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f4ba7346-9a87-4673-94e4-dbb21f0eba6b | academic-resource-text-level-multi-label | 2203.10743 | null | https://arxiv.org/abs/2203.10743v1 | https://arxiv.org/pdf/2203.10743v1.pdf | Academic Resource Text Level Multi-label Classification based on Attention | Hierarchical multi-label academic text classification (HMTC) is to assign academic texts into a hierarchically structured labeling system. We propose an attention-based hierarchical multi-label classification algorithm of academic texts (AHMCA) by integrating features such as text, keywords, and hierarchical structure,... | ['Ang Li', 'Yawen Li', 'Yue Wang'] | 2022-03-21 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-2.90732890e-01 -1.34925783e-01 -5.40476382e-01 -1.71691850e-01
-7.31886148e-01 -6.24140978e-01 4.71060783e-01 5.75998247e-01
-1.52722076e-01 1.82194680e-01 7.65030503e-01 -4.00386095e-01
-2.32270986e-01 -5.57834864e-01 9.18891653e-02 -4.59154993e-01
6.81722999e-01 4.59406555e-01 4.03919816e-02 1.52703971... | [10.332283020019531, 6.678091526031494] |
7c5b3261-15d5-4630-bcac-4ad7d225b082 | data-efficient-direct-speech-to-text | 1911.04283 | null | https://arxiv.org/abs/1911.04283v2 | https://arxiv.org/pdf/1911.04283v2.pdf | Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning | End-to-end Speech Translation (ST) models have several advantages such as lower latency, smaller model size, and less error compounding over conventional pipelines that combine Automatic Speech Recognition (ASR) and text Machine Translation (MT) models. However, collecting large amounts of parallel data for ST task is ... | ['Sathish Indurthi', 'Sangha Kim', 'Houjeung Han', 'Chanwoo Kim', 'Nikhil Kumar Lakumarapu', 'Insoo Chung', 'Beomseok Lee'] | 2019-11-11 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 4.70912486e-01 1.79728903e-02 -1.40732467e-01 -3.96386653e-01
-1.58919704e+00 -6.90446377e-01 7.52053678e-01 -2.12046906e-01
-5.10350406e-01 7.83478439e-01 3.29860479e-01 -8.41350019e-01
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6.05159998e-01 9.23000991e-01 7.12035969e-02 -3.54841590... | [14.487459182739258, 7.2103190422058105] |
ab8a0386-c7e6-4796-bdc2-c0c80d33a182 | image-coupled-volume-propagation-for-stereo | 2301.00695 | null | https://arxiv.org/abs/2301.00695v1 | https://arxiv.org/pdf/2301.00695v1.pdf | Image-Coupled Volume Propagation for Stereo Matching | Several leading methods on public benchmarks for depth-from-stereo rely on memory-demanding 4D cost volumes and computationally intensive 3D convolutions for feature matching. We suggest a new way to process the 4D cost volume where we merge two different concepts in one deeply integrated framework to achieve a symbiot... | ['Eduard Zell', 'Oh-Hun Kwon'] | 2022-12-30 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 3.32333893e-02 1.37255937e-01 2.95105964e-01 -4.98772919e-01
-6.84467554e-01 -5.11946261e-01 5.79229236e-01 2.66197920e-01
-6.36461794e-01 2.05128506e-01 1.87074497e-01 -1.78802073e-01
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1.54271066e-01 2.29919672e-01 7.22983658e-01 -3.05323482... | [8.748842239379883, -2.4117431640625] |
7fd43ec0-788a-4f17-9ab5-a933631449a1 | dannte-a-case-study-of-a-turbo-machinery | 2201.03850 | null | https://arxiv.org/abs/2201.03850v1 | https://arxiv.org/pdf/2201.03850v1.pdf | DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift | We propose an adversarial learning method to tackle a Domain Adaptation (DA) time series regression task (DANNTe). The regression aims at building a virtual copy of a sensor installed on a gas turbine, to be used in place of the physical sensor which can be missing in certain situations. Our DA approach is to search fo... | ['Giacomo Veneri', 'Valentina Gori', 'Luca Strazzera'] | 2022-01-11 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 5.51410139e-01 3.46606970e-01 -1.16120271e-01 -2.03753158e-01
-7.29029775e-01 -8.01530838e-01 8.66385102e-01 -3.22249010e-02
-2.84010142e-01 7.65226483e-01 -3.50523174e-01 -1.15119234e-01
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-2.62987524e-01 5.63157439e-01 -4.04658429e-02 -3.15885484... | [7.850674152374268, 2.5315794944763184] |
7cd33eaf-dd1d-4e3a-ae31-bf194c1c849b | patch-based-3d-natural-scene-generation-from | 2304.12670 | null | https://arxiv.org/abs/2304.12670v2 | https://arxiv.org/pdf/2304.12670v2.pdf | Patch-based 3D Natural Scene Generation from a Single Example | We target a 3D generative model for general natural scenes that are typically unique and intricate. Lacking the necessary volumes of training data, along with the difficulties of having ad hoc designs in presence of varying scene characteristics, renders existing setups intractable. Inspired by classical patch-based im... | ['Baoquan Chen', 'Jue Wang', 'Xuelin Chen', 'Weiyu Li'] | 2023-04-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Patch-Based_3D_Natural_Scene_Generation_From_a_Single_Example_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Patch-Based_3D_Natural_Scene_Generation_From_a_Single_Example_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-generation'] | ['computer-vision'] | [ 6.18110895e-01 1.45888910e-01 3.67552578e-01 -6.87039867e-02
-7.07056761e-01 -6.95648909e-01 9.55457151e-01 -3.60639930e-01
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-5.21543510e-02 4.84425455e-01 -1.19174220e-01 -4.57783788... | [9.250343322753906, -3.224567413330078] |
57181681-62b7-4fec-93d5-92dd7a9a5c57 | metaems-a-meta-reinforcement-learning-based | 2210.12590 | null | https://arxiv.org/abs/2210.12590v1 | https://arxiv.org/pdf/2210.12590v1.pdf | MetaEMS: A Meta Reinforcement Learning-based Control Framework for Building Energy Management System | The building sector has been recognized as one of the primary sectors for worldwide energy consumption. Improving the energy efficiency of the building sector can help reduce the operation cost and reduce the greenhouse gas emission. The energy management system (EMS) can monitor and control the operations of built-in ... | ['Benoit Boulet', 'Di wu', 'Huiliang Zhang'] | 2022-10-23 | null | null | null | null | ['energy-management'] | ['time-series'] | [-9.39271450e-02 -1.09799579e-01 -2.68403143e-01 1.54263511e-01
-3.88087273e-01 -8.50578323e-02 3.28301609e-01 1.80084392e-01
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-3.72728050e-01 -1.23133981e+00 -2.95224190e-01 -1.04510307e+00
1.49190575e-01 -1.10430256e-01 1.08804196e-01 -3.57913077... | [5.629508018493652, 2.4268252849578857] |
7d90a3cb-3737-427c-8889-f875fa7f990f | towards-general-robustness-to-bad-training | null | null | https://openreview.net/forum?id=kz6rsFehYjd | https://openreview.net/pdf?id=kz6rsFehYjd | Towards General Robustness to Bad Training Data | In this paper, we focus on the problem of identifying bad training data when the underlying cause is unknown in advance. Our key insight is that regardless of how bad data are generated, they tend to contribute little to training a model with good prediction performance or more generally, to some utility function of th... | ['Ruoxi Jia', 'Ming Jin', 'Yi Zeng', 'Tianhao Wang'] | 2021-09-29 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 1.64145738e-01 2.16423571e-01 -6.92751825e-01 -2.65452228e-02
-1.34793675e+00 -7.48071611e-01 5.41019738e-01 6.29282236e-01
-2.27013022e-01 9.37220037e-01 3.51942003e-01 -3.78006816e-01
-3.73752147e-01 -5.55191100e-01 -7.80822396e-01 -1.06351364e+00
3.49152684e-02 6.25025988e-01 -1.51770130e-01 -8.06223303... | [8.705284118652344, 4.2943196296691895] |
079e51e7-175f-4882-880a-a2da4acd70df | describing-textures-in-the-wild | 1311.3618 | null | http://arxiv.org/abs/1311.3618v2 | http://arxiv.org/pdf/1311.3618v2.pdf | Describing Textures in the Wild | Patterns and textures are defining characteristics of many natural objects: a
shirt can be striped, the wings of a butterfly can be veined, and the skin of
an animal can be scaly. Aiming at supporting this analytical dimension in image
understanding, we address the challenging problem of describing textures with
semant... | ['Subhransu Maji', 'Mircea Cimpoi', 'Andrea Vedaldi', 'Sammy Mohamed', 'Iasonas Kokkinos'] | 2013-11-14 | describing-textures-in-the-wild-1 | http://openaccess.thecvf.com/content_cvpr_2014/html/Cimpoi_Describing_Textures_in_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Cimpoi_Describing_Textures_in_2014_CVPR_paper.pdf | cvpr-2014-6 | ['material-recognition'] | ['computer-vision'] | [ 4.31609839e-01 -3.58592272e-01 -2.47941256e-01 -4.46199298e-01
-5.30532479e-01 -7.77015448e-01 9.88337398e-01 -1.76656604e-01
2.73339719e-01 3.41490060e-01 7.75901452e-02 1.13502830e-01
-5.61267138e-01 -7.88332582e-01 -6.58788264e-01 -1.03694773e+00
-7.15112314e-02 6.12684667e-01 6.10621907e-02 -3.79885465... | [10.223735809326172, -0.16886340081691742] |
3ce3e435-2504-4b29-83c7-d61928e0ea10 | convolutional-neural-networks-trained-to | 2302.03992 | null | https://arxiv.org/abs/2302.03992v3 | https://arxiv.org/pdf/2302.03992v3.pdf | Convolutional Neural Networks Trained to Identify Words Provide a Surprisingly Good Account of Visual Form Priming Effects | A wide variety of orthographic coding schemes and models of visual word identification have been developed to account for masked priming data that provide a measure of orthographic similarity between letter strings. These models tend to include hand-coded orthographic representations with single unit coding for specifi... | ['Jeffrey Bowers', 'Valerio Biscione', 'Dong Yin'] | 2023-02-08 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 1.08270928e-01 -1.46193087e-01 8.14764053e-02 -1.73780918e-01
2.81679958e-01 -8.75103652e-01 9.98590112e-01 3.57804239e-01
-7.99406409e-01 5.87638505e-02 5.98896384e-01 -8.14980328e-01
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1.41195476e-01 1.42453149e-01 2.83836275e-01 -4.59995002... | [10.336335182189941, 2.2362051010131836] |
1a23d03c-3840-4c6e-a872-701e46bad63e | hierarchical-reinforcement-learning-of | 2203.10616 | null | https://arxiv.org/abs/2203.10616v1 | https://arxiv.org/pdf/2203.10616v1.pdf | Hierarchical Reinforcement Learning of Locomotion Policies in Response to Approaching Objects: A Preliminary Study | Animals such as rabbits and birds can instantly generate locomotion behavior in reaction to a dynamic, approaching object, such as a person or a rock, despite having possibly never seen the object before and having limited perception of the object's properties. Recently, deep reinforcement learning has enabled complex ... | ['George Konidaris', 'Kaiyu Zheng', 'Sreehari Rammohan', 'Shangqun Yu'] | 2022-03-20 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-6.87722266e-02 2.74191916e-01 1.83287695e-01 8.07044357e-02
2.77275473e-01 -4.57474411e-01 4.38656956e-01 -3.17914896e-02
-8.59299481e-01 1.08907998e+00 -3.47083867e-01 7.30432123e-02
-1.45529360e-01 -8.22015047e-01 -8.21899712e-01 -8.54794919e-01
-7.46971548e-01 4.75975752e-01 4.65222090e-01 -5.93140960... | [4.559063911437988, 1.1442644596099854] |
c302fcb0-ea6e-4aa0-851c-eb82162aa5b8 | beyond-rewards-a-hierarchical-perspective-on | 2206.09046 | null | https://arxiv.org/abs/2206.09046v3 | https://arxiv.org/pdf/2206.09046v3.pdf | Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis | Each year, expert-level performance is attained in increasingly-complex multiagent domains, where notable examples include Go, Poker, and StarCraft II. This rapid progression is accompanied by a commensurate need to better understand how such agents attain this performance, to enable their safe deployment, identify lim... | ['Been Kim', 'Lucas Dixon', 'Yannick Assogba', 'Andrei Kapishnikov', 'Shayegan Omidshafiei'] | 2022-06-17 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-5.03845036e-01 -4.39154916e-02 -4.71056461e-01 1.52102588e-02
-5.44591963e-01 -8.06228518e-01 9.76024091e-01 2.39410594e-01
-4.14538473e-01 8.77354801e-01 5.87258078e-02 -3.65847170e-01
-5.28988600e-01 -4.32074726e-01 -4.11964297e-01 -7.45688915e-01
-6.79659843e-01 9.00086701e-01 1.01745903e-01 -2.84922004... | [3.746082067489624, 2.019031286239624] |
14cbe56c-0fb9-4956-a318-7a3a2e4a35f1 | pricing-algorithmic-insurance | 2106.00839 | null | https://arxiv.org/abs/2106.00839v2 | https://arxiv.org/pdf/2106.00839v2.pdf | Algorithmic Insurance | As machine learning algorithms start to get integrated into the decision-making process of companies and organizations, insurance products are being developed to protect their owners from liability risk. Algorithmic liability differs from human liability since it is based on a single model compared to multiple heteroge... | ['Agni Orfanoudaki', 'Dimitris Bertsimas'] | 2021-06-01 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.82603055e-01 6.39796257e-01 -3.79461676e-01 -4.76034194e-01
-8.92790675e-01 -5.10333002e-01 1.93117514e-01 4.47517365e-01
-4.00589168e-01 6.80369377e-01 8.32312256e-02 -7.79822707e-01
-6.84237480e-01 -8.40654194e-01 -7.07113683e-01 -4.54023600e-01
1.91819891e-01 5.74105561e-01 -3.23528945e-01 -3.25794667... | [7.642518997192383, 5.01938009262085] |
e8596996-f980-4c7c-b81d-9def68cc71ff | simhaze-game-engine-simulated-data-for-real | 2305.16481 | null | https://arxiv.org/abs/2305.16481v1 | https://arxiv.org/pdf/2305.16481v1.pdf | SimHaze: game engine simulated data for real-world dehazing | Deep models have demonstrated recent success in single-image dehazing. Most prior methods consider fully supervised training and learn from paired clean and hazy images, where a hazy image is synthesized based on a clean image and its estimated depth map. This paradigm, however, can produce low-quality hazy images due ... | ['Yu Hen Hu', 'Yin Li', 'Bochen Guan', 'Jiang Li', 'Liang Shang', 'XiaoYu Zhang', 'Yanli Liu', 'Fangzhou Mu', 'Huan Xu', 'Zhengyang Lou'] | 2023-05-25 | null | null | null | null | ['image-dehazing', 'single-image-dehazing'] | ['computer-vision', 'computer-vision'] | [ 5.41786194e-01 2.34075457e-01 6.30207956e-01 -9.09422617e-03
-7.14386165e-01 -3.15668702e-01 6.43810630e-01 -2.94725478e-01
-3.27593833e-02 7.40828335e-01 -5.22652231e-02 -2.14492753e-01
3.43772054e-01 -1.11316812e+00 -9.78693187e-01 -9.65126395e-01
3.11239183e-01 1.95640236e-01 3.68674070e-01 -5.16891837... | [10.902064323425293, -3.16693377494812] |
1f97cd94-f106-413b-b9f5-ed0deae4f11e | domain-adaptation-in-lidar-semantic | 2010.12239 | null | https://arxiv.org/abs/2010.12239v3 | https://arxiv.org/pdf/2010.12239v3.pdf | Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions | LiDAR semantic segmentation provides 3D semantic information about the environment, an essential cue for intelligent systems during their decision making processes. Deep neural networks are achieving state-of-the-art results on large public benchmarks on this task. Unfortunately, finding models that generalize well or ... | ['Ana C. Murillo', 'Luis Montesano', 'Luis Riazuelo', 'Inigo Alonso'] | 2020-10-23 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 3.22063059e-01 1.52433245e-02 -2.97573775e-01 -7.90796578e-01
-6.03762627e-01 -5.97115278e-01 4.92792368e-01 2.49391958e-01
-7.27783382e-01 7.88233280e-01 -4.93825600e-02 -1.00921139e-01
-1.98809922e-01 -9.07478869e-01 -8.65783215e-01 -4.34773833e-01
3.66290629e-01 1.26170099e+00 9.02161658e-01 -2.84407765... | [8.20639419555664, -2.5773825645446777] |
14a54db4-1ea6-4c05-ae6e-d6be3cbf01d7 | a-reinforcement-learning-based-energy | 2104.04443 | null | https://arxiv.org/abs/2104.04443v2 | https://arxiv.org/pdf/2104.04443v2.pdf | A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline | Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and v... | ['Robert P. Dick', 'Li Shang', 'Ning Gu', 'Tun Lu', 'Dongsheng Li', 'Qin Lv', 'Da Feng', 'Yujiang Wang', 'Mingzhi Dong', 'Yingying Zhao'] | 2021-04-09 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.35428244e-02 -3.64156306e-01 -2.77411044e-01 -2.02191528e-02
-4.36408073e-01 -4.85778064e-01 2.40234584e-01 1.86751671e-02
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-1.93200603e-01 -5.88102818e-01 -7.30624735e-01 -6.61361516e-01
-2.28439152e-01 -3.18998173e-02 3.06300074e-01 3.79709005... | [8.978630065917969, 0.1571565419435501] |
c666bf34-ab03-4fe7-94c4-b7a298ba641e | backbone-is-all-your-need-a-simplified | 2203.05328 | null | https://arxiv.org/abs/2203.05328v2 | https://arxiv.org/pdf/2203.05328v2.pdf | Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking | Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest. However, existing tracking approaches rely on customized sub-modules and need prior knowledge for architecture selection, hindering the tracking development in a more general system.... | ['Wanli Ouyang', 'Wei Wu', 'Weihao Gan', 'Bo Li', 'Qiuhong Shen', 'Lei Qiao', 'Lei Bai', 'Peixia Li', 'BoYu Chen'] | 2022-03-10 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [ 2.49693412e-02 -7.58431926e-02 -1.96345374e-01 -5.49014509e-02
-4.65368569e-01 -8.34761679e-01 4.05680299e-01 -5.55657387e-01
-6.23509705e-01 3.91812891e-01 6.22051656e-02 -8.97690579e-02
1.72971580e-02 -2.36810729e-01 -8.49966645e-01 -7.01520681e-01
2.94858694e-01 1.31645754e-01 8.58259916e-01 8.24484322... | [6.2879414558410645, -2.116497039794922] |
ebf774ad-7273-4900-9f77-7bed09a1b64a | synthetic-data-for-face-recognition-current | 2305.01021 | null | https://arxiv.org/abs/2305.01021v1 | https://arxiv.org/pdf/2305.01021v1.pdf | Synthetic Data for Face Recognition: Current State and Future Prospects | Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However, these technologies are foreseen to face a major challenge in the next years due to the legal and ethical concerns about using authentic ... | ['Naser Damer', 'Julian Fierrez', 'Vitomir Struc', 'Fadi Boutros'] | 2023-05-01 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 3.39898556e-01 2.02225819e-01 3.31071764e-01 -9.86466587e-01
-5.42185068e-01 -1.50696516e-01 7.15019166e-01 -6.94223881e-01
-4.87892628e-01 7.39953816e-01 -1.38151824e-01 1.91399232e-01
-4.21700962e-02 -8.44263256e-01 -3.93619448e-01 -5.34565806e-01
1.68854982e-01 6.40466511e-01 -8.21072757e-01 -1.84888884... | [12.919297218322754, 0.7312828302383423] |
714a14f0-0012-44a1-80da-a69e56267251 | panoptic-segmentation-with-an-end-to-end-cell | null | null | https://doi.org/10.1007/978-3-030-00934-2_27 | https://www.semanticscholar.org/paper/Panoptic-Segmentation-with-an-End-to-End-Cell-R-CNN-Zhang-Song/be403b1a9fa44c860044e79b8d9704cc0b217417 | Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis | The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers. In order to obtain the quantitative morphological information, we present an end-to-end network for panoptic segmentation of pathology images. Recently, many methods have been proposed, focusing on the sem... | ['Si-Qi Liu', 'Yang song', 'Weidong Cai', 'Heng Huang', 'Haozhe Jia', 'Donghao Zhang', 'Dongnan Liu', 'Yong Xia'] | 2018-09-28 | null | null | null | miccai-2018-2018-9 | ['nuclear-segmentation'] | ['medical'] | [ 3.51651371e-01 2.93875873e-01 -2.40546897e-01 -2.81980932e-01
-9.68500078e-01 -2.65352786e-01 2.56593198e-01 6.29827976e-01
-7.42295623e-01 5.63156962e-01 -2.99026459e-01 -1.16895281e-01
1.23403724e-02 -6.62764370e-01 -3.33798856e-01 -1.25723028e+00
3.01590841e-02 5.57059526e-01 6.46983087e-01 2.89332092... | [14.960859298706055, -3.0541627407073975] |
32fa9865-4561-4fec-b78f-4fa5dc7140bd | autonomous-golf-putting-with-data-driven-and | 2211.08081 | null | https://arxiv.org/abs/2211.08081v1 | https://arxiv.org/pdf/2211.08081v1.pdf | Autonomous Golf Putting with Data-Driven and Physics-Based Methods | We are developing a self-learning mechatronic golf robot using combined data-driven and physics-based methods, to have the robot autonomously learn to putt the ball from an arbitrary point on the green. Apart from the mechatronic control design of the robot, this task is accomplished by a camera system with image recog... | ['Ansgar Trächtler', 'Julia Timmermann', 'Niklas Fittkau', 'Annika Junker'] | 2022-11-15 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-2.15143621e-01 3.43955129e-01 -5.48842400e-02 1.12310641e-01
-1.11406237e-01 -2.53868967e-01 2.15203598e-01 -3.46809059e-01
-4.05439079e-01 3.18484098e-01 -5.73232710e-01 -2.86832482e-01
-6.52465940e-01 -8.48939180e-01 -1.16675067e+00 -5.72660148e-01
1.03248641e-01 9.37090874e-01 9.83150229e-02 -5.67609310... | [4.840149879455566, 1.29497230052948] |
ec1c8aa4-8aa3-402f-93a5-abdb2bbd244b | carbon-efficient-neural-architecture-search | 2307.04131 | null | https://arxiv.org/abs/2307.04131v1 | https://arxiv.org/pdf/2307.04131v1.pdf | Carbon-Efficient Neural Architecture Search | This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The proposed framework, called carbon-efficient NAS (CE-NAS), consists of NAS evaluation algorithms with different energy requirements, a multi-objecti... | ['Tian Guo', 'Yiyang Zhao'] | 2023-07-09 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-2.46448033e-02 -7.13947058e-01 -3.30681264e-01 -2.50358731e-01
-5.94281673e-01 -5.30772328e-01 4.41775858e-01 -1.76135227e-01
-7.15543270e-01 6.09591067e-01 -8.06012005e-02 -5.34868956e-01
-1.27894625e-01 -1.02775288e+00 -8.04200232e-01 -6.11924231e-01
3.19414884e-01 6.42815769e-01 7.39298910e-02 2.62078077... | [8.387685775756836, 3.244055986404419] |
6338210b-b6d9-4b07-a021-1be08e749473 | amplitude-spectrum-transformation-for-open | 2202.04287 | null | https://arxiv.org/abs/2202.04287v1 | https://arxiv.org/pdf/2202.04287v1.pdf | Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation | Open compound domain adaptation (OCDA) has emerged as a practical adaptation setting which considers a single labeled source domain against a compound of multi-modal unlabeled target data in order to generalize better on novel unseen domains. We hypothesize that an improved disentanglement of domain-related and task-re... | ['R. Venkatesh Babu', 'Varun Jampani', 'Suvaansh Bhambri', 'Akshay Kulkarni', 'Jogendra Nath Kundu'] | 2022-02-09 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 5.40866852e-01 6.44240826e-02 -6.59907833e-02 -5.11343300e-01
-1.07432151e+00 -1.15165544e+00 8.50790858e-01 -2.57092237e-01
-3.52124423e-01 4.82301831e-01 3.39140519e-02 -1.49026886e-01
-5.87168820e-02 -5.11452317e-01 -9.33804989e-01 -7.68367589e-01
2.26594314e-01 4.59432125e-01 2.32264008e-02 -2.20429227... | [9.809328079223633, 2.005810260772705] |
1a1ab5da-4070-486e-80e2-7b123c08f3b4 | avis-autonomous-visual-information-seeking | 2306.08129 | null | https://arxiv.org/abs/2306.08129v1 | https://arxiv.org/pdf/2306.08129v1.pdf | AVIS: Autonomous Visual Information Seeking with Large Language Models | In this paper, we propose an autonomous information seeking visual question answering framework, AVIS. Our method leverages a Large Language Model (LLM) to dynamically strategize the utilization of external tools and to investigate their outputs, thereby acquiring the indispensable knowledge needed to provide answers t... | ['Alireza Fathi', 'Cordelia Schmid', 'David A Ross', 'Yizhou Sun', 'Kai-Wei Chang', 'Chen Sun', 'Ahmet Iscen', 'Ziniu Hu'] | 2023-06-13 | null | null | null | null | ['visual-question-answering-1', 'question-answering'] | ['computer-vision', 'natural-language-processing'] | [ 1.16703644e-01 1.79175600e-01 -1.45398071e-02 -4.17176336e-01
-6.77395403e-01 -1.31303668e+00 7.12237537e-01 3.25847477e-01
-1.88857779e-01 -4.96608876e-02 4.54806656e-01 -8.48708153e-01
-1.72535479e-01 -7.36830890e-01 -4.25047636e-01 7.93442056e-02
3.42030793e-01 5.57673395e-01 4.23931539e-01 -1.96076259... | [9.02309799194336, 7.105389595031738] |
0f84f319-3e92-4eb6-b5c2-ce85ee19c609 | decoupled-novel-object-captioner | 1804.03803 | null | http://arxiv.org/abs/1804.03803v2 | http://arxiv.org/pdf/1804.03803v2.pdf | Decoupled Novel Object Captioner | Image captioning is a challenging task where the machine automatically
describes an image by sentences or phrases. It often requires a large number of
paired image-sentence annotations for training. However, a pre-trained
captioning model can hardly be applied to a new domain in which some novel
object categories exist... | ['Linchao Zhu', 'Yu Wu', 'Yi Yang', 'Lu Jiang'] | 2018-04-11 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 6.90129876e-01 2.27118745e-01 -2.57473327e-02 -2.54887879e-01
-8.56589317e-01 -4.79048580e-01 5.69510877e-01 2.15483442e-01
-4.28134024e-01 7.56659150e-01 -5.74119911e-02 -1.25492434e-03
5.94594538e-01 -6.52581573e-01 -1.13061750e+00 -7.46434093e-01
4.61960346e-01 6.08718932e-01 5.10693371e-01 -2.39487141... | [10.84164047241211, 1.0852768421173096] |
58e796c6-3205-4fff-a32c-5a0dbe16a0cc | improving-noise-robustness-of-contrastive | 2110.15430 | null | https://arxiv.org/abs/2110.15430v1 | https://arxiv.org/pdf/2110.15430v1.pdf | Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction | Noise robustness is essential for deploying automatic speech recognition (ASR) systems in real-world environments. One way to reduce the effect of noise interference is to employ a preprocessing module that conducts speech enhancement, and then feed the enhanced speech to an ASR backend. In this work, instead of suppre... | ['DeLiang Wang', 'Jinyu Li', 'Takuya Yoshioka', 'Shujie Liu', 'Chengyi Wang', 'Yiming Wang', 'Xiaofei Wang', 'Yao Qian', 'Heming Wang'] | 2021-10-28 | null | null | null | null | ['auxiliary-learning', 'noisy-speech-recognition'] | ['methodology', 'speech'] | [ 5.20390332e-01 6.73222542e-02 5.17829895e-01 -4.77180839e-01
-1.52534580e+00 -2.87521601e-01 4.68131900e-01 -9.43317339e-02
-8.63836348e-01 4.16062683e-01 4.63896215e-01 -5.44481456e-01
2.06737071e-01 -3.94689828e-01 -6.56049371e-01 -9.01866019e-01
4.56438392e-01 -5.89208007e-02 -2.20081490e-02 -5.09984314... | [14.765353202819824, 6.235950469970703] |
52d5e71c-488d-4067-b189-de1c448a282c | non-backtracking-walks-reveal-compartments-in | 2003.09949 | null | https://arxiv.org/abs/2003.09949v2 | https://arxiv.org/pdf/2003.09949v2.pdf | Non-backtracking walks reveal compartments in sparse chromatin interaction networks | Chromatin communities stabilized by protein machinery play essential role in gene regulation and refine global polymeric folding of the chromatin fiber. However, treatment of these communities in the framework of the classical network theory (stochastic block model, SBM) does not take into account intrinsic linear conn... | ['S. Ulianov', 'S. V. Razin', 'S. Nechaev', 'A. Gorsky', 'K. Polovnikov'] | 2020-03-22 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.61142844e-01 2.88867921e-01 -6.98923916e-02 3.59029591e-01
1.51523873e-01 -9.97845054e-01 7.05696046e-01 3.73707592e-01
-3.50440413e-01 1.02047002e+00 4.76327762e-02 -3.38903219e-01
-8.03244948e-01 -9.39111352e-01 -7.27506042e-01 -1.32033777e+00
-1.07179224e-01 1.00187874e+00 6.99973226e-01 -1.57648534... | [6.869426250457764, 5.152101516723633] |
9f1ed521-02b0-4cc9-8854-3a9c29de8c17 | efficient-learning-of-pinball-twsvm-using | 2107.06744 | null | https://arxiv.org/abs/2107.06744v1 | https://arxiv.org/pdf/2107.06744v1.pdf | Efficient Learning of Pinball TWSVM using Privileged Information and its applications | In any learning framework, an expert knowledge always plays a crucial role. But, in the field of machine learning, the knowledge offered by an expert is rarely used. Moreover, machine learning algorithms (SVM based) generally use hinge loss function which is sensitive towards the noise. Thus, in order to get the advant... | ['Aman Pal', 'Reshma Rastogi'] | 2021-07-14 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 2.20014751e-01 -2.16286570e-01 -3.12928528e-01 -3.23025405e-01
-3.14072967e-02 -3.51829320e-01 1.70869783e-01 2.20331162e-01
-5.53140819e-01 1.12689829e+00 -2.93267936e-01 -5.13087809e-01
-4.69543517e-01 -8.77186954e-01 -4.24430668e-01 -8.69033873e-01
3.75305206e-01 3.03382259e-02 4.38149393e-01 -1.82466224... | [8.221360206604004, 4.045428276062012] |
7adc6d81-aacd-41b6-b5be-0b9857472362 | concept-aware-clustering-for-decentralized | 2306.12768 | null | https://arxiv.org/abs/2306.12768v1 | https://arxiv.org/pdf/2306.12768v1.pdf | Concept-aware clustering for decentralized deep learning under temporal shift | Decentralized deep learning requires dealing with non-iid data across clients, which may also change over time due to temporal shifts. While non-iid data has been extensively studied in distributed settings, temporal shifts have received no attention. To the best of our knowledge, we are first with tackling the novel a... | ['Olof Mogren', 'Martin Willbo', 'Edvin Listo Zec', 'Emilie Klefbom', 'Marcus Toftås'] | 2023-06-22 | null | null | null | null | ['clustering'] | ['methodology'] | [-4.79634404e-01 -3.47603530e-01 -1.03710622e-01 -4.66687530e-01
-3.14921498e-01 -6.37299895e-01 4.76581484e-01 1.26331210e-01
-5.23866713e-01 9.77015138e-01 5.85151836e-03 -7.50548989e-02
-4.97460455e-01 -4.46595252e-01 -6.38291001e-01 -7.99383163e-01
-6.54087067e-01 1.18649602e+00 4.03162479e-01 2.21551582... | [5.871539115905762, 6.113372325897217] |
99163416-6b5b-4aca-bd81-075827c11b7d | variance-reduced-proxskip-algorithm-theory | 2207.04338 | null | https://arxiv.org/abs/2207.04338v1 | https://arxiv.org/pdf/2207.04338v1.pdf | Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning | We study distributed optimization methods based on the {\em local training (LT)} paradigm: achieving communication efficiency by performing richer local gradient-based training on the clients before parameter averaging. Looking back at the progress of the field, we {\em identify 5 generations of LT methods}: 1) heurist... | ['Peter Richtárik', 'Kai Yi', 'Grigory Malinovsky'] | 2022-07-09 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-1.53219386e-03 2.19339520e-01 -2.10303336e-01 -5.85346967e-02
-1.19784701e+00 -4.72667515e-01 5.51169634e-01 3.36087979e-02
-8.19765925e-01 1.09559178e+00 -1.33907601e-01 -5.81039488e-01
-6.89665675e-01 -5.54496765e-01 -9.44733739e-01 -1.05969918e+00
-3.75854909e-01 7.62890160e-01 2.13643983e-02 -3.68445888... | [6.304009437561035, 4.8940510749816895] |
82aca780-8264-4635-a6e2-fe4c40335a2c | normalized-multivariate-time-series-causality | 2104.11360 | null | https://arxiv.org/abs/2104.11360v1 | https://arxiv.org/pdf/2104.11360v1.pdf | Normalized multivariate time series causality analysis and causal graph reconstruction | Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as real physical notion so as to formulate it from first principles, however, seems to go unnoticed. This study introduce... | ['X. San Liang'] | 2021-04-23 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 4.64981467e-01 3.31568420e-01 7.03405812e-02 1.78058669e-01
1.62668273e-01 -6.63046777e-01 9.96509969e-01 3.98497432e-01
-2.40394115e-01 9.94770706e-01 5.12412935e-02 -5.67369580e-01
-7.87742615e-01 -9.68792081e-01 -5.21342039e-01 -1.18673015e+00
-7.48642564e-01 2.60233879e-01 1.62815645e-01 -2.94171929... | [7.627429008483887, 5.177863597869873] |
0009ab29-983b-4088-a5c8-f9ee02f271bd | prediction-assistant-frame-super-resolution | 2103.09455 | null | https://arxiv.org/abs/2103.09455v1 | https://arxiv.org/pdf/2103.09455v1.pdf | Prediction-assistant Frame Super-Resolution for Video Streaming | Video frame transmission delay is critical in real-time applications such as online video gaming, live show, etc. The receiving deadline of a new frame must catch up with the frame rendering time. Otherwise, the system will buffer a while, and the user will encounter a frozen screen, resulting in unsatisfactory user ex... | ['Zhiyong Gao', 'Jerry W Hu', 'Charlie L Wang', 'Guangtao Zhai', 'Wenbo Bao', 'Wang Shen'] | 2021-03-17 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 5.54076433e-01 -1.94622323e-01 -1.29722223e-01 -2.03765437e-01
-2.21385449e-01 4.02386747e-02 -2.79537946e-01 -4.08122689e-02
-5.27556181e-01 9.24738467e-01 -1.41290829e-01 -1.89476103e-01
1.25441596e-01 -9.22375798e-01 -5.51574469e-01 -6.43011808e-01
-2.98904598e-01 -2.26985335e-01 8.44676197e-01 -1.47682086... | [11.10410213470459, -1.7639203071594238] |
7527d52a-b5a3-4397-abe3-34e817ebaa28 | on-information-plane-analyses-of-neural | 2003.09671 | null | https://arxiv.org/abs/2003.09671v3 | https://arxiv.org/pdf/2003.09671v3.pdf | On Information Plane Analyses of Neural Network Classifiers -- A Review | We review the current literature concerned with information plane analyses of neural network classifiers. While the underlying information bottleneck theory and the claim that information-theoretic compression is causally linked to generalization are plausible, empirical evidence was found to be both supporting and con... | ['Bernhard C. Geiger'] | 2020-03-21 | null | null | null | null | ['mutual-information-estimation', 'information-plane'] | ['methodology', 'methodology'] | [ 7.18635738e-01 4.70748365e-01 -2.37852767e-01 -5.90146601e-01
8.75700787e-02 -5.58398306e-01 6.33479238e-01 4.76590186e-01
-6.97201431e-01 6.10232413e-01 1.12911411e-01 -6.82582200e-01
-1.02814853e+00 -5.53691983e-01 -5.67182124e-01 -9.41075623e-01
-2.69614935e-01 3.77997786e-01 -7.46433903e-03 9.34778750... | [8.038516998291016, 3.497786521911621] |
87c7acc2-854d-4446-a4de-80ff7ca41d08 | cfr-icl-cascade-forward-refinement-with | 2303.05620 | null | https://arxiv.org/abs/2303.05620v1 | https://arxiv.org/pdf/2303.05620v1.pdf | CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation | The click-based interactive segmentation aims to extract the object of interest from an image with the guidance of user clicks. Recent work has achieved great overall performance by employing the segmentation from the previous output. However, in most state-of-the-art approaches, 1) the inference stage involves inflexi... | ['Luca Capriotti', 'Tiankai Yao', 'Fei Xu', 'Min Xian', 'Shoukun Sun'] | 2023-03-09 | null | null | null | null | ['image-augmentation', 'interactive-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.48633081e-01 -1.41449690e-01 -2.30590001e-01 -4.89183903e-01
-1.14294720e+00 -4.87491310e-01 2.64439344e-01 -1.10477738e-01
-5.91431260e-01 4.81122941e-01 -3.80145520e-01 -4.41695809e-01
2.16489151e-01 -6.69776261e-01 -9.32169318e-01 -5.35193861e-01
4.14839447e-01 4.14021343e-01 8.28537822e-01 9.80817303... | [9.444198608398438, -0.059335965663194656] |
b5440c21-f9c9-4bdb-995d-c12d868e92db | image-segmentation-via-probabilistic-graph | 2305.07954 | null | https://arxiv.org/abs/2305.07954v1 | https://arxiv.org/pdf/2305.07954v1.pdf | Image Segmentation via Probabilistic Graph Matching | This work presents an unsupervised and semi-automatic image segmentation approach where we formulate the segmentation as a inference problem based on unary and pairwise assignment probabilities computed using low-level image cues. The inference is solved via a probabilistic graph matching scheme, which allows rigorous ... | ['Yosi Keller', 'Ayelet Heimowitz'] | 2023-05-13 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 9.32408512e-01 4.84507918e-01 -2.30489433e-01 -6.23789608e-01
-8.03739190e-01 -6.09179139e-01 6.18294597e-01 3.49290729e-01
-7.76727140e-01 4.65439975e-01 -2.82361805e-01 -1.79990843e-01
-4.25657362e-01 -5.83561182e-01 -3.07282329e-01 -7.06743717e-01
4.52105515e-02 9.80843186e-01 6.13550544e-01 1.10150225... | [9.47188949584961, 0.26979753375053406] |
e5f364e4-1405-4561-9d06-5549ecec9b0f | semantic-scene-completion-from-a-single-depth | 1611.08974 | null | http://arxiv.org/abs/1611.08974v1 | http://arxiv.org/pdf/1611.08974v1.pdf | Semantic Scene Completion from a Single Depth Image | This paper focuses on semantic scene completion, a task for producing a
complete 3D voxel representation of volumetric occupancy and semantic labels
for a scene from a single-view depth map observation. Previous work has
considered scene completion and semantic labeling of depth maps separately.
However, we observe tha... | ['Angel X. Chang', 'Thomas Funkhouser', 'Manolis Savva', 'Fisher Yu', 'Shuran Song', 'Andy Zeng'] | 2016-11-28 | semantic-scene-completion-from-a-single-depth-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Song_Semantic_Scene_Completion_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Song_Semantic_Scene_Completion_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 4.42483068e-01 4.45267558e-01 1.31925747e-01 -7.52319932e-01
-6.98795319e-01 -6.25562310e-01 6.62330270e-01 1.46489948e-01
-2.80281097e-01 3.12816054e-01 4.15629953e-01 -1.50083289e-01
2.75074631e-01 -7.42931962e-01 -8.64620805e-01 -1.73210338e-01
1.60135739e-02 5.45968890e-01 4.66052651e-01 3.54145974... | [8.504201889038086, -2.942798137664795] |
c4a44ec6-92eb-4c09-a7ae-ef2336e0bb08 | mots-r-cnn-cosine-margin-triplet-loss-for | 2102.03512 | null | https://arxiv.org/abs/2102.03512v1 | https://arxiv.org/pdf/2102.03512v1.pdf | MOTS R-CNN: Cosine-margin-triplet loss for multi-object tracking | One of the central tasks of multi-object tracking involves learning a distance metric that is consistent with the semantic similarities of objects. The design of an appropriate loss function that encourages discriminative feature learning is among the most crucial challenges in deep neural network-based metric learning... | ['Renu M. Rameshan', 'Amit Satish Unde'] | 2021-02-06 | null | null | null | null | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [-1.78840920e-01 -6.38248265e-01 5.86597212e-02 -5.92133701e-01
-8.15115988e-01 -4.17827874e-01 3.93155456e-01 2.55799890e-02
-7.55561113e-01 5.92186749e-01 -2.91645378e-01 7.16400966e-02
-5.62331080e-01 -6.56414330e-01 -7.14295983e-01 -8.03061724e-01
1.29695982e-01 4.85777080e-01 4.17121559e-01 -1.92882076... | [6.312716960906982, -2.1321845054626465] |
7debb7fa-eba1-48ca-b843-64c500ca1cd6 | scir-qa-at-semeval-2017-task-3-cnn-model | null | null | https://aclanthology.org/S17-2049 | https://aclanthology.org/S17-2049.pdf | SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity | We describe a method of calculating the similarity of questions in community QA. Question in cQA are usually very long and there are a lot of useless information about calculating the similarity of questions. Therefore,we implement a CNN model based on similar and dissimilar information between question{'}s keywords. W... | ['Ting Liu', 'Le Qi', 'Yu Zhang'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['graph-ranking', 'question-similarity'] | ['graphs', 'natural-language-processing'] | [-6.96313202e-01 -4.49859411e-01 3.68570745e-01 -6.40245140e-01
-6.42073154e-01 -6.81326687e-01 3.24446052e-01 6.46701574e-01
-7.45515883e-01 2.53855765e-01 7.39245534e-01 -1.85270328e-02
-3.83477211e-01 -1.08765042e+00 -1.92883953e-01 -2.09694684e-01
3.16344649e-01 4.96781677e-01 5.91623008e-01 -8.32519293... | [11.243217468261719, 8.010148048400879] |
0824c9a6-0a37-4f18-b201-ea5e6855cb5d | cut-inner-layers-a-structured-pruning | 2206.14658 | null | https://arxiv.org/abs/2206.14658v1 | https://arxiv.org/pdf/2206.14658v1.pdf | Cut Inner Layers: A Structured Pruning Strategy for Efficient U-Net GANs | Pruning effectively compresses overparameterized models. Despite the success of pruning methods for discriminative models, applying them for generative models has been relatively rarely approached. This study conducts structured pruning on U-Net generators of conditional GANs. A per-layer sensitivity analysis confirms ... | ['Hancheol Park', 'Shinkook Choi', 'Bo-Kyeong Kim'] | 2022-06-29 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 5.83363533e-01 6.96471930e-01 -1.75169762e-02 -3.96826476e-01
-9.60497499e-01 -4.37656432e-01 6.02150798e-01 -5.87606847e-01
-1.70647383e-01 9.30396855e-01 4.93584424e-01 -5.34967005e-01
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3.86730552e-01 4.73682135e-01 -9.97398570e-02 2.11030334... | [11.372920989990234, -0.1926930695772171] |
8ccbd1f9-5637-4bb6-a13e-caf8ca8a2471 | sentiarabic-a-sentiment-analyzer-for-standard | null | null | https://aclanthology.org/L18-1195 | https://aclanthology.org/L18-1195.pdf | SentiArabic: A Sentiment Analyzer for Standard Arabic | null | ['Ramy er', 'Esk'] | 2018-05-01 | sentiarabic-a-sentiment-analyzer-for-standard-1 | https://aclanthology.org/L18-1195 | https://aclanthology.org/L18-1195.pdf | lrec-2018-5 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-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... | [-7.2081990242004395, 3.594397783279419] |
72713e3c-082d-471e-8d4d-5813b6f53f3e | high-quality-task-division-for-large-scale | 2208.10366 | null | https://arxiv.org/abs/2208.10366v1 | https://arxiv.org/pdf/2208.10366v1.pdf | High-quality Task Division for Large-scale Entity Alignment | Entity Alignment (EA) aims to match equivalent entities that refer to the same real-world objects and is a key step for Knowledge Graph (KG) fusion. Most neural EA models cannot be applied to large-scale real-life KGs due to their excessive consumption of GPU memory and time. One promising solution is to divide a large... | ['Xia Zhang', 'Genghong Zhao', 'Guido Zuccon', 'Wen Hua', 'Bing Liu'] | 2022-08-22 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [ 2.04380214e-01 3.46796602e-01 -9.41709504e-02 -8.55088979e-02
-7.63031363e-01 -4.30645019e-01 5.17334819e-01 4.99935180e-01
-3.65919709e-01 7.74205804e-01 2.75501937e-01 -3.04987133e-01
-3.86218339e-01 -1.08574986e+00 -9.75817502e-01 -4.22563374e-01
-1.12906866e-01 6.86946750e-01 5.84902763e-01 -2.05347717... | [8.985014915466309, 8.221659660339355] |
77ec96f6-57ab-4653-b5fd-9426871ed7d4 | face-age-progression-with-attribute | 2106.07696 | null | https://arxiv.org/abs/2106.07696v1 | https://arxiv.org/pdf/2106.07696v1.pdf | Face Age Progression With Attribute Manipulation | Face is one of the predominant means of person recognition. In the process of ageing, human face is prone to many factors such as time, attributes, weather and other subject specific variations. The impact of these factors were not well studied in the literature of face aging. In this paper, we propose a novel holistic... | ['Anurag Mittal', 'Athira Nambiar', 'Sinzith Tatikonda'] | 2021-06-14 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 3.37261021e-01 1.51156023e-01 1.92020416e-01 -6.75534308e-01
-3.67735699e-02 -3.61121982e-01 6.53439164e-01 -3.88111591e-01
-1.22452609e-01 6.33930147e-01 1.67243287e-01 2.62850702e-01
2.37383008e-01 -8.91997755e-01 -6.94691718e-01 -8.82545173e-01
3.98785695e-02 1.50760248e-01 -3.97663921e-01 -2.29489133... | [13.224575996398926, 0.5192817449569702] |
4068d51d-d146-4643-8dbb-a43ba5ed579c | semantically-aligned-universal-tree | 2010.06823 | null | https://arxiv.org/abs/2010.06823v1 | https://arxiv.org/pdf/2010.06823v1.pdf | Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems | A practical automatic textual math word problems (MWPs) solver should be able to solve various textual MWPs while most existing works only focused on one-unknown linear MWPs. Herein, we propose a simple but efficient method called Universal Expression Tree (UET) to make the first attempt to represent the equations of v... | ['Liang Lin', 'Rumin Zhang', 'Xiaodan Liang', 'Lihui Lin', 'Jinghui Qin'] | 2020-10-14 | null | https://aclanthology.org/2020.emnlp-main.309 | https://aclanthology.org/2020.emnlp-main.309.pdf | emnlp-2020-11 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 2.01700971e-01 1.30354524e-01 -1.33538038e-01 -3.15520525e-01
-8.82591486e-01 -4.53392684e-01 -6.22033775e-02 -3.12179089e-01
5.98053746e-02 8.22337985e-01 4.73590381e-02 -4.29846406e-01
-2.57341444e-01 -1.22796977e+00 -8.59064877e-01 -2.95213312e-01
5.67446709e-01 5.81586361e-01 -1.00160055e-02 -4.82631832... | [9.728458404541016, 7.480113983154297] |
c2739af3-85ef-45a4-bc57-814cd7824cfb | cross-lingual-pretraining-methods-for-spoken | null | null | https://openreview.net/forum?id=c1oDhu_hagR | https://openreview.net/pdf?id=c1oDhu_hagR | Cross-Lingual Pretraining Methods for Spoken Dialog | There has been an increasing interest among NLP researchers towards learning generic representations. However, in the field of multilingual spoken dialogue systems, this problem remains overlooked. Indeed most of the pre-training methods focus on learning representations for written and non-conversational data or are r... | ['Anonymous'] | 2021-03-17 | null | null | null | null | ['spoken-dialogue-systems'] | ['speech'] | [-2.82955337e-02 3.47024739e-01 -6.52666092e-02 -7.22322941e-01
-1.18906474e+00 -7.16799915e-01 8.77663732e-01 1.15975693e-01
-6.56173885e-01 1.19137073e+00 6.09079719e-01 -5.46881735e-01
1.75804973e-01 -5.40861428e-01 -6.44697309e-01 -4.45157111e-01
-1.27914965e-01 7.90686190e-01 -1.76329628e-01 -6.61443651... | [12.423635482788086, 8.362923622131348] |
1e3ac908-1324-4d66-bde2-306e8f683e20 | improving-few-and-zero-shot-reaction-template | null | null | https://pubs.acs.org/doi/abs/10.1021/acs.jcim.1c01065 | https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.1c01065 | Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks | Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed, which rely on a single-step model of chemical reactivity. In this study, we introduce a template-based single-step retrosynt... | ['and Günter Klambauer', 'Sepp Hochreiter', 'Marwin Segler', 'Jörg K. Wegner', 'Jonas Verhoeven', 'Paulo Neves', 'Natalia Dyubankova', 'Philipp Renz', 'Philipp Seidl'] | 2022-01-15 | null | null | null | journal-of-chemical-information-and-modeling-1 | ['retrosynthesis'] | ['medical'] | [ 4.53079492e-01 -3.39450827e-03 -4.58764017e-01 -1.23125732e-01
-5.94304502e-01 -1.09514463e+00 8.87959540e-01 5.62464833e-01
-3.04733634e-01 9.86269593e-01 2.15398967e-01 -4.96496350e-01
-1.96113423e-01 -8.12436640e-01 -8.49551499e-01 -8.20791721e-01
5.51566929e-02 4.20189619e-01 8.09448361e-02 -3.53414446... | [4.510021686553955, 6.088146686553955] |
96323041-cb70-4c20-94e2-641e4c8b9677 | deep-implicit-statistical-shape-models-for-3d | 2104.02847 | null | https://arxiv.org/abs/2104.02847v2 | https://arxiv.org/pdf/2104.02847v2.pdf | Deep Implicit Statistical Shape Models for 3D Medical Image Delineation | 3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Prior to deep learning, statistical shape models that imposed anatomical constraints and... | ['Adam P. Harrison', 'Junzhou Huang', 'Dakai Jin', 'Le Lu', 'Shun Miao', 'Ashwin Raju'] | 2021-04-07 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.91038936e-01 5.85929871e-01 2.30421975e-01 -3.37292314e-01
-1.03118336e+00 -6.55918062e-01 7.02776849e-01 2.14197859e-01
-3.46321076e-01 4.08852547e-01 1.90725029e-01 -5.41156530e-01
-3.25769395e-01 -3.81456643e-01 -6.76418841e-01 -8.53914320e-01
-5.00975311e-01 5.75215876e-01 2.01653570e-01 1.42618820... | [14.22671890258789, -2.4550859928131104] |
6416121d-3bd7-46b0-9ff9-ab30cf2eafb5 | neural-chronos-ode-unveiling-temporal | 2307.01023 | null | https://arxiv.org/abs/2307.01023v1 | https://arxiv.org/pdf/2307.01023v1.pdf | Neural Chronos ODE: Unveiling Temporal Patterns and Forecasting Future and Past Trends in Time Series Data | This work introduces Neural Chronos Ordinary Differential Equations (Neural CODE), a deep neural network architecture that fits a continuous-time ODE dynamics for predicting the chronology of a system both forward and backward in time. To train the model, we solve the ODE as an initial value problem and a final value p... | ['L. L. Ferrás', 'M. Fernanda P. Costa', 'C. Coelho'] | 2023-07-03 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [-6.07881427e-01 -2.18640938e-01 6.71578273e-02 1.74724385e-01
-1.21342167e-01 -5.11842191e-01 3.73107582e-01 -3.70290816e-01
-1.19799383e-01 8.41359913e-01 3.12931597e-01 -1.09340513e+00
-2.13938847e-01 -3.73633862e-01 -8.45111489e-01 -6.07563853e-01
-6.47372127e-01 4.69770133e-01 -2.26518571e-01 -5.17364800... | [7.303421974182129, 3.379690408706665] |
a2d08ddf-368f-4370-902f-f6365e5a100b | adversarial-and-safely-scaled-question | 2210.09467 | null | https://arxiv.org/abs/2210.09467v1 | https://arxiv.org/pdf/2210.09467v1.pdf | Adversarial and Safely Scaled Question Generation | Question generation has recently gained a lot of research interest, especially with the advent of large language models. In and of itself, question generation can be considered 'AI-hard', as there is a lack of unanimously agreed sense of what makes a question 'good' or 'bad'. In this paper, we tackle two fundamental pr... | ['Zhihang Dong', 'Sreehari Sankar'] | 2022-10-17 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 2.9179075e-01 6.0719067e-01 5.4796749e-01 -4.7686324e-02
-1.1921796e+00 -9.3489861e-01 7.4762106e-01 5.5717921e-01
-4.4752771e-01 9.1502142e-01 2.6233265e-01 -5.8108324e-01
4.2222708e-02 -1.1454009e+00 -6.9601673e-01 -6.5200455e-02
5.2448398e-01 5.5315077e-01 2.9209462e-01 -7.0746905e-01
3.6448875e-01... | [11.5302095413208, 8.312457084655762] |
c02da36d-881d-4922-a55f-26c72fd31aa0 | dynamical-hyperspectral-unmixing-with | 2303.10566 | null | https://arxiv.org/abs/2303.10566v1 | https://arxiv.org/pdf/2303.10566v1.pdf | Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks | Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the analysis of hyperspectral image sequences. It reveals the dynamical evolution of the materials (endmembers) and of their proportions (abundances) in a given scene. However, adequately accounting for the spatial and temporal variability of the endm... | ['Pau Closas', 'Tales Imbiriba', 'Ricardo Augusto Borsoi'] | 2023-03-19 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.65400577e-01 -7.10622489e-01 3.56707215e-01 3.00271176e-02
-1.38166696e-01 -3.87301922e-01 6.20428860e-01 -7.57014379e-02
-1.13414332e-01 8.21558297e-01 -1.82773676e-02 1.53445723e-02
-3.55732590e-01 -7.76636362e-01 -5.96517086e-01 -1.41235399e+00
3.35936427e-01 2.35063523e-01 -1.60182908e-01 -8.99785459... | [10.062758445739746, -2.06974720954895] |
c69718db-ff37-4d67-881a-d502fac41c0d | adaptive-generation-of-privileged | 2307.03240 | null | https://arxiv.org/abs/2307.03240v1 | https://arxiv.org/pdf/2307.03240v1.pdf | Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification | Visible-infrared person re-identification seeks to retrieve images of the same individual captured over a distributed network of RGB and IR sensors. Several V-I ReID approaches directly integrate both V and I modalities to discriminate persons within a shared representation space. However, given the significant gap in ... | ['Eric Granger', 'Rafael M. O. Cruz', 'Pourya Shamsolmoali', 'Arthur Josi', 'Mahdi Alehdaghi'] | 2023-07-06 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 4.47878838e-01 -3.30795272e-04 -6.86693937e-02 -5.17221689e-01
-7.17512131e-01 -8.42740476e-01 6.15854323e-01 -3.72631878e-01
-4.05137360e-01 7.14822054e-01 1.77176312e-01 2.64697045e-01
-2.51212001e-01 -9.00044084e-01 -5.97426355e-01 -8.46009374e-01
3.19419593e-01 2.28972629e-01 -4.44196552e-01 -2.39834487... | [14.706624984741211, 0.9618216753005981] |
50733e0a-5ebd-4dab-a2b9-224e845de4f3 | explaining-predictive-uncertainty-with | 2306.05724 | null | https://arxiv.org/abs/2306.05724v1 | https://arxiv.org/pdf/2306.05724v1.pdf | Explaining Predictive Uncertainty with Information Theoretic Shapley Values | Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models. By contrast, explaining the $\textit{uncertainty}$ of model outputs has received relatively little attention. We adapt the popular Shapley value framewor... | ['Ido Guy', 'Richard Mudd', 'Niek Tax', "Joshua O'Hara", 'David S. Watson'] | 2023-06-09 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 4.33114856e-01 8.69837999e-01 -7.47802913e-01 -7.45112062e-01
-6.73770785e-01 -5.20655096e-01 1.86220080e-01 4.42042947e-01
-4.03509200e-01 1.29466176e+00 -1.58738017e-01 -3.66733134e-01
-8.04605246e-01 -7.37995207e-01 -5.94189465e-01 -7.07407951e-01
-2.85427868e-01 7.22690463e-01 -2.94938684e-01 1.81185052... | [8.571634292602539, 5.390944957733154] |
129d19ec-3d04-4d02-a542-71df5e6dbe83 | cms-rcnn-contextual-multi-scale-region-based | 1606.05413 | null | http://arxiv.org/abs/1606.05413v1 | http://arxiv.org/pdf/1606.05413v1.pdf | CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection | Robust face detection in the wild is one of the ultimate components to
support various facial related problems, i.e. unconstrained face recognition,
facial periocular recognition, facial landmarking and pose estimation, facial
expression recognition, 3D facial model construction, etc. Although the face
detection proble... | ['Khoa Luu', 'Yutong Zheng', 'Marios Savvides', 'Chenchen Zhu'] | 2016-06-17 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 6.39670715e-02 -2.77845144e-01 -8.69646892e-02 -4.79311496e-01
-3.84298563e-01 -2.49163702e-01 5.14510334e-01 -7.15849876e-01
-3.37103873e-01 4.20744210e-01 -1.61859378e-01 4.05524932e-02
1.31439492e-01 -5.30916572e-01 -4.07917678e-01 -9.54661489e-01
-1.68784652e-02 1.15796529e-01 4.41446677e-02 -3.49466830... | [13.384683609008789, 0.6343944668769836] |
78a1f210-2389-4af2-9c32-0c1aabb22f3a | in-context-analogical-reasoning-with-pre | 2305.17626 | null | https://arxiv.org/abs/2305.17626v2 | https://arxiv.org/pdf/2305.17626v2.pdf | In-Context Analogical Reasoning with Pre-Trained Language Models | Analogical reasoning is a fundamental capacity of human cognition that allows us to reason abstractly about novel situations by relating them to past experiences. While it is thought to be essential for robust reasoning in AI systems, conventional approaches require significant training and/or hard-coding of domain kno... | ['Joyce Chai', 'Richard L. Lewis', 'Shane Storks', 'Xiaoyang Hu'] | 2023-05-28 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 3.48627061e-01 2.18081623e-01 2.45451123e-01 -2.86495656e-01
5.94829035e-04 -6.12859011e-01 9.87583280e-01 7.15845406e-01
-4.49456185e-01 1.86586782e-01 4.46131676e-01 -8.17854643e-01
-5.26944101e-01 -8.88472974e-01 -5.67836821e-01 3.89038287e-02
-1.75538972e-01 6.49652123e-01 2.10901618e-01 -6.95264041... | [10.596675872802734, 2.3239083290100098] |
5ce1c916-8c9b-401a-a4e3-f765fafd099e | subpixel-heatmap-regression-for-facial | 2111.02360 | null | https://arxiv.org/abs/2111.02360v1 | https://arxiv.org/pdf/2111.02360v1.pdf | Subpixel Heatmap Regression for Facial Landmark Localization | Deep Learning models based on heatmap regression have revolutionized the task of facial landmark localization with existing models working robustly under large poses, non-uniform illumination and shadows, occlusions and self-occlusions, low resolution and blur. However, despite their wide adoption, heatmap regression a... | ['Georgios Tzimiropoulos', 'Enrique Sanchez', 'Adrian Bulat'] | 2021-11-03 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-2.77684648e-02 -7.41635486e-02 -4.20820341e-02 -8.12845528e-01
-1.07020867e+00 -3.77409875e-01 7.00542808e-01 -1.87164932e-01
-1.80003822e-01 4.37005788e-01 6.88018352e-02 2.20423594e-01
2.02314377e-01 -5.05113006e-01 -8.09453070e-01 -6.49149179e-01
-6.94040209e-02 3.89241070e-01 -1.86908215e-01 3.73123027... | [13.444022178649902, 0.37481269240379333] |
06a17d19-9c18-4f53-bbef-95616ce7c0a0 | hifisinger-towards-high-fidelity-neural | 2009.01776 | null | https://arxiv.org/abs/2009.01776v1 | https://arxiv.org/pdf/2009.01776v1.pdf | HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis | High-fidelity singing voices usually require higher sampling rate (e.g., 48kHz) to convey expression and emotion. However, higher sampling rate causes the wider frequency band and longer waveform sequences and throws challenges for singing voice synthesis (SVS) in both frequency and time domains. Conventional SVS syste... | ['Tie-Yan Liu', 'Xu Tan', 'Tao Qin', 'Jian Luan', 'Jiawei Chen'] | 2020-09-03 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 1.84529901e-01 -1.40343262e-02 1.11068867e-01 1.11776911e-01
-9.86194968e-01 -7.37704992e-01 1.96343854e-01 -7.37059116e-01
1.81818575e-01 6.45085156e-01 4.89217550e-01 -1.94418028e-01
3.63744497e-01 -6.80806220e-01 -5.86688697e-01 -7.05875754e-01
1.24546647e-01 -2.67559677e-01 -1.56611856e-02 -3.42474490... | [15.507224082946777, 6.187005519866943] |
401b8f8d-8acd-4d54-92f0-2c69240777b2 | double-matching-under-complementary | 2301.10230 | null | https://arxiv.org/abs/2301.10230v2 | https://arxiv.org/pdf/2301.10230v2.pdf | Double Matching Under Complementary Preferences | In this paper, we propose a new algorithm for addressing the problem of matching markets with complementary preferences, where agents' preferences are unknown a priori and must be learned from data. The presence of complementary preferences can lead to instability in the matching process, making this problem challengin... | ['Xiaowu Dai', 'Guang Cheng', 'Yuantong Li'] | 2023-01-24 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 1.88098233e-02 1.33358404e-01 -9.37618732e-01 -1.54094785e-01
-1.11789167e+00 -7.75012255e-01 3.30919206e-01 -2.54094880e-02
-3.85866523e-01 8.98574173e-01 -3.51077989e-02 -6.54576421e-01
-7.23692834e-01 -8.01884115e-01 -7.71312475e-01 -6.82466269e-01
4.70687635e-02 8.11298251e-01 -6.59652352e-02 6.62237331... | [4.47898530960083, 3.245450019836426] |
70545602-f99d-44b2-ad68-179d8981017a | temporal-hallucinating-for-action-recognition | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Temporal_Hallucinating_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Temporal_Hallucinating_for_CVPR_2018_paper.pdf | Temporal Hallucinating for Action Recognition With Few Still Images | Action recognition in still images has been recently promoted by deep learning. However, the success of these deep models heavily depends on huge amount of training images for various action categories, which may not be available in practice. Alternatively, humans can classify new action categories after seeing few ima... | ['Yu Qiao', 'Yali Wang', 'Lei Zhou'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['action-recognition-in-still-images'] | ['computer-vision'] | [ 2.60649204e-01 -3.36059034e-01 -3.43712032e-01 -3.13128531e-01
-3.99327546e-01 -3.01266789e-01 6.34701431e-01 -2.81173855e-01
-4.20199066e-01 5.56173503e-01 3.15753520e-01 2.26057425e-01
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-2.97820829e-02 -1.03427611e-01 7.14088082e-01 2.79512286... | [8.637910842895508, 0.5901516079902649] |
03082a8c-55ed-4c57-9f43-ae96c572c5fa | unsupervised-joint-training-of-bilingual-word | null | null | https://aclanthology.org/P19-1312 | https://aclanthology.org/P19-1312.pdf | Unsupervised Joint Training of Bilingual Word Embeddings | State-of-the-art methods for unsupervised bilingual word embeddings (BWE) train a mapping function that maps pre-trained monolingual word embeddings into a bilingual space. Despite its remarkable results, unsupervised mapping is also well-known to be limited by the original dissimilarity between the word embedding spac... | ['Atsushi Fujita', 'Benjamin Marie'] | 2019-07-01 | null | null | null | acl-2019-7 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-1.04743056e-01 -2.12273989e-02 -5.83250999e-01 -4.32308614e-01
-9.41112280e-01 -6.48577511e-01 9.29806113e-01 3.26105654e-02
-8.21071446e-01 8.86268735e-01 6.37870967e-01 -6.44954801e-01
2.78562486e-01 -6.50393307e-01 -8.35927248e-01 -3.74986112e-01
1.83365479e-01 1.00521243e+00 -2.09840983e-01 -4.97138798... | [11.058248519897461, 10.039112091064453] |
41727ab7-689b-48dd-8935-4ab0be6c2fb9 | ghost-free-multi-exposure-image-fusion | null | null | https://www.sciencedirect.com/science/article/pii/S1047320319301750 | https://www.sciencedirect.com/science/article/pii/S1047320319301750 | Ghost-free multi exposure image fusion technique using dense SIFT descriptor and guided filter | A ghost-free multi-exposure image fusion technique using the dense SIFT descriptor and the guided filter is proposed in this paper. The results suggest that the presented scheme produces high-quality images using ordinary cameras and that too without the ghosting artifact. To do so, the dense SIFT descriptor is used to... | ['Muhammad Imran', 'Naila Hayat'] | 2019-07-01 | null | null | null | journal-2019-7 | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 2.88171947e-01 -8.89066637e-01 1.96904898e-01 -1.60607129e-01
-3.31182986e-01 -2.11399104e-02 4.17641729e-01 2.00829003e-02
-5.77016175e-01 5.39023995e-01 3.59079614e-02 4.05051112e-01
-2.22538814e-01 -6.70479178e-01 -2.05225393e-01 -1.10449886e+00
1.02949198e-02 -5.73637605e-01 4.96476710e-01 -1.91407919... | [10.919923782348633, -2.446815252304077] |
d8cd8ac6-1128-48d1-916d-541dc34635fe | uncertainty-based-class-activation-maps-for | 2002.10309 | null | https://arxiv.org/abs/2002.10309v1 | https://arxiv.org/pdf/2002.10309v1.pdf | Uncertainty based Class Activation Maps for Visual Question Answering | Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we... | ['Vinay P. Namboodiri', 'Mayank Lunayach', 'Badri N. Patro'] | 2020-01-23 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-3.00491333e-01 4.28886205e-01 -1.87290132e-01 -5.87156475e-01
-1.11500978e+00 -5.55459142e-01 7.87589788e-01 2.58614123e-01
-1.87559009e-01 6.58586323e-01 2.50198096e-01 -4.79683518e-01
-1.19336382e-01 -7.00516939e-01 -9.96824443e-01 -3.50599885e-01
1.76656321e-01 5.04035652e-01 2.60938019e-01 1.63505480... | [10.819918632507324, 1.8040192127227783] |
36e46cfb-c4c0-43a3-a904-594754917979 | adversarial-consistent-learning-on-partial | 2009.09289 | null | https://arxiv.org/abs/2009.09289v1 | https://arxiv.org/pdf/2009.09289v1.pdf | Adversarial Consistent Learning on Partial Domain Adaptation of PlantCLEF 2020 Challenge | Domain adaptation is one of the most crucial techniques to mitigate the domain shift problem, which exists when transferring knowledge from an abundant labeled sourced domain to a target domain with few or no labels. Partial domain adaptation addresses the scenario when target categories are only a subset of source cat... | ['Youshan Zhang', 'Brian D. Davison'] | 2020-09-19 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 4.36919212e-01 7.91239887e-02 -3.14276904e-01 -6.22454047e-01
-6.99643672e-01 -9.92335618e-01 4.06609863e-01 -1.22626813e-03
9.43615474e-03 8.87136042e-01 -1.47482559e-01 1.21355906e-01
-3.11081239e-04 -8.18427622e-01 -1.06316245e+00 -5.47144771e-01
1.76985085e-01 4.93504196e-01 3.22252333e-01 -3.11849654... | [10.355196952819824, 3.1032400131225586] |
a2c97185-e551-4e72-bb2c-e6578ad0ca45 | xer-an-explainable-model-for-entity | null | null | https://aclanthology.org/2021.trustnlp-1.5 | https://aclanthology.org/2021.trustnlp-1.5.pdf | xER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem | In this paper, we propose a global, self- explainable solution to solve a prominent NLP problem: Entity Resolution (ER). We formu- late ER as a graph partitioning problem. Every mention of a real-world entity is represented by a node in the graph, and the pairwise sim- ilarity scores between the mentions are used to as... | ['Wen-mei Hwu', 'JinJun Xiong', 'Rakesh Nagi', 'Samhita Vadrevu'] | null | null | null | null | naacl-trustnlp-2021-6 | ['graph-partitioning', 'entity-resolution'] | ['graphs', 'natural-language-processing'] | [ 1.58212125e-01 9.70002353e-01 -3.32351893e-01 -2.93942750e-01
-6.78363979e-01 -7.85482407e-01 -1.20691448e-01 4.72295702e-01
-3.58418263e-02 1.26062167e+00 -3.03703725e-01 -2.69030660e-01
-5.25409520e-01 -1.03378296e+00 -7.81979501e-01 -1.91826150e-01
-4.55652297e-01 1.35720825e+00 3.87066305e-02 2.03703297... | [7.0887885093688965, 5.337768077850342] |
1d755f68-9143-447e-82f7-1cb278c04412 | ssd-single-shot-multibox-detector | 1512.02325 | null | http://arxiv.org/abs/1512.02325v5 | http://arxiv.org/pdf/1512.02325v5.pdf | SSD: Single Shot MultiBox Detector | We present a method for detecting objects in images using a single deep
neural network. Our approach, named SSD, discretizes the output space of
bounding boxes into a set of default boxes over different aspect ratios and
scales per feature map location. At prediction time, the network generates
scores for the presence ... | ['Cheng-Yang Fu', 'Christian Szegedy', 'Wei Liu', 'Scott Reed', 'Dumitru Erhan', 'Dragomir Anguelov', 'Alexander C. Berg'] | 2015-12-08 | null | null | null | null | ['lidar-semantic-segmentation', 'surgical-tool-detection'] | ['computer-vision', 'computer-vision'] | [ 1.08210770e-02 -2.54739136e-01 -3.28592658e-02 -5.93914211e-01
-6.78719401e-01 -6.15865171e-01 1.96285963e-01 -2.09802866e-01
-6.70670331e-01 2.34567493e-01 -3.82237405e-01 -3.76337171e-01
5.60735464e-01 -8.02847028e-01 -9.76746798e-01 -3.13789189e-01
2.38942549e-01 2.28738949e-01 9.35607731e-01 1.61014661... | [8.813780784606934, -0.060054875910282135] |
46a1f327-8eef-4165-8f71-79cef3d1f752 | a-comprehensive-survey-on-multi-hop-machine | 2212.04070 | null | https://arxiv.org/abs/2212.04070v1 | https://arxiv.org/pdf/2212.04070v1.pdf | A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics | Multi-hop Machine reading comprehension is a challenging task with aim of answering a question based on disjoint pieces of information across the different passages. The evaluation metrics and datasets are a vital part of multi-hop MRC because it is not possible to train and evaluate models without them, also, the prop... | ['Ahmad Baraani', 'Reza Ramezani', 'Azade Mohammadi'] | 2022-12-08 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 3.16360146e-01 1.82886839e-01 -2.52423257e-01 -2.67352253e-01
-1.35058415e+00 -5.83154321e-01 2.11930469e-01 7.33909667e-01
-6.48897052e-01 1.06008041e+00 5.26493847e-01 -3.38825017e-01
-7.28178442e-01 -5.71700871e-01 -6.54344440e-01 -3.39237034e-01
-1.28902672e-02 8.44313875e-02 3.34773451e-01 -6.14363551... | [11.364263534545898, 8.117300033569336] |
84c05116-9fd3-4b11-81eb-72e3a7b80e5f | instance-segmentation-based-6d-pose | null | null | https://www.sciencedirect.com/science/article/pii/S0736584523000170 | https://www.sciencedirect.com/science/article/pii/S0736584523000170 | Instance segmentation based 6D pose estimation of industrial objects using point clouds for robotic bin-picking | 3D object pose estimation for robotic grasping and manipulation is a crucial task in the manufacturing industry.
In cluttered and occluded scenes, the 6D pose estimation of the low-textured or textureless industrial object is a
challenging problem due to the lack of color information. Thus, point cloud that is hard... | ['Han Ding', 'Shaofei Li', 'Chungang Zhuang'] | 2023-08-10 | null | null | null | robotics-and-computer-integrated | ['6d-pose-estimation-1', 'robotic-grasping'] | ['computer-vision', 'robots'] | [-0.03628322 -0.24296111 0.19885643 -0.3988318 -0.34179145 -0.29685462
0.25174007 -0.04430562 -0.01561185 0.18019919 -0.6746481 0.05891504
-0.32839248 -0.79985505 -0.8830565 -0.7314285 0.03896917 1.0126407
0.24635424 0.033198 0.3229553 0.9352481 -1.5696867 0.04656348
0.70864886 1.3899217 0.7... | [7.314042568206787, -2.509852170944214] |
59968885-289c-484e-a04e-4f92f22de5cf | chatting-makes-perfect-chat-based-image | 2305.20062 | null | https://arxiv.org/abs/2305.20062v1 | https://arxiv.org/pdf/2305.20062v1.pdf | Chatting Makes Perfect -- Chat-based Image Retrieval | Chats emerge as an effective user-friendly approach for information retrieval, and are successfully employed in many domains, such as customer service, healthcare, and finance. However, existing image retrieval approaches typically address the case of a single query-to-image round, and the use of chats for image retrie... | ['Dani Lischinski', 'Nir Darshan', 'Rami Ben-Ari', 'Matan Levy'] | 2023-05-31 | null | null | null | null | ['chat-based-image-retrieval', 'information-retrieval'] | ['computer-vision', 'natural-language-processing'] | [ 3.36302191e-01 -8.96096155e-02 -7.99925253e-02 -3.29076707e-01
-1.50816298e+00 -7.65345573e-01 7.08160639e-01 2.56510191e-02
-6.74328208e-01 4.36012834e-01 2.94256121e-01 -2.74647385e-01
6.24929145e-02 -3.28456312e-01 -3.25945616e-01 -5.19690990e-01
2.90583283e-01 7.35867918e-01 2.94947267e-01 -3.56900781... | [10.920382499694824, 1.3651435375213623] |
60dfa1e0-8c84-459f-a9e1-60831f677219 | construction-of-english-resume-corpus-and | 2208.03219 | null | https://arxiv.org/abs/2208.03219v2 | https://arxiv.org/pdf/2208.03219v2.pdf | Construction of English Resume Corpus and Test with Pre-trained Language Models | Information extraction(IE) has always been one of the essential tasks of NLP. Moreover, one of the most critical application scenarios of information extraction is the information extraction of resumes. Constructed text is obtained by classifying each part of the resume. It is convenient to store these texts for later ... | ['Tatsunori Mori', 'Chengguang Gan'] | 2022-08-05 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 4.38176244e-01 -7.33154938e-02 -3.30332577e-01 -3.48580390e-01
-5.88953257e-01 -5.88231504e-01 5.24514437e-01 5.15442371e-01
-4.54714715e-01 1.01030672e+00 1.96678728e-01 -4.48489130e-01
-2.28638723e-01 -6.44371688e-01 -3.16801101e-01 -3.05245489e-01
1.10323250e-01 4.46204066e-01 2.06466794e-01 -3.33191961... | [9.787022590637207, 8.624054908752441] |
635ba76e-d967-4476-b3ee-1585f0a6594c | faster-dan-multi-target-queries-with-document | 2301.10593 | null | https://arxiv.org/abs/2301.10593v1 | https://arxiv.org/pdf/2301.10593v1.pdf | Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document Recognition | Recent advances in handwritten text recognition enabled to recognize whole documents in an end-to-end way: the Document Attention Network (DAN) recognizes the characters one after the other through an attention-based prediction process until reaching the end of the document. However, this autoregressive process leads t... | ['Thierry Paquet', 'Clément Chatelain', 'Denis Coquenet'] | 2023-01-25 | null | null | null | null | ['handwritten-document-recognition'] | ['computer-vision'] | [ 2.57712990e-01 -2.85332024e-01 -2.33504087e-01 -4.13612664e-01
-9.58655596e-01 -8.28448832e-01 7.54005492e-01 -2.19288602e-01
-3.72649103e-01 3.41003031e-01 2.45468408e-01 -3.68335068e-01
4.57949974e-02 -4.36872452e-01 -7.78239369e-01 -6.70043468e-01
5.60209513e-01 1.05102682e+00 1.16853036e-01 1.59461752... | [11.875056266784668, 2.4249794483184814] |
890ebf4f-3ee4-42c7-831e-f337b503fde6 | dynamic-virtual-network-embedding-algorithm | 2202.02140 | null | https://arxiv.org/abs/2202.02140v1 | https://arxiv.org/pdf/2202.02140v1.pdf | Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning | Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by heuristic method, but this m... | ['Lei Liu', 'Weishan Zhang', 'Neeraj Kumar', 'Chao Wang', 'Peiying Zhang'] | 2022-02-03 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-3.03720713e-01 -4.49837923e-01 -3.76194715e-01 3.46407950e-01
9.08434510e-01 -2.73327410e-01 1.56741254e-02 -5.45489788e-01
-4.26645845e-01 8.49247277e-01 -4.58293885e-01 -6.53000236e-01
-7.48091936e-01 -1.09611750e+00 -7.22403824e-03 -6.93004966e-01
-4.28218901e-01 3.85835111e-01 1.27089560e-01 -3.57056260... | [5.858583927154541, 1.7233357429504395] |
c5e53df2-f62d-419b-ab6d-f5a16f110e35 | opinesum-entailment-based-self-training-for | 2212.10791 | null | https://arxiv.org/abs/2212.10791v1 | https://arxiv.org/pdf/2212.10791v1.pdf | OpineSum: Entailment-based self-training for abstractive opinion summarization | A typical product or place often has hundreds of reviews, and summarization of these texts is an important and challenging problem. Recent progress on abstractive summarization in domains such as news has been driven by supervised systems trained on hundreds of thousands of news articles paired with human-written summa... | ['Joshua Maynez', 'Annie Louis'] | 2022-12-21 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.98839408e-01 2.86193430e-01 -4.96731102e-01 -3.14389974e-01
-1.37180138e+00 -4.67007101e-01 7.08082378e-01 1.03968596e+00
-1.49473444e-01 9.89273667e-01 9.79900897e-01 1.78725436e-01
1.69015333e-01 -5.91405213e-01 -4.35534179e-01 -3.67581934e-01
3.99245203e-01 6.54174984e-01 2.08440140e-01 -4.24842596... | [12.456968307495117, 9.425171852111816] |
afa5698f-7740-4419-8939-af4dfb0019df | deep-blind-video-decaptioning-by-temporal | 1905.02949 | null | https://arxiv.org/abs/1905.02949v1 | https://arxiv.org/pdf/1905.02949v1.pdf | Deep Blind Video Decaptioning by Temporal Aggregation and Recurrence | Blind video decaptioning is a problem of automatically removing text overlays and inpainting the occluded parts in videos without any input masks. While recent deep learning based inpainting methods deal with a single image and mostly assume that the positions of the corrupted pixels are known, we aim at automatic text... | ['Joon-Young Lee', 'Dahun Kim', 'Sanghyun Woo', 'In So Kweon'] | 2019-05-08 | deep-blind-video-decaptioning-by-temporal-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kim_Deep_Blind_Video_Decaptioning_by_Temporal_Aggregation_and_Recurrence_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Deep_Blind_Video_Decaptioning_by_Temporal_Aggregation_and_Recurrence_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-denoising', 'video-to-video-synthesis', 'video-inpainting'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.89546424e-01 -1.04977809e-01 -1.28689501e-02 4.65416498e-02
-5.98485172e-01 -5.30044734e-01 4.05953944e-01 -4.38075185e-01
-3.77834082e-01 6.38756931e-01 5.70474148e-01 5.43136373e-02
3.66991580e-01 -1.20498903e-01 -1.22674382e+00 -6.64709747e-01
1.89289629e-01 -5.73529266e-02 2.53276378e-01 -4.27750051... | [10.801613807678223, -1.3425040245056152] |
1130125d-1302-4e15-b536-6b21fd00441c | dr2-diffusion-based-robust-degradation | 2303.06885 | null | https://arxiv.org/abs/2303.06885v3 | https://arxiv.org/pdf/2303.06885v3.pdf | DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration | Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training, while more complex cases could happen in the real world. This gap between the assumed and actual degradation hurts the restoration performance where artifacts are often observed in the output. However... | ['Yanfeng Wang', 'Ya zhang', 'Mingyuan Zhou', 'Huangjie Zheng', 'Ziying Zhang', 'Xiaoyun Zhang', 'Zhixin Wang'] | 2023-03-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_DR2_Diffusion-Based_Robust_Degradation_Remover_for_Blind_Face_Restoration_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_DR2_Diffusion-Based_Robust_Degradation_Remover_for_Blind_Face_Restoration_CVPR_2023_paper.pdf | cvpr-2023-1 | ['blind-face-restoration'] | ['computer-vision'] | [ 5.44058979e-01 -4.44357127e-01 2.57054240e-01 -1.50377244e-01
-6.46638453e-01 -3.84525359e-01 5.03912032e-01 -4.82321680e-01
8.89587179e-02 6.81862772e-01 6.08460248e-01 -1.83643121e-03
-1.13945276e-01 -7.22276568e-01 -6.24557257e-01 -1.22349751e+00
2.52703130e-01 -9.20040309e-02 1.31077528e-01 -7.07755834... | [11.469862937927246, -2.186467170715332] |
f181a9ac-b639-481a-bce0-1bb229344b12 | impakt-a-dataset-for-open-schema-knowledge | 2212.10770 | null | https://arxiv.org/abs/2212.10770v1 | https://arxiv.org/pdf/2212.10770v1.pdf | ImPaKT: A Dataset for Open-Schema Knowledge Base Construction | Large language models have ushered in a golden age of semantic parsing. The seq2seq paradigm allows for open-schema and abstractive attribute and relation extraction given only small amounts of finetuning data. Language model pretraining has simultaneously enabled great strides in natural language inference, reasoning ... | ['Sumit Sanghai', 'Patrick Murray', 'Bhargav Kanagal', 'Zach Fisher', 'Luke Vilnis'] | 2022-12-21 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 3.01501483e-01 8.51017892e-01 -5.06488383e-01 -1.03526843e+00
-8.04737628e-01 -9.36427712e-01 5.51603734e-01 5.78264534e-01
-3.85192692e-01 8.95317972e-01 6.60975456e-01 -4.88844067e-01
-3.24451596e-01 -1.03785980e+00 -7.71844864e-01 1.14683159e-01
-6.50872290e-02 1.24554145e+00 -5.27810492e-02 -6.34109855... | [10.058904647827148, 8.275453567504883] |
943dd672-f0f9-48f9-a478-557d6f7d4bcb | a-gated-cross-domain-collaborative-network | 2306.14141 | null | https://arxiv.org/abs/2306.14141v1 | https://arxiv.org/pdf/2306.14141v1.pdf | A Gated Cross-domain Collaborative Network for Underwater Object Detection | Underwater object detection (UOD) plays a significant role in aquaculture and marine environmental protection. Considering the challenges posed by low contrast and low-light conditions in underwater environments, several underwater image enhancement (UIE) methods have been proposed to improve the quality of underwater ... | ['Mengyuan Liu', 'Pinhao Song', 'Hong Liu', 'Linhui Dai'] | 2023-06-25 | null | null | null | null | ['image-enhancement', 'uie'] | ['computer-vision', 'computer-vision'] | [ 3.54066312e-01 -1.29054010e-01 9.48201895e-01 -1.32929981e-01
-2.25899875e-01 -2.51685917e-01 1.83919668e-01 1.77179247e-01
-7.86292911e-01 4.74684268e-01 -6.22858014e-03 3.74324381e-01
-3.08040917e-01 -1.27321267e+00 -5.36828041e-01 -1.27125156e+00
-3.80646557e-01 -6.37391746e-01 6.23325884e-01 -4.94736284... | [10.662798881530762, -3.482184648513794] |
88660fd8-4112-4e24-a6ab-0be91ea65049 | an-evolutionary-approach-to-dynamic | 2205.12755 | null | https://arxiv.org/abs/2205.12755v6 | https://arxiv.org/pdf/2205.12755v6.pdf | An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems | Multitask learning assumes that models capable of learning from multiple tasks can achieve better quality and efficiency via knowledge transfer, a key feature of human learning. Though, state of the art ML models rely on high customization for each task and leverage size and data scale rather than scaling the number of... | ['Jeff Dean', 'Andrea Gesmundo'] | 2022-05-25 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 1.81520641e-01 -1.47853941e-01 8.27271864e-02 -9.32558402e-02
-8.05373847e-01 -4.62171584e-01 4.40952301e-01 1.30818263e-02
-6.60537481e-01 1.08031094e+00 -2.72938371e-01 -7.93539211e-02
-3.41202497e-01 -5.04914224e-01 -1.08462083e+00 -7.66605675e-01
-5.22468723e-02 7.69346714e-01 5.40667892e-01 -1.04338586... | [9.642034530639648, 3.419278383255005] |
72d82952-cd62-409a-9e42-deae5e4d0970 | transformers-in-healthcare-a-survey | 2307.00067 | null | https://arxiv.org/abs/2307.00067v1 | https://arxiv.org/pdf/2307.00067v1.pdf | Transformers in Healthcare: A Survey | With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Languag... | ['Parisa Rashidi', 'Kia Khezeli', 'Azra Bihorac', 'Benjamin Shickel', 'Jessica Sena', 'Brandon Silva', 'Aysegul Bumin', 'Scott Siegel', 'Miguel Contreras', 'Jiaqing Zhang', 'Sabyasachi Bandyopadhyay', 'Subhash Nerella'] | 2023-06-30 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 3.82992595e-01 4.49879616e-01 -2.53251731e-01 -3.98134202e-01
-4.12600607e-01 -3.05751324e-01 1.11020513e-01 6.56623840e-01
-6.50861442e-01 1.04920566e+00 4.87331122e-01 -6.22524798e-01
-3.12824547e-01 -6.00679576e-01 -4.14524555e-01 -5.98430157e-01
-1.90651789e-01 5.74608505e-01 -6.26039922e-01 1.95643112... | [7.9151458740234375, 6.972306251525879] |
7a157816-dbbb-4ac2-ab75-d3b3c50c2668 | snore-scalable-unsupervised-learning-of | 2009.04535 | null | https://arxiv.org/abs/2009.04535v2 | https://arxiv.org/pdf/2009.04535v2.pdf | SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations | Learning from complex real-life networks is a lively research area, with recent advances in learning information-rich, low-dimensional network node representations. However, state-of-the-art methods are not necessarily interpretable and are therefore not fully applicable to sensitive settings in biomedical or user prof... | ['Blaž Škrlj', 'Nada Lavrač', 'Sebastian Mežnar'] | 2020-09-08 | null | null | null | null | ['structural-node-embedding'] | ['graphs'] | [ 5.03482580e-01 9.22922492e-01 -3.89980137e-01 -4.16414201e-01
6.41437545e-02 -2.00268030e-01 7.95003057e-01 8.45986009e-01
-3.18023890e-01 7.21853077e-01 3.41916949e-01 -4.62076783e-01
-6.00230098e-01 -8.93144429e-01 -5.81999242e-01 -7.39437401e-01
-4.05056059e-01 1.03895283e+00 5.15709817e-02 -4.82368499... | [7.244147777557373, 6.466592311859131] |
fcca6d3a-d99b-4607-8a83-a9e934fab7f2 | shapelet-based-counterfactual-explanations | 2208.10462 | null | https://arxiv.org/abs/2208.10462v1 | https://arxiv.org/pdf/2208.10462v1.pdf | Shapelet-Based Counterfactual Explanations for Multivariate Time Series | As machine learning and deep learning models have become highly prevalent in a multitude of domains, the main reservation in their adoption for decision-making processes is their black-box nature. The Explainable Artificial Intelligence (XAI) paradigm has gained a lot of momentum lately due to its ability to reduce mod... | ['Shah Muhammad Hamdi', 'Soukaina Filali Boubrahimi', 'Omar Bahri'] | 2022-08-22 | null | null | null | null | ['counterfactual-explanation', 'solar-flare-prediction'] | ['miscellaneous', 'time-series'] | [ 3.29621851e-01 5.47508657e-01 -3.03693175e-01 -3.94199580e-01
-1.42527074e-01 -6.36443019e-01 1.00211108e+00 1.63019553e-01
2.79499143e-01 9.51278090e-01 5.36036253e-01 -8.85150135e-01
-4.61946070e-01 -7.95307040e-01 -6.86518312e-01 -1.86651587e-01
-8.68347511e-02 2.99117386e-01 -4.58491772e-01 -1.04889832... | [8.730120658874512, 5.642559051513672] |
9d5f2d13-321c-4022-9fb7-700f2cd37059 | statistical-user-simulation-for-spoken | null | null | https://aclanthology.org/W12-1805 | https://aclanthology.org/W12-1805.pdf | Statistical User Simulation for Spoken Dialogue Systems: What for, Which Data, Which Future? | null | ['Olivier Pietquin'] | 2012-06-01 | null | null | null | ws-2012-6 | ['user-simulation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.238363265991211, 3.7262535095214844] |
1b383c88-4a43-4fcc-9bf5-6cbba3700ea5 | kleister-key-information-extraction-datasets | 2105.05796 | null | https://arxiv.org/abs/2105.05796v1 | https://arxiv.org/pdf/2105.05796v1.pdf | Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts | The relevance of the Key Information Extraction (KIE) task is increasingly important in natural language processing problems. But there are still only a few well-defined problems that serve as benchmarks for solutions in this area. To bridge this gap, we introduce two new datasets (Kleister NDA and Kleister Charity). T... | ['Przemysław Biecek', 'Bartosz Topolski', 'Paulina Rosalska', 'Agnieszka Kaliska', 'Dawid Lipiński', 'Anna Wróblewska', 'Filip Graliński', 'Tomasz Stanisławek'] | 2021-05-12 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [-2.58140057e-01 4.96387929e-01 -4.15895879e-01 -2.48396188e-01
-1.19394505e+00 -1.10666525e+00 8.87229502e-01 4.37144816e-01
-6.22608781e-01 1.10065711e+00 3.50293070e-01 -5.67908645e-01
-4.65215534e-01 -8.81722689e-01 -7.61192799e-01 -7.32174665e-02
-3.19082588e-01 8.50059032e-01 -7.02202171e-02 8.15702975... | [9.415894508361816, 8.620926856994629] |
23a0bd31-6772-49d8-88ab-093f9dbe5613 | towards-automatic-pulmonary-nodule-management | 1610.09157 | null | http://arxiv.org/abs/1610.09157v2 | http://arxiv.org/pdf/1610.09157v2.pdf | Towards automatic pulmonary nodule management in lung cancer screening with deep learning | The introduction of lung cancer screening programs will produce an
unprecedented amount of chest CT scans in the near future, which radiologists
will have to read in order to decide on a patient follow-up strategy. According
to the current guidelines, the workup of screen-detected nodules strongly
relies on nodule size... | ['Alfonso Marchiano', 'Mathilde M. W. Wille', 'Cornelia Schaefer-Prokop', 'Colin Jacobs', 'Paul K. Gerke', 'Arnaud Arindra Adiyoso Setio', 'Mathias Prokop', 'Bram van Ginneken', 'Sarah J. van Riel', 'Kaman Chung', 'Ugo Pastorino', 'Francesco Ciompi', 'Ernst Th. Scholten'] | 2016-10-28 | null | null | null | null | ['pulmonary-nodules-classification', 'lung-nodule-detection', 'lung-nodule-classification'] | ['medical', 'medical', 'medical'] | [ 3.77164215e-01 3.61716777e-01 -1.73163712e-01 -4.77794915e-01
-8.36340904e-01 -5.55701911e-01 3.43652397e-01 5.40533841e-01
-6.25721633e-01 -5.31537645e-02 7.47334212e-02 -8.10521066e-01
-1.65356010e-01 -1.04635847e+00 -5.30835450e-01 -4.67991650e-01
-5.82306609e-02 9.70409751e-01 7.82541454e-01 1.26121391... | [15.37608814239502, -2.154320478439331] |
55c4784d-5aab-429a-9bf4-c3f439b0e3fe | filtered-noise-shaping-for-time-domain-room | 2107.07503 | null | https://arxiv.org/abs/2107.07503v1 | https://arxiv.org/pdf/2107.07503v1.pdf | Filtered Noise Shaping for Time Domain Room Impulse Response Estimation From Reverberant Speech | Deep learning approaches have emerged that aim to transform an audio signal so that it sounds as if it was recorded in the same room as a reference recording, with applications both in audio post-production and augmented reality. In this work, we propose FiNS, a Filtered Noise Shaping network that directly estimates th... | ['Paul Calamia', 'Vamsi Krishna Ithapu', 'Christian J. Steinmetz'] | 2021-07-15 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 1.87082574e-01 -3.74817342e-01 9.95278537e-01 -1.88376874e-01
-1.45878518e+00 -6.40022993e-01 2.47969091e-01 -2.81569332e-01
-1.38326466e-01 3.61063212e-01 9.78727877e-01 -2.90296286e-01
7.57739693e-02 -4.65749234e-01 -7.95915127e-01 -5.90576172e-01
-6.53082654e-02 -9.59089622e-02 -1.37707040e-01 -3.39603364... | [15.21505355834961, 5.9222636222839355] |
bca46701-17dc-477d-8f21-ff3fc6edcbbc | fedftn-personalized-federated-learning-with | 2304.00570 | null | https://arxiv.org/abs/2304.00570v1 | https://arxiv.org/pdf/2304.00570v1.pdf | FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising | Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, ... | ['Chi Liu', 'James S. Duncan', 'Kuangyu Shi', 'Axel Rominger', 'Biao Li', 'S. Kevin Zhou', 'Zhicheng Feng', 'Xueqi Guo', 'Xiongchao Chen', 'Qiong Liu', 'Huidong Xie', 'Bo Zhou'] | 2023-04-02 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [ 2.88405232e-02 -2.95083642e-01 -1.76547602e-01 -5.72764695e-01
-1.40401256e+00 -5.29312372e-01 1.33320615e-01 2.41934985e-01
-7.62346923e-01 8.27542126e-01 3.17534506e-01 -1.94489226e-01
-3.40240419e-01 -8.64943445e-01 -6.95706367e-01 -1.11118686e+00
2.30529979e-01 4.30766433e-01 1.05189309e-01 1.96430087... | [6.0938262939453125, 6.441249370574951] |
41f5961e-2d92-4353-9aaf-4dfe1f1699e5 | 3d-siammask-vision-based-multi-rotor-aerial | null | null | https://www.mdpi.com/1945298 | https://www.mdpi.com/1945298 | 3D-SiamMask: Vision-Based Multi-Rotor Aerial-Vehicle Tracking for a Moving Object | This paper aims to develop a multi-rotor-based visual tracker for a specified moving object. Visual object-tracking algorithms for multi-rotors are challenging due to multiple issues such as occlusion, quick camera motion, and out-of-view scenarios. Hence, algorithmic changes are required for dealing with images or vid... | ['Alexandr Klimchik', 'Geesara Kulathunga', 'Mohamad Al Mdfaa'] | 2022-11-14 | null | null | null | remote-sensing-2022-11 | ['3d-single-object-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [-3.46312761e-01 -4.48370099e-01 2.92854309e-02 3.64193507e-02
-2.66844600e-01 -3.43893439e-01 3.60241622e-01 -8.61740485e-02
-5.92446566e-01 3.86457950e-01 -5.91485143e-01 1.10315777e-01
1.84491307e-01 -2.98345506e-01 -6.75565839e-01 -8.78674448e-01
2.76019037e-01 4.76338357e-01 1.00883424e+00 1.10016219... | [6.850841522216797, -1.9749561548233032] |
9679e364-6b1d-4323-aea4-6e60dfbf7ba2 | an-updated-duet-model-for-passage-re-ranking | 1903.07666 | null | http://arxiv.org/abs/1903.07666v1 | http://arxiv.org/pdf/1903.07666v1.pdf | An Updated Duet Model for Passage Re-ranking | We propose several small modifications to Duet---a deep neural ranking
model---and evaluate the updated model on the MS MARCO passage ranking task. We
report significant improvements from the proposed changes based on an ablation
study. | ['Bhaskar Mitra', 'Nick Craswell'] | 2019-03-18 | null | null | null | null | ['passage-ranking', 'passage-re-ranking'] | ['natural-language-processing', 'natural-language-processing'] | [-3.69197428e-02 -1.66206792e-01 -1.28663465e-01 -5.52958071e-01
-1.51409960e+00 -5.21188796e-01 5.42914510e-01 4.92115915e-01
-1.15797567e+00 1.16079247e+00 7.94559062e-01 -4.91197228e-01
-3.80820841e-01 -1.95443049e-01 -7.08514392e-01 1.72382474e-01
-7.37085640e-01 7.68815637e-01 3.14365983e-01 -9.95285332... | [11.494245529174805, 7.683866024017334] |
2aad236f-764d-4de0-89c5-f1292ffe7dea | on-boosting-single-frame-3d-human-pose | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_On_Boosting_Single-Frame_3D_Human_Pose_Estimation_via_Monocular_Videos_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_On_Boosting_Single-Frame_3D_Human_Pose_Estimation_via_Monocular_Videos_ICCV_2019_paper.pdf | On Boosting Single-Frame 3D Human Pose Estimation via Monocular Videos | The premise of training an accurate 3D human pose estimation network is the possession of huge amount of richly annotated training data. Nonetheless, manually obtaining rich and accurate annotations is, even not impossible, tedious and slow. In this paper, we propose to exploit monocular videos to complement the traini... | [' Peilin Jiang', ' Fei Wang', ' Xuan Wang', 'Zhi Li'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['weakly-supervised-3d-human-pose-estimation'] | ['computer-vision'] | [-1.83897801e-02 1.66282237e-01 -1.21894889e-01 -2.93839216e-01
-9.45673525e-01 -6.25558734e-01 4.10470575e-01 -4.24989045e-01
-7.46690571e-01 7.48318613e-01 8.88722390e-02 3.36076885e-01
4.88414049e-01 -1.34573013e-01 -9.24450099e-01 -4.22611237e-01
1.53684229e-01 7.38550425e-01 2.90577173e-01 -5.60597889... | [7.080169677734375, -0.9424672722816467] |
fa8aafb1-429e-402f-a981-d77d99c52768 | esrgan-enhanced-super-resolution-generative | 1809.00219 | null | http://arxiv.org/abs/1809.00219v2 | http://arxiv.org/pdf/1809.00219v2.pdf | ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks | The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work
that is capable of generating realistic textures during single image
super-resolution. However, the hallucinated details are often accompanied with
unpleasant artifacts. To further enhance the visual quality, we thoroughly
study three key com... | ['Xiaoou Tang', 'Yu Qiao', 'Xintao Wang', 'Shixiang Wu', 'Jinjin Gu', 'Chen Change Loy', 'Chao Dong', 'Yihao Liu', 'Ke Yu'] | 2018-09-01 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 5.42773843e-01 1.54262125e-01 1.71308815e-01 -2.76891470e-01
-7.69023299e-01 -2.92305648e-01 4.63113040e-01 -7.84654737e-01
8.28170255e-02 9.10218418e-01 3.91533822e-01 4.86056320e-03
2.26708546e-01 -9.43512857e-01 -8.54323924e-01 -1.05323911e+00
2.43937761e-01 -4.04992074e-01 -8.74841437e-02 -5.41426957... | [11.277533531188965, -1.7270007133483887] |
985addc5-1b4d-430d-8e21-86c382d196b6 | alexsis-a-dataset-for-lexical-simplification | null | null | https://aclanthology.org/2022.lrec-1.383 | https://aclanthology.org/2022.lrec-1.383.pdf | ALEXSIS: A Dataset for Lexical Simplification in Spanish | Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier to read (or understand) expressions while preserving the original information and meaning. In this paper we introduce ALEXSIS, a new dataset for this task, and we use ALEXSIS to benchmark Lexical ... | ['Horacio Saggion', 'Daniel Ferrés'] | null | null | null | null | lrec-2022-6 | ['lexical-simplification'] | ['natural-language-processing'] | [ 5.56216575e-02 4.74735141e-01 -2.76516825e-02 -3.88063967e-01
-5.37914157e-01 -3.06860983e-01 5.62473714e-01 4.60250318e-01
-7.59349823e-01 1.11745405e+00 6.06319249e-01 -1.23543210e-01
-4.92839925e-02 -8.66865396e-01 -2.19128907e-01 -1.93311512e-01
7.28297949e-01 9.21602428e-01 2.14749485e-01 -1.02812433... | [10.917851448059082, 10.431655883789062] |
1de07644-633f-4dc1-9ae3-d9046d237803 | text-to-face-generation-with-stylegan2 | 2205.12512 | null | https://arxiv.org/abs/2205.12512v1 | https://arxiv.org/pdf/2205.12512v1.pdf | Text-to-Face Generation with StyleGAN2 | Synthesizing images from text descriptions has become an active research area with the advent of Generative Adversarial Networks. The main goal here is to generate photo-realistic images that are aligned with the input descriptions. Text-to-Face generation (T2F) is a sub-domain of Text-to-Image generation (T2I) that is... | ['Sarasi Munasinghe', 'D. M. A. Ayanthi'] | 2022-05-25 | null | null | null | null | ['text-to-face-generation'] | ['computer-vision'] | [ 4.56115931e-01 4.69194174e-01 2.81505674e-01 -3.49378496e-01
-6.42299056e-01 -4.37619686e-01 9.66966748e-01 -1.02167487e+00
9.24693048e-02 8.28630805e-01 2.00329587e-01 3.32765281e-01
2.11505219e-01 -1.08696258e+00 -8.16456378e-01 -8.09929430e-01
4.31281924e-01 4.01796967e-01 -3.03031445e-01 -2.92157233... | [12.317617416381836, -0.16529656946659088] |
81bbfd0a-59c1-461e-98c7-d2fa844dad05 | explainable-multi-agent-reinforcement | 2305.10378 | null | https://arxiv.org/abs/2305.10378v1 | https://arxiv.org/pdf/2305.10378v1.pdf | Explainable Multi-Agent Reinforcement Learning for Temporal Queries | As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors of MARL agents in complex environments. This work presents an approach for generating policy-level contrastive explanations for MARL to answe... | ['Lu Feng', 'Sarit Kraus', 'Kayla Boggess'] | 2023-05-17 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-3.21650878e-02 7.50032961e-01 -6.91379458e-02 -2.56394982e-01
-7.13054478e-01 -5.99073172e-01 9.91497755e-01 5.18027127e-01
-1.32498488e-01 1.28832352e+00 -1.53691217e-01 -3.37583959e-01
-3.72985959e-01 -6.88082933e-01 -6.22873724e-01 -3.83211195e-01
-4.49912697e-01 9.70309019e-01 4.41838592e-01 -3.56355488... | [4.50866174697876, 1.871673822402954] |
dd54bfac-3aa6-497f-ac53-a42a3edbc0c5 | rola-a-real-time-online-lightweight-anomaly | 2305.16509 | null | https://arxiv.org/abs/2305.16509v1 | https://arxiv.org/pdf/2305.16509v1.pdf | RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series | A multivariate time series refers to observations of two or more variables taken from a device or a system simultaneously over time. There is an increasing need to monitor multivariate time series and detect anomalies in real time to ensure proper system operation and good service quality. It is also highly desirable t... | ['Jia-Chun Lin', 'Ming-Chang Lee'] | 2023-05-25 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [-1.37402713e-01 -9.10943329e-01 3.57390344e-01 -2.93485641e-01
-1.97605416e-01 -3.86497319e-01 3.92463475e-01 7.05321431e-01
-2.42016241e-01 1.65143475e-01 -4.07663524e-01 -3.90068203e-01
-4.01897162e-01 -7.93187499e-01 -1.68930873e-01 -6.95773721e-01
-7.32048571e-01 3.87120217e-01 4.97697949e-01 -1.14927255... | [7.265730857849121, 2.7464351654052734] |
288e7e27-f1e4-4706-92a2-07b5269713c8 | semi-supervised-community-detection-via | 2306.01089 | null | https://arxiv.org/abs/2306.01089v1 | https://arxiv.org/pdf/2306.01089v1.pdf | Semi-supervised Community Detection via Structural Similarity Metrics | Motivated by social network analysis and network-based recommendation systems, we study a semi-supervised community detection problem in which the objective is to estimate the community label of a new node using the network topology and partially observed community labels of existing nodes. The network is modeled using... | ['Tracy Ke', 'Yicong Jiang'] | 2023-06-01 | null | null | null | null | ['stochastic-block-model', 'community-detection'] | ['graphs', 'graphs'] | [ 5.81343994e-02 5.06704748e-01 -3.80919158e-01 -2.35110059e-01
-2.86001027e-01 -7.56182730e-01 3.19648325e-01 6.61917627e-01
-1.42306566e-01 5.00059843e-01 -2.60263234e-01 -7.82345608e-02
-4.89894867e-01 -9.10186231e-01 -3.05398405e-01 -7.09678888e-01
-6.24970376e-01 1.02501714e+00 2.52750427e-01 4.01018590... | [6.909959316253662, 5.176337242126465] |
abea5a3f-8744-440c-b8dd-2563345991eb | fairness-index-measures-to-evaluate-bias-in | 2306.10919 | null | https://arxiv.org/abs/2306.10919v1 | https://arxiv.org/pdf/2306.10919v1.pdf | Fairness Index Measures to Evaluate Bias in Biometric Recognition | The demographic disparity of biometric systems has led to serious concerns regarding their societal impact as well as applicability of such systems in private and public domains. A quantitative evaluation of demographic fairness is an important step towards understanding, assessment, and mitigation of demographic bias ... | ['Sebastien Marcel', 'Ketan Kotwal'] | 2023-06-19 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 2.99677312e-01 -9.84318182e-02 -9.55931395e-02 -8.75715196e-01
-1.72084495e-01 -6.06929362e-01 6.50725245e-01 6.52113080e-01
-6.34296238e-01 8.85460377e-01 2.25759000e-01 -2.78729290e-01
-3.08205515e-01 -8.59078944e-01 -7.04751238e-02 -7.53944874e-01
3.32248390e-01 2.32176855e-01 -3.74559611e-01 -7.72811696... | [13.032125473022461, 1.301426887512207] |
ed83896c-3643-4204-bcf7-c87abf43e487 | recent-developments-in-machine-learning | 2303.10257 | null | https://arxiv.org/abs/2303.10257v1 | https://arxiv.org/pdf/2303.10257v1.pdf | Recent Developments in Machine Learning Methods for Stochastic Control and Games | Stochastic optimal control and games have found a wide range of applications, from finance and economics to social sciences, robotics and energy management. Many real-world applications involve complex models which have driven the development of sophisticated numerical methods. Recently, computational methods based on ... | ['Mathieu Laurière', 'Ruimeng Hu'] | 2023-03-17 | null | null | null | null | ['energy-management'] | ['time-series'] | [-9.78554338e-02 -8.59414190e-02 -1.78098470e-01 4.98531669e-01
-4.64946568e-01 -1.98381469e-01 6.45862281e-01 -1.03043132e-01
-6.08093679e-01 1.08500946e+00 -3.01286578e-01 -4.38916445e-01
-4.74155992e-01 -8.84434938e-01 -2.64962852e-01 -1.11409867e+00
-2.46154666e-01 6.12889886e-01 1.40767574e-01 -4.29470599... | [4.1397223472595215, 2.4264612197875977] |
270419e0-25fb-41c9-9428-d1f313712cba | time-series-continuous-modeling-for | 2306.05880 | null | https://arxiv.org/abs/2306.05880v2 | https://arxiv.org/pdf/2306.05880v2.pdf | Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations | Although widely explored, time series modeling continues to encounter significant challenges when confronted with real-world data. We propose a novel modeling approach leveraging Implicit Neural Representations (INR). This approach enables us to effectively capture the continuous aspect of time series and provides a na... | ['Vincent Guigue', 'Nicolas Baskiotis', 'Ghislain Agoua', 'Patrick Gallinari', 'Yuan Yin', 'Léon Migus', 'Louis Serrano', 'Etienne Le Naour'] | 2023-06-09 | null | null | null | null | ['imputation', 'meta-learning', 'imputation', 'imputation'] | ['computer-vision', 'methodology', 'miscellaneous', 'time-series'] | [ 5.11368811e-01 -3.96766037e-01 -5.12336195e-01 -3.43744665e-01
-9.21221256e-01 -3.74726057e-01 7.53632724e-01 -3.05411890e-02
-2.04118043e-01 8.42385113e-01 2.50219345e-01 -4.29153711e-01
-4.55584526e-01 -5.45583129e-01 -8.14854741e-01 -4.60125506e-01
-4.06680435e-01 1.71268687e-01 -6.05758190e-01 -5.35519831... | [7.029759407043457, 3.142416000366211] |
396c8a7f-ae4c-4109-9837-734e9ce02f61 | abstractive-meeting-summarization | 1609.07035 | null | http://arxiv.org/abs/1609.07035v1 | http://arxiv.org/pdf/1609.07035v1.pdf | Abstractive Meeting Summarization UsingDependency Graph Fusion | Automatic summarization techniques on meeting conversations developed so far
have been primarily extractive, resulting in poor summaries. To improve this,
we propose an approach to generate abstractive summaries by fusing important
content from several utterances. Any meeting is generally comprised of several
discussio... | ['Siddhartha Banerjee', 'Kazunari Sugiyama', 'Prasenjit Mitra'] | 2016-09-22 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 5.51073194e-01 7.35880196e-01 -8.46543089e-02 -5.61401427e-01
-1.72712028e+00 -3.39870483e-01 6.93885863e-01 7.99365163e-01
4.38801870e-02 1.31438017e+00 1.36219978e+00 1.87024727e-01
2.88232118e-01 -3.62616360e-01 -4.20687497e-01 -4.84944671e-01
1.85585663e-01 3.82970333e-01 -1.43454492e-01 -2.32354015... | [12.61026382446289, 9.381625175476074] |
5fca7d4a-24fd-4593-a694-5441e13a053d | circular-antenna-array-design-for-breast | 1801.05068 | null | http://arxiv.org/abs/1801.05068v1 | http://arxiv.org/pdf/1801.05068v1.pdf | Circular Antenna Array Design for Breast Cancer Detection | Microwave imaging for breast cancer detection is based on the contrast in the
electrical properties of healthy fatty breast tissues. This paper presents an
industrial, scientific and medical (ISM) bands comparative study of five
microstrip patch antennas for microwave imaging at a frequency of 2.45 GHz. The
choice of o... | ['Kalthoum Ouerghi', 'Najib Fadlallah', 'Amor Smida', 'Jaouhar Fattahi', 'Ridha Ghayoula', 'Noureddine Boulejfen'] | 2018-01-15 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.02239037e-01 4.37403798e-01 1.00988992e-01 -1.14125282e-01
-1.72195032e-01 -1.12821236e-01 -8.19636509e-02 4.80525708e-03
-2.14764494e-02 3.06932122e-01 -1.41057326e-02 -9.24274802e-01
-2.65555441e-01 -8.57584715e-01 -8.66579264e-02 -1.01750660e+00
-3.88041079e-01 9.51477513e-02 2.39438608e-01 2.50963569... | [15.187472343444824, -2.776240348815918] |
76b38ebb-7cbb-4c3d-9c83-ed4c4ca05942 | deep-reflection-prior | 1912.03623 | null | https://arxiv.org/abs/1912.03623v2 | https://arxiv.org/pdf/1912.03623v2.pdf | Deep Reflection Prior | Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass. The problem of removing reflection artifacts is important but challenging due to its ill-posed nature. Recent learning-based approaches have demonstrated a significant improvement in removi... | ['Dong-Dong Chen', 'Carola-Bibiane Schnlieb', 'Angelica Aviles-Rivero', 'Qingnan Fan', 'Yingda Yin', 'Yujie Wang', 'Ruoteng Li', 'Dani Lischinski', 'Baoquan Chen'] | 2019-12-08 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 7.72563100e-01 -1.68314159e-01 5.36615014e-01 -3.51342231e-01
-1.09066391e+00 -1.59083799e-01 4.45360065e-01 -6.84028387e-01
-1.18806921e-01 4.63690490e-01 2.24223301e-01 -1.05776362e-01
3.51594478e-01 -7.43080020e-01 -8.41575146e-01 -1.05336618e+00
4.45333540e-01 -1.37412727e-01 1.70308128e-01 -2.37074737... | [10.544488906860352, -2.77262020111084] |
9d401349-9d65-440d-a6a9-da01284708a9 | multi-layer-personalized-federated-learning | 2212.02985 | null | https://arxiv.org/abs/2212.02985v1 | https://arxiv.org/pdf/2212.02985v1.pdf | Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics | Traditional learning-based approaches to student modeling (e.g., predicting grades based on measured activities) generalize poorly to underrepresented/minority student groups due to biases in data availability. In this paper, we propose a Multi-Layer Personalized Federated Learning (MLPFL) methodology which optimizes i... | ['Christopher Brinton', 'Andrew Lan', 'Kerrie Douglas', 'Laura Cruz', 'Elizabeth Tenorio', 'Seyyedali Hosseinalipour', 'Yun-Wei Chu'] | 2022-12-05 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-4.60925838e-03 1.48855999e-01 -7.79597342e-01 -7.52219737e-01
-8.39796782e-01 -8.36030483e-01 4.73300785e-01 7.46608078e-01
-2.82301068e-01 4.88005072e-01 6.00246549e-01 -6.09579027e-01
-5.52914381e-01 -1.03687882e+00 -8.02284062e-01 -2.92689115e-01
1.82858407e-01 2.80380189e-01 8.94173980e-02 1.25918275... | [10.127290725708008, 7.177610874176025] |
92c27560-8367-4601-9bb3-12a38720a218 | masked-conditional-neural-networks-for-1 | 1805.10004 | null | http://arxiv.org/abs/1805.10004v2 | http://arxiv.org/pdf/1805.10004v2.pdf | Masked Conditional Neural Networks for Environmental Sound Classification | The ConditionaL Neural Network (CLNN) exploits the nature of the temporal
sequencing of the sound signal represented in a spectrogram, and its variant
the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in
frequency bands by embedding a filterbank-like sparseness over the network's
links using a ... | ['John Robinson', 'Fady Medhat', 'David Chesmore'] | 2018-05-25 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 3.64064157e-01 6.69070520e-03 3.27436954e-01 -2.37908170e-01
-6.06946409e-01 -2.99656391e-01 3.14349204e-01 -4.54625815e-01
-5.61737120e-01 5.36195755e-01 1.61601797e-01 -2.29945630e-01
-2.75310367e-01 -4.80347931e-01 -6.20108366e-01 -7.46277869e-01
-7.71884084e-01 -2.21211433e-01 3.07933748e-01 9.67659131... | [15.3378324508667, 5.326955318450928] |
be32bf41-366c-44ec-bbc4-e5c7ad75ca58 | a-slot-is-not-built-in-one-utterance-spoken-1 | 2203.10759 | null | https://arxiv.org/abs/2203.10759v1 | https://arxiv.org/pdf/2203.10759v1.pdf | A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots | A slot value might be provided segment by segment over multiple-turn interactions in a dialog, especially for some important information such as phone numbers and names. It is a common phenomenon in daily life, but little attention has been paid to it in previous work. To fill the gap, this paper defines a new task nam... | ['Xiaojie Wang', 'Caixia Yuan', 'Jian Sun', 'Yongbin Li', 'Jiaman Wu', 'Yuchuan Wu', 'Yuwei Hu', 'Sai Zhang'] | 2022-03-21 | null | https://aclanthology.org/2022.findings-acl.27 | https://aclanthology.org/2022.findings-acl.27.pdf | findings-acl-2022-5 | ['sstod'] | ['natural-language-processing'] | [-5.05787849e-01 2.29647115e-01 -2.18035206e-01 -8.33180010e-01
-4.04462725e-01 -9.52910662e-01 8.20905983e-01 -3.71692181e-01
-3.03744644e-01 1.24936354e+00 6.81230724e-01 -5.39948225e-01
2.33657137e-01 -4.23984796e-01 1.72696307e-01 -2.77012527e-01
3.34329009e-01 1.12947798e+00 6.72251821e-01 -9.90665138... | [12.780228614807129, 7.928560256958008] |
ae9f64fa-e62a-494e-b9fa-11cea7c06f17 | learnable-gated-temporal-shift-module-for | 1907.01131 | null | https://arxiv.org/abs/1907.01131v2 | https://arxiv.org/pdf/1907.01131v2.pdf | Learnable Gated Temporal Shift Module for Deep Video Inpainting | How to efficiently utilize temporal information to recover videos in a consistent way is the main issue for video inpainting problems. Conventional 2D CNNs have achieved good performance on image inpainting but often lead to temporally inconsistent results where frames will flicker when applied to videos (see https://w... | ['Kuan-Ying Lee', 'Ya-Liang Chang', 'Winston Hsu', 'Zhe Yu Liu'] | 2019-07-02 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 1.04981087e-01 -7.70944357e-02 -3.95149142e-01 -2.58864313e-01
-4.08651114e-01 -2.37969398e-01 1.81744710e-01 -7.13907838e-01
-2.56449789e-01 7.88558066e-01 1.00678265e-01 -3.88367712e-01
1.65161744e-01 -6.73197329e-01 -1.16391861e+00 -5.40376008e-01
-3.16050723e-02 -1.61824033e-01 7.38320798e-02 -1.18334077... | [10.907159805297852, -1.2936288118362427] |
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