paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6cab27ec-7f83-4930-9b7b-e6e7ccffa97d | where2comm-communication-efficient | 2209.12836 | null | https://arxiv.org/abs/2209.12836v1 | https://arxiv.org/pdf/2209.12836v1.pdf | Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps | Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication. It inevitably results in a fundamental trade-off between perception performance and communication bandwidth. To tackle this bottleneck ... | ['Siheng Chen', 'Yiqi Zhong', 'Zixing Lei', 'Shaoheng Fang', 'Yue Hu'] | 2022-09-26 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [-2.23582506e-01 -1.14234962e-01 2.70298034e-01 -3.00734714e-02
-3.62280667e-01 -8.89866412e-01 7.47525811e-01 6.63588047e-01
-6.82468593e-01 5.94332516e-01 1.31361544e-01 -2.32622966e-01
-3.37237120e-01 -1.16676092e+00 -5.36060929e-01 -5.82565188e-01
-5.21111667e-01 4.49833065e-01 7.70899236e-01 -3.13201696... | [7.147497653961182, -1.9383258819580078] |
e8da2d09-d511-4997-87ad-f01f37b1de50 | using-snomed-to-recognize-and-index-chemical | null | null | https://aclanthology.org/D19-5718 | https://aclanthology.org/D19-5718.pdf | Using Snomed to recognize and index chemical and drug mentions. | In this paper we describe a new named entity extraction system. Our work proposes a system for the identification and annotation of drug names in Spanish biomedical texts based on machine learning and deep learning models. Subsequently, a standardized code using Snomed is assigned to these drugs, for this purpose, Natu... | ['L. Alfonso Urena Lopez', "Manuel Carlos D{\\'\\i}az Galiano", "Pilar L{\\'o}pez {\\'U}beda", 'Maite Martin'] | 2019-11-01 | null | null | null | ws-2019-11 | ['entity-extraction'] | ['natural-language-processing'] | [-2.41922334e-01 4.76073027e-01 -4.77039188e-01 -2.53373176e-01
-6.94394767e-01 -4.77727681e-01 5.50122142e-01 9.84207630e-01
-7.99610078e-01 1.24328792e+00 5.01517914e-02 -7.42449239e-02
-3.83610427e-01 -7.60990739e-01 -4.95224327e-01 -5.62536240e-01
-1.23661563e-01 9.76300418e-01 -4.85026799e-02 2.07502004... | [8.495266914367676, 8.758156776428223] |
e5954286-0ed3-4b86-96a8-7498ebe5ac59 | warm-start-alphazero-self-play-search | 2004.12357 | null | https://arxiv.org/abs/2004.12357v1 | https://arxiv.org/pdf/2004.12357v1.pdf | Warm-Start AlphaZero Self-Play Search Enhancements | Recently, AlphaZero has achieved landmark results in deep reinforcement learning, by providing a single self-play architecture that learned three different games at super human level. AlphaZero is a large and complicated system with many parameters, and success requires much compute power and fine-tuning. Reproducing r... | ['Hui Wang', 'Aske Plaat', 'Mike Preuss'] | 2020-04-26 | null | null | null | null | ['board-games'] | ['playing-games'] | [-3.42913479e-01 -1.14105850e-01 -1.20701902e-01 -9.04837996e-03
-4.43925112e-01 -6.38540149e-01 5.05648255e-01 -1.79725781e-01
-8.45550895e-01 1.03011286e+00 -1.58477992e-01 -3.83218586e-01
-3.57942760e-01 -9.67102051e-01 -5.66537857e-01 -8.31567764e-01
-2.50811338e-01 7.36027300e-01 6.23105407e-01 -1.24122882... | [3.561708927154541, 1.514366865158081] |
e2d2905b-8c6b-40e1-b541-5ba8bd09a725 | joint-optimization-of-cascade-ranking-models | null | null | https://dl.acm.org/citation.cfm?id=3290986 | http://culpepper.io/publications/gcbc19-wsdm.pdf | Joint Optimization of Cascade Ranking Models | Reducing excessive costs in feature acquisition and model evaluation has been a long-standing challenge in learning-to-rank systems. A cascaded ranking architecture turns ranking into a pipeline of multiple stages, and has been shown to be a powerful approach to balancing efficiency and effectiveness trade-offs in larg... | ['Ruey-Chen Chen', 'Roi Blanco', 'J. Shane Culpepper', 'Luke Gallagher'] | 2019-02-11 | null | null | null | wsdm-2019-2 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 2.89018769e-02 -3.45727175e-01 -4.17149216e-01 -7.26551890e-01
-1.27060866e+00 -6.96198761e-01 4.73834038e-01 1.78875387e-01
-5.77952683e-01 4.60947871e-01 1.52871951e-01 -3.51964921e-01
-6.42795384e-01 -4.70352620e-01 -6.85255826e-01 -3.61676008e-01
-3.28214675e-01 9.97532189e-01 1.68408662e-01 -4.20231730... | [10.179295539855957, 5.211532115936279] |
7ec74284-fdd3-4352-918a-377cba89e0a5 | towards-improving-faithfulness-in-abstractive | 2210.01877 | null | https://arxiv.org/abs/2210.01877v1 | https://arxiv.org/pdf/2210.01877v1.pdf | Towards Improving Faithfulness in Abstractive Summarization | Despite the success achieved in neural abstractive summarization based on pre-trained language models, one unresolved issue is that the generated summaries are not always faithful to the input document. There are two possible causes of the unfaithfulness problem: (1) the summarization model fails to understand or captu... | ['Xiangliang Zhang', 'Xin Gao', 'Mingzhe Li', 'Xiuying Chen'] | 2022-10-04 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.98224145e-01 6.59087598e-01 -1.45072207e-01 -1.94756433e-01
-9.38655853e-01 -4.54155952e-01 6.96235597e-01 3.03321272e-01
-1.59026667e-01 8.54101360e-01 9.82537031e-01 -2.58319288e-01
2.65013516e-01 -7.66421258e-01 -8.26415420e-01 -2.76756883e-01
6.37784839e-01 3.56880605e-01 2.29207858e-01 -3.69760185... | [12.392855644226074, 9.351301193237305] |
6b32edac-c9c1-4df1-b17d-bc94a4f74c7b | pi-qt-opt-predictive-information-improves | 2210.08217 | null | https://arxiv.org/abs/2210.08217v2 | https://arxiv.org/pdf/2210.08217v2.pdf | PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale | The predictive information, the mutual information between the past and future, has been shown to be a useful representation learning auxiliary loss for training reinforcement learning agents, as the ability to model what will happen next is critical to success on many control tasks. While existing studies are largely ... | ['Yao Lu', 'Ian Fischer', 'Paul Wohlhart', 'Adrian Li', 'Ted Xiao', 'Kuang-Huei Lee'] | 2022-10-15 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.78850347e-01 -6.98044151e-02 -3.85574624e-02 -1.82111651e-01
-6.44462049e-01 -4.47599262e-01 5.87407649e-01 -1.79122351e-02
-8.22217941e-01 9.74816680e-01 -2.72578776e-01 -4.87218983e-02
-5.87168396e-01 -2.81020850e-01 -1.06748915e+00 -5.91522634e-01
-7.14542389e-01 1.01323771e+00 2.02090278e-01 -4.01174128... | [4.393594741821289, 1.066947340965271] |
c4b9ae20-ab82-4b66-a704-01e29c17076d | towards-generalized-open-information | 2211.15987 | null | https://arxiv.org/abs/2211.15987v1 | https://arxiv.org/pdf/2211.15987v1.pdf | Towards Generalized Open Information Extraction | Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts. However, the prevailing solutions evaluate OpenIE models on in-domain test sets aside from the training corpus, which certainly violates the initial task principle of domain-independence. In this paper, we propose to advance Op... | ['Bin Wang', 'Yongbin Li', 'Jian Sun', 'Tingwen Liu', 'Haiyang Yu', 'Jingyang Li', 'Zhenyu Zhang', 'Bowen Yu'] | 2022-11-29 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [-6.24814443e-02 3.90861481e-01 -4.77915019e-01 -2.00188577e-01
-7.34458208e-01 -1.12993395e+00 5.92932820e-01 1.22868806e-01
-2.11445153e-01 1.12404180e+00 3.27603258e-02 -3.28443676e-01
-3.85637403e-01 -8.03697288e-01 -7.78655887e-01 -3.00727040e-01
-8.75725821e-02 7.69458771e-01 3.87011856e-01 -3.93377036... | [9.977495193481445, 8.697132110595703] |
a6d8a6e9-d2a2-4a53-989a-c431076226a3 | incorporating-deep-q-network-with-multiclass | 2307.03908 | null | https://arxiv.org/abs/2307.03908v1 | https://arxiv.org/pdf/2307.03908v1.pdf | Incorporating Deep Q -- Network with Multiclass Classification Algorithms | In this study, we explore how Deep Q-Network (DQN) might improve the functionality of multiclass classification algorithms. We will use a benchmark dataset from Kaggle to create a framework incorporating DQN with existing supervised multiclass classification algorithms. The findings of this study will bring insight int... | ['Ravindranath Sawane', 'Noopur Zambare'] | 2023-07-08 | null | null | null | null | ['classification-1', 'management'] | ['methodology', 'miscellaneous'] | [-8.73963833e-02 -4.08675261e-02 -4.14894938e-01 -5.88382483e-01
-3.20823073e-01 -5.67619324e-01 1.15560569e-01 3.24307293e-01
-2.92107224e-01 7.20382214e-01 1.60135254e-01 -7.11715698e-01
-4.27872986e-01 -1.09732640e+00 -5.86736798e-02 -4.87916172e-01
2.40504503e-01 4.02299583e-01 -2.58554697e-01 -4.17763054... | [4.653134822845459, 4.227510929107666] |
4db98423-c9bb-4461-961e-2e9ad41e6c09 | self-explaining-structures-improve-nlp-models | 2012.01786 | null | https://arxiv.org/abs/2012.01786v2 | https://arxiv.org/pdf/2012.01786v2.pdf | Self-Explaining Structures Improve NLP Models | Existing approaches to explaining deep learning models in NLP usually suffer from two major drawbacks: (1) the main model and the explaining model are decoupled: an additional probing or surrogate model is used to interpret an existing model, and thus existing explaining tools are not self-explainable; (2) the probing ... | ['Jiwei Li', 'Fei Wu', 'Yuxian Meng', 'Xiaofei Sun', 'Qinghong Han', 'Chun Fan', 'Zijun Sun'] | 2020-12-03 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 1.71321541e-01 8.25853884e-01 -5.42320788e-01 -5.76683939e-01
-3.48013401e-01 -2.35398486e-01 4.44951415e-01 2.16450736e-01
4.38276026e-03 7.28106141e-01 3.66113305e-01 -3.65338683e-01
-1.04290776e-01 -5.75753629e-01 -8.49250376e-01 -3.67628187e-01
2.40376472e-01 6.15870178e-01 1.67405188e-01 -1.02584176... | [9.231389045715332, 6.094292640686035] |
3426de2e-34fb-40da-84fb-37aec80dd68d | zero-shot-transfer-for-implicit-discourse | 1907.12885 | null | https://arxiv.org/abs/1907.12885v1 | https://arxiv.org/pdf/1907.12885v1.pdf | Zero-shot transfer for implicit discourse relation classification | Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation. It becomes even more challenging by the fact that annotated training data exists only for a small number of languages, such as English and Chinese. We present a... | ['Robert Östling', 'Murathan Kurfali'] | 2019-07-30 | null | null | null | null | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 3.55700813e-02 6.81402504e-01 -5.43074965e-01 -2.44222328e-01
-8.39799643e-01 -4.73185152e-01 9.48163211e-01 3.21123183e-01
-5.13021231e-01 1.24072862e+00 5.35913646e-01 -5.03379941e-01
3.07458609e-01 -7.83625424e-01 -3.61864567e-01 -2.98781127e-01
-6.10588454e-02 8.07726085e-01 5.18191040e-01 -7.95052826... | [10.813067436218262, 9.320127487182617] |
d126cba8-6818-4675-89e4-d0f62b2c947d | applying-the-decisiveness-and-robustness | 2006.00058 | null | https://arxiv.org/abs/2006.00058v1 | https://arxiv.org/pdf/2006.00058v1.pdf | Applying the Decisiveness and Robustness Metrics to Convolutional Neural Networks | We review three recently-proposed classifier quality metrics and consider their suitability for large-scale classification challenges such as applying convolutional neural networks to the 1000-class ImageNet dataset. These metrics, referred to as the "geometric accuracy," "decisiveness," and "robustness," are based on ... | ['Eduardo A. Barrera', 'Kenric P. Nelson', 'Christopher A. George'] | 2020-05-29 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 2.70724714e-01 -7.56773576e-02 -2.75902063e-01 -7.86152542e-01
-4.15668368e-01 -3.62499595e-01 5.98271847e-01 -1.39666066e-01
-8.52386177e-01 8.21871221e-01 -1.50954530e-01 -4.39620912e-01
-6.18823469e-01 -6.93259358e-01 -4.60159391e-01 -7.20741034e-01
-1.77302912e-01 1.05819285e-01 1.15539446e-01 -1.61746174... | [8.014339447021484, -0.7650129795074463] |
41e86e31-1851-49fc-9bda-e76264af1730 | machine-learning-and-chord-based-feature | 1902.03283 | null | http://arxiv.org/abs/1902.03283v1 | http://arxiv.org/pdf/1902.03283v1.pdf | Machine learning and chord based feature engineering for genre prediction in popular Brazilian music | Music genre can be hard to describe: many factors are involved, such as
style, music technique, and historical context. Some genres even have
overlapping characteristics. Looking for a better understanding of how music
genres are related to musical harmonic structures, we gathered data about the
music chords for thousa... | ['Walmes M. Zeviani', 'Bruna D. Wundervald'] | 2019-02-08 | null | null | null | null | ['music-genre-recognition'] | ['music'] | [-8.60552117e-02 -3.64635706e-01 -1.79152712e-01 -1.64360017e-01
-5.98083258e-01 -1.06695914e+00 6.51559472e-01 2.45258734e-01
-2.02471763e-01 6.94066048e-01 3.77798378e-01 -1.04521073e-01
-7.58615613e-01 -9.02604282e-01 -2.78175205e-01 -5.87409139e-01
-1.52461812e-01 5.28323054e-01 4.66801286e-01 -5.58828890... | [15.937097549438477, 5.239450454711914] |
4d7cc9be-fbbd-44ca-a863-e8bc36eb5e6d | superpixel-based-graph-laplacian | 2007.14033 | null | https://arxiv.org/abs/2007.14033v2 | https://arxiv.org/pdf/2007.14033v2.pdf | Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing | An efficient spatial regularization method using superpixel segmentation and graph Laplacian regularization is proposed for sparse hyperspectral unmixing method. Since it is likely to find spectrally similar pixels in a homogeneous region, we use a superpixel segmentation algorithm to extract the homogeneous regions by... | ['Taner Ince'] | 2020-07-28 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 6.54104173e-01 6.46101311e-02 -2.54838467e-01 5.04138926e-03
-2.95203209e-01 -3.08658063e-01 -1.24209009e-01 -5.94697371e-02
-2.03878894e-01 6.35765910e-01 2.90645957e-02 1.79168805e-01
-2.02422440e-01 -8.89321864e-01 -4.35492098e-01 -1.09245801e+00
-4.93063219e-02 -2.22889669e-02 2.84285277e-01 2.90330052... | [10.093822479248047, -2.00382924079895] |
21255155-d06e-4f8d-8311-d44ce76de011 | learn-to-race-challenge-2022-benchmarking | 2205.02953 | null | https://arxiv.org/abs/2205.02953v2 | https://arxiv.org/pdf/2205.02953v2.pdf | Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing | We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in autonomous driving and to help advance the state of the art on a realistic benchmark. Analogous to racing being used to test cut... | ['Ivan Zhukov', 'Vrushank Vyas', 'Eric Nyberg', 'Jean Oh', 'Anirudh Koul', 'Max Kumskoy', 'Sahika Genc', 'Ayush Shivani', 'Jyotish Poonganam', 'Sidharth Kathpal', 'Siddha Ganju', 'Bingqing Chen', 'Jonathan Francis'] | 2022-05-05 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.59849271e-01 -1.70249324e-02 -1.40105963e-01 -2.59915829e-01
-9.77122605e-01 -8.04240048e-01 7.42551863e-01 -1.18407853e-01
-7.21504211e-01 8.27721953e-01 -1.48103803e-01 -5.66795230e-01
-1.98366478e-01 -5.29396355e-01 -1.03617990e+00 -2.63567477e-01
-5.95593452e-01 8.47835004e-01 3.69047433e-01 -9.58574116... | [5.061178684234619, 1.2172703742980957] |
41898670-cc22-4850-815e-f22de3506ad6 | doubly-stochastic-subspace-clustering | 2011.14859 | null | https://arxiv.org/abs/2011.14859v2 | https://arxiv.org/pdf/2011.14859v2.pdf | Doubly Stochastic Subspace Clustering | Many state-of-the-art subspace clustering methods follow a two-step process by first constructing an affinity matrix between data points and then applying spectral clustering to this affinity. Most of the research into these methods focuses on the first step of generating the affinity, which often exploits the self-exp... | ['Benjamin D. Haeffele', 'René Vidal', 'Derek Lim'] | 2020-11-30 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 1.48958623e-01 -2.32055351e-01 -1.15866311e-01 -3.24027479e-01
-8.94177616e-01 -7.61706054e-01 4.09223765e-01 -1.75350189e-01
-3.54363114e-01 1.86954737e-01 3.71825755e-01 8.38926435e-02
-4.88300949e-01 -4.27863508e-01 -5.92997789e-01 -1.24209571e+00
1.05449267e-01 7.10183740e-01 2.41399258e-02 1.70192614... | [7.845475196838379, 4.361932277679443] |
eec90b40-8b9b-46ed-9844-9315b0507f1c | cost-effective-photonic-super-resolution | 2210.04280 | null | https://arxiv.org/abs/2210.04280v1 | https://arxiv.org/pdf/2210.04280v1.pdf | Cost-effective photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection | A cost-effective millimeter-wave (MMW) joint radar-communication (JRC) system with super resolution is proposed and experimentally demonstrated, using optical heterodyne up-conversion and self-coherent detection down-conversion techniques. The point lies in the designed coherent dual-band constant envelope linear frequ... | ['Bin Luo', 'Lianshan Yan', 'Wei Pan', 'Ningyuan Zhong', 'Xihua Zou', 'Peixuan Li', 'Wenlin Bai'] | 2022-10-09 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 3.11824024e-01 -2.35587768e-02 -1.36847631e-03 -1.59934074e-01
-6.08500957e-01 -2.38956034e-01 5.83657205e-01 -7.34164178e-01
-4.63343114e-01 1.11272955e+00 2.01577842e-01 -2.58806586e-01
-7.05355942e-01 -1.00996017e+00 2.67460257e-01 -1.08520532e+00
-4.68375117e-01 9.67841372e-02 -2.65683651e-01 -1.22001901... | [6.4202399253845215, 1.2502952814102173] |
61ea3486-93b0-4a69-9987-bc98045f34c4 | uniform-convergence-with-square-root | 2306.13188 | null | https://arxiv.org/abs/2306.13188v1 | https://arxiv.org/pdf/2306.13188v1.pdf | Uniform Convergence with Square-Root Lipschitz Loss | We establish generic uniform convergence guarantees for Gaussian data in terms of the Rademacher complexity of the hypothesis class and the Lipschitz constant of the square root of the scalar loss function. We show how these guarantees substantially generalize previous results based on smoothness (Lipschitz constant of... | ['Nathan Srebro', 'Frederic Koehler', 'Zhen Dai', 'Lijia Zhou'] | 2023-06-22 | null | null | null | null | ['retrieval'] | ['methodology'] | [-2.90014502e-02 3.19619834e-01 -2.49904290e-01 -3.63376230e-01
-1.34189510e+00 -4.94380295e-01 2.27272734e-01 4.69872952e-01
-5.17713606e-01 8.06169152e-01 -3.97459827e-02 -2.06720948e-01
-5.38833201e-01 -6.45412266e-01 -8.92202139e-01 -1.10346508e+00
-5.83284736e-01 3.44811410e-01 2.98147142e-01 -8.87620673... | [7.277900695800781, 4.11313009262085] |
f63c3a3c-9091-4b02-aad3-c14838678367 | deep-convolutional-neural-network-based-1 | 2005.11780 | null | https://arxiv.org/abs/2005.11780v1 | https://arxiv.org/pdf/2005.11780v1.pdf | Deep Convolutional Neural Network-based Bernoulli Heatmap for Head Pose Estimation | Head pose estimation is a crucial problem for many tasks, such as driver attention, fatigue detection, and human behaviour analysis. It is well known that neural networks are better at handling classification problems than regression problems. It is an extremely nonlinear process to let the network output the angle val... | ['Yang Xing', 'Jie Liu', 'Chen Lv', 'Zhongxu Hu', 'Peng Hang'] | 2020-05-24 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-2.62606084e-01 -1.45935128e-03 -4.81723845e-02 -8.02668810e-01
-5.38737416e-01 3.02777201e-01 1.69226646e-01 -5.00090532e-02
-7.89666593e-01 5.43371677e-01 2.53329992e-01 2.99460310e-02
2.20837463e-02 -7.05866992e-01 -6.77308619e-01 -9.37214077e-01
1.29070699e-01 5.74190635e-03 2.39211664e-01 -1.74944371... | [13.750085830688477, 0.2735692858695984] |
9ece6ff1-1d96-4e3f-b473-6b835c4618a5 | systematicity-emerges-in-transformers-when | null | null | https://aclanthology.org/2022.naacl-srw.1 | https://aclanthology.org/2022.naacl-srw.1.pdf | Systematicity Emerges in Transformers when Abstract Grammatical Roles Guide Attention | Systematicity is thought to be a key inductive bias possessed by humans that is lacking in standard natural language processing systems such as those utilizing transformers. In this work, we investigate the extent to which the failure of transformers on systematic generalization tests can be attributed to a lack of lin... | ['Randall O’Reilly', 'Jacob Labe Russin', 'Ayush K Chakravarthy'] | null | null | null | null | naacl-acl-2022-7 | ['systematic-generalization'] | ['reasoning'] | [ 4.33443218e-01 9.82527342e-03 -2.12954402e-01 -5.47035038e-01
4.68685851e-03 -7.71351099e-01 6.07618272e-01 5.26394188e-01
-7.05706000e-01 5.08137465e-01 4.16526496e-01 -6.76811755e-01
-1.44884679e-02 -1.05067205e+00 -6.46012604e-01 -2.55805969e-01
1.85062476e-02 4.47714210e-01 7.44115591e-01 -4.75896984... | [10.456609725952148, 8.422676086425781] |
33dc3a66-4d55-42f2-af07-c88778a5e489 | bottlesum-unsupervised-and-self-supervised | 1909.07405 | null | https://arxiv.org/abs/1909.07405v2 | https://arxiv.org/pdf/1909.07405v2.pdf | BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle | The principle of the Information Bottleneck (Tishby et al. 1999) is to produce a summary of information X optimized to predict some other relevant information Y. In this paper, we propose a novel approach to unsupervised sentence summarization by mapping the Information Bottleneck principle to a conditional language mo... | ['Yejin Choi', 'Jan Buys', 'Peter West', 'Ari Holtzman'] | 2019-09-16 | bottlesum-unsupervised-and-self-supervised-1 | https://aclanthology.org/D19-1389 | https://aclanthology.org/D19-1389.pdf | ijcnlp-2019-11 | ['unsupervised-extractive-summarization', 'abstractive-sentence-summarization', 'unsupervised-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 8.12328756e-01 8.26894462e-01 -5.12607038e-01 -4.26625401e-01
-1.39189065e+00 -4.82817084e-01 6.15593195e-01 8.25569928e-01
-4.14130688e-01 9.33017850e-01 1.18556547e+00 -4.38607670e-02
8.64770543e-03 -4.62515563e-01 -7.42819190e-01 -2.57126957e-01
1.54940709e-01 6.28336668e-01 -2.96373814e-02 -9.71239284... | [12.500020980834961, 9.499228477478027] |
31754f43-49da-4987-98dc-121379f175ce | a-study-on-agreement-in-pico-span-annotations | 1904.09557 | null | http://arxiv.org/abs/1904.09557v1 | http://arxiv.org/pdf/1904.09557v1.pdf | A Study on Agreement in PICO Span Annotations | In evidence-based medicine, relevance of medical literature is determined by
predefined relevance conditions. The conditions are defined based on PICO
elements, namely, Patient, Intervention, Comparator, and Outcome. Hence, PICO
annotations in medical literature are essential for automatic relevant document
filtering. ... | ['Aixin Sun', 'Grace E. Lee'] | 2019-04-21 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 3.41664702e-01 4.17804182e-01 -4.97450858e-01 -2.66845912e-01
-9.07958031e-01 -1.04684103e+00 3.33557785e-01 1.05452907e+00
-4.53388989e-01 8.41541767e-01 5.14601469e-01 -6.04131758e-01
-6.97372139e-01 -4.21409398e-01 -3.33444774e-01 -3.47925454e-01
2.92778492e-01 4.24616843e-01 3.15299958e-01 2.24815235... | [8.453250885009766, 8.683704376220703] |
083f4742-fd3d-4660-b573-4890bc566867 | direction-of-arrival-estimation-for-non | 2011.02083 | null | https://arxiv.org/abs/2011.02083v1 | https://arxiv.org/pdf/2011.02083v1.pdf | Direction of Arrival Estimation for Non-Coherent Sub-Arrays via Joint Sparse and Low-Rank Signal Recovery | Estimating the directions of arrival (DOAs) of multiple sources from a single snapshot obtained by a coherent antenna array is a well-known problem, which can be addressed by sparse signal reconstruction methods, where the DOAs are estimated from the peaks of the recovered high-dimensional signal. In this paper, we con... | ['Oded Bialer', 'Tom Tirer'] | 2020-11-04 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 2.66941398e-01 -1.64624900e-01 4.62265849e-01 7.59986788e-02
-9.10042048e-01 -8.70023370e-01 2.51923770e-01 -1.37586057e-01
-1.58008978e-01 7.34244108e-01 6.06822014e-01 2.80716449e-01
-5.86522102e-01 -3.61029238e-01 -8.38132858e-01 -1.36017835e+00
-3.26106340e-01 3.15979272e-01 -1.87252238e-01 -2.33061947... | [6.494715690612793, 1.3287110328674316] |
3a02c5f8-3485-4a11-a159-d1fe9cf5ad23 | global-context-aware-attention-lstm-networks | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Liu_Global_Context-Aware_Attention_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Liu_Global_Context-Aware_Attention_CVPR_2017_paper.pdf | Global Context-Aware Attention LSTM Networks for 3D Action Recognition | Long Short-Term Memory (LSTM) networks have shown superior performance in 3D human action recognition due to their power in modeling the dynamics and dependencies in sequential data. Since not all joints are informative for action analysis and the irrelevant joints often bring a lot of noise, we need to pay more attent... | ['Ling-Yu Duan', 'Ping Hu', 'Gang Wang', 'Alex C. Kot', 'Jun Liu'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['action-analysis', 'one-shot-3d-action-recognition', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.48778746e-01 -5.55964783e-02 -3.00777227e-01 -2.01117560e-01
-6.44120812e-01 2.40924001e-01 4.79724497e-01 -4.31996316e-01
-3.65484685e-01 3.94538939e-01 6.24895155e-01 7.33181983e-02
6.55329376e-02 -3.90172899e-01 -7.12753832e-01 -8.54950905e-01
-8.48540291e-02 2.85242468e-01 5.23637652e-01 -4.51931693... | [7.934614658355713, 0.4385104477405548] |
7a495859-1f42-48d9-a1fa-252677d79999 | d-lema-deep-learning-ensembles-from-multiple | 2012.07206 | null | https://arxiv.org/abs/2012.07206v2 | https://arxiv.org/pdf/2012.07206v2.pdf | D-LEMA: Deep Learning Ensembles from Multiple Annotations -- Application to Skin Lesion Segmentation | Medical image segmentation annotations suffer from inter- and intra-observer variations even among experts due to intrinsic differences in human annotators and ambiguous boundaries. Leveraging a collection of annotators' opinions for an image is an interesting way of estimating a gold standard. Although training deep m... | ['Ghassan Hamarneh', 'Saeed Izadi', 'Kumar Abhishek', 'Zahra Mirikharaji'] | 2020-12-14 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 4.12755698e-01 6.60603940e-01 -8.17477554e-02 -9.39547181e-01
-1.26244533e+00 -5.71678638e-01 2.65248418e-01 3.75411958e-01
-8.04334402e-01 8.45779896e-01 -1.33835584e-01 -1.41047817e-02
-6.50519282e-02 -2.33446330e-01 -8.10205400e-01 -7.98563957e-01
4.57326263e-01 9.10852015e-01 5.52758515e-01 2.91837186... | [14.65034294128418, -2.1045825481414795] |
36f44186-ffd0-4f38-a2de-78c88b1228b9 | iiitk-dravidianlangtech-eacl2021-offensive | null | null | https://aclanthology.org/2021.dravidianlangtech-1.30 | https://aclanthology.org/2021.dravidianlangtech-1.30.pdf | IIITK@DravidianLangTech-EACL2021: Offensive Language Identification and Meme Classification in Tamil, Malayalam and Kannada | This paper describes the IIITK team’s submissions to the offensive language identification, and troll memes classification shared tasks for Dravidian languages at DravidianLangTech 2021 workshop@EACL 2021. Our best configuration for Tamil troll meme classification achieved 0.55 weighted average F1 score, and for offens... | ['Bharathi Raja Chakravarthi', 'Ruba Priyadharshini', 'Sajeetha Thavareesan', 'Parameswari Krishnamurthy', 'Nikhil Ghanghor'] | 2021-04-17 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-5.81272900e-01 -4.75913942e-01 -4.74065393e-01 2.65805542e-01
-9.87007201e-01 -1.36167979e+00 1.27708042e+00 -3.61147337e-02
-8.65652323e-01 8.29699337e-01 4.53859657e-01 -7.32986569e-01
8.18918943e-02 -3.13447118e-01 1.35737821e-01 -4.91174430e-01
-2.81649902e-02 7.74116337e-01 -2.76852697e-01 -7.34668970... | [9.674905776977539, 10.700288772583008] |
e9f4e8ef-cc02-4155-aad1-bceddd1b4094 | learning-unnormalized-statistical-models-via | 2306.07485 | null | https://arxiv.org/abs/2306.07485v1 | https://arxiv.org/pdf/2306.07485v1.pdf | Learning Unnormalized Statistical Models via Compositional Optimization | Learning unnormalized statistical models (e.g., energy-based models) is computationally challenging due to the complexity of handling the partition function. To eschew this complexity, noise-contrastive estimation~(NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artific... | ['Lijun Zhang', 'Tianbao Yang', 'Changyou Chen', 'Lingyu Wu', 'Jiayu Qin', 'Wei Jiang'] | 2023-06-13 | null | null | null | null | ['density-estimation'] | ['methodology'] | [ 3.96144181e-01 -7.82416761e-02 -4.17250022e-02 -1.07854858e-01
-1.02380443e+00 -4.14516628e-01 5.13110578e-01 -6.32310733e-02
-6.54443562e-01 8.81969392e-01 -7.73321241e-02 -1.66216597e-01
-1.77073181e-01 -6.76098466e-01 -9.19735014e-01 -1.10183823e+00
1.32088542e-01 4.95257825e-01 1.54349491e-01 1.44138128... | [7.145923137664795, 3.9552276134490967] |
7910dee7-bb08-4675-ae89-6eedef44ded7 | learn-more-for-food-recognition-via | 2303.05073 | null | https://arxiv.org/abs/2303.05073v1 | https://arxiv.org/pdf/2303.05073v1.pdf | Learn More for Food Recognition via Progressive Self-Distillation | Food recognition has a wide range of applications, such as health-aware recommendation and self-service restaurants. Most previous methods of food recognition firstly locate informative regions in some weakly-supervised manners and then aggregate their features. However, location errors of informative regions limit the... | ['Jiang Tian', 'Linhu Liu', 'Yaohui Zhu'] | 2023-03-09 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 3.92519951e-01 3.57501626e-01 -4.46297646e-01 -5.43328106e-01
-3.51976514e-01 -4.88935381e-01 1.64404169e-01 5.15871763e-01
-4.10240293e-01 4.41465527e-01 2.07493320e-01 1.35240108e-01
1.74021721e-02 -1.15748131e+00 -6.85314178e-01 -9.73902881e-01
-3.76343466e-02 1.72766268e-01 5.55896223e-01 1.07591180... | [11.391328811645508, 4.163058280944824] |
c1f21150-30d7-4bea-968c-dc434a0cffc5 | motr-end-to-end-multiple-object-tracking-with | 2105.03247 | null | https://arxiv.org/abs/2105.03247v4 | https://arxiv.org/pdf/2105.03247v4.pdf | MOTR: End-to-End Multiple-Object Tracking with Transformer | Temporal modeling of objects is a key challenge in multiple object tracking (MOT). Existing methods track by associating detections through motion-based and appearance-based similarity heuristics. The post-processing nature of association prevents end-to-end exploitation of temporal variations in video sequence. In thi... | ['Xiangyu Zhang', 'Tiancai Wang', 'Yuang Zhang', 'Yichen Wei', 'Bin Dong', 'Fangao Zeng'] | 2021-05-07 | null | null | null | null | ['multiple-object-tracking-with-transformer'] | ['computer-vision'] | [-1.48110792e-01 -4.81398493e-01 -7.22358108e-01 -3.64785314e-01
-9.70213234e-01 -5.29385626e-01 4.25077885e-01 5.52266315e-02
-4.49604124e-01 5.71151972e-01 3.85258384e-02 1.79855540e-01
-1.73324928e-01 -2.39670932e-01 -8.09683800e-01 -5.33021867e-01
-4.20453072e-01 6.79529607e-01 9.56319869e-01 2.32860178... | [6.2995219230651855, -2.0284571647644043] |
e651ed6e-a046-44d2-943b-97a20f7ba5dc | automatic-identification-and-classification | 2203.05840 | null | https://arxiv.org/abs/2203.05840v1 | https://arxiv.org/pdf/2203.05840v1.pdf | Automatic Identification and Classification of Bragging in Social Media | Bragging is a speech act employed with the goal of constructing a favorable self-image through positive statements about oneself. It is widespread in daily communication and especially popular in social media, where users aim to build a positive image of their persona directly or indirectly. In this paper, we present t... | ['Nikolaos Aletras', 'A. Seza Doğruöz', 'Daniel Preoţiuc-Pietro', 'Mali Jin'] | 2022-03-11 | null | https://aclanthology.org/2022.acl-long.273 | https://aclanthology.org/2022.acl-long.273.pdf | acl-2022-5 | ['type-prediction'] | ['computer-code'] | [ 2.40185544e-01 6.53189898e-01 -6.35788083e-01 -7.70396948e-01
-6.74144804e-01 -1.09921440e-01 1.07089531e+00 5.59943557e-01
-5.35763383e-01 6.15305126e-01 9.69379902e-01 -2.03091785e-01
8.66971612e-02 -7.24841595e-01 -3.36013108e-01 -4.33639884e-01
2.53323615e-01 6.05524004e-01 -1.06456362e-01 -5.66640913... | [9.075824737548828, 10.605148315429688] |
5a361fb5-0f3b-4e32-aef4-d8bc5e4e2a8f | dirichlet-survival-process-scalable-inference | 2212.05996 | null | https://arxiv.org/abs/2212.05996v1 | https://arxiv.org/pdf/2212.05996v1.pdf | Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks | Information spread on networks can be efficiently modeled by considering three features: documents' content, time of publication relative to other publications, and position of the spreader in the network. Most previous works model up to two of those jointly, or rely on heavily parametric approaches. Building on recent... | ['Sabine Loudcher', 'Julien Velcin', 'Gaël Poux-Médard'] | 2022-12-12 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 4.04034136e-03 1.38034612e-01 -5.60544312e-01 -1.30393282e-01
-7.78671026e-01 -7.36444533e-01 1.45481455e+00 3.47185016e-01
-3.18089545e-01 5.79899311e-01 4.38725471e-01 -2.98898667e-01
-3.93750966e-01 -8.85618269e-01 -6.27845943e-01 -8.15697551e-01
-3.02117229e-01 1.41037130e+00 4.36505347e-01 4.70649451... | [7.1276140213012695, 5.219654560089111] |
9ca694c9-9ac5-41db-96e3-2826acbbb791 | direct-multi-view-multi-person-3d-pose | 2111.04076 | null | https://arxiv.org/abs/2111.04076v2 | https://arxiv.org/pdf/2111.04076v2.pdf | Direct Multi-view Multi-person 3D Pose Estimation | We present Multi-view Pose transformer (MvP) for estimating multi-person 3D poses from multi-view images. Instead of estimating 3D joint locations from costly volumetric representation or reconstructing the per-person 3D pose from multiple detected 2D poses as in previous methods, MvP directly regresses the multi-perso... | ['Jiashi Feng', 'Shuicheng Yan', 'Yujun Cai', 'Jianfeng Zhang', 'Tao Wang'] | 2021-11-07 | direct-multi-view-multi-person-3d-pose-1 | http://proceedings.neurips.cc/paper/2021/hash/6da9003b743b65f4c0ccd295cc484e57-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/6da9003b743b65f4c0ccd295cc484e57-Paper.pdf | neurips-2021-12 | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.85034567e-01 5.45189083e-02 3.28758918e-02 -2.48670965e-01
-1.21859825e+00 -5.90163887e-01 5.15531957e-01 -1.08205549e-01
-4.09805626e-01 2.75035977e-01 3.93115729e-01 4.00394499e-01
2.57281214e-01 -6.50622666e-01 -9.96445239e-01 -3.72159481e-01
2.28423148e-01 9.22047675e-01 1.61307618e-01 -1.12812862... | [7.0486602783203125, -0.9362524151802063] |
74a4e163-66f7-4d83-a373-453b65da98d1 | skghoi-spatial-semantic-knowledge-graph-for | 2303.04253 | null | https://arxiv.org/abs/2303.04253v3 | https://arxiv.org/pdf/2303.04253v3.pdf | TMHOI: Translational Model for Human-Object Interaction Detection | Detecting human-object interactions (HOIs) is an intricate challenge in the field of computer vision. Existing methods for HOI detection heavily rely on appearance-based features, but these may not fully capture all the essential characteristics necessary for accurate detection. To overcome these challenges, we propose... | ['Shuteng Niu', 'Qing Tian', 'Acharya Kamal', 'Houbing Song', 'Alvaro Velasquez', 'Qizhen Lan', 'Lijing Zhu'] | 2023-03-07 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 2.33085513e-01 -1.61903828e-01 -2.20053792e-01 9.71969664e-02
-2.96391279e-01 -4.02866155e-01 3.14965218e-01 2.24693269e-01
-6.17158972e-02 7.60612488e-02 2.61401445e-01 -6.60073161e-02
-1.28923990e-02 -8.50815058e-01 -5.74035645e-01 -6.26590014e-01
-2.51257598e-01 -7.67390281e-02 5.60454667e-01 -1.41883090... | [9.538262367248535, 1.3809270858764648] |
ac92284b-a69b-41e8-8504-62a00cdc9706 | free-form-video-inpainting-with-3d-gated | 1904.10247 | null | https://arxiv.org/abs/1904.10247v3 | https://arxiv.org/pdf/1904.10247v3.pdf | Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN | Free-form video inpainting is a very challenging task that could be widely used for video editing such as text removal. Existing patch-based methods could not handle non-repetitive structures such as faces, while directly applying image-based inpainting models to videos will result in temporal inconsistency (see http:/... | ['Winston Hsu', 'Kuan-Ying Lee', 'Ya-Liang Chang', 'Zhe Yu Liu'] | 2019-04-23 | free-form-video-inpainting-with-3d-gated-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Chang_Free-Form_Video_Inpainting_With_3D_Gated_Convolution_and_Temporal_PatchGAN_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Chang_Free-Form_Video_Inpainting_With_3D_Gated_Convolution_and_Temporal_PatchGAN_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-inpainting'] | ['computer-vision'] | [ 2.50062287e-01 -1.54766023e-01 -8.15965161e-02 -3.88519049e-01
-5.52536368e-01 -2.92486757e-01 3.16128492e-01 -7.31654704e-01
2.20978539e-03 7.34240830e-01 1.17234983e-01 -3.70654613e-02
-7.27226911e-03 -4.95077670e-01 -1.21241355e+00 -4.15860623e-01
3.43118720e-02 4.24891785e-02 8.44916031e-02 -1.05511732... | [10.940652847290039, -1.2598581314086914] |
e7fdff61-5f24-4a99-b3fb-5d7a7ef7277d | deep-factor-model-a-novel-approach-for-motion | 2304.00102 | null | https://arxiv.org/abs/2304.00102v1 | https://arxiv.org/pdf/2304.00102v1.pdf | Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI | Recent quantitative parameter mapping methods including MR fingerprinting (MRF) collect a time series of images that capture the evolution of magnetization. The focus of this work is to introduce a novel approach termed as Deep Factor Model(DFM), which offers an efficient representation of the multi-contrast image time... | ['Mathews Jacob', 'Vincent Magnotta', 'Curtis Corum', 'James H. Holmes', 'Yan Chen'] | 2023-03-31 | null | null | null | null | ['motion-estimation'] | ['computer-vision'] | [ 3.40555638e-01 -4.42668140e-01 -1.61194101e-01 -2.73464382e-01
-4.99255270e-01 -1.64733082e-01 6.02388084e-01 -6.94693103e-02
-7.64033973e-01 6.73441350e-01 7.74354935e-02 5.73643036e-02
-5.52619994e-01 -4.01220053e-01 -3.08387488e-01 -8.72485995e-01
-4.21190351e-01 3.71661007e-01 5.62700450e-01 -1.92610435... | [13.51130485534668, -2.4118289947509766] |
07fb11cc-b074-4daa-ba60-7cf9c41afdf7 | snowflakenet-point-cloud-completion-by | 2108.04444 | null | https://arxiv.org/abs/2108.04444v2 | https://arxiv.org/pdf/2108.04444v2.pdf | SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer | Point cloud completion aims to predict a complete shape in high accuracy from its partial observation. However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it hard to reveal fine local geometric details on the complete shape. ... | ['Zhizhong Han', 'Wen Zheng', 'Pengfei Wan', 'Yan-Pei Cao', 'Yu-Shen Liu', 'Xin Wen', 'Peng Xiang'] | 2021-08-10 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xiang_SnowflakeNet_Point_Cloud_Completion_by_Snowflake_Point_Deconvolution_With_Skip-Transformer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xiang_SnowflakeNet_Point_Cloud_Completion_by_Snowflake_Point_Deconvolution_With_Skip-Transformer_ICCV_2021_paper.pdf | iccv-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [-2.29567423e-01 1.48000523e-01 1.88255087e-01 -1.40827149e-01
-6.53330684e-01 -5.20840526e-01 5.30420661e-01 6.30113035e-02
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1.36652052e-01 -1.09521794e+00 -1.26246989e+00 -5.82445323e-01
2.19466053e-02 8.25358152e-01 1.89357147e-01 -4.42198038... | [8.380166053771973, -3.611647129058838] |
28ec7d3d-3a30-4a18-91a0-214771c6cb6f | penalizing-proposals-using-classifiers-for | 2205.13219 | null | https://arxiv.org/abs/2205.13219v2 | https://arxiv.org/pdf/2205.13219v2.pdf | Penalizing Proposals using Classifiers for Semi-Supervised Object Detection | Obtaining gold standard annotated data for object detection is often costly, involving human-level effort. Semi-supervised object detection algorithms solve the problem with a small amount of gold-standard labels and a large unlabelled dataset used to generate silver-standard labels. But training on the silver standard... | ['Pallab Dasgupta', 'Somnath Hazra'] | 2022-05-26 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 4.59415287e-01 5.15520334e-01 1.20077170e-01 -6.72013760e-01
-1.09584951e+00 -6.59210682e-01 5.87904871e-01 3.13494802e-01
-8.83887708e-01 8.26590896e-01 -2.25625917e-01 7.96139464e-02
3.76974881e-01 -4.95506823e-01 -7.16926098e-01 -6.40330434e-01
2.52363503e-01 6.73334956e-01 8.54545534e-01 3.07877243... | [9.261161804199219, 1.2303614616394043] |
3099bbc3-feb7-4165-b676-df50f2006a9e | fedcp-separating-feature-information-for | 2307.01217 | null | https://arxiv.org/abs/2307.01217v1 | https://arxiv.org/pdf/2307.01217v1.pdf | FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy | Recently, personalized federated learning (pFL) has attracted increasing attention in privacy protection, collaborative learning, and tackling statistical heterogeneity among clients, e.g., hospitals, mobile smartphones, etc. Most existing pFL methods focus on exploiting the global information and personalized informat... | ['Haibing Guan', 'Ruhui Ma', 'Zhengui Xue', 'Tao Song', 'Hao Wang', 'Yang Hua', 'Jianqing Zhang'] | 2023-07-01 | null | null | null | null | ['federated-learning', 'personalized-federated-learning'] | ['methodology', 'methodology'] | [-2.21771255e-01 -1.00155197e-01 -5.66448867e-01 -7.43986547e-01
-8.89256239e-01 -3.28393102e-01 5.06052673e-01 4.28476110e-02
-2.16705739e-01 7.27006137e-01 3.94114345e-01 -2.35938802e-01
-1.13898307e-01 -6.71924531e-01 -5.96586406e-01 -8.73853743e-01
7.15486109e-02 3.48194242e-01 1.08486257e-01 3.90737355... | [5.849491596221924, 6.342510223388672] |
42746194-d990-42ff-b0d3-df1e33905b39 | explanationlp-abductive-reasoning-for | 2010.13128 | null | https://arxiv.org/abs/2010.13128v1 | https://arxiv.org/pdf/2010.13128v1.pdf | ExplanationLP: Abductive Reasoning for Explainable Science Question Answering | We propose a novel approach for answering and explaining multiple-choice science questions by reasoning on grounding and abstract inference chains. This paper frames question answering as an abductive reasoning problem, constructing plausible explanations for each choice and then selecting the candidate with the best e... | ['André Freitas', 'Marco Valentino', 'Mokanarangan Thayaparan'] | 2020-10-25 | null | null | null | null | ['science-question-answering', 'answer-selection'] | ['miscellaneous', 'natural-language-processing'] | [ 2.65543312e-01 1.13538861e+00 -5.45153618e-01 -6.95133150e-01
-9.98406887e-01 -5.72134614e-01 5.32881498e-01 3.41528535e-01
9.41716060e-02 7.42464721e-01 5.07391334e-01 -7.68897772e-01
-8.03717434e-01 -8.83782744e-01 -8.75215590e-01 2.02797055e-02
4.96437699e-02 1.13276350e+00 3.14960301e-01 -2.39148572... | [10.833137512207031, 7.782648086547852] |
6582102a-e716-4ee2-a5f7-d8c845406190 | weakly-supervised-video-anomaly-detection | 2101.10030 | null | https://arxiv.org/abs/2101.10030v3 | https://arxiv.org/pdf/2101.10030v3.pdf | Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning | Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets. Although current methods show effective detection performance, their... | ['Gustavo Carneiro', 'Johan W. Verjans', 'Rajvinder Singh', 'Yuanhong Chen', 'Guansong Pang', 'Yu Tian'] | 2021-01-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Tian_Weakly-Supervised_Video_Anomaly_Detection_With_Robust_Temporal_Feature_Magnitude_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Tian_Weakly-Supervised_Video_Anomaly_Detection_With_Robust_Temporal_Feature_Magnitude_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 2.49558941e-01 -3.86649132e-01 -3.16536099e-01 -3.52478325e-01
-9.06177461e-01 -3.76484275e-01 6.59818709e-01 4.97712269e-02
-2.72183597e-01 4.31790739e-01 8.57208073e-02 -1.28089618e-02
-7.26380944e-02 -4.62608576e-01 -9.56507206e-01 -7.56381154e-01
-6.92085862e-01 2.82065552e-02 1.29473552e-01 7.58536011... | [7.851905822753906, 1.590477705001831] |
5128f321-a66f-4e01-806c-5f32e9eb77a1 | detecting-out-of-distribution-inputs-in-deep | 1910.10307 | null | https://arxiv.org/abs/1910.10307v1 | https://arxiv.org/pdf/1910.10307v1.pdf | Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output | Deep neural networks achieve superior performance in challenging tasks such as image classification. However, deep classifiers tend to incorrectly classify out-of-distribution (OOD) inputs, which are inputs that do not belong to the classifier training distribution. Several approaches have been proposed to detect OOD i... | ['Taylor Denounden', 'Krzysztof Czarnecki', 'Rick Salay', 'Sachin Vernekar', 'Vahdat Abdelzad', 'Buu Phan'] | 2019-10-23 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 2.28591278e-01 9.74532142e-02 -2.35000044e-01 -3.81245852e-01
-4.83761579e-01 -5.34354031e-01 6.22466207e-01 3.73484135e-01
-4.06903982e-01 3.32862318e-01 -1.98592722e-01 -2.83678532e-01
3.11750442e-01 -9.04519975e-01 -5.98015726e-01 -5.58464468e-01
7.25743547e-02 4.94927198e-01 6.09423459e-01 3.08865547... | [9.342658042907715, 2.9456374645233154] |
2f364245-258e-48f8-b017-506f6dda1096 | morpheus-a-neural-network-for-jointly | null | null | https://aclanthology.org/W19-4205 | https://aclanthology.org/W19-4205.pdf | Morpheus: A Neural Network for Jointly Learning Contextual Lemmatization and Morphological Tagging | In this study, we present Morpheus, a joint contextual lemmatizer and morphological tagger. Morpheus is based on a neural sequential architecture where inputs are the characters of the surface words in a sentence and the outputs are the minimum edit operations between surface words and their lemmata as well as the morp... | ['A. C{\\"u}neyd Tantu{\\u{g}}', 'Eray Yildiz'] | 2019-08-01 | null | null | null | ws-2019-8 | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.80171475e-01 3.06887627e-01 1.11049667e-01 -4.43038195e-01
-9.53530133e-01 -9.26082492e-01 3.33771735e-01 4.79685664e-01
-8.35499167e-01 5.21600664e-01 4.04426754e-01 -5.98466694e-01
3.48306924e-01 -7.08868861e-01 -8.79923761e-01 -3.11040580e-01
2.93755412e-01 6.77586436e-01 1.39734700e-01 -8.42706189... | [10.434189796447754, 10.056742668151855] |
4a832185-98d6-4950-9ab3-e6fffdd9d29c | explaining-image-classifiers-using | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6192_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730392.pdf | Explaining Image Classifiers using Statistical Fault Localization | The black-box nature of deep neural networks (DNNs) makes it impossible to understand why a particular output is produced, creating demand for “Explainable AI”. In this paper, we show that statistical fault localization (SFL) techniques from software engineering deliver high quality explanations of the outputs of DNNs,... | ['Daniel\xa0Kroening', 'Xiaowei\xa0Huang', 'Hana\xa0Chockler', 'Youcheng\xa0Sun'] | null | null | null | null | eccv-2020-8 | ['fault-localization'] | ['computer-code'] | [ 2.07237244e-01 7.42385626e-01 -1.00609578e-01 -6.34987712e-01
-3.04793209e-01 -4.69130009e-01 5.17577589e-01 -1.49871454e-01
4.19449449e-01 7.45429516e-01 1.13661319e-01 -5.64095676e-01
-5.07368088e-01 -6.27982378e-01 -1.04041588e+00 -3.65473360e-01
1.03098825e-02 3.68253440e-01 2.57843196e-01 6.19659498... | [8.8857421875, 5.713723659515381] |
4fd29c63-a2f0-44c0-8183-433a748c9959 | query-utterance-attention-with-joint-modeling | 2303.04487 | null | https://arxiv.org/abs/2303.04487v3 | https://arxiv.org/pdf/2303.04487v3.pdf | Query-Utterance Attention with Joint modeling for Query-Focused Meeting Summarization | Query-focused meeting summarization (QFMS) aims to generate summaries from meeting transcripts in response to a given query. Previous works typically concatenate the query with meeting transcripts and implicitly model the query relevance only at the token level with attention mechanism. However, due to the dilution of ... | ['Yajing Xu', 'Bo Xiao', 'Bin Duan', 'Xingxian Liu'] | 2023-03-08 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 4.11661327e-01 3.58364910e-01 -1.80776611e-01 -2.97787666e-01
-1.98091483e+00 -2.73970097e-01 7.26585150e-01 5.90522647e-01
-1.71210602e-01 7.26103723e-01 1.16812813e+00 3.00516665e-01
-4.73714992e-02 -5.65406799e-01 -5.20127416e-01 -3.42747688e-01
1.59058318e-01 6.69173360e-01 2.79108763e-01 -5.19901335... | [12.66324234008789, 9.423822402954102] |
410fbb80-644a-42b0-a4b0-f3ec7090d4f9 | ml-doctor-holistic-risk-assessment-of | 2102.02551 | null | https://arxiv.org/abs/2102.02551v2 | https://arxiv.org/pdf/2102.02551v2.pdf | ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models | Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied, in depth, several kinds of attacks, they have done so in isolation. As a result, we lack a comprehensive picture of the risks caused by the ... | ['Yang Zhang', 'Mario Fritz', 'Emiliano De Cristofaro', 'Michael Backes', 'Zhikun Zhang', 'Ahmed Salem', 'Xinlei He', 'Rui Wen', 'Yugeng Liu'] | 2021-02-04 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 1.84731588e-01 9.69770700e-02 -2.33353093e-01 -1.40373155e-01
-6.03407502e-01 -1.06889760e+00 7.20770299e-01 1.24436744e-01
-2.19966903e-01 3.33587527e-01 -3.08043003e-01 -9.90405858e-01
-1.19947366e-01 -8.73080254e-01 -8.72958601e-01 -5.64292073e-01
-5.27957343e-02 3.60454947e-01 3.80764812e-01 -2.11614862... | [5.884347438812256, 7.436987400054932] |
40f38670-11a9-47c4-b55c-c96024c0466a | making-parameter-efficient-tuning-more | null | null | https://aclanthology.org/2022.coling-1.615 | https://aclanthology.org/2022.coling-1.615.pdf | Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks | Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. Recent studies have explored parameter-efficient PLM tuning, which only updates a small amount of task-specific parameters while achieving both high efficiency and comparable performance against standard fine-tun... | ['Wei Wu', 'Rui Xie', 'Xuanjing Huang', 'Qi Zhang', 'Tao Gui', 'Xuanting Chen', 'Yicheng Zou', 'Ruotian Ma', 'Xin Zhou'] | null | null | null | null | coling-2022-10 | ['sentence-classification'] | ['natural-language-processing'] | [-2.13412300e-01 -3.84254724e-01 -4.76414204e-01 -5.28874278e-01
-8.90421987e-01 -5.29736340e-01 3.08928728e-01 2.29267851e-02
-6.44112527e-01 6.69724464e-01 -1.65010080e-01 -5.29926360e-01
2.74409145e-01 -5.00378907e-01 -4.05081838e-01 -5.77058971e-01
5.30057251e-01 4.74883616e-01 3.31612855e-01 1.28871307... | [10.885740280151367, 8.311603546142578] |
c71b5a87-6ca3-4599-918d-fe659bff0358 | data-augmentation-for-low-resource-keyphrase | 2305.17968 | null | https://arxiv.org/abs/2305.17968v1 | https://arxiv.org/pdf/2305.17968v1.pdf | Data Augmentation for Low-Resource Keyphrase Generation | Keyphrase generation is the task of summarizing the contents of any given article into a few salient phrases (or keyphrases). Existing works for the task mostly rely on large-scale annotated datasets, which are not easy to acquire. Very few works address the problem of keyphrase generation in low-resource settings, but... | ['Cornelia Caragea', 'Jishnu Ray Chowdhury', 'Krishna Garg'] | 2023-05-29 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 1.57724023e-01 1.75913990e-01 -6.16494894e-01 7.73656229e-03
-1.21547210e+00 -7.99871147e-01 8.53768468e-01 5.07440746e-01
-4.45282161e-01 9.93194878e-01 7.32426047e-01 -1.11381181e-01
3.06876361e-01 -5.18768668e-01 -8.95835519e-01 -2.43948266e-01
1.69284269e-01 3.61353487e-01 1.58390984e-01 -3.98034155... | [12.298221588134766, 8.896663665771484] |
4e52da4d-7e1f-4d95-82e9-da1250566f99 | modeling-and-utilizing-user-s-internal-state | 2012.03118 | null | https://arxiv.org/abs/2012.03118v1 | https://arxiv.org/pdf/2012.03118v1.pdf | Modeling and Utilizing User's Internal State in Movie Recommendation Dialogue | Intelligent dialogue systems are expected as a new interface between humans and machines. Such an intelligent dialogue system should estimate the user's internal state (UIS) in dialogues and change its response appropriately according to the estimation result. In this paper, we model the UIS in dialogues, taking movie ... | ['Sadao Kurohashi', 'Ribeka Tanaka', 'Takashi Kodama'] | 2020-12-05 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.02137730e-01 8.50623310e-01 -3.07002086e-02 -9.98218000e-01
-2.03336060e-01 -7.16417074e-01 9.07443345e-01 -1.21817462e-01
-4.31453615e-01 7.93869257e-01 7.37361729e-01 -4.62570302e-02
2.80566067e-01 -7.24448442e-01 8.13881904e-02 1.47082031e-01
4.38176751e-01 6.54140353e-01 4.74109501e-01 -6.75838649... | [12.916879653930664, 8.004467010498047] |
05aa360e-6503-40bf-8034-ed3129aff948 | is-swarm-intelligence-able-to-create-mazes | 1601.06580 | null | http://arxiv.org/abs/1601.06580v1 | http://arxiv.org/pdf/1601.06580v1.pdf | Is swarm intelligence able to create mazes? | In this paper, the idea of applying Computational Intelligence in the process
of creation board games, in particular mazes, is presented. For two different
algorithms the proposed idea has been examined. The results of the experiments
are shown and discussed to present advantages and disadvantages. | ['Dawid Polap', 'Christian Napoli', 'Marcin Wozniak', 'Emiliano Tramontana'] | 2016-01-25 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.45582235e-04 4.21128422e-01 4.61226493e-01 -1.07697472e-01
5.27578652e-01 -4.73711610e-01 4.44786429e-01 -1.55943647e-01
-6.66458666e-01 1.35576570e+00 -1.71910256e-01 -5.78094006e-01
-9.32464838e-01 -1.22309721e+00 1.11684024e-01 -3.89788508e-01
-4.27101254e-01 6.99185431e-01 3.81640315e-01 -9.77423429... | [3.4573404788970947, 1.5077226161956787] |
35423515-86d1-4fd8-ba28-39bac1dc04fa | conformal-uncertainty-sets-for-robust | 2105.14957 | null | https://arxiv.org/abs/2105.14957v2 | https://arxiv.org/pdf/2105.14957v2.pdf | Conformal Uncertainty Sets for Robust Optimization | Decision-making under uncertainty is hugely important for any decisions sensitive to perturbations in observed data. One method of incorporating uncertainty into making optimal decisions is through robust optimization, which minimizes the worst-case scenario over some uncertainty set. We connect conformal prediction re... | ['Bruce Cox', 'Chancellor Johnstone'] | 2021-05-31 | null | null | null | null | ['decision-making-under-uncertainty', 'multi-target-regression', 'decision-making-under-uncertainty'] | ['medical', 'miscellaneous', 'reasoning'] | [ 3.03428769e-01 7.28732944e-01 4.46261428e-02 -8.64429355e-01
-1.48190379e+00 -9.77846146e-01 5.89247167e-01 3.66284758e-01
-1.18133454e-02 1.16089797e+00 5.69416285e-01 -3.66007388e-01
-1.17847848e+00 -9.08499062e-01 -7.35658228e-01 -4.92637366e-01
-6.87962323e-02 9.89610553e-01 -1.53044313e-01 1.46394193... | [5.287027359008789, 3.897360324859619] |
62e67138-6166-4897-b92f-c6dc2f06b2ad | transfer-learning-for-relation-extraction-via | 1908.08507 | null | https://arxiv.org/abs/1908.08507v1 | https://arxiv.org/pdf/1908.08507v1.pdf | Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning | Relation extraction aims to extract relational facts from sentences. Previous models mainly rely on manually labeled datasets, seed instances or human-crafted patterns, and distant supervision. However, the human annotation is expensive, while human-crafted patterns suffer from semantic drift and distant supervision sa... | ['Wei zhang', 'Ningyu Zhang', 'Jiaoyan Chen', 'Huajun Chen', 'Shumin Deng', 'Zhanlin Sun'] | 2019-08-22 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 3.20754230e-01 3.29838037e-01 -7.81113923e-01 -7.01690495e-01
-6.03408694e-01 -4.95780796e-01 5.80584824e-01 2.45666161e-01
-3.18150073e-01 1.17572236e+00 1.00075297e-01 -5.92740178e-02
-2.56361842e-01 -1.05265141e+00 -6.44452691e-01 -5.09366989e-01
2.27271140e-01 8.96221578e-01 3.73986959e-01 -3.79905015... | [9.201451301574707, 8.493167877197266] |
7b485709-1ce2-4c19-93b2-b364a271f419 | online-3d-bin-packing-with-constrained-deep | 2006.14978 | null | https://arxiv.org/abs/2006.14978v5 | https://arxiv.org/pdf/2006.14978v5.pdf | Online 3D Bin Packing with Constrained Deep Reinforcement Learning | We solve a challenging yet practically useful variant of 3D Bin Packing Problem (3D-BPP). In our problem, the agent has limited information about the items to be packed into the bin, and an item must be packed immediately after its arrival without buffering or readjusting. The item's placement also subjects to the cons... | ['Qijin She', 'Yin Yang', 'Hang Zhao', 'Kai Xu', 'Chenyang Zhu'] | 2020-06-26 | null | null | null | null | ['3d-bin-packing'] | ['miscellaneous'] | [-2.31668368e-01 1.55134246e-01 -5.30982137e-01 -1.32719144e-01
-4.50672567e-01 -6.89239740e-01 4.34635282e-02 3.34928006e-01
-6.36108637e-01 8.66813540e-01 -8.82302001e-02 -6.76632404e-01
-3.69148748e-03 -7.04422712e-01 -1.13946450e+00 -6.74967587e-01
-5.65729976e-01 9.59897578e-01 1.46847695e-01 -2.94448018... | [4.926309108734131, 2.6476681232452393] |
fb9535eb-c91d-4a9c-88e5-324aad4a9701 | i-msv-2022-indic-multilingual-and-multi | 2302.13209 | null | https://arxiv.org/abs/2302.13209v1 | https://arxiv.org/pdf/2302.13209v1.pdf | I-MSV 2022: Indic-Multilingual and Multi-sensor Speaker Verification Challenge | Speaker Verification (SV) is a task to verify the claimed identity of the claimant using his/her voice sample. Though there exists an ample amount of research in SV technologies, the development concerning a multilingual conversation is limited. In a country like India, almost all the speakers are polyglot in nature. C... | ['S. R. Mahadeva Prasanna', 'Mrinmoy Bhattacharjee', 'Jagabandhu Mishra'] | 2023-02-26 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-1.73006877e-01 -2.39047334e-02 5.02665080e-02 -7.16909051e-01
-1.35576069e+00 -7.66790390e-01 4.15829211e-01 -8.20766836e-02
-3.17753881e-01 6.75698876e-01 4.50002104e-02 -5.25049567e-01
6.07480049e-01 -1.33173645e-01 -5.17522693e-01 -3.48001808e-01
7.76576772e-02 1.52489990e-01 -1.72690749e-01 -3.07542920... | [14.263619422912598, 6.204905986785889] |
4cb0ffca-8aa2-4bc7-b9ec-6e0428969c7d | exploring-the-representation-power-of-splade | 2306.16680 | null | https://arxiv.org/abs/2306.16680v1 | https://arxiv.org/pdf/2306.16680v1.pdf | Exploring the Representation Power of SPLADE Models | The SPLADE (SParse Lexical AnD Expansion) model is a highly effective approach to learned sparse retrieval, where documents are represented by term impact scores derived from large language models. During training, SPLADE applies regularization to ensure postings lists are kept sparse -- with the aim of mimicking the p... | ['Guido Zuccon', 'Shengyao Zhuang', 'Joel Mackenzie'] | 2023-06-29 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 8.07416886e-02 -1.89679682e-01 -7.59213746e-01 3.64724211e-02
-1.18971229e+00 -6.38700068e-01 8.02454710e-01 6.13135993e-01
-5.03574848e-01 6.17120326e-01 1.09596527e+00 -5.39818257e-02
-4.88655657e-01 -7.66083300e-01 -7.65896201e-01 -2.64145464e-01
-2.59075552e-01 5.44375777e-01 -5.08746952e-02 -3.07507426... | [11.471431732177734, 7.630578517913818] |
b260227f-8af6-4eb0-a108-3e9fa3173b19 | leveraging-virtual-and-real-person-for | 1811.02074 | null | http://arxiv.org/abs/1811.02074v1 | http://arxiv.org/pdf/1811.02074v1.pdf | Leveraging Virtual and Real Person for Unsupervised Person Re-identification | Person re-identification (re-ID) is a challenging problem especially when no
labels are available for training. Although recent deep re-ID methods have
achieved great improvement, it is still difficult to optimize deep re-ID model
without annotations in training data. To address this problem, this study
introduces a no... | ['Fengxiang Yang', 'Zhun Zhong', 'Shaozi Li', 'Zhiming Luo', 'Sheng Lian'] | 2018-11-05 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-9.79482159e-02 4.17850502e-02 2.36214444e-01 -6.11637235e-01
-4.71619189e-01 -4.29463118e-01 8.38313520e-01 -8.68856907e-02
-7.42863894e-01 7.19344258e-01 4.08211827e-01 3.30606014e-01
2.30613306e-01 -7.95445561e-01 -6.75075650e-01 -3.26772660e-01
1.92887083e-01 1.21107602e+00 -9.25028101e-02 -1.30916104... | [14.8267822265625, 1.0881916284561157] |
cc0facb2-c209-4a2e-8faf-1665b0bfca45 | implicit-feature-refinement-for-instance | 2112.04709 | null | https://arxiv.org/abs/2112.04709v1 | https://arxiv.org/pdf/2112.04709v1.pdf | Implicit Feature Refinement for Instance Segmentation | We propose a novel implicit feature refinement module for high-quality instance segmentation. Existing image/video instance segmentation methods rely on explicitly stacked convolutions to refine instance features before the final prediction. In this paper, we first give an empirical comparison of different refinement s... | ['Xiangyu Zhang', 'Xiu Li', 'Jiangpeng Yan', 'Bin Dong', 'Tiancai Wang', 'Lufan Ma'] | 2021-12-09 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.51031837e-01 9.47597176e-02 -2.04162151e-01 -3.02855134e-01
-7.50363410e-01 -2.92669743e-01 3.47182751e-01 -3.35407168e-01
-6.12506390e-01 4.30574208e-01 -4.43621635e-01 -4.17070925e-01
1.02200486e-01 -7.07787633e-01 -9.43498254e-01 -7.28827536e-01
-1.30680548e-02 1.97002918e-01 6.56428874e-01 -2.45190598... | [9.516234397888184, 0.059627994894981384] |
b2aadaf1-be43-46b2-bc21-73d518c8eba1 | explore-more-guidance-a-task-aware | 2204.05953 | null | https://arxiv.org/abs/2204.05953v3 | https://arxiv.org/pdf/2204.05953v3.pdf | Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation | Sign language recognition and translation first uses a recognition module to generate glosses from sign language videos and then employs a translation module to translate glosses into spoken sentences. Most existing works focus on the recognition step, while paying less attention to sign language translation. In this w... | ['Hwang Kai', 'Zhengdao Li', 'Long Hu', 'Guangyong Chen', 'Min Chen', 'Xianzhi Li', 'Wei Li', 'Yong Cao'] | 2022-04-12 | null | https://aclanthology.org/2022.findings-naacl.205 | https://aclanthology.org/2022.findings-naacl.205.pdf | findings-naacl-2022-7 | ['sign-language-recognition', 'sign-language-translation'] | ['computer-vision', 'computer-vision'] | [ 2.71584332e-01 -2.51212984e-01 -4.33937967e-01 -4.88122612e-01
-8.55937302e-01 -3.54482323e-01 6.47955477e-01 -9.21750903e-01
-4.79895473e-01 4.86063272e-01 6.02467477e-01 -2.16902152e-01
3.53106976e-01 -5.37212074e-01 -6.22413933e-01 -7.08495557e-01
6.61943436e-01 3.88119549e-01 3.43141444e-02 -2.27717951... | [9.208115577697754, -6.520098686218262] |
46d160fb-d22d-471a-ad29-6a0a82e92f65 | kimera-from-slam-to-spatial-perception-with | 2101.06894 | null | https://arxiv.org/abs/2101.06894v3 | https://arxiv.org/pdf/2101.06894v3.pdf | Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs | Humans are able to form a complex mental model of the environment they move in. This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e.g., objects, rooms, buildings), includes static and dynamic entities and their relations (e.g., a person... | ['Luca Carlone', 'Arjun Gupta', 'Jingnan Shi', 'Yun Chang', 'Nathan Hughes', 'Marcus Abate', 'Andrew Violette', 'Antoni Rosinol'] | 2021-01-18 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [-3.07723641e-01 3.25887464e-02 3.50070477e-01 -4.72240806e-01
-2.15209916e-01 -4.49028015e-01 6.48822844e-01 4.32681769e-01
-2.06470490e-01 4.87750530e-01 1.83383122e-01 2.09314078e-02
-2.62960121e-02 -1.10409939e+00 -8.74418855e-01 -1.61298797e-01
-3.88638705e-01 1.26822424e+00 5.97887695e-01 -5.46172619... | [4.850970268249512, 0.3603166937828064] |
6443eb50-79f3-4480-ba97-002581a6efb4 | clicking-matters-towards-interactive-human | 2111.06162 | null | https://arxiv.org/abs/2111.06162v2 | https://arxiv.org/pdf/2111.06162v2.pdf | Clicking Matters:Towards Interactive Human Parsing | In this work, we focus on Interactive Human Parsing (IHP), which aims to segment a human image into multiple human body parts with guidance from users' interactions. This new task inherits the class-aware property of human parsing, which cannot be well solved by traditional interactive image segmentation approaches tha... | ['Yunchao Wei', 'Yidong Li', 'Songhe Feng', 'Congyan Lang', 'Liqian Liang', 'Yutong Gao'] | 2021-11-11 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 6.38349414e-01 4.10896122e-01 -4.07029130e-02 -5.28662145e-01
-8.34675133e-01 -4.31119889e-01 4.23385650e-02 -4.43535522e-02
-6.40237093e-01 4.60244507e-01 -1.46701396e-01 -1.32950202e-01
2.78389394e-01 -6.69851661e-01 -9.03065979e-01 -4.35462952e-01
4.17938054e-01 3.20627183e-01 6.68344080e-01 -2.31464636... | [9.071419715881348, 0.154776468873024] |
b154a2e6-dc25-4ae1-a169-25790e9e395b | skeleton-based-action-analysis-for-adhd | 2304.09751 | null | https://arxiv.org/abs/2304.09751v1 | https://arxiv.org/pdf/2304.09751v1.pdf | Skeleton-based action analysis for ADHD diagnosis | Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorder worldwide. While extensive research has focused on machine learning methods for ADHD diagnosis, most research relies on high-cost equipment, e.g., MRI machine and EEG patch. Therefore, low-cost diagnostic methods based on the action ch... | ['Syed Mohsen Naqvi', 'Rajesh Nair', 'Yi Li', 'YiChun Li'] | 2023-04-14 | null | null | null | null | ['skeleton-based-action-recognition', 'action-analysis', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.88603836e-01 -2.52889782e-01 -5.64484000e-01 -2.44180173e-01
-6.19276345e-01 1.35521933e-01 7.02138841e-02 2.36462682e-01
-1.29811361e-01 5.67063093e-01 -8.48499611e-02 -2.74454318e-02
-5.22527874e-01 -6.28364503e-01 -2.28476226e-01 -7.13601887e-01
1.31630272e-01 5.84302485e-01 3.26578617e-01 3.81381601... | [12.76740550994873, 2.988612413406372] |
5b99765e-f97b-4508-9ce9-35ddda2b0703 | frustratingly-easy-model-ensemble-for | null | null | https://aclanthology.org/D18-1449 | https://aclanthology.org/D18-1449.pdf | Frustratingly Easy Model Ensemble for Abstractive Summarization | Ensemble methods, which combine multiple models at decoding time, are now widely known to be effective for text-generation tasks. However, they generally increase computational costs, and thus, there have been many studies on compressing or distilling ensemble models. In this paper, we propose an alternative, simple bu... | ['Hayato Kobayashi'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['headline-generation'] | ['natural-language-processing'] | [ 2.59339243e-01 -1.09831885e-01 1.94909304e-01 -4.33167994e-01
-9.54592168e-01 -3.81706327e-01 8.13936532e-01 2.58548051e-01
-4.34027255e-01 1.12323833e+00 3.43256801e-01 -5.11181653e-01
-1.56560596e-02 -6.46051288e-01 -4.51948196e-01 -8.45740020e-01
1.40669599e-01 5.40471733e-01 -4.39270847e-02 -1.75568551... | [11.94503116607666, 9.262096405029297] |
ffe5e07e-c2a9-4909-b884-01ebbae6412d | deepaste-inpainting-for-pasting | 2112.10600 | null | https://arxiv.org/abs/2112.10600v2 | https://arxiv.org/pdf/2112.10600v2.pdf | DeePaste -- Inpainting for Pasting | One of the challenges of supervised learning training is the need to procure an substantial amount of tagged data. A well-known method of solving this problem is to use synthetic data in a copy-paste fashion, so that we cut objects and paste them onto relevant backgrounds. Pasting the objects naively results in artifac... | ['Levi Kassel Michael Werman'] | 2021-12-20 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 6.97481036e-01 1.45157784e-01 4.78717200e-02 -3.79141927e-01
-8.93224955e-01 -5.86442351e-01 4.56981361e-01 1.90130845e-01
-4.87907588e-01 5.96114516e-01 -3.05020660e-01 -5.00246622e-02
3.04574311e-01 -5.62711775e-01 -9.65032578e-01 -6.85576618e-01
1.37329891e-01 5.39509952e-01 7.28739142e-01 1.23457983... | [9.673212051391602, 0.2980881929397583] |
a2fefdd1-5dff-4156-9e66-948bc319dd18 | recursive-context-aware-lexical | null | null | https://aclanthology.org/D19-1491 | https://aclanthology.org/D19-1491.pdf | Recursive Context-Aware Lexical Simplification | This paper presents a novel architecture for recursive context-aware lexical simplification, REC-LS, that is capable of (1) making use of the wider context when detecting the words in need of simplification and suggesting alternatives, and (2) taking previous simplification steps into account. We show that our system o... | ['Sian Gooding', 'Ekaterina Kochmar'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['lexical-simplification'] | ['natural-language-processing'] | [ 2.04425544e-01 2.70006597e-01 -8.59932303e-02 -3.84625673e-01
-6.51708841e-01 -3.96734446e-01 3.49549621e-01 8.07535827e-01
-7.75753379e-01 7.13728487e-01 6.75304711e-01 -6.34057343e-01
1.80630296e-01 -8.69520843e-01 -3.81584287e-01 1.42859176e-01
4.37063426e-01 8.57589722e-01 2.25261450e-01 -8.30572784... | [10.909907341003418, 10.387351036071777] |
ab76b450-1198-463e-bd27-27f213584cf5 | fb-mstcn-a-full-band-single-channel-speech | 2203.07684 | null | https://arxiv.org/abs/2203.07684v1 | https://arxiv.org/pdf/2203.07684v1.pdf | FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on Multi-Scale Temporal Convolutional Network | In recent years, deep learning-based approaches have significantly improved the performance of single-channel speech enhancement. However, due to the limitation of training data and computational complexity, real-time enhancement of full-band (48 kHz) speech signals is still very challenging. Because of the low energy ... | ['Mingjiang Wang', 'Heng Li', 'Yukun Qian', 'Xuyi Zhuang', 'Lu Zhang', 'Zehua Zhang'] | 2022-03-15 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 3.22654575e-01 -2.93121815e-01 1.67210072e-01 -3.75420034e-01
-1.16734433e+00 -1.22270718e-01 -1.20872989e-01 3.29525806e-02
-6.56177640e-01 5.31947792e-01 5.00060976e-01 -2.93868512e-01
-9.62935165e-02 -5.60692728e-01 -3.36711466e-01 -9.99076724e-01
-6.16567321e-02 -8.02749932e-01 8.83397013e-02 -3.72407854... | [14.978921890258789, 5.910974979400635] |
635d0d33-fb21-42ca-b4e5-e27b7626ab34 | openvidial-2-0-a-larger-scale-open-domain | 2109.12761 | null | https://arxiv.org/abs/2109.12761v2 | https://arxiv.org/pdf/2109.12761v2.pdf | OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts | In order to better simulate the real human conversation process, models need to generate dialogue utterances based on not only preceding textual contexts but also visual contexts. However, with the development of multi-modal dialogue learning, the dataset scale gradually becomes a bottleneck. In this report, we release... | ['Jiwei Li', 'Rongbin Ouyang', 'Xiaofei Sun', 'Xiaoya Li', 'Yuxian Meng', 'Shuhe Wang'] | 2021-09-27 | null | null | null | null | ['multi-modal-dialogue-generation'] | ['natural-language-processing'] | [-2.23303393e-01 2.10355178e-01 1.45785362e-01 -3.76883239e-01
-9.43198204e-01 -9.39878047e-01 9.02828336e-01 -3.96794677e-01
4.49858652e-03 9.59248126e-01 8.42901111e-01 -5.63414618e-02
8.25540483e-01 -8.63390565e-01 -1.13701962e-01 -4.44872230e-01
5.15130758e-01 8.36869001e-01 1.57921582e-01 -6.91151679... | [12.827534675598145, 7.838142395019531] |
c8e52a9f-bff6-4983-8611-80f18c60c846 | robust-optimization-structure-control-co | 2306.08472 | null | https://arxiv.org/abs/2306.08472v1 | https://arxiv.org/pdf/2306.08472v1.pdf | Robust Optimization, Structure/Control co-design, Distributed Optimization, Monolithic Optimization, Robust Control, Parametric Uncertainty | This paper presents an end-to-end framework for robust structure/control optimization of an industrial benchmark. When dealing with space structures, a reduction of the spacecraft mass is paramount to minimize the mission cost and maximize the propellant availability. However, a lighter design comes with a bigger struc... | ['Finn Ankersen', 'Pedro Simplicio', 'Mark Watt', 'Andy Kiley', 'Daniel Alazard', 'Francesco Sanfedino'] | 2023-06-14 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-1.37388840e-01 4.29405957e-01 3.10984999e-01 1.31671965e-01
7.96022117e-02 -8.31474781e-01 7.22546399e-01 2.89435148e-01
-3.21539193e-01 1.15821326e+00 -2.56317496e-01 -1.17913522e-01
-1.09100306e+00 -5.31601846e-01 -4.96874690e-01 -9.03776109e-01
-1.66808784e-01 8.14032555e-01 -2.64413804e-01 -5.32745063... | [5.381500244140625, 2.370124340057373] |
126a262f-850c-4393-abc1-0e1a9829fbaf | task-wise-sampling-convolutions-for-arbitrary | 2209.02200 | null | https://arxiv.org/abs/2209.02200v1 | https://arxiv.org/pdf/2209.02200v1.pdf | Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images | Arbitrary-oriented object detection (AOOD) has been widely applied to locate and classify objects with diverse orientations in remote sensing images. However, the inconsistent features for the localization and classification tasks in AOOD models may lead to ambiguity and low-quality object predictions, which constrains... | ['Ran Tao', 'Hao Wang', 'Xiang-Gen Xia', 'Wei Li', 'Zhanchao Huang'] | 2022-09-06 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 3.32462400e-01 -5.47006130e-01 3.14075984e-02 -8.20530653e-01
-4.55751777e-01 -5.09154558e-01 3.72753412e-01 -5.11355996e-02
-4.10963953e-01 2.44830027e-01 2.01008245e-02 1.00287274e-01
-5.77414215e-01 -6.67984068e-01 -3.98866177e-01 -1.09913039e+00
-1.10307969e-01 2.85737723e-01 5.13761640e-01 1.26266122... | [8.84967041015625, -0.7477682828903198] |
8332d878-1a75-4028-9fc4-afefc5ee904c | class-adaptive-self-training-for-relation | 2306.09697 | null | https://arxiv.org/abs/2306.09697v1 | https://arxiv.org/pdf/2306.09697v1.pdf | Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data | Relation extraction (RE) aims to extract relations from sentences and documents. Existing relation extraction models typically rely on supervised machine learning. However, recent studies showed that many RE datasets are incompletely annotated. This is known as the false negative problem in which valid relations are fa... | ['Hwee Tou Ng', 'Lidong Bing', 'Lu Xu', 'Qingyu Tan'] | 2023-06-16 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 2.22204402e-01 4.43009019e-01 -5.98345876e-01 -6.61897838e-01
-9.43167984e-01 -4.41546082e-01 4.56454068e-01 5.42296171e-01
-3.67729813e-01 1.40499830e+00 -1.33811040e-02 -2.74137914e-01
-7.14040920e-02 -9.97867763e-01 -6.01833701e-01 -6.50571823e-01
2.81474233e-01 6.58360779e-01 8.95717815e-02 6.18813597... | [9.093921661376953, 8.614165306091309] |
e45a9bcf-2468-4d8d-b4c8-32c305fd4d00 | a-mutual-learning-method-for-salient-object | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Wu_A_Mutual_Learning_Method_for_Salient_Object_Detection_With_Intertwined_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_A_Mutual_Learning_Method_for_Salient_Object_Detection_With_Intertwined_CVPR_2019_paper.pdf | A Mutual Learning Method for Salient Object Detection With Intertwined Multi-Supervision | Though deep learning techniques have made great progress in salient object detection recently, the predicted saliency maps still suffer from incomplete predictions due to the internal complexity of objects and inaccurate boundaries caused by strides in convolution and pooling operations. To alleviate these issues, we p... | [' Errui Ding', ' Huchuan Lu', ' Dong Wang', ' Wenlong Guan', ' Mengyang Feng', 'Runmin Wu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['contour-detection'] | ['computer-vision'] | [ 4.63514507e-01 3.26225050e-02 -2.80421585e-01 -2.73099691e-01
-4.98061478e-01 -1.03290610e-01 3.94820958e-01 3.91494520e-02
-1.52480274e-01 4.77713376e-01 1.51160985e-01 -4.33635190e-02
3.32292765e-01 -6.36876941e-01 -8.01642060e-01 -7.28626192e-01
1.99562788e-01 -2.37212062e-01 1.18584943e+00 1.68564186... | [9.789856910705566, -0.3667083978652954] |
451bc5a5-c052-4f30-8b09-319bb4db97cb | unit3d-a-unified-transformer-for-3d-dense | 2212.00836 | null | https://arxiv.org/abs/2212.00836v1 | https://arxiv.org/pdf/2212.00836v1.pdf | UniT3D: A Unified Transformer for 3D Dense Captioning and Visual Grounding | Performing 3D dense captioning and visual grounding requires a common and shared understanding of the underlying multimodal relationships. However, despite some previous attempts on connecting these two related tasks with highly task-specific neural modules, it remains understudied how to explicitly depict their shared... | ['Angel X. Chang', 'Matthias Nießner', 'Xinlei Chen', 'Ronghang Hu', 'Dave Zhenyu Chen'] | 2022-12-01 | null | null | null | null | ['dense-captioning', '3d-dense-captioning'] | ['computer-vision', 'computer-vision'] | [ 2.13657722e-01 3.84333819e-01 -2.17058837e-01 -5.88620305e-01
-9.30346727e-01 -6.65816128e-01 6.50512934e-01 -3.18067700e-01
1.19886115e-01 5.46574831e-01 6.04848385e-01 -3.84414643e-01
2.73597091e-01 -4.18116063e-01 -1.00003839e+00 -3.36943150e-01
8.59299302e-02 5.22904813e-01 -3.94058138e-01 -2.90527791... | [8.181294441223145, -3.3220622539520264] |
7928faa3-8f7a-44b4-91ed-def38557319b | non-local-attention-learning-on-large | null | null | https://ieeexplore.ieee.org/document/9006463 | https://xiaoyuxin1002.github.io/docs/NLAH.pdf | Non-local Attention Learning on Large Heterogeneous Information Networks | Heterogeneous information network (HIN) summarizes rich structural information in real-world datasets and plays an important role in many big data applications. Recently, graph neural networks have been extended to the representation learning of HIN. One very recent advancement is the hierarchical attention mechanism w... | ['ChengXiang Zhai', 'Zecheng Zhang', 'Yuxin Xiao', 'Carl Yang'] | 2019-12-12 | null | null | null | 2019-ieee-international-conference-on-big-2 | ['heterogeneous-node-classification'] | ['graphs'] | [-2.37738296e-01 2.95498937e-01 -6.22361779e-01 -3.15166414e-01
-2.81378776e-01 -2.20317483e-01 4.67832148e-01 3.05265188e-01
-1.43661425e-01 4.88319039e-01 4.75265741e-01 -2.68738329e-01
-1.93181574e-01 -1.28444684e+00 -5.61039567e-01 -6.00254536e-01
6.10408485e-02 1.62527099e-01 4.85260427e-01 -2.86969662... | [7.270221710205078, 6.2669243812561035] |
e5fc6dcb-f991-4c03-aa71-dd14451ef8e1 | classification-and-online-clustering-of-zero | 2305.00605 | null | https://arxiv.org/abs/2305.00605v1 | https://arxiv.org/pdf/2305.00605v1.pdf | Classification and Online Clustering of Zero-Day Malware | A large amount of new malware is constantly being generated, which must not only be distinguished from benign samples, but also classified into malware families. For this purpose, investigating how existing malware families are developed and examining emerging families need to be explored. This paper focuses on the onl... | ['Róbert Lórencz', 'Martin Jureček', 'Olha Jurečková'] | 2023-05-01 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 2.21598998e-01 -2.66422033e-01 -8.87275040e-02 -2.96978861e-01
1.68172553e-01 -6.81871116e-01 6.59086883e-01 6.35544717e-01
-2.21477985e-01 5.62613130e-01 -3.61003995e-01 -5.01977742e-01
8.61896351e-02 -7.93204367e-01 -4.08060431e-01 -1.02358615e+00
-4.73366112e-01 7.32515097e-01 4.53760356e-01 2.20941558... | [14.414718627929688, 9.639486312866211] |
f700fd19-6b44-466d-8692-45940dd0c6b6 | unsupervised-dependency-parsing-with-acoustic | null | null | https://aclanthology.org/Q13-1006 | https://aclanthology.org/Q13-1006.pdf | Unsupervised Dependency Parsing with Acoustic Cues | Unsupervised parsing is a difficult task that infants readily perform. Progress has been made on this task using text-based models, but few computational approaches have considered how infants might benefit from acoustic cues. This paper explores the hypothesis that word duration can help with learning syntax. We descr... | ['Sharon Goldwater', 'John K Pate'] | 2013-01-01 | null | null | null | tacl-2013-1 | ['unsupervised-dependency-parsing'] | ['natural-language-processing'] | [ 2.31084719e-01 4.45302993e-01 -1.88024983e-01 -1.04029989e+00
-8.07920277e-01 -6.15459740e-01 2.05527961e-01 7.08395600e-01
-9.87469256e-01 2.58872300e-01 7.38811731e-01 -5.54387808e-01
3.91715169e-01 -5.13462603e-01 -6.83137238e-01 -3.59926432e-01
-2.16704145e-01 2.89258331e-01 3.82274806e-01 1.60869136... | [10.568007469177246, 9.505419731140137] |
e339de18-ed70-407c-81e6-88a9f81ee17e | argus-efficient-activity-detection-system-for | null | null | http://openaccess.thecvf.com/content_WACVW_2020/html/w5/Liu_Argus_Efficient_Activity_Detection_System_for_Extended_Video_Analysis_WACVW_2020_paper.html | http://openaccess.thecvf.com/content_WACVW_2020/papers/w5/Liu_Argus_Efficient_Activity_Detection_System_for_Extended_Video_Analysis_WACVW_2020_paper.pdf | Argus: Efficient Activity Detection System for Extended Video Analysis | We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario. For the spatial-temporal event detection in the surveillance video, we first generate video proposals by applying object detection and tracking algorithm which shared the detection features. After that, w... | ['Xiaojun Chang', 'Po-Yao Huang', 'Junwei Liang', 'Guoliang Kang', 'Wenhe Liu', 'Liangke Gui', 'Jing Wen', 'Yijun Qian', 'Peng Chen'] | 2020-03-02 | null | null | null | proceedings-of-the-ieee-winter-conference-on | ['video-object-tracking'] | ['computer-vision'] | [ 3.00921679e-01 -1.13405399e-02 6.35621417e-03 -2.32474118e-01
-9.54426467e-01 -7.48586714e-01 8.64451885e-01 8.32948983e-02
-9.98151898e-01 7.23001599e-01 2.22629815e-01 8.76852348e-02
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-3.02994192e-01 -7.47823939e-02 9.33701992e-01 3.50110531... | [8.3054838180542, 0.4329882264137268] |
5e565518-ea66-4d23-b253-9b0055a2dceb | impact-of-acoustic-noise-on-alzheimer-s | 2203.17110 | null | https://arxiv.org/abs/2203.17110v2 | https://arxiv.org/pdf/2203.17110v2.pdf | Impact of Environmental Noise on Alzheimer's Disease Detection from Speech: Should You Let a Baby Cry? | Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the acoustics of spontaneous speech, speech processing and machine learning (ML) provide promising techniques for reliably detecting... | ['Jekaterina Novikova'] | 2022-03-31 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 2.90675700e-01 -3.21864933e-01 2.41651878e-01 -4.44811642e-01
-1.16579723e+00 6.92794099e-02 2.54363716e-01 2.15895638e-01
-5.05717158e-01 4.07824129e-01 5.29091656e-01 -2.00191587e-01
1.02135979e-01 -6.26304150e-01 -3.55649322e-01 -4.29182023e-01
-4.20530707e-01 3.98380384e-02 3.71902376e-01 3.80743947... | [13.947796821594238, 5.397353649139404] |
f496e9a3-fc51-4477-99c2-16b1b4c2c6e8 | mt-vae-learning-motion-transformations-to | 1808.04545 | null | http://arxiv.org/abs/1808.04545v1 | http://arxiv.org/pdf/1808.04545v1.pdf | MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics | Long-term human motion can be represented as a series of motion
modes---motion sequences that capture short-term temporal dynamics---with
transitions between them. We leverage this structure and present a novel Motion
Transformation Variational Auto-Encoders (MT-VAE) for learning motion sequence
generation. Our model j... | ['Sunil Hadap', 'Ersin Yumer', 'Xinchen Yan', 'Kalyan Sunkavalli', 'Honglak Lee', 'Eli Shechtman', 'Akash Rastogi', 'Ruben Villegas'] | 2018-08-14 | mt-vae-learning-motion-transformations-to-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Xinchen_Yan_Generating_Multimodal_Human_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xinchen_Yan_Generating_Multimodal_Human_ECCV_2018_paper.pdf | eccv-2018-9 | ['human-pose-forecasting', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [ 2.33149484e-01 2.88609087e-01 -3.47571343e-01 -1.67171165e-01
-5.21778882e-01 -4.51741576e-01 1.04922152e+00 -9.93339121e-01
1.64105058e-01 7.73833215e-01 8.76964211e-01 8.32668170e-02
2.63731450e-01 -7.64357924e-01 -9.25656676e-01 -6.47792220e-01
-3.30763310e-02 2.27775991e-01 4.49573770e-02 -3.70210350... | [7.398108959197998, -0.20243282616138458] |
aa00f972-88a6-4433-9eea-097037575ea0 | qrnet-optimal-regulator-design-with-lqr | 2009.05686 | null | https://arxiv.org/abs/2009.05686v2 | https://arxiv.org/pdf/2009.05686v2.pdf | QRnet: optimal regulator design with LQR-augmented neural networks | In this paper we propose a new computational method for designing optimal regulators for high-dimensional nonlinear systems. The proposed approach leverages physics-informed machine learning to solve high-dimensional Hamilton-Jacobi-Bellman equations arising in optimal feedback control. Concretely, we augment linear qu... | ['Tenavi Nakamura-Zimmerer', 'Wei Kang', 'Qi Gong'] | 2020-09-11 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-1.66273177e-01 4.30856079e-01 -4.56039071e-01 5.27752519e-01
-7.27624297e-01 -4.79588896e-01 3.91237944e-01 -3.61689001e-01
-1.00938477e-01 1.09974229e+00 5.88541254e-02 -5.25139809e-01
-4.23119873e-01 -1.40816495e-01 -7.58934498e-01 -8.52887094e-01
-2.98791319e-01 4.98953253e-01 -3.03125143e-01 -5.57135940... | [6.299037456512451, 3.3827760219573975] |
6a51b8fb-dc1a-4d4d-96b2-36dd48e6140a | open-access-to-orbit-and-runaway-space-debris | 2202.07442 | null | https://arxiv.org/abs/2202.07442v1 | https://arxiv.org/pdf/2202.07442v1.pdf | Open access to orbit and runaway space debris growth | As Earth's orbits fill with satellites and debris, debris-producing collisions between orbiting bodies become more likely. Runaway space debris growth, known as Kessler Syndrome, may render Earth's orbits unusable for centuries. We present a dynamic physico-economic model of Earth orbit use under rational expectations ... | ['Giacomo Rondina', 'Akhil Rao'] | 2022-02-12 | null | null | null | null | ['2048'] | ['playing-games'] | [-9.71183479e-01 5.92919350e-01 -4.17167068e-01 8.62265289e-01
3.02476466e-01 -6.76282763e-01 9.84901726e-01 -2.30655476e-01
-2.31176242e-01 1.16357291e+00 4.79174465e-01 -8.81393075e-01
-6.79014772e-02 -9.05282497e-01 -8.24448645e-01 -4.67163414e-01
-6.59575045e-01 6.40285373e-01 8.06904808e-02 -3.18092644... | [6.100048542022705, 3.634866237640381] |
b01e6b29-e39a-4318-b439-fcdfd7407e67 | teacher-student-training-and-triplet-loss-to | 2111.10561 | null | https://arxiv.org/abs/2111.10561v1 | https://arxiv.org/pdf/2111.10561v1.pdf | Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion | We study a series of recognition tasks in two realistic scenarios requiring the analysis of faces under strong occlusion. On the one hand, we aim to recognize facial expressions of people wearing Virtual Reality (VR) headsets. On the other hand, we aim to estimate the age and identify the gender of people wearing surgi... | ['Radu Tudor Ionescu', 'Georgian Duta', 'Mariana-Iuliana Georgescu'] | 2021-11-20 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 1.16435841e-01 5.79566002e-01 -1.13468945e-01 -5.55713952e-01
-5.66692472e-01 -3.22066694e-01 3.80436599e-01 -3.11475426e-01
-4.88360375e-01 7.57765353e-01 -1.05800167e-01 -1.06616959e-01
-1.14566483e-01 -3.62090409e-01 -9.47477520e-01 -6.56235576e-01
-5.84577508e-02 3.81787926e-01 -4.42674756e-01 -2.79344711... | [13.447114944458008, 1.1882466077804565] |
00a90ccd-f88e-4a2b-93c9-afa1c65ebe8d | location-aware-feature-selection-for-scene | 2004.10999 | null | https://arxiv.org/abs/2004.10999v2 | https://arxiv.org/pdf/2004.10999v2.pdf | Location-Aware Feature Selection Text Detection Network | Regression-based text detection methods have already achieved promising performances with simple network structure and high efficiency. However, they are behind in accuracy comparing with recent segmentation-based text detectors. In this work, we discover that one important reason to this case is that regression-based ... | ['Haojie Li', 'Wanli Ouyang', 'Zengyuan Guo', 'Wen Gao', 'Zilin Wang', 'Zhihui Wang'] | 2020-04-23 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [-2.92019427e-01 -5.16431391e-01 -3.23674619e-01 -4.47354347e-01
-6.87369406e-01 -3.30262810e-01 3.00672352e-01 3.02846819e-01
-4.28949028e-01 4.61192280e-01 -2.53825784e-01 4.33448069e-02
-1.45193696e-01 -1.04761338e+00 -4.83299106e-01 -7.11870670e-01
3.27770263e-01 5.50303102e-01 1.02871013e+00 -1.53804913... | [12.098637580871582, 2.3023080825805664] |
8a18df9c-0d45-4836-a055-09a9af700a8f | bootstrapping-text-anonymization-models-with-1 | 2205.06895 | null | https://arxiv.org/abs/2205.06895v1 | https://arxiv.org/pdf/2205.06895v1.pdf | Bootstrapping Text Anonymization Models with Distant Supervision | We propose a novel method to bootstrap text anonymization models based on distant supervision. Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals. This knowledge graph is employed ... | ['Ildikó Pilán', 'Lilja Øvrelid', 'Pierre Lison', 'Anthi Papadopoulou'] | 2022-05-13 | null | https://aclanthology.org/2022.lrec-1.476 | https://aclanthology.org/2022.lrec-1.476.pdf | lrec-2022-6 | ['text-anonymization'] | ['natural-language-processing'] | [ 2.92534292e-01 6.47940993e-01 -3.00369471e-01 -5.41661561e-01
-7.55185306e-01 -1.00533378e+00 6.66199148e-01 5.91487110e-01
-5.79005659e-01 1.09765697e+00 4.86693054e-01 -1.31085023e-01
-7.22453892e-02 -8.48360956e-01 -6.31251216e-01 -5.16605616e-01
1.76423013e-01 7.60906577e-01 -1.68519437e-01 -6.96406979... | [6.153720855712891, 7.015879154205322] |
021b4816-37f9-40ea-81da-32035f5682e4 | end-to-end-human-pose-and-mesh-reconstruction | 2012.09760 | null | https://arxiv.org/abs/2012.09760v3 | https://arxiv.org/pdf/2012.09760v3.pdf | End-to-End Human Pose and Mesh Reconstruction with Transformers | We present a new method, called MEsh TRansfOrmer (METRO), to reconstruct 3D human pose and mesh vertices from a single image. Our method uses a transformer encoder to jointly model vertex-vertex and vertex-joint interactions, and outputs 3D joint coordinates and mesh vertices simultaneously. Compared to existing techni... | ['Zicheng Liu', 'Lijuan Wang', 'Kevin Lin'] | 2020-12-17 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lin_End-to-End_Human_Pose_and_Mesh_Reconstruction_with_Transformers_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lin_End-to-End_Human_Pose_and_Mesh_Reconstruction_with_Transformers_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [-2.90022820e-01 2.17505172e-01 -1.02115810e-01 -2.38193497e-01
-6.55953526e-01 -2.90008396e-01 5.32729268e-01 -3.09275597e-01
-1.22852124e-01 3.90364259e-01 3.41201782e-01 1.76642358e-01
1.66532010e-01 -6.95324063e-01 -1.19880474e+00 -2.87956208e-01
9.34280753e-02 1.21070468e+00 2.93451428e-01 -1.88049987... | [6.999768257141113, -1.1811882257461548] |
c9666b61-9e3c-4799-8fa6-bc848236dadb | interpretable-visualizations-with | 2006.06640 | null | https://arxiv.org/abs/2006.06640v1 | https://arxiv.org/pdf/2006.06640v1.pdf | Interpretable Visualizations with Differentiating Embedding Networks | We present a visualization algorithm based on a novel unsupervised Siamese neural network training regime and loss function, called Differentiating Embedding Networks (DEN). The Siamese neural network finds differentiating or similar features between specific pairs of samples in a dataset, and uses these features to em... | ['Isaac Robinson'] | 2020-06-11 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-1.19261026e-01 -1.35711610e-01 -1.96410745e-01 -4.09472018e-01
-2.48960868e-01 -8.16228807e-01 4.86789376e-01 8.50884095e-02
-1.98750019e-01 1.47705942e-01 3.74707669e-01 -3.51663470e-01
-5.65012276e-01 -4.84550953e-01 -6.26007140e-01 -8.33867669e-01
-6.12764716e-01 5.88094831e-01 -1.77144334e-01 8.35646614... | [7.978028774261475, 4.444721698760986] |
58bbaac6-6f01-4219-9892-9638f7219f2d | metacovid-a-siamese-neural-network-framework | null | null | https://reader.elsevier.com/reader/sd/pii/S0031320320305033 | https://reader.elsevier.com/reader/sd/pii/S0031320320305033?token=A078019693958EA8EF7D7236196492ACBFED6694FECCCC2FC640FA775670FD4C55081AF13AD614D7920C53BFF7C25DA9&originRegion=eu-west-1&originCreation=20211205131409 | MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients | Various AI functionalities such as pattern recognition and prediction can effectively be used to diagnose (recognize) and predict coronavirus disease 2019 (COVID-19) infections and propose timely response (remedial action) to minimize the spread and impact of the virus. Motivated by this, an AI system based on deep met... | ['M. ShamimHossain', 'Mohammad Shorfuzzaman'] | 2020-10-17 | null | null | null | pattern-recognition-2020-10 | ['covid-19-detection', 'pneumonia-detection'] | ['medical', 'medical'] | [ 4.24066752e-01 -3.75195414e-01 -5.77707402e-02 -2.65189737e-01
-6.31666064e-01 -4.61164266e-01 3.04083705e-01 6.33597896e-02
-3.19392085e-01 4.34001684e-01 1.53876171e-01 -3.68526369e-01
-4.89754379e-01 -5.28722465e-01 -6.09363437e-01 -5.86216092e-01
-2.03361079e-01 8.42146575e-01 -2.16562673e-01 3.17229867... | [15.546623229980469, -1.7338091135025024] |
07f48685-b537-4adc-92c4-0de59e5ef85e | global-proxy-based-hard-mining-for-visual | 2302.14217 | null | https://arxiv.org/abs/2302.14217v1 | https://arxiv.org/pdf/2302.14217v1.pdf | Global Proxy-based Hard Mining for Visual Place Recognition | Learning deep representations for visual place recognition is commonly performed using pairwise or triple loss functions that highly depend on the hardness of the examples sampled at each training iteration. Existing techniques address this by using computationally and memory expensive offline hard mining, which consis... | ['Philippe Giguère', 'Brahim Chaib-Draa', 'Amar Ali-bey'] | 2023-02-28 | null | null | null | null | ['metric-learning', 'image-similarity-search', 'visual-place-recognition', 'metric-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [-1.66154757e-01 -7.62564242e-02 -2.43466780e-01 -4.62448180e-01
-1.42448187e+00 -5.70588231e-01 6.11657977e-01 5.13699174e-01
-6.18019521e-01 7.37995863e-01 -1.82701088e-02 -1.60036832e-02
-1.20021343e-01 -7.47678638e-01 -1.00021064e+00 -6.27290249e-01
-3.10868949e-01 7.96023607e-01 1.97909713e-01 1.19889807... | [8.014788627624512, -1.8310340642929077] |
436ea698-06f7-47b2-89b4-4facfa6ddb0d | value-aware-transformers-for-1-5d-data | null | null | https://openreview.net/forum?id=S3qhbZwzq3H | https://openreview.net/pdf?id=S3qhbZwzq3H | Value-aware transformers for 1.5d data | Sparse sequential highly-multivariate data of the form characteristic of hospital in-patient investigation and treatment poses a considerable challenge for representation learning. Such data is neither faithfully reducible to 1d nor dense enough to constitute multivariate series. Conventional models compromise their da... | ['Parashkev Nachev', 'Amy Nelson', 'Amy R Tso', 'Timothy J Roberts', 'James F Cann'] | 2021-09-29 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [ 4.63479072e-01 -5.28443046e-02 -3.74078900e-01 -4.89892274e-01
-8.00254524e-01 -5.36523700e-01 5.16754568e-01 7.73271620e-01
-5.19508123e-01 7.30557323e-01 6.46694541e-01 -8.97759318e-01
-5.92507064e-01 -6.43317044e-01 -4.95663166e-01 -5.30289352e-01
-7.65811920e-01 9.60904658e-01 -3.72457594e-01 -1.78714335... | [7.967310428619385, 6.2387166023254395] |
7731d09c-202b-4646-914b-a73d5fffd015 | multi-color-balance-for-color-constancy | 2105.10228 | null | https://arxiv.org/abs/2105.10228v1 | https://arxiv.org/pdf/2105.10228v1.pdf | Multi-color balance for color constancy | In this paper, we propose a novel multi-color balance adjustment for color constancy. The proposed method, called "n-color balancing," allows us not only to perfectly correct n target colors on the basis of corresponding ground truth colors but also to correct colors other than the n colors. In contrast, although white... | ['Hitoshi Kiya', 'Yuma Kinoshita', 'Teruaki Akazawa'] | 2021-05-21 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-2.81228591e-02 -6.30309165e-01 1.19072525e-02 -2.22640201e-01
-3.48667771e-01 -6.31413162e-01 1.57980159e-01 -9.84968990e-02
-2.73315042e-01 8.35905135e-01 -1.65333733e-01 -2.68019348e-01
1.31132886e-01 -6.21493936e-01 -2.85518050e-01 -6.27476394e-01
6.61315203e-01 3.38356644e-01 2.17240021e-01 -5.30136943... | [10.502717971801758, -2.5415666103363037] |
23beb4bb-b4ff-48e7-8022-bd55ec668ba1 | kernelized-multi-graph-matching | 2210.05206 | null | https://arxiv.org/abs/2210.05206v1 | https://arxiv.org/pdf/2210.05206v1.pdf | Kernelized multi-graph matching | Multigraph matching is a recent variant of the graph matching problem. In this framework, the optimization procedure considers several graphs and enforces the consistency of the matches along the graphs. This constraint can be formalized as a cycle consistency across the pairwise permutation matrices, which implies the... | ['S. Takerkart', 'Guillaume Auzias', 'Rohit Yadav', 'François-Xavier Dupé'] | 2022-10-11 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 3.68481427e-01 1.49357766e-01 -1.79185256e-01 -1.48841888e-01
-4.53666508e-01 -6.84118688e-01 6.00827634e-01 3.35488021e-01
-1.57642946e-01 4.11578655e-01 1.02662943e-01 -1.14972191e-03
-7.00737774e-01 -7.60181308e-01 -6.98413670e-01 -8.03299069e-01
-5.85140251e-02 6.64132893e-01 8.33429694e-02 -5.20258695... | [7.164320468902588, 5.194514274597168] |
95fb191a-c49e-43d6-84c5-ad3f1e6a6d7a | divide-and-denoise-learning-from-noisy-labels | null | null | https://openreview.net/forum?id=LJPfn2jgIrW | https://openreview.net/pdf?id=LJPfn2jgIrW | Divide and Denoise: Learning from Noisy Labels in Fine-grained Entity Typing with Cluster-wise Loss Correction | Fine-grained Entity Typing(FET) has witnessed great progress since distant supervision was introduced, but still suffers from label noise. Existing noise control methods applied to FET rely on predicted distribution and deals instances isolately, thus suffers from confirmation bias. In this work, We propose to tackle t... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['entity-typing'] | ['natural-language-processing'] | [ 2.74540409e-02 -5.05382232e-02 -1.39907792e-01 -5.40180326e-01
-1.14476860e+00 -2.57491142e-01 4.82757390e-01 1.82416603e-01
-8.04873586e-01 1.07978737e+00 1.27137706e-01 1.97078437e-01
3.25195156e-02 -6.85999870e-01 -8.21038425e-01 -7.52921700e-01
1.68069810e-01 6.02972269e-01 2.76316553e-01 1.56599939... | [9.509174346923828, 8.79630184173584] |
983a34a3-39e8-4125-b93c-ad7f9012e8d9 | adversarially-tuned-scene-generation | 1701.00405 | null | http://arxiv.org/abs/1701.00405v2 | http://arxiv.org/pdf/1701.00405v2.pdf | Adversarially Tuned Scene Generation | Generalization performance of trained computer vision systems that use
computer graphics (CG) generated data is not yet effective due to the concept
of 'domain-shift' between virtual and real data. Although simulated data
augmented with a few real world samples has been shown to mitigate domain shift
and improve transf... | ['V. S. R. Veeravasarapu', 'Ramesh Visvanathan', 'Constantin Rothkopf'] | 2017-01-02 | adversarially-tuned-scene-generation-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Veeravasarapu_Adversarially_Tuned_Scene_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Veeravasarapu_Adversarially_Tuned_Scene_CVPR_2017_paper.pdf | cvpr-2017-7 | ['scene-generation'] | ['computer-vision'] | [ 3.70621681e-01 3.33183080e-01 5.86247444e-01 -4.69659418e-01
-8.42766404e-01 -7.37139761e-01 8.56102347e-01 -4.09875095e-01
-2.78133750e-01 8.31264079e-01 -1.24423824e-01 -2.41289422e-01
2.04110995e-01 -1.03328228e+00 -1.09088922e+00 -7.61655688e-01
4.02476639e-01 1.15918112e+00 4.03977811e-01 -3.68457675... | [9.848800659179688, 1.1870567798614502] |
49caba48-ae92-446f-be3f-011d18c0bc29 | authorship-verification-average-similarity | null | null | https://aclanthology.org/R15-1012 | https://aclanthology.org/R15-1012.pdf | Authorship Verification, Average Similarity Analysis | null | ['Rafael Mu{\\~n}oz Guillena', "Mar{\\'\\i}a Pelaez Brioso", 'Daniel Castro Castro', 'Yaritza Adame Arcia'] | 2015-09-01 | authorship-verification-average-similarity-1 | https://aclanthology.org/R15-1012 | https://aclanthology.org/R15-1012.pdf | ranlp-2015-9 | ['authorship-verification'] | ['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... | [-7.279503345489502, 3.6174473762512207] |
50d18110-1003-47ea-aaf9-a928eca6f357 | nonnegative-low-rank-tensor-completion-via | 2305.07976 | null | https://arxiv.org/abs/2305.07976v1 | https://arxiv.org/pdf/2305.07976v1.pdf | Nonnegative Low-Rank Tensor Completion via Dual Formulation with Applications to Image and Video Completion | Recent approaches to the tensor completion problem have often overlooked the nonnegative structure of the data. We consider the problem of learning a nonnegative low-rank tensor, and using duality theory, we propose a novel factorization of such tensors. The factorization decouples the nonnegative constraints from the ... | ['Pawan Kumar', 'Jayadev Naram', 'Tanmay Kumar Sinha'] | 2023-05-13 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.20462021e-02 -4.07652915e-01 7.91085809e-02 -1.66265324e-01
-6.80773258e-01 -6.55442894e-01 3.83603394e-01 -6.07890666e-01
-4.01936114e-01 4.41425562e-01 3.89019758e-01 -3.83135498e-01
-3.48854780e-01 -4.02717292e-02 -5.39820433e-01 -8.76313448e-01
-1.81403026e-01 -3.48950177e-03 -5.15127718e-01 -3.38717312... | [7.375734329223633, 4.513314723968506] |
ef794dde-a9c1-4553-b880-bd3f93da557d | hdr-chipqa-no-reference-quality-assessment-on | 2304.13156 | null | https://arxiv.org/abs/2304.13156v1 | https://arxiv.org/pdf/2304.13156v1.pdf | HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos | We present a no-reference video quality model and algorithm that delivers standout performance for High Dynamic Range (HDR) videos, which we call HDR-ChipQA. HDR videos represent wider ranges of luminances, details, and colors than Standard Dynamic Range (SDR) videos. The growing adoption of HDR in massively scaled vid... | ['Alan C. Bovik', 'Sriram Sethuraman', 'Hai Wei', 'Yongjun Wu', 'Zaixi Shang', 'Joshua P. Ebenezer'] | 2023-04-25 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [ 1.57926098e-01 -7.51388788e-01 -8.16512182e-02 -3.94577920e-01
-7.81613350e-01 -6.23535097e-01 4.27882522e-01 -2.38453910e-01
-1.08356282e-01 4.58130568e-01 4.68862742e-01 -6.60240948e-02
-1.85170561e-01 -7.90496349e-01 -5.24066508e-01 -6.25930905e-01
-4.15477663e-01 -1.89218223e-01 3.96342218e-01 -7.25990474... | [11.5588960647583, -1.9111452102661133] |
ede036c7-7a8f-4474-ae39-aeae5ce3217c | towards-understanding-distributional | 2110.03155 | null | https://arxiv.org/abs/2110.03155v4 | https://arxiv.org/pdf/2110.03155v4.pdf | Interpreting Distributional Reinforcement Learning: A Regularization Perspective | Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive... | ['Bei Jiang', 'Xiaodong Yan', 'Linglong Kong', 'Yafei Wang', 'Enze Shi', 'Yi Liu', 'Yingnan Zhao', 'Ke Sun'] | 2021-10-07 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.77833617e-01 3.98522764e-01 -2.97113299e-01 -1.97060362e-01
-8.33137691e-01 -4.14571047e-01 4.10168499e-01 2.30166182e-01
-7.90522099e-01 8.17084491e-01 5.09367645e-01 -4.03169751e-01
-4.42063242e-01 -7.24911571e-01 -8.29927444e-01 -1.09152567e+00
-1.28674544e-02 8.43800455e-02 -3.73283952e-01 -2.45693788... | [4.111146450042725, 2.535659074783325] |
4a8835a9-5ad6-47f8-b64f-cac259699247 | an-online-sequence-to-sequence-model-for | 1706.06428 | null | http://arxiv.org/abs/1706.06428v1 | http://arxiv.org/pdf/1706.06428v1.pdf | An online sequence-to-sequence model for noisy speech recognition | Generative models have long been the dominant approach for speech
recognition. The success of these models however relies on the use of
sophisticated recipes and complicated machinery that is not easily accessible
to non-practitioners. Recent innovations in Deep Learning have given rise to an
alternative - discriminati... | ['George Tucker', 'Chung-Cheng Chiu', 'Yuping Luo', 'Navdeep Jaitly', 'Kevin Swersky', 'Ilya Sutskever', 'Dieterich Lawson'] | 2017-06-16 | null | null | null | null | ['noisy-speech-recognition'] | ['speech'] | [ 3.52104753e-01 2.11301446e-01 7.68530443e-02 -6.17757261e-01
-7.63114870e-01 -8.63573253e-01 6.60355508e-01 -3.07587624e-01
-2.97406465e-01 7.17488348e-01 -2.78102476e-02 -5.06870747e-01
9.12052244e-02 -6.35396957e-01 -8.67781818e-01 -7.55820572e-01
-7.75327533e-02 6.01122558e-01 7.96007216e-02 -3.38453621... | [14.482097625732422, 6.815150737762451] |
89fd9007-21f5-4f4c-b8d2-6e93f4dabdb0 | learning-in-implicit-generative-models | 1610.03483 | null | http://arxiv.org/abs/1610.03483v4 | http://arxiv.org/pdf/1610.03483v4.pdf | Learning in Implicit Generative Models | Generative adversarial networks (GANs) provide an algorithmic framework for
constructing generative models with several appealing properties: they do not
require a likelihood function to be specified, only a generating procedure;
they provide samples that are sharp and compelling; and they allow us to
harness our knowl... | ['Shakir Mohamed', 'Balaji Lakshminarayanan'] | 2016-10-11 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 5.90094507e-01 4.54445571e-01 -2.53017485e-01 -4.31413591e-01
-8.52529943e-01 -6.17370367e-01 8.87712300e-01 -4.61241335e-01
-4.27653193e-02 9.75361407e-01 1.21204779e-01 -5.18318176e-01
-3.50777805e-01 -1.22561300e+00 -7.20450163e-01 -9.93645251e-01
1.76680043e-01 6.93618953e-01 -3.51517856e-01 -4.89673577... | [11.568778038024902, -0.043232399970293045] |
5b7499c2-114a-4421-94fa-1fba56a3653c | tddiscourse-a-dataset-for-discourse-level | null | null | https://aclanthology.org/W19-5929 | https://aclanthology.org/W19-5929.pdf | TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events | Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs. This restricts the ability of systems to learn temporal links between global pairs, since reliance on local syntactic features suffic... | ['Luke Breitfeller', 'Aakanksha Naik', 'Carolyn Rose'] | 2019-09-01 | null | null | null | ws-2019-9 | ['temporal-relation-classification'] | ['natural-language-processing'] | [-4.74180467e-02 4.07696754e-01 -6.41638398e-01 -5.22357166e-01
-9.15457726e-01 -9.70358431e-01 1.26819706e+00 7.57907093e-01
-4.23417926e-01 9.10588086e-01 7.92512715e-01 -6.31039739e-01
-3.10629815e-01 -5.51277936e-01 -4.02805686e-01 -2.46563390e-01
-7.34313011e-01 6.62080765e-01 6.18854463e-01 -4.15754110... | [9.101238250732422, 9.205011367797852] |
3d21f9e0-d75f-451d-b648-3f8f141c5d97 | m2-net-multi-stages-specular-highlight | 2207.09965 | null | https://arxiv.org/abs/2207.09965v1 | https://arxiv.org/pdf/2207.09965v1.pdf | M2-Net: Multi-stages Specular Highlight Detection and Removal in Multi-scenes | In this paper, we propose a novel uniformity framework for highlight detection and removal in multi-scenes, including synthetic images, face images, natural images, and text images. The framework consists of three main components, highlight feature extractor module, highlight coarse removal module, and highlight refine... | ['Xingjun Wang', 'Kun Hu', 'Zhaoyangfan Huang'] | 2022-07-20 | null | null | null | null | ['highlight-detection', 'highlight-removal'] | ['computer-vision', 'computer-vision'] | [ 5.96418977e-01 -4.31000531e-01 2.62317151e-01 4.23691422e-02
-4.45403486e-01 -3.70637357e-01 4.86714184e-01 1.69452317e-02
-3.41257840e-01 6.73398793e-01 2.41076306e-01 3.20945680e-01
3.91356051e-02 -5.02704203e-01 -5.03011703e-01 -7.77014315e-01
1.52136475e-01 -7.66173601e-01 5.15907645e-01 -1.40496448... | [10.872462272644043, -2.566481113433838] |
4700f97d-d58b-420f-a049-155520892ed5 | theoretical-analysis-of-an-xgboost-framework | 2112.01566 | null | https://arxiv.org/abs/2112.01566v1 | https://arxiv.org/pdf/2112.01566v1.pdf | Theoretical Analysis of an XGBoost Framework for Product Cannibalization | This paper is an extension of our work where we presented a three-stage XGBoost algorithm for forecasting sales under product cannibalization scenario. Previously we developed the model based on our intuition and provided empirical evidence on its performance. In this study we would briefly go over the algorithm and th... | ['Mohammad Bari', 'Gautham Bekal'] | 2021-12-02 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-1.49131984e-01 1.55529067e-01 -9.96259332e-01 -7.81912982e-01
1.83278114e-01 -5.96090436e-01 6.58787012e-01 4.12000865e-01
-1.64214805e-01 2.84354419e-01 -1.63373441e-01 -9.72134173e-01
-5.87531090e-01 -7.50334024e-01 -6.49517953e-01 -4.31619644e-01
-2.30834797e-01 5.89654624e-01 -1.69603348e-01 -5.05073309... | [9.402813911437988, 5.818057060241699] |
3f6d8a0c-3632-427b-967a-ef2a1b36e06a | lung-nodule-detection-and-classification-from | null | null | https://doi.org/10.1016/j.jksuci.2020.03.013 | https://www.sciencedirect.com/science/article/pii/S1319157820303335 | Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning | Lung malignancy is one of the most common causes of death in the world caused by malignant lung nodules which commonly diagnosed radiologically by radiologists. Unfortunately, the continuous flow of medical images in hospitals drives radiologists to prioritize quantity over quality. This work condition allows misinterp... | ['Tjeng Wawan Cenggoro', 'Suryadiputra Liawatimena', 'Ivan William Harsono'] | 2020-04-08 | null | null | null | journal-of-king-saud-university-computer-and-2 | ['lung-nodule-3d-detection', 'lung-nodule-3d-classification', 'lung-nodule-detection', 'lung-nodule-classification'] | ['computer-vision', 'computer-vision', 'medical', 'medical'] | [ 1.39395013e-01 3.34155500e-01 -5.90442605e-02 2.44915560e-01
-6.96459711e-01 -3.48167300e-01 3.96940708e-01 -2.24496111e-01
-4.30081725e-01 4.66616005e-01 9.58525091e-02 -4.91225183e-01
-1.80830657e-01 -8.52733195e-01 -4.10086244e-01 -6.61770284e-01
3.34644355e-02 6.29420340e-01 8.42553496e-01 3.23517531... | [15.40157699584961, -2.142075538635254] |
fc0a6efc-19df-45fc-bd68-e5bf950b37e9 | nonlinear-equivariant-imaging-learning-multi | 2211.12786 | null | https://arxiv.org/abs/2211.12786v1 | https://arxiv.org/pdf/2211.12786v1.pdf | Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI | Current state-of-the-art reconstruction for quantitative tissue maps from fast, compressive, Magnetic Resonance Fingerprinting (MRF), use supervised deep learning, with the drawback of requiring high-fidelity ground truth tissue map training data which is limited. This paper proposes NonLinear Equivariant Imaging (NLEI... | ['Mohammad Golbabaee', 'Peter Hall', 'Marion I. Menzel', 'Carolin M. Pirkl', 'Kwai Y. Chau', 'Ketan Fatania'] | 2022-11-23 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 7.93807268e-01 2.94631451e-01 -1.62071899e-01 -4.84006554e-01
-1.00834835e+00 -3.44645202e-01 6.29751623e-01 -2.81378955e-01
-5.67247570e-01 7.04238117e-01 4.64020282e-01 3.20217013e-02
-6.42600417e-01 -3.61200154e-01 -1.02167785e+00 -8.83504152e-01
-3.89449239e-01 6.22499526e-01 -2.25650333e-02 1.43209994... | [13.508493423461914, -2.410276412963867] |
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