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f50bf9a7-0170-4ea9-a4f6-0ee74ae7ea5a | docformer-end-to-end-transformer-for-document | 2106.11539 | null | https://arxiv.org/abs/2106.11539v2 | https://arxiv.org/pdf/2106.11539v2.pdf | DocFormer: End-to-End Transformer for Document Understanding | We present DocFormer -- a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU). VDU is a challenging problem which aims to understand documents in their varied formats (forms, receipts etc.) and layouts. In addition, DocFormer is pre-trained in an unsupervised fashion using car... | ['R. Manmatha', 'Yusheng Xie', 'Bhargava Urala Kota', 'Bhavan Jasani', 'Srikar Appalaraju'] | 2021-06-22 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Appalaraju_DocFormer_End-to-End_Transformer_for_Document_Understanding_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Appalaraju_DocFormer_End-to-End_Transformer_for_Document_Understanding_ICCV_2021_paper.pdf | iccv-2021-1 | ['document-image-classification'] | ['computer-vision'] | [-1.26122132e-01 -9.86198038e-02 8.28227252e-02 -2.48685941e-01
-6.84568942e-01 -1.14412677e+00 1.28299510e+00 2.91809112e-01
-1.69558063e-01 8.54025260e-02 7.93523192e-01 -4.15978849e-01
1.24367788e-01 -4.51419950e-01 -1.04182053e+00 -3.92606258e-01
2.53195167e-01 7.91896999e-01 1.26994234e-02 -8.39500576... | [11.339566230773926, 2.110960006713867] |
210a14bf-bdc8-48b7-833d-d16ae19cfbb0 | incorporating-domain-knowledge-in-deep-neural | 2306.00016 | null | https://arxiv.org/abs/2306.00016v1 | https://arxiv.org/pdf/2306.00016v1.pdf | Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models | Discrete choice models (DCM) are widely employed in travel demand analysis as a powerful theoretical econometric framework for understanding and predicting choice behaviors. DCMs are formed as random utility models (RUM), with their key advantage of interpretability. However, a core requirement for the estimation of th... | ['Tomer Toledo', 'Omar Mansour', 'Shadi Haj-Yahia'] | 2023-05-30 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-3.76820974e-02 3.61268491e-01 -9.87811863e-01 -8.94350231e-01
-3.95321459e-01 -3.58359367e-01 5.25956333e-01 6.22817054e-02
-1.33576632e-01 8.82206202e-01 2.45441571e-01 -6.35513783e-01
-6.46681845e-01 -8.26176465e-01 -4.05461013e-01 -4.49767321e-01
-1.83821499e-01 6.33183241e-01 -4.79205936e-01 -1.81775376... | [9.059266090393066, 5.496286869049072] |
956b5243-88d4-4da8-a414-711b8cf465cb | a-new-search-paradigm-for-natural-language | null | null | https://openreview.net/forum?id=YeaNQgfFwlQ | https://openreview.net/pdf?id=YeaNQgfFwlQ | A New Search Paradigm for Natural Language Code Search | Code search can accelerate the efficiency of software development by finding code snippets for the given query. The dominant code search paradigm is to learn the semantic matching between code snippets and queries by neural networks. However, this search paradigm causes the gap transferring and expansion between code s... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.58955956e-01 -5.19148171e-01 -3.48369867e-01 -3.47383291e-01
-8.46486449e-01 -6.36126816e-01 1.41155720e-01 1.36918485e-01
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2.44923994e-01 2.83656448e-01 5.40892720e-01 -4.85928625... | [7.503477573394775, 8.080648422241211] |
3a3ca133-e5a3-4e5d-918d-6e381f08ebb4 | transprompt-towards-an-automatic-transferable | null | null | https://aclanthology.org/2021.emnlp-main.221 | https://aclanthology.org/2021.emnlp-main.221.pdf | TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification | Recent studies have shown that prompts improve the performance of large pre-trained language models for few-shot text classification. Yet, it is unclear how the prompting knowledge can be transferred across similar NLP tasks for the purpose of mutual reinforcement. Based on continuous prompt embeddings, we propose Tran... | ['Ming Gao', 'Jun Huang', 'Minghui Qiu', 'Jianing Wang', 'Chengyu Wang'] | null | null | null | null | emnlp-2021-11 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.70455480e-01 6.53265938e-02 -4.66138899e-01 -4.91842508e-01
-1.18026507e+00 -4.33017582e-01 9.23601329e-01 3.51186186e-01
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-6.01070793e-03 -5.60304523e-01 -6.75566316e-01 -3.73770833e-01
2.87008673e-01 5.26314199e-01 5.00972390e-01 -4.83704150... | [10.831937789916992, 7.932953834533691] |
c3bfa77d-692a-490d-bb36-eb071825ee08 | multi-view-silhouette-and-depth-decomposition | 1802.09987 | null | http://arxiv.org/abs/1802.09987v3 | http://arxiv.org/pdf/1802.09987v3.pdf | Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation | We consider the problem of scaling deep generative shape models to
high-resolution. Drawing motivation from the canonical view representation of
objects, we introduce a novel method for the fast up-sampling of 3D objects in
voxel space through networks that perform super-resolution on the six
orthographic depth project... | ['Scott Fujimoto', 'Edward Smith', 'David Meger'] | 2018-02-27 | multi-view-silhouette-and-depth-decomposition-1 | http://papers.nips.cc/paper/7883-multi-view-silhouette-and-depth-decomposition-for-high-resolution-3d-object-representation | http://papers.nips.cc/paper/7883-multi-view-silhouette-and-depth-decomposition-for-high-resolution-3d-object-representation.pdf | neurips-2018-12 | ['3d-object-reconstruction', '3d-object-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 3.59764934e-01 4.46727902e-01 5.48828244e-01 -3.99646878e-01
-1.03129792e+00 -2.40467638e-01 6.10038877e-01 -3.52296233e-01
-1.90332323e-01 6.40336275e-01 1.73598483e-01 1.78749293e-01
5.19876406e-02 -1.35271120e+00 -9.16480482e-01 -3.60836565e-01
-4.00851555e-02 1.17148256e+00 5.23255706e-01 -8.02782848... | [8.901082992553711, -3.354342460632324] |
f2a4fc1f-001a-466d-a352-3c17ff17a699 | systemic-formalisation-of-cyber-physical | 2104.05710 | null | https://arxiv.org/abs/2104.05710v1 | https://arxiv.org/pdf/2104.05710v1.pdf | Systemic formalisation of Cyber-Physical-Social System (CPSS): A systematic literature review | The notion of Cyber-Physical-Social System (CPSS) is an emerging concept developed as a result of the need to understand the impact of Cyber-Physical Systems (CPS) on humans and vice versa. This paradigm shift from CPS to CPSS was mainly attributed to the increasing use of sensor-enabled smart devices and the tight lin... | ['Yannick Naudet', 'Hervé Panetto', 'Bereket Abera Yilma'] | 2021-04-11 | null | null | null | null | ['human-dynamics'] | ['computer-vision'] | [ 1.65553689e-01 3.51171702e-01 -2.65663087e-01 3.12862396e-01
1.05770715e-01 -7.17992604e-01 1.04657519e+00 4.49812412e-01
-4.76568304e-02 4.52707052e-01 3.06702197e-01 -4.78066266e-01
-6.40922487e-01 -8.08153629e-01 -3.28176498e-01 -4.78058398e-01
8.91197324e-02 -7.91981351e-03 3.19444895e-01 -5.61121106... | [8.859177589416504, 6.649916172027588] |
478248df-35cf-4432-9531-8456a575f3b8 | controllable-lexical-simplification-for | 2302.02900 | null | https://arxiv.org/abs/2302.02900v1 | https://arxiv.org/pdf/2302.02900v1.pdf | Controllable Lexical Simplification for English | Fine-tuning Transformer-based approaches have recently shown exciting results on sentence simplification task. However, so far, no research has applied similar approaches to the Lexical Simplification (LS) task. In this paper, we present ConLS, a Controllable Lexical Simplification system fine-tuned with T5 (a Transfor... | ['Horacio Saggion', 'Daniel Ferrés', 'Kim Cheng SHEANG'] | 2023-02-06 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [-1.94141790e-01 3.39309484e-01 1.32675260e-01 -4.25522476e-01
-9.39858317e-01 -4.03478354e-01 6.77674890e-01 9.83977690e-02
-4.87425685e-01 9.33753312e-01 5.59450209e-01 -3.44510198e-01
1.19208321e-01 -6.01636887e-01 -5.54230869e-01 -1.74114078e-01
5.40879846e-01 8.36525202e-01 1.80777147e-01 -1.04238284... | [11.057673454284668, 10.353551864624023] |
5d82ca71-86bd-402f-af79-0209ea15d592 | scaling-up-visual-and-vision-language | 2102.05918 | null | https://arxiv.org/abs/2102.05918v2 | https://arxiv.org/pdf/2102.05918v2.pdf | Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision | Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human annotations, visual and vision-language representations still rely heavily on curated training datasets that are expensive or require expert kno... | ['Tom Duerig', 'Zhen Li', 'YunHsuan Sung', 'Quoc V. Le', 'Hieu Pham', 'Zarana Parekh', 'Yi-Ting Chen', 'Ye Xia', 'Yinfei Yang', 'Chao Jia'] | 2021-02-11 | null | null | null | null | ['zero-shot-transfer-image-classification', 'zero-shot-cross-modal-retrieval'] | ['computer-vision', 'miscellaneous'] | [ 5.27208924e-01 -4.55171913e-02 -3.43103617e-01 -2.94243008e-01
-1.15843904e+00 -7.14820206e-01 1.01298141e+00 1.89600959e-01
-6.81941628e-01 4.95444745e-01 1.99105188e-01 -2.97574878e-01
1.77546784e-01 -5.34213960e-01 -9.69450116e-01 -5.12933671e-01
5.04640222e-01 5.65639079e-01 -1.03984125e-01 -2.01782718... | [10.554529190063477, 1.6556657552719116] |
76730c98-1ea1-4847-819a-2eca5557ab2b | jointist-simultaneous-improvement-of-multi | 2302.00286 | null | https://arxiv.org/abs/2302.00286v2 | https://arxiv.org/pdf/2302.00286v2.pdf | Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training | In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of an instrument recognition module that conditions the other two modules: a transcription module that outp... | ['Dorien Herremans', 'Yun-Ning Hung', 'Ju-Chiang Wang', 'Minz Won', 'Bochen Li', 'Qiuqiang Kong', 'Keunwoo Choi', 'Kin Wai Cheuk'] | 2023-02-01 | null | null | null | null | ['chord-recognition', 'instrument-recognition', 'music-source-separation'] | ['audio', 'audio', 'music'] | [ 1.42263412e-01 -3.82722408e-01 -6.56878874e-02 2.63243765e-01
-1.47195327e+00 -1.08900654e+00 2.25117475e-01 -5.47677279e-02
-1.53145120e-01 3.91904384e-01 1.61136791e-01 4.24866118e-02
-3.03688228e-01 -2.33814478e-01 -5.06374717e-01 -6.81499839e-01
1.35960905e-02 2.60995835e-01 2.71531083e-02 -2.92136222... | [15.787206649780273, 5.426917552947998] |
a9ae7feb-0863-4097-9022-02a89c126ea2 | up-to-down-network-fusing-multi-scale-context | null | null | https://ieeexplore.ieee.org/abstract/document/9635888 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9635888 | Up-to-Down Network: Fusing Multi-Scale Context for 3D Semantic Scene Completion | An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems. In this paper, we focus on semantic scene completion, which is a task of jointly estimating the volumetric occupancy and semantic labels of objects. Since the real-world data is sparse and occluded, this is an e... | ['Hongbo Zhang', 'Feng Wen', 'Wanlong Li', 'Yong liu', 'Chujuan Zhang', 'Tianxin Huang', 'Xuemeng Yang', 'Hao Zou'] | 2021-09-27 | null | null | null | ieee-international-workshop-on-intelligent | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 1.08190998e-01 -1.53713584e-01 8.83359686e-02 -4.58992004e-01
-6.63059592e-01 -9.92977917e-02 5.72825372e-01 1.41032666e-01
-4.18264896e-01 4.40314293e-01 1.68908983e-01 -3.40919830e-02
5.15814163e-02 -1.02944136e+00 -8.71223867e-01 -6.45111978e-01
2.49216929e-01 2.78669745e-01 7.48473465e-01 -2.04422325... | [8.397635459899902, -2.732337474822998] |
8c297c22-7bad-4f1b-97d6-020865a2a12a | stochastic-conservative-contextual-linear | 2203.15629 | null | https://arxiv.org/abs/2203.15629v1 | https://arxiv.org/pdf/2203.15629v1.pdf | Stochastic Conservative Contextual Linear Bandits | Many physical systems have underlying safety considerations that require that the strategy deployed ensures the satisfaction of a set of constraints. Further, often we have only partial information on the state of the system. We study the problem of safe real-time decision making under uncertainty. In this paper, we fo... | ['Baskar Ganapathysubramanian', 'Soumik Sarkar', 'Shana Moothedath', 'Talukder Jubery', 'Xian Yeow Lee', 'Jiabin Lin'] | 2022-03-29 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 4.82471466e-01 3.48125488e-01 -3.12505037e-01 -1.55290633e-01
-8.22775841e-01 -9.30130661e-01 1.85630828e-01 4.05072927e-01
-2.67072886e-01 1.01166999e+00 -4.16606158e-01 -8.34871650e-01
-7.07990348e-01 -9.14395452e-01 -1.10578561e+00 -1.00646806e+00
-1.86274499e-01 4.30877626e-01 2.81990580e-02 3.49910744... | [4.568202972412109, 3.1694788932800293] |
50db2290-bf38-43fb-932e-e7bfea3ca650 | infant-brain-mri-segmentation-with-dilated | 1912.12570 | null | https://arxiv.org/abs/1912.12570v2 | https://arxiv.org/pdf/1912.12570v2.pdf | Infant brain MRI segmentation with dilated convolution pyramid downsampling and self-attention | In this paper, we propose a dual aggregation network to adaptively aggregate different information in infant brain MRI segmentation. More precisely, we added two modules based on 3D-UNet to better model information at different levels and locations. The dilated convolution pyramid downsampling module is mainly to solve... | ['Ying WEI', 'Zhihao Lei', 'Yunlong Zhou', 'Lin Qi'] | 2019-12-29 | null | null | null | null | ['infant-brain-mri-segmentation'] | ['medical'] | [-1.30568653e-01 6.19490333e-02 2.22793102e-01 -4.29413348e-01
-2.83673644e-01 -1.20640494e-01 2.07115576e-01 8.15023109e-02
-7.46074319e-01 5.01758337e-01 4.78686750e-01 1.44533724e-01
-7.27624223e-02 -4.90179658e-01 -4.93515581e-01 -7.36487806e-01
-2.93023497e-01 9.68632475e-02 5.22408307e-01 9.69425440... | [14.305098533630371, -2.3886730670928955] |
24b70ddf-53b8-47dd-84f8-0f3bb245415d | hierarchical-explanations-for-video-action | 2301.00436 | null | https://arxiv.org/abs/2301.00436v3 | https://arxiv.org/pdf/2301.00436v3.pdf | Hierarchical Explanations for Video Action Recognition | To interpret deep neural networks, one main approach is to dissect the visual input and find the prototypical parts responsible for the classification. However, existing methods often ignore the hierarchical relationship between these prototypes, and thus can not explain semantic concepts at both higher level (e.g., wa... | ['Nanne van Noord', 'Teng Long', 'Sadaf Gulshad'] | 2023-01-01 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 9.56393927e-02 4.18979973e-01 -1.96532056e-01 -4.36834127e-01
1.96233138e-01 -6.44669354e-01 5.60882092e-01 2.72039890e-01
-9.58168283e-02 3.68018121e-01 3.68496835e-01 -2.54115671e-01
-2.44855955e-01 -8.83747458e-01 -7.55361676e-01 -4.52832580e-01
1.78207159e-01 2.70257473e-01 4.87413079e-01 5.83848581... | [8.945298194885254, 0.9106769561767578] |
6d8f5f2f-1b0f-427f-9082-67b022b2229b | target-specified-sequence-labeling-with-multi | null | null | https://aclanthology.org/2021.naacl-main.145 | https://aclanthology.org/2021.naacl-main.145.pdf | Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction | Opinion target extraction and opinion term extraction are two fundamental tasks in Aspect Based Sentiment Analysis (ABSA). Many recent works on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the corresponding opinion words for a given opinion target. TOWE can be furth... | ['He Liu', 'Ninghua Wang', 'Yuyao Tang', 'Yanghui Rao', 'Yuhao Feng'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['target-oriented-opinion-words-extraction'] | ['natural-language-processing'] | [ 3.88592571e-01 -3.68604176e-02 -5.39411865e-02 -5.08175194e-01
-1.27846229e+00 -6.08930767e-01 6.79575086e-01 8.11053962e-02
-2.61234850e-01 2.83332437e-01 3.90613616e-01 -5.23977935e-01
4.25985157e-01 -6.38278067e-01 -3.70710641e-01 -7.81500936e-01
3.37554216e-01 3.66997898e-01 -5.90764694e-02 -5.42663455... | [11.47574520111084, 6.632970809936523] |
4b0b917c-b97f-4526-8d1a-079b337f8f4f | localize-to-binauralize-audio-spatialization | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Rachavarapu_Localize_to_Binauralize_Audio_Spatialization_From_Visual_Sound_Source_Localization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Rachavarapu_Localize_to_Binauralize_Audio_Spatialization_From_Visual_Sound_Source_Localization_ICCV_2021_paper.pdf | Localize to Binauralize: Audio Spatialization From Visual Sound Source Localization | Videos with binaural audios provide an immersive viewing experience by enabling 3D sound sensation. Recent works attempt to generate binaural audio in a multimodal learning framework using large quantities of videos with accompanying binaural audio. In contrast, we attempt a more challenging problem -- synthesizing... | ['A. N. Rajagopalan', 'Vignesh Sundaresha', 'Aakanksha', 'Kranthi Kumar Rachavarapu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['audio-generation'] | ['audio'] | [ 3.61857951e-01 6.35288060e-02 2.48784155e-01 -8.96402225e-02
-1.30220568e+00 -6.11708999e-01 3.20595920e-01 -2.84888476e-01
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3.73921126e-01 -5.36715567e-01 -1.23954439e+00 -6.39304698e-01
2.12766640e-02 -1.79835707e-01 2.67224580e-01 -2.08847091... | [14.980798721313477, 5.0881667137146] |
f9be64ff-be04-41b3-98c2-1d38113c34c6 | quatde-dynamic-quaternion-embedding-for | 2105.09002 | null | https://arxiv.org/abs/2105.09002v2 | https://arxiv.org/pdf/2105.09002v2.pdf | QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion | Knowledge graph embedding has been an active research topic for knowledge base completion (KGC), with progressive improvement from the initial TransE, TransH, RotatE et al to the current state-of-the-art QuatE. However, QuatE ignores the multi-faceted nature of the entity and the complexity of the relation, only using ... | ['Ke Qin', 'Jim Wilson Owusu', 'Rufai Yusuf Zakari', 'Yuxue Yang', 'Kun Yang', 'Haipeng Gao'] | 2021-05-19 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-3.42417806e-01 1.32745862e-01 -3.62106115e-01 1.28158748e-01
-7.76252747e-02 -5.26372135e-01 4.44611400e-01 1.77068144e-01
-4.31905836e-01 5.55146277e-01 3.38814139e-01 6.12661541e-02
-5.27472079e-01 -1.03845310e+00 -6.01379156e-01 -5.09841740e-01
-2.84732580e-01 5.88445008e-01 1.61427513e-01 -7.36368835... | [8.659262657165527, 7.791315078735352] |
e6e736a4-d1b1-4160-a35d-1e5f98833fab | detection-recovery-in-online-multi-object | 2205.00968 | null | https://arxiv.org/abs/2205.00968v2 | https://arxiv.org/pdf/2205.00968v2.pdf | Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker | In existing joint detection and tracking methods, pairwise relational features are used to match previous tracklets to current detections. However, the features may not be discriminative enough for a tracker to identify a target from a large number of detections. Selecting only high-scored detections for tracking may l... | ['Dit-yan Yeung', 'Dongyoon Wee', 'Myunggu Kang', 'Jeongseok Hyun'] | 2022-05-02 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-4.41241354e-01 -2.71698684e-01 -3.55876267e-01 1.58245864e-04
-5.43654799e-01 -7.38148510e-01 4.48919505e-01 2.11157724e-01
-1.51853904e-01 6.61855400e-01 -3.96451615e-02 2.98633844e-01
-2.90820956e-01 -7.34741509e-01 -7.67103553e-01 -7.26520360e-01
-7.48453736e-01 3.32409918e-01 9.65818644e-01 1.42352402... | [6.323513507843018, -2.122504949569702] |
89325676-761c-4755-a9ee-12b5e0c49bae | a-marketplace-for-web-scale-analytics-and | null | null | https://aclanthology.org/C14-2022 | https://aclanthology.org/C14-2022.pdf | A Marketplace for Web Scale Analytics and Text Annotation Services | null | ['Holger D{\\"u}wiger', 'er', 'Alex L{\\"o}ser', 'Peter Adolphs', 'Torsten Kilias', 'Holmer Hemsen', 'Johannes Kirschnick', 'Heiko Ehrig'] | 2014-08-01 | a-marketplace-for-web-scale-analytics-and-1 | https://aclanthology.org/C14-2022 | https://aclanthology.org/C14-2022.pdf | coling-2014-8 | ['text-annotation'] | ['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... | [-7.292387962341309, 3.742838144302368] |
e07a0751-3415-4198-b74d-9d0af204a54f | pre-training-strategies-and-datasets-for | 2103.16554 | null | https://arxiv.org/abs/2103.16554v2 | https://arxiv.org/pdf/2103.16554v2.pdf | Pre-training strategies and datasets for facial representation learning | What is the best way to learn a universal face representation? Recent work on Deep Learning in the area of face analysis has focused on supervised learning for specific tasks of interest (e.g. face recognition, facial landmark localization etc.) but has overlooked the overarching question of how to find a facial repres... | ['Georgios Tzimiropoulos', 'Enrique Sanchez', 'Andrew Garbett', 'Jing Yang', 'Shiyang Cheng', 'Adrian Bulat'] | 2021-03-30 | null | null | null | null | ['face-alignment', '3d-facial-landmark-localization', 'facial-action-unit-detection', 'unsupervised-pre-training'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 2.12049231e-01 -5.48326038e-02 -1.66437671e-01 -6.98247492e-01
-7.20457911e-01 -2.96065062e-01 7.57570803e-01 -1.66212097e-01
-1.27369776e-01 4.61167604e-01 1.92192852e-01 1.22310624e-01
-2.50710219e-01 -4.18809801e-01 -4.77870822e-01 -6.24617338e-01
-4.33987349e-01 3.59607309e-01 -2.16349155e-01 -2.17034414... | [13.39468765258789, 1.1921461820602417] |
37fa7fe2-d7ab-4355-a4c5-b2f5945a440f | multi-task-learning-for-radar-signal | 2306.13105 | null | https://arxiv.org/abs/2306.13105v1 | https://arxiv.org/pdf/2306.13105v1.pdf | Multi-task Learning for Radar Signal Characterisation | Radio signal recognition is a crucial task in both civilian and military applications, as accurate and timely identification of unknown signals is an essential part of spectrum management and electronic warfare. The majority of research in this field has focused on applying deep learning for modulation classification, ... | ['Terrence Martin', 'Clinton Fookes', 'Simon Denman', 'Akila Pemasiri', 'Zi Huang'] | 2023-06-19 | null | null | null | null | ['classification-1', 'multi-task-learning', 'management'] | ['methodology', 'methodology', 'miscellaneous'] | [ 1.03943336e+00 -5.36746025e-01 -1.05891176e-01 -3.14029098e-01
-1.20083594e+00 -4.63778138e-01 8.38240027e-01 -1.51138306e-01
-4.11081761e-01 7.01132655e-01 -1.29095107e-01 -7.16893494e-01
-6.67549014e-01 -3.84705901e-01 -1.47719279e-01 -8.84521127e-01
-3.63270432e-01 4.74176943e-01 -2.66508553e-02 -3.97947371... | [6.524192810058594, 1.5140364170074463] |
6522ef93-b0a7-4230-abb1-63708a9e9a6d | model-driven-deep-learning-for-physical-layer | 1809.06059 | null | http://arxiv.org/abs/1809.06059v2 | http://arxiv.org/pdf/1809.06059v2.pdf | Model-Driven Deep Learning for Physical Layer Communications | Intelligent communication is gradually considered as the mainstream direction
in future wireless communications. As a major branch of machine learning, deep
learning (DL) has been applied in physical layer communications and has
demonstrated an impressive performance improvement in recent years. However,
most of the ex... | ['Chao-Kai Wen', 'Hengtao He', 'Zongben Xu', 'Feifei Gao', 'Geoffrey Ye Li', 'Shi Jin'] | 2018-09-17 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [ 3.37625831e-01 1.64536253e-01 -8.15417767e-01 -3.73738587e-01
-5.61731398e-01 8.29213783e-02 2.15444431e-01 -9.49509367e-02
-2.59223819e-01 1.10506761e+00 -1.74650893e-01 -8.47739398e-01
-2.17001796e-01 -8.33243132e-01 -3.29043627e-01 -9.17382240e-01
-3.94713581e-01 -1.39845116e-02 -2.63353944e-01 -5.30879721... | [6.30243444442749, 1.4449907541275024] |
b0f9252b-e4df-46c2-9b72-bd4dcc66ce1a | time-reversed-diffusion-tensor-transformer-a | 2210.16897 | null | https://arxiv.org/abs/2210.16897v1 | https://arxiv.org/pdf/2210.16897v1.pdf | Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection | In this paper, we tackle the challenging problem of Few-shot Object Detection. Existing FSOD pipelines (i) use average-pooled representations that result in information loss; and/or (ii) discard position information that can help detect object instances. Consequently, such pipelines are sensitive to large intra-class a... | ['Piotr Koniusz', 'Lei Wang', 'Naila Murray', 'Shan Zhang'] | 2022-10-30 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 6.44014999e-02 -4.79491532e-01 7.79702421e-03 -2.95901984e-01
-6.23595417e-01 -6.33144975e-01 6.22905135e-01 1.62429839e-01
-3.20262790e-01 -9.53058824e-02 -9.52639803e-02 4.66593713e-01
-2.49447629e-01 -6.92650080e-01 -5.51353514e-01 -6.72413170e-01
-2.85432577e-01 4.07186240e-01 1.08200157e+00 -9.67239738... | [9.40718936920166, 0.9685880541801453] |
5afbcb0a-0658-4eb7-9222-bab25063a867 | is-a-pet-all-you-need-a-multi-modal-study-for | 2207.02094 | null | https://arxiv.org/abs/2207.02094v1 | https://arxiv.org/pdf/2207.02094v1.pdf | Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs | Alzheimer's Disease (AD) is the most common form of dementia and often difficult to diagnose due to the multifactorial etiology of dementia. Recent works on neuroimaging-based computer-aided diagnosis with deep neural networks (DNNs) showed that fusing structural magnetic resonance images (sMRI) and fluorodeoxyglucose ... | ['Christian Wachinger', 'Igor Yakushev', 'Aldana Lizarraga', 'Sebastian Pölsterl', 'Ignacio Sarasua', 'Marla Narazani'] | 2022-07-05 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 8.5127898e-02 -2.4631521e-01 -2.9800743e-01 -3.9773268e-01
-6.2191308e-01 -3.4697267e-01 5.1654935e-01 -1.1261822e-01
-8.7291658e-01 1.1246879e+00 4.6353388e-01 -3.4983775e-01
-4.0188140e-01 -8.7090886e-01 -7.0212975e-02 -5.7902050e-01
-3.3386078e-01 1.0248049e+00 3.1341752e-01 2.7523133e-01
-4.7762915e-01... | [14.139301300048828, -1.769507646560669] |
66d9c66d-40ac-46b8-a395-118a7a05f701 | improving-conversational-passage-re-ranking | 2304.13290 | null | https://arxiv.org/abs/2304.13290v1 | https://arxiv.org/pdf/2304.13290v1.pdf | Improving Conversational Passage Re-ranking with View Ensemble | This paper presents ConvRerank, a conversational passage re-ranker that employs a newly developed pseudo-labeling approach. Our proposed view-ensemble method enhances the quality of pseudo-labeled data, thus improving the fine-tuning of ConvRerank. Our experimental evaluation on benchmark datasets shows that combining ... | ['Chuan-Ju Wang', 'Ming-Feng Tsai', 'Sheng-Chieh Lin', 'Jia-Huei Ju'] | 2023-04-26 | null | null | null | null | ['passage-re-ranking', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing'] | [-2.95767546e-01 -1.24243021e-01 -1.79085344e-01 -5.69528759e-01
-1.37327325e+00 -9.24147010e-01 9.76293504e-01 5.36230654e-02
-6.04589403e-01 7.69712627e-01 1.04724944e+00 -2.69551307e-01
3.97357997e-03 -5.52827001e-01 -3.59998137e-01 -2.30219677e-01
2.43719459e-01 9.04803753e-01 2.89155394e-01 -5.39926946... | [12.039189338684082, 7.813551902770996] |
74fe67bf-b9ed-4d9c-8932-a2daeca41f9e | viewpoint-invariant-change-captioning | 1901.02527 | null | http://arxiv.org/abs/1901.02527v2 | http://arxiv.org/pdf/1901.02527v2.pdf | Robust Change Captioning | Describing what has changed in a scene can be useful to a user, but only if
generated text focuses on what is semantically relevant. It is thus important
to distinguish distractors (e.g. a viewpoint change) from relevant changes
(e.g. an object has moved). We present a novel Dual Dynamic Attention Model
(DUDA) to perfo... | ['Anna Rohrbach', 'Trevor Darrell', 'Dong Huk Park'] | 2019-01-08 | robust-change-captioning | http://openaccess.thecvf.com/content_ICCV_2019/html/Park_Robust_Change_Captioning_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Park_Robust_Change_Captioning_ICCV_2019_paper.pdf | iccv-2019-10 | ['natural-language-visual-grounding'] | ['reasoning'] | [ 1.97978705e-01 -3.82296085e-01 1.94445401e-01 -3.34391117e-01
-8.34347546e-01 -9.78817105e-01 9.12657201e-01 1.10033348e-01
-4.20912951e-01 4.85291213e-01 4.50300574e-01 -1.18079416e-01
4.11280960e-01 -4.85528052e-01 -1.14066815e+00 -6.63391471e-01
4.71056737e-02 4.04405862e-01 6.11862838e-01 -4.41354007... | [10.351526260375977, 1.2268550395965576] |
c3aee6b1-abf6-474e-aeb2-cb390a7b3c2a | feature-engineering-based-detection-of-buffer | 2306.07981 | null | https://arxiv.org/abs/2306.07981v1 | https://arxiv.org/pdf/2306.07981v1.pdf | Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks | One of the most significant challenges in the field of software code auditing is the presence of vulnerabilities in software source code. Every year, more and more software flaws are discovered, either internally in proprietary code or publicly disclosed. These flaws are highly likely to be exploited and can lead to sy... | ['Alfredo Cuzzocrea', 'Sheikh Iqbal Ahamed', 'Juan Rodriguez Cardenas', 'Hossain Shahriar', 'Mst Shapna Akter'] | 2023-06-01 | null | null | null | null | ['feature-engineering', 'vulnerability-detection'] | ['methodology', 'miscellaneous'] | [-2.00672299e-01 1.14890918e-01 6.53113611e-03 -8.13457370e-02
-3.75472039e-01 -6.79455459e-01 9.68748182e-02 5.59811115e-01
-2.64226168e-01 1.98757827e-01 1.27536625e-01 -1.01529408e+00
1.45403251e-01 -1.00943744e+00 -6.44421756e-01 -4.14910577e-02
-3.83465618e-01 -4.55171078e-01 3.07778716e-01 -3.40284526... | [7.057379245758057, 7.776902675628662] |
83e709f5-9328-436f-a445-24839f027d2b | dcase-2022-challenge-task-6b-language-based | 2206.06108 | null | https://arxiv.org/abs/2206.06108v3 | https://arxiv.org/pdf/2206.06108v3.pdf | Language-based Audio Retrieval Task in DCASE 2022 Challenge | Language-based audio retrieval is a task, where natural language textual captions are used as queries to retrieve audio signals from a dataset. It has been first introduced into DCASE 2022 Challenge as Subtask 6B of task 6, which aims at developing computational systems to model relationships between audio signals and ... | ['Tuomas Virtanen', 'Samuel Lipping', 'Huang Xie'] | 2022-06-13 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 5.26318312e-01 3.50779714e-03 3.26706320e-01 -1.52614221e-01
-2.22903609e+00 -7.69491971e-01 6.94716454e-01 3.31521034e-01
-1.24502957e-01 6.25048339e-01 7.93061554e-01 1.52375758e-01
-1.24251775e-01 -8.87225196e-02 -8.34304810e-01 5.88714750e-03
-5.26182234e-01 4.86790329e-01 2.84027904e-01 -3.71710271... | [15.262482643127441, 4.8621649742126465] |
2a1f3469-e17e-45d9-8157-815139ec9f95 | gensf-simultaneous-adaptation-of-generative | 2106.07055 | null | https://arxiv.org/abs/2106.07055v1 | https://arxiv.org/pdf/2106.07055v1.pdf | GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling | In transfer learning, it is imperative to achieve strong alignment between a pre-trained model and a downstream task. Prior work has done this by proposing task-specific pre-training objectives, which sacrifices the inherent scalability of the transfer learning paradigm. We instead achieve strong alignment by simultane... | ['Maxine Eskenazi', 'Shikib Mehri'] | 2021-06-13 | null | https://aclanthology.org/2021.sigdial-1.51 | https://aclanthology.org/2021.sigdial-1.51.pdf | sigdial-acl-2021-7 | ['zero-shot-slot-filling', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.79038692e-01 9.06871498e-01 -3.02087575e-01 -5.74045300e-01
-1.04674494e+00 -3.75566244e-01 7.64531076e-01 -3.53983082e-02
-5.52271962e-01 6.91386461e-01 6.43929482e-01 -5.81236959e-01
2.49154299e-01 -7.91597545e-01 -5.93588352e-01 -3.00755471e-01
2.56158829e-01 8.90337408e-01 3.68400544e-01 -3.79918694... | [12.582409858703613, 7.4868011474609375] |
0ab1ab3d-098f-4aa6-b78d-d4ed4012caa8 | molecule-morphology-contrastive-pretraining | 2305.09790 | null | https://arxiv.org/abs/2305.09790v2 | https://arxiv.org/pdf/2305.09790v2.pdf | Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation | Image-based profiling techniques have become increasingly popular over the past decade for their applications in target identification, mechanism-of-action inference, and assay development. These techniques have generated large datasets of cellular morphologies, which are typically used to investigate the effects of sm... | ['Kim M. Branson', 'Dante Pertusi', 'Cuong Q. Nguyen'] | 2023-04-27 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 3.37702900e-01 -4.76257563e-01 -5.26704729e-01 3.36818509e-02
-7.52972543e-01 -1.10150707e+00 6.58937454e-01 8.73987317e-01
-2.97675967e-01 8.59468043e-01 -5.74044436e-02 -5.51983595e-01
-3.58281843e-02 -6.84200108e-01 -1.06502068e+00 -8.05280685e-01
-1.42685771e-01 3.88246000e-01 7.25021735e-02 -4.24117409... | [5.147570610046387, 5.860888481140137] |
44fd0ca1-9435-4f6f-85ac-11296a886c07 | identity-expression-ambiguity-in-3d-morphable | 2109.14203 | null | https://arxiv.org/abs/2109.14203v1 | https://arxiv.org/pdf/2109.14203v1.pdf | Identity-Expression Ambiguity in 3D Morphable Face Models | 3D Morphable Models are a class of generative models commonly used to model faces. They are typically applied to ill-posed problems such as 3D reconstruction from 2D data. Several ambiguities in this problem's image formation process have been studied explicitly. We demonstrate that non-orthogonality of the variation i... | ['Joshua Tenenbaum', 'Safa C. Medin', 'Skylar Sutherland', 'Bernhard Egger'] | 2021-09-29 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 6.37706697e-01 5.71110487e-01 4.48916823e-01 -3.93665969e-01
-6.29373193e-01 -7.71818697e-01 9.41180527e-01 -5.03789186e-01
-2.23123953e-02 4.67713624e-01 2.46168688e-01 -6.25519361e-03
-7.35941753e-02 -6.80515528e-01 -5.83898962e-01 -6.71583176e-01
4.69277576e-02 8.13638866e-01 -1.29457623e-01 -4.52988327... | [12.70760726928711, -0.31205445528030396] |
8c918f1b-b56a-48d6-b0f6-b4d459471980 | convolutional-sparse-coding-fast | 2106.15296 | null | https://arxiv.org/abs/2106.15296v1 | https://arxiv.org/pdf/2106.15296v1.pdf | Convolutional Sparse Coding Fast Approximation with Application to Seismic Reflectivity Estimation | In sparse coding, we attempt to extract features of input vectors, assuming that the data is inherently structured as a sparse superposition of basic building blocks. Similarly, neural networks perform a given task by learning features of the training data set. Recently both data-driven and model-driven feature extract... | ['Anthony A. Vassiliou', 'Israel Cohen', 'Deborah Pereg'] | 2021-06-29 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 3.72021765e-01 1.65306285e-01 -1.51382508e-02 -2.59476930e-01
-3.43782902e-01 -1.95813909e-01 4.77118611e-01 3.57491642e-01
-5.27903080e-01 7.31057525e-01 -1.19941369e-01 1.82906359e-01
-3.43081415e-01 -9.52494442e-01 -7.05806196e-01 -9.46043432e-01
-2.01646730e-01 4.89608169e-01 -8.93700197e-02 -3.41743082... | [11.599015235900879, -2.207329750061035] |
02955497-946b-486a-a526-353ecdc9b0f8 | hilmeme-a-human-in-the-loop-machine | 2211.05201 | null | https://arxiv.org/abs/2211.05201v1 | https://arxiv.org/pdf/2211.05201v1.pdf | HilMeMe: A Human-in-the-Loop Machine Translation Evaluation Metric Looking into Multi-Word Expressions | With the fast development of Machine Translation (MT) systems, especially the new boost from Neural MT (NMT) models, the MT output quality has reached a new level of accuracy. However, many researchers criticised that the current popular evaluation metrics such as BLEU can not correctly distinguish the state-of-the-art... | ['Lifeng Han'] | 2022-11-09 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.85662264e-01 -7.85509299e-04 -4.60312963e-01 -5.08800387e-01
-9.81117725e-01 -7.56894290e-01 9.71733928e-01 2.87632793e-01
-5.38622081e-01 8.11992943e-01 3.02948356e-01 -7.32024670e-01
5.22972569e-02 -4.87997085e-01 -4.56554353e-01 -1.63093746e-01
5.47424674e-01 8.64683867e-01 -2.84863740e-01 -5.89646578... | [11.471527099609375, 10.215839385986328] |
2fb35184-6e4e-4817-b500-435ba203d9af | learning-to-answer-by-learning-to-ask-getting | 1911.02365 | null | https://arxiv.org/abs/1911.02365v1 | https://arxiv.org/pdf/1911.02365v1.pdf | Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds | Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we prop... | ['Moin Nabi', 'Tassilo Klein'] | 2019-11-06 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 3.22931051e-01 8.09565306e-01 6.27971411e-01 -3.68820578e-01
-1.39434493e+00 -6.43997312e-01 1.04976547e+00 1.09370574e-01
-2.53749251e-01 8.20501149e-01 6.21763170e-01 -3.93222898e-01
-1.10140547e-01 -1.06351960e+00 -7.14833379e-01 -1.72367185e-01
6.48320735e-01 7.93399751e-01 1.79828733e-01 -7.25809038... | [11.481911659240723, 8.238142013549805] |
d07527ba-19fa-40fc-9c9a-80e999195477 | gcnnmatch-graph-convolutional-neural-networks | 2010.00067 | null | https://arxiv.org/abs/2010.00067v4 | https://arxiv.org/pdf/2010.00067v4.pdf | GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization | This paper proposes a novel method for online Multi-Object Tracking (MOT) using Graph Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature matching for object association. The Graph based approach incorporates both appearance and geometry of objects at past frames as well as the current f... | ['Abhijit Sarkar', 'Ioannis Papakis', 'Anuj Karpatne'] | 2020-09-30 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-1.47348568e-01 -2.42214039e-01 -2.29898214e-01 -3.15696180e-01
-2.80565113e-01 -3.07545692e-01 3.60481024e-01 2.27072313e-01
-3.45352948e-01 2.96578497e-01 -2.32568964e-01 1.41364589e-01
-1.21380962e-01 -7.89223850e-01 -9.37508881e-01 -3.62996429e-01
-2.98438996e-01 4.78771001e-01 6.43428743e-01 -1.22886583... | [6.3703179359436035, -2.1100995540618896] |
9abbf8a0-5ed2-4beb-988a-a319d2d92872 | a-classical-approach-to-handcrafted-feature | 2201.10102 | null | https://arxiv.org/abs/2201.10102v1 | https://arxiv.org/pdf/2201.10102v1.pdf | A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition | Bangla Handwritten Digit recognition is a significant step forward in the development of Bangla OCR. However, intricate shape, structural likeness and distinctive composition style of Bangla digits makes it relatively challenging to distinguish. Thus, in this paper, we benchmarked four rigorous classifiers to recognize... | ['Md. Shohanur Islam Sobuj', 'Md. Fahim Shahriar', 'Md. Ferdous Wahid'] | 2022-01-25 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-2.65577585e-01 -8.55539918e-01 -2.26020768e-01 -4.48058575e-01
-2.19448999e-01 -7.24909425e-01 7.52661109e-01 -1.43045202e-01
-3.03099006e-01 8.28759789e-01 -6.90979809e-02 -4.18643087e-01
-3.18753481e-01 -8.29602361e-01 -8.35026577e-02 -1.12467611e+00
1.30309388e-01 1.54275253e-01 2.12396264e-01 -8.59259069... | [11.850691795349121, 2.6709163188934326] |
042dd3ab-a98e-4956-b6ab-fd6f12fa6e67 | re-2-region-aware-relation-extraction-from | 2305.14590 | null | https://arxiv.org/abs/2305.14590v1 | https://arxiv.org/pdf/2305.14590v1.pdf | RE$^2$: Region-Aware Relation Extraction from Visually Rich Documents | Current research in form understanding predominantly relies on large pre-trained language models, necessitating extensive data for pre-training. However, the importance of layout structure (i.e., the spatial relationship between the entity blocks in the visually rich document) to relation extraction has been overlooked... | ['Lifu Huang', 'Joy Rimchala', 'Lalla Mouatadid', 'Sijia Wang', 'Pritika Ramu'] | 2023-05-24 | null | null | null | null | ['graph-attention', 'relation-extraction'] | ['graphs', 'natural-language-processing'] | [ 1.33538306e-01 2.73843646e-01 -3.56266618e-01 -4.11629975e-01
-2.45836768e-02 -6.47663593e-01 6.22764468e-01 4.20821637e-01
-1.21422730e-01 3.06463927e-01 3.88053179e-01 -6.18514895e-01
-2.31524974e-01 -1.26365745e+00 -8.63209426e-01 -1.17085571e-03
-3.09902459e-01 3.37320119e-02 -9.90340561e-02 -7.86972940... | [9.216463088989258, 8.207855224609375] |
9107a8da-1896-4f77-8b7f-891c25ef50c7 | ynu-hpcc-at-semeval-2021-task-10-using-a | null | null | https://aclanthology.org/2021.semeval-1.184 | https://aclanthology.org/2021.semeval-1.184.pdf | YNU-HPCC at SemEval-2021 Task 10: Using a Transformer-based Source-Free Domain Adaptation Model for Semantic Processing | Data sharing restrictions are common in NLP datasets. The purpose of this task is to develop a model trained in a source domain to make predictions for a target domain with related domain data. To address the issue, the organizers provided the models that fine-tuned a large number of source domain data on pre-trained m... | ['Xuejie Zhang', 'Jin Wang', 'Zhewen Yu'] | 2021-08-01 | null | null | null | semeval-2021 | ['source-free-domain-adaptation'] | ['computer-vision'] | [-4.28934395e-01 5.41486979e-01 -9.28753689e-02 -9.26612914e-01
-6.58421516e-01 -9.26108122e-01 5.80352664e-01 1.22317925e-01
-8.48822057e-01 1.36040962e+00 4.33387756e-01 9.10116509e-02
-2.33368110e-03 -8.16676378e-01 -5.97198904e-01 9.69771743e-02
4.21452016e-01 1.12169015e+00 5.20857215e-01 -5.93661845... | [9.81234073638916, 9.488790512084961] |
aee28961-ea2b-45bf-9f2c-3bb4c6470818 | experimental-evidence-supporting-a-new | 1201.0912 | null | https://arxiv.org/abs/1201.0912v2 | https://arxiv.org/pdf/1201.0912v2.pdf | Experimental Evidence Supporting a New "Osmosis Law & Theory" Derived New Formula that Improves van't Hoff Osmotic Pressure Equation | Experimental data were used to support a new concept of osmotic force and a new osmotic law that can explain the osmotic process without the difficulties encountered with van't Hoff osmotic pressure theory. Derived new osmotic formula with curvilinear equation (via new osmotic law) overcomes the limitations and incompl... | ['Hung-Chung Huang', 'Gaochao Lin', 'Rongqing Xie'] | 2012-01-02 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-6.52363300e-02 -1.15379527e-01 -1.22199476e-01 -1.07796378e-01
6.14338398e-01 -4.85015422e-01 4.15731594e-03 7.41526842e-01
-4.51462626e-01 1.25992703e+00 -2.95552909e-01 -6.73143327e-01
-2.14829624e-01 -6.30724072e-01 -4.92333204e-01 -6.36685312e-01
-2.49478921e-01 2.49326870e-01 9.85392649e-03 -4.11231846... | [4.816150665283203, 5.012392997741699] |
d182fe48-32b5-4995-a661-3afaba73b3e0 | optimizing-credit-limit-adjustments-under | 2306.15585 | null | https://arxiv.org/abs/2306.15585v1 | https://arxiv.org/pdf/2306.15585v1.pdf | Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning | Reinforcement learning has been explored for many problems, from video games with deterministic environments to portfolio and operations management in which scenarios are stochastic; however, there have been few attempts to test these methods in banking problems. In this study, we sought to find and automatize an optim... | ['Cristián Bravo', 'Kristina P. Sendova', 'Alejandro Correa-Bahnsen', 'Jesús Solano', 'Sherly Alfonso-Sánchez'] | 2023-06-27 | null | null | null | null | ['q-learning', 'decision-making'] | ['methodology', 'reasoning'] | [-5.24800308e-02 6.17381670e-02 -3.89124215e-01 1.01201572e-02
-3.70169014e-01 -6.54594302e-01 2.36592516e-01 3.85333300e-02
-7.30572104e-01 8.82479370e-01 -1.35982290e-01 -9.19810176e-01
-4.07680243e-01 -1.09849584e+00 -6.09180450e-01 -5.22940576e-01
-1.37053683e-01 5.52830279e-01 5.30958168e-05 -4.89080101... | [4.322490692138672, 2.6020255088806152] |
4a38594b-be6e-4924-98c7-28a2cdf52008 | are-pre-trained-transformers-robust-in-intent | null | null | https://aclanthology.org/2022.nlp4convai-1.2 | https://aclanthology.org/2022.nlp4convai-1.2.pdf | Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection | Pre-trained Transformer-based models were reported to be robust in intent classification. In this work, we first point out the importance of in-domain out-of-scope detection in few-shot intent recognition tasks and then illustrate the vulnerability of pre-trained Transformer-based models against samples that are in-dom... | ['Philip Yu', 'Caiming Xiong', 'Ye Liu', 'Zhiwei Liu', 'Yao Wan', 'Kazuma Hashimoto', 'JianGuo Zhang'] | null | null | null | null | nlp4convai-acl-2022-5 | ['intent-recognition'] | ['natural-language-processing'] | [ 2.38854110e-01 -1.75901219e-01 -3.20670038e-01 -4.20977086e-01
-9.37339365e-01 -2.70674020e-01 6.96161985e-01 1.08372048e-01
-3.64422500e-01 3.95694703e-01 3.84446591e-01 -1.24337360e-01
-5.46282642e-02 -6.05483174e-01 -9.22553465e-02 -1.85679540e-01
-1.17631257e-01 4.50305432e-01 5.48503697e-01 -6.42846704... | [12.01559066772461, 7.519038200378418] |
4fcc68db-d44a-48dd-bb0c-b8267c1e4a6d | semantic-parsing-of-colonoscopy-videos-with | 2306.06960 | null | https://arxiv.org/abs/2306.06960v1 | https://arxiv.org/pdf/2306.06960v1.pdf | Semantic Parsing of Colonoscopy Videos with Multi-Label Temporal Networks | Following the successful debut of polyp detection and characterization, more advanced automation tools are being developed for colonoscopy. The new automation tasks, such as quality metrics or report generation, require understanding of the procedure flow that includes activities, events, anatomical landmarks, etc. In ... | ['Roman Goldenberg', 'Ehud Rivlin', 'Or Weinstein', 'Ori Kelner'] | 2023-06-12 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 5.12891293e-01 1.10301889e-01 -2.69213527e-01 -5.13329148e-01
-7.18693912e-01 -9.72683251e-01 4.94439423e-01 9.20030355e-01
-3.80314857e-01 4.94766176e-01 4.26920056e-01 -4.29286391e-01
-2.42121950e-01 -5.69908798e-01 -5.67376137e-01 -3.37174088e-01
-3.45649302e-01 2.63194710e-01 4.46957558e-01 3.17388147... | [14.100101470947266, -3.2736968994140625] |
ecab1b7b-7546-4d39-b16a-ab508c94efe0 | mlc-at-hecktor-2022-the-effect-and-importance | 2211.16834 | null | https://arxiv.org/abs/2211.16834v1 | https://arxiv.org/pdf/2211.16834v1.pdf | MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning | Head and neck cancers are the fifth most common cancer worldwide, and recently, analysis of Positron Emission Tomography (PET) and Computed Tomography (CT) images has been proposed to identify patients with a prognosis. Even though the results look promising, more research is needed to further validate and improve the ... | ['Michael A. Riegler', 'Pål Halvorsen', 'Steven A. Hicks', 'Andrea M. Storås', 'Vajira Thambawita'] | 2022-11-30 | null | null | null | null | ['kidney-function'] | ['medical'] | [-4.62403409e-02 2.86223888e-01 -4.24689651e-01 -5.89859068e-01
-9.52506185e-01 -3.95141274e-01 5.40124714e-01 4.15554255e-01
-7.52637386e-01 7.86917150e-01 3.15511018e-01 -6.47863507e-01
-1.96103722e-01 -6.98699653e-01 -4.71763253e-01 -8.32775712e-01
1.19579703e-01 9.05152023e-01 5.00163078e-01 2.25725234... | [14.79146957397461, -2.565415143966675] |
d8a78f33-d94d-4f22-8e58-93f4bc951df1 | property-inference-attack-graph-neural | 2209.01100 | null | https://arxiv.org/abs/2209.01100v1 | https://arxiv.org/pdf/2209.01100v1.pdf | Property inference attack; Graph neural networks; Privacy attacks and defense; Trustworthy machine learning | With the fast adoption of machine learning (ML) techniques, sharing of ML models is becoming popular. However, ML models are vulnerable to privacy attacks that leak information about the training data. In this work, we focus on a particular type of privacy attacks named property inference attack (PIA) which infers the ... | ['Wendy Hui Wang', 'Xiuling Wang'] | 2022-09-02 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 3.39171320e-01 2.80346781e-01 -3.53103340e-01 -8.36660415e-02
-2.74887592e-01 -1.10260475e+00 5.37928760e-01 2.10743576e-01
6.05701879e-02 5.68769753e-01 -4.63367820e-01 -8.82499993e-01
-3.14653099e-01 -1.21786797e+00 -1.05648994e+00 -6.26689970e-01
-5.24277627e-01 -2.41074041e-02 3.85124922e-01 1.02913432... | [5.98232889175415, 7.2423624992370605] |
495e2190-11de-448f-85f2-7623794b91ae | lingglewrite-a-coaching-system-for-essay | null | null | https://aclanthology.org/2020.acl-demos.17 | https://aclanthology.org/2020.acl-demos.17.pdf | LinggleWrite: a Coaching System for Essay Writing | This paper presents LinggleWrite, a writing coach that provides writing suggestions, assesses writing proficiency levels, detects grammatical errors, and offers corrective feedback in response to user{'}s essay. The method involves extracting grammar patterns, training models for automated essay scoring (AES) and gramm... | ['Ching-Yu Yang', 'Jhih-Jie Chen', 'Chung-Ting Tsai', 'Jason S. Chang'] | 2020-07-01 | null | null | null | acl-2020-6 | ['automated-essay-scoring', 'grammatical-error-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-9.78869870e-02 -4.43506353e-02 -1.76353812e-01 -3.99952143e-01
-8.92696559e-01 -7.33829558e-01 2.12168574e-01 7.69775152e-01
-4.28972781e-01 8.50132465e-01 2.93561459e-01 -7.86971629e-01
-1.85551509e-01 -5.29073000e-01 -5.26661053e-02 3.89547944e-01
9.53832090e-01 4.49957758e-01 2.26621434e-01 -6.10338509... | [11.326905250549316, 9.303570747375488] |
e3860700-af9b-476c-a5d0-b125b515dd8a | fast-improving-controllability-for-text | 2210.03167 | null | https://arxiv.org/abs/2210.03167v1 | https://arxiv.org/pdf/2210.03167v1.pdf | FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training | Controllable text generation systems often leverage control codes to direct various properties of the output like style and length. Inspired by recent work on causal inference for NLP, this paper reveals a previously overlooked flaw in these control code-based conditional text generation algorithms. Spurious correlatio... | ['Yi Liu', 'Chenguang Zhu', 'Konstantin Golobokov', 'Victor Ye Dong', 'Reid Pryzant', 'Junyi Chai'] | 2022-10-06 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 6.68147266e-01 7.82255292e-01 -4.39292997e-01 -3.63722175e-01
-1.02579212e+00 -8.92391026e-01 1.17654598e+00 2.32496083e-01
-1.08481839e-01 1.25024009e+00 8.26012015e-01 -5.64296842e-01
-1.56386092e-01 -8.99725556e-01 -1.01882768e+00 -4.23250616e-01
2.99794286e-01 6.00551426e-01 -3.25214535e-01 -3.11268121... | [11.586310386657715, 8.893105506896973] |
95e95cc9-4fa6-4137-8382-095ff68dee49 | towards-efficient-modularity-in-industrial | 2210.01971 | null | https://arxiv.org/abs/2210.01971v3 | https://arxiv.org/pdf/2210.01971v3.pdf | Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint | The industrial drying process consumes approximately 12% of the total energy used in manufacturing, with the potential for a 40% reduction in energy usage through improved process controls and the development of new drying technologies. To achieve cost-efficient and high-performing drying, multiple drying technologies ... | ['Srinivasa Salapaka', 'Hao Feng', 'Amir Malvandi', 'Amber Srivastava', 'Alisina Bayati'] | 2022-10-05 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 3.75156790e-01 8.81975666e-02 -1.85726330e-01 -2.46554658e-01
-2.77177900e-01 -5.73869944e-01 1.69701148e-02 9.14838374e-01
-1.21002614e-01 7.27129757e-01 -3.65245074e-01 -2.51479328e-01
-6.12033248e-01 -9.73304272e-01 -2.95547307e-01 -7.84413218e-01
-7.81169608e-02 4.65159088e-01 -5.00523865e-01 -1.58728063... | [5.689939498901367, 3.031780242919922] |
cccb0759-51dc-462d-b1a6-b800cd18c5fd | binaural-lcmv-beamforming-with-partial-noise | 1905.04050 | null | https://arxiv.org/abs/1905.04050v2 | https://arxiv.org/pdf/1905.04050v2.pdf | Binaural LCMV Beamforming with Partial Noise Estimation | Besides reducing undesired sources (interfering sources and background noise), another important objective of a binaural beamforming algorithm is to preserve the spatial impression of the acoustic scene, which can be achieved by preserving the binaural cues of all sound sources. While the binaural minimum variance dist... | ['Simon Doclo', 'Sharon Gannot', 'Elior Hadad', 'Nico Gößling'] | 2019-05-10 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 1.50924191e-01 -2.49055594e-01 6.81711614e-01 3.07411700e-01
-6.71395957e-01 -5.13353109e-01 4.18249965e-01 7.58265406e-02
-2.66750395e-01 4.90545630e-01 5.55428147e-01 -2.14213267e-01
-4.87531453e-01 -6.64845407e-01 -5.15073299e-01 -1.09520471e+00
5.83672039e-02 -3.96862507e-01 5.74452579e-01 -2.08735526... | [15.126298904418945, 5.7947211265563965] |
5ed70a80-e2c6-45b9-b1d4-703c2b9105b1 | trust-but-verify-cross-modality-fusion-for-hd | 2212.07312 | null | https://arxiv.org/abs/2212.07312v1 | https://arxiv.org/pdf/2212.07312v1.pdf | Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection | High-definition (HD) map change detection is the task of determining when sensor data and map data are no longer in agreement with one another due to real-world changes. We collect the first dataset for the task, which we entitle the Trust, but Verify (TbV) dataset, by mining thousands of hours of data from over 9 mont... | ['James Hays', 'John Lambert'] | 2022-12-14 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [-3.40699971e-01 -2.47360760e-04 -1.06768340e-01 -8.64910126e-01
-6.30955279e-01 -8.53584588e-01 8.58605683e-01 1.47816360e-01
-2.89212853e-01 8.00815344e-01 3.09353825e-02 -4.39769208e-01
-1.08678630e-02 -9.98575211e-01 -1.19595623e+00 -3.63494158e-01
-5.55957437e-01 7.34858692e-01 4.72796410e-01 -3.55020583... | [7.7961626052856445, -1.9003843069076538] |
ee9793f1-b33c-48a1-bf69-7b518c7547b6 | when-is-nontrivial-estimation-possible-for | 1604.01871 | null | http://arxiv.org/abs/1604.01871v1 | http://arxiv.org/pdf/1604.01871v1.pdf | When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models? | Block graphons (also called stochastic block models) are an important and
widely-studied class of models for random networks. We provide a lower bound on
the accuracy of estimators for block graphons with a large number of blocks. We
show that, given only the number $k$ of blocks and an upper bound $\rho$ on the
values... | ['Adam Smith', 'Audra McMillan'] | 2016-04-07 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 6.20236956e-02 5.91741085e-01 -3.67323875e-01 8.64756182e-02
-5.57296813e-01 -7.35233366e-01 2.37901732e-02 3.17003101e-01
-2.84861982e-01 8.57778609e-01 -5.33268094e-01 -6.64056659e-01
-6.67113900e-01 -1.19757736e+00 -9.18238461e-01 -7.92851985e-01
-7.28945136e-01 4.16424066e-01 3.96140218e-01 -1.21243507... | [6.736847400665283, 5.022206783294678] |
aee928db-b4fb-4bda-9fcc-b6ce10af6850 | single-image-object-counting-and-localizing | 2111.08383 | null | https://arxiv.org/abs/2111.08383v1 | https://arxiv.org/pdf/2111.08383v1.pdf | Single Image Object Counting and Localizing using Active-Learning | The need to count and localize repeating objects in an image arises in different scenarios, such as biological microscopy studies, production lines inspection, and surveillance recordings analysis. The use of supervised Convoutional Neural Networks (CNNs) achieves accurate object detection when trained over large class... | ['Raanan Fattal', 'Inbar Huberman-Spiegelglas'] | 2021-11-16 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 4.75919873e-01 -2.25468472e-01 9.74861719e-03 -3.72791678e-01
-7.13147759e-01 -7.72206068e-01 3.91039819e-01 4.87998694e-01
-1.23184013e+00 5.01811266e-01 -6.27582371e-01 -7.19982460e-02
2.23054990e-01 -5.93077064e-01 -7.28758395e-01 -7.40550280e-01
6.57953024e-02 5.93196630e-01 4.22672719e-01 6.03072584... | [9.052270889282227, 0.528303325176239] |
370856c9-80ff-4f2e-99c6-83f33c5cd669 | sense-disambiguation-of-compound-constituents | 2204.00429 | null | https://arxiv.org/abs/2204.00429v1 | https://arxiv.org/pdf/2204.00429v1.pdf | Sense disambiguation of compound constituents | In distributional semantic accounts of the meaning of noun-noun compounds (e.g. starfish, bank account, houseboat) the important role of constituent polysemy remains largely unaddressed(cf. the meaning of star in starfish vs. star cluster vs. star athlete). Instead of semantic vectors that average over the different me... | ['Ingo Plag', 'Stefan Conrad', 'Carlo Schackow'] | 2022-04-01 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 3.12969126e-02 -1.06453128e-01 -4.38371114e-02 -1.44447029e-01
-1.95744082e-01 -1.19718480e+00 8.49724174e-01 6.17066622e-01
-7.56378651e-01 5.55207849e-01 6.24489486e-01 -3.78342837e-01
-2.90702730e-01 -8.85223091e-01 -3.00768554e-01 -7.12919176e-01
2.75424808e-01 5.73064804e-01 4.95517582e-01 -7.39114642... | [10.323955535888672, 9.081868171691895] |
001f33bf-c4cc-4921-91d1-7b37d1add753 | a-step-by-step-gradient-penalty-with | null | null | https://link.springer.com/article/10.1007/s11063-022-11031-0 | https://link.springer.com/article/10.1007/s11063-022-11031-0 | A Step-by-Step Gradient Penalty with Similarity Calculation for Text Summary Generation | The summary generation model equipped with gradient penalty avoids overfitting and makes the model more stable. However, the traditional gradient penalty faces two issues: (i) calculating the gradient twice increases training time, and (ii) the disturbance factor requires repeated trials to find the best value. To this... | ['Shuai Zhao'] | 2022-09-20 | null | null | null | neural-processing-letters-2022-9 | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 5.02372161e-02 -3.57419074e-01 -3.51134896e-01 -2.93477297e-01
-9.40949380e-01 -4.68717963e-01 3.91263783e-01 3.46958935e-01
-6.03673220e-01 7.81318128e-01 1.59189746e-01 -1.97693914e-01
-1.29906207e-01 -6.89753413e-01 -4.48451370e-01 -4.82937038e-01
2.01707199e-01 1.02914058e-01 5.92232943e-01 -8.69375616... | [12.071192741394043, 8.96774673461914] |
d20f79b5-d969-4426-995b-521fbca2f8d8 | learning-spatiotemporal-features-via-video | 2001.05691 | null | https://arxiv.org/abs/2001.05691v3 | https://arxiv.org/pdf/2001.05691v3.pdf | Learning Spatiotemporal Features via Video and Text Pair Discrimination | Current video representations heavily rely on learning from manually annotated video datasets which are time-consuming and expensive to acquire. We observe videos are naturally accompanied by abundant text information such as YouTube titles and Instagram captions. In this paper, we leverage this visual-textual connecti... | ['Li-Min Wang', 'Tianhao Li'] | 2020-01-16 | null | https://openreview.net/forum?id=Bw7VC-DJUM | https://openreview.net/pdf?id=Bw7VC-DJUM | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 9.57936123e-02 -5.24860144e-01 -6.51740909e-01 -2.18821675e-01
-1.08597875e+00 -6.03739858e-01 7.09732711e-01 -1.52425051e-01
-6.25978529e-01 6.02625847e-01 2.96686411e-01 -4.38679866e-02
9.41675529e-02 -3.88807178e-01 -1.00901520e+00 -7.04566300e-01
-1.25849172e-01 2.16830254e-01 1.82308644e-01 -7.34205963... | [8.817800521850586, 0.852346658706665] |
74cec07c-0c73-4c0c-9b94-08ceac9da82e | personalized-face-modeling-for-improved-face | 2007.06759 | null | https://arxiv.org/abs/2007.06759v2 | https://arxiv.org/pdf/2007.06759v2.pdf | Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting | Traditional methods for image-based 3D face reconstruction and facial motion retargeting fit a 3D morphable model (3DMM) to the face, which has limited modeling capacity and fail to generalize well to in-the-wild data. Use of deformation transfer or multilinear tensor as a personalized 3DMM for blendshape interpolation... | ['Linda Shapiro', 'Bindita Chaudhuri', 'Noranart Vesdapunt', 'Baoyuan Wang'] | 2020-07-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2986_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500137.pdf | eccv-2020-8 | ['motion-retargeting'] | ['computer-vision'] | [-2.10066125e-01 -5.71529940e-02 -7.98386112e-02 -7.44501233e-01
-3.68183017e-01 -5.14583826e-01 3.81626397e-01 -9.49834824e-01
1.50018156e-01 1.70409635e-01 3.36421251e-01 5.06951809e-01
2.02726811e-01 -3.97102982e-01 -6.26360953e-01 -7.39565551e-01
8.84135142e-02 4.29122806e-01 -3.69397908e-01 -3.51102769... | [12.809592247009277, -0.31808707118034363] |
363e0b47-6ad1-46d7-98ec-8f3e08cdc385 | covariate-balancing-using-the-integral | 2305.13715 | null | https://arxiv.org/abs/2305.13715v1 | https://arxiv.org/pdf/2305.13715v1.pdf | Covariate balancing using the integral probability metric for causal inference | Weighting methods in causal inference have been widely used to achieve a desirable level of covariate balancing. However, the existing weighting methods have desirable theoretical properties only when a certain model, either the propensity score or outcome regression model, is correctly specified. In addition, the corr... | ['Yongdai Kim', 'Kwonsang Lee', 'Joonhyuk Jung', 'Yuha Park', 'Insung Kong'] | 2023-05-23 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 3.52253675e-01 1.93686616e-02 -9.82319117e-01 -5.51721096e-01
-7.74796605e-01 -2.74506032e-01 4.78267938e-01 3.85025650e-01
-3.59466732e-01 9.66395378e-01 4.37412381e-01 -3.96512687e-01
-6.25335097e-01 -1.01266098e+00 -5.33109426e-01 -7.38341630e-01
-7.91722015e-02 4.10456836e-01 6.58703670e-02 3.52005512... | [7.930051803588867, 5.207375526428223] |
94c08e85-7a49-4095-95ee-064ad6407e5a | gta-global-temporal-attention-for-video | 2012.08510 | null | https://arxiv.org/abs/2012.08510v3 | https://arxiv.org/pdf/2012.08510v3.pdf | GTA: Global Temporal Attention for Video Action Understanding | Self-attention learns pairwise interactions to model long-range dependencies, yielding great improvements for video action recognition. In this paper, we seek a deeper understanding of self-attention for temporal modeling in videos. We first demonstrate that the entangled modeling of spatio-temporal information by flat... | ['Abhinav Shrivastava', 'Ser-Nam Lim', 'Hao Chen', 'Zuxuan Wu', 'Xitong Yang', 'Bo He'] | 2020-12-15 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 9.62276757e-03 -2.27512777e-01 -5.82629561e-01 -3.27360719e-01
-6.20187521e-01 -4.31418359e-01 8.08104634e-01 -1.90489128e-01
-1.16452619e-01 2.89955676e-01 7.78819025e-01 -1.68610781e-01
-4.93428111e-02 -4.59535182e-01 -9.67640340e-01 -6.44590795e-01
-5.60244858e-01 -2.07469743e-02 2.89424807e-01 -2.34177746... | [8.796674728393555, 0.5911589860916138] |
7009f50b-d8b0-40e6-847a-7e797b9bf77c | focus-or-not-a-baseline-for-anomaly-event | 2303.11668 | null | https://arxiv.org/abs/2303.11668v2 | https://arxiv.org/pdf/2303.11668v2.pdf | Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite Images | In recent years, monitoring the world wide area with satellite images has been emerged as an important issue. Site monitoring task can be divided into two independent tasks; 1) Change Detection and 2) Anomaly Event Detection. Unlike to change detection research is actively conducted based on the numerous datasets(\eg L... | ['Junsik Kim', 'Hyunguk Choi', 'Doyoung Jeong', 'Youngtack Oh', 'Yongjin Jeon'] | 2023-03-21 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [-1.35509238e-01 -3.08103025e-01 4.51813191e-02 -2.57296950e-01
-4.29022312e-01 -3.50117952e-01 9.05677617e-01 4.16231573e-01
-3.24252516e-01 5.30478597e-01 8.73747319e-02 -4.52857137e-01
2.12712660e-02 -1.06847143e+00 -3.03060502e-01 -7.19660103e-01
-5.56845367e-01 2.42070794e-01 4.22824860e-01 -3.75863105... | [7.61887788772583, 2.2360100746154785] |
1f6c5d9e-004b-4d3b-b3e3-267577c83a11 | closed-book-question-generation-via | 2210.06781 | null | https://arxiv.org/abs/2210.06781v2 | https://arxiv.org/pdf/2210.06781v2.pdf | Closed-book Question Generation via Contrastive Learning | Question Generation (QG) is a fundamental NLP task for many downstream applications. Recent studies on open-book QG, where supportive answer-context pairs are provided to models, have achieved promising progress. However, generating natural questions under a more practical closed-book setting that lacks these supportin... | ['James Caverlee', 'Jianling Wang', 'Jiaying Lu', 'Xiangjue Dong'] | 2022-10-13 | null | null | null | null | ['natural-questions', 'question-generation', 'open-domain-question-answering'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 3.40139261e-03 3.77995044e-01 -8.97921342e-03 -4.25367415e-01
-1.58703804e+00 -7.60229409e-01 6.58410251e-01 1.37898028e-01
-2.60773599e-01 8.25529516e-01 6.29233181e-01 -4.90720123e-01
-1.03059664e-01 -9.26196992e-01 -7.83573806e-01 7.55977854e-02
4.78435487e-01 7.06209898e-01 6.49576843e-01 -8.61435235... | [11.418457984924316, 8.063614845275879] |
f4e93679-3e47-43d6-a33c-5dc8e0b6572d | where-in-the-world-is-this-image-transformer | 2204.13861 | null | https://arxiv.org/abs/2204.13861v2 | https://arxiv.org/pdf/2204.13861v2.pdf | Where in the World is this Image? Transformer-based Geo-localization in the Wild | Predicting the geographic location (geo-localization) from a single ground-level RGB image taken anywhere in the world is a very challenging problem. The challenges include huge diversity of images due to different environmental scenarios, drastic variation in the appearance of the same location depending on the time o... | ['Rama Chellappa', 'Carlos D. Castillo', 'Joshua Gleason', 'Ewa M. Nowara', 'Shraman Pramanick'] | 2022-04-29 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 1.38882408e-02 -4.13747221e-01 1.39644518e-01 -2.68978834e-01
-7.85302937e-01 -5.72913527e-01 4.78425980e-01 -3.46174613e-02
-3.62016857e-01 5.60405910e-01 -1.12258650e-01 -1.28287613e-01
-1.96299981e-02 -9.06585813e-01 -8.54004323e-01 -7.60558188e-01
3.39662582e-02 1.95022509e-01 4.09220934e-01 -1.58993497... | [7.682118892669678, -1.8271548748016357] |
836c6faf-3a59-4747-bd66-8816301c9d18 | text-is-all-you-need-personalizing-asr-models | 2303.14885 | null | https://arxiv.org/abs/2303.14885v1 | https://arxiv.org/pdf/2303.14885v1.pdf | Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis | Adapting generic speech recognition models to specific individuals is a challenging problem due to the scarcity of personalized data. Recent works have proposed boosting the amount of training data using personalized text-to-speech synthesis. Here, we ask two fundamental questions about this strategy: when is synthetic... | ['Oncel Tuzel', 'Hema Swetha Koppula', 'Jen-Hao Rick Chang', 'Ting-yao Hu', 'Karren Yang'] | 2023-03-27 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 4.51177329e-01 6.30042180e-02 -1.15851812e-01 -3.66092980e-01
-7.51506805e-01 -5.73190928e-01 4.66566086e-01 -2.83944964e-01
-2.24921823e-01 6.60475791e-01 6.68596864e-01 -1.39424384e-01
8.56469572e-02 -3.37383866e-01 -5.34512460e-01 -6.64664626e-01
4.20493007e-01 6.20960176e-01 1.94844350e-01 -6.55486584... | [14.588102340698242, 6.6042561531066895] |
827436e5-0e33-4a68-992b-3f0739df8660 | segmentation-and-tracking-of-vegetable-plants | 2306.13518 | null | https://arxiv.org/abs/2306.13518v2 | https://arxiv.org/pdf/2306.13518v2.pdf | Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural Robots | With the increasing deployment of agricultural robots, the traditional manual spray of liquid fertilizer and pesticide is gradually being replaced by agricultural robots. For robotic precision spray application in vegetable farms, accurate plant phenotyping through instance segmentation and robust plant tracking are of... | ['Yu Tan', 'Yongliang Qiao', 'Zimeng Wang', 'Huiyu Zhong', 'Xuechang Wang', 'Shuo Wang', 'Daobilige Su', 'Nan Hu'] | 2023-06-23 | null | null | null | null | ['object-tracking', 'multiple-object-tracking', 'plant-phenotyping', 'instance-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.72016889e-01 -1.21283740e-01 -2.62866825e-01 8.21604878e-02
3.29945922e-01 -1.09887302e+00 -1.23214699e-01 8.92123699e-01
-1.58521667e-01 3.63027096e-01 -1.09878302e+00 -5.24316847e-01
-3.34361762e-01 -9.71420109e-01 -6.96712554e-01 -9.31831360e-01
-1.11644179e-01 4.54201102e-01 6.14607394e-01 -1.56653240... | [9.035737991333008, -1.5670366287231445] |
679a766a-75cd-4822-bf1f-8728a15d2baa | graph-differentiable-architecture-search-with | null | null | http://proceedings.neurips.cc/paper/2021/hash/8c9f32e03aeb2e3000825c8c875c4edd-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8c9f32e03aeb2e3000825c8c875c4edd-Paper.pdf | Graph Differentiable Architecture Search with Structure Learning | Discovering ideal Graph Neural Networks (GNNs) architectures for different tasks is labor intensive and time consuming. To save human efforts, Neural Architecture Search (NAS) recently has been used to automatically discover adequate GNN architectures for certain tasks in order to achieve competitive or even better per... | ['Wenwu Zhu', 'Zeyang Zhang', 'Xin Wang', 'Yijian Qin'] | 2021-12-01 | null | https://openreview.net/forum?id=kSv_AMdehh3 | https://openreview.net/pdf?id=kSv_AMdehh3 | neurips-2021-12 | ['graph-structure-learning'] | ['graphs'] | [ 1.25454426e-01 1.87993065e-01 1.14108855e-02 -1.75072432e-01
-7.61511996e-02 -4.01031315e-01 2.53391862e-01 -7.51517564e-02
-1.66680232e-01 1.49471194e-01 -1.72261283e-01 -4.91228461e-01
-4.58785534e-01 -7.95009732e-01 -6.41014516e-01 -5.50137997e-01
-2.67200116e-02 5.00563860e-01 2.41619721e-02 -4.51323718... | [8.623514175415039, 3.439345359802246] |
9a3a43de-a5ee-40f5-ad04-077d7adf9b6b | counterfactual-inference-for-text | null | null | https://aclanthology.org/2021.acl-long.422 | https://aclanthology.org/2021.acl-long.422.pdf | Counterfactual Inference for Text Classification Debiasing | Today{'}s text classifiers inevitably suffer from unintended dataset biases, especially the document-level label bias and word-level keyword bias, which may hurt models{'} generalization. Many previous studies employed data-level manipulations or model-level balancing mechanisms to recover unbiased distributions and th... | ['Pengjun Xie', 'Chunping Ma', 'Lijie Wen', 'Fuli Feng', 'Chen Qian'] | 2021-08-01 | null | null | null | acl-2021-5 | ['counterfactual-inference'] | ['miscellaneous'] | [ 2.58760363e-01 2.25017026e-01 -8.73860836e-01 -5.82291126e-01
-6.08906746e-01 -5.30637562e-01 8.06692302e-01 2.08494291e-02
-6.62809193e-01 1.09683394e+00 3.45510006e-01 -7.10035622e-01
-1.32524073e-01 -8.08801889e-01 -8.34268332e-01 -7.60702252e-01
4.58752394e-01 1.54726833e-01 -5.40779054e-01 1.06656313... | [10.271683692932129, 7.7074666023254395] |
b8ed3ee1-fe3d-423e-bd01-7015c6988625 | an-attentive-listening-system-with-android | null | null | https://aclanthology.org/2020.sigdial-1.15 | https://aclanthology.org/2020.sigdial-1.15.pdf | An Attentive Listening System with Android ERICA: Comparison of Autonomous and WOZ Interactions | We describe an attentive listening system for the autonomous android robot ERICA. The proposed system generates several types of listener responses: backchannels, repeats, elaborating questions, assessments, generic sentimental responses, and generic responses. In this paper, we report a subjective experiment with 20 e... | ['Tatsuya Kawahara', 'Katsuya Takanashi', 'Shizuka Nakamura', 'Kenta Yamamoto', 'Divesh Lala', 'Koji Inoue'] | null | null | null | null | sigdial-acl-2020-7 | ['dialogue-understanding'] | ['natural-language-processing'] | [-3.40823978e-01 9.56440687e-01 2.06163406e-01 -7.52309918e-01
-4.31822628e-01 -2.90478021e-01 1.16685137e-01 2.26797596e-01
-3.91126722e-01 9.07459021e-01 5.65809548e-01 1.88650578e-01
3.45294267e-01 -3.28645468e-01 -9.75622144e-03 -4.16772276e-01
2.21386209e-01 1.80266336e-01 -7.16003627e-02 -9.01798487... | [13.129096031188965, 7.665170192718506] |
19306d76-2728-4927-aaf6-ffa7f248198c | a-dual-source-approach-for-3d-human-pose | 1705.02883 | null | http://arxiv.org/abs/1705.02883v2 | http://arxiv.org/pdf/1705.02883v2.pdf | A Dual-Source Approach for 3D Human Pose Estimation from a Single Image | In this work we address the challenging problem of 3D human pose estimation
from single images. Recent approaches learn deep neural networks to regress 3D
pose directly from images. One major challenge for such methods, however, is
the collection of training data. Specifically, collecting large amounts of
training data... | ['Björn Krüger', 'Hashim Yasin', 'Andreas Weber', 'Andreas Doering', 'Umar Iqbal', 'Juergen Gall'] | 2017-05-08 | null | null | null | null | ['monocular-3d-human-pose-estimation', 'pose-retrieval'] | ['computer-vision', 'computer-vision'] | [ 1.34571642e-01 -4.66895513e-02 -4.17738140e-01 -3.09120148e-01
-1.16081917e+00 -6.27566576e-01 2.74898052e-01 -3.59482080e-01
-6.52447224e-01 4.94664103e-01 2.16389984e-01 1.15094252e-01
1.25185177e-01 -5.25108635e-01 -9.88302827e-01 -3.34144592e-01
2.14970231e-01 9.31009471e-01 1.64233923e-01 1.18186593... | [6.952550411224365, -0.9700822234153748] |
55f8929c-18d5-40d1-ba36-20eec26289b2 | etrica-event-triggered-context-aware-story | 2210.12463 | null | https://arxiv.org/abs/2210.12463v1 | https://arxiv.org/pdf/2210.12463v1.pdf | EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention | One of the key challenges of automatic story generation is how to generate a long narrative that can maintain fluency, relevance, and coherence. Despite recent progress, current story generation systems still face the challenge of how to effectively capture contextual and event features, which has a profound impact on ... | ['Zhihao Zhang', 'Frank Guerin', 'Henglin Huang', 'Chenghua Lin', 'Chen Tang'] | 2022-10-22 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 2.27195442e-01 1.20398574e-01 -3.47832918e-01 -1.85932815e-01
-6.80365086e-01 -3.96668673e-01 1.20052135e+00 5.12114912e-02
7.75541551e-03 9.09749985e-01 1.26055694e+00 2.83222586e-01
1.89327210e-01 -1.00516498e+00 -5.08179903e-01 -6.34870157e-02
4.41687480e-02 1.99012548e-01 4.32407204e-03 -5.66093087... | [11.644362449645996, 8.851446151733398] |
c453c668-4502-4297-accd-12a1306a8068 | controlling-hallucinations-at-word-level-in | 2102.02810 | null | https://arxiv.org/abs/2102.02810v2 | https://arxiv.org/pdf/2102.02810v2.pdf | Controlling Hallucinations at Word Level in Data-to-Text Generation | Data-to-Text Generation (DTG) is a subfield of Natural Language Generation aiming at transcribing structured data in natural language descriptions. The field has been recently boosted by the use of neural-based generators which exhibit on one side great syntactic skills without the need of hand-crafted pipelines; on th... | ['Patrick Gallinari', 'Rossella Cancelliere', 'Geoffrey Scoutheeten', 'Laure Soulier', 'Marco Roberti', 'Clément Rebuffel'] | 2021-02-04 | null | null | null | null | ['table-to-text-generation', 'data-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.87999541e-01 7.66486108e-01 2.10682005e-01 -2.43152410e-01
-9.21533525e-01 -4.45566833e-01 8.75862300e-01 3.26989204e-01
-3.08049530e-01 8.82518888e-01 5.26807249e-01 -1.10792421e-01
2.67053358e-02 -8.45603287e-01 -5.87308407e-01 -5.10095179e-01
2.80851781e-01 8.43718052e-01 -9.30644870e-02 -6.70063436... | [11.565491676330566, 8.983193397521973] |
680c4192-3555-4b9f-a11e-2b52c7a6b493 | tw-star-at-semeval-2017-task-4-sentiment | null | null | https://aclanthology.org/S17-2110 | https://aclanthology.org/S17-2110.pdf | Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets | In this paper, we present our contribution in SemEval 2017 international workshop. We have tackled task 4 entitled {``}Sentiment analysis in Twitter{''}, specifically subtask 4A-Arabic. We propose two Arabic sentiment classification models implemented using supervised and unsupervised learning strategies. In both model... | ['Ismail Babaoglu', 'Mourad Gridach', 'Hatem Haddad', 'Hala Mulki'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 1.31981105e-01 7.59209916e-02 7.93135446e-03 -8.05159986e-01
-6.73406422e-01 -7.62465954e-01 8.66035759e-01 9.63989258e-01
-9.23384428e-01 6.33213639e-01 2.64903247e-01 -2.24635780e-01
-1.89947709e-02 -7.53094256e-01 -2.34577090e-01 -4.85900491e-01
-2.85920411e-01 4.51841503e-01 1.15553603e-01 -1.10359812... | [11.134836196899414, 6.914978504180908] |
d79f698a-91aa-4d01-bcd2-d53317e91064 | recognition-of-handwritten-chinese-text-by | 2207.14801 | null | https://arxiv.org/abs/2207.14801v1 | https://arxiv.org/pdf/2207.14801v1.pdf | Recognition of Handwritten Chinese Text by Segmentation: A Segment-annotation-free Approach | Online and offline handwritten Chinese text recognition (HTCR) has been studied for decades. Early methods adopted oversegmentation-based strategies but suffered from low speed, insufficient accuracy, and high cost of character segmentation annotations. Recently, segmentation-free methods based on connectionist tempora... | ['Jing Li', 'Shenggao Zhu', 'Hesuo Zhang', 'Canyu Xie', 'Weihong Ma', 'Lianwen Jin', 'Dezhi Peng'] | 2022-07-29 | null | null | null | null | ['handwritten-chinese-text-recognition', 'handwritten-chinese-text-recognition'] | ['computer-vision', 'natural-language-processing'] | [ 2.55153060e-01 -6.12458467e-01 -2.50464439e-01 -4.56317753e-01
-6.62776470e-01 -5.36768496e-01 4.15023655e-01 -4.12246324e-02
-6.62346542e-01 5.19300401e-01 1.96786858e-02 -5.84579170e-01
3.49280596e-01 -4.72994089e-01 -4.47641134e-01 -7.72268593e-01
6.31862700e-01 3.31979662e-01 4.36329931e-01 1.67153969... | [11.90496826171875, 2.3856348991394043] |
b2664328-b6ea-48ac-82a0-2938ac4d32be | hakg-hierarchy-aware-knowledge-gated-network | 2204.04959 | null | https://arxiv.org/abs/2204.04959v1 | https://arxiv.org/pdf/2204.04959v1.pdf | HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation | Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability. A recent technical trend is to design end-to-end models based on information propagation schemes. However, existing propagation-based methods fail to (1) model the underlying hierarchical structures... | ['Yunjun Gao', 'Baihua Zheng', 'Lu Chen', 'Xinjun Zhu', 'Yuntao Du'] | 2022-04-11 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [-5.72631121e-01 -3.59272398e-02 -5.77728748e-01 -2.18336895e-01
1.24303594e-01 -4.22227591e-01 2.40685463e-01 4.58558321e-01
-5.42027168e-02 3.08083981e-01 8.23236108e-01 -3.50010172e-02
-8.65991950e-01 -9.97485220e-01 -4.36317444e-01 -6.14555717e-01
-4.82951671e-01 2.11746663e-01 2.72028327e-01 -2.36125156... | [10.263258934020996, 5.671534061431885] |
1e34d3a8-33e6-4cfa-9aa2-54d6dbf14bf6 | anomaly-detection-in-video-sequence-with | 1908.06351 | null | https://arxiv.org/abs/1908.06351v1 | https://arxiv.org/pdf/1908.06351v1.pdf | Anomaly Detection in Video Sequence with Appearance-Motion Correspondence | Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated mo... | ['Trong Nguyen Nguyen', 'Jean Meunier'] | 2019-08-17 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 3.07207763e-01 -2.29866073e-01 2.36715630e-01 -3.11144143e-01
-2.22451523e-01 -2.94673890e-01 7.83568025e-01 -1.09829932e-01
-4.40433443e-01 3.86737436e-01 -1.01664849e-01 -1.31907240e-01
2.62635678e-01 -5.38411021e-01 -1.19124126e+00 -7.61104882e-01
-1.81067988e-01 4.74671014e-02 9.10985589e-01 -5.58873080... | [7.913956642150879, 1.3588083982467651] |
51d1a6d9-08ac-496c-bc7c-41c3de301879 | a-new-sparse-auto-encoder-based-framework | 2201.12493 | null | https://arxiv.org/abs/2201.12493v1 | https://arxiv.org/pdf/2201.12493v1.pdf | A new Sparse Auto-encoder based Framework using Grey Wolf Optimizer for Data Classification Problem | One of the most important properties of deep auto-encoders (DAEs) is their capability to extract high level features from row data. Hence, especially recently, the autoencoders are preferred to be used in various classification problems such as image and voice recognition, computer security, medical data analysis, etc.... | ['Ahmad Mozaffer Karim'] | 2022-01-29 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [-1.80413052e-02 -3.34298283e-01 1.41757075e-03 -4.84980382e-02
-9.54749212e-02 9.92763862e-02 4.47088510e-01 2.78182983e-01
-5.46022058e-01 1.14763987e+00 5.60806617e-02 3.59401494e-01
-7.06564367e-01 -9.06414986e-01 -5.23925006e-01 -1.12187278e+00
-1.98212504e-01 6.62318468e-01 -1.43079862e-01 -5.06980181... | [8.090047836303711, 3.280696153640747] |
2e2d66b4-bdcc-433d-ace9-e3459caa455a | amos-an-automated-model-order-selection | 1609.06457 | null | http://arxiv.org/abs/1609.06457v1 | http://arxiv.org/pdf/1609.06457v1.pdf | AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering | One of the longstanding problems in spectral graph clustering (SGC) is the
so-called model order selection problem: automated selection of the correct
number of clusters. This is equivalent to the problem of finding the number of
connected components or communities in an undirected graph. In this paper, we
propose AMOS... | ['Pin-Yu Chen', 'Thibaut Gensollen', 'Alfred O. Hero III'] | 2016-09-21 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [-7.44303167e-02 1.23387776e-01 6.11887313e-02 1.91740766e-01
-2.65571743e-01 -8.35578799e-01 2.39011392e-01 6.13612771e-01
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-6.88873291e-01 -8.45568419e-01 -1.73084304e-01 -6.37463808e-01
-5.88895738e-01 1.02457690e+00 3.63048613e-01 2.23622650... | [7.03914737701416, 5.189591884613037] |
a579e9f7-774f-455e-ae1d-0f5d79aaaa6f | adversarial-semantic-collisions | 2011.04743 | null | https://arxiv.org/abs/2011.04743v1 | https://arxiv.org/pdf/2011.04743v1.pdf | Adversarial Semantic Collisions | We study semantic collisions: texts that are semantically unrelated but judged as similar by NLP models. We develop gradient-based approaches for generating semantic collisions and demonstrate that state-of-the-art models for many tasks which rely on analyzing the meaning and similarity of texts-- including paraphrase ... | ['Vitaly Shmatikov', 'Alexander M. Rush', 'Congzheng Song'] | 2020-11-09 | null | https://aclanthology.org/2020.emnlp-main.344 | https://aclanthology.org/2020.emnlp-main.344.pdf | emnlp-2020-11 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 4.99236763e-01 -6.20460622e-02 -1.19239055e-01 -3.83929878e-01
-1.55041742e+00 -8.51343334e-01 7.84397125e-01 8.17899764e-01
-4.97536182e-01 5.47795653e-01 1.00553012e+00 -2.92256504e-01
-3.25751007e-01 -5.94028711e-01 -5.45382679e-01 -2.14635819e-01
3.84450912e-01 7.05965936e-01 3.47667187e-01 -6.21318161... | [12.155407905578613, 9.28640365600586] |
ec99b15f-6076-4f05-a646-c2d2f4ff31aa | generative-adversarial-networks-based-skin | 2305.18164 | null | https://arxiv.org/abs/2305.18164v1 | https://arxiv.org/pdf/2305.18164v1.pdf | Generative Adversarial Networks based Skin Lesion Segmentation | Skin cancer is a serious condition that requires accurate identification and treatment. One way to assist clinicians in this task is by using computer-aided diagnosis (CAD) tools that can automatically segment skin lesions from dermoscopic images. To this end, a new adversarial learning-based framework called EGAN has ... | ['Sharath Chandra Guntuku', 'Sanjay Talbar', 'Ujjwal Baid', 'Venu Pokuri', 'Bhakti Baheti', 'Prasad Dutande', 'Shubham Innani'] | 2023-05-29 | null | null | null | null | ['skin-lesion-segmentation', 'lesion-segmentation'] | ['medical', 'medical'] | [ 7.31884003e-01 5.07051706e-01 -7.33944261e-03 -1.40680268e-01
-6.67689860e-01 -5.43121576e-01 4.96144265e-01 1.61859453e-01
-2.95291632e-01 5.60480297e-01 -2.23149955e-01 -2.01908365e-01
9.61372256e-02 -8.42122257e-01 -3.06210160e-01 -7.98374295e-01
1.62370801e-01 1.03291739e-02 4.62846696e-01 1.38239592... | [15.575652122497559, -2.9113235473632812] |
803e9aa3-c210-4a85-bb0d-813be0dfbfea | probabilistic-domain-adaptation-for | 2303.11790 | null | https://arxiv.org/abs/2303.11790v1 | https://arxiv.org/pdf/2303.11790v1.pdf | Probabilistic Domain Adaptation for Biomedical Image Segmentation | Segmentation is a key analysis tasks in biomedical imaging. Given the many different experimental settings in this field, the lack of generalization limits the use of deep learning in practice. Domain adaptation is a promising remedy: it trains a model for a given task on a source dataset with labels and adapts it to a... | ['Constantin Pape', 'Anwai Archit'] | 2023-03-21 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 8.85687232e-01 4.10114050e-01 -4.93754178e-01 -8.15083206e-01
-1.03262150e+00 -5.89349806e-01 5.18250823e-01 1.65080085e-01
-8.61115336e-01 9.11736071e-01 2.97290217e-02 -1.03276856e-01
6.75718440e-03 -3.04965794e-01 -7.25553930e-01 -8.99471819e-01
3.45111191e-01 9.95618403e-01 7.11822689e-01 3.60213548... | [14.690811157226562, -1.9817419052124023] |
b059fffe-7f65-4275-bb44-99f962f657d0 | distilling-universal-and-joint-knowledge-for | 2307.03347 | null | https://arxiv.org/abs/2307.03347v1 | https://arxiv.org/pdf/2307.03347v1.pdf | Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data | For many real-world time series tasks, the computational complexity of prevalent deep leaning models often hinders the deployment on resource-limited environments (e.g., smartphones). Moreover, due to the inevitable domain shift between model training (source) and deploying (target) stages, compressing those deep model... | ['Zhenghua Chen', 'Kezhi Mao', 'XiaoLi Li', 'Min Wu', 'Qing Xu'] | 2023-07-07 | null | null | null | null | ['model-compression', 'transfer-learning'] | ['methodology', 'miscellaneous'] | [ 3.65754634e-01 -2.48015150e-01 -4.27227885e-01 -3.39140505e-01
-7.30713785e-01 -4.84570265e-01 4.37410116e-01 7.86895081e-02
-3.14814389e-01 7.13016927e-01 -7.20580518e-02 -3.17528009e-01
-2.90894747e-01 -8.42660308e-01 -7.96195149e-01 -6.22682869e-01
1.31255612e-01 5.28432190e-01 9.83043611e-02 -1.64339274... | [10.169921875, 3.120453357696533] |
7e5a9bf4-8820-43db-b7f2-ccb279120f9b | interpolated-convolutional-networks-for-3d | 1908.04512 | null | https://arxiv.org/abs/1908.04512v1 | https://arxiv.org/pdf/1908.04512v1.pdf | Interpolated Convolutional Networks for 3D Point Cloud Understanding | Point cloud is an important type of 3D representation. However, directly applying convolutions on point clouds is challenging due to the sparse, irregular and unordered data structure. In this paper, we propose a novel Interpolated Convolution operation, InterpConv, to tackle the point cloud feature learning and unders... | ['Hongsheng Li', 'Jiageng Mao', 'Xiaogang Wang'] | 2019-08-13 | interpolated-convolutional-networks-for-3d-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Mao_Interpolated_Convolutional_Networks_for_3D_Point_Cloud_Understanding_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Mao_Interpolated_Convolutional_Networks_for_3D_Point_Cloud_Understanding_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-part-segmentation'] | ['computer-vision'] | [ 2.13021822e-02 -3.83984685e-01 -1.44689053e-01 -8.06983232e-01
-3.22904766e-01 -5.86123109e-01 2.73387045e-01 2.50419259e-01
-1.15085848e-01 1.29959553e-01 -1.33506447e-01 -4.00847226e-01
-1.70492023e-01 -1.16689646e+00 -1.41236472e+00 -2.12116674e-01
-1.02833614e-01 4.14835721e-01 5.10121822e-01 6.28880560... | [7.946135520935059, -3.595989942550659] |
9ca819e1-cf1f-4e88-a16e-65f1652ce3bf | two-person-graph-convolutional-network-for | 2208.06174 | null | https://arxiv.org/abs/2208.06174v2 | https://arxiv.org/pdf/2208.06174v2.pdf | Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition | Graph convolutional networks (GCNs) have been the predominant methods in skeleton-based human action recognition, including human-human interaction recognition. However, when dealing with interaction sequences, current GCN-based methods simply split the two-person skeleton into two discrete graphs and perform graph con... | ['Jingyong Su', 'Tong Zhang', 'Linlin Tang', 'Yueran Li', 'Zhengcen Li'] | 2022-08-12 | null | null | null | null | ['action-classification', 'human-interaction-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.24635869e-01 1.52365401e-01 -3.81261706e-01 -3.43698502e-01
-5.28786518e-02 -3.98301333e-02 4.96673733e-01 -2.90139318e-01
-4.29924816e-01 4.04983878e-01 4.23568875e-01 9.81297344e-02
8.13323408e-02 -8.08009565e-01 -5.20370603e-01 -3.67578685e-01
-1.83714181e-01 6.71912432e-01 5.51969528e-01 -3.88093233... | [7.933310031890869, 0.39629408717155457] |
ff969025-e3ef-468a-8be0-b693aa537237 | sequence-to-sequence-convolutional-neural | null | null | https://aclanthology.org/2019.rocling-1.12 | https://aclanthology.org/2019.rocling-1.12.pdf | Sequence to Sequence Convolutional Neural Network for Automatic Spelling Correction | null | ['Yuan-Fu Liao', 'Ján Staš', 'Matúš Pleva', 'Daniel Hládek'] | null | null | null | null | rocling-2019-10 | ['spelling-correction'] | ['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... | [-7.294840335845947, 3.683072090148926] |
9dcc72ee-9494-415d-9caa-00faac2b4555 | universal-morphology-control-via-contextual | 2302.11070 | null | https://arxiv.org/abs/2302.11070v1 | https://arxiv.org/pdf/2302.11070v1.pdf | Universal Morphology Control via Contextual Modulation | Learning a universal policy across different robot morphologies can significantly improve learning efficiency and generalization in continuous control. However, it poses a challenging multi-task reinforcement learning problem, as the optimal policy may be quite different across robots and critically depend on the morph... | ['Shimon Whiteson', 'Jacob Beck', 'Zheng Xiong'] | 2023-02-22 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 5.96243590e-02 -1.44555017e-01 -1.29970804e-01 -1.31810037e-02
-2.46462896e-01 -7.13345826e-01 4.19463813e-01 5.12511022e-02
-4.99422103e-01 7.12542593e-01 3.38388532e-02 1.52513646e-02
-3.78063619e-01 -7.36355424e-01 -9.62917924e-01 -8.90160739e-01
-4.25629094e-02 4.47297722e-01 4.65265840e-01 -7.01983690... | [4.3846893310546875, 1.0477122068405151] |
dfea1615-5e53-438b-9243-edb47947457e | understanding-autoencoders-with-information | 1804.00057 | null | https://arxiv.org/abs/1804.00057v3 | https://arxiv.org/pdf/1804.00057v3.pdf | Understanding Autoencoders with Information Theoretic Concepts | Despite their great success in practical applications, there is still a lack of theoretical and systematic methods to analyze deep neural networks. In this paper, we illustrate an advanced information theoretic methodology to understand the dynamics of learning and the design of autoencoders, a special type of deep lea... | ['Jose C. Principe', 'Shujian Yu'] | 2018-03-30 | null | null | null | null | ['information-plane'] | ['methodology'] | [-4.11469080e-02 3.11098605e-01 1.94064915e-01 -6.33077472e-02
3.10000181e-01 -4.62391615e-01 6.45489097e-01 3.07900429e-01
-6.53074205e-01 6.28058851e-01 2.96724271e-02 -4.99751151e-01
-8.70640993e-01 -5.77964842e-01 -6.52523398e-01 -1.06642914e+00
-4.22481686e-01 1.57079548e-01 9.57625732e-02 -2.58250892... | [7.950762748718262, 3.564326047897339] |
af07f3cf-96c3-4c51-915c-8802486e6dd9 | sample-efficient-on-policy-imitation-learning | 2306.09805 | null | https://arxiv.org/abs/2306.09805v1 | https://arxiv.org/pdf/2306.09805v1.pdf | Sample-Efficient On-Policy Imitation Learning from Observations | Imitation learning from demonstrations (ILD) aims to alleviate numerous shortcomings of reinforcement learning through the use of demonstrations. However, in most real-world applications, expert action guidance is absent, making the use of ILD impossible. Instead, we consider imitation learning from observations (ILO),... | ['Alexandros Kalousis', 'Naoya Takeishi', 'Lionel Blondé', 'João A. Cândido Ramos'] | 2023-06-16 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [ 4.33268733e-02 2.80654758e-01 -3.67812276e-01 1.58871785e-01
-6.06642902e-01 -6.09617054e-01 6.52238250e-01 -2.26539001e-01
-6.17637575e-01 1.19088292e+00 -3.49842608e-01 -5.81763983e-01
-2.80788392e-02 -4.75407362e-01 -1.04567170e+00 -6.65760934e-01
-8.51669535e-02 4.00505573e-01 1.16645031e-01 -1.87956586... | [4.311412334442139, 1.5652445554733276] |
dbf225e1-8b15-4707-9668-00467aa3d96c | counterfactual-data-augmentation-using | 2007.02863 | null | https://arxiv.org/abs/2007.02863v2 | https://arxiv.org/pdf/2007.02863v2.pdf | Counterfactual Data Augmentation using Locally Factored Dynamics | Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses. Though the subprocesses are not independent, their interactions are often sparse, and the dynamics at any given time step can often be decomposed into locally independent cau... | ['Animesh Garg', 'Silviu Pitis', 'Elliot Creager'] | 2020-07-06 | null | http://proceedings.neurips.cc/paper/2020/hash/294e09f267683c7ddc6cc5134a7e68a8-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/294e09f267683c7ddc6cc5134a7e68a8-Paper.pdf | neurips-2020-12 | ['multi-goal-reinforcement-learning'] | ['methodology'] | [ 2.76729792e-01 3.79264563e-01 -4.79664117e-01 -9.71657410e-02
-5.43942571e-01 -6.39711201e-01 1.32765090e+00 4.94750477e-02
-3.30196351e-01 1.19204366e+00 1.05871105e+00 -4.19166416e-01
-5.49524665e-01 -6.49202824e-01 -1.11156321e+00 -8.12184453e-01
-6.64219141e-01 6.64158821e-01 -1.76652715e-01 5.02838120... | [4.185120105743408, 1.5516901016235352] |
c4ea119d-16d5-4f33-9609-656215fc4d1b | mutual-contrastive-learning-for-visual | 2104.12565 | null | https://arxiv.org/abs/2104.12565v2 | https://arxiv.org/pdf/2104.12565v2.pdf | Mutual Contrastive Learning for Visual Representation Learning | We present a collaborative learning method called Mutual Contrastive Learning (MCL) for general visual representation learning. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks. A crucial component of MCL is Interactive Contrastive Learning (ICL)... | ['Yongjun Xu', 'Linhang Cai', 'Zhulin An', 'Chuanguang Yang'] | 2021-04-26 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.58236651e-02 1.66185368e-02 -5.25925815e-01 -3.02797496e-01
-4.34098452e-01 -6.09875441e-01 7.78587878e-01 -1.99002791e-02
-1.55761927e-01 4.01986480e-01 1.42327458e-01 -2.26067707e-01
-4.01394695e-01 -7.36942410e-01 -5.70555389e-01 -5.87333322e-01
-5.44915915e-01 -8.89450498e-03 7.34937713e-02 2.51422767... | [9.465226173400879, 2.930659770965576] |
8b3d0ce1-95b0-4d89-83fd-1c6a7ce045c5 | agile-gesture-recognition-for-capacitive | 2305.07624 | null | https://arxiv.org/abs/2305.07624v1 | https://arxiv.org/pdf/2305.07624v1.pdf | Agile gesture recognition for capacitive sensing devices: adapting on-the-job | Automated hand gesture recognition has been a focus of the AI community for decades. Traditionally, work in this domain revolved largely around scenarios assuming the availability of the flow of images of the user hands. This has partly been due to the prevalence of camera-based devices and the wide availability of ima... | ['Ivan Y. Tyukin', 'Evgeny Mirkes', 'Alexander Gorban', 'Yuxiang Huang', 'Valeri A. Makarov', 'Liucheng Guo', 'Ying Liu'] | 2023-05-12 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition', 'dimensionality-reduction'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 3.66958976e-01 -3.68590564e-01 2.10235700e-01 -7.09324107e-02
-4.11676764e-01 -4.90221679e-01 3.30967128e-01 -4.70308810e-01
-8.54088306e-01 2.44615465e-01 -1.99722290e-01 -1.23608798e-01
-2.11114749e-01 -4.71985430e-01 -4.10805941e-01 -8.40091169e-01
1.29674405e-01 4.12240326e-01 2.55252808e-01 -9.24490020... | [6.708060264587402, -0.011256803758442402] |
37c00db7-1bdb-4642-bb4f-c8d1de9f0f39 | intelligent-reflecting-surface-aided-mobile | 2205.02194 | null | https://arxiv.org/abs/2205.02194v1 | https://arxiv.org/pdf/2205.02194v1.pdf | Intelligent Reflecting Surface Aided Mobile Edge Computing With Binary Offloading: Energy Minimization for IoT Devices | Mobile edge computing (MEC) is envisioned as a promising technique to support computation-intensive and timecritical applications in future Internet of Things (IoT) era. However, the uplink transmission performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission powe... | ['Yik-Chung Wu', 'Yi Gong', 'Yizhen Yang'] | 2022-05-04 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 2.21772969e-01 -5.76066934e-02 -3.06680262e-01 2.84502804e-01
-1.00753628e-01 -4.65702444e-01 -4.72614095e-02 -2.11575687e-01
-1.92641541e-01 6.98022366e-01 -1.97737023e-01 -5.17867208e-01
-3.74144673e-01 -8.42579722e-01 -3.83052498e-01 -1.12043881e+00
2.09074263e-02 3.25133950e-01 -2.91460659e-02 1.38242140... | [5.938512325286865, 1.5986454486846924] |
17f3946b-74ad-4fda-8383-896b25333816 | bae-bert-based-adversarial-examples-for-text | 2004.01970 | null | https://arxiv.org/abs/2004.01970v3 | https://arxiv.org/pdf/2004.01970v3.pdf | BAE: BERT-based Adversarial Examples for Text Classification | Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Recent works in NLP use rule-based synonym replacement strategies to generate adversarial examples. These strategies can lead to out-of-context... | ['Goutham Ramakrishnan', 'Siddhant Garg'] | 2020-04-04 | null | https://aclanthology.org/2020.emnlp-main.498 | https://aclanthology.org/2020.emnlp-main.498.pdf | emnlp-2020-11 | ['adversarial-text'] | ['adversarial'] | [ 6.13924682e-01 6.81544185e-01 2.27708727e-01 -3.90603781e-01
-8.02119195e-01 -1.23570490e+00 1.07407069e+00 2.46833295e-01
-3.00737202e-01 1.05616558e+00 3.08738112e-01 -4.11209196e-01
3.94038796e-01 -7.49680281e-01 -7.49680400e-01 -3.37147176e-01
2.06936017e-01 7.22093880e-01 -1.03942007e-01 -5.47120392... | [6.018619060516357, 8.14523983001709] |
309ad20c-4fd1-4ba1-9d68-5adc5dd74cb3 | parametric-depth-based-feature-representation | 2307.04106 | null | https://arxiv.org/abs/2307.04106v1 | https://arxiv.org/pdf/2307.04106v1.pdf | Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's Eye View | Recent vision-only perception models for autonomous driving achieved promising results by encoding multi-view image features into Bird's-Eye-View (BEV) space. A critical step and the main bottleneck of these methods is transforming image features into the BEV coordinate frame. This paper focuses on leveraging geometry ... | ['Miaomiao Liu', 'Jose M. Alvarez', 'Enze Xie', 'Jiayu Yang'] | 2023-07-09 | null | null | null | null | ['semantic-segmentation', 'object-detection', 'autonomous-driving', 'representation-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 6.22529984e-02 1.52110696e-01 -2.25144569e-02 -5.18430352e-01
-4.60924387e-01 -6.46488428e-01 6.75542474e-01 -7.96589553e-02
-3.79268050e-01 3.49357098e-01 2.39136014e-02 -1.04768582e-01
1.30178958e-01 -1.00391209e+00 -7.09358752e-01 -6.73609495e-01
4.97926623e-01 3.09752822e-01 6.66653633e-01 -1.15640007... | [8.233762741088867, -2.423077344894409] |
26e0148d-9968-4cd9-ab31-d2511d4b7005 | training-verifiers-to-solve-math-word | 2110.14168 | null | https://arxiv.org/abs/2110.14168v2 | https://arxiv.org/pdf/2110.14168v2.pdf | Training Verifiers to Solve Math Word Problems | State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning. To diagnose the failures of current models and support research, we introduce GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word pro... | ['John Schulman', 'Christopher Hesse', 'Reiichiro Nakano', 'Jerry Tworek', 'Matthias Plappert', 'Lukasz Kaiser', 'Heewoo Jun', 'Mark Chen', 'Jacob Hilton', 'Mohammad Bavarian', 'Vineet Kosaraju', 'Karl Cobbe'] | 2021-10-27 | null | null | null | null | ['gsm8k', 'mathematical-reasoning'] | ['natural-language-processing', 'natural-language-processing'] | [-2.23056540e-01 -1.08149208e-01 -3.98259670e-01 -4.09113109e-01
-1.07788646e+00 -9.77873623e-01 4.32700038e-01 3.89428049e-01
-4.09703016e-01 8.40859711e-01 2.10604772e-01 -6.80586576e-01
-5.35767555e-01 -8.90980840e-01 -8.52657914e-01 8.13144818e-02
3.39130282e-01 9.31443274e-01 -6.14939779e-02 -2.23524615... | [9.712186813354492, 7.365890979766846] |
222241d0-0677-4bf7-8f69-362bf949c8a3 | financial-risk-management-on-a-neutral-atom | 2212.03223 | null | https://arxiv.org/abs/2212.03223v1 | https://arxiv.org/pdf/2212.03223v1.pdf | Financial Risk Management on a Neutral Atom Quantum Processor | Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In t... | ["Didier M'tamon", 'Hacene Isselnane', 'Oumaima Hammammi', 'Achraf Seddik', 'Roman Orus', 'Michel Kurek', 'Irene Caceres', 'Samuel Mugel', 'Andoni Duarte', 'Faysal Ishtiaq', 'Luc Andrea', 'Maitree Shah', 'Usman Ayub Sheikh', 'Gianni Del Bimbo', 'Loïc Henriet', 'Adrien Signoles', 'Vincent E. Elfving', 'Julia R. K. Cline... | 2022-12-06 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.20518602e-01 2.85419285e-01 -6.42099511e-03 -5.95880628e-01
-8.77149582e-01 -4.82072622e-01 6.17311239e-01 4.29851115e-01
-3.91084611e-01 7.27714658e-01 2.18643323e-01 -7.86964417e-01
-3.82827342e-01 -1.21894455e+00 -4.46458787e-01 -5.14154494e-01
-1.67763814e-01 1.03575039e+00 -1.65323302e-01 -6.28891945... | [5.600744247436523, 5.033929347991943] |
b35a7c31-ba6e-4be6-a7e8-e72ca4df4a12 | a-mixer-layer-is-worth-one-graph-convolution | 2304.03532 | null | https://arxiv.org/abs/2304.03532v1 | https://arxiv.org/pdf/2304.03532v1.pdf | A Mixer Layer is Worth One Graph Convolution: Unifying MLP-Mixers and GCNs for Human Motion Prediction | The past few years has witnessed the dominance of Graph Convolutional Networks (GCNs) over human motion prediction, while their performance is still far from satisfactory. Recently, MLP-Mixers show competitive results on top of being more efficient and simple. To extract features, GCNs typically follow an aggregate-and... | ['Mengyuan Liu', 'Chen Chen', 'Shen Zhao', 'Xinshun Wang'] | 2023-04-07 | null | null | null | null | ['motion-prediction', 'human-motion-prediction'] | ['computer-vision', 'time-series'] | [-4.78270091e-02 1.36222020e-01 -4.32500780e-01 -7.26915747e-02
-3.97527218e-01 -3.28042388e-01 5.78772008e-01 7.33043477e-02
-4.27409530e-01 3.64605606e-01 4.20196921e-01 -3.55296373e-01
2.51274067e-03 -1.02682817e+00 -7.45606780e-01 -5.42672813e-01
-5.39841473e-01 3.32867384e-01 5.97341299e-01 -4.10432369... | [7.302338123321533, -0.06006191670894623] |
4ceeab14-c58d-4e4c-b44d-7fc29801a475 | analysis-open-published-17-june-2019 | null | null | https://www.nature.com/articles/s41597-019-0103-9 | https://www.nature.com/articles/s41597-019-0103-9.pdf | Analysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series data | Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absen... | ['Hrant Khachatrian', 'Aram Galstyan', 'Hrayr Harutyunyan', 'Greg Ver Steeg', 'David C. Kale'] | 2019-06-17 | null | null | null | nature-scientific-data-2019-6 | ['computational-phenotyping', 'length-of-stay-prediction', 'phenotype-classification'] | ['medical', 'medical', 'medical'] | [ 3.13054800e-01 -5.78374118e-02 -3.89048904e-01 -6.68176532e-01
-1.02479899e+00 -3.02353084e-01 2.27289483e-01 9.57562804e-01
-5.22778749e-01 6.20725811e-01 4.46601182e-01 -7.14599609e-01
-2.35350326e-01 -4.18598890e-01 -4.94892567e-01 -3.44416469e-01
-3.59107137e-01 6.25527263e-01 -2.94809610e-01 3.08194548... | [7.963992118835449, 6.270141124725342] |
51b5d30c-1c75-48e3-9960-429a6ea964aa | temporal-convolutional-attention-neural | null | null | https://ieeexplore.ieee.org/abstract/document/9534351 | https://www.researchgate.net/profile/Yang-Lin-27/publication/354797495_Temporal_Convolutional_Attention_Neural_Networks_for_Time_Series_Forecasting/links/61558599ab3c1324134c8883/Temporal-Convolutional-Attention-Neural-Networks-for-Time-Series-Forecasting.pdf | Temporal Convolutional Attention Neural Networks for Time Series Forecasting | Temporal Convolutional Neural Networks (TCNNs) have been applied for various sequence modelling tasks including time series forecasting. However, TCNNs may require many convolutional layers if the input sequence is long and are not able to provide interpretable results. In this paper, we present TCAN, a novel deep lear... | ['Mashud Rana', 'Irena Koprinska', 'Yang Lin'] | 2021-09-23 | null | null | null | international-joint-conference-on-neural-1 | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-1.55156046e-01 -2.74851352e-01 1.25804588e-01 -4.06286627e-01
9.99643579e-02 -5.92705786e-01 1.06901324e+00 -1.61965564e-01
4.00657840e-02 6.83536589e-01 4.20608193e-01 -7.21268773e-01
-2.83635497e-01 -9.41159368e-01 -6.58697605e-01 -9.09526587e-01
-2.84805864e-01 2.30632752e-01 2.18116686e-01 -1.36981800... | [6.6540141105651855, 2.848098039627075] |
65369582-e76a-4946-8af9-4250da27bd74 | fsd-fully-specialized-detector-via-neural | 2305.16649 | null | https://arxiv.org/abs/2305.16649v3 | https://arxiv.org/pdf/2305.16649v3.pdf | FSD: Fully-Specialized Detector via Neural Architecture Search | Most generic object detectors are mainly built for standard object detection tasks such as COCO and PASCAL VOC. They might not work well and/or efficiently on tasks of other domains consisting of images that are visually different from standard datasets. To this end, many advances have been focused on adapting a genera... | ['Yudian Li', 'Zhe Huang'] | 2023-05-26 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.89216867e-01 2.51292080e-01 -2.95933843e-01 -2.14954272e-01
-5.78266442e-01 -1.32825851e-01 4.02860314e-01 5.48491292e-02
-5.36247790e-01 1.49530455e-01 -1.47510678e-01 -4.50033456e-01
2.91444719e-01 -4.92009163e-01 -3.81655604e-01 -4.83211637e-01
-1.49755254e-01 4.43424523e-01 1.21431005e+00 -1.28668159... | [15.095234870910645, -2.379183769226074] |
083f8efd-9f97-479c-912f-030a0da67881 | sketch-a-shape-zero-shot-sketch-to-3d-shape | 2307.03869 | null | https://arxiv.org/abs/2307.03869v1 | https://arxiv.org/pdf/2307.03869v1.pdf | Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generation | Significant progress has recently been made in creative applications of large pre-trained models for downstream tasks in 3D vision, such as text-to-shape generation. This motivates our investigation of how these pre-trained models can be used effectively to generate 3D shapes from sketches, which has largely remained a... | ['Saeid Asgari Taghanaki', 'Evan Atherton', 'Hooman Shayani', 'Joseph Lambourne', 'Arianna Rampini', 'Pradeep Kumar Jayaraman', 'Aditya Sanghi'] | 2023-07-08 | null | null | null | null | ['3d-shape-generation', 'text-to-shape-generation'] | ['computer-vision', 'computer-vision'] | [ 4.31356072e-01 2.01714769e-01 4.50386852e-01 -2.51657039e-01
-6.14332080e-01 -9.45711136e-01 1.12672317e+00 -3.43106866e-01
4.91536781e-02 2.83148825e-01 1.16692506e-01 -3.86682749e-01
1.73309445e-01 -9.43482280e-01 -1.09300125e+00 -3.80397677e-01
2.49977201e-01 6.50489986e-01 1.15672108e-02 -2.08807141... | [9.104023933410645, -3.498377799987793] |
ce4dffa9-621c-49e0-8a65-be29586d942f | energy-optimization-for-hvac-systems-in-multi | 2306.13333 | null | https://arxiv.org/abs/2306.13333v1 | https://arxiv.org/pdf/2306.13333v1.pdf | Energy Optimization for HVAC Systems in Multi-VAV Open Offices: A Deep Reinforcement Learning Approach | With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). With HVAC systems accounting for about 40% of the total energy cost in the commercial sector, we propose a low-complexity DRL-based model w... | ['Abolfazl Razi', 'Edward. Duffy', 'Natan Vital', 'Xiwen Chen', 'Hao Wang'] | 2023-06-23 | null | null | null | null | ['total-energy', 'energy-management'] | ['miscellaneous', 'time-series'] | [-5.27063385e-02 6.43192679e-02 1.19582705e-01 -1.75613418e-01
-4.20320958e-01 -6.34949625e-01 7.56021440e-02 2.09824115e-01
-9.95899513e-02 6.75917923e-01 -1.30898431e-01 -4.66772795e-01
-3.63058716e-01 -1.02546358e+00 -4.33627039e-01 -1.04305172e+00
9.55998898e-02 -6.63725520e-03 -2.83709884e-01 -2.35242456... | [5.628588676452637, 2.447730302810669] |
6813cfa6-83df-47b1-a291-8b614b321ab7 | towards-pose-invariant-face-recognition-in | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhao_Towards_Pose_Invariant_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhao_Towards_Pose_Invariant_CVPR_2018_paper.pdf | Towards Pose Invariant Face Recognition in the Wild | Pose variation is one key challenge in face recognition. As opposed to current techniques for pose invariant face recognition, which either directly extract pose invariant features for recognition, or first normalize profile face images to frontal pose before feature extraction, we argue that it is more desirable to pe... | ['ShengMei Shen', 'Sugiri Pranata', 'Yan Xu', 'Lin Xiong', 'Junliang Xing', 'Jian Zhao', 'Yu Cheng', 'Jianshu Li', 'Fang Zhao', 'Karlekar Jayashree', 'Jiashi Feng', 'Shuicheng Yan'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['robust-face-recognition'] | ['computer-vision'] | [ 4.02591079e-01 1.46438539e-01 2.56855208e-02 -7.52207339e-01
-7.72588015e-01 -5.76487303e-01 7.35133708e-01 -9.62663889e-01
6.98617473e-02 4.87719864e-01 3.33313406e-01 1.07853949e-01
-4.14967462e-02 -7.23270237e-01 -8.63919795e-01 -9.24881339e-01
8.71725455e-02 3.42621744e-01 -5.94712913e-01 -1.50639504... | [13.056832313537598, 0.32404589653015137] |
849e6f98-6e87-4008-a5bb-0c10df5a8552 | a-multi-dimensional-cross-domain-and | null | null | https://aclanthology.org/2022.coling-1.45 | https://aclanthology.org/2022.coling-1.45.pdf | A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging | Dialogue Act tagging with the ISO 24617-2 standard is a difficult task that involves multi-label text classification across a diverse set of labels covering semantic, syntactic and pragmatic aspects of dialogue. The lack of an adequately sized training set annotated with this standard is a major problem when using the ... | ['Alan Blair', 'Wayne Wobcke', 'Stefano Mezza'] | null | null | null | null | coling-2022-10 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 7.62723237e-02 4.49791729e-01 -1.05733119e-01 -6.16136372e-01
-7.30315447e-01 -9.40459728e-01 9.25087094e-01 2.49293655e-01
-7.37208128e-01 1.06281376e+00 8.79530966e-01 -2.38901585e-01
4.35098857e-02 -3.36150795e-01 1.86391518e-01 -1.96251288e-01
1.95486601e-02 9.36816931e-01 3.37185532e-01 -8.20220470... | [12.782687187194824, 7.909097671508789] |
c0b6595f-0227-4560-9e88-9f0c49e635f6 | where-are-we-in-named-entity-recognition-from | null | null | https://aclanthology.org/2020.lrec-1.556 | https://aclanthology.org/2020.lrec-1.556.pdf | Where are we in Named Entity Recognition from Speech? | Named entity recognition (NER) from speech is usually made through a pipeline process that consists in (i) processing audio using an automatic speech recognition system (ASR) and (ii) applying a NER to the ASR outputs. The latest data available for named entity extraction from speech in French were produced during the ... | ['Yannick Est{\\`e}ve', 'Antoine Caubri{\\`e}re', 'Antoine Laurent', 'Sophie Rosset', 'Emmanuel Morin'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['entity-extraction'] | ['natural-language-processing'] | [ 4.42030840e-02 2.86687613e-01 6.27002180e-01 -5.65734506e-01
-1.19304276e+00 -6.81927383e-01 6.08747721e-01 8.50036889e-02
-1.08722603e+00 4.51885700e-01 7.33642638e-01 -2.87532061e-01
1.47078529e-01 -4.20626700e-01 -5.14030099e-01 -6.51711971e-02
-6.29625237e-03 3.89488429e-01 3.39167893e-01 -4.41326737... | [14.210550308227539, 7.002226829528809] |
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