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1539734e-4aec-400a-a0f0-111740e8324d | uniter-learning-universal-image-text-1 | 1909.11740 | null | https://arxiv.org/abs/1909.11740v3 | https://arxiv.org/pdf/1909.11740v3.pdf | UNITER: UNiversal Image-TExt Representation Learning | Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text... | ['Yu Cheng', 'Yen-Chun Chen', 'Ahmed El Kholy', 'Linjie Li', 'Licheng Yu', 'Jingjing Liu', 'Zhe Gan', 'Faisal Ahmed'] | 2019-09-25 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/7093_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750103.pdf | eccv-2020-8 | ['zero-shot-cross-modal-retrieval', 'visual-commonsense-reasoning', 'visual-entailment'] | ['miscellaneous', 'reasoning', 'reasoning'] | [ 4.50695723e-01 2.11754605e-01 -3.54839534e-01 -5.21988213e-01
-1.06118226e+00 -7.61642277e-01 9.09041941e-01 8.78316984e-02
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5.26391745e-01 3.31624001e-01 -2.95853585e-01 -1.58188954... | [10.85666275024414, 1.6290507316589355] |
b5d3da2a-a82c-4068-be42-b46b7445e64a | dsac-differentiable-ransac-for-camera | 1611.05705 | null | http://arxiv.org/abs/1611.05705v4 | http://arxiv.org/pdf/1611.05705v4.pdf | DSAC - Differentiable RANSAC for Camera Localization | RANSAC is an important algorithm in robust optimization and a central
building block for many computer vision applications. In recent years,
traditionally hand-crafted pipelines have been replaced by deep learning
pipelines, which can be trained in an end-to-end fashion. However, RANSAC has
so far not been used as part... | ['Frank Michel', 'Jamie Shotton', 'Eric Brachmann', 'Carsten Rother', 'Stefan Gumhold', 'Sebastian Nowozin', 'Alexander Krull'] | 2016-11-17 | dsac-differentiable-ransac-for-camera-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Brachmann_DSAC_-_Differentiable_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Brachmann_DSAC_-_Differentiable_CVPR_2017_paper.pdf | cvpr-2017-7 | ['camera-localization'] | ['computer-vision'] | [-1.17501587e-01 -2.23781943e-01 1.62334248e-01 -4.47497547e-01
-9.20409560e-01 -8.62703443e-01 7.37137854e-01 -1.69537887e-02
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2.52756894e-01 6.10252917e-01 2.62507707e-01 -8.68976489... | [7.835072040557861, -2.139061212539673] |
2c137b6d-eee3-4d31-b536-00c440ffa8df | on-learning-latent-models-with-multi-instance | 2306.13796 | null | https://arxiv.org/abs/2306.13796v1 | https://arxiv.org/pdf/2306.13796v1.pdf | On Learning Latent Models with Multi-Instance Weak Supervision | We consider a weakly supervised learning scenario where the supervision signal is generated by a transition function $\sigma$ of labels associated with multiple input instances. We formulate this problem as \emph{multi-instance Partial Label Learning (multi-instance PLL)}, which is an extension to the standard PLL prob... | ['Dan Roth', 'Efi Tsamoura', 'Kaifu Wang'] | 2023-06-23 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 7.14256108e-01 5.64841688e-01 -5.71854889e-01 -3.49804252e-01
-1.06645620e+00 -6.18873179e-01 3.32283109e-01 9.80308093e-03
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-5.51571667e-01 -4.07171160e-01 -1.08717012e+00 -1.05521595e+00
-1.78871185e-01 3.93674999e-01 -5.57047576e-02 9.50284395... | [9.291696548461914, 4.051496982574463] |
15311f0d-78c0-4cbc-b289-2943f3884f27 | active-uncertainty-reduction-for-safe-and | 2302.00171 | null | https://arxiv.org/abs/2302.00171v1 | https://arxiv.org/pdf/2302.00171v1.pdf | Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach | The ability to accurately predict the opponent's behavior is central to the safety and efficiency of robotic systems in interactive settings, such as human-robot interaction and multi-robot teaming tasks. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as opponent's... | ['Jaime F. Fisac', 'Sangjae Bae', 'David Isele', 'Haimin Hu'] | 2023-02-01 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 3.70563149e-01 7.96489179e-01 -4.35651988e-01 -8.05870816e-02
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-2.89263308e-01 8.06680083e-01 3.43462020e-01 -4.29736197... | [4.771781921386719, 2.043571949005127] |
0659acfa-2d7e-4ac9-995f-3f71ac98f869 | singaug-data-augmentation-for-singing-voice | 2203.17001 | null | https://arxiv.org/abs/2203.17001v2 | https://arxiv.org/pdf/2203.17001v2.pdf | SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy | Deep learning based singing voice synthesis (SVS) systems have been demonstrated to flexibly generate singing with better qualities, compared to conventional statistical parametric based methods. However, neural systems are generally data-hungry and have difficulty to reach reasonable singing quality with limited publi... | ['Qin Jin', 'Shinji Watanabe', 'Tao Qian', 'Jiatong Shi', 'Shuai Guo'] | 2022-03-31 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-0.05403638 -0.35265535 -0.25203028 -0.1389206 -0.9976493 -0.4579129
0.14300822 -0.65442044 0.11830502 0.6264856 0.31998152 -0.0990121
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-0.0199067 -0.5068553 -0.19698502 0.19866252 -1.7162216 0.1449011
0.94719595 0.86189157 0.166... | [15.504133224487305, 6.167813777923584] |
b8645337-39d0-49e3-9d1f-243fc3665379 | knowledge-enriched-transformer-for-emotion | 1909.10681 | null | https://arxiv.org/abs/1909.10681v2 | https://arxiv.org/pdf/1909.10681v2.pdf | Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations | Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the con... | ['Chunyan Miao', 'Di Wang', 'Peixiang Zhong'] | 2019-09-24 | knowledge-enriched-transformer-for-emotion-1 | https://aclanthology.org/D19-1016 | https://aclanthology.org/D19-1016.pdf | ijcnlp-2019-11 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 3.33286196e-01 2.74252027e-01 -5.70725277e-02 -6.35539830e-01
2.06092070e-03 -4.90726948e-01 8.82141471e-01 1.99162856e-01
-1.57563880e-01 6.75518572e-01 6.47455931e-01 -5.62461950e-02
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5.64365573e-02 1.72229826e-01 -5.62861487e-02 -7.67437994... | [12.901302337646484, 6.290820598602295] |
b41e3a2e-2d47-4f40-a9f7-707c63a7ad96 | dagad-data-augmentation-for-graph-anomaly | 2210.09766 | null | https://arxiv.org/abs/2210.09766v1 | https://arxiv.org/pdf/2210.09766v1.pdf | DAGAD: Data Augmentation for Graph Anomaly Detection | Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when ... | ['Charu C. Aggarwal', 'Quan Z. Sheng', 'Hao Peng', 'Chuan Zhou', 'Amin Beheshti', 'Shan Xue', 'Jian Yang', 'Jia Wu', 'Xiaoxiao Ma', 'Fanzhen Liu'] | 2022-10-18 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 4.37244296e-01 5.73095202e-01 -2.40668610e-01 -3.03918988e-01
-7.22590163e-02 -1.96984887e-01 5.91555178e-01 8.58637035e-01
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-9.23320726e-02 -1.03061938e+00 -5.93275130e-01 -7.96947300e-01
-5.44505358e-01 4.85542774e-01 2.91158676e-01 -2.59619027... | [6.623826503753662, 5.802565097808838] |
102ba60d-bbfb-4c30-86de-9fa75625fcbb | sia-ftp-a-spoken-instruction-aware-flight | 2305.01661 | null | https://arxiv.org/abs/2305.01661v1 | https://arxiv.org/pdf/2305.01661v1.pdf | SIA-FTP: A Spoken Instruction Aware Flight Trajectory Prediction Framework | Ground-air negotiation via speech communication is a vital prerequisite for ensuring safety and efficiency in air traffic control (ATC) operations. However, with the increase in traffic flow, incorrect instructions caused by human factors bring a great threat to ATC safety. Existing flight trajectory prediction (FTP) a... | ['Yi Lin', 'Jianwei Zhang', 'Dongyue Guo'] | 2023-05-02 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 1.98112205e-01 -9.92777124e-02 -2.18320295e-01 -3.93377602e-01
-5.74899733e-01 -4.22758877e-01 4.58173275e-01 4.43946272e-02
-3.43454272e-01 3.95549744e-01 4.22003090e-01 -7.43263483e-01
-4.92829323e-01 -7.03596234e-01 -3.45755905e-01 -4.99608189e-01
7.62081221e-02 2.97796547e-01 3.42051953e-01 -6.68733060... | [6.879857063293457, 2.6370041370391846] |
842d5267-2091-4cdb-b467-d30b492c1cd4 | open-ended-reinforcement-learning-with-neural | 2202.08266 | null | https://arxiv.org/abs/2202.08266v2 | https://arxiv.org/pdf/2202.08266v2.pdf | Open-Ended Reinforcement Learning with Neural Reward Functions | Inspired by the great success of unsupervised learning in Computer Vision and Natural Language Processing, the Reinforcement Learning community has recently started to focus more on unsupervised discovery of skills. Most current approaches, like DIAYN or DADS, optimize some form of mutual information objective. We prop... | ['Asier Mujika', 'Robert Meier'] | 2022-02-16 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-1.84796214e-01 2.13084042e-01 6.48702681e-02 -5.29112875e-01
-3.10597301e-01 -5.41491508e-01 3.85264426e-01 1.80324331e-01
-7.74807274e-01 1.05531204e+00 9.55458451e-03 1.03926612e-02
-5.07718563e-01 -8.10104847e-01 -8.41905117e-01 -5.89363873e-01
-4.71183389e-01 5.54626644e-01 1.43124864e-01 -5.62879980... | [4.028858661651611, 1.4600903987884521] |
dd360ea4-de23-4e08-9af8-b1d710c6af43 | efficient-implementation-of-a-multi-layer | 2305.19468 | null | https://arxiv.org/abs/2305.19468v1 | https://arxiv.org/pdf/2305.19468v1.pdf | Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA | This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first network to have end-to-end multi-layer online local supervised training without using gradients and has the combined adaptation of weights and thr... | ['Saeed Afshar', 'Andrew Wabnitz', 'André van Schaik', 'Yeshwanth Bethi', 'Ali Mehrabi'] | 2023-05-31 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 2.46704757e-01 -3.22903007e-01 4.36308503e-01 -5.71266413e-01
-9.92627442e-02 -2.31895670e-01 9.09556001e-02 1.87506899e-01
-1.27090180e+00 8.11463892e-01 -6.57873809e-01 1.87015533e-02
-8.36686790e-02 -6.05721176e-01 -1.08535731e+00 -9.48474944e-01
-4.31029409e-01 2.16434062e-01 1.22906160e+00 -1.83409750... | [8.217490196228027, 2.452011823654175] |
e2ea0b9c-1d03-49ff-99df-7841a8fb22fd | detrs-beat-yolos-on-real-time-object | 2304.08069 | null | https://arxiv.org/abs/2304.08069v2 | https://arxiv.org/pdf/2304.08069v2.pdf | DETRs Beat YOLOs on Real-time Object Detection | Recently, end-to-end transformer-based detectors~(DETRs) have achieved remarkable performance. However, the issue of the high computational cost of DETRs has not been effectively addressed, limiting their practical application and preventing them from fully exploiting the benefits of no post-processing, such as non-max... | ['Yi Liu', 'Qingqing Dang', 'Yuning Du', 'Cheng Cui', 'Jinman Wei', 'Guanzhong Wang', 'Yian Zhao', 'Shangliang Xu', 'Wenyu Lv'] | 2023-04-17 | null | null | null | null | ['2d-object-detection', 'real-time-object-detection'] | ['computer-vision', 'computer-vision'] | [-1.59947112e-01 -4.29477662e-01 -6.48126900e-02 -2.28004456e-01
-1.06683958e+00 -4.81354594e-01 2.41441667e-01 -4.85098176e-02
-8.55556786e-01 1.07016839e-01 -3.59791994e-01 -1.87858030e-01
4.63778079e-01 -6.73236907e-01 -1.01413989e+00 -4.33962673e-01
2.03358516e-01 2.53065765e-01 8.63193512e-01 -7.43144527... | [8.686598777770996, -0.27994105219841003] |
c7e37baa-0db7-4028-a48c-68d4fc476322 | reinforcement-learning-with-simple-sequence | 2305.17109 | null | https://arxiv.org/abs/2305.17109v1 | https://arxiv.org/pdf/2305.17109v1.pdf | Reinforcement Learning with Simple Sequence Priors | Everything else being equal, simpler models should be preferred over more complex ones. In reinforcement learning (RL), simplicity is typically quantified on an action-by-action basis -- but this timescale ignores temporal regularities, like repetitions, often present in sequential strategies. We therefore propose an R... | ['Eric Schulz', 'Marcel Binz', 'Peter Dayan', 'Noémi Éltető', 'Tankred Saanum'] | 2023-05-26 | null | null | null | null | ['continuous-control', 'data-compression'] | ['playing-games', 'time-series'] | [ 2.80150741e-01 2.51790255e-01 -6.51045918e-01 -2.02658951e-01
-6.85927272e-01 -4.19378012e-01 9.12511349e-01 1.30918294e-01
-8.86110902e-01 9.59476233e-01 4.44410592e-01 -2.25267917e-01
-6.94205344e-01 -5.35474777e-01 -8.14304590e-01 -7.51989484e-01
-4.63903099e-01 7.69463122e-01 -7.85396993e-02 -1.45083010... | [4.0957536697387695, 1.881567120552063] |
d6e4b7af-206c-4def-992a-e489cf43ff6f | rank-one-network-an-effective-framework-for | 2011.12610 | null | https://arxiv.org/abs/2011.12610v1 | https://arxiv.org/pdf/2011.12610v1.pdf | Rank-One Network: An Effective Framework for Image Restoration | The principal rank-one (RO) components of an image represent the self-similarity of the image, which is an important property for image restoration. However, the RO components of a corrupted image could be decimated by the procedure of image denoising. We suggest that the RO property should be utilized and the decimati... | ['Xiahai Zhuang', 'Shangqi Gao'] | 2020-11-25 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 7.33594120e-01 -4.81902689e-01 4.90286827e-01 -5.92417782e-03
-6.23170376e-01 3.81731130e-02 2.79291719e-02 -6.35882437e-01
-2.34454215e-01 4.43228245e-01 3.33614200e-01 1.12613797e-01
-1.93005040e-01 -7.44126141e-01 -4.02258247e-01 -1.22430599e+00
2.29817897e-01 -4.85343844e-01 4.52049226e-02 -3.95968080... | [11.281034469604492, -2.4567904472351074] |
3525e417-721d-4964-a901-8132bbfb8e9e | clipmasterprints-fooling-contrastive-language | 2307.03798 | null | https://arxiv.org/abs/2307.03798v1 | https://arxiv.org/pdf/2307.03798v1.pdf | CLIPMasterPrints: Fooling Contrastive Language-Image Pre-training Using Latent Variable Evolution | Models leveraging both visual and textual data such as Contrastive Language-Image Pre-training (CLIP), are increasingly gaining importance. In this work, we show that despite their versatility, such models are vulnerable to what we refer to as fooling master images. Fooling master images are capable of maximizing the c... | ['Sebastian Risi', 'Anders Sundnes Løvlie', 'Peter Kun', 'Matthias Freiberger'] | 2023-07-07 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 4.73032504e-01 1.06758378e-01 -1.04912175e-02 -2.08758876e-01
-1.23049295e+00 -1.16718042e+00 9.66093421e-01 -1.21344730e-01
-1.92472368e-01 3.55679452e-01 2.26008832e-01 -3.36470962e-01
3.16023305e-02 -4.57973450e-01 -1.13427913e+00 -5.60852408e-01
5.50104026e-03 1.87981158e-01 2.53128577e-02 -2.25539520... | [5.845086574554443, 7.831084728240967] |
38a60d84-3a70-4d4f-8e8e-f5ab10a55b10 | the-halliday-centre-tagger-an-online-platform | null | null | https://aclanthology.org/L14-1593 | https://aclanthology.org/L14-1593.pdf | The Halliday Centre Tagger: An Online Platform for Semi-automatic Text Annotation and Analysis | This paper reports the latest development of The Halliday Centre Tagger (the Tagger), an online platform provided with semi-automatic features to facilitate text annotation and analysis. The Tagger is featured for its web-based architecture with all functionalities and file storage space provided online, and a theory-n... | ['Billy T. M. Wong', 'Hengbin Yan', 'Jonathan J. Webster', 'Ian C. Chow'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['text-annotation'] | ['natural-language-processing'] | [-2.88450837e-01 5.30714869e-01 -5.39962314e-02 -3.49737376e-01
-4.12388116e-01 -9.86263335e-01 8.59329462e-01 8.28891575e-01
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-6.10306740e-01 -5.09964049e-01 2.53719985e-01 -5.03887653e-01
-1.61887944e-01 7.31671512e-01 1.19171515e-01 -6.52818441... | [9.47098159790039, 9.01158332824707] |
bbadc7ef-f774-4ff1-bd63-a8f1628cc4cf | securing-optimized-code-against-power-side | 2207.02614 | null | https://arxiv.org/abs/2207.02614v2 | https://arxiv.org/pdf/2207.02614v2.pdf | Securing Optimized Code Against Power Side Channels | Side-channel attacks impose a serious threat to cryptographic algorithms, including widely employed ones, such as AES and RSA. These attacks take advantage of the algorithm implementation in hardware or software to extract secret information via side channels. Software masking is a mitigation approach against power sid... | ['Panagiotis Papadimitratos', 'Elena Troubitsyna', 'Roberto Castañeda Lozano', 'Rodothea Myrsini Tsoupidi'] | 2022-07-06 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 3.56663525e-01 -4.31333780e-02 -4.87861633e-01 1.04238968e-02
-7.27559447e-01 -1.05979562e+00 3.93014073e-01 4.43564892e-01
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3.27377498e-01 -9.99860764e-01 -7.01154411e-01 -6.65977359e-01
-3.49653453e-01 -4.67829257e-01 2.94751227e-01 -3.84057015... | [5.750420093536377, 7.391849517822266] |
aa586f97-239e-4f2c-bf70-0362564427b3 | a-real-time-junk-food-recognition-system | 2203.11836 | null | https://arxiv.org/abs/2203.11836v1 | https://arxiv.org/pdf/2203.11836v1.pdf | A Real-time Junk Food Recognition System based on Machine Learning | $ $As a result of bad eating habits, humanity may be destroyed. People are constantly on the lookout for tasty foods, with junk foods being the most common source. As a consequence, our eating patterns are shifting, and we're gravitating toward junk food more than ever, which is bad for our health and increases our ris... | ['Md. Kishor Morol', 'Niloy Kumar', 'Nila Maitra Chaity', 'Sabikunnahar Talukder Pyaasa', 'Takitazwar Parthib', 'Sirajum Munira Shifat'] | 2022-03-22 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [-2.22838536e-01 -4.85672027e-01 -3.66302520e-01 -4.05229717e-01
2.65849978e-01 -2.81267464e-01 -1.27296090e-01 4.78026211e-01
-3.79961222e-01 3.44290882e-01 -6.14923164e-02 -1.12820581e-01
1.09784879e-01 -1.42410219e+00 -5.56153417e-01 -7.05083132e-01
-7.34040663e-02 -1.05517387e-01 -3.47606651e-02 -5.74986160... | [11.55932903289795, 4.405973434448242] |
9b2e2a5e-a880-4108-8511-adee5406a93b | a-hierarchical-architecture-for-optimal-unit | 2306.16119 | null | https://arxiv.org/abs/2306.16119v1 | https://arxiv.org/pdf/2306.16119v1.pdf | A Hierarchical Architecture for Optimal Unit Commitment and Control of an Ensemble of Steam Generators | A hierarchical architecture for the optimal management of an ensemble of steam generators is presented. The subsystems are coordinated by a multilayer scheme for jointly sustaining a common load. The high level optimizes the load allocation and the generator schedule, considering activation dynamics by a hybrid model. ... | ['Andrea Ballarino', 'Marcello Farina', 'Stefano Spinelli'] | 2023-06-28 | null | null | null | null | ['management'] | ['miscellaneous'] | [-4.07002896e-01 3.56228113e-01 1.39194995e-01 4.84312117e-01
4.34925854e-02 -1.00217247e+00 7.31622398e-01 1.14771955e-01
4.28925902e-01 1.02454591e+00 -5.80652021e-02 -7.17155710e-02
-3.91933054e-01 -8.34690750e-01 -3.46178800e-01 -1.44488001e+00
-3.50758165e-01 7.52039135e-01 6.00617565e-02 -6.80248559... | [5.4842143058776855, 2.431853771209717] |
96ff4e25-4cbf-4b0b-89a9-b4d93ccf736c | ad-hoc-table-retrieval-using-semantic | 1802.06159 | null | https://arxiv.org/abs/1802.06159v3 | https://arxiv.org/pdf/1802.06159v3.pdf | Ad Hoc Table Retrieval using Semantic Similarity | We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables. This task is not only interesting on its own account, but is also being used as a core component in many other table-based information access scenarios, such as table completion or table mining. The m... | ['Krisztian Balog', 'Shuo Zhang'] | 2018-02-16 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [ 2.59206146e-01 5.59291393e-02 -4.86397803e-01 -4.67806667e-01
-1.40195346e+00 -8.91714633e-01 7.78421402e-01 1.05727029e+00
-1.12069637e-01 6.81261241e-01 6.83079123e-01 -2.32382659e-02
-3.66880685e-01 -1.18688309e+00 -7.89168358e-01 -2.43803356e-02
-1.00070417e-01 1.21217692e+00 4.00946289e-01 -6.17458582... | [9.650022506713867, 7.894441604614258] |
f601665a-3d9f-472b-99bf-fea7f18c0c55 | scrnet-a-retinex-structure-based-low-light | 2305.08053 | null | https://arxiv.org/abs/2305.08053v1 | https://arxiv.org/pdf/2305.08053v1.pdf | SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency | Images captured under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reproduction. These factors can significantly hinder the performance of computer vision tasks such as object detection and image segmentation. As ... | ['Shenghui Zhong', 'Yiqing Shen', 'Miao Zhang'] | 2023-05-14 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 5.44287384e-01 -5.15770495e-01 -1.06228486e-01 -2.22183973e-01
-2.43573770e-01 -2.40776062e-01 4.40780103e-01 -8.90216082e-02
-2.81576663e-01 6.90866768e-01 -1.52553454e-01 -8.20550546e-02
-2.93832961e-02 -7.87982523e-01 -5.51991343e-01 -9.16577995e-01
4.44976181e-01 -6.52765393e-01 6.92902684e-01 -3.17408115... | [10.72945499420166, -2.509983539581299] |
5343fad1-7695-4478-b335-2290d9736e5a | masked-language-model-based-textual | 2304.08767 | null | https://arxiv.org/abs/2304.08767v2 | https://arxiv.org/pdf/2304.08767v2.pdf | Masked Language Model Based Textual Adversarial Example Detection | Adversarial attacks are a serious threat to the reliable deployment of machine learning models in safety-critical applications. They can misguide current models to predict incorrectly by slightly modifying the inputs. Recently, substantial work has shown that adversarial examples tend to deviate from the underlying dat... | ['Leo Yu Zhang', 'Shengshan Hu', 'Yanjun Zhang', 'Xufei Zheng', 'Qi Zhong', 'Zhaoxi Zhang', 'Xiaomei Zhang'] | 2023-04-18 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 8.06899071e-02 -3.21238972e-02 -1.29255489e-01 3.24037448e-02
-1.05084264e+00 -1.25867987e+00 9.59236681e-01 -5.80380410e-02
-2.53741909e-02 2.59476990e-01 -2.27792576e-01 -8.02652240e-01
1.78344965e-01 -7.79100537e-01 -8.32479119e-01 -7.36546814e-01
-3.67492348e-01 1.78329140e-01 1.55326799e-01 -5.07988751... | [5.795899391174316, 7.826888561248779] |
b7a24046-566b-4d63-a43d-0e91c3816c8e | latent-space-laplacian-pyramids-for | 1912.06466 | null | https://arxiv.org/abs/1912.06466v1 | https://arxiv.org/pdf/1912.06466v1.pdf | Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds | Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design. Despite the recent progress in deep generative modelling, synthesis of finely detailed 3D surfaces, such as high-resolution point clouds, from scr... | ['Vage Egiazarian', 'Savva Ignatyev', 'Youyi Zheng', 'Oleg Voynov', 'Andrey Kravchenko', 'Alexey Artemov', 'Luiz Velho', 'Evgeny Burnaev'] | 2019-12-13 | null | null | null | null | ['generating-3d-point-clouds'] | ['computer-vision'] | [ 9.78346467e-02 7.54866973e-02 4.45617527e-01 -1.46671236e-01
-8.39703619e-01 -4.82984841e-01 8.56199503e-01 -4.55432504e-01
6.89457178e-01 4.86288339e-01 1.19635105e-01 -2.16014042e-01
1.21686466e-01 -1.17384291e+00 -7.56981492e-01 -5.20570457e-01
4.30544734e-01 7.60890067e-01 4.97215129e-02 -2.02385187... | [8.881938934326172, -3.6552371978759766] |
81493768-e023-4a97-9eb8-0db0193f207c | leveraging-world-knowledge-in-implicit-hate | 2212.14100 | null | https://arxiv.org/abs/2212.14100v1 | https://arxiv.org/pdf/2212.14100v1.pdf | Leveraging World Knowledge in Implicit Hate Speech Detection | While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems. This paper presents the first attempt to apply Entity Linking (EL) techniques to bo... | ['Jessica Lin'] | 2022-12-28 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.33131325e-01 2.28043109e-01 -3.72197092e-01 5.59253506e-02
-3.45175564e-01 -8.36656332e-01 7.64013469e-01 5.52302122e-01
-2.60173112e-01 8.08883429e-01 6.74776495e-01 -3.22349459e-01
1.95184454e-01 -5.21305859e-01 -2.94354975e-01 -3.96454692e-01
-7.47177452e-02 -1.32522732e-01 1.31486624e-01 -3.96609008... | [8.68252944946289, 10.496410369873047] |
9c722f4d-450d-4652-a37d-a1b90d692e80 | learning-to-execute-or-ask-clarification | null | null | https://openreview.net/forum?id=6c8_5EyZYxH | https://openreview.net/pdf?id=6c8_5EyZYxH | Learning to execute or ask clarification questions | Collaborative tasks are ubiquitous activities where a form of communication is required in order to reach a joint goal. Collaborative building is one of such tasks. To this end, we wish to develop an intelligent builder agent in a simulated building environment (Minecraft) that can build whatever users wish to build by... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['learning-to-execute'] | ['computer-code'] | [ 8.72929096e-02 3.82134050e-01 4.41696197e-01 -9.11677063e-01
-1.10582256e+00 -6.85540497e-01 8.60163927e-01 -8.21263865e-02
-2.15657458e-01 7.19796419e-01 6.99959159e-01 -3.67287189e-01
-1.90281346e-01 -7.41609693e-01 -5.75204790e-01 -4.90516484e-01
1.14504099e-01 9.35194969e-01 1.39223963e-01 -5.38335383... | [12.623699188232422, 7.985926151275635] |
3168b632-a7dd-471c-bcf0-57e90e518b4a | optimism-and-adaptivity-in-policy | 2306.10587 | null | https://arxiv.org/abs/2306.10587v1 | https://arxiv.org/pdf/2306.10587v1.pdf | Optimism and Adaptivity in Policy Optimization | We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) through \emph{optimism} \& \emph{adaptivity}. Leveraging the deep connection between policy iteration and policy gradient methods, we recast seemingly unrelated policy optimization algorithms as the repeated ... | ['Sebastian Flennerhag', 'Doina Precup', 'Arthur Guez', 'Tom Zahavy', 'Veronica Chelu'] | 2023-06-18 | null | null | null | null | ['meta-learning', 'policy-gradient-methods'] | ['methodology', 'methodology'] | [ 1.05404615e-01 4.19539034e-01 -6.48109674e-01 -9.08995047e-02
-6.91170335e-01 -4.25708652e-01 8.58781099e-01 1.66714489e-01
-8.54018748e-01 1.04366481e+00 4.46427494e-01 -6.56148195e-01
-3.58105689e-01 -3.79405499e-01 -8.47240388e-01 -7.91155577e-01
-1.46622047e-01 4.94737417e-01 -3.00717831e-01 -5.40987790... | [4.081685543060303, 2.232409715652466] |
2a342b98-df1d-4c37-820f-587abd4bb1fa | convergent-bregman-plug-and-play-image | 2306.03466 | null | https://arxiv.org/abs/2306.03466v1 | https://arxiv.org/pdf/2306.03466v1.pdf | Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems | Plug-and-Play (PnP) methods are efficient iterative algorithms for solving ill-posed image inverse problems. PnP methods are obtained by using deep Gaussian denoisers instead of the proximal operator or the gradient-descent step within proximal algorithms. Current PnP schemes rely on data-fidelity terms that have eithe... | ['Nicolas Papadakis', 'Arthur Leclaire', 'Ulugbek Kamilov', 'Samuel Hurault'] | 2023-06-06 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 2.43851095e-02 5.28079756e-02 2.12654039e-01 -4.26175371e-02
-1.06551135e+00 -1.06544666e-01 4.44480687e-01 -3.93442452e-01
-3.49876910e-01 7.88719893e-01 2.56216109e-01 7.96190128e-02
-6.20023489e-01 -6.08607352e-01 -1.08083236e+00 -1.08236849e+00
1.16730347e-01 4.39560592e-01 -9.82868075e-02 -3.21805716... | [11.826600074768066, -2.468158006668091] |
ea10240a-36d6-4ac8-84af-1cb749939128 | gaitsada-self-aligned-domain-adaptation-for | 2301.13384 | null | https://arxiv.org/abs/2301.13384v3 | https://arxiv.org/pdf/2301.13384v3.pdf | GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition | mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. This technology offers privacy protection and is resilient to weather and lighting conditions. However, its generalization performance is yet unknown and limits its practical d... | ['Zhi Sun', 'Chen Chen', 'Qucheng Peng', 'Minwoo Lee', 'Pu Wang', 'Kalvik Jakkala', 'Ayman Ali', 'Ekkasit Pinyoanuntapong'] | 2023-01-31 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 2.14542776e-01 -3.63225818e-01 -2.39547387e-01 -3.32218379e-01
-7.77963758e-01 -5.14062762e-01 3.82924408e-01 -2.00956374e-01
-1.65552080e-01 7.78883517e-01 1.24004893e-01 1.18717074e-01
-1.31892607e-01 -6.61573768e-01 -1.68872491e-01 -1.15571785e+00
-1.21643439e-01 4.42337781e-01 -7.38571212e-02 2.90138647... | [14.228645324707031, 1.4475243091583252] |
aed4949e-e6a3-4785-9742-b0a5ca3b17d3 | deep-self-taught-learning-for-remote-sensing | 1710.07096 | null | http://arxiv.org/abs/1710.07096v2 | http://arxiv.org/pdf/1710.07096v2.pdf | Deep Self-taught Learning for Remote Sensing Image Classification | This paper addresses the land cover classification task for remote sensing
images by deep self-taught learning. Our self-taught learning approach learns
suitable feature representations of the input data using sparse representation
and undercomplete dictionary learning. We propose a deep learning framework
which extrac... | ['Susanne Wenzel', 'Anika Bettge', 'Ribana Roscher'] | 2017-10-19 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 3.12721044e-01 -2.15453119e-03 -3.02207053e-01 -7.52763391e-01
-5.31123817e-01 -1.37047186e-01 3.99242461e-01 1.13386668e-01
-4.21975404e-01 9.18005049e-01 1.07387349e-01 -3.26962978e-01
-2.11888790e-01 -1.48071373e+00 -6.97207987e-01 -7.94670701e-01
-4.87284482e-01 4.23130751e-01 -3.30243915e-01 -5.69660604... | [9.689135551452637, -1.4584401845932007] |
9d6ab21c-a736-498d-a614-edbfa1671cb5 | shadow-detection-a-survey-and-comparative | 1304.1233 | null | http://arxiv.org/abs/1304.1233v1 | http://arxiv.org/pdf/1304.1233v1.pdf | Shadow Detection: A Survey and Comparative Evaluation of Recent Methods | This paper presents a survey and a comparative evaluation of recent
techniques for moving cast shadow detection. We identify shadow removal as a
critical step for improving object detection and tracking. The survey covers
methods published during the last decade, and places them in a feature-based
taxonomy comprised of... | ['Brian C. Lovell', 'Andres Sanin', 'Conrad Sanderson'] | 2013-04-04 | null | null | null | null | ['shadow-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision'] | [ 5.00756323e-01 -6.13942802e-01 1.24193043e-01 -3.28574218e-02
-2.55270243e-01 -5.79858720e-01 6.27999187e-01 -2.75433034e-01
-2.60796666e-01 9.28699911e-01 -1.09473422e-01 -6.13742948e-01
2.06824597e-02 -4.82170045e-01 6.84307469e-03 -1.22239113e+00
-1.18132018e-01 4.64126080e-01 1.17635453e+00 -3.43636274... | [10.805706977844238, -4.037639617919922] |
721e2069-735b-4ae8-93a7-88a30f4422a2 | epillid-dataset-a-low-shot-fine-grained | 2005.14288 | null | https://arxiv.org/abs/2005.14288v2 | https://arxiv.org/pdf/2005.14288v2.pdf | ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification | Identifying prescription medications is a frequent task for patients and medical professionals; however, this is an error-prone task as many pills have similar appearances (e.g. white round pills), which increases the risk of medication errors. In this paper, we introduce ePillID, the largest public benchmark on pill i... | ['Naoto Usuyama', 'Natalia Larios Delgado', 'Jessica Lundin', 'Amanda K. Hall'] | 2020-05-28 | null | null | null | null | ['fine-grained-image-recognition', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [ 2.25269020e-01 -5.48958898e-01 -6.44670606e-01 -3.87186825e-01
-1.12615561e+00 -7.83985257e-01 3.90680194e-01 3.13440293e-01
8.17066990e-03 6.69135749e-01 2.59742826e-01 -7.23604783e-02
1.27184048e-01 -6.09775364e-01 -6.67298019e-01 -8.37458014e-01
4.38293479e-02 5.56682646e-01 -2.23364756e-01 1.28799632... | [14.993497848510742, -2.4963319301605225] |
c5a0a855-fb98-4646-aa77-04f2161449fb | controllable-unsupervised-text-attribute | 1905.12926 | null | https://arxiv.org/abs/1905.12926v2 | https://arxiv.org/pdf/1905.12926v2.pdf | Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation | Unsupervised text attribute transfer automatically transforms a text to alter a specific attribute (e.g. sentiment) without using any parallel data, while simultaneously preserving its attribute-independent content. The dominant approaches are trying to model the content-independent attribute separately, e.g., learning... | ['Ke Wang', 'Xiaojun Wan', 'Hang Hua'] | 2019-05-30 | controllable-unsupervised-text-attribute-1 | http://papers.nips.cc/paper/9284-controllable-unsupervised-text-attribute-transfer-via-editing-entangled-latent-representation | http://papers.nips.cc/paper/9284-controllable-unsupervised-text-attribute-transfer-via-editing-entangled-latent-representation.pdf | neurips-2019-12 | ['text-attribute-transfer'] | ['natural-language-processing'] | [ 6.63512886e-01 3.70180726e-01 -7.94302151e-02 -8.28216314e-01
-8.74665439e-01 -8.22396576e-01 7.74214864e-01 1.76046580e-01
-4.00037736e-01 6.40461802e-01 3.51244509e-01 -4.23446186e-02
2.20828801e-01 -1.02605534e+00 -8.64522576e-01 -8.50750864e-01
6.45108163e-01 8.24195027e-01 -1.66744024e-01 -7.80057237... | [11.553152084350586, 0.24832504987716675] |
05bb6c89-a7bb-48af-89c2-39520a2f65a5 | egocentric-activity-prediction-via-event | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Yang_Shen_Egocentric_Activity_Prediction_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yang_Shen_Egocentric_Activity_Prediction_ECCV_2018_paper.pdf | Egocentric Activity Prediction via Event Modulated Attention | Predicting future activities from an egocentric viewpoint is of particular interest in assisted living. However, state-of-the-art egocentric activity understanding techniques are mostly NOT capable of predictive tasks, as their synchronous processing architecture performs poorly in either modeling event dependency or p... | ['Bingbing Ni', 'Yang Shen', 'Zefan Li', 'Ning Zhuang'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 4.13808674e-01 2.72101402e-01 -2.50962704e-01 -5.68529904e-01
3.03197037e-02 2.05147922e-01 6.65039718e-01 2.24166304e-01
-2.03264102e-01 6.47231221e-01 6.74048662e-01 2.48148233e-01
-3.34075838e-01 -4.54539061e-01 -9.39909592e-02 -7.39376307e-01
-3.93632919e-01 5.76367183e-03 3.32646012e-01 -1.44870952... | [8.371609687805176, 0.6240119934082031] |
3dd1df53-7069-4cd4-89c0-f4dfe485d387 | conditional-graph-information-bottleneck-for | 2305.01520 | null | https://arxiv.org/abs/2305.01520v2 | https://arxiv.org/pdf/2305.01520v2.pdf | Conditional Graph Information Bottleneck for Molecular Relational Learning | Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. Recently, graph neural networks have recently shown great success in molecular relational learning by modeling a molecule as a grap... | ['Chanyoung Park', 'Junseok Lee', 'Sungwon Kim', 'Gyoung S. Na', 'Dongmin Hyun', 'Namkyeong Lee'] | 2023-04-29 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 3.39059353e-01 1.35838598e-01 -5.30853391e-01 -1.84282556e-01
-1.06442511e-01 -6.68183208e-01 5.46025693e-01 8.03979158e-01
1.44233003e-01 7.55729795e-01 -2.10199016e-03 -6.97778821e-01
-3.29953551e-01 -1.17574000e+00 -1.09406173e+00 -9.87243891e-01
-2.19789162e-01 3.64214242e-01 2.76402887e-02 -9.84751061... | [5.239808082580566, 5.919036388397217] |
79d66c26-c39c-4161-9dad-66eb8a378643 | ace-cooperative-multi-agent-q-learning-with | 2211.16068 | null | https://arxiv.org/abs/2211.16068v2 | https://arxiv.org/pdf/2211.16068v2.pdf | ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency | Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which is the ever-changing targets at every iteration when multiple agents update their policies at the same time. Starting from first principle, in this paper, we manage to solve the non-stationarity problem by proposing bidirectional... | ['Wanli Ouyang', 'Yu Liu', 'Yaodong Yang', 'Yazhe Niu', 'Yuhong Wei', 'Yinmin Zhang', 'Jie Liu', 'Chuming Li'] | 2022-11-29 | null | null | null | null | ['smac-1', 'starcraft', 'smac'] | ['playing-games', 'playing-games', 'playing-games'] | [-2.64952779e-02 5.76988719e-02 -5.42679310e-01 -1.72542393e-01
-6.56862676e-01 -6.42976761e-01 7.89776146e-01 1.97445571e-01
-9.30079222e-01 1.13019919e+00 -1.21123921e-02 -2.04050496e-01
-4.39642668e-01 -8.75376225e-01 -8.51786971e-01 -9.39740419e-01
-3.74444067e-01 9.58227277e-01 1.89851880e-01 -2.96950638... | [3.6787474155426025, 2.0430305004119873] |
0a523909-0cc0-4c42-a970-514d42cd1af7 | learning-to-control-latent-representations | 1911.08542 | null | https://arxiv.org/abs/1911.08542v1 | https://arxiv.org/pdf/1911.08542v1.pdf | Learning to Control Latent Representations for Few-Shot Learning of Named Entities | Humans excel in continuously learning with small data without forgetting how to solve old problems. However, neural networks require large datasets to compute latent representations across different tasks while minimizing a loss function. For example, a natural language understanding (NLU) system will often deal with e... | ['Omar U. Florez', 'Erik Mueller'] | 2019-11-19 | null | https://openreview.net/forum?id=rJleFREKDr | https://openreview.net/pdf?id=rJleFREKDr | null | ['small-data'] | ['computer-vision'] | [ 1.68768927e-01 5.77923238e-01 -1.94882691e-01 -4.68632758e-01
-4.03984219e-01 -4.76547986e-01 5.81122696e-01 3.10607225e-01
-7.32894003e-01 8.35391223e-01 3.08807850e-01 -2.01225430e-02
8.59706625e-02 -8.60249162e-01 -8.52692246e-01 -1.72714964e-01
-1.88090220e-01 9.79896605e-01 1.56418011e-01 -1.09996133... | [10.895224571228027, 7.824802398681641] |
ef2d6111-7f76-44e3-974c-0f45c9bfa344 | a-comparative-assessment-of-deep-learning | 2302.12168 | null | https://arxiv.org/abs/2302.12168v1 | https://arxiv.org/pdf/2302.12168v1.pdf | A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers | Short-term load forecasting (STLF) is vital for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. To that end, different forecasting methods have been proposed in the literature for day-ahead lo... | ['Dimitris Askounis', 'Spiros Mouzakitis', 'Evangelos Karakolis', 'Theodosios Pountridis', 'Evangelos Spiliotis', 'Ioannis-Konstantinos Seisopoulos', 'Sotiris Pelekis'] | 2023-02-23 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-5.57627499e-01 -5.11892915e-01 7.28567839e-02 -3.52971494e-01
-3.06660473e-01 -6.00303531e-01 9.20548260e-01 1.86343104e-01
-2.16622241e-02 6.27440870e-01 3.72747302e-01 -8.68385196e-01
-5.92095733e-01 -8.34066451e-01 -2.57005960e-01 -9.60581124e-01
-4.18612480e-01 3.53038073e-01 -4.71547246e-01 -2.70798415... | [6.18838357925415, 2.821852684020996] |
5690d088-3737-4722-8282-cded402e5b02 | no-reference-quality-assessment-of-contrast | 1904.08879 | null | http://arxiv.org/abs/1904.08879v1 | http://arxiv.org/pdf/1904.08879v1.pdf | No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement | No-reference image quality assessment (NR-IQA) aims to measure the image
quality without reference image. However, contrast distortion has been
overlooked in the current research of NR-IQA. In this paper, we propose a very
simple but effective metric for predicting quality of contrast-altered images
based on the fact t... | ['Xin Fu', 'Jie Li', 'Jia Yan'] | 2019-04-18 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 5.07833600e-01 -5.53259850e-01 1.10464640e-01 -3.96702290e-01
-6.46438420e-01 -1.58325449e-01 2.66797543e-01 1.51437461e-01
-4.35608238e-01 6.32261038e-01 1.43317372e-01 -6.28973357e-03
-2.97451377e-01 -8.17099094e-01 -2.99092591e-01 -9.62483943e-01
3.58477756e-02 -5.75973690e-01 2.07925886e-01 1.58943224... | [11.694169044494629, -1.9636958837509155] |
909cb669-b363-42e8-9a10-8a168f535351 | dynamic-review-based-recommenders | 2110.14747 | null | https://arxiv.org/abs/2110.14747v2 | https://arxiv.org/pdf/2110.14747v2.pdf | Dynamic Review-based Recommenders | Just as user preferences change with time, item reviews also reflect those same preference changes. In a nutshell, if one is to sequentially incorporate review content knowledge into recommender systems, one is naturally led to dynamical models of text. In the present work we leverage the known power of reviews to enha... | ['Cesar Ojeda', 'Christian Bauckhage', 'Ramses J. Sanchez', 'Kostadin Cvejoski'] | 2021-10-27 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [-1.32012144e-01 1.33578870e-02 -6.10599697e-01 -3.45292032e-01
7.16551486e-03 -8.92598569e-01 9.81488705e-01 4.21251804e-01
-1.01266980e-01 5.35032392e-01 6.80828035e-01 -5.11022270e-01
-2.85348684e-01 -9.89907146e-01 -4.40842599e-01 -9.65982676e-02
3.91170979e-02 4.41854715e-01 1.36841731e-02 -7.60763347... | [10.152397155761719, 5.742184638977051] |
aeb9bbe5-5418-477e-b5d7-20efed97bc6f | optimizing-sampling-patterns-for-compressed | 2306.03284 | null | https://arxiv.org/abs/2306.03284v1 | https://arxiv.org/pdf/2306.03284v1.pdf | Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models | Diffusion-based generative models have been used as powerful priors for magnetic resonance imaging (MRI) reconstruction. We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI that leverages pre-trained diffusion generative models. Crucially, during training we use a single... | ['Alexandros G. Dimakis', 'Jonathan I. Tamir', 'Ajil Jalal', 'Brett Levac', 'Sriram Ravula'] | 2023-06-05 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 6.15375280e-01 2.98190445e-01 -1.32543713e-01 -3.16861510e-01
-1.05039537e+00 -2.49646544e-01 5.94866991e-01 -1.77718058e-01
-5.31574309e-01 5.62903762e-01 6.65665329e-01 -1.98315173e-01
-2.60985941e-01 -3.53415012e-01 -5.53239882e-01 -8.89346540e-01
-3.81216705e-01 6.80545270e-01 2.71046609e-01 2.29884103... | [13.51187515258789, -2.380202293395996] |
4abaa12a-53cf-4130-a8e3-dce990a7340d | deep-recurrent-semi-supervised-eeg | 2107.13505 | null | https://arxiv.org/abs/2107.13505v1 | https://arxiv.org/pdf/2107.13505v1.pdf | Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition | EEG-based emotion recognition often requires sufficient labeled training samples to build an effective computational model. Labeling EEG data, on the other hand, is often expensive and time-consuming. To tackle this problem and reduce the need for output labels in the context of EEG-based emotion recognition, we propos... | ['Ali Etemad', 'Guangyi Zhang'] | 2021-07-28 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.58230796e-01 1.60283238e-01 2.94573367e-01 -7.45895922e-01
-8.76203954e-01 -3.93206328e-01 1.70166433e-01 1.23629779e-01
-5.44826925e-01 8.37069750e-01 2.98130959e-01 2.51957357e-01
7.52327889e-02 -5.01542568e-01 -7.10144818e-01 -7.05345631e-01
1.22847766e-01 4.43921506e-01 -3.49278659e-01 1.29970327... | [13.233990669250488, 3.5177812576293945] |
6156dea4-8150-4dae-bf4c-5caba102984a | detection-of-adversarial-examples-in-text | null | null | https://aclanthology.org/2022.findings-acl.289 | https://aclanthology.org/2022.findings-acl.289.pdf | Detection of Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation | Word-level adversarial attacks have shown success in NLP models, drastically decreasing the performance of transformer-based models in recent years. As a countermeasure, adversarial defense has been explored, but relatively few efforts have been made to detect adversarial examples. However, detecting adversarial exampl... | ['Nojun Kwak', 'Jiho Jang', 'Jangho Kim', 'KiYoon Yoo'] | null | null | null | null | findings-acl-2022-5 | ['adversarial-defense'] | ['adversarial'] | [ 4.79116142e-02 -1.14420965e-01 -2.63484687e-01 -3.04074347e-01
-1.31499195e+00 -1.15268469e+00 1.08857417e+00 3.50381315e-01
-3.31390202e-01 6.57752097e-01 2.49749050e-01 -6.63466930e-01
2.82443017e-01 -9.19670463e-01 -4.65034932e-01 -6.16317987e-01
9.25010368e-02 3.84973437e-01 -4.21502553e-02 -3.79448146... | [6.023985862731934, 8.079489707946777] |
f440a88a-7a23-4954-ab7a-4c01da670abd | replug-retrieval-augmented-black-box-language | 2301.12652 | null | https://arxiv.org/abs/2301.12652v4 | https://arxiv.org/pdf/2301.12652v4.pdf | REPLUG: Retrieval-Augmented Black-Box Language Models | We introduce REPLUG, a retrieval-augmented language modeling framework that treats the language model (LM) as a black box and augments it with a tuneable retrieval model. Unlike prior retrieval-augmented LMs that train language models with special cross attention mechanisms to encode the retrieved text, REPLUG simply p... | ['Wen-tau Yih', 'Luke Zettlemoyer', 'Mike Lewis', 'Rich James', 'Minjoon Seo', 'Michihiro Yasunaga', 'Sewon Min', 'Weijia Shi'] | 2023-01-30 | null | null | null | null | ['multi-task-language-understanding'] | ['methodology'] | [-3.74064654e-01 -8.91244859e-02 -5.52714825e-01 -1.12181671e-01
-1.68960905e+00 -5.05641639e-01 6.11253202e-01 -1.22965492e-01
-4.59428817e-01 4.12536800e-01 2.24605784e-01 -6.48922861e-01
1.17599271e-01 -5.61915517e-01 -9.52265084e-01 -3.87579203e-01
-8.35668668e-02 8.73925388e-01 3.35906535e-01 -3.54707509... | [11.355252265930176, 7.854794979095459] |
6acea42d-fe63-49ae-9a9b-36914ac38054 | revisiting-hate-speech-benchmarks-from-data | 2306.01105 | null | https://arxiv.org/abs/2306.01105v2 | https://arxiv.org/pdf/2306.01105v2.pdf | Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment | Social media is awash with hateful content, much of which is often veiled with linguistic and topical diversity. The benchmark datasets used for hate speech detection do not account for such divagation as they are predominantly compiled using hate lexicons. However, capturing hate signals becomes challenging in neutral... | ['Tanmoy Chakraborty', 'Vikram Goyal', 'Sarah Masud', 'Atharva Kulkarni'] | 2023-06-01 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-4.26853985e-01 -7.60932341e-02 -1.99341252e-01 1.47349030e-01
-3.90695900e-01 -9.48052108e-01 9.54690456e-01 5.34962416e-02
-3.27003092e-01 4.44754809e-01 6.46747768e-01 1.08682990e-01
3.78659368e-01 -3.48060787e-01 -3.99472922e-01 -5.57086110e-01
1.09444484e-02 2.27725402e-01 1.33640319e-02 -5.40387928... | [8.703996658325195, 10.631386756896973] |
64237826-e7e0-42dc-86ef-447084590ada | robust-neural-routing-through-space | 2012.04746 | null | https://arxiv.org/abs/2012.04746v2 | https://arxiv.org/pdf/2012.04746v2.pdf | Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments | Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc. Recent advances estimate the camera pose via optimization over the 2D/3D-3D correspondences established between the coordinates in 2D/3D camera space and 3D world space. Such a mapping is estimated ... | ['Leonidas Guibas', 'Baoquan Chen', 'Thomas Funkhouser', 'Li Yi', 'Ji Shi', 'He Wang', 'Qingnan Fan', 'Siyan Dong'] | 2020-12-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Dong_Robust_Neural_Routing_Through_Space_Partitions_for_Camera_Relocalization_in_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Dong_Robust_Neural_Routing_Through_Space_Partitions_for_Camera_Relocalization_in_CVPR_2021_paper.pdf | cvpr-2021-1 | ['camera-relocalization'] | ['computer-vision'] | [-5.01437187e-02 -3.43452752e-01 1.73026741e-01 -3.66115838e-01
-5.75941145e-01 -5.48198819e-01 4.45520043e-01 2.07975790e-01
-6.65467322e-01 1.94355369e-01 -6.43363893e-02 -3.00253212e-01
-2.41899192e-02 -6.40934944e-01 -1.13064134e+00 -4.58776683e-01
4.93505877e-03 6.39287055e-01 3.90375078e-01 1.44419000... | [7.650956153869629, -2.1955161094665527] |
b0e540ba-3618-470c-a5c8-0453c6eeb390 | volume-droid-a-real-time-implementation-of | 2306.06850 | null | https://arxiv.org/abs/2306.06850v1 | https://arxiv.org/pdf/2306.06850v1.pdf | Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM | This paper presents Volume-DROID, a novel approach for Simultaneous Localization and Mapping (SLAM) that integrates Volumetric Mapping and Differentiable Recurrent Optimization-Inspired Design (DROID). Volume-DROID takes camera images (monocular or stereo) or frames from a video as input and combines DROID-SLAM, point ... | ['Emaad Gerami', 'Nibarkavi Amutha', 'Ashwin Saxena', 'Sandilya Sai Garimella', 'Peter Stratton'] | 2023-06-12 | null | null | null | null | ['simultaneous-localization-and-mapping', 'point-cloud-registration', 'point-cloud-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.14649484e-01 -3.36500481e-02 1.54019430e-01 -4.11041379e-01
-7.82059669e-01 -8.46139550e-01 5.89241087e-01 -8.43003467e-02
-5.20842075e-01 4.73929495e-01 -3.21880579e-01 4.68268394e-02
-1.92187995e-01 -7.94781387e-01 -1.10691154e+00 -3.01008701e-01
1.54243587e-02 8.95901501e-01 5.17838001e-01 5.63852340... | [7.401644229888916, -2.2533328533172607] |
b8085460-20b3-4569-b5ef-ad5b9f1c864c | grow-and-clip-informative-yet-concise | 2201.05088 | null | https://arxiv.org/abs/2201.05088v2 | https://arxiv.org/pdf/2201.05088v2.pdf | Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation | Interpreting the predictions of existing Question Answering (QA) models is critical to many real-world intelligent applications, such as QA systems for healthcare, education, and finance. However, existing QA models lack interpretability and provide no feedback or explanation for end-users to help them understand why a... | ['Bang Liu', 'Yanghua Xiao', 'Yuyan Chen'] | 2022-01-13 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 3.19025181e-02 3.94116253e-01 -8.15453902e-02 -8.13412249e-01
-9.67696190e-01 -5.44354975e-01 4.54331487e-01 4.71842468e-01
-2.81034172e-01 8.20037365e-01 6.27683818e-01 -5.46621263e-01
-3.72211844e-01 -7.32021928e-01 -5.89654207e-01 -1.66586176e-01
5.38360775e-01 3.64125192e-01 5.34728646e-01 -3.56101453... | [11.163124084472656, 8.071039199829102] |
042bb71e-243b-442e-8818-67b090c98514 | mmvc-learned-multi-mode-video-compression | 2304.02273 | null | https://arxiv.org/abs/2304.02273v1 | https://arxiv.org/pdf/2304.02273v1.pdf | MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding | Learning-based video compression has been extensively studied over the past years, but it still has limitations in adapting to various motion patterns and entropy models. In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal m... | ['Hun-Seok Kim', 'Shiyu Liu', 'Rakesh Chowdary Machineni', 'Yu Chen', 'Bowen Liu'] | 2023-04-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_MMVC_Learned_Multi-Mode_Video_Compression_With_Block-Based_Prediction_Mode_Selection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_MMVC_Learned_Multi-Mode_Video_Compression_With_Block-Based_Prediction_Mode_Selection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['ms-ssim'] | ['computer-vision'] | [ 4.74000007e-01 -4.76478428e-01 -4.21068400e-01 -2.36044303e-01
-4.99403596e-01 -8.89083650e-03 5.49919128e-01 -1.76657051e-01
-4.59709942e-01 7.30726004e-01 6.33780241e-01 1.06205545e-01
-3.16841573e-01 -8.03838730e-01 -5.77139914e-01 -8.20635498e-01
-4.30187494e-01 -3.41603681e-02 3.23139399e-01 -2.04419401... | [11.337339401245117, -1.5742260217666626] |
0ecbd528-bf6a-4051-9ac6-d3d73d1bd48d | far-field-speaker-recognition-benchmark | null | null | https://aclanthology.org/2022.lrec-1.209 | https://aclanthology.org/2022.lrec-1.209.pdf | Far-Field Speaker Recognition Benchmark Derived From The DiPCo Corpus | In this paper, we present a far-field speaker verification benchmark derived from the publicly-available DiPCo corpus. This corpus comprise three different tasks that involve enrollment and test conditions with single- and/or multi-channels recordings. The main goal of this corpus is to foster research in far-field and... | ['Mohammad Mohammadamini', 'Mickael Rouvier'] | null | null | null | null | lrec-2022-6 | ['text-independent-speaker-verification'] | ['speech'] | [ 4.52299975e-02 -4.65482324e-01 2.00324148e-01 -7.67861485e-01
-1.16868174e+00 -5.13653100e-01 4.41192806e-01 -4.21389997e-01
-2.86027133e-01 3.82918358e-01 4.26203579e-01 -3.86576682e-01
4.33049649e-01 -7.57421479e-02 -3.61156255e-01 -9.56867099e-01
4.02662195e-02 8.56367126e-02 -1.92077041e-01 -4.36464220... | [14.369770050048828, 6.140002250671387] |
b7fcc354-a453-4955-a5c4-cb577cca82bc | deformable-convolutions-and-lstm-based | 2306.00834 | null | https://arxiv.org/abs/2306.00834v1 | https://arxiv.org/pdf/2306.00834v1.pdf | Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network for Motion Deblurring | Event cameras differ from conventional RGB cameras in that they produce asynchronous data sequences. While RGB cameras capture every frame at a fixed rate, event cameras only capture changes in the scene, resulting in sparse and asynchronous data output. Despite the fact that event data carries useful information that ... | ['Mehmet Yamac', 'Dan Yang'] | 2023-06-01 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 2.47376814e-01 -7.45425999e-01 1.48549289e-01 -1.70690101e-02
-1.01429373e-01 -3.99277180e-01 5.98369360e-01 -2.56173164e-01
-7.93168724e-01 6.47871971e-01 2.36000940e-01 7.85410479e-02
1.13468789e-01 -7.60283470e-01 -7.16169596e-01 -7.42935658e-01
3.20599740e-03 -2.64351249e-01 6.17497444e-01 1.17039114... | [10.638762474060059, -1.6479182243347168] |
1d55f5c2-0c92-485c-aa88-5df068bfc0cc | active-learning-applied-to-patient-adaptive | null | null | http://papers.nips.cc/paper/4091-active-learning-applied-to-patient-adaptive-heartbeat-classification | http://papers.nips.cc/paper/4091-active-learning-applied-to-patient-adaptive-heartbeat-classification.pdf | Active Learning Applied to Patient-Adaptive Heartbeat Classification | While clinicians can accurately identify different types of heartbeats in electrocardiograms (ECGs) from different patients, researchers have had limited success in applying supervised machine learning to the same task. The problem is made challenging by the variety of tasks, inter- and intra-patient differences, an of... | ['Jenna Wiens', 'John V. Guttag'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['heartbeat-classification'] | ['medical'] | [ 5.42582631e-01 1.02362163e-01 -2.75477499e-01 -5.62220931e-01
-1.23600996e+00 -6.22707665e-01 -2.06205338e-01 6.17448866e-01
-4.72738922e-01 8.39128733e-01 -1.52499616e-01 -4.57031518e-01
-4.16760385e-01 -8.40071216e-02 1.52637765e-01 -6.30367219e-01
-4.24244314e-01 8.89957428e-01 -5.05871652e-03 5.11659384... | [14.298779487609863, 3.311234474182129] |
f1606381-5381-4693-97a4-d4cbb8d44aad | lcaunet-a-skin-lesion-segmentation-network | 2305.00837 | null | https://arxiv.org/abs/2305.00837v1 | https://arxiv.org/pdf/2305.00837v1.pdf | LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion | Accurate segmentation of skin lesions in dermatoscopic images is crucial for the early diagnosis of skin cancer and improving the survival rate of patients. However, it is still a challenging task due to the irregularity of lesion areas, the fuzziness of boundaries, and other complex interference factors. In this paper... | ['Yuhai Zhao', 'Lisheng Xu', 'Gao Wang', 'Keming Mao', 'Qisen Ma'] | 2023-05-01 | null | null | null | null | ['skin-lesion-segmentation', 'lesion-segmentation'] | ['medical', 'medical'] | [ 5.30552268e-01 -1.09190978e-01 -2.74131745e-01 -2.93171406e-01
-6.81092918e-01 -3.98683310e-01 3.12205970e-01 7.17463195e-02
-2.48408586e-01 5.15446603e-01 1.04898669e-01 -1.25507951e-01
-1.82307437e-01 -7.25940645e-01 -6.11379206e-01 -7.15816557e-01
3.25217098e-01 -2.82474667e-01 3.85755628e-01 -1.69287920... | [15.62130069732666, -2.9142258167266846] |
bfd2dde2-ee5e-454d-ae57-ab33c43fd84d | generative-ai-empowered-simulation-for | 2302.08418 | null | https://arxiv.org/abs/2302.08418v1 | https://arxiv.org/pdf/2302.08418v1.pdf | Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses | In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside... | ['Zhu Han', 'Shiwen Mao', 'Zehui Xiong', 'Jiawen Kang', 'Hongliang Zhang', 'Junlong Chen', 'Dusit Niyato', 'Minrui Xu'] | 2023-02-16 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-6.50542974e-01 2.48755500e-01 -3.88190806e-01 -3.40528518e-01
-4.41921234e-01 -2.88049430e-01 5.16349375e-01 -6.67133033e-01
-1.07267238e-01 8.65595937e-01 -3.70224863e-01 -5.44584692e-01
-2.38336578e-01 -1.04813910e+00 -7.88910568e-01 -5.41427732e-01
-3.65883082e-01 5.45756876e-01 6.18790984e-01 -6.00604773... | [5.739319324493408, 1.358502745628357] |
ebd5f190-87cf-4f4a-94c0-9cbe54030c49 | resus-warm-up-cold-users-via-meta-learning | 2210.16080 | null | https://arxiv.org/abs/2210.16080v1 | https://arxiv.org/pdf/2210.16080v1.pdf | RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction | Click-Through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge, which either perform few-shot user representation learning or adopt optimization-based meta-learning. However, existing methods suffer from ... | ['Kangyi Lin', 'Wenwen Zhou', 'Zibin Zhang', 'Weiyu Cheng', 'Lifan Zhao', 'Yanyan Shen'] | 2022-10-28 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-1.98979214e-01 -8.90779734e-01 -5.16367972e-01 -4.94821042e-01
-8.78151000e-01 -2.47174561e-01 3.93392295e-01 7.23194778e-02
-4.35624838e-01 5.16974986e-01 6.90308928e-01 3.15683894e-02
-2.61957794e-01 -6.70876443e-01 -1.66533366e-01 -7.39891708e-01
3.40951741e-01 3.98712039e-01 1.03660859e-01 -4.76582766... | [10.101317405700684, 5.605418682098389] |
80e74965-3391-4379-a740-303d0d76ebe6 | mind-the-gap-cross-lingual-information | 2112.13510 | null | https://arxiv.org/abs/2112.13510v1 | https://arxiv.org/pdf/2112.13510v1.pdf | Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement | Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written in a language different from the user's query. The intrinsic gap between different languages is an essential challenge for CLIR. In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the sufficient informat... | ['Qing He', 'Yi Wei', 'Fuzhen Zhuang', 'Dehong Gao', 'Xiang Ao', 'Zhao Zhang', 'Fuwei Zhang'] | 2021-12-27 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-5.21464407e-01 -3.16484213e-01 -4.95221257e-01 -2.59297222e-01
-1.24161422e+00 -6.70592308e-01 6.96482599e-01 3.49834323e-01
-7.85342932e-01 3.82758170e-01 6.44713402e-01 9.94877443e-02
-4.14987564e-01 -6.22546196e-01 -2.71703333e-01 -3.33037347e-01
3.96938682e-01 5.67664623e-01 6.01774693e-01 -7.29619265... | [11.250670433044434, 9.730918884277344] |
9e05ef7f-05ea-4d87-875f-9bdcf6e815be | gator-graph-aware-transformer-with-motion | 2303.05652 | null | https://arxiv.org/abs/2303.05652v1 | https://arxiv.org/pdf/2303.05652v1.pdf | GATOR: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose | 3D human mesh recovery from a 2D pose plays an important role in various applications. However, it is hard for existing methods to simultaneously capture the multiple relations during the evolution from skeleton to mesh, including joint-joint, joint-vertex and vertex-vertex relations, which often leads to implausible r... | ['Runwei Ding', 'Ti Wang', 'Wenhao Li', 'Xia Li', 'Hong Liu', 'Yingxuan You'] | 2023-03-10 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [ 1.44146970e-02 1.77664548e-01 -2.14222297e-01 -2.43060678e-01
-6.95997179e-01 -1.67920440e-01 4.09948736e-01 -1.22277699e-01
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-8.24250430e-02 8.18786919e-01 2.99843073e-01 -2.33421519... | [7.170871257781982, -0.8813808560371399] |
ba5ca3cb-d9d0-475b-bb32-36b00b33180d | how-trial-to-trial-learning-shapes-mappings | 2207.00430 | null | https://arxiv.org/abs/2207.00430v2 | https://arxiv.org/pdf/2207.00430v2.pdf | How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning | Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning durin... | ['R. Harald Baayen', 'Yu-Ying Chuang', 'Maria Heitmeier'] | 2022-07-01 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 1.04177922e-01 -1.02207437e-01 -8.71018171e-02 -3.69329989e-01
-1.86631903e-01 -5.81315696e-01 9.62142646e-01 7.29557991e-01
-1.17230308e+00 6.08224213e-01 5.67111233e-03 -6.95982218e-01
-2.30386138e-01 -6.84393764e-01 -5.26788116e-01 -3.38298529e-01
-1.99136376e-01 2.80713826e-01 3.20670605e-01 -3.01618159... | [10.429052352905273, 9.131461143493652] |
a4dffe1d-b147-413b-8d2c-bcdf056dc176 | multi-time-horizon-solar-forecasting-using | 1807.05459 | null | http://arxiv.org/abs/1807.05459v1 | http://arxiv.org/pdf/1807.05459v1.pdf | Multi-time-horizon Solar Forecasting Using Recurrent Neural Network | The non-stationarity characteristic of the solar power renders traditional
point forecasting methods to be less useful due to large prediction errors.
This results in increased uncertainties in the grid operation, thereby
negatively affecting the reliability and increased cost of operation. This
research paper proposes... | ['Praveen Palanisamy', 'Sakshi Mishra'] | 2018-07-14 | null | null | null | null | ['3d-anomaly-detection-and-segmentation'] | ['methodology'] | [-2.20428675e-01 -4.52637702e-01 1.48665756e-01 -3.94777894e-01
-2.68891096e-01 -5.93161583e-01 7.12006927e-01 1.99605469e-02
3.73095214e-01 8.99797738e-01 7.99718052e-02 -6.48501039e-01
-2.22221911e-01 -1.07323217e+00 -1.07795894e-01 -9.21048522e-01
-1.70438498e-01 3.24526802e-02 -3.49493682e-01 -4.06822413... | [6.173717498779297, 2.80155086517334] |
8995476e-5e55-4ea1-8836-157a00c53317 | outlier-detection-by-consistent-data | 1712.04129 | null | http://arxiv.org/abs/1712.04129v2 | http://arxiv.org/pdf/1712.04129v2.pdf | Outlier Detection by Consistent Data Selection Method | Often the challenge associated with tasks like fraud and spam detection[1] is
the lack of all likely patterns needed to train suitable supervised learning
models. In order to overcome this limitation, such tasks are attempted as
outlier or anomaly detection tasks. We also hypothesize that out- liers have
behavioral pat... | ['Smruthi Mukund', 'Utkarsh Porwal'] | 2017-12-12 | null | null | null | null | ['one-class-classifier', 'spam-detection'] | ['methodology', 'natural-language-processing'] | [-7.57032260e-02 -2.47747764e-01 4.14953493e-02 -4.95632082e-01
-5.37990332e-01 -1.94192544e-01 4.99934047e-01 6.11182153e-01
-3.58389705e-01 6.22266471e-01 -1.41012341e-01 7.84922913e-02
-3.90929788e-01 -6.40765071e-01 -6.39727116e-01 -5.61524510e-01
-1.96807891e-01 7.35079229e-01 5.08223414e-01 1.06301725... | [7.603435039520264, 2.6772549152374268] |
7b3341f9-76bb-416e-b3ca-0d665c8d3277 | autism-spectrum-disorder-classification-based | 2208.08902 | null | https://arxiv.org/abs/2208.08902v2 | https://arxiv.org/pdf/2208.08902v2.pdf | Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning? | Research in machine learning for autism spectrum disorder (ASD) classification bears the promise to improve clinical diagnoses. However, recent studies in clinical imaging have shown the limited generalization of biomarkers across and beyond benchmark datasets. Despite increasing model complexity and sample size in neu... | ['Vanessa Reindl', 'Martin Schulte-Rüther', 'Jana Kruppa', 'Kerstin Konrad', 'Christian Gerloff'] | 2022-08-17 | null | null | null | null | ['classification'] | ['methodology'] | [ 5.87937355e-01 5.28024495e-01 -3.63867655e-02 -4.08336580e-01
1.59817174e-01 -2.44426996e-01 3.68529916e-01 8.24275851e-01
-1.61061183e-01 1.63296714e-01 2.87596852e-01 -1.25096634e-01
-8.11879575e-01 -5.33752203e-01 -1.05297871e-01 -4.43288058e-01
-5.09179056e-01 5.98010957e-01 -5.26654646e-02 -9.60941315... | [12.610495567321777, 3.1509342193603516] |
b2bbe440-e1a3-4df5-aeaa-627395139c2a | improving-twitter-sentiment-analysis-with | null | null | https://aclanthology.org/P14-2071 | https://aclanthology.org/P14-2071.pdf | Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training | null | ['Liang Zhou', 'Bing Xiang'] | 2014-06-01 | null | null | null | acl-2014-6 | ['twitter-sentiment-analysis', 'stock-prediction'] | ['natural-language-processing', 'time-series'] | [-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... | [-7.340675354003906, 3.7936513423919678] |
dcc44aef-602e-4fff-bae6-f0e0d3a54b9b | boosting-offline-reinforcement-learning-with-1 | 2306.03362 | null | https://arxiv.org/abs/2306.03362v1 | https://arxiv.org/pdf/2306.03362v1.pdf | Boosting Offline Reinforcement Learning with Action Preference Query | Training practical agents usually involve offline and online reinforcement learning (RL) to balance the policy's performance and interaction costs. In particular, online fine-tuning has become a commonly used method to correct the erroneous estimates of out-of-distribution data learned in the offline training phase. Ho... | ['Gao Huang', 'Shiji Song', 'Matthieu Gaetan Lin', 'Shenzhi Wang', 'Qisen Yang'] | 2023-06-06 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.55218497e-01 2.40911305e-01 -4.19587731e-01 -2.70372897e-01
-8.05530429e-01 -4.18514669e-01 4.34632778e-01 3.54618281e-01
-8.35708857e-01 1.31573844e+00 -1.02735475e-01 -3.14339697e-01
-4.28203493e-01 -6.05462611e-01 -1.03137898e+00 -8.80787492e-01
-2.81516790e-01 7.49405265e-01 6.57363087e-02 -2.10624337... | [4.005281925201416, 2.271998643875122] |
fa59c25e-7c13-43cb-a4b8-f4465358c9b3 | fact-enhanced-synthetic-news-generation | 2012.04778 | null | https://arxiv.org/abs/2012.04778v2 | https://arxiv.org/pdf/2012.04778v2.pdf | Fact-Enhanced Synthetic News Generation | The advanced text generation methods have witnessed great success in text summarization, language translation, and synthetic news generation. However, these techniques can be abused to generate disinformation and fake news. To better understand the potential threats of synthetic news, we develop a new generation method... | ['Huan Liu', 'Kaize Ding', 'Yichuan Li', 'Kai Shu'] | 2020-12-08 | null | null | null | null | ['news-generation'] | ['natural-language-processing'] | [ 2.27637425e-01 8.32051694e-01 -6.03799880e-01 2.33448431e-01
-8.83739233e-01 -8.47793818e-01 1.20103967e+00 2.16874272e-01
1.79984152e-01 1.68881691e+00 1.07861245e+00 -2.46380001e-01
6.27373993e-01 -1.05720067e+00 -8.15486729e-01 -2.83578634e-01
4.93403286e-01 4.65199262e-01 1.59602121e-01 -4.97550845... | [12.073492050170898, 9.222092628479004] |
6cb0e755-c362-4942-988f-5a22433497fe | perturbation-based-frequency-domain-linear | 2105.03973 | null | https://arxiv.org/abs/2105.03973v1 | https://arxiv.org/pdf/2105.03973v1.pdf | Perturbation-based Frequency Domain Linear and Nonlinear Noise Estimation | In this paper, a new method for the separation of noise categories based on Four-Wave Mixing is presented. The theoretical analysis is grounded in the Gaussian Noise model and verified by split step simulations. The noise categories react differently to the introduced perturbations, by performing a set of perturbations... | ['S. J. Savory', 'D. J. Ives', 'F. J. Vaquero-Caballero'] | 2021-05-09 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 1.96835220e-01 -2.98201859e-01 5.70849180e-01 -8.56315047e-02
-4.00221258e-01 -3.72490823e-01 3.79027247e-01 2.11756259e-01
-5.71508229e-01 8.77688468e-01 -1.87952146e-01 -2.51368999e-01
-6.99114263e-01 -6.17597580e-01 -4.51160856e-02 -1.02516973e+00
-1.93374783e-01 5.28537929e-01 3.15986276e-01 -3.73284966... | [12.037007331848145, -2.338021993637085] |
87bcf354-df35-4542-ae4c-bfd175808b2f | evolving-three-dimension-3d-abstract-art | 2304.12932 | null | https://arxiv.org/abs/2304.12932v1 | https://arxiv.org/pdf/2304.12932v1.pdf | Evolving Three Dimension (3D) Abstract Art: Fitting Concepts by Language | Computational creativity has contributed heavily to abstract art in modern era, allowing artists to create high quality, abstract two dimension (2D) arts with a high level of controllability and expressibility. However, even with computational approaches that have promising result in making concrete 3D art, computation... | ['Yingtao Tian'] | 2023-04-24 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 3.69942367e-01 1.59282625e-01 3.01715583e-01 1.78683281e-01
4.49060313e-02 -1.05691040e+00 6.70364320e-01 -3.40760827e-01
3.46977562e-01 5.54946542e-01 3.93196754e-02 -2.53714681e-01
-1.10091589e-01 -8.59685063e-01 -3.65339965e-01 -4.01017129e-01
3.43851410e-02 5.08040845e-01 -1.06011063e-01 -2.96460718... | [9.129100799560547, -3.5193676948547363] |
7130a8f5-b8a0-410d-9c50-e4e7c917320a | contrasinver-voxel-wise-contrastive-semi | 2302.06441 | null | https://arxiv.org/abs/2302.06441v2 | https://arxiv.org/pdf/2302.06441v2.pdf | ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion | Recent studies have shown that learning theories have been very successful in hydrocarbon exploration. Inversion of seismic into various attributes through the relationship of 1D well-logs and 3D seismic is an essential step in reservoir description, among which, acoustic impedance is one of the most critical attribute... | ['Zhifeng Xu', 'Hongjie Duan', 'Kewen Li', 'Timing Li', 'YiMin Dou'] | 2023-02-13 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [-1.16044335e-01 -3.74872051e-02 1.38129652e-01 -2.73894995e-01
-1.30192411e+00 -2.96559751e-01 6.72120273e-01 -6.02319650e-03
-6.18719697e-01 7.41056383e-01 8.73534977e-02 -5.24268031e-01
-6.07431293e-01 -9.66110885e-01 -8.82446468e-01 -1.07432878e+00
-6.82991982e-01 1.02987444e+00 2.89629459e-01 -3.42238247... | [6.862648010253906, 2.5156211853027344] |
b8609b55-bf8e-4b96-a925-eec800fd6f8a | learning-canonical-embeddings-for | 2209.02152 | null | https://arxiv.org/abs/2209.02152v2 | https://arxiv.org/pdf/2209.02152v2.pdf | Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations | We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding algorithm (LLE) -- originally designed for nonlinear dimensionality reduction -- for shape correspondence. The key idea is to find dense corres... | ['Anand Rangarajan', 'Sanjay Ranka', 'Patrick Emami', 'Pan He'] | 2022-09-05 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [-3.50305170e-01 -1.28077762e-02 -9.23399720e-03 -4.73959923e-01
-9.34941173e-01 -7.88055718e-01 8.75021040e-01 3.51704180e-01
-7.61546120e-02 -2.04703454e-02 3.65841746e-01 1.27370626e-01
-3.65531713e-01 -9.11558807e-01 -6.63077116e-01 -6.34564340e-01
8.50965921e-03 1.17781663e+00 5.71482144e-02 3.53623480... | [8.058754920959473, -3.2696971893310547] |
233d10e3-3a0a-4c77-8cd8-65f1b43166ea | example-based-motion-synthesis-via-generative | 2306.00378 | null | https://arxiv.org/abs/2306.00378v1 | https://arxiv.org/pdf/2306.00378v1.pdf | Example-based Motion Synthesis via Generative Motion Matching | We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inher... | ['Baoquan Chen', 'Olga Sorkine-Hornung', 'Peizhuo Li', 'Xuelin Chen', 'Weiyu Li'] | 2023-06-01 | null | null | null | null | ['motion-synthesis'] | ['computer-vision'] | [ 2.58314192e-01 1.07240304e-01 -1.29607886e-01 1.11838482e-01
-8.37315500e-01 -6.66522443e-01 7.20441282e-01 -4.21453893e-01
-3.88455428e-02 6.53482437e-01 4.14725453e-01 -1.54430047e-01
-9.65727642e-02 -7.72699773e-01 -7.14390337e-01 -5.72304904e-01
8.95333514e-02 5.59785604e-01 3.40800762e-01 -2.08697528... | [7.364650726318359, -0.3473231792449951] |
00b1e039-456b-4bc1-aca1-6dab6accb1f1 | towards-source-free-domain-adaptive-semantic | 2306.01598 | null | https://arxiv.org/abs/2306.01598v2 | https://arxiv.org/pdf/2306.01598v2.pdf | Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning | Domain adaptive semantic segmentation enables robust pixel-wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods. It utilizes a well-trained source mo... | ['Yaonan Wang', 'Kailun Yang', 'Zheng Xiao', 'Xiao Lu', 'HUI ZHANG', 'Yihong Cao'] | 2023-06-02 | null | null | null | null | ['source-free-domain-adaptation', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 4.76157993e-01 1.29240468e-01 -5.90562284e-01 -6.94039106e-01
-1.05641353e+00 -5.81967413e-01 4.10948247e-01 -7.94834569e-02
-6.01488709e-01 7.79841542e-01 -9.59187672e-02 -1.00499392e-01
1.58406064e-01 -5.48614025e-01 -8.52940857e-01 -7.94205368e-01
5.71268439e-01 4.27945077e-01 5.27681649e-01 -1.58647783... | [9.663946151733398, 1.362302303314209] |
edc2b1b9-6d99-4b00-bb39-28242129a0e3 | iit-bhu-system-description-for-lsdsem17 | null | null | https://aclanthology.org/W17-0912 | https://aclanthology.org/W17-0912.pdf | IIT (BHU): System Description for LSDSem'17 Shared Task | This paper describes an ensemble system submitted as part of the LSDSem Shared Task 2017 - the Story Cloze Test. The main conclusion from our results is that an approach based on semantic similarity alone may not be enough for this task. We test various approaches and compare them with two ensemble systems. One is base... | ['Anil Kumar Singh', 'Pranav Goel'] | 2017-04-01 | null | null | null | ws-2017-4 | ['cloze-test'] | ['natural-language-processing'] | [-1.82909250e-01 -6.44191983e-04 2.73768157e-01 -5.75893462e-01
-6.74782574e-01 -4.11957353e-01 8.31518710e-01 4.81151849e-01
-5.58623493e-01 8.70529056e-01 4.27565575e-01 -9.13503543e-02
-3.06612134e-01 -5.51856935e-01 -1.63754269e-01 -6.13565922e-01
4.21778917e-01 6.07161999e-01 4.93401408e-01 -9.06867385... | [9.105437278747559, 10.506221771240234] |
3f4019b8-9ae9-4123-ae8a-73f9fe6ca8ab | two-stage-robust-and-sparse-distributed | 2208.08230 | null | https://arxiv.org/abs/2208.08230v1 | https://arxiv.org/pdf/2208.08230v1.pdf | Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data | In this paper, we address the problem of conducting statistical inference in settings involving large-scale data that may be high-dimensional and contaminated by outliers. The high volume and dimensionality of the data require distributed processing and storage solutions. We propose a two-stage distributed and robust s... | ['Visa Koivunen', 'Emadaldin Mozafari-Majd'] | 2022-08-17 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.62978470e-01 -3.82276148e-01 -3.44986081e-01 -3.71310651e-01
-1.10201097e+00 -3.82647544e-01 2.15395585e-01 2.74687588e-01
-6.71983957e-02 1.50201333e+00 2.77984850e-02 -5.85964806e-02
-5.68905592e-01 -8.97971153e-01 -7.58780479e-01 -9.98351932e-01
-2.64009058e-01 7.60895371e-01 -2.62574494e-01 3.76202017... | [7.2157368659973145, 4.454105854034424] |
35ec9d14-5d3a-4a07-b48f-57f238c4a520 | the-2019-bbn-cross-lingual-information | null | null | https://aclanthology.org/2020.clssts-1.8 | https://aclanthology.org/2020.clssts-1.8.pdf | The 2019 BBN Cross-lingual Information Retrieval System | In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English. Foreign audio documents are first transcribed by ... | ['Manaj Srivastava', 'Richard Schwartz', 'Le Zhang', 'Lee Tarlin', 'Damianos Karakos', 'Sanjay Krishna Gouda', 'Lingjun Zhao', 'David Akodes', 'Numra Bathool', 'John Makhoul', 'Zhuolin Jiang', 'William Hartmann'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 3.42743427e-01 -2.33227223e-01 -6.11720562e-01 -2.76766390e-01
-2.38560081e+00 -8.34979475e-01 4.85997885e-01 2.10441843e-01
-4.87379968e-01 7.87356794e-01 5.31972229e-01 -2.97008395e-01
-7.79540986e-02 -4.19395864e-01 -6.93971634e-01 -2.43655503e-01
3.05164576e-01 7.79783845e-01 1.64323464e-01 -3.95886362... | [14.442571640014648, 7.1860833168029785] |
8c86fbf0-8e69-45e2-9fb2-ba5de5a03f42 | fitting-mixed-logit-random-regret | 2301.01091 | null | https://arxiv.org/abs/2301.01091v1 | https://arxiv.org/pdf/2301.01091v1.pdf | Fitting mixed logit random regret minimization models using maximum simulated likelihood | This article describes the mixrandregret command, which extends the randregret command introduced in Guti\'errez-Vargas et al. (2021, The Stata Journal 21: 626-658) incorporating random coefficients for Random Regret Minimization models. The newly developed command mixrandregret allows the inclusion of random coefficie... | ['Martina Vandebroek', 'Álvaro A. Gutiérrez-Vargas', 'Ziyue Zhu'] | 2023-01-03 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.97015184e-01 4.98710945e-02 -4.85107899e-01 -6.07156456e-01
-8.32642674e-01 -7.13579059e-01 5.18925309e-01 5.48620895e-03
-6.65076613e-01 1.25188005e+00 3.33613724e-01 -9.41574216e-01
-4.94982630e-01 -8.68036330e-01 -7.41635561e-01 -3.69681478e-01
-1.99690759e-01 2.87872761e-01 -1.40001550e-01 -2.56726630... | [6.4947075843811035, 3.964688777923584] |
0819565f-a395-44a5-8313-77cf6043137b | styleflow-disentangle-latent-representations | 2212.09670 | null | https://arxiv.org/abs/2212.09670v1 | https://arxiv.org/pdf/2212.09670v1.pdf | StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer | Text style transfer aims to alter the style of a sentence while preserving its content. Due to the lack of parallel corpora, most recent work focuses on unsupervised methods and often uses cycle construction to train models. Since cycle construction helps to improve the style transfer ability of the model by rebuilding... | ['Xiaoguang Mao', 'Ruifeng Luo', 'Zhiliang Tian', 'Kangchen Zhu'] | 2022-12-19 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 4.19539183e-01 -1.69216972e-02 9.02639255e-02 -6.68015659e-01
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8.91869962e-01 5.91344051e-02 5.33596091e-02 -3.39060128... | [11.725117683410645, 9.542169570922852] |
f505ac82-b5f9-493f-a2d1-78d748ed981f | inference-optimized-ai-and-high-performance | 2201.11133 | null | https://arxiv.org/abs/2201.11133v2 | https://arxiv.org/pdf/2201.11133v2.pdf | Inference-optimized AI and high performance computing for gravitational wave detection at scale | We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 hours. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our infer... | ['Huihuo Zheng', 'E. A. Huerta', 'Minyang Tian', 'Asad Khan', 'Pranshu Chaturvedi'] | 2022-01-26 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-8.14264178e-01 -3.76721658e-02 2.54978925e-01 1.32979630e-02
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-3.68812293e-01 1.56518590e+00 2.44139120e-01 -9.15582702... | [7.785970687866211, 3.1455962657928467] |
665b796a-36fa-4e64-b027-011f3122705c | medical-dialogue-generation-via-dual-flow | 2305.18109 | null | https://arxiv.org/abs/2305.18109v1 | https://arxiv.org/pdf/2305.18109v1.pdf | Medical Dialogue Generation via Dual Flow Modeling | Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription. Since most patients cannot precisely describe their symptoms, dialogue understanding is challenging for MDS. Previous studies mainly addressed this by extracting the mentioned medical entities as critical d... | ['Wenjie Li', 'Jian Wang', 'Yi Cheng', 'Wenjun Hou', 'Kaishuai Xu'] | 2023-05-29 | null | null | null | null | ['dialogue-generation', 'dialogue-understanding', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 2.12920919e-01 9.87674952e-01 -3.06740493e-01 -6.55260623e-01
-2.31039003e-01 -3.05627495e-01 9.38085854e-01 6.48321450e-01
-1.75003007e-01 9.03692186e-01 9.90092278e-01 -4.15107876e-01
-1.69077422e-03 -8.60069931e-01 2.16958940e-01 -1.49497226e-01
7.05881417e-02 1.02747595e+00 1.37497753e-01 -5.63867629... | [12.421953201293945, 8.318760871887207] |
50c6aae7-a993-42f9-b6d3-89280c3b030e | evaluation-of-deep-learning-models-for-1 | null | null | https://ieeexplore.ieee.org/document/9275976 | https://ieeexplore.ieee.org/document/9275976 | Evaluation of Deep Learning Models for Kannada Handwritten Digit Recognition | Handwritten digit recognition is a basic and important problem in computer vision. While it has been studied intensively, the newer datasets keep bring new challenges to existed solutions. A new Kannada-MNIST dataset is mainly digital images of the Kannada language, which contains 70,000 28×28 gray-scale sample images ... | ['Qisheng Hu'] | 2020-12-09 | null | null | null | 01-02-august-2020-12 | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.78041101e-01 -5.48972428e-01 -1.02137879e-01 -4.36325014e-01
-4.68517870e-01 -2.44166523e-01 5.69871783e-01 -5.81673503e-01
-5.77071548e-01 6.96854115e-01 -2.97464609e-01 -3.83229196e-01
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2.33694483e-02 4.60114986e-01 1.01711310e-01 -8.91015958... | [11.877254486083984, 2.5609140396118164] |
8463abaa-9b93-4259-af4b-b984637c1f3e | deep-set-to-set-matching-and-learning | 1910.09972 | null | https://arxiv.org/abs/1910.09972v2 | https://arxiv.org/pdf/1910.09972v2.pdf | Exchangeable deep neural networks for set-to-set matching and learning | Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract features to match a correct pair of different sets and also preserve two types of exchangeability required for set-to-set matching: the pair of ... | ['Hirotaka Hachiya', 'Yuki Saito', 'Kenji Fukumizu', 'Takuma Nakamura'] | 2019-10-22 | exchangeable-deep-neural-networks-for-set-to | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2844_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620613.pdf | eccv-2020-8 | ['set-matching'] | ['computer-vision'] | [ 1.45610690e-01 -5.46932042e-01 -2.67190278e-01 -7.23877966e-01
-7.04988182e-01 -5.64212084e-01 1.76252171e-01 3.41166139e-01
-1.18785337e-01 4.32669222e-01 -3.11075081e-03 2.27359250e-01
-6.16358459e-01 -9.16066408e-01 -7.76042402e-01 -4.30546671e-01
1.55556038e-01 7.75591075e-01 -8.12107846e-02 -5.26954472... | [10.098301887512207, 5.294515132904053] |
30fa5010-7609-4ebf-a695-b3200b66d7bf | does-bert-understand-idioms-a-probing-based | null | null | https://aclanthology.org/2021.ranlp-main.156 | https://aclanthology.org/2021.ranlp-main.156.pdf | Does BERT Understand Idioms? A Probing-Based Empirical Study of BERT Encodings of Idioms | Understanding idioms is important in NLP. In this paper, we study to what extent pre-trained BERT model can encode the meaning of a potentially idiomatic expression (PIE) in a certain context. We make use of a few existing datasets and perform two probing tasks: PIE usage classification and idiom paraphrase identificat... | ['Jing Jiang', 'Minghuan Tan'] | null | null | https://aclanthology.org/2021.ranlp-1.156 | https://aclanthology.org/2021.ranlp-1.156.pdf | ranlp-2021-9 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 2.18444854e-01 -5.59937954e-02 -9.20069396e-01 -6.43710375e-01
-1.86524391e-01 -1.09504604e+00 8.31323564e-01 -5.09966761e-02
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3.12247962e-01 1.10689902e+00 -1.61572039e-01 -6.01043701... | [10.689491271972656, 9.40930461883545] |
a4c55280-7a72-4ac4-b26c-ae8a717cf9bc | reinforcement-learning-based-control-of-4 | 2306.03951 | null | https://arxiv.org/abs/2306.03951v2 | https://arxiv.org/pdf/2306.03951v2.pdf | Reinforcement Learning-Based Control of CrazyFlie 2.X Quadrotor | The objective of the project is to explore synergies between classical control algorithms such as PID and contemporary reinforcement learning algorithms to come up with a pragmatic control mechanism to control the CrazyFlie 2.X quadrotor. The primary objective would be performing PID tuning using reinforcement learning... | ['Valentín López Jiménez', 'Arshad Javeed'] | 2023-06-06 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.93005991e-01 3.90565425e-01 7.00645149e-02 1.76946849e-01
-4.39421535e-01 -7.02324927e-01 6.25891447e-01 -3.82697403e-01
-8.05311739e-01 1.32236850e+00 -2.23892316e-01 -4.38900858e-01
-4.63087052e-01 -8.31016600e-01 -6.71959281e-01 -7.79905736e-01
-4.28165078e-01 5.05841911e-01 1.13919057e-01 -1.17024159... | [4.5200324058532715, 1.4365955591201782] |
9759daf2-46ef-45ab-9413-c85af39c2533 | winner-weakly-supervised-hierarchical | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_WINNER_Weakly-Supervised_hIerarchical_decompositioN_and_aligNment_for_Spatio-tEmporal_Video_gRounding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_WINNER_Weakly-Supervised_hIerarchical_decompositioN_and_aligNment_for_Spatio-tEmporal_Video_gRounding_CVPR_2023_paper.pdf | WINNER: Weakly-Supervised hIerarchical decompositioN and aligNment for Spatio-tEmporal Video gRounding | Spatio-temporal video grounding aims to localize the aligned visual tube corresponding to a language query. Existing techniques achieve such alignment by exploiting dense boundary and bounding box annotations, which can be prohibitively expensive. To bridge the gap, we investigate the weakly-supervised setting, where m... | ['Fei Wu', 'Wei Ji', 'Shengyu Zhang', 'Zhou Zhao', 'Jiaxu Miao', 'Wenqiao Zhang', 'Han Wang', 'Mengze Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-grounding', 'spatio-temporal-video-grounding'] | ['computer-vision', 'computer-vision'] | [ 1.2105909e-01 7.2719276e-02 -6.4015412e-01 -2.0845827e-01
-1.0655062e+00 -7.0667738e-01 3.5119471e-01 1.1758636e-01
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1.2075223e-02... | [10.068958282470703, 0.7958270311355591] |
5f347457-9f3d-4e6a-8892-8a049f1d9b78 | geometry-and-uncertainty-aware-3d-point-cloud | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Geometry_and_Uncertainty-Aware_3D_Point_Cloud_Class-Incremental_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Geometry_and_Uncertainty-Aware_3D_Point_Cloud_Class-Incremental_Semantic_Segmentation_CVPR_2023_paper.pdf | Geometry and Uncertainty-Aware 3D Point Cloud Class-Incremental Semantic Segmentation | Despite the significant recent progress made on 3D point cloud semantic segmentation, the current methods require training data for all classes at once, and are not suitable for real-life scenarios where new categories are being continuously discovered. Substantial memory storage and expensive re-training is requir... | ['Yinjie Lei', 'Chao Ren', 'Zhao Jin', 'Munawar Hayat', 'Yuwei Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 1.55922771e-01 1.50491789e-01 -1.13155171e-01 -5.52519500e-01
-5.47859669e-01 -6.66207314e-01 4.65815067e-01 3.69679809e-01
-3.66522163e-01 6.18086278e-01 -4.33800578e-01 -3.02909404e-01
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-1.88107401e-01 9.77623940e-01 8.88746798e-01 6.45329729... | [8.010835647583008, -3.113762855529785] |
814293b7-5b09-4fab-8185-bb04ecdeab63 | robust-tumor-detection-from-coarse | 2303.16533 | null | https://arxiv.org/abs/2303.16533v1 | https://arxiv.org/pdf/2303.16533v1.pdf | Robust Tumor Detection from Coarse Annotations via Multi-Magnification Ensembles | Cancer detection and classification from gigapixel whole slide images of stained tissue specimens has recently experienced enormous progress in computational histopathology. The limitation of available pixel-wise annotated scans shifted the focus from tumor localization to global slide-level classification on the basis... | ['Maria Kalweit', 'Joschka Boedecker', 'Marc Metzger', 'Philipp Poxleitner', 'Ignacio Mastroleo', 'Gabriel Kalweit', 'Mehdi Naouar'] | 2023-03-29 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 6.60936236e-01 1.39841884e-01 -5.91475248e-01 -1.61973909e-01
-1.46795964e+00 -4.53755528e-01 4.47345883e-01 5.61912835e-01
-6.62191629e-01 8.19984257e-01 -7.69338608e-02 -7.41569817e-01
-1.05001502e-01 -5.70546091e-01 -3.17373186e-01 -1.55123305e+00
-9.70156714e-02 4.49615419e-01 2.77568460e-01 8.88776928... | [15.056703567504883, -2.972780227661133] |
763da000-31ed-41c9-8f2f-744606545df9 | clustering-us-counties-to-find-patterns | 2303.11936 | null | https://arxiv.org/abs/2303.11936v1 | https://arxiv.org/pdf/2303.11936v1.pdf | Clustering US Counties to Find Patterns Related to the COVID-19 Pandemic | When COVID-19 first started spreading and quarantine was implemented, the Society for Industrial and Applied Mathematics (SIAM) Student Chapter at the University of Minnesota-Twin Cities began a collaboration with Ecolab to use our skills as data scientists and mathematicians to extract useful insights from relevant da... | ['Cooper Zhao', 'Tianyi Sun', 'Sarah Milstein', 'Cora Brown'] | 2023-03-19 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-5.55788934e-01 -3.83482367e-01 -8.44797119e-03 -1.83672875e-01
-3.79837990e-01 -6.15463555e-01 4.21694249e-01 4.39786553e-01
-2.72351682e-01 5.47447205e-01 3.28614354e-01 -6.57072067e-01
-4.39768165e-01 -8.18274975e-01 -1.28616318e-01 -5.01584709e-01
-7.73155391e-01 6.53716624e-01 3.56293283e-02 -3.67843598... | [7.233373641967773, 5.051487445831299] |
bb9faac8-829d-4c22-a8bc-45fb840af3d9 | nonlinear-feature-aggregation-two-algorithms | 2306.11143 | null | https://arxiv.org/abs/2306.11143v1 | https://arxiv.org/pdf/2306.11143v1.pdf | Nonlinear Feature Aggregation: Two Algorithms driven by Theory | Many real-world machine learning applications are characterized by a huge number of features, leading to computational and memory issues, as well as the risk of overfitting. Ideally, only relevant and non-redundant features should be considered to preserve the complete information of the original data and limit the dim... | ['Marcello Restelli', 'Alberto Maria Metelli', 'Paolo Bonetti'] | 2023-06-19 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 3.90102535e-01 -1.09484047e-01 5.30972481e-02 -3.00888151e-01
-3.06825012e-01 -4.52093720e-01 4.95282680e-01 3.79975080e-01
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-7.12082922e-01 -9.76050496e-01 -4.33818400e-01 -9.87848520e-01
-1.84705094e-01 3.38682413e-01 8.60378519e-02 -8.02717581... | [7.962353229522705, 4.2588677406311035] |
93a869a1-78a2-46b7-8794-d46609379240 | shape-completion-with-points-in-the-shadow | 2209.08345 | null | https://arxiv.org/abs/2209.08345v3 | https://arxiv.org/pdf/2209.08345v3.pdf | Shape Completion with Points in the Shadow | Single-view point cloud completion aims to recover the full geometry of an object based on only limited observation, which is extremely hard due to the data sparsity and occlusion. The core challenge is to generate plausible geometries to fill the unobserved part of the object based on a partial scan, which is under-co... | ['Ruizhen Hu', 'He Wang', 'Xi Zhao', 'BoWen Zhang'] | 2022-09-17 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 2.34424636e-01 7.09752813e-02 2.93184906e-01 -2.10819349e-01
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5.23392439e-01 8.75126779e-01 3.64304245e-01 -4.05536778... | [8.913376808166504, -3.183237075805664] |
e8eb096c-edbb-4361-9484-971bacd25069 | supervised-anomaly-detection-via-conditional | 2104.11952 | null | https://arxiv.org/abs/2104.11952v1 | https://arxiv.org/pdf/2104.11952v1.pdf | Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning | Anomaly detection has wide applications in machine intelligence but is still a difficult unsolved problem. Major challenges include the rarity of labeled anomalies and it is a class highly imbalanced problem. Traditional unsupervised anomaly detectors are suboptimal while supervised models can easily make biased predic... | ['Guoping Qiu', 'Li Kang', 'Jiang Duan', 'Zhi Chen'] | 2021-04-24 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 4.18196559e-01 2.91603148e-01 -1.22945376e-01 -4.11433846e-01
-8.14472139e-01 -3.05847764e-01 2.38890067e-01 8.22698548e-02
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6.34676069e-02 6.34366333e-01 1.54874504e-01 -1.26338631... | [7.62150239944458, 2.3645360469818115] |
0693e47b-445d-4b4d-ace3-0e19215a0e97 | contrastive-learning-of-natural-language-and | null | null | https://openreview.net/forum?id=eiAkrltBTh4 | https://openreview.net/pdf?id=eiAkrltBTh4 | Contrastive Learning of Natural Language and Code Representations for Semantic Code Search | Retrieving semantically relevant code functions given a natural language (NL) or programming language (PL) query is a task of great practical value towards building productivity enhancing tools for software developers. Recent approaches to solve this task involve leveraging transformer based masked language models that... | ['Anonymous'] | 2021-06-16 | null | null | null | acl-arr-jun-2021-6 | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 2.02882141e-01 7.78543353e-02 -3.97535175e-01 -3.35498065e-01
-1.49884903e+00 -7.96864152e-01 5.75011671e-01 2.71464344e-02
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-5.87988943e-02 2.07805589e-01 1.93940818e-01 -4.03952450... | [7.5502028465271, 8.063084602355957] |
69b32453-f69a-4e4c-82e7-66de9cb2e3af | gender-prediction-from-tweets-improving | 1908.09919 | null | https://arxiv.org/abs/1908.09919v2 | https://arxiv.org/pdf/1908.09919v2.pdf | Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features | Author profiling is the characterization of an author through some key attributes such as gender, age, and language. In this paper, a RNN model with Attention (RNNwA) is proposed to predict the gender of a twitter user using their tweets. Both word level and tweet level attentions are utilized to learn 'where to look'.... | ['Ozan Polatbilek', 'Selma Tekir', 'Erhan Sezerer'] | 2019-08-22 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-5.07688642e-01 -6.99427724e-02 -4.36856598e-01 -5.74977279e-01
-6.82434499e-01 -2.74773747e-01 7.78913796e-01 3.50254834e-01
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2.46521682e-02 6.61395311e-01 -6.96026742e-01 -2.86127269... | [9.491924285888672, 10.399659156799316] |
83502243-21f6-4b1b-8f31-da8cbefd9f28 | when-transformer-meets-robotic-grasping | 2202.11911 | null | https://arxiv.org/abs/2202.11911v3 | https://arxiv.org/pdf/2202.11911v3.pdf | When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection | In this paper, we present a transformer-based architecture, namely TF-Grasp, for robotic grasp detection. The developed TF-Grasp framework has two elaborate designs making it well suitable for visual grasping tasks. The first key design is that we adopt the local window attention to capture local contextual information... | ['Zhen Kan', 'Zhangli Zhou', 'Shaochen Wang'] | 2022-02-24 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-1.2138499e-01 -4.1422090e-01 7.2731704e-02 -3.6944649e-01
-5.3393179e-01 -5.0907898e-01 8.3744451e-02 9.4446756e-02
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-9.2039846e-02 -7.6070184e-01 -1.1692297e+00 -9.8181713e-01
-6.6635388e-01 1.8628510e-02 3.0745888e-01 -8.1420876e-02
3.7562099e-01... | [5.781398296356201, -0.8920032382011414] |
eb7698f9-b226-4404-89ce-cd5c22914d67 | neural-light-field-estimation-for-street | 2208.09480 | null | https://arxiv.org/abs/2208.09480v1 | https://arxiv.org/pdf/2208.09480v1.pdf | Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion | We consider the challenging problem of outdoor lighting estimation for the goal of photorealistic virtual object insertion into photographs. Existing works on outdoor lighting estimation typically simplify the scene lighting into an environment map which cannot capture the spatially-varying lighting effects in outdoor ... | ['Sanja Fidler', 'Jan Kautz', 'David Acuna', 'Wenzheng Chen', 'Zian Wang'] | 2022-08-19 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 2.76792675e-01 9.71255153e-02 3.95670921e-01 -6.37195408e-01
-4.28668648e-01 -4.69546854e-01 2.54636198e-01 -7.84149945e-01
-3.60802300e-02 5.03346086e-01 6.66293576e-02 -2.73470134e-01
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4.99611825e-01 1.04737608e-02 -8.53671283e-02 -3.05913538... | [9.75115966796875, -2.990790843963623] |
f4e511a8-fc18-49c6-b5a7-f37ca0e8ba4d | coarse-to-fine-semantic-segmentation-from | 1812.10885 | null | http://arxiv.org/abs/1812.10885v1 | http://arxiv.org/pdf/1812.10885v1.pdf | Coarse-to-fine Semantic Segmentation from Image-level Labels | Deep neural network-based semantic segmentation generally requires
large-scale cost extensive annotations for training to obtain better
performance. To avoid pixel-wise segmentation annotations which are needed for
most methods, recently some researchers attempted to use object-level labels
(e.g. bounding boxes) or ima... | ['Yu-cheng Chen', 'YingLi Tian', 'Longlong Jing'] | 2018-12-28 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 6.81702256e-01 3.20118040e-01 -2.15421483e-01 -6.06670499e-01
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2.65001297e-01 5.07733166e-01 1.02902174e+00 3.15112382... | [9.587610244750977, 0.5144661664962769] |
09ddd167-9082-4f86-9e7c-ec18d618ffa3 | control-flow-in-active-inference-systems | 2303.01514 | null | https://arxiv.org/abs/2303.01514v1 | https://arxiv.org/pdf/2303.01514v1.pdf | Control flow in active inference systems | Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. We show here that when systems are described as executing active inf... | ['Antonino Marciano', 'Michael Levin', 'Hananel Hazan', 'James F. Glazebrook', 'Karl Friston', 'Filippo Fabrocini', 'Chris Fields'] | 2023-02-25 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 4.45549726e-01 4.79889035e-01 -6.48978651e-02 -1.12316705e-01
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-4.29629594e-01 9.20410037e-01 3.90994996e-02 -9.21673700e-02
-4.99463230e-01 -1.14132845e+00 -4.00367349e-01 -1.09391868e+00
-4.75044131e-01 3.52010459e-01 2.13452190e-01 -4.59567726... | [5.822856426239014, 4.353854179382324] |
1adc13be-c287-4e23-8552-75b642974829 | progressive-class-semantic-matching-for-semi-1 | 2205.10189 | null | https://arxiv.org/abs/2205.10189v1 | https://arxiv.org/pdf/2205.10189v1.pdf | Progressive Class Semantic Matching for Semi-supervised Text Classification | Semi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressive performance. In this work, we further investigate the marriage between semi-supervised learning an... | ['Ehsan Abbasnejad', 'Lingqiao Liu', 'Hai-Ming Xu'] | 2022-05-20 | null | https://aclanthology.org/2022.naacl-main.219 | https://aclanthology.org/2022.naacl-main.219.pdf | naacl-2022-7 | ['classification', 'semi-supervised-text-classification-1'] | ['methodology', 'natural-language-processing'] | [ 2.98654288e-01 3.80927682e-01 -6.51776373e-01 -1.02027690e+00
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3.73021662e-01 8.83751631e-01 2.92238295e-01 4.56012562... | [10.574933052062988, 7.348547458648682] |
2ca065e0-3c54-4198-a7cc-cfd99cc3bfb4 | temporal-analysis-of-reddit-networks-via-role | 1908.05192 | null | https://arxiv.org/abs/1908.05192v1 | https://arxiv.org/pdf/1908.05192v1.pdf | Temporal Analysis of Reddit Networks via Role Embeddings | Inspired by diachronic word analysis from the field of natural language processing, we propose an approach for uncovering temporal insights regarding user roles from social networks using graph embedding methods. Specifically, we apply the role embedding algorithm, struc2vec, to a collection of social networks exhibiti... | ['Siobhan Grayson', 'Derek Greene'] | 2019-08-14 | null | null | null | null | ['role-embedding'] | ['graphs'] | [-1.74765244e-01 1.14149913e-01 -2.77083039e-01 -6.67943135e-02
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-5.86289406e-01 3.94158602e-01 -9.65622440e-02 -6.20193660... | [9.854347229003906, 8.603254318237305] |
2ca34838-3c09-4018-a016-35ad237c3959 | joint-segmentation-and-discontinuity | 2211.13828 | null | https://arxiv.org/abs/2211.13828v1 | https://arxiv.org/pdf/2211.13828v1.pdf | Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images | Medical image registration is a challenging task involving the estimation of spatial transformations to establish anatomical correspondence between pairs or groups of images. Recently, deep learning-based image registration methods have been widely explored, and demonstrated to enable fast and accurate image registrati... | ['Alejandro F Frangi', 'Nishant Ravikumar', 'Yan Xia', 'Xiang Chen'] | 2022-11-24 | null | null | null | null | ['medical-image-registration'] | ['medical'] | [ 3.91392231e-01 -4.11718376e-02 -8.32944214e-02 -5.28806090e-01
-8.95509005e-01 -4.46056247e-01 4.31562960e-01 2.95253873e-01
-4.72992897e-01 3.58880997e-01 1.27151310e-01 2.50072759e-02
-2.51619905e-01 -5.65626085e-01 -5.77256799e-01 -7.95922399e-01
-2.54977822e-01 4.45917130e-01 1.89721018e-01 -8.78092498... | [13.981281280517578, -2.5619328022003174] |
1174923f-772f-4cdf-8753-81867a95b100 | open-vocabulary-one-stage-detection-with | 2203.10593 | null | https://arxiv.org/abs/2203.10593v1 | https://arxiv.org/pdf/2203.10593v1.pdf | Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation | Open-vocabulary object detection aims to detect novel object categories beyond the training set. The advanced open-vocabulary two-stage detectors employ instance-level visual-to-visual knowledge distillation to align the visual space of the detector with the semantic space of the Pre-trained Visual-Language Model (PVLM... | ['Weiming Hu', 'Congxuan Zhang', 'Shaoru Wang', 'Yuxin Chen', 'Liang Li', 'Jin Gao', 'Guan Luo', 'Zongyang Ma'] | 2022-03-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ma_Open-Vocabulary_One-Stage_Detection_With_Hierarchical_Visual-Language_Knowledge_Distillation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ma_Open-Vocabulary_One-Stage_Detection_With_Hierarchical_Visual-Language_Knowledge_Distillation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 5.88370040e-02 1.17924348e-01 -6.01053238e-02 -1.53050572e-03
-7.55202949e-01 -4.78047997e-01 6.05763674e-01 2.23751068e-01
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3.28836977e-01 1.83648109e-01 7.12420881e-01 -1.44790150... | [9.589719772338867, 1.4703240394592285] |
43edcc5f-c2fd-4120-a969-dc16e887ed9b | how-to-augment-a-small-learning-set-for | 1801.04076 | null | http://arxiv.org/abs/1801.04076v2 | http://arxiv.org/pdf/1801.04076v2.pdf | How to augment a small learning set for improving the performances of a CNN-based steganalyzer? | Deep learning and convolutional neural networks (CNN) have been intensively
used in many image processing topics during last years. As far as steganalysis
is concerned, the use of CNN allows reaching the state-of-the-art results. The
performances of such networks often rely on the size of their learning
database. An ob... | ['Frédéric Comby', 'Marc Chaumont', 'Mehdi Yedroudj'] | 2018-01-12 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 2.76751995e-01 4.52037714e-02 2.56363600e-01 -5.01771681e-02
1.00679301e-01 -1.07210390e-01 4.90668416e-01 -2.08588704e-01
-7.51115024e-01 4.58720118e-01 -3.00454676e-01 -4.17319685e-01
3.91657874e-02 -1.09739065e+00 -7.25164056e-01 -1.10858047e+00
1.69044212e-01 8.79209116e-02 4.93503571e-01 -5.85246682... | [4.3322954177856445, 8.043479919433594] |
db38a810-da67-4943-bcdf-ef261630a3b1 | ranking-social-media-news-feeds-a-comparative | null | null | https://link.springer.com/chapter/10.1007/978-3-030-96311-8_19 | http://dspace.univ-eloued.dz/bitstream/123456789/10831/1/ranking%20social%20media%20news%20feeds%20a%20comparative%20study%20of%20personalized%20and%20non%20personalized.pdf | Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models | Home Artificial Intelligence and Its Applications Conference paper
Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models
Sami Belkacem, Kamel Boukhalfa & Omar Boussaid
Conference paper
First Online: 12 March 2022
416 Accesses
Part of the Lecture Notes in ... | ['Omar Boussaid', 'Kamel Boukhalfa', 'Sami Belkacem'] | 2022-03-12 | null | null | null | international-conference-on-artificial-5 | ['social-media-popularity-prediction', 'social-media-popularity-prediction'] | ['miscellaneous', 'time-series'] | [-0.27327722 0.02101434 -0.40591705 -0.49153116 -0.2782803 -0.33777633
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1.0885302 0.93156326 0.... | [10.065836906433105, 5.828140735626221] |
b28ab7fc-c221-4d65-943e-cb64d27e5eca | data-leakage-and-evaluation-issues-in-micro | 2211.11425 | null | https://arxiv.org/abs/2211.11425v2 | https://arxiv.org/pdf/2211.11425v2.pdf | Data Leakage and Evaluation Issues in Micro-Expression Analysis | Micro-expressions have drawn increasing interest lately due to various potential applications. The task is, however, difficult as it incorporates many challenges from the fields of computer vision, machine learning and emotional sciences. Due to the spontaneous and subtle characteristics of micro-expressions, the avail... | ['Guoying Zhao', 'Wei Peng', 'Yante Li', 'Tuomas Varanka'] | 2022-11-21 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 9.24385935e-02 -1.21397629e-01 -3.55547071e-01 -7.37850964e-01
-7.95785725e-01 -6.28085613e-01 2.98267066e-01 -1.66484401e-01
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1.43971387e-03 -3.01752001e-01 -2.44701266e-01 -2.78434873... | [13.574027061462402, 1.8603975772857666] |
88e75766-6952-4231-b948-b883824e4b83 | span-labeling-approach-for-vietnamese-and | 2110.00156 | null | https://arxiv.org/abs/2110.00156v1 | https://arxiv.org/pdf/2110.00156v1.pdf | Span Labeling Approach for Vietnamese and Chinese Word Segmentation | In this paper, we propose a span labeling approach to model n-gram information for Vietnamese word segmentation, namely SPAN SEG. We compare the span labeling approach with the conditional random field by using encoders with the same architecture. Since Vietnamese and Chinese have similar linguistic phenomena, we evalu... | ['Ngan Luu-Thuy Nguyen', 'Dang Van Thin', 'Linh-Bao Vo', 'Duc-Vu Nguyen'] | 2021-10-01 | null | null | null | null | ['vietnamese-word-segmentation', 'chinese-word-segmentation'] | ['natural-language-processing', 'natural-language-processing'] | [-8.16107914e-02 6.00405484e-02 -5.53986788e-01 -4.79972363e-01
-1.13810372e+00 -4.94759798e-01 1.16810858e-01 -7.60238692e-02
-1.10127819e+00 1.05021393e+00 2.54200071e-01 -6.02296233e-01
6.64764404e-01 -7.80798852e-01 -6.33317590e-01 -3.74069005e-01
7.35917455e-03 5.40153563e-01 5.75051010e-01 -8.90709576... | [10.0691556930542, 10.033583641052246] |
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