1.pdf | Learn to Cluster Faces via Pairwise Classification | Face clustering plays an essential role in exploiting massive unlabeled face data. Recently, graph-based face clustering methods are getting popular for their satisfying performances. However, they usually suffer from excessive memory consumption especially on large-scale graphs, and rely on empirical thresholds to det... | ['Xiaolin Wei', 'Pengfei Yan', 'Di Qiu', 'Junfu Liu'] | 2022-05-26 | learn-to-cluster-faces-via-pairwise | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Learn_To_Cluster_Faces_via_Pairwise_Classification_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Learn_To_Cluster_Faces_via_Pairwise_Classification_ICCV_2021_paper.pdf | iccv-2021-1 | ['face-clustering'] | ['computer-vision'] | [-1.22733675e-01 -3.26497853e-01 -1.95349947e-01 -5.51350713e-01
-4.76880759e-01 -2.59593785e-01 3.96248460e-01 -4.20262478e-03
-1.97777376e-01 3.92869055e-01 -1.80158406e-01 -1.31012291e-01
-4.26228166e-01 -8.00336599e-01 -3.00241202e-01 -1.06210756e+00
-3.91716436e-02 6.57705963e-01 1.99914247e-01 1.92449987... | [13.467852592468262, 1.051177740097046] |
5553b5fa-0ef9-415d-a834-1b3c68d73e32 | trusted-multi-view-classification-1 | 2102.02051 | null | https://arxiv.org/abs/2102.02051v1 | https://arxiv.org/pdf/2102.02051v1.pdf | Trusted Multi-View Classification | Multi-view classification (MVC) generally focuses on improving classification accuracy by using information from different views, typically integrating them into a unified comprehensive representation for downstream tasks. However, it is also crucial to dynamically assess the quality of a view for different samples in ... | ['Joey Tianyi Zhou', 'Huazhu Fu', 'Changqing Zhang', 'Zongbo Han'] | 2021-02-03 | trusted-multi-view-classification | https://openreview.net/forum?id=OOsR8BzCnl5 | https://openreview.net/pdf?id=OOsR8BzCnl5 | iclr-2021-1 | ['multi-view-learning'] | ['computer-vision'] | [-3.83373767e-01 -1.29720837e-01 -4.25942779e-01 -7.20126748e-01
-1.23778033e+00 -6.42236412e-01 6.69019282e-01 3.48311305e-01
2.95510083e-01 7.05277443e-01 4.20330688e-02 2.51558214e-01
-3.56348932e-01 -8.40233207e-01 -4.79696065e-01 -1.14743674e+00
4.57354009e-01 4.89604175e-01 1.01616502e-01 2.88013816... | [8.51975154876709, 4.5271124839782715] |
feee37ea-12dd-43d2-9913-0e1e0c3b7826 | the-architecture-of-a-biologically-plausible | 2306.15364 | null | https://arxiv.org/abs/2306.15364v1 | https://arxiv.org/pdf/2306.15364v1.pdf | The Architecture of a Biologically Plausible Language Organ | We present a simulated biologically plausible language organ, made up of stylized but realistic neurons, synapses, brain areas, plasticity, and a simplified model of sensory perception. We show through experiments that this model succeeds in an important early step in language acquisition: the learning of nouns, verbs,... | ['Christos H. Papadimitriou', 'Daniel Mitropolsky'] | 2023-06-27 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 1.33469924e-01 7.02795029e-01 3.09974760e-01 2.10311660e-03
2.17887312e-01 -3.95033002e-01 8.45477939e-01 3.15732569e-01
-5.79151809e-01 7.68682539e-01 4.34737414e-01 -2.43549511e-01
1.97594240e-01 -9.22486305e-01 -9.77458954e-01 -2.72180885e-01
-3.55954506e-02 3.92070025e-01 5.93518436e-01 -5.32360017... | [10.217816352844238, 8.661703109741211] |
cdab9bb4-5c89-4ff4-ab2b-938919c61b45 | random-walk-model-from-the-point-of-view-of | 1908.04333 | null | https://arxiv.org/abs/1908.04333v1 | https://arxiv.org/pdf/1908.04333v1.pdf | Random walk model from the point of view of algorithmic trading | Despite the fact that an intraday market price distribution is not normal, the random walk model of price behaviour is as important for the understanding of basic principles of the market as the pendulum model is a starting point of many fundamental theories in physics. This model is a good zero order approximation for... | ['Alexandre Argenson', 'Bruce Bland', 'Oleh Danyliv'] | 2019-08-12 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-5.01666486e-01 -1.30478367e-01 -1.19683087e-01 -1.33519005e-02
-4.46298532e-02 -6.71666980e-01 6.03784204e-01 5.80598056e-01
-6.55376017e-01 6.73168182e-01 -4.45229769e-01 -8.27687502e-01
-4.82412517e-01 -1.09903002e+00 -6.35581911e-01 -7.28506386e-01
-2.75875211e-01 7.79661655e-01 8.56086671e-01 -3.28722954... | [4.755852222442627, 4.060290813446045] |
290b56cd-5026-4b5d-8412-319c75e3ff6f | going-full-tilt-boogie-on-document | 2102.09550 | null | https://arxiv.org/abs/2102.09550v3 | https://arxiv.org/pdf/2102.09550v3.pdf | Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer | We address the challenging problem of Natural Language Comprehension beyond plain-text documents by introducing the TILT neural network architecture which simultaneously learns layout information, visual features, and textual semantics. Contrary to previous approaches, we rely on a decoder capable of unifying a variety... | ['Gabriela Pałka', 'Michał Pietruszka', 'Tomasz Dwojak', 'Dawid Jurkiewicz', 'Łukasz Borchmann', 'Rafał Powalski'] | 2021-02-18 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 3.25855762e-01 4.37869668e-01 1.62121788e-01 -4.99142259e-01
-7.68798590e-01 -8.73123884e-01 8.56811762e-01 4.26600009e-01
-3.87952477e-01 1.27422586e-01 6.33721769e-01 -6.99648440e-01
1.39105290e-01 -7.56291628e-01 -9.23234582e-01 -1.91585824e-01
2.65885085e-01 6.64683104e-01 7.81931952e-02 -2.78950542... | [11.122445106506348, 1.996045470237732] |
7a62dd80-f69a-40c9-80f2-114acdb2bdc2 | real-time-audio-video-enhancement-with-a | 2303.00949 | null | https://arxiv.org/abs/2303.00949v1 | https://arxiv.org/pdf/2303.00949v1.pdf | Real-time Audio Video Enhancement \\with a Microphone Array and Headphones | This paper presents a complete hardware and software pipeline for real-time speech enhancement in noisy and reverberant conditions. The device consists of a microphone array and a camera mounted on eyeglasses, connected to an embedded system that enhances speech and plays back the audio in headphones, with a latency of... | ['François Grondin', 'Jérémy Bélec', 'Amélie Rioux-Joyal', 'Olivier Bergeron', 'Félix Ducharme-Turcotte', 'Francis Cardinal', 'Étienne Deshaies-Samson', 'Anthony Gosselin', 'Jacob Kealey'] | 2023-03-02 | null | null | null | null | ['face-detection', 'video-enhancement', 'speech-enhancement'] | ['computer-vision', 'computer-vision', 'speech'] | [ 3.61116320e-01 -1.52816884e-02 8.65486443e-01 -2.15325758e-01
-5.63165605e-01 -3.65605205e-01 3.04795325e-01 -1.91979215e-01
-5.45255125e-01 2.49258503e-01 2.84518421e-01 -5.42949021e-01
6.21000640e-02 -2.30363861e-01 -2.91787297e-01 -7.72844374e-01
-1.36170417e-01 -4.10590380e-01 3.07833225e-01 -5.22177406... | [14.954854965209961, 5.7980217933654785] |
b11f5dfb-d1bf-43c3-b8dc-f32782536eea | cholectriplet2021-a-benchmark-challenge-for | 2204.04746 | null | https://arxiv.org/abs/2204.04746v2 | https://arxiv.org/pdf/2204.04746v2.pdf | CholecTriplet2021: A benchmark challenge for surgical action triplet recognition | Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details abou... | ['Nicolas Padoy', 'Cristians Gonzalez', 'Barbara Seeliger', 'Pietro Mascagni', 'Didier Mutter', 'Danail Stoyanov', 'Alexander Jenke', 'Lalithkumar Seenivasan', 'Mobarakol Islam', 'Mengya Xu', 'Nicolas Elini van der Kar', 'Jakob-Anton Aschenbrenner', 'Shuai Ding', 'Yuanbo Zhu', 'Imanol Luengo', 'Debdoot Sheet', 'Velmuru... | 2022-04-10 | null | null | null | null | ['action-triplet-recognition'] | ['computer-vision'] | [ 4.04021263e-01 2.97491640e-01 -5.26082993e-01 -2.81183124e-01
-8.69706571e-01 -6.84033513e-01 4.49767828e-01 3.78017247e-01
-5.80543935e-01 2.94167161e-01 9.13187742e-01 -4.68878597e-01
-6.93794906e-01 -1.55769676e-01 -5.29976666e-01 -9.17676747e-01
-4.26983535e-01 2.69685805e-01 -2.96938956e-01 -1.56377759... | [14.057258605957031, -3.4049859046936035] |
b2ebb58c-0e4c-480a-bd55-783e3b0f8cd8 | brazilian-lyrics-based-music-genre | 2003.05377 | null | https://arxiv.org/abs/2003.05377v1 | https://arxiv.org/pdf/2003.05377v1.pdf | Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network | Organize songs, albums, and artists in groups with shared similarity could be done with the help of genre labels. In this paper, we present a novel approach for automatic classifying musical genre in Brazilian music using only the song lyrics. This kind of classification remains a challenge in the field of Natural Lang... | ['Hélio Cortês Vieira Lopes', 'Rômulo César Costa de Sousa', 'Raul de Araújo Lima', 'Simone Diniz Junqueira Barbosa'] | 2020-03-06 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [-1.96970895e-01 -4.12514865e-01 4.09460478e-02 -5.11967354e-02
-5.69622159e-01 -8.42913687e-01 4.25530553e-01 2.74327904e-01
-6.36066318e-01 7.55869150e-01 3.86231095e-01 1.17272735e-01
-3.34147871e-01 -7.91699767e-01 -3.14398140e-01 -7.45966673e-01
-5.51460199e-02 3.51144254e-01 1.03535596e-02 -3.39323968... | [15.87685489654541, 5.23203182220459] |
c0ed55cd-6707-4567-8e04-6e9ec776c2d8 | modeling-what-to-ask-and-how-to-ask-for | 2305.03088 | null | https://arxiv.org/abs/2305.03088v1 | https://arxiv.org/pdf/2305.03088v1.pdf | Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation | Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast into two different settings: answer-aware and answer-unaware. While the former facilitates the models by exposing the expected answer, the lat... | ['Ai Ti Aw', 'Nancy F. Chen', 'Liangming Pan', 'Anh Tai Tran', 'Shafiq Joty', 'Bowei Zou', 'Xuan Long Do'] | 2023-05-04 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 2.69580781e-01 4.56807852e-01 5.68538867e-02 -6.08126640e-01
-8.43475342e-01 -7.97257841e-01 7.37240911e-01 1.50416434e-01
3.70590799e-02 7.01180935e-01 5.59994161e-01 -5.60543239e-01
-1.25444055e-01 -9.02862668e-01 -1.91796809e-01 -2.93836594e-01
6.05806708e-01 6.22560620e-01 4.12552625e-01 -6.30188048... | [11.842032432556152, 8.070246696472168] |
1eeba2dc-2f81-47e5-a557-f916b2217930 | dependency-grammar-induction-with-neural | 1708.00801 | null | http://arxiv.org/abs/1708.00801v1 | http://arxiv.org/pdf/1708.00801v1.pdf | Dependency Grammar Induction with Neural Lexicalization and Big Training Data | We study the impact of big models (in terms of the degree of lexicalization)
and big data (in terms of the training corpus size) on dependency grammar
induction. We experimented with L-DMV, a lexicalized version of Dependency
Model with Valence and L-NDMV, our lexicalized extension of the Neural
Dependency Model with V... | ['Kewei Tu', 'Yong Jiang', 'Wenjuan Han'] | 2017-08-02 | dependency-grammar-induction-with-neural-1 | https://aclanthology.org/D17-1176 | https://aclanthology.org/D17-1176.pdf | emnlp-2017-9 | ['dependency-grammar-induction'] | ['natural-language-processing'] | [-5.23891330e-01 6.02722168e-01 -5.56078792e-01 -3.31446737e-01
-6.04508460e-01 -7.97851562e-01 5.40784061e-01 1.05630226e-01
-6.06974006e-01 7.54023910e-01 4.86758381e-01 -7.38270164e-01
3.07643831e-01 -8.37793887e-01 -6.66764140e-01 -3.10618341e-01
-1.34813473e-01 1.02980816e+00 1.14636287e-01 -5.39306998... | [10.609827041625977, 9.514890670776367] |
a01c00d5-49e4-4d22-9244-ba3b29340685 | a-study-on-the-integration-of-pipeline-and | 2305.01620 | null | https://arxiv.org/abs/2305.01620v2 | https://arxiv.org/pdf/2305.01620v2.pdf | A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge | Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken semantic parsing system for the quality track (Track 1) in Spoken Language Understanding Grand Challenge which is part of ICASSP Signal Process... | ['Shinji Watanabe', 'Brian Yan', 'Emiru Tsunoo', 'Yosuke Kashiwagi', 'Yifan Peng', 'Jessica Huynh', 'Shih-Lun Wu', 'Hayato Futami', 'Siddhant Arora'] | 2023-05-02 | null | null | null | null | ['spoken-language-understanding', 'semantic-parsing', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.10712051e-01 3.99234384e-01 3.42573285e-01 -1.10164690e+00
-1.53479481e+00 -5.68541348e-01 6.47836804e-01 -2.68861592e-01
-5.57779908e-01 3.44400436e-01 7.49029219e-01 -4.09022242e-01
5.06977260e-01 -1.89136505e-01 -6.20774448e-01 2.92384950e-03
1.30930077e-02 5.73444724e-01 1.95196152e-01 -3.63071084... | [14.031609535217285, 7.009063243865967] |
9bdb2fe2-2ddc-4776-90ce-5fbf511b1159 | grenzlinie-at-semeval-2021-task-7-detecting | null | null | https://aclanthology.org/2021.semeval-1.34 | https://aclanthology.org/2021.semeval-1.34.pdf | Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and Offense | This paper introduces the result of Team Grenzlinie{'}s experiment in SemEval-2021 task 7: HaHackathon: Detecting and Rating Humor and Offense. This task has two subtasks. Subtask1 includes the humor detection task, the humor rating prediction task, and the humor controversy detection task. Subtask2 is an offensive rat... | ['Xiaobing Zhou', 'Renyuan Liu'] | 2021-08-01 | null | null | null | semeval-2021 | ['humor-detection'] | ['natural-language-processing'] | [-3.55941772e-01 1.03452660e-01 2.66173501e-02 4.59388457e-02
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3.87642950e-01 1.94172725e-01 -3.53749208e-02 -7.02928901... | [8.867189407348633, 11.078749656677246] |
b0af9c95-bca5-47b9-9a87-c9253d45535b | semantic-and-effective-communication-for | 2301.05901 | null | https://arxiv.org/abs/2301.05901v1 | https://arxiv.org/pdf/2301.05901v1.pdf | Semantic and Effective Communication for Remote Control Tasks with Dynamic Feature Compression | The coordination of robotic swarms and the remote wireless control of industrial systems are among the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that needs to be shared over the wireless medium can overload even high-capacity connections. Consequently, solving... | ['Michele Zorzi', 'Andrea Zanella', 'Federico Chiariotti', 'Francesco Pase', 'Pietro Talli'] | 2023-01-14 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 1.18906744e-01 4.85346764e-01 -2.59496160e-02 -2.63589267e-02
-3.39558989e-01 -1.71512663e-01 4.13566947e-01 2.66568124e-01
-4.16932911e-01 9.58303034e-01 -2.81108558e-01 -2.95826942e-01
-5.09094536e-01 -9.51939762e-01 -6.39745176e-01 -1.19778919e+00
-2.79864609e-01 6.55541718e-01 8.13898444e-02 -3.67618769... | [4.502495288848877, 1.9630489349365234] |
c0494c9d-55e7-40f2-9808-412139f50699 | on-the-structural-generalization-in-text-to | 2301.04790 | null | https://arxiv.org/abs/2301.04790v2 | https://arxiv.org/pdf/2301.04790v2.pdf | On the Structural Generalization in Text-to-SQL | Exploring the generalization of a text-to-SQL parser is essential for a system to automatically adapt the real-world databases. Previous works provided investigations focusing on lexical diversity, including the influence of the synonym and perturbations in both natural language questions and databases. However, resear... | ['Kai Yu', 'Hanchong Zhang', 'Zhi Chen', 'Hongshen Xu', 'Su Zhu', 'Ruisheng Cao', 'Lu Chen', 'Jieyu Li'] | 2023-01-12 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 2.74038196e-01 1.16659418e-01 -7.61574507e-02 -8.38394046e-01
-6.15832865e-01 -7.66153276e-01 3.27195048e-01 3.50537747e-01
-6.06606230e-02 5.68663836e-01 1.22532077e-01 -6.38405740e-01
-1.71585754e-02 -1.07751083e+00 -1.05880439e+00 -5.35643138e-02
3.60078782e-01 6.23565972e-01 4.58641022e-01 -6.19547427... | [9.827136039733887, 7.834438323974609] |
485792ef-6f0b-47da-9328-957b21955a91 | cream-weakly-supervised-object-localization | 2205.13922 | null | https://arxiv.org/abs/2205.13922v1 | https://arxiv.org/pdf/2205.13922v1.pdf | CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping | Weakly Supervised Object Localization (WSOL) aims to localize objects with image-level supervision. Existing works mainly rely on Class Activation Mapping (CAM) derived from a classification model. However, CAM-based methods usually focus on the most discriminative parts of an object (i.e., incomplete localization prob... | ['Shang Gao', 'Xuequan Lu', 'Tao Zhang', 'Rui-Wei Zhao', 'Rui Feng', 'Yuejie Zhang', 'Junlin Hou', 'Jilan Xu'] | 2022-05-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_CREAM_Weakly_Supervised_Object_Localization_via_Class_RE-Activation_Mapping_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_CREAM_Weakly_Supervised_Object_Localization_via_Class_RE-Activation_Mapping_CVPR_2022_paper.pdf | cvpr-2022-1 | ['weakly-supervised-object-localization'] | ['computer-vision'] | [-6.55398443e-02 -1.58793375e-01 -4.67498034e-01 -3.56904387e-01
-8.80636871e-01 -3.60898077e-01 5.90779066e-01 1.18895367e-01
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3.06809634e-01 2.55959123e-01 4.31549191e-01 2.38282472... | [9.62022876739502, 0.774587869644165] |
f6af8459-4d90-4f14-a2c8-61b849af8e39 | robust-point-light-source-estimation-using | 1812.04857 | null | http://arxiv.org/abs/1812.04857v1 | http://arxiv.org/pdf/1812.04857v1.pdf | Robust Point Light Source Estimation Using Differentiable Rendering | Illumination estimation is often used in mixed reality to re-render a scene
from another point of view, to change the color/texture of an object, or to
insert a virtual object consistently lit into a real video or photograph.
Specifically, the estimation of a point light source is required for the
shadows cast by the i... | ['Grégoire Nieto', 'Philippe Robert', 'Salma Jiddi'] | 2018-12-12 | null | null | null | null | ['light-source-estimation'] | ['computer-vision'] | [ 7.78831363e-01 -1.17651515e-01 6.21288836e-01 -3.50835532e-01
-2.96679944e-01 -6.01842523e-01 4.52515960e-01 -4.02449042e-01
-2.02847496e-01 5.60310006e-01 -3.92035931e-01 -6.17531203e-02
1.31592840e-01 -7.66611159e-01 -8.22107971e-01 -9.19146299e-01
7.73470938e-01 3.19348454e-01 1.68919042e-02 -6.30146638... | [9.78016185760498, -3.0352940559387207] |
b1c694b5-409c-4019-bb58-2052d8b5384f | peer-learning-for-unbiased-scene-graph | 2301.00146 | null | https://arxiv.org/abs/2301.00146v2 | https://arxiv.org/pdf/2301.00146v2.pdf | Peer Learning for Unbiased Scene Graph Generation | Unbiased scene graph generation (USGG) is a challenging task that requires predicting diverse and heavily imbalanced predicates between objects in an image. To address this, we propose a novel framework peer learning that uses predicate sampling and consensus voting (PSCV) to encourage multiple peers to learn from each... | ['Yangsheng Xu', 'Tin Lun Lam', 'Yuhongze Zhou', 'Junjie Hu', 'Liguang Zhou'] | 2022-12-31 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 3.03889364e-01 4.61364955e-01 -5.20470798e-01 -4.43263203e-01
-8.03448677e-01 -1.55282065e-01 5.57303488e-01 3.66696239e-01
2.22452044e-01 1.10752869e+00 3.24152499e-01 1.15026914e-01
7.10140541e-02 -9.55184042e-01 -1.00473166e+00 -7.12437928e-01
2.29471028e-02 8.93444479e-01 8.60782921e-01 1.23180404... | [10.235395431518555, 1.8018825054168701] |
2d70c0d5-c26e-4d6b-9d58-0a89a958a6f0 | knowledge-graph-papers-iclr-2021 | null | null | https://openreview.net/forum?id=2P7cGsM14Mj | https://openreview.net/pdf?id=2P7cGsM14Mj | Knowledge Graph Papers @ ICLR 2021 | This post aims at providing an overview of ICLR 2021 papers focusing on knowledge graphs (KGs). In particular, we highlight the research in four wide areas: complex query answering and reasoning in KGs, temporal logics and KGs, NLP point of view and entity linking, multimodal question answering with KGs. We hope this p... | ['Anonymous'] | 2022-01-17 | null | null | null | iclr-track-blog-2022-5 | ['complex-query-answering'] | ['knowledge-base'] | [-4.90500689e-01 8.73377264e-01 -5.67186832e-01 -2.19881326e-01
-6.11005962e-01 -8.89725208e-01 4.96317297e-01 7.27847576e-01
-1.18313596e-01 1.12568009e+00 3.14463139e-01 -4.05651331e-01
-9.01975811e-01 -1.13928819e+00 -6.18248582e-01 2.14229152e-01
-3.71900976e-01 8.62285376e-01 7.45337188e-01 -5.00930965... | [9.551115989685059, 8.019603729248047] |
94d7c04b-ed36-4b60-96cc-9d48e570ce8c | contrastive-entity-linkage-mining-variational | null | null | https://openreview.net/forum?id=fR44nF03Rb | https://openreview.net/pdf?id=fR44nF03Rb | Contrastive Entity Linkage: Mining Variational Attributes from Large Catalogs for Entity Linkage | Presence of near identical, but distinct, entities called entity variations makes the task of data integration challenging. For example, in the domain of grocery products, variations share the same value for attributes such as brand, manufacturer and product line, but differ in other attributes, called variational attr... | ['Lise Getoor', 'Christos Faloutsos', 'Xin Luna Dong', 'Hao Wei', 'Bunyamin Sisman', 'Varun Embar'] | 2020-02-14 | null | null | null | akbc-2020-6 | ['data-integration'] | ['knowledge-base'] | [-2.97329664e-01 -7.03278705e-02 -2.84368515e-01 -4.95305389e-01
-7.24541128e-01 -9.79527950e-01 4.11994636e-01 1.04583097e+00
-1.96134105e-01 8.86431873e-01 -9.88644809e-02 2.03902796e-01
-3.32175672e-01 -9.35496926e-01 -1.00196624e+00 -2.65074193e-01
1.71499443e-03 1.03533137e+00 4.51064169e-01 -2.46550456... | [9.249128341674805, 8.13854694366455] |
b3cf0579-f9b9-422f-9e6b-4630dccdee51 | optimizing-a-digital-twin-for-fault-diagnosis | 2212.03564 | null | https://arxiv.org/abs/2212.03564v1 | https://arxiv.org/pdf/2212.03564v1.pdf | Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters -- A Bayesian Approach | In this paper, a hyperparameter tuning based Bayesian optimization of digital twins is carried out to diagnose various faults in grid connected inverters. As fault detection and diagnosis require very high precision, we channelize our efforts towards an online optimization of the digital twins, which, in turn, allows a... | ['Pedro H. J. Nardelli', 'Charalampos Kalalas', 'Subham Sahoo', 'Pavol Mulinka'] | 2022-12-07 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-3.76671314e-01 -2.26950362e-01 -1.05913512e-01 -5.04029691e-02
-6.06484771e-01 -5.46932578e-01 2.57394552e-01 -2.83591390e-01
4.96310085e-01 8.45921338e-01 -4.35724646e-01 -4.13665861e-01
-7.11444318e-01 -7.93291628e-01 -2.79524088e-01 -1.00734985e+00
-1.78088620e-01 8.21531057e-01 -1.13295078e-01 -1.71361879... | [6.009566783905029, 2.671999216079712] |
ae8e65a1-ce5f-47e2-8729-029d9d2ce7fb | cgnn-traffic-classification-with-graph-neural | 2110.09726 | null | https://arxiv.org/abs/2110.09726v1 | https://arxiv.org/pdf/2110.09726v1.pdf | CGNN: Traffic Classification with Graph Neural Network | Traffic classification associates packet streams with known application labels, which is vital for network security and network management. With the rise of NAT, port dynamics, and encrypted traffic, it is increasingly challenging to obtain unified traffic features for accurate classification. Many state-of-the-art tra... | ['Yan Jia', 'Qing Liao', 'Ye Wang', 'Siyuan Ren', 'Yongquan Fu', 'Bo Pang'] | 2021-10-19 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.57511830e-02 -4.72211331e-01 -4.71010536e-01 -4.12204295e-01
1.95753574e-01 -5.00646830e-01 2.85825670e-01 1.00165576e-01
-7.81686902e-02 5.13957739e-01 -4.69036192e-01 -9.87676322e-01
-1.89051345e-01 -1.15321267e+00 -3.17151874e-01 -3.58594239e-01
-4.66425329e-01 3.72223318e-01 5.79455197e-01 -1.33155897... | [5.063797473907471, 7.238203525543213] |
a2088f43-758f-4610-a3a1-607fb0ae6f29 | exploring-timbre-disentanglement-in-non | 2110.07192 | null | https://arxiv.org/abs/2110.07192v3 | https://arxiv.org/pdf/2110.07192v3.pdf | Exploring Timbre Disentanglement in Non-Autoregressive Cross-Lingual Text-to-Speech | In this paper, we study the disentanglement of speaker and language representations in non-autoregressive cross-lingual TTS models from various aspects. We propose a phoneme length regulator that solves the length mismatch problem between IPA input sequence and monolingual alignment results. Using the phoneme length re... | ['Yue Lin', 'Yang Zhang', 'Haitong Zhang', 'Xinyuan Yu', 'Haoyue Zhan'] | 2021-10-14 | null | null | null | null | ['voice-cloning'] | ['speech'] | [-1.22162983e-01 -5.40437996e-02 -4.30389017e-01 -4.81640100e-01
-1.16940379e+00 -8.60420883e-01 4.01531368e-01 -7.83388674e-01
-7.70107582e-02 3.70350897e-01 4.58641440e-01 -8.21703970e-01
2.16633052e-01 5.90340756e-02 -5.43725073e-01 -5.16034722e-01
2.49708742e-01 4.11701709e-01 -3.40818346e-01 -3.06415945... | [14.790995597839355, 6.687067985534668] |
f4284085-52c0-4227-8282-b3d4d95cb947 | towards-bio-inspired-unsupervised | 2106.09326 | null | https://arxiv.org/abs/2106.09326v1 | https://arxiv.org/pdf/2106.09326v1.pdf | Towards bio-inspired unsupervised representation learning for indoor aerial navigation | Aerial navigation in GPS-denied, indoor environments, is still an open challenge. Drones can perceive the environment from a richer set of viewpoints, while having more stringent compute and energy constraints than other autonomous platforms. To tackle that problem, this research displays a biologically inspired deep-l... | ['Bart Dhoedt', 'Matthias Hartmann', 'Tim Verbelen', 'Ozan Catal', 'Ni Wang'] | 2021-06-17 | null | null | null | null | ['drone-navigation'] | ['computer-vision'] | [ 2.14659378e-01 -3.58414352e-01 1.35957837e-01 -1.28528103e-01
3.94211262e-02 -7.85333395e-01 3.87857705e-01 -1.30785340e-02
-4.52587575e-01 8.26829135e-01 -2.51444638e-01 7.71830380e-02
-4.89008576e-01 -9.17476058e-01 -4.81515288e-01 -8.05710018e-01
-4.34096217e-01 2.54881918e-01 1.68708235e-01 -4.50803548... | [7.330127239227295, -1.9147083759307861] |
db61bd60-b148-4809-aa46-37452a318009 | improving-the-intent-classification-accuracy | 2303.06585 | null | https://arxiv.org/abs/2303.06585v1 | https://arxiv.org/pdf/2303.06585v1.pdf | Improving the Intent Classification accuracy in Noisy Environment | Intent classification is a fundamental task in the spoken language understanding field that has recently gained the attention of the scientific community, mainly because of the feasibility of approaching it with end-to-end neural models. In this way, avoiding using intermediate steps, i.e. automatic speech recognition,... | ['Daniele Falavigna', 'Alessio Brutti', 'Mohamed Nabih Ali'] | 2023-03-12 | null | null | null | null | ['spoken-language-understanding', 'intent-classification', 'spoken-language-understanding', 'speech-enhancement'] | ['natural-language-processing', 'natural-language-processing', 'speech', 'speech'] | [ 3.14250588e-01 1.54361427e-01 6.82258666e-01 -5.17118156e-01
-4.58109379e-01 -1.41230956e-01 5.54925382e-01 1.91281170e-01
-8.89551759e-01 4.16247278e-01 4.58617985e-01 -1.13913499e-01
-2.32424557e-01 -5.96495807e-01 -1.83517471e-01 -6.69711113e-01
-9.81662497e-02 3.12179267e-01 -1.29543602e-01 -4.75741118... | [14.846537590026855, 5.899835109710693] |
604ef74a-4043-42d0-9c74-a9d52f852afd | better-early-than-late-fusing-topics-with | 2007.11314 | null | https://arxiv.org/abs/2007.11314v1 | https://arxiv.org/pdf/2007.11314v1.pdf | Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification | Question paraphrase identification is a key task in Community Question Answering (CQA) to determine if an incoming question has been previously asked. Many current models use word embeddings to identify duplicate questions, but the use of topic models in feature-engineered systems suggests that they can be helpful for ... | ['Nicole Peinelt', 'Dong Nguyen', 'Maria Liakata'] | 2020-07-22 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [-1.59537923e-02 1.64618358e-01 2.17378005e-01 -3.58660638e-01
-1.33302057e+00 -6.41133010e-01 8.06469500e-01 6.62767470e-01
-4.82687593e-01 2.62390733e-01 9.68619525e-01 -4.91362691e-01
-1.83987632e-01 -7.95309007e-01 -3.69414866e-01 -2.28320435e-02
5.81252158e-01 5.32305121e-01 5.19344270e-01 -3.34143907... | [11.3596773147583, 8.080748558044434] |
6048ba1b-ede1-46dc-88f5-2a6663f20a18 | tokenwise-contrastive-pretraining-for-finer | 2204.05188 | null | https://arxiv.org/abs/2204.05188v2 | https://arxiv.org/pdf/2204.05188v2.pdf | Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems | Recent advances in End-to-End (E2E) Spoken Language Understanding (SLU) have been primarily due to effective pretraining of speech representations. One such pretraining paradigm is the distillation of semantic knowledge from state-of-the-art text-based models like BERT to speech encoder neural networks. This work is a ... | ['Brian Kingsbury', 'Hong-Kwang J. Kuo', 'Samuel Thomas', 'Eric Fosler-Lussier', 'Vishal Sunder'] | 2022-04-11 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 3.24752480e-01 3.35842341e-01 -1.96637902e-02 -9.30125356e-01
-1.21267509e+00 -4.15112674e-01 7.76614606e-01 1.94530457e-01
-8.25795829e-01 4.81094778e-01 9.27141488e-01 -4.89872366e-01
4.03022289e-01 -5.24147809e-01 -8.05676997e-01 -3.47326756e-01
4.45895791e-02 6.02288783e-01 -1.15234852e-02 -3.32281590... | [14.02847671508789, 6.9848737716674805] |
49d8efc8-b2e5-4f85-ac44-4387dfff3425 | policy-diagnosis-via-measuring-role-diversity | 2207.05683 | null | https://arxiv.org/abs/2207.05683v1 | https://arxiv.org/pdf/2207.05683v1.pdf | Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL | Cooperative multi-agent reinforcement learning (MARL) is making rapid progress for solving tasks in a grid world and real-world scenarios, in which agents are given different attributes and goals, resulting in different behavior through the whole multi-agent task. In this study, we quantify the agent's behavior differe... | ['Xiaojun Chang', 'Xiaodan Liang', 'Chuanlong Xie', 'Siyi Hu'] | 2022-06-01 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-7.40678430e-01 -5.47917664e-01 -1.80840269e-01 1.19326442e-01
-4.35554236e-01 -4.96914297e-01 7.21039057e-01 3.79884958e-01
-7.91381836e-01 1.15086722e+00 -5.80812953e-02 2.77675893e-02
-6.26156628e-01 -5.67810059e-01 -3.19517553e-01 -1.11838305e+00
-4.66508359e-01 7.93029547e-01 3.24349344e-01 -8.12140524... | [3.7713568210601807, 1.974765419960022] |
6f999e85-6fc9-4fa7-9dc1-beabb3aec8f1 | evaluating-foveated-video-quality-using | 2106.06817 | null | https://arxiv.org/abs/2106.06817v1 | https://arxiv.org/pdf/2106.06817v1.pdf | Evaluating Foveated Video Quality Using Entropic Differencing | Virtual Reality is regaining attention due to recent advancements in hardware technology. Immersive images / videos are becoming widely adopted to carry omnidirectional visual information. However, due to the requirements for higher spatial and temporal resolution of real video data, immersive videos require significan... | ['Alan Bovik', 'Anjul Patney', 'Yize Jin'] | 2021-06-12 | null | null | null | null | ['foveation'] | ['computer-vision'] | [ 9.75046754e-02 -4.21476066e-01 1.82483748e-01 -9.90154594e-02
-8.32563698e-01 -2.44860068e-01 2.55503803e-01 -2.91120976e-01
-6.55110002e-01 6.51831210e-01 2.04081237e-01 -2.47750804e-01
-3.36796135e-01 -4.01965559e-01 -6.42133057e-01 -5.27928352e-01
-2.86760390e-01 -7.63317406e-01 3.40535104e-01 -5.06802425... | [11.565446853637695, -1.8800292015075684] |
907e9d41-d2c4-4671-bb57-e3503c869aa6 | deepfake-captcha-a-method-for-preventing-fake | 2301.03064 | null | https://arxiv.org/abs/2301.03064v1 | https://arxiv.org/pdf/2301.03064v1.pdf | Deepfake CAPTCHA: A Method for Preventing Fake Calls | Deep learning technology has made it possible to generate realistic content of specific individuals. These `deepfakes' can now be generated in real-time which enables attackers to impersonate people over audio and video calls. Moreover, some methods only need a few images or seconds of audio to steal an identity. Exist... | ['Yisroel Mirsky', 'Fred M. Grabovski', 'Guy Frankovits', 'Lior Yasur'] | 2023-01-08 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 1.05070181e-01 7.10219219e-02 2.09554762e-01 1.64122239e-01
-1.02490103e+00 -1.13662076e+00 6.44956470e-01 -2.35190257e-01
-4.16558832e-01 6.44317508e-01 1.85662225e-01 -4.09532227e-02
5.58587790e-01 -6.42521024e-01 -5.11138380e-01 -6.12451017e-01
-5.50647154e-02 5.65353990e-01 4.59243357e-01 -1.97597057... | [12.577014923095703, 1.3027251958847046] |
b4570c5c-945d-47e0-a781-eafb4d55e82f | arm-order-recognition-in-multi-armed-bandit | 2005.13085 | null | https://arxiv.org/abs/2005.13085v1 | https://arxiv.org/pdf/2005.13085v1.pdf | Arm order recognition in multi-armed bandit problem with laser chaos time series | By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series. Although the algorithm detects the arm with the highest reward... | ['Nicolas Chauvet', 'Naoki Narisawa', 'Mikio Hasegawa', 'Makoto Naruse'] | 2020-05-26 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 3.87700784e-05 -2.52047777e-01 -4.02437180e-01 1.86525747e-01
-7.80460358e-01 -9.97041464e-01 5.17817378e-01 -2.08173454e-01
-6.22223556e-01 1.08172548e+00 -4.55084592e-02 -5.50903499e-01
-8.12472880e-01 -6.41685426e-01 -3.48936051e-01 -9.07861829e-01
-2.11139560e-01 6.28957808e-01 -2.46678740e-01 -5.20773567... | [4.564005374908447, 3.315481424331665] |
e58e6048-ea76-4358-8b68-8a0ce49b37d8 | two-parents-one-child-dual-transfer-for-low | null | null | https://aclanthology.org/2021.findings-acl.241 | https://aclanthology.org/2021.findings-acl.241.pdf | Two Parents, One Child: Dual Transfer for Low-Resource Neural Machine Translation | null | ['Qun Liu', 'Liangyou Li', 'Meng Zhang'] | null | null | null | null | findings-acl-2021-8 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.375491619110107, 3.628530263900757] |
976ab4ae-b7c5-4001-9ea8-6a7d242e97a3 | multi-timescale-event-detection-in | 2211.10721 | null | https://arxiv.org/abs/2211.10721v1 | https://arxiv.org/pdf/2211.10721v1.pdf | Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle | Load event detection is the fundamental step for the event-based non-intrusive load monitoring (NILM). However, existing event detection methods with fixed parameters may fail in coping with the inherent multi-timescale characteristics of events and their event detection accuracy is easily affected by the load fluctuat... | ['Yixin Yu', 'Zishuai Liu', 'Wenpeng Luan', 'Jianfeng Zhang', 'Bo Liu'] | 2022-11-19 | null | null | null | null | ['activity-detection', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['computer-vision', 'knowledge-base', 'miscellaneous', 'time-series'] | [ 2.60568172e-01 -6.32363141e-01 -2.56000787e-01 -1.46068379e-01
-5.59160233e-01 -3.43328238e-01 2.40456462e-01 5.77066720e-01
-1.59941971e-01 6.17214382e-01 2.35537246e-01 -7.82361776e-02
-4.10152525e-01 -8.54032695e-01 1.90547686e-02 -7.20675349e-01
-1.05495185e-01 2.09031124e-02 5.05264938e-01 1.87702850... | [6.168606758117676, 2.646420478820801] |
4ddb5b4d-fcad-4971-9e80-ec9041474e5c | the-computational-limits-of-deep-learning | 2007.05558 | null | https://arxiv.org/abs/2007.05558v2 | https://arxiv.org/pdf/2007.05558v2.pdf | The Computational Limits of Deep Learning | Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, translation, and other tasks. But this progress has come with a voracious appetite for computing power. This article catalogs the extent of t... | ['Kristjan Greenewald', 'Keeheon Lee', 'Neil C. Thompson', 'Gabriel F. Manso'] | 2020-07-10 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 6.36763424e-02 -2.91839302e-01 -7.01589212e-02 -1.56571984e-01
-7.41622746e-01 -4.92972702e-01 7.30637014e-01 -1.86885640e-01
-5.79011381e-01 4.40194070e-01 1.57970294e-01 -6.64461434e-01
-8.78389552e-03 -6.07990801e-01 -2.23716781e-01 -5.61852574e-01
-1.94781899e-01 3.82030338e-01 1.33781908e-02 -4.00484264... | [8.990997314453125, 6.385815143585205] |
939a4d2e-33cf-4826-a2f9-84812d1834a4 | chid-a-large-scale-chinese-idiom-dataset-for | 1906.01265 | null | https://arxiv.org/abs/1906.01265v3 | https://arxiv.org/pdf/1906.01265v3.pdf | ChID: A Large-scale Chinese IDiom Dataset for Cloze Test | Cloze-style reading comprehension in Chinese is still limited due to the lack of various corpora. In this paper we propose a large-scale Chinese cloze test dataset ChID, which studies the comprehension of idiom, a unique language phenomenon in Chinese. In this corpus, the idioms in a passage are replaced by blank symbo... | ['Aixin Sun', 'Minlie Huang', 'Chujie Zheng'] | 2019-06-04 | chid-a-large-scale-chinese-idiom-dataset-for-1 | https://aclanthology.org/P19-1075 | https://aclanthology.org/P19-1075.pdf | acl-2019-7 | ['cloze-test'] | ['natural-language-processing'] | [-1.22327931e-01 -2.23077133e-01 -2.56020784e-01 -3.59328479e-01
-7.29597151e-01 -7.57898331e-01 4.80352730e-01 1.87891256e-02
-2.48620585e-01 6.17235065e-01 8.04131091e-01 -6.08592510e-01
1.27223909e-01 -6.10026240e-01 -2.35327870e-01 -1.17289253e-01
3.56316924e-01 6.51235282e-01 4.36304659e-01 -6.51595175... | [10.915220260620117, 9.016057014465332] |
08a852a9-5bad-4456-b99b-8a6d35cf48df | collection-space-navigator-an-interactive | 2305.06809 | null | https://arxiv.org/abs/2305.06809v1 | https://arxiv.org/pdf/2305.06809v1.pdf | Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets | We introduce the Collection Space Navigator (CSN), a browser-based visualization tool to explore, research, and curate large collections of visual digital artifacts that are associated with multidimensional data, such as vector embeddings or tables of metadata. Media objects such as images are often encoded as numerica... | ['Maximilian Schich', 'Andres Karjus', 'Mar Canet Solà', 'Tillmann Ohm'] | 2023-05-11 | null | null | null | null | ['dimensionality-reduction', 'data-visualization', 'data-visualization', 'embeddings-evaluation'] | ['methodology', 'methodology', 'miscellaneous', 'natural-language-processing'] | [-1.22345299e-01 -4.35404390e-01 1.07664630e-01 2.99703982e-02
-1.97873071e-01 -1.23256123e+00 7.70542324e-01 5.06606877e-01
-2.77230740e-01 1.60739556e-01 4.80297357e-01 -7.73048580e-01
-4.02601808e-01 -8.60161841e-01 -6.36009127e-02 -3.86878908e-01
-3.46260190e-01 2.55147010e-01 2.43296787e-01 -1.48517594... | [7.997768878936768, 4.593198299407959] |
57ca94b0-ae31-440d-a6f5-36d39b0dd408 | riddle-reversible-and-diversified-de | 2303.05171 | null | https://arxiv.org/abs/2303.05171v3 | https://arxiv.org/pdf/2303.05171v3.pdf | RiDDLE: Reversible and Diversified De-identification with Latent Encryptor | This work presents RiDDLE, short for Reversible and Diversified De-identification with Latent Encryptor, to protect the identity information of people from being misused. Built upon a pre-learned StyleGAN2 generator, RiDDLE manages to encrypt and decrypt the facial identity within the latent space. The design of RiDDLE... | ['Tieniu Tan', 'Jing Dong', 'Kang Zhao', 'Wei Wang', 'Dongze Li'] | 2023-03-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_RiDDLE_Reversible_and_Diversified_De-Identification_With_Latent_Encryptor_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_RiDDLE_Reversible_and_Diversified_De-Identification_With_Latent_Encryptor_CVPR_2023_paper.pdf | cvpr-2023-1 | ['de-identification'] | ['natural-language-processing'] | [ 1.99484900e-01 1.00849129e-01 -1.88030049e-01 2.61495709e-02
-2.68033653e-01 -1.17971587e+00 5.68202019e-01 -5.47563493e-01
-2.59218037e-01 7.43030488e-01 3.59841466e-01 -1.73326299e-01
2.50054210e-01 -8.19417179e-01 -3.17699552e-01 -9.07808363e-01
-1.53754532e-01 -1.11640878e-02 -4.77862686e-01 2.23957729... | [12.731184959411621, 0.7557722330093384] |
6d66af5a-8e06-4792-8866-5b3070cfca84 | semantic-query-by-example-speech-search-using | 1904.07078 | null | http://arxiv.org/abs/1904.07078v1 | http://arxiv.org/pdf/1904.07078v1.pdf | Semantic query-by-example speech search using visual grounding | A number of recent studies have started to investigate how speech systems can
be trained on untranscribed speech by leveraging accompanying images at
training time. Examples of tasks include keyword prediction and within- and
across-mode retrieval. Here we consider how such models can be used for
query-by-example (QbE)... | ['Karen Livescu', 'Aristotelis Anastassiou', 'Herman Kamper'] | 2019-04-15 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.56948522e-01 1.76338479e-01 -3.86026800e-02 -4.81536478e-01
-1.61866808e+00 -5.36779523e-01 8.24291706e-01 1.30330101e-01
-4.76181418e-01 1.68742701e-01 5.50716162e-01 -8.74257162e-02
-1.81392003e-02 -2.57721543e-01 -7.37821400e-01 -5.27683556e-01
-1.34090498e-01 6.29787982e-01 2.44477868e-01 -2.36657694... | [10.724120140075684, 1.3386119604110718] |
b85265ca-2333-448d-a2e8-ece2590c7b76 | learning-to-predict-navigational-patterns | 2304.13242 | null | https://arxiv.org/abs/2304.13242v2 | https://arxiv.org/pdf/2304.13242v2.pdf | Learning to Predict Navigational Patterns from Partial Observations | Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unma... | ['Kazuya Takeda', 'Kento Ohtani', 'Keisuke Fujii', 'Francisco Lepe-Salazar', 'Alexander Carballo', 'Robin Karlsson'] | 2023-04-26 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [ 3.86703223e-01 6.84862792e-01 -4.51413929e-01 -7.92377293e-01
-3.38487178e-01 -5.23369193e-01 6.81783974e-01 -1.01472050e-01
-2.93790191e-01 9.84273255e-01 2.67632872e-01 -7.28132546e-01
-4.75947261e-01 -9.87402439e-01 -1.11480761e+00 -5.03399670e-01
-5.15620589e-01 7.67520547e-01 4.94204730e-01 -3.00119400... | [5.0732741355896, 0.6083695888519287] |
8a08c706-fc4f-4860-acc3-639e558555f7 | text-attentional-convolutional-neural-network | null | null | https://arxiv.org/abs/1510.03283 | https://arxiv.org/pdf/1510.03283.pdf | Text-attentional convolutional neural network for scene text detection | Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components
in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representati... | ['Weilin Huang', 'Yu Qiao', 'Jian Yao', 'Tong He'] | 2016-03-24 | null | null | null | ieee-trans-on-image-processing-2016-2016-3 | ['scene-text-detection'] | ['computer-vision'] | [ 5.66950321e-01 -4.45494294e-01 -3.29610892e-02 -1.35411382e-01
-7.45809793e-01 -2.66151190e-01 7.80436277e-01 1.65801272e-01
-4.79822904e-01 4.47196186e-01 -8.22741389e-02 9.07215998e-02
3.38119090e-01 -7.26835966e-01 -5.75320780e-01 -1.18248689e+00
4.45006758e-01 2.41270810e-01 4.82323885e-01 -2.00725291... | [12.072467803955078, 2.2720956802368164] |
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