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229654b5-bd4f-44cf-b087-4f0ee545015b | towards-a-foundation-model-for-generalist | 2305.10455 | null | https://arxiv.org/abs/2305.10455v2 | https://arxiv.org/pdf/2305.10455v2.pdf | Towards Generalist Robots: A Promising Paradigm via Generative Simulation | This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots. The purpose of this document is to share the excitement of the authors with the community and highlight a promising research direction in robotics and AI. The authors believe the proposed paradi... | ['Yian Wang', 'Tsun-Hsuan Wang', 'Yi-Ling Qiao', 'Zhenjia Xu', 'Theophile Gervet', 'Zhou Xian'] | 2023-05-17 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 2.04644963e-01 5.92568398e-01 -2.07308188e-01 -1.40267044e-01
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-3.09361994e-01 5.76249063e-01 2.85641346e-02 -4.46837485e-01
-2.60427982e-01 -6.86941504e-01 -8.57577562e-01 -6.77584946e-01
1.77843109e-01 4.17840749e-01 -1.90708667e-01 -4.54656541... | [4.588624477386475, 0.9622118473052979] |
94ca3772-dc9b-47b6-8246-f3c04a65dc04 | ltiatcmu-at-semeval-2020-task-11 | 2008.04820 | null | https://arxiv.org/abs/2008.04820v2 | https://arxiv.org/pdf/2008.04820v2.pdf | LTIatCMU at SemEval-2020 Task 11: Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification | In this paper we describe our submission for the task of Propaganda Span Identification in news articles. We introduce a BERT-BiLSTM based span-level propaganda classification model that identifies which token spans within the sentence are indicative of propaganda. The "multi-granular" model incorporates linguistic kno... | ['Alan W. black', 'Rishabh Joshi', 'Yulia Tsvetkov', 'Sopan Khosla', 'Ritam Dutt'] | 2020-08-11 | null | https://aclanthology.org/2020.semeval-1.230 | https://aclanthology.org/2020.semeval-1.230.pdf | semeval-2020 | ['propaganda-span-identification'] | ['natural-language-processing'] | [-3.87092233e-02 3.91695388e-02 -6.08306170e-01 -4.00254935e-01
-8.88848364e-01 -5.54734528e-01 9.09949064e-01 4.75132108e-01
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1.11610321e-02 2.24033576e-02 -1.68055639e-01 -4.87134129... | [8.468348503112793, 10.612937927246094] |
11bd89ef-73d7-40cc-b01c-ca3ad5127661 | learning-to-navigate-the-energy-landscape | 1603.05772 | null | http://arxiv.org/abs/1603.05772v1 | http://arxiv.org/pdf/1603.05772v1.pdf | Learning to Navigate the Energy Landscape | In this paper, we present a novel and efficient architecture for addressing
computer vision problems that use `Analysis by Synthesis'. Analysis by
synthesis involves the minimization of the reconstruction error which is
typically a non-convex function of the latent target variables.
State-of-the-art methods adopt a hyb... | ['Shahram Izadi', 'Matthias Nießner', 'Julien Valentin', 'Cem Keskin', 'Philip Torr', 'Angela Dai', 'Pushmeet Kohli'] | 2016-03-18 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 2.10450783e-01 -4.10964519e-01 -2.45312244e-01 -2.49411613e-01
-1.15715873e+00 -7.05216885e-01 4.66710120e-01 -3.97905111e-01
-6.46314502e-01 6.46314800e-01 -1.21394796e-02 -6.75293431e-02
-3.03534955e-01 -3.66479784e-01 -8.30295205e-01 -8.58066261e-01
3.55099589e-01 8.48379672e-01 2.48040155e-01 -1.83356434... | [6.860119819641113, -0.9302965998649597] |
c20d3c48-cac1-4da1-85e2-ff459c05ed98 | coordinating-policies-among-multiple-agents | 2205.10607 | null | https://arxiv.org/abs/2205.10607v2 | https://arxiv.org/pdf/2205.10607v2.pdf | Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel | In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another. In this paper, we propose an alternative approach whereby agents communicate through an intelligent facilitator that learns to sift through and interpret signals provided b... | ['Yoshua Bengio', 'Nicolas Heess', 'Michael Mozer', 'Tianmin Shu', 'Anirudh Goyal', 'Cristian Meo', 'Oussama Boussif', 'Vedant Shah', 'Dianbo Liu'] | 2022-05-21 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [-2.37908766e-01 4.43129301e-01 -3.65883380e-01 -2.40413755e-01
-5.68860292e-01 -6.49496138e-01 8.43217552e-01 4.33723897e-01
-9.97530580e-01 1.17607188e+00 2.32494175e-01 8.60483050e-02
-8.86141788e-04 -9.25879240e-01 -7.49809444e-01 -8.10300231e-01
-5.64804375e-01 7.35488415e-01 3.21914047e-01 -6.04365051... | [3.8005502223968506, 2.042858600616455] |
c5a9541b-367c-4465-973d-f48b69117f1c | adaptive-deep-learning-for-nonparametric-time | 2207.02546 | null | https://arxiv.org/abs/2207.02546v1 | https://arxiv.org/pdf/2207.02546v1.pdf | Adaptive deep learning for nonparametric time series regression | In this paper, we develop a general theory for adaptive nonparametric estimation of mean functions of nonstationary and nonlinear time series using deep neural networks (DNNs). We first consider two types of DNN estimators, non-penalized and sparse-penalized DNN estimators, and establish their generalization error boun... | ['Yuta Koike', 'Riku Fukami', 'Daisuke Kurisu'] | 2022-07-06 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.75034508e-01 -4.29447740e-01 -2.13452727e-01 -4.93305445e-01
-7.25235462e-01 -4.87406880e-01 1.92863479e-01 -5.78366280e-01
-2.32784376e-01 9.81033385e-01 1.81117401e-01 -1.76480357e-02
-4.90647495e-01 -5.31061709e-01 -8.81695747e-01 -9.89266157e-01
-5.69167614e-01 7.55455673e-01 -2.77475089e-01 -2.95167584... | [7.012728214263916, 3.5960886478424072] |
a9edd959-3bc3-400c-927f-e6acc72a71c8 | graphcspn-geometry-aware-depth-completion-via | 2210.10758 | null | https://arxiv.org/abs/2210.10758v1 | https://arxiv.org/pdf/2210.10758v1.pdf | GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs | Image guided depth completion aims to recover per-pixel dense depth maps from sparse depth measurements with the help of aligned color images, which has a wide range of applications from robotics to autonomous driving. However, the 3D nature of sparse-to-dense depth completion has not been fully explored by previous me... | ['Shengjin Wang', 'YaLi Li', 'Bo wang', 'Xiaofei Shao', 'Xin Liu'] | 2022-10-19 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 2.57237405e-01 2.97760814e-01 5.27644642e-02 -4.90537226e-01
-3.34468216e-01 -1.85493499e-01 4.38532084e-01 -1.30672604e-01
-4.04286832e-01 5.92655718e-01 2.04371780e-01 -3.37773860e-02
-1.76514283e-01 -1.03521478e+00 -7.56391287e-01 -5.55265188e-01
-6.63046986e-02 3.28896016e-01 2.15153053e-01 -6.09993301... | [8.79610824584961, -2.577909469604492] |
a0a13299-2cbf-4cfb-9ade-f65ec3f26eea | how-does-pretraining-improve-discourse-aware | 2305.19847 | null | https://arxiv.org/abs/2305.19847v1 | https://arxiv.org/pdf/2305.19847v1.pdf | How Does Pretraining Improve Discourse-Aware Translation? | Pretrained language models (PLMs) have produced substantial improvements in discourse-aware neural machine translation (NMT), for example, improved coherence in spoken language translation. However, the underlying reasons for their strong performance have not been well explained. To bridge this gap, we introduce a prob... | ['Derek F. Wong', 'Siyou Liu', 'Longyue Wang', 'Zhihong Huang'] | 2023-05-31 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 3.25563908e-01 5.40167034e-01 -5.18647134e-01 -3.54618937e-01
-9.84042108e-01 -4.81234610e-01 1.06966913e+00 -1.39455900e-01
-2.43948415e-01 7.42017388e-01 9.29261267e-01 -7.54437506e-01
3.02143425e-01 -3.99387568e-01 -1.02946031e+00 -3.40025336e-01
3.35714370e-01 6.72685623e-01 -6.91214427e-02 -3.81997049... | [11.646907806396484, 10.010966300964355] |
383684e6-c5b3-4160-8994-57c5e0c82658 | knowledge-enhanced-masked-language-model-for | null | null | https://aclanthology.org/2021.naacl-main.376 | https://aclanthology.org/2021.naacl-main.376.pdf | Knowledge Enhanced Masked Language Model for Stance Detection | Detecting stance on Twitter is especially challenging because of the short length of each tweet, the continuous coinage of new terminology and hashtags, and the deviation of sentence structure from standard prose. Fine-tuned language models using large-scale in-domain data have been shown to be the new state-of-the-art... | ['Lisa Singh', 'Kornraphop Kawintiranon'] | 2021-05-26 | null | null | null | naacl-2021-4 | ['stance-detection-us-election-2020-biden', 'stance-detection-us-election-2020-trump'] | ['natural-language-processing', 'natural-language-processing'] | [-1.95476804e-02 1.93693087e-01 -8.18321526e-01 -4.93763715e-01
-1.20925295e+00 -7.67839730e-01 1.00383687e+00 6.15957201e-01
-7.25128531e-01 8.68004143e-01 8.66966188e-01 -5.01832247e-01
4.43486571e-01 -8.60157013e-01 -4.35758948e-01 -3.46110135e-01
1.99715078e-01 4.76255864e-01 5.59010766e-02 -5.87962747... | [8.839011192321777, 9.950091361999512] |
7a2ad700-ebc6-4460-9847-52a9f8cf66fe | text-independent-speaker-verification-using | 1705.09422 | null | http://arxiv.org/abs/1705.09422v7 | http://arxiv.org/pdf/1705.09422v7.pdf | Text-Independent Speaker Verification Using 3D Convolutional Neural Networks | In this paper, a novel method using 3D Convolutional Neural Network (3D-CNN)
architecture has been proposed for speaker verification in the text-independent
setting. One of the main challenges is the creation of the speaker models. Most
of the previously-reported approaches create speaker models based on averaging
the ... | ['Nasser M. Nasrabadi', 'Jeremy Dawson', 'Amirsina Torfi'] | 2017-05-26 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-1.05907932e-01 -1.86041355e-01 1.34567991e-01 -7.18496859e-01
-6.69026256e-01 -4.24682081e-01 7.50597298e-01 -7.67084509e-02
-2.77368486e-01 -1.01052068e-01 2.11030126e-01 -3.12522531e-01
2.14210764e-01 -3.23289424e-01 -3.20002913e-01 -6.85741723e-01
1.78631172e-01 3.15332055e-01 -2.47249663e-01 -2.30803654... | [14.324189186096191, 6.08082914352417] |
10771f26-3ba3-4a06-8ee6-7f1baafadc47 | human-object-interaction-prediction-in-videos | 2306.03597 | null | https://arxiv.org/abs/2306.03597v1 | https://arxiv.org/pdf/2306.03597v1.pdf | Human-Object Interaction Prediction in Videos through Gaze Following | Understanding the human-object interactions (HOIs) from a video is essential to fully comprehend a visual scene. This line of research has been addressed by detecting HOIs from images and lately from videos. However, the video-based HOI anticipation task in the third-person view remains understudied. In this paper, we ... | ['Dongheui Lee', 'Hyemin Ahn', 'Esteve Valls Mascaró', 'Zhifan Ni'] | 2023-06-06 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 1.86990991e-01 -3.38389218e-01 -2.04096556e-01 -4.33460563e-01
-4.49181348e-01 -3.56967270e-01 4.40444797e-01 -3.20635825e-01
-2.23170340e-01 2.93153644e-01 4.06458676e-01 2.87800133e-01
6.35965765e-02 1.03788882e-01 -6.81432843e-01 -4.01434630e-01
-1.27885893e-01 -7.63217360e-02 1.97970480e-01 1.91070110... | [14.022576332092285, 0.05528607591986656] |
3e974c55-bce7-49e0-b6c6-7452c530c18d | image-inpainting-via-conditional-texture-and | 2108.09760 | null | https://arxiv.org/abs/2108.09760v1 | https://arxiv.org/pdf/2108.09760v1.pdf | Image Inpainting via Conditional Texture and Structure Dual Generation | Deep generative approaches have recently made considerable progress in image inpainting by introducing structure priors. Due to the lack of proper interaction with image texture during structure reconstruction, however, current solutions are incompetent in handling the cases with large corruptions, and they generally s... | ['Di Huang', 'Hongyu Yang', 'Xiefan Guo'] | 2021-08-22 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Guo_Image_Inpainting_via_Conditional_Texture_and_Structure_Dual_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Guo_Image_Inpainting_via_Conditional_Texture_and_Structure_Dual_Generation_ICCV_2021_paper.pdf | iccv-2021-1 | ['texture-synthesis'] | ['computer-vision'] | [ 1.69189468e-01 -1.53996632e-01 -1.15685746e-01 -2.94016570e-01
-8.98297966e-01 -1.81415096e-01 5.83560348e-01 -1.47792488e-01
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1.44286025e-02 -9.58493888e-01 -7.19828665e-01 -9.76351142e-01
4.28297341e-01 1.94963440e-01 8.14756602e-02 -2.69919127... | [11.252473831176758, -1.3114808797836304] |
104ca499-2159-4a3e-a461-1677a9fc0efb | principled-inference-of-hyperedges-and | 2204.05646 | null | https://arxiv.org/abs/2204.05646v2 | https://arxiv.org/pdf/2204.05646v2.pdf | Inference of hyperedges and overlapping communities in hypergraphs | Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on statistical inference to characterize the structural organization of hypergraphs. The method allows to i... | ['Caterina De Bacco', 'Federico Battiston', 'Martina Contisciani'] | 2022-04-12 | null | null | null | null | ['hyperedge-prediction'] | ['graphs'] | [ 2.35788599e-01 3.06005567e-01 -2.21520923e-02 -5.70966825e-02
3.71388346e-02 -7.06663609e-01 7.32460082e-01 3.88427764e-01
2.24910881e-02 8.52524519e-01 1.57461062e-01 -1.32024378e-01
-8.52137625e-01 -1.15703773e+00 -7.75430560e-01 -7.83189893e-01
-8.40638936e-01 1.20896566e+00 5.84015071e-01 -4.48949523... | [6.891613960266113, 5.301851749420166] |
7608227b-2ee6-46e5-b0f1-4fd24c1beb77 | a-survey-on-text-generation-using-generative | 2212.11119 | null | https://arxiv.org/abs/2212.11119v1 | https://arxiv.org/pdf/2212.11119v1.pdf | A survey on text generation using generative adversarial networks | This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called "natural" language. Nevertheless, adversarial text generation is... | ['João Paulo Papa', 'Gustavo Henrique de Rosa'] | 2022-12-20 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 5.15534461e-01 4.35725242e-01 6.78570345e-02 -2.10422538e-02
-7.26495028e-01 -6.46676898e-01 1.06854260e+00 -2.05468044e-01
-3.08011621e-01 1.45930386e+00 6.31118566e-02 -1.82861969e-01
9.59675163e-02 -1.14181352e+00 -6.94329560e-01 -7.45497584e-01
2.35622287e-01 4.64300662e-01 -3.80258471e-01 -4.45231616... | [15.441539764404297, 6.041910648345947] |
8bbcec31-a726-483e-be40-dd618c55f98d | d2conv3d-dynamic-dilated-convolutions-for | null | null | https://openaccess.thecvf.com/content/WACV2022/html/Schmidt_D2Conv3D_Dynamic_Dilated_Convolutions_for_Object_Segmentation_in_Videos_WACV_2022_paper.html | https://openaccess.thecvf.com/content/WACV2022/papers/Schmidt_D2Conv3D_Dynamic_Dilated_Convolutions_for_Object_Segmentation_in_Videos_WACV_2022_paper.pdf | D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos | Despite receiving significant attention from the research community, the task of segmenting and tracking objects in monocular videos still has much room for improvement. Existing works have simultaneously justified the efficacy of dilated and deformable convolutions for various image-level segmentation tasks. This give... | ['Bastian Leibe', 'Sabarinath Mahadevan', 'Ali Athar', 'Christian Schmidt'] | 2021-11-15 | null | null | null | wacv-2021-11 | ['video-instance-segmentation', 'unsupervised-video-object-segmentation', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.69441824e-02 -1.43289983e-01 -1.34790912e-01 -4.13524777e-01
-2.31708765e-01 -9.49476302e-01 4.96151090e-01 -4.79773313e-01
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-1.38397768e-01 -1.06112827e-02 8.29360962e-01 7.48905865... | [9.349564552307129, 0.09860693663358688] |
9ae9e70d-0d67-4560-8232-df430bad987a | dick-preston-and-morbo-at-semeval-2019-task-4 | null | null | https://aclanthology.org/S19-2160 | https://aclanthology.org/S19-2160.pdf | Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News Detection | In a world of information operations, influence campaigns, and fake news, classification of news articles as following hyperpartisan argumentation or not is becoming increasingly important. We present a deep learning-based approach in which a pre-trained language model has been fine-tuned on domain-specific data and us... | ['Tim Isbister', 'Fredrik Johansson'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['news-classification'] | ['natural-language-processing'] | [-3.77884865e-01 5.39839745e-01 -6.84537113e-01 -7.64237866e-02
-9.31156397e-01 -8.86392951e-01 1.40213954e+00 7.93366373e-01
-5.29779971e-01 8.46329749e-01 6.66565061e-01 -7.41338015e-01
1.09740891e-01 -9.44736004e-01 -1.14340961e+00 -3.55827749e-01
2.25109637e-01 9.99732852e-01 2.28241339e-01 -3.94191802... | [8.175292015075684, 10.341153144836426] |
c8ca5532-4672-4828-897b-c80cb3e79ec3 | simultaneous-denoising-and-dereverberation | 2004.02420 | null | https://arxiv.org/abs/2004.02420v1 | https://arxiv.org/pdf/2004.02420v1.pdf | Simultaneous Denoising and Dereverberation Using Deep Embedding Features | Monaural speech dereverberation is a very challenging task because no spatial cues can be used. When the additive noises exist, this task becomes more challenging. In this paper, we propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features, which is based on the... | ['Jian-Hua Tao', 'Cunhang Fan', 'Bin Liu', 'Zhengqi Wen', 'Jiangyan Yi'] | 2020-04-06 | null | null | null | null | ['speech-denoising', 'speech-dereverberation'] | ['speech', 'speech'] | [-2.03614980e-01 -4.67484862e-01 7.02173114e-01 -1.10760339e-01
-1.13813531e+00 -3.22553188e-01 1.68728203e-01 -3.00724834e-01
-2.28479177e-01 3.39203805e-01 6.44758701e-01 -2.24565476e-01
-8.69347528e-03 -3.15185130e-01 -5.19779444e-01 -1.45847011e+00
2.21099719e-01 -3.22563052e-01 -1.43297791e-01 -9.00477730... | [15.005694389343262, 5.840943813323975] |
0c2947e7-88c4-463f-8e40-72583a8c2c09 | meta-learning-extractors-for-music-source | 2002.07016 | null | https://arxiv.org/abs/2002.07016v1 | https://arxiv.org/pdf/2002.07016v1.pdf | Meta-learning Extractors for Music Source Separation | We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models. This enables efficient parameter-sharing, while still allowing for instrument-specific parameterization. Meta-TasNet is shown to be m... | ['Jason Naradowsky', 'Aditya Ganeshan', 'David Samuel'] | 2020-02-17 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 1.84260085e-01 -1.83995180e-02 -2.76644558e-01 9.61144790e-02
-1.32177138e+00 -6.25898838e-01 3.44068974e-01 -3.81039351e-01
-2.62268811e-01 2.32269615e-01 5.58062136e-01 1.71823636e-01
-2.60926038e-01 -1.94347501e-01 -2.73290873e-01 -6.52651906e-01
-1.16227090e-01 4.55470473e-01 -1.37616038e-01 -1.71137854... | [15.797202110290527, 5.4274001121521] |
96cd49c9-4cd1-4efd-bd58-1e63f682137b | blind-analysis-of-ct-image-noise-using | 1605.07650 | null | http://arxiv.org/abs/1605.07650v1 | http://arxiv.org/pdf/1605.07650v1.pdf | Blind Analysis of CT Image Noise Using Residual Denoised Images | CT protocol design and quality control would benefit from automated tools to
estimate the quality of generated CT images. These tools could be used to
identify erroneous CT acquisitions or refine protocols to achieve certain
signal to noise characteristics. This paper investigates blind estimation
methods to determine ... | ['Adam Alessio', 'Sohini Roychowdhury', 'Nathan Hollraft'] | 2016-05-24 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 2.34460905e-01 -4.12579924e-01 3.02568525e-01 -4.50635582e-01
-1.05217063e+00 -3.96600991e-01 4.08913255e-01 4.68694866e-01
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-4.96785462e-01 -6.36869133e-01 1.04953945e-01 -8.47544968e-01
-3.34944725e-01 4.30400014e-01 6.03845239e-01 1.54627576... | [13.423848152160645, -2.5327208042144775] |
39316861-c14a-4897-b187-9d3ebf19b1e6 | look-backward-and-forward-self-knowledge | 2203.05248 | null | https://arxiv.org/abs/2203.05248v2 | https://arxiv.org/pdf/2203.05248v2.pdf | Look Backward and Forward: Self-Knowledge Distillation with Bidirectional Decoder for Neural Machine Translation | Neural Machine Translation(NMT) models are usually trained via unidirectional decoder which corresponds to optimizing one-step-ahead prediction. However, this kind of unidirectional decoding framework may incline to focus on local structure rather than global coherence. To alleviate this problem, we propose a novel met... | ['Yanjun Miao', 'Liang Wang', 'Disheng Pan', 'Libin Shen', 'Xuanwei Zhang'] | 2022-03-10 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 1.20166153e-01 6.16700292e-01 -6.16858661e-01 -5.19522667e-01
-8.83139014e-01 -3.35532606e-01 8.99946928e-01 -5.20326018e-01
-1.91412926e-01 9.06556368e-01 8.69789124e-01 -7.80961454e-01
6.50084615e-01 -6.63345158e-01 -1.23353112e+00 -4.06376779e-01
4.89891350e-01 6.48113370e-01 -2.44140014e-01 -4.36506331... | [11.696146011352539, 10.098954200744629] |
1774fd76-73dd-4e3c-b8ae-5f9bdf754cf2 | core-gpt-combining-open-access-research-and | 2307.04683 | null | https://arxiv.org/abs/2307.04683v1 | https://arxiv.org/pdf/2307.04683v1.pdf | CORE-GPT: Combining Open Access research and large language models for credible, trustworthy question answering | In this paper, we present CORE-GPT, a novel question-answering platform that combines GPT-based language models and more than 32 million full-text open access scientific articles from CORE. We first demonstrate that GPT3.5 and GPT4 cannot be relied upon to provide references or citations for generated text. We then int... | ['Petr Knoth', 'Matteo Cancellieri', 'David Pride'] | 2023-07-06 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [-7.09762156e-01 6.50663555e-01 -8.97881836e-02 1.92251727e-01
-1.50865424e+00 -1.00092697e+00 5.56432009e-01 5.71922123e-01
-2.06489384e-01 1.03835106e+00 5.15225887e-01 -6.26270473e-01
-4.49384570e-01 -5.83233297e-01 -6.49127066e-01 1.15646452e-01
5.24532497e-01 5.96611440e-01 2.92090207e-01 -2.44617268... | [11.271529197692871, 8.072635650634766] |
545d3fbe-089d-416f-9410-01b33e464b9e | interpretable-eeg-seizure-prediction-using-a | null | null | https://www.nature.com/articles/s41598-022-08322-w | https://www.nature.com/articles/s41598-022-08322-w | Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm | Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seizure prediction models involves defining the pre-ictal period, a transition stage between inter-ictal brain acti... | ['César Teixeira', 'Pedro Martins', 'António Dourado', 'Fábio Lopes', 'Adriana Leal', 'Tiago Coelho', 'Mauro Pinto'] | 2022-03-15 | null | null | null | scientific-reports-2022-3 | ['seizure-prediction'] | ['medical'] | [ 2.03265995e-01 1.51414216e-01 2.27012888e-01 -2.75636047e-01
-6.70234561e-01 -5.73541939e-01 6.00142241e-01 4.63096559e-01
-3.71920019e-01 5.41428089e-01 1.81384444e-01 -4.44025755e-01
-6.42152071e-01 -4.67603445e-01 -2.21835151e-01 -7.99399078e-01
-8.26083601e-01 5.24346173e-01 3.41569744e-02 -1.41902128... | [13.234273910522461, 3.522299289703369] |
59a4c687-df57-4f78-9d71-658fd5c01c09 | rw-resnet-a-novel-speech-anti-spoofing-model | 2108.05684 | null | https://arxiv.org/abs/2108.05684v2 | https://arxiv.org/pdf/2108.05684v2.pdf | RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform | In recent years, synthetic speech generated by advanced text-to-speech (TTS) and voice conversion (VC) systems has caused great harms to automatic speaker verification (ASV) systems, urging us to design a synthetic speech detection system to protect ASV systems. In this paper, we propose a new speech anti-spoofing mode... | ['Shugong Xu', 'Zongze Ren', 'Youxuan Ma'] | 2021-08-12 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [-1.40762255e-02 -1.68322250e-01 6.04810799e-03 -3.49064134e-02
-4.15410489e-01 -1.79552883e-01 4.50370640e-01 -3.31809938e-01
-3.14219773e-01 4.50077057e-01 6.71474397e-01 -7.46076584e-01
5.20525515e-01 -5.32852471e-01 -2.05582976e-01 -7.42004156e-01
9.66666862e-02 -4.92597818e-01 5.90771735e-01 -5.42931437... | [14.149917602539062, 5.894186496734619] |
1d95f548-94b5-4c88-b126-2a05187899e0 | a-token-wise-cnn-based-method-for-sentence | 2009.11260 | null | https://arxiv.org/abs/2009.11260v1 | https://arxiv.org/pdf/2009.11260v1.pdf | A Token-wise CNN-based Method for Sentence Compression | Sentence compression is a Natural Language Processing (NLP) task aimed at shortening original sentences and preserving their key information. Its applications can benefit many fields e.g. one can build tools for language education. However, current methods are largely based on Recurrent Neural Network (RNN) models whic... | ['Piotr Koniusz', 'Sabrina Caldwell', 'Tom Gedeon', 'Weiwei Hou', 'Hanna Suominen'] | 2020-09-23 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 4.58778232e-01 2.74957478e-01 4.21364903e-02 -2.55172819e-01
-7.27079690e-01 -1.56549320e-01 4.29848433e-01 3.62752169e-01
-6.50411308e-01 7.98035622e-01 8.35821211e-01 -6.68149412e-01
2.47429624e-01 -9.55756366e-01 -8.53518784e-01 -3.34052563e-01
2.67811030e-01 1.70128658e-01 6.31337985e-02 -6.28516018... | [12.05704116821289, 9.223612785339355] |
a41adec2-6e6e-4e4e-b5ae-2a95d77f49a2 | surveying-the-research-on-fake-news-in-social | 2109.07909 | null | https://arxiv.org/abs/2109.07909v2 | https://arxiv.org/pdf/2109.07909v2.pdf | Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language | With the explosive growth of online social media, the ancient problem of information disorders interfering with news diffusion has surfaced with a renewed intensity threatening our democracies, public health, and news outlets' credibility. Therefore, thousands of scientific papers have been published in a relatively sh... | ['Paolo Rosso', 'Anastasia Giachanou', 'Alfonso Semeraro', 'Giancarlo Ruffo'] | 2021-09-13 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [ 6.91942172e-03 5.39590657e-01 -8.09310555e-01 3.66958231e-01
-7.14315251e-02 -5.73616326e-01 6.30555511e-01 9.11644280e-01
-5.88424861e-01 5.18292427e-01 6.27280712e-01 -6.53473616e-01
-2.46670097e-01 -8.75168681e-01 -3.26405942e-01 -1.03128918e-01
9.27910358e-02 1.63971066e-01 2.98714131e-01 -5.17287493... | [8.472614288330078, 10.017128944396973] |
348b4bf9-0b47-46fd-b793-66f83d548e43 | atp-amrize-then-parse-enhancing-amr-parsing | 2204.08875 | null | https://arxiv.org/abs/2204.08875v2 | https://arxiv.org/pdf/2204.08875v2.pdf | ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs | As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing. We find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring more performance gain than other tas... | ['Baobao Chang', 'Zhifang Sui', 'Tianyu Liu', 'Runxin Xu', 'Peiyi Wang', 'Liang Chen'] | 2022-04-19 | null | https://aclanthology.org/2022.findings-naacl.190 | https://aclanthology.org/2022.findings-naacl.190.pdf | findings-naacl-2022-7 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 6.87951386e-01 8.48200619e-01 -5.19887090e-01 -5.82031250e-01
-1.20443058e+00 -5.91832280e-01 5.33098221e-01 2.60325551e-01
-2.38411680e-01 6.96043015e-01 9.22422528e-01 -5.37053704e-01
1.66334450e-01 -8.10808778e-01 -9.22976017e-01 -2.35550910e-01
2.86240220e-01 5.76967359e-01 2.12431014e-01 -4.93052363... | [10.503780364990234, 9.313820838928223] |
ed9ad00f-cdb4-40e9-b1c2-80b72aaa9bb7 | do-we-really-need-scene-specific-pose | 2012.12014 | null | https://arxiv.org/abs/2012.12014v1 | https://arxiv.org/pdf/2012.12014v1.pdf | Do We Really Need Scene-specific Pose Encoders? | Visual pose regression models estimate the camera pose from a query image with a single forward pass. Current models learn pose encoding from an image using deep convolutional networks which are trained per scene. The resulting encoding is typically passed to a multi-layer perceptron in order to regress the pose. In th... | ['Ron Ferens', 'Yoli Shavit'] | 2020-12-22 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [ 2.99195468e-01 2.26636559e-01 -3.29446271e-02 -6.54220819e-01
-9.49824870e-01 -6.67534530e-01 6.13861561e-01 2.12055683e-01
-7.55941927e-01 3.98275256e-01 -9.67019200e-02 -3.67430039e-02
1.30178362e-01 -5.15967727e-01 -1.41340852e+00 -4.14940834e-01
7.20556974e-02 7.78428495e-01 3.24181139e-01 -1.98014021... | [7.720593452453613, -2.1309449672698975] |
d4c424a7-0800-46a3-b4e0-2c96b90f024b | learning-quality-aware-dynamic-memory-for | 2207.07922 | null | https://arxiv.org/abs/2207.07922v1 | https://arxiv.org/pdf/2207.07922v1.pdf | Learning Quality-aware Dynamic Memory for Video Object Segmentation | Recently, several spatial-temporal memory-based methods have verified that storing intermediate frames and their masks as memory are helpful to segment target objects in videos. However, they mainly focus on better matching between the current frame and the memory frames without explicitly paying attention to the quali... | ['Yujiu Yang', 'Weihao Xia', 'Wei Zhao', 'Xinyuan Zhao', 'Fei Yin', 'Ran Yu', 'Yong liu'] | 2022-07-16 | null | null | null | null | ['semi-supervised-video-object-segmentation'] | ['computer-vision'] | [-1.91044196e-01 -4.48204517e-01 -3.43038529e-01 -1.40841365e-01
-4.63786900e-01 -3.13861877e-01 9.23059210e-02 1.11660115e-01
-4.75914806e-01 3.77515465e-01 -1.72334418e-01 -1.05703451e-01
2.47987270e-01 -9.08005655e-01 -7.24832833e-01 -6.13962889e-01
9.34114829e-02 -2.63631362e-02 1.16810346e+00 1.94933221... | [9.173556327819824, -0.11341516673564911] |
dd4d8828-c6c1-4f29-88b0-cecf3662a65f | weakly-supervised-action-localization-by-2 | 2003.12424 | null | https://arxiv.org/abs/2003.12424v2 | https://arxiv.org/pdf/2003.12424v2.pdf | Weakly-Supervised Action Localization by Generative Attention Modeling | Weakly-supervised temporal action localization is a problem of learning an action localization model with only video-level action labeling available. The general framework largely relies on the classification activation, which employs an attention model to identify the action-related frames and then categorizes them in... | ['Qi Dai', 'Baifeng Shi', 'Yadong Mu', 'Jingdong Wang'] | 2020-03-27 | weakly-supervised-action-localization-by-3 | http://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Weakly-Supervised_Action_Localization_by_Generative_Attention_Modeling_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_Weakly-Supervised_Action_Localization_by_Generative_Attention_Modeling_CVPR_2020_paper.pdf | cvpr-2020-6 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 4.11493450e-01 1.73129991e-01 -5.54623246e-01 -3.20229799e-01
-7.64568508e-01 -2.07400039e-01 5.42922437e-01 -1.44767329e-01
-2.82720596e-01 6.60711884e-01 6.36019111e-01 2.79320508e-01
2.00962380e-01 -3.15489560e-01 -6.95120752e-01 -9.10353899e-01
4.94687147e-02 4.79806252e-02 5.06284952e-01 2.71935523... | [8.475275039672852, 0.6185852885246277] |
5411f983-da0a-4e64-9f3d-18b0ea552b72 | innovations-in-neural-data-to-text-generation | 2207.12571 | null | https://arxiv.org/abs/2207.12571v2 | https://arxiv.org/pdf/2207.12571v2.pdf | Innovations in Neural Data-to-text Generation: A Survey | The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG). This survey offers a consolidated view into the neural DTG paradigm with a structured examination of the approaches, benchmark datasets, and ... | ['Naren Ramakrishnan', 'Ajay Gogineni', 'Mandar Sharma'] | 2022-07-25 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 2.80118614e-01 8.03140223e-01 -2.95498639e-01 -5.13696074e-01
-5.36190629e-01 -6.35202527e-01 1.10445893e+00 2.12880582e-01
-3.12151790e-01 8.72764587e-01 1.07843900e+00 -5.21779597e-01
1.34833828e-01 -9.23126996e-01 -1.95513979e-01 -2.75358856e-01
2.55027175e-01 2.12465629e-01 -9.04530227e-01 -4.37970877... | [11.722847938537598, 9.03788948059082] |
7387e99a-812f-47de-ac2a-e64567b251c8 | learning-to-super-resolve-blurry-images-with | 2302.13766 | null | https://arxiv.org/abs/2302.13766v1 | https://arxiv.org/pdf/2302.13766v1.pdf | Learning to Super-Resolve Blurry Images with Events | Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution. In this paper, we employ events to alleviate the burden of SRB and propose an Event-enhanced SRB (E-SRB) algorithm, which can generate a sequence of sharp an... | ['Gui-Song Xia', 'Jianzhuang Liu', 'Wen Yang', 'Haijian Zhang', 'Xiang Zhang', 'Bishan Wang', 'Lei Yu'] | 2023-02-27 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 4.24481273e-01 -6.46959543e-01 6.15434013e-02 -2.95919269e-01
-8.84551048e-01 1.26180887e-01 3.37936491e-01 -7.70050883e-01
-6.51873648e-02 1.22992790e+00 6.36421740e-01 1.82598859e-01
-1.80044189e-01 -4.12151605e-01 -7.64059365e-01 -8.51697445e-01
1.41839221e-01 -3.71855170e-01 4.27367389e-01 3.17396000... | [11.214738845825195, -2.2743759155273438] |
4869a839-0dea-4f26-a31d-c40fcc6330dd | bi-directional-dermoscopic-feature-learning | 2002.08694 | null | https://arxiv.org/abs/2002.08694v1 | https://arxiv.org/pdf/2002.08694v1.pdf | Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation | Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin l... | ['Jun Liu', 'Henghui Ding', 'Xudong Jiang', 'Xiaohong Wang'] | 2020-02-20 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.18712938e-01 -9.31243226e-02 -5.35088778e-01 -5.17292678e-01
-8.11191738e-01 -5.05597532e-01 3.14031810e-01 3.37880015e-01
-2.59419411e-01 3.25184196e-01 -1.21259876e-02 -2.31759608e-01
-2.95450896e-01 -8.01267564e-01 -3.25247556e-01 -9.16740835e-01
3.80344719e-01 5.98387979e-02 4.38627571e-01 1.12788007... | [15.639713287353516, -2.920912027359009] |
d476fb90-cdb3-48e1-a7a3-afcbdc4047bb | sliced-wasserstein-on-symmetric-positive | 2303.05798 | null | https://arxiv.org/abs/2303.05798v2 | https://arxiv.org/pdf/2303.05798v2.pdf | Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals | When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals. Learning with these matrices requires using Riemanian geometry to account for their structure. In this paper, we propose a new method to deal with distri... | ['Nicolas Courty', 'Matthieu Kowalski', 'Thomas Moreau', 'Lucas Drumetz', 'Alain Rakotomamonjy', 'Benoît Malézieux', 'Clément Bonet'] | 2023-03-10 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.23349738e-02 1.52829945e-01 4.70037133e-01 -5.22117555e-01
-4.31373090e-01 -1.06344447e-01 2.84744322e-01 -9.75797549e-02
-6.69056296e-01 6.01442218e-01 -2.04362422e-02 -2.73437440e-01
-7.31995344e-01 -3.16716939e-01 -6.42937660e-01 -8.22061479e-01
-7.68944979e-01 5.07702827e-01 -1.62167147e-01 6.57168180... | [12.821236610412598, 3.5282480716705322] |
d3367fc5-6615-4bd6-aa3e-38b81ae744c7 | trier-template-guided-neural-networks-for | 2009.05407 | null | https://arxiv.org/abs/2009.05407v1 | https://arxiv.org/pdf/2009.05407v1.pdf | TRIER: Template-Guided Neural Networks for Robust and Interpretable Sleep Stage Identification from EEG Recordings | Neural networks often obtain sub-optimal representations during training, which degrade robustness as well as classification performances. This is a severe problem in applying deep learning to bio-medical domains, since models are vulnerable to being harmed by irregularities and scarcities in data. In this study, we pr... | ['Jeonghwan Hwang', 'Taeheon Lee', 'Honggu Lee'] | 2020-09-10 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 2.75067508e-01 -6.33125659e-03 -7.68974274e-02 -3.49795252e-01
-2.61043370e-01 -2.80923456e-01 2.17832610e-01 3.15226644e-01
-3.38399589e-01 7.25870728e-01 7.23724812e-02 -1.24068528e-01
-4.65299755e-01 -5.89617670e-01 -3.36068004e-01 -7.49170065e-01
-2.41454303e-01 5.23138717e-02 1.11671619e-01 -2.87671924... | [13.68143367767334, 3.450352191925049] |
341a172a-b4da-4be4-ba2a-d17ce5928689 | trainable-time-warping-aligning-time-series | 1903.09245 | null | http://arxiv.org/abs/1903.09245v1 | http://arxiv.org/pdf/1903.09245v1.pdf | Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain | DTW calculates the similarity or alignment between two signals, subject to
temporal warping. However, its computational complexity grows exponentially
with the number of time-series. Although there have been algorithms developed
that are linear in the number of time-series, they are generally quadratic in
time-series l... | ['Soheil Khorram', 'Melvin G McInnis', 'Emily Mower Provost'] | 2019-03-21 | null | null | null | null | ['time-series-averaging'] | ['time-series'] | [ 3.79442275e-01 -7.22749889e-01 4.14531901e-02 -4.14847642e-01
-1.21394563e+00 -7.67489195e-01 3.86003256e-01 -3.49676274e-02
-6.18794799e-01 4.36704636e-01 7.89229423e-02 -2.04485461e-01
-2.89587945e-01 -3.77180636e-01 -4.50426847e-01 -7.73978591e-01
-7.18561709e-01 1.76979348e-01 2.70222753e-01 -3.08256745... | [7.320582389831543, 3.3139266967773438] |
5a37cd89-c540-464e-987c-f7faf0a0c8b2 | trajectory-modeling-and-prediction-with | 1811.08576 | null | https://arxiv.org/abs/1811.08576v4 | https://arxiv.org/pdf/1811.08576v4.pdf | CM Modeling of Trajectory | Information about the waypoints of a moving object, e.g., an airliner in an air traffic control (ATC) problem, should be considered in trajectory modeling and prediction. Due to the ATC regulations, trajectory design criteria, and restricted motion capability of airliners there are long-range dependencies in trajectori... | ['X. Rong Li', 'Reza Rezaie'] | 2018-11-21 | null | null | null | null | ['trajectory-modeling'] | ['time-series'] | [-2.43602723e-01 -7.00126886e-01 -6.89111531e-01 -2.41637230e-01
1.33817539e-01 -7.16573060e-01 5.23799658e-01 2.77835280e-02
-3.18809658e-01 6.13052130e-01 2.75201887e-01 -1.11242807e+00
-9.74573255e-01 -7.46386468e-01 -3.11453998e-01 -5.45954883e-01
-8.86519477e-02 2.48174101e-01 3.39626729e-01 -1.83851764... | [5.769467830657959, 1.2200042009353638] |
f139f321-8f5e-4b0d-b9b8-e1010d54ad90 | terminology-extraction-using-co-occurrence | null | null | https://aclanthology.org/2022.term-1.5 | https://aclanthology.org/2022.term-1.5.pdf | Terminology extraction using co-occurrence patterns as predictors of semantic relevance | We propose a method for automatic term extraction based on a statistical measure that ranks term candidates according to their semantic relevance to a specialised domain. As a measure of relevance we use term co-occurrence, defined as the repeated instantiation of two terms in the same sentences, in indifferent order a... | ['David Lindemann', 'Rogelio Nazar'] | null | null | null | null | term-lrec-2022-6 | ['term-extraction'] | ['natural-language-processing'] | [ 2.92384565e-01 5.77441789e-02 -6.84229806e-02 -4.10983294e-01
-7.98438370e-01 -6.59064353e-01 9.54621315e-01 8.36138785e-01
-1.00744116e+00 9.66240108e-01 3.96381557e-01 -3.46257180e-01
-5.62775910e-01 -8.10109437e-01 1.75764353e-03 -5.98923922e-01
-8.33365023e-02 7.87356377e-01 4.71275032e-01 -5.71579933... | [10.130942344665527, 8.912832260131836] |
833a29ac-39cc-4637-9062-91b02a66cf25 | energy-flows-towards-determinant-free | 2206.06672 | null | https://arxiv.org/abs/2206.06672v2 | https://arxiv.org/pdf/2206.06672v2.pdf | Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows | Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians. This paper studies the likelihood-free training of flows and proposes the energy objective, an alternative sample-based loss based on proper scoring rules. The energy objectiv... | ['Volodymyr Kuleshov', 'Yair Schiff', 'Subham Sekhar Sahoo', 'Zeyi Chen', 'Phillip Si'] | 2022-06-14 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [-1.13165438e-01 9.31444019e-02 -2.27136552e-01 -2.33782142e-01
-8.78371656e-01 -6.05732262e-01 9.42360163e-01 -4.33205605e-01
-3.82206410e-01 1.02841115e+00 3.70729446e-01 -4.09169316e-01
-3.45064998e-01 -8.18168581e-01 -4.72640067e-01 -5.24985015e-01
-5.60661890e-02 6.13719523e-01 -8.51770211e-03 1.44819498... | [7.151242733001709, 3.7968366146087646] |
85022017-3be2-4858-a003-320e9670e59b | revisiting-spectral-graph-clustering-with | 1709.04594 | null | http://arxiv.org/abs/1709.04594v2 | http://arxiv.org/pdf/1709.04594v2.pdf | Revisiting Spectral Graph Clustering with Generative Community Models | The methodology of community detection can be divided into two principles:
imposing a network model on a given graph, or optimizing a designed objective
function. The former provides guarantees on theoretical detectability but falls
short when the graph is inconsistent with the underlying model. The latter is
model-fre... | ['Pin-Yu Chen', 'Lingfei Wu'] | 2017-09-14 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [ 3.52520704e-01 2.30358597e-02 1.03315048e-01 1.47347495e-01
-5.75246274e-01 -5.73041975e-01 4.70757306e-01 4.58251506e-01
3.68990228e-02 3.00158083e-01 -3.76571208e-01 -2.14379877e-01
-4.17198360e-01 -9.59980905e-01 -4.15879101e-01 -8.79101276e-01
-4.59558785e-01 5.95293462e-01 4.75769162e-01 1.29549205... | [6.985329627990723, 5.248591423034668] |
578a1625-26e5-429e-9d8a-7ece9e315dfa | non-asymptotic-analysis-of-langevin-type | 2303.12407 | null | https://arxiv.org/abs/2303.12407v4 | https://arxiv.org/pdf/2303.12407v4.pdf | Non-asymptotic analysis of Langevin-type Monte Carlo algorithms | We study Langevin-type algorithms for sampling from Gibbs distributions such that the potentials are dissipative and their weak gradients have finite moduli of continuity not necessarily convergent to zero. Our main result is a non-asymptotic upper bound of the 2-Wasserstein distance between a Gibbs distribution and th... | ['Shogo Nakakita'] | 2023-03-22 | null | null | null | null | ['type'] | ['speech'] | [-1.33940116e-01 3.74054015e-01 1.96971193e-01 -2.40749940e-01
-8.01310241e-01 -4.47164118e-01 4.91409153e-01 -9.47313681e-02
-6.32430315e-01 1.20156014e+00 -2.37538651e-01 -7.98312128e-02
9.47348587e-03 -9.74758983e-01 -7.28683710e-01 -1.20279396e+00
-3.18902582e-01 9.08298492e-01 2.35717878e-01 1.54849058... | [7.009736061096191, 4.158105373382568] |
5d310e25-6c25-4c9f-b559-9edd0d2a13ad | referring-image-segmentation-by-generative | null | null | https://www.semanticscholar.org/paper/Referring-Image-Segmentation-by-Generative-Learning-Qiu-Zhao/863ce99910dad0efa42385e8a3bcbeb1f400acfb | https://jianbojiao.com/pdfs/TMM.pdf | Referring Image Segmentation by Generative Adversarial Learning | Referring expression is a kind of language expression being used for referring to particular objects. In this paper, we focus on the problem of image segmentation from natural language referring expressions. Existing works tackle this problem by augmenting the convolutional semantic segmentation networks with an LSTM s... | ['Shuang Qiu', 'Shikui Wei', 'Jianbo Jiao', 'Yunchao Wei', 'Yao Zhao'] | 2020-04-20 | null | null | null | ieee-2020-4 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 4.11147714e-01 4.35942292e-01 -3.14303786e-02 -5.15866160e-01
-8.05503786e-01 -2.93100685e-01 4.47681457e-01 -3.74095351e-01
-3.36506754e-01 6.63659930e-01 1.99554995e-01 -9.19572264e-02
7.34955147e-02 -9.79643047e-01 -1.03880262e+00 -7.83292353e-01
6.95270002e-01 1.89836711e-01 2.55260974e-01 -2.99533427... | [10.25454330444336, 1.173134684562683] |
95eb1565-836a-4590-a7c8-55ddc1db8292 | task-oriented-human-object-interactions | 2303.13129 | null | https://arxiv.org/abs/2303.13129v1 | https://arxiv.org/pdf/2303.13129v1.pdf | Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations | Digital human motion synthesis is a vibrant research field with applications in movies, AR/VR, and video games. Whereas methods were proposed to generate natural and realistic human motions, most only focus on modeling humans and largely ignore object movements. Generating task-oriented human-object interaction motions... | ['Bo Dai', 'Chen Change Loy', 'Jingbo Wang', 'Quanzhou Li'] | 2023-03-23 | null | null | null | null | ['human-object-interaction-detection', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.87170953e-02 -3.77760716e-02 -9.70176980e-02 9.85018983e-02
-2.41646975e-01 -6.29263759e-01 5.64243972e-01 -4.11264241e-01
-2.74138570e-01 4.49882686e-01 3.91997576e-01 -5.73221780e-02
1.34849906e-01 -4.83413070e-01 -4.34064060e-01 -5.12930572e-01
1.39773130e-01 3.70410681e-01 3.97539318e-01 -2.24374160... | [10.694328308105469, -0.7021053433418274] |
fc8c906b-ca52-42d2-a47e-62a3b7690783 | rethinking-eye-blink-assessing-task | 2102.06690 | null | https://arxiv.org/abs/2102.06690v1 | https://arxiv.org/pdf/2102.06690v1.pdf | Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking | Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysi... | ['Youngjun Cho'] | 2021-02-12 | null | null | null | null | ['mental-workload-estimation'] | ['computer-vision'] | [ 1.99295431e-01 -3.67311567e-01 1.72595292e-01 -1.03075139e-01
-3.38105977e-01 -3.73535305e-01 1.37468487e-01 1.55718327e-01
-3.24702531e-01 6.15508676e-01 -4.50221933e-02 -4.22748655e-01
-4.57984746e-01 -1.11414321e-01 -1.93066821e-01 -4.59464073e-01
3.31237227e-01 -4.63384509e-01 1.64591298e-01 -2.52141446... | [14.077163696289062, 0.17837637662887573] |
489a2683-3434-4058-8c72-7fe02b1b5638 | beyond-known-reality-exploiting | 2307.02131 | null | https://arxiv.org/abs/2307.02131v1 | https://arxiv.org/pdf/2307.02131v1.pdf | Beyond Known Reality: Exploiting Counterfactual Explanations for Medical Research | This study employs counterfactual explanations to explore "what if?" scenarios in medical research, with the aim of expanding our understanding beyond existing boundaries. Specifically, we focus on utilizing MRI features for diagnosing pediatric posterior fossa brain tumors as a case study. The field of artificial inte... | ['Bilgin Keserci', 'Serkan Ayvaz', 'Toygar Tanyel'] | 2023-07-05 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 4.99072641e-01 8.40891540e-01 -6.61690056e-01 -5.52290142e-01
-4.18051839e-01 -9.28622484e-02 6.97625816e-01 4.21296299e-01
-4.52600867e-01 1.02844214e+00 7.76289701e-01 -9.09036636e-01
-5.36732316e-01 -2.69966125e-01 -5.69314361e-01 -5.96632004e-01
-1.63479671e-01 4.61690813e-01 -7.02262640e-01 1.92997098... | [8.477760314941406, 5.671731472015381] |
a4fecac1-ef4d-4d3a-9b64-507e0acc573e | blind-mask-to-improve-intelligibility-of-non | 2008.09175 | null | https://arxiv.org/abs/2008.09175v1 | https://arxiv.org/pdf/2008.09175v1.pdf | Blind Mask to Improve Intelligibility of Non-Stationary Noisy Speech | This letter proposes a novel blind acoustic mask (BAM) designed to adaptively detect noise components and preserve target speech segments in time-domain. A robust standard deviation estimator is applied to the non-stationary noisy speech to identify noise masking elements. The main contribution of the proposed solution... | ['R. Coelho', 'F. Farias'] | 2020-08-20 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 5.25471807e-01 -3.41360301e-01 3.15537125e-01 -9.92727503e-02
-7.98042357e-01 -4.35727358e-01 4.08805251e-01 -1.19974464e-01
-5.87982595e-01 6.26678407e-01 4.83914584e-01 -4.70374912e-01
-3.43785703e-01 -2.23947346e-01 -3.70559394e-02 -9.86230016e-01
-1.02273777e-01 -3.64276201e-01 2.94707060e-01 -1.32457316... | [15.005290031433105, 5.788326263427734] |
afc9581d-c5c5-44c3-a8a2-97f3e096be71 | neurar-neural-uncertainty-for-autonomous-3d | 2207.10985 | null | https://arxiv.org/abs/2207.10985v2 | https://arxiv.org/pdf/2207.10985v2.pdf | NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations | Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, where a robot is required to explore a scene and plan a view path for the reconstruction, has not been ... | ['Qi Ye', 'Jiming Chen', 'Gimhee Lee', 'Yingfeng Chen', 'Lincheng Li', 'Shibo He', 'Jing Zeng', 'Yunlong Ran'] | 2022-07-22 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 1.55684203e-01 3.71381164e-01 1.21838711e-01 -4.84453559e-01
-7.59230912e-01 -3.98970693e-01 4.97095674e-01 -1.85949564e-01
-2.75353462e-01 4.96694177e-01 1.70793816e-01 -6.70728311e-02
-3.48435968e-01 -6.32798374e-01 -8.75910223e-01 -6.88529253e-01
-1.30749808e-03 6.07321441e-01 -1.63980961e-01 -1.96259871... | [8.47435474395752, -2.8217222690582275] |
9141d692-dcdc-4bc4-8c37-9e353bc662d5 | context-aware-unsupervised-clustering-for | 2110.01341 | null | https://arxiv.org/abs/2110.01341v1 | https://arxiv.org/pdf/2110.01341v1.pdf | Context-Aware Unsupervised Clustering for Person Search | The existing person search methods use the annotated labels of person identities to train deep networks in a supervised manner that requires a huge amount of time and effort for human labeling. In this paper, we first introduce a novel framework of person search that is able to train the network in the absence of the p... | ['Jae-Young Sim', 'Kuhyeun Ko', 'Byeong-Ju Han'] | 2021-10-04 | null | null | null | null | ['person-search', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 1.17395036e-01 -1.17945649e-01 7.41372108e-02 -6.05771244e-01
-1.16811179e-01 -3.78942102e-01 7.74720788e-01 -2.06668470e-02
-1.05036938e+00 6.47876978e-01 -2.12579757e-01 2.28295505e-01
-2.77007103e-01 -7.50874817e-01 -3.56044054e-01 -6.87622607e-01
2.57253259e-01 1.08771789e+00 8.02804977e-02 1.08427957... | [14.795513153076172, 1.025801658630371] |
0412240b-ee74-4da7-a001-26db26479215 | deep-image-prior | 1711.10925 | null | https://arxiv.org/abs/1711.10925v4 | https://arxiv.org/pdf/1711.10925v4.pdf | Deep Image Prior | Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is suf... | ['Dmitry Ulyanov', 'Andrea Vedaldi', 'Victor Lempitsky'] | 2017-11-29 | deep-image-prior-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Ulyanov_Deep_Image_Prior_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Ulyanov_Deep_Image_Prior_CVPR_2018_paper.pdf | cvpr-2018-6 | ['jpeg-compression-artifact-reduction'] | ['computer-vision'] | [ 5.35793662e-01 1.20653465e-01 8.44370797e-02 -2.00353757e-01
-6.73515975e-01 -3.56554091e-01 6.26676798e-01 -3.63753676e-01
-2.68293530e-01 9.05609012e-01 2.17848167e-01 -9.09477100e-02
-1.17183834e-01 -8.31279099e-01 -9.34738696e-01 -9.24239337e-01
4.13196385e-01 1.75264105e-01 8.01844075e-02 -4.99018669... | [11.492607116699219, -2.221374034881592] |
a9a5f2d3-4b5b-4522-a8ed-73c578c3b584 | skeleton-aware-multi-scale-heatmap-regression | 2105.10904 | null | https://arxiv.org/abs/2105.10904v1 | https://arxiv.org/pdf/2105.10904v1.pdf | Skeleton-aware multi-scale heatmap regression for 2D hand pose estimation | Existing RGB-based 2D hand pose estimation methods learn the joint locations from a single resolution, which is not suitable for different hand sizes. To tackle this problem, we propose a new deep learning-based framework that consists of two main modules. The former presents a segmentation-based approach to detect the... | ['Yakup Genc', 'Ikram Kourbane'] | 2021-05-23 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-1.50524914e-01 -3.82551700e-01 -1.75380155e-01 3.70667316e-02
-6.77908182e-01 -4.59734291e-01 1.83443680e-01 -3.79200518e-01
-6.00874305e-01 4.77504849e-01 9.59363058e-02 1.42475322e-01
9.48875099e-02 -5.61035752e-01 -4.59230930e-01 -5.70808470e-01
3.19814980e-01 8.84142101e-01 6.17130280e-01 4.51482758... | [6.6169867515563965, -0.7522726058959961] |
b98363f3-f4c9-40ee-9539-5a5e9d0d0b7f | improving-temporal-action-proposal-generation | 1906.06496 | null | https://arxiv.org/abs/1906.06496v4 | https://arxiv.org/pdf/1906.06496v4.pdf | Accelerating temporal action proposal generation via high performance computing | Temporal action recognition always depends on temporal action proposal generation to hypothesize actions and algorithms usually need to process very long video sequences and output the starting and ending times of each potential action in each video suffering from high computation cost. To address this, based on bounda... | ['Youyou Jiang', 'Choi Chang', 'Tian Wang', 'Hichem Snoussi', 'Guangcun Shan', 'Shiye Lei'] | 2019-06-15 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 1.97377652e-01 -5.25387406e-01 -1.34127870e-01 -9.06231701e-02
-2.63133943e-01 -1.65098682e-02 4.89716023e-01 -2.57072449e-01
-7.63204455e-01 5.89572728e-01 1.81138426e-01 1.42800119e-02
-7.64790699e-02 -8.40846181e-01 -5.30967891e-01 -9.90469754e-01
-3.39704365e-01 9.95984748e-02 8.99568975e-01 5.01098372... | [8.37679672241211, 0.4050038456916809] |
07168c5e-1124-4982-a7a0-611440b118e7 | leveraging-gans-for-data-scarcity-of-covid-19 | 2304.03536 | null | https://arxiv.org/abs/2304.03536v1 | https://arxiv.org/pdf/2304.03536v1.pdf | Leveraging GANs for data scarcity of COVID-19: Beyond the hype | Artificial Intelligence (AI)-based models can help in diagnosing COVID-19 from lung CT scans and X-ray images; however, these models require large amounts of data for training and validation. Many researchers studied Generative Adversarial Networks (GANs) for producing synthetic lung CT scans and X-Ray images to improv... | ['Zubair Shah', 'Christer Gronlund', 'Hazrat Ali'] | 2023-04-07 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 4.31268722e-01 6.69405341e-01 -2.90741086e-01 -3.06426078e-01
-9.82382238e-01 -3.87715846e-01 2.52801359e-01 -8.67748708e-02
-2.89242178e-01 8.18601370e-01 4.04576033e-01 -6.13178492e-01
2.01442003e-01 -8.85062337e-01 -7.10213363e-01 -8.24842930e-01
2.53825128e-01 7.64482856e-01 -1.67104736e-01 7.89266229... | [14.371482849121094, -1.9287341833114624] |
2fe51e68-407a-4721-88db-5d6375c433ef | learning-image-adaptive-codebooks-for-class | 2306.06513 | null | https://arxiv.org/abs/2306.06513v2 | https://arxiv.org/pdf/2306.06513v2.pdf | Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration | Recent work on discrete generative priors, in the form of codebooks, has shown exciting performance for image reconstruction and restoration, as the discrete prior space spanned by the codebooks increases the robustness against diverse image degradations. Nevertheless, these methods require separate training of codeboo... | ['Jinwei Gu', 'Inchang Choi', 'Yitong Jiang', 'Kechun Liu'] | 2023-06-10 | null | null | null | null | ['image-super-resolution', 'image-reconstruction', 'super-resolution', 'image-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 5.12849927e-01 -2.21529216e-01 -1.26603236e-02 -3.39501113e-01
-7.40013897e-01 -3.13872755e-01 7.23559022e-01 -4.01200026e-01
-6.93700463e-02 4.70127136e-01 4.99582559e-01 1.74035639e-01
1.94813702e-02 -6.85099304e-01 -6.83661699e-01 -9.11086380e-01
2.44975671e-01 4.09525484e-01 1.70673542e-02 -2.01056346... | [11.243682861328125, -1.9691118001937866] |
0f499947-87ad-4616-965d-4784afa58681 | do-gpts-produce-less-literal-translations | 2305.16806 | null | https://arxiv.org/abs/2305.16806v4 | https://arxiv.org/pdf/2305.16806v4.pdf | Do GPTs Produce Less Literal Translations? | Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. Howev... | ['Hany Hassan Awadalla', 'Matt Post', 'Arul Menezes', 'Vikas Raunak'] | 2023-05-26 | null | null | null | null | ['nmt', 'word-alignment'] | ['computer-code', 'natural-language-processing'] | [ 4.21884030e-01 4.59460258e-01 -5.94111621e-01 -4.02866960e-01
-1.05058825e+00 -6.86286986e-01 1.07033980e+00 -2.98129115e-03
-2.38131106e-01 1.07899320e+00 5.26511550e-01 -5.61010242e-01
1.61309958e-01 -6.35391235e-01 -8.09850037e-01 -2.40802437e-01
3.26440871e-01 8.61695826e-01 -4.07735497e-01 -7.40176201... | [11.595191955566406, 10.141671180725098] |
2f98b2eb-ca60-42b9-a849-691ff72cabc2 | q-diffusion-quantizing-diffusion-models | 2302.04304 | null | https://arxiv.org/abs/2302.04304v3 | https://arxiv.org/pdf/2302.04304v3.pdf | Q-Diffusion: Quantizing Diffusion Models | Diffusion models have achieved great success in image synthesis through iterative noise estimation using deep neural networks. However, the slow inference, high memory consumption, and computation intensity of the noise estimation model hinder the efficient adoption of diffusion models. Although post-training quantizat... | ['Long Lian', 'Kurt Keutzer', 'Shanghang Zhang', 'Daniel Kang', 'Zhen Dong', 'Huanrui Yang', 'Yijiang Liu', 'Xiuyu Li'] | 2023-02-08 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 3.60348374e-01 -1.17360383e-01 -6.62982315e-02 -8.78411904e-02
-8.46211135e-01 -3.53745610e-01 6.78264916e-01 3.33860442e-02
-6.49189472e-01 4.09649849e-01 -5.29831387e-02 -4.16425318e-01
1.45024983e-02 -9.76586759e-01 -7.55594969e-01 -7.94560969e-01
2.02482179e-01 3.70969623e-01 4.54452813e-01 -2.06712976... | [11.212867736816406, -0.4453328549861908] |
58bf8704-048f-40e1-a1ea-ea02a31772ad | unsupervised-learning-of-neurosymbolic | 2107.13132 | null | https://arxiv.org/abs/2107.13132v2 | https://arxiv.org/pdf/2107.13132v2.pdf | Unsupervised Learning of Neurosymbolic Encoders | We present a framework for the unsupervised learning of neurosymbolic encoders, which are encoders obtained by composing neural networks with symbolic programs from a domain-specific language. Our framework naturally incorporates symbolic expert knowledge into the learning process, which leads to more interpretable and... | ['Swarat Chaudhuri', 'Yisong Yue', 'Ann Kennedy', 'Jennifer J. Sun', 'Eric Zhan'] | 2021-07-28 | unsupervised-learning-of-neurosymbolic-1 | https://openreview.net/forum?id=aJ_GcB4vcT0 | https://openreview.net/pdf?id=aJ_GcB4vcT0 | null | ['sports-analytics'] | ['computer-vision'] | [ 5.30214846e-01 4.36767370e-01 -3.90967309e-01 -3.90599012e-01
-2.89231032e-01 -5.85990012e-01 8.77257288e-01 2.16481119e-01
-2.78985947e-02 5.02583444e-01 5.59984922e-01 -3.82527024e-01
-1.44478589e-01 -9.43570554e-01 -1.40252852e+00 -4.57395107e-01
-1.13151759e-01 5.42994916e-01 -1.05641358e-01 -3.65747362... | [8.596863746643066, 7.2019548416137695] |
d18dc00d-9127-49f8-a2da-20c9254b20da | multimodal-deep-learning-to-differentiate | 2302.14124 | null | https://arxiv.org/abs/2302.14124v1 | https://arxiv.org/pdf/2302.14124v1.pdf | Multimodal Deep Learning to Differentiate Tumor Recurrence from Treatment Effect in Human Glioblastoma | Differentiating tumor progression (TP) from treatment-related necrosis (TN) is critical for clinical management decisions in glioblastoma (GBM). Dynamic FDG PET (dPET), an advance from traditional static FDG PET, may prove advantageous in clinical staging. dPET includes novel methods of a model-corrected blood input fu... | ['Bijoy Kundu', 'Miaomiao Zhang', 'David Schiff', 'Sohil Patel', 'Thomas Eluvathingal Muttikkal', 'Nivetha Jayakumar', 'Zoraiz Qureshi', 'Tonmoy Hossain'] | 2023-02-27 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [-2.62314510e-02 -2.46639922e-01 -2.78299451e-01 -3.78806353e-01
-1.00612617e+00 -2.64399707e-01 3.91781300e-01 4.71193880e-01
-1.04342341e+00 1.10931599e+00 -1.52342003e-02 -6.72805250e-01
6.01451397e-02 -8.72385621e-01 -2.59197623e-01 -9.96491730e-01
-1.60193801e-01 5.17035663e-01 2.06874490e-01 2.20544681... | [14.797369956970215, -2.488004684448242] |
5f590126-d7b5-4365-baf3-184d24eb395f | balancing-fairness-and-accuracy-in-sentiment | 2204.10940 | null | https://arxiv.org/abs/2204.10940v1 | https://arxiv.org/pdf/2204.10940v1.pdf | Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models | Sentiment detection is an important building block for multiple information retrieval tasks such as product recommendation, cyberbullying detection, and misinformation detection. Unsurprisingly, multiple commercial APIs, each with different levels of accuracy and fairness, are now available for sentiment detection. Whi... | ['Vivek K. Singh', 'Abdulaziz A. Almuzaini'] | 2022-04-22 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 1.02528512e-01 -1.64228886e-01 -5.98967135e-01 -7.30232120e-01
-9.78116453e-01 -6.14404380e-01 5.89420199e-01 5.72302520e-01
-4.07169342e-01 2.56916374e-01 1.76191986e-01 -2.51047075e-01
3.25998664e-01 -4.68062460e-01 -5.64455390e-01 -3.10383856e-01
3.29047024e-01 -1.80358440e-01 5.36266156e-02 -2.20943362... | [13.015795707702637, 1.37960684299469] |
c4db4746-ac62-4e8c-908b-af318cf0d75f | freezing-of-gait-prediction-from | 2307.03475 | null | https://arxiv.org/abs/2307.03475v1 | https://arxiv.org/pdf/2307.03475v1.pdf | Freezing of Gait Prediction From Accelerometer Data Using a Simple 1D-Convolutional Neural Network -- 8th Place Solution for Kaggle's Parkinson's Freezing of Gait Prediction Competition | Freezing of Gait (FOG) is a common motor symptom in patients with Parkinson's disease (PD). During episodes of FOG, patients suddenly lose their ability to stride as intended. Patient-worn accelerometers can capture information on the patient's movement during these episodes and machine learning algorithms can potentia... | ['Jan Brederecke'] | 2023-07-07 | null | null | null | null | ['management'] | ['miscellaneous'] | [-7.65734017e-02 -1.79325882e-02 -1.32305980e-01 -1.20684236e-01
-7.94085085e-01 -1.15182111e-02 -5.07086180e-02 -1.34904087e-01
-5.99668562e-01 7.57703185e-01 6.76695347e-01 3.69670689e-02
1.32112443e-01 -5.40825069e-01 -1.93007663e-01 -3.33607554e-01
-5.22456765e-01 6.59451246e-01 2.61896223e-01 -2.52704799... | [7.128357887268066, 0.3528047800064087] |
a018fee3-7be9-4c11-a57a-363e472b10fd | video-graph-transformer-for-video-question | 2207.05342 | null | https://arxiv.org/abs/2207.05342v3 | https://arxiv.org/pdf/2207.05342v3.pdf | Video Graph Transformer for Video Question Answering | This paper proposes a Video Graph Transformer (VGT) model for Video Quetion Answering (VideoQA). VGT's uniqueness are two-fold: 1) it designs a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations, and dynamics for complex spatio-temporal reasoning; and 2) it ... | ['Shuicheng Yan', 'Tat-Seng Chua', 'Pan Zhou', 'Junbin Xiao'] | 2022-07-12 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [-6.52494356e-02 3.31605487e-02 -2.41523087e-01 -1.77052930e-01
-8.53477657e-01 -7.51838982e-01 6.67897284e-01 -3.08723688e-01
-1.74442623e-02 3.06000918e-01 5.24050713e-01 -4.51351553e-01
-2.60398686e-01 -6.20016754e-01 -9.97371495e-01 -4.29109335e-01
-7.36403018e-02 7.30404377e-01 1.96740046e-01 -4.87428725... | [10.25669002532959, 0.9966393113136292] |
8b083318-6775-45d5-9e00-c73f71c5dc76 | generative-probabilistic-image-colorization | 2109.14518 | null | https://arxiv.org/abs/2109.14518v1 | https://arxiv.org/pdf/2109.14518v1.pdf | Generative Probabilistic Image Colorization | We propose Generative Probabilistic Image Colorization, a diffusion-based generative process that trains a sequence of probabilistic models to reverse each step of noise corruption. Given a line-drawing image as input, our method suggests multiple candidate colorized images. Therefore, our method accounts for the ill-p... | ['Yuri Odagiri', 'Michael Li', 'Shinya Kitaoka', 'Chie Furusawa'] | 2021-09-29 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 5.84849656e-01 -1.31564379e-01 4.43779558e-01 -1.07465029e-01
-1.00818038e+00 -6.40829861e-01 8.66302669e-01 -3.03561896e-01
-4.43057537e-01 8.06021214e-01 -9.97966751e-02 -1.87677369e-01
1.02154970e-01 -8.00628364e-01 -7.95934498e-01 -7.95455515e-01
5.50801396e-01 6.17621005e-01 1.05587982e-01 1.95749089... | [11.413546562194824, -0.4228103458881378] |
150025dc-2c2e-4109-8eaa-a61199bd07ad | ai-audit-a-card-game-to-reflect-on-everyday | 2305.17910 | null | https://arxiv.org/abs/2305.17910v1 | https://arxiv.org/pdf/2305.17910v1.pdf | AI Audit: A Card Game to Reflect on Everyday AI Systems | An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems. Previous work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing responsible AI. The... | ['Cynthia Breazeal', 'Vishesh Kumar', 'Safinah Ali'] | 2023-05-29 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 1.50356710e-01 1.12712169e+00 7.84710944e-02 -3.09129916e-02
-1.62009612e-01 -6.02566779e-01 2.61839718e-01 9.86184999e-02
-3.71500164e-01 3.46014202e-01 5.23958564e-01 -7.27303684e-01
-2.15090349e-01 -7.72127151e-01 -8.30938220e-01 -2.73627251e-01
5.57456732e-01 1.92530394e-01 1.96989104e-01 -7.62617171... | [10.003620147705078, 7.051984786987305] |
cd477be3-7b37-4b53-92d1-a701682f02d5 | continual-segment-towards-a-single-unified | 2302.00162 | null | https://arxiv.org/abs/2302.00162v3 | https://arxiv.org/pdf/2302.00162v3.pdf | Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans | Deep learning empowers the mainstream medical image segmentation methods. Nevertheless current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new incremental segmentation classes (along with new training datasets or not) are required to be added... | ['Dakai Jin', 'Qifeng Wang', 'Mingchen Gao', 'Le Lu', 'Jingren Zhou', 'Minfeng Xu', 'Xianghua Ye', 'Jia Ge', 'Ke Yan', 'Puyang Wang', 'Dazhou Guo', 'Zhanghexuan Ji'] | 2023-02-01 | null | null | null | null | ['continual-semantic-segmentation'] | ['computer-vision'] | [ 5.41394353e-01 5.65445662e-01 -2.75914073e-01 -5.28584838e-01
-9.26288188e-01 -7.23761916e-01 8.97387713e-02 3.66071612e-01
-4.89490002e-01 7.24552453e-01 -2.77734309e-01 -4.64091569e-01
9.29275528e-02 -5.96699178e-01 -8.22235405e-01 -5.31362414e-01
2.35943198e-02 9.46713924e-01 5.91671348e-01 2.44105488... | [14.699909210205078, -2.3374366760253906] |
426ec053-4c0f-4fd8-a01f-1a5c1aa191b4 | yedrouj-net-an-efficient-cnn-for-spatial | 1803.00407 | null | http://arxiv.org/abs/1803.00407v1 | http://arxiv.org/pdf/1803.00407v1.pdf | Yedrouj-Net: An efficient CNN for spatial steganalysis | For about 10 years, detecting the presence of a secret message hidden in an
image was performed with an Ensemble Classifier trained with Rich features. In
recent years, studies such as Xu et al. have indicated that well-designed
convolutional Neural Networks (CNN) can achieve comparable performance to the
two-step mach... | ['Marc Chaumont', 'Mehdi Yedroudj', 'Frederic Comby'] | 2018-02-26 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 4.27068532e-01 2.18945533e-01 3.11088301e-02 -1.75275147e-01
-2.67896622e-01 -1.19463973e-01 8.14911187e-01 -3.95835899e-02
-6.18466675e-01 6.35306954e-01 -7.74102286e-02 -4.78682131e-01
1.41921386e-01 -8.99437129e-01 -5.96289158e-01 -9.90464211e-01
-2.79855907e-01 -2.48245850e-01 4.94457543e-01 -7.32561350... | [4.326910972595215, 8.044705390930176] |
dcc707ae-8d73-4991-bb35-d37c6cbc4b03 | satellite-image-forgery-detection-and | 1802.04881 | null | http://arxiv.org/abs/1802.04881v1 | http://arxiv.org/pdf/1802.04881v1.pdf | Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier | Current satellite imaging technology enables shooting high-resolution
pictures of the ground. As any other kind of digital images, overhead pictures
can also be easily forged. However, common image forensic techniques are often
developed for consumer camera images, which strongly differ in their nature
from satellite o... | ['David Güera', 'Sri Kalyan Yarlagadda', 'Fengqing Maggie Zhu', 'Edward J. Delp', 'Paolo Bestagini', 'Stefano Tubaro'] | 2018-02-13 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 5.93956470e-01 -3.07676971e-01 3.98133814e-01 -1.55981943e-01
-6.13335192e-01 -9.29623783e-01 4.78902727e-01 -4.71605994e-02
-3.53964895e-01 7.53074706e-01 -5.25307953e-01 -3.49329650e-01
1.02569163e-01 -1.09403658e+00 -8.72713447e-01 -1.10854411e+00
4.92642932e-02 8.78769811e-03 2.21858472e-01 -8.51041526... | [12.403716087341309, 0.9604350924491882] |
6cd8ba9e-5fef-4772-a64e-ae1c57b8ff9c | image-to-video-person-re-identification-by | 1810.03989 | null | http://arxiv.org/abs/1810.03989v2 | http://arxiv.org/pdf/1810.03989v2.pdf | Image-to-Video Person Re-Identification by Reusing Cross-modal Embeddings | Image-to-video person re-identification identifies a target person by a probe
image from quantities of pedestrian videos captured by non-overlapping cameras.
Despite the great progress achieved,it's still challenging to match in the
multimodal scenario,i.e. between image and video. Currently,state-of-the-art
approaches... | ['Zhongwei Xie', 'Luo Zhong', 'Xian Zhong', 'Lin Li'] | 2018-10-04 | null | null | null | null | ['image-to-video-person-re-identification'] | ['computer-vision'] | [ 1.12847961e-01 -4.31786895e-01 -1.97819307e-01 -4.06391084e-01
-8.48834097e-01 -5.65259039e-01 6.79819763e-01 -1.46594793e-01
-6.56499028e-01 5.32314241e-01 3.92053992e-01 3.45487773e-01
1.10727139e-02 -3.13416719e-01 -7.54792750e-01 -6.00483179e-01
2.08326206e-01 1.44887313e-01 -4.62969840e-02 1.67730004... | [14.637727737426758, 0.9495022892951965] |
6ffe1a82-af8d-4539-9cd7-43cd9ce09c54 | on-distances-paths-and-connections-for | 1603.08497 | null | http://arxiv.org/abs/1603.08497v1 | http://arxiv.org/pdf/1603.08497v1.pdf | On distances, paths and connections for hyperspectral image segmentation | The present paper introduces the $\eta$ and {\eta} connections in order to
add regional information on $\lambda$-flat zones, which only take into account
a local information. A top-down approach is considered. First $\lambda$-flat
zones are built in a way leading to a sub-segmentation. Then a finer
segmentation is obta... | ['Guillaume Noyel', 'Jesus Angulo', 'Dominique Jeulin'] | 2016-02-02 | null | null | null | null | ['hyperspectral-image-segmentation'] | ['computer-vision'] | [ 9.56045836e-02 6.97533041e-02 2.52796650e-01 -2.32102826e-01
-2.30867758e-01 -3.48971575e-01 2.21352544e-04 6.84933186e-01
-5.26358664e-01 6.67407393e-01 -5.34228265e-01 -4.08638835e-01
-8.56502652e-01 -1.61400998e+00 -3.18431169e-01 -9.41516757e-01
-4.18779075e-01 5.16260207e-01 4.13136363e-01 -1.57845467... | [7.557278633117676, 4.56078577041626] |
822fda65-5116-447c-b7a7-ee1d5d2c5644 | adversarial-attacks-on-multi-task-visual | 2107.07449 | null | https://arxiv.org/abs/2107.07449v2 | https://arxiv.org/pdf/2107.07449v2.pdf | Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving | Deep neural networks (DNNs) have accomplished impressive success in various applications, including autonomous driving perception tasks, in recent years. On the other hand, current deep neural networks are easily fooled by adversarial attacks. This vulnerability raises significant concerns, particularly in safety-criti... | ['Senthil Yogamani', 'Varun Ravi Kumar', 'Ahmed Hamed', 'Ibrahim Sobh'] | 2021-07-15 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 2.50384331e-01 1.96457714e-01 2.08976626e-01 -3.20939362e-01
-2.88506299e-01 -1.15664601e+00 7.49482095e-01 -1.65567175e-02
-7.49160171e-01 5.27010202e-01 -1.69154823e-01 -6.04140341e-01
2.20307574e-01 -5.69767058e-01 -7.65496016e-01 -8.88353705e-01
-1.13459677e-01 -1.42430604e-01 5.46265543e-01 -2.37165168... | [5.47832727432251, 7.900060653686523] |
8d38b851-b55c-4d2a-a665-c1ac1ef8418a | joint-entity-and-relation-extraction-from | null | null | http://ceur-ws.org/Vol-3004/paper2.pdf | http://ceur-ws.org/Vol-3004/paper2.pdf | Joint Entity and Relation Extraction from Scientific Documents: Role of Linguistic Information and Entity Types | Scientific articles contain various types of domain-specific entities and relations between them. The entities and their relations
succinctly capture important information about the topic of the
document and hence, they are crucial to the understanding and
automatic analysis of the documents. In this paper, we aim t... | ['Partha Pratim Das', 'Debarshi Kumar Sanyal', 'Sudakshina Dutta', 'Prantika Chakraborty', 'T Y S S Santosh'] | 2021-09-30 | null | null | null | extraction-and-evaluation-of-knowledge | ['joint-entity-and-relation-extraction-on', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.66835815e-03 4.22794938e-01 -4.50952172e-01 -4.72666055e-01
-5.46797276e-01 -7.11312175e-01 7.99462140e-01 8.73397529e-01
-6.62627995e-01 8.78080487e-01 5.81459045e-01 -3.67503822e-01
-1.04397386e-01 -1.16926014e+00 -8.74942601e-01 -2.79203117e-01
-2.31154531e-01 4.70869541e-01 -1.96526438e-01 1.73031569... | [9.367447853088379, 8.643671035766602] |
a1754114-e7e4-4ef3-8a50-aef99feb2046 | cheap-translation-for-cross-lingual-named | null | null | https://aclanthology.org/D17-1269 | https://aclanthology.org/D17-1269.pdf | Cheap Translation for Cross-Lingual Named Entity Recognition | Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e.g., the availability of Wikipedia in the target language. We propose a simple method for cross-lingual named entity recognition (NER) that works well in settings with \textit... | ['Chen-Tse Tsai', 'Dan Roth', 'Stephen Mayhew'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-4.67894673e-01 4.71383408e-02 -3.12462598e-01 -1.16307624e-01
-1.40468526e+00 -9.72414911e-01 5.71952760e-01 1.64038718e-01
-1.18039501e+00 1.20406735e+00 2.03740224e-01 -3.38391930e-01
2.18061149e-01 -7.04045832e-01 -8.54071319e-01 -3.69228609e-02
1.08490616e-01 6.85491860e-01 1.71811044e-01 -5.71597040... | [9.968934059143066, 9.730037689208984] |
fb2a0ed8-fda2-404a-9b59-78b1cb7abd83 | does-order-matter-an-empirical-study-on | 1909.03590 | null | https://arxiv.org/abs/1909.03590v4 | https://arxiv.org/pdf/1909.03590v4.pdf | Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence | Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation. Existing studies concatenate target keyphrases in different orders but no study has examined the effects of ordering on models' behavior. In this paper, we propose several orderings fo... | ['Peter Brusilovsky', 'Xingdi Yuan', 'Tong Wang', 'Rui Meng', 'Daqing He', 'Adam Trischler'] | 2019-09-09 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.86932915e-01 -2.99675196e-01 -7.62165964e-01 1.00048386e-01
-7.33530402e-01 -1.05779719e+00 1.21973753e+00 4.41903532e-01
-5.32969356e-01 9.23065960e-01 6.56399190e-01 -6.98421776e-01
-4.74404246e-02 -6.03446364e-01 -6.77470565e-01 -4.99615282e-01
-7.86394328e-02 4.36199866e-02 1.18328765e-01 -3.84404749... | [12.274487495422363, 8.872258186340332] |
275af6aa-61ae-4119-b7fb-e466f9dd65f8 | crosssplit-mitigating-label-noise | 2212.01674 | null | https://arxiv.org/abs/2212.01674v2 | https://arxiv.org/pdf/2212.01674v2.pdf | CrossSplit: Mitigating Label Noise Memorization through Data Splitting | We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label correction and co-teaching methods, we propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit, which uses a pair of neural networks trained... | ['Simon Lacoste-Julien', 'Yan Zhang', 'Aristide Baratin', 'JiHye Kim'] | 2022-12-03 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.76570415e-01 1.83952272e-01 4.94533032e-02 -6.27178073e-01
-7.97193527e-01 -4.54517961e-01 3.15803587e-01 3.38137895e-01
-6.53359711e-01 7.74642825e-01 -1.54900566e-01 -1.18731540e-02
4.98711132e-02 -5.98124146e-01 -1.02341831e+00 -7.70182490e-01
2.87365973e-01 6.56870306e-01 6.07213974e-01 3.12556401... | [9.40255355834961, 3.8368208408355713] |
9688d16b-b28c-4eaa-8fa0-0a61be80f232 | a-trainable-multiplication-layer-for-auto | 1905.12871 | null | https://arxiv.org/abs/1905.12871v1 | https://arxiv.org/pdf/1905.12871v1.pdf | A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction | In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image as an input, the TML raises each pixel value to the power of a weight and then multiplies them, thereby extracting the higher-order local aut... | ['Seiichi Uchida', 'Hideaki Hayashi'] | 2019-05-30 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 3.92196834e-01 4.68663909e-02 5.36780097e-02 -6.53949678e-01
-1.07396487e-02 -3.53009999e-01 4.78561312e-01 2.30768755e-01
-6.97983682e-01 2.10539967e-01 -3.53154182e-01 -3.52161527e-01
-3.12071741e-01 -8.38347316e-01 -8.50146353e-01 -8.55806053e-01
-3.36658269e-01 -1.31602466e-01 1.59247130e-01 1.19822219... | [9.189428329467773, 2.2937657833099365] |
c50e659a-af1a-4e27-8b9c-5b72787cea0a | an-analysis-of-massively-multilingual-neural | null | null | https://aclanthology.org/2020.lrec-1.458 | https://aclanthology.org/2020.lrec-1.458.pdf | An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages | In this work, we explore massively multilingual low-resource neural machine translation. Using translations of the Bible (which have parallel structure across languages), we train models with up to 1,107 source languages. We create various multilingual corpora, varying the number and relatedness of source languages. Us... | ['David Yarowsky', 'Winston Wu', 'Garrett Nicolai', 'Arya D. McCarthy', 'Dylan Lewis', 'Aaron Mueller'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [-1.03338756e-01 -5.71590662e-01 -4.93710935e-01 -3.71393830e-01
-1.13579047e+00 -1.04508495e+00 5.52762449e-01 2.64641028e-02
-8.53191614e-01 1.17276299e+00 4.31787461e-01 -9.15858090e-01
2.73396224e-01 -5.34875453e-01 -8.40764523e-01 -4.48522836e-01
2.46317208e-01 9.17218208e-01 6.50032461e-02 -7.15607643... | [11.32319450378418, 10.192167282104492] |
bbcdbaa6-6bbd-4456-8630-8047b13873c9 | humset-dataset-of-multilingual-information | 2210.04573 | null | https://arxiv.org/abs/2210.04573v3 | https://arxiv.org/pdf/2210.04573v3.pdf | HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response | Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce a... | ['Nicolò Tamagnone', 'Navid Rekabsaz', 'Ewan Oglethorpe', 'Ximena Contla', 'Ranjan Shrestha', 'Benjamin Minixhofer', 'Selim Fekih'] | 2022-10-10 | null | null | null | null | ['text-annotation', 'multilingual-nlp'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.57897503e-03 8.12946260e-02 -5.40071547e-01 -2.92383224e-01
-1.53891027e+00 -9.49662149e-01 9.33071494e-01 6.94609940e-01
-1.06110966e+00 1.01108253e+00 1.34839511e+00 -6.22265100e-01
2.74648294e-02 -2.12313071e-01 3.91368987e-03 -2.82565653e-01
-3.53603363e-02 1.40833020e+00 -4.13217813e-01 -5.60569406... | [8.88757038116455, 9.42541217803955] |
4cb79e9d-bb11-41a3-a805-64944fd05a07 | aggregated-semantic-matching-for-short-text | null | null | https://aclanthology.org/K18-1046 | https://aclanthology.org/K18-1046.pdf | Aggregated Semantic Matching for Short Text Entity Linking | The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base. Entity linking in long text has been well studied in previous work. However, short text entity linking is more challenging since the text are noisy and less coherent. To better utilize the loc... | ['Rong pan', 'Chin-Yew Lin', 'Feng Nie', 'Jinpeng Wang', 'Jing Liu', 'Shuyan Zhou'] | 2018-10-01 | null | null | null | conll-2018-10 | ['card-games'] | ['playing-games'] | [-1.89174697e-01 1.63870096e-01 -6.69429183e-01 -2.73294389e-01
-8.73866379e-01 -2.37877116e-01 7.82164991e-01 8.32956553e-01
-6.76616609e-01 8.72311413e-01 7.28838563e-01 4.45066124e-01
-3.88711840e-01 -1.02663314e+00 -5.84038019e-01 -1.45993337e-01
5.91351837e-02 7.87527502e-01 4.09441829e-01 -5.07783890... | [9.327123641967773, 8.622517585754395] |
08c49add-939b-4f7e-be46-8c43274b7971 | pointcnn-convolution-on-mathcalx-transformed | 1801.07791 | null | http://arxiv.org/abs/1801.07791v5 | http://arxiv.org/pdf/1801.07791v5.pdf | PointCNN: Convolution On $\mathcal{X}$-Transformed Points | We present a simple and general framework for feature learning from point
clouds. The key to the success of CNNs is the convolution operator that is
capable of leveraging spatially-local correlation in data represented densely
in grids (e.g. images). However, point clouds are irregular and unordered, thus
directly conv... | ['Rui Bu', 'Yangyan Li', 'Xinhan Di', 'Wei Wu', 'Mingchao Sun', 'Baoquan Chen'] | 2018-01-23 | null | null | null | neurips-2018 | ['3d-instance-segmentation-1', '3d-part-segmentation', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.41651509e-02 -4.18814987e-01 3.62750947e-01 -4.43572998e-01
-2.35611960e-01 -4.10413384e-01 6.34323597e-01 2.98989564e-01
-4.71640110e-01 4.35665935e-01 -2.35324413e-01 -8.57382715e-02
-4.70143586e-01 -1.14322662e+00 -1.13098192e+00 -8.32600594e-01
-2.72108495e-01 3.12865436e-01 1.61841154e-01 -1.32818207... | [7.926904201507568, -3.603771924972534] |
20fc6b09-44a1-4886-9d10-1af667ca063e | mc-hands-1m-a-glove-wearing-hand-dataset-for | 2210.10428 | null | https://arxiv.org/abs/2210.10428v1 | https://arxiv.org/pdf/2210.10428v1.pdf | MC-hands-1M: A glove-wearing hand dataset for pose estimation | Nowadays, the need for large amounts of carefully and complexly annotated data for the training of computer vision modules continues to grow. Furthermore, although the research community presents state of the art solutions to many problems, there exist special cases, such as the pose estimation and tracking of a glove-... | ['Petros Daras', 'Anastasios Dimou', 'Konstantinos Konstantoudakis', 'Zisis Batzos', 'Prodromos Boutis'] | 2022-10-19 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-2.62466669e-02 -1.94815502e-01 1.39169365e-01 -1.93349466e-01
-3.49359393e-01 -7.73632228e-01 3.92083585e-01 -4.81311291e-01
-2.96247751e-01 3.25927943e-01 -2.13744864e-02 -1.00045249e-01
-1.26498505e-01 -1.14659578e-01 -4.71787006e-01 -6.14428878e-01
2.01990455e-01 6.35061681e-01 1.21850982e-01 -2.72191733... | [6.6066484451293945, -0.7197695374488831] |
b97a66f2-d1e6-4084-a52f-ef3700906a4a | when-and-how-to-fool-explainable-models-and | 2107.01943 | null | https://arxiv.org/abs/2107.01943v2 | https://arxiv.org/pdf/2107.01943v2.pdf | When and How to Fool Explainable Models (and Humans) with Adversarial Examples | Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpretability and the lack of robustness against adversarial examples or out-of-distribution inputs. In this exploratory review, we explore the ... | ['Jose A. Lozano', 'Roberto Santana', 'Jon Vadillo'] | 2021-07-05 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 4.85947460e-01 8.60850453e-01 1.65156975e-01 -4.17155117e-01
-2.97858030e-01 -1.05559552e+00 5.97600460e-01 -9.52345654e-02
1.18810639e-01 7.42753625e-01 -2.42675096e-01 -7.64062345e-01
-3.98862690e-01 -5.83482921e-01 -7.58417010e-01 -5.03902256e-01
2.51667034e-02 4.45968956e-01 -2.94662088e-01 -3.22627366... | [5.9207940101623535, 7.738814830780029] |
2c4ed654-d094-4697-9f4a-5bbe39b6c625 | real-time-speech-emotion-recognition-based-on | 2204.11382 | null | https://arxiv.org/abs/2204.11382v3 | https://arxiv.org/pdf/2204.11382v3.pdf | Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction | Speech emotion recognition systems have high prediction latency because of the high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition syst... | ['Cheng-Shan Jiang', 'Wei-Hua Cao', 'Min Wu', 'Zhen-Tao Liu', 'Abdul Rehman'] | 2022-04-25 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 1.41285688e-01 9.01716109e-03 5.03988564e-01 -5.10608435e-01
-9.25552666e-01 -1.39754072e-01 7.83969462e-03 2.07656741e-01
-6.88993514e-01 5.24616599e-01 1.19678028e-01 -2.28874519e-01
1.79596215e-01 -4.14305836e-01 -3.31511378e-01 -5.92961252e-01
-2.11672366e-01 -1.93506703e-02 -7.95511678e-02 -3.29793602... | [13.746426582336426, 5.776284217834473] |
6e1f1c44-2c7f-457e-a38b-4029cd949c03 | dp-hypo-an-adaptive-private-hyperparameter | 2306.05734 | null | https://arxiv.org/abs/2306.05734v1 | https://arxiv.org/pdf/2306.05734v1.pdf | DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework | Hyperparameter optimization, also known as hyperparameter tuning, is a widely recognized technique for improving model performance. Regrettably, when training private ML models, many practitioners often overlook the privacy risks associated with hyperparameter optimization, which could potentially expose sensitive info... | ['Milan Shen', 'Weijie J. Su', 'Huanyu Zhang', 'Sheng Gao', 'Hua Wang'] | 2023-06-09 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 1.07242569e-01 -3.60683352e-02 -3.19642961e-01 -3.50838363e-01
-1.11784315e+00 -1.08255923e+00 2.04986975e-01 2.52783567e-01
-4.66428488e-01 8.63539398e-01 -1.07962517e-02 -2.96825647e-01
-1.09396927e-01 -8.10958445e-01 -7.16620803e-01 -1.23265707e+00
1.09401487e-01 3.24869990e-01 -5.73107123e-01 3.37628275... | [5.965239524841309, 6.792746543884277] |
ec850c2e-f899-4a34-9f1a-6725bdbec182 | t-former-an-efficient-transformer-for-image | 2305.07239 | null | https://arxiv.org/abs/2305.07239v2 | https://arxiv.org/pdf/2305.07239v2.pdf | T-former: An Efficient Transformer for Image Inpainting | Benefiting from powerful convolutional neural networks (CNNs), learning-based image inpainting methods have made significant breakthroughs over the years. However, some nature of CNNs (e.g. local prior, spatially shared parameters) limit the performance in the face of broken images with diverse and complex forms. Recen... | ['Jinjun Wang', 'Deyu Meng', 'Sanping Zhou', 'Siqi Hui', 'Ye Deng'] | 2023-05-12 | null | null | null | null | ['image-inpainting', 'long-range-modeling'] | ['computer-vision', 'natural-language-processing'] | [ 3.44608328e-03 -2.53434777e-01 -1.94209307e-01 -2.23969400e-01
-7.16669083e-01 7.06123188e-02 1.68703079e-01 -2.17033550e-01
-3.30594748e-01 5.42685449e-01 8.31063688e-02 -2.16503382e-01
3.08323968e-02 -8.26230168e-01 -8.40313792e-01 -5.06463528e-01
2.76605844e-01 -8.81964862e-02 2.18991369e-01 -2.47187287... | [11.158308982849121, -1.5115965604782104] |
1ea7e928-9aa4-41f1-8eb8-f65bef6fbcdc | advances-in-very-deep-convolutional-neural | 1604.01792 | null | http://arxiv.org/abs/1604.01792v2 | http://arxiv.org/pdf/1604.01792v2.pdf | Advances in Very Deep Convolutional Neural Networks for LVCSR | Very deep CNNs with small 3x3 kernels have recently been shown to achieve
very strong performance as acoustic models in hybrid NN-HMM speech recognition
systems. In this paper we investigate how to efficiently scale these models to
larger datasets. Specifically, we address the design choice of pooling and
padding along... | ['Vaibhava Goel', 'Tom Sercu'] | 2016-04-06 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 6.53129816e-02 -5.89817725e-02 1.98888406e-01 -5.12973070e-01
-9.31600988e-01 -5.30407250e-01 4.14747179e-01 -2.52314150e-01
-1.08937705e+00 3.44551027e-01 1.02644570e-01 -8.23257208e-01
2.20030114e-01 -2.34995246e-01 -6.93515182e-01 -7.15062618e-01
-8.64422917e-02 2.05430791e-01 4.06052440e-01 -1.43020660... | [14.402082443237305, 6.402997970581055] |
829498ee-7bee-478e-8e99-76c97dcb63fe | a-novel-dataset-towards-extracting-virus-host | 2305.13317 | null | https://arxiv.org/abs/2305.13317v1 | https://arxiv.org/pdf/2305.13317v1.pdf | A Novel Dataset Towards Extracting Virus-Host Interactions | We describe a novel dataset for the automated recognition of named taxonomic and other entities relevant to the association of viruses with their hosts. We further describe some initial results using pre-trained models on the named-entity recognition (NER) task on this novel dataset. We propose that our dataset of manu... | ['Beckett Sterner', 'Nathan S. Upham', 'Atriya Sen', 'Rasha Alshawi'] | 2023-05-11 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [ 6.78222626e-02 2.16700807e-01 -3.40802670e-01 -2.63472855e-01
-4.70948130e-01 -5.16695321e-01 8.44347179e-01 7.81467915e-01
-7.28585839e-01 1.22878075e+00 3.62006843e-01 -5.96177697e-01
-1.74243413e-02 -7.41678119e-01 -5.75344265e-01 -3.74560505e-01
-4.08176720e-01 1.00275743e+00 7.46319909e-03 1.59566298... | [8.524945259094238, 8.744165420532227] |
88c9a115-4678-43a5-8f9e-7bd3feff7f54 | deepfont-identify-your-font-from-an-image | 1507.03196 | null | http://arxiv.org/abs/1507.03196v1 | http://arxiv.org/pdf/1507.03196v1.pdf | DeepFont: Identify Your Font from An Image | As font is one of the core design concepts, automatic font identification and
similar font suggestion from an image or photo has been on the wish list of
many designers. We study the Visual Font Recognition (VFR) problem, and advance
the state-of-the-art remarkably by developing the DeepFont system. First of
all, we bu... | ['Aseem Agarwala', 'Zhangyang Wang', 'Jianchao Yang', 'Thomas S. Huang', 'Hailin Jin', 'Eli Shechtman', 'Jonathan Brandt'] | 2015-07-12 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 4.87668425e-01 -5.22891164e-01 7.45057315e-02 -4.63796347e-01
-4.47275311e-01 -8.02708745e-01 4.85770345e-01 -2.38816857e-01
-7.62841552e-02 4.57436621e-01 5.36701530e-02 -5.11867225e-01
1.50974661e-01 -5.11306107e-01 -7.79197276e-01 -4.33124185e-01
6.26156092e-01 2.47055218e-01 2.24531159e-01 -3.38047534... | [11.949542999267578, 1.9488672018051147] |
019d7bff-af3d-4dc1-abb6-4ec5b32e488b | calibration-of-multiple-fish-eye-cameras | 1407.1267 | null | http://arxiv.org/abs/1407.1267v1 | http://arxiv.org/pdf/1407.1267v1.pdf | Calibration of Multiple Fish-Eye Cameras Using a Wand | Fish-eye cameras are becoming increasingly popular in computer vision, but
their use for 3D measurement is limited partly due to the lack of an accurate,
efficient and user-friendly calibration procedure. For such a purpose, we
propose a method to calibrate the intrinsic and extrinsic parameters (including
radial disto... | ['Kai-Yuan Cai', 'Qiang Fu', 'Quan Quan'] | 2014-07-04 | null | null | null | null | ['camera-auto-calibration'] | ['computer-vision'] | [-2.61674792e-01 -3.61022115e-01 2.11450011e-01 -1.47184581e-01
-1.04694314e-01 -6.56552553e-01 2.73522228e-01 -4.65725511e-01
-6.13824487e-01 3.34624827e-01 -4.37050790e-01 -1.34207964e-01
1.15281366e-01 -6.87599853e-02 -4.40413892e-01 -8.05870295e-01
6.10123873e-01 -5.37206754e-02 3.04558128e-01 1.46973342... | [7.9852776527404785, -2.207223653793335] |
de1bef8d-a4ca-43ba-a89a-6c19033ce44b | tlnets-transformation-learning-networks-for | 2305.15770 | null | https://arxiv.org/abs/2305.15770v1 | https://arxiv.org/pdf/2305.15770v1.pdf | TLNets: Transformation Learning Networks for long-range time-series prediction | Time series prediction is a prevalent issue across various disciplines, such as meteorology, traffic surveillance, investment, and energy production and consumption. Many statistical and machine-learning strategies have been developed to tackle this problem. However, these approaches either lack explainability or exhib... | ['Hao Sun', 'Yang Liu', 'Wei Wang'] | 2023-05-25 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-6.21244125e-02 -4.23940629e-01 -1.51476219e-01 -4.92409915e-01
-1.92889869e-01 -1.56130716e-01 8.56775343e-01 -1.44765764e-01
-9.04674977e-02 5.88932395e-01 3.48047853e-01 -3.56527537e-01
-3.49437505e-01 -8.46068859e-01 -5.63475370e-01 -8.87993515e-01
-1.80371657e-01 -2.38678187e-01 6.43185675e-02 -4.46515948... | [6.8760271072387695, 2.865030288696289] |
59469a8f-0b14-4b28-b356-235f21fc0013 | partially-observable-mean-field-multi-agent | 2304.12653 | null | https://arxiv.org/abs/2304.12653v1 | https://arxiv.org/pdf/2304.12653v1.pdf | Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention | Traditional multi-agent reinforcement learning algorithms are difficultly applied in a large-scale multi-agent environment. The introduction of mean field theory has enhanced the scalability of multi-agent reinforcement learning in recent years. This paper considers partially observable multi-agent reinforcement learni... | ['Ziyuan Zhou', 'Guanjun Liu', 'Min Yang'] | 2023-04-25 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-2.21870705e-01 6.41978607e-02 -3.30429167e-01 1.40158996e-01
-6.32527947e-01 -1.99359596e-01 7.79369295e-01 4.10103083e-01
-7.56860673e-01 1.02216744e+00 3.46960455e-01 1.75735548e-01
-3.15022200e-01 -1.04354024e+00 -9.22538638e-01 -9.77203190e-01
-6.03076577e-01 7.37666905e-01 3.90251070e-01 -4.06198442... | [3.774188756942749, 1.9418425559997559] |
47095f12-f326-4754-9b33-097a47e21ad7 | gliding-vertex-on-the-horizontal-bounding-box | 1911.09358 | null | https://arxiv.org/abs/1911.09358v2 | https://arxiv.org/pdf/1911.09358v2.pdf | Gliding vertex on the horizontal bounding box for multi-oriented object detection | Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this paper, we propose a simple yet effective framework to detect multi-oriented objects.... | ['Gui-Song Xia', 'Yongchao Xu', 'Kai Chen', 'Qimeng Wang', 'Yukang Wang', 'Xiang Bai', 'Mingtao Fu'] | 2019-11-21 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 6.65208371e-03 -3.88068467e-01 -5.78320250e-02 -2.43923873e-01
-2.89006680e-01 -5.07078290e-01 2.23161384e-01 1.24974124e-01
-5.56991816e-01 2.45626584e-01 -3.21615547e-01 -4.17054176e-01
1.18436985e-01 -7.21650958e-01 -6.75031722e-01 -9.18344736e-01
4.27632369e-02 3.12341545e-02 7.55029976e-01 -1.08228855... | [8.70501708984375, -0.7616028189659119] |
5253296c-245a-40c6-9cf9-6d91b8ce3f32 | on-generalisability-of-machine-learning-based | 2205.04112 | null | https://arxiv.org/abs/2205.04112v1 | https://arxiv.org/pdf/2205.04112v1.pdf | On Generalisability of Machine Learning-based Network Intrusion Detection Systems | Many of the proposed machine learning (ML) based network intrusion detection systems (NIDSs) achieve near perfect detection performance when evaluated on synthetic benchmark datasets. Though, there is no record of if and how these results generalise to other network scenarios, in particular to real-world networks. In t... | ['Marius Portmann', 'Siamak Layeghy'] | 2022-05-09 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 2.60035396e-01 -8.83711502e-02 -6.89908490e-02 -3.47851813e-01
1.73411518e-01 -5.19998550e-01 1.06115150e+00 5.40481448e-01
-5.53714991e-01 9.15014446e-01 -6.08040869e-01 -6.24428928e-01
-7.99639940e-01 -8.45675170e-01 -2.87783444e-01 -7.85781920e-01
-4.07539606e-01 6.90699220e-01 5.86084723e-01 -3.22704524... | [5.238846778869629, 7.2058563232421875] |
124c0ab3-a540-4db4-badd-074a6f18f0a2 | a-feature-embedding-strategy-for-high-level | 1705.04301 | null | http://arxiv.org/abs/1705.04301v1 | http://arxiv.org/pdf/1705.04301v1.pdf | A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets | Following the rapidly growing digital image usage, automatic image
categorization has become preeminent research area. It has broaden and adopted
many algorithms from time to time, whereby multi-feature (generally,
hand-engineered features) based image characterization comes handy to improve
accuracy. Recently, in mach... | ['Wei Jiang', 'Thangarajah Akilan', 'Q. M. Jonathan Wu'] | 2017-05-11 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 5.58764279e-01 -3.03484797e-01 -2.65039861e-01 -5.15272260e-01
-6.41175687e-01 -1.57026842e-01 1.04530096e+00 2.69906223e-01
-8.45243335e-01 6.70923352e-01 8.30069259e-02 1.00873308e-02
-6.13065004e-01 -5.59105158e-01 -3.40058774e-01 -9.62109745e-01
-7.92694688e-02 -1.55271113e-01 5.49743958e-02 -9.64440107... | [9.424650192260742, 2.2360732555389404] |
5edc7a3f-e207-4b09-81d9-b045e70d7f27 | metagross-meta-gated-recursive-controller | null | null | https://openreview.net/forum?id=Sygn20VtwH | https://openreview.net/pdf?id=Sygn20VtwH | Metagross: Meta Gated Recursive Controller Units for Sequence Modeling | This paper proposes Metagross (Meta Gated Recursive Controller), a new neural sequence modeling unit. Our proposed unit is characterized by recursive parameterization of its gating functions, i.e., gating mechanisms of Metagross are controlled by instances of itself, which are repeatedly called in a recursive fashion. ... | ['Yew Soon Ong', 'Alvin Chan', 'Yikang Shen', 'Yi Tay'] | 2020-01-01 | null | null | null | iclr-2020-1 | ['music-modeling'] | ['music'] | [ 7.54900515e-01 1.67140946e-01 -1.26295507e-01 -2.23552108e-01
-4.52487439e-01 -4.87398416e-01 4.95266259e-01 -6.36714473e-02
-1.47112936e-01 5.95319271e-01 1.02720231e-01 -5.88755131e-01
3.41947883e-01 -9.66456413e-01 -9.90489721e-01 -8.11707795e-01
-6.71681985e-02 2.48941898e-01 1.51868880e-01 -1.04261465... | [10.64295768737793, 7.006781101226807] |
54f23ed7-b4b9-4d52-8b20-b0b7618082f8 | bayesbeat-a-bayesian-deep-learning-approach | 2011.00753 | null | https://arxiv.org/abs/2011.00753v2 | https://arxiv.org/pdf/2011.00753v2.pdf | BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data | Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities. To further widen their health tracking capabilities, in recent years researchers have started to look into the possibility of Atrial Fibrillation (AF)... | ['Mohammed Eunus Ali', 'Mohammad Mehedy Masud', 'Atif Rahman', 'Md. Saiful Islam', 'Masum Rahman', 'Subangkar Karmaker Shanto', 'Sarkar Snigdha Sarathi Das'] | 2020-11-02 | null | null | null | null | ['photoplethysmography-ppg', 'atrial-fibrillation-detection'] | ['medical', 'medical'] | [-1.14490673e-01 -1.75174624e-01 -4.17028189e-01 -3.81822675e-01
-9.06382024e-01 -3.03954840e-01 -9.00583565e-02 -1.37120724e-01
-7.23986402e-02 8.88002634e-01 4.75660652e-01 -3.31842184e-01
8.43636319e-02 -6.60287976e-01 -2.07466364e-01 -7.07053602e-01
-1.37018129e-01 8.65068063e-02 -2.26906985e-01 5.43105960... | [14.013350486755371, 3.101423740386963] |
4516632f-0750-4943-89c8-b6e81d7bb869 | distill-the-image-to-nowhere-inversion | 2210.04468 | null | https://arxiv.org/abs/2210.04468v2 | https://arxiv.org/pdf/2210.04468v2.pdf | Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation | Past works on multimodal machine translation (MMT) elevate bilingual setup by incorporating additional aligned vision information. However, an image-must requirement of the multimodal dataset largely hinders MMT's development -- namely that it demands an aligned form of [image, source text, target text]. This limitatio... | ['Junbo Zhao', 'Yawen Zeng', 'Ru Peng'] | 2022-10-10 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.49462914e-01 9.42373574e-02 -8.58260319e-02 -2.74850965e-01
-1.13146794e+00 -7.49140739e-01 1.03697932e+00 -2.68323988e-01
-5.88813424e-01 7.17178464e-01 -2.16437310e-01 -7.33484626e-01
2.19779998e-01 -5.15682399e-01 -1.00592244e+00 -7.01551378e-01
7.80467093e-01 9.44730461e-01 -3.16986531e-01 -3.10189158... | [11.46141529083252, 1.5102437734603882] |
3bc46bd6-4db9-480e-a84a-bc3054005d49 | cogvideo-large-scale-pretraining-for-text-to | 2205.15868 | null | https://arxiv.org/abs/2205.15868v1 | https://arxiv.org/pdf/2205.15868v1.pdf | CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers | Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation. Its application to video generation is still facing many challenges: The potential huge computation cost makes the training from scratch unaffordable; The scarcity and weak relevance of text-vi... | ['Jie Tang', 'Xinghan Liu', 'Wendi Zheng', 'Ming Ding', 'Wenyi Hong'] | 2022-05-29 | null | null | null | null | ['text-to-video-generation'] | ['natural-language-processing'] | [ 3.66184235e-01 1.42570600e-01 -3.58578116e-01 1.76065285e-02
-9.16482866e-01 -4.43972260e-01 8.98696482e-01 -7.73739100e-01
-9.54518616e-02 6.71891332e-01 5.90780497e-01 -2.85832703e-01
2.09616289e-01 -5.61919153e-01 -1.25311363e+00 -4.61949736e-01
2.59506702e-01 5.09639084e-01 3.13625842e-01 -2.42418349... | [10.884493827819824, -0.4553844630718231] |
ea62dee8-9e73-491b-85a6-511f218a5c70 | peano-learning-formal-mathematical-reasoning | 2211.15864 | null | https://arxiv.org/abs/2211.15864v1 | https://arxiv.org/pdf/2211.15864v1.pdf | Peano: Learning Formal Mathematical Reasoning | General mathematical reasoning is computationally undecidable, but humans routinely solve new problems. Moreover, discoveries developed over centuries are taught to subsequent generations quickly. What structure enables this, and how might that inform automated mathematical reasoning? We posit that central to both puzz... | ['Noah D. Goodman', 'Gabriel Poesia'] | 2022-11-29 | null | null | null | null | ['automated-theorem-proving', 'mathematical-reasoning', 'automated-theorem-proving'] | ['miscellaneous', 'natural-language-processing', 'reasoning'] | [-1.32352859e-01 4.31119055e-01 2.68985406e-02 2.00997353e-01
-2.67232060e-01 -1.10868430e+00 4.98934627e-01 9.05571058e-02
-1.05814815e-01 8.36143196e-01 1.73655245e-02 -9.30232465e-01
-7.64526725e-01 -1.35876715e+00 -9.94842887e-01 -3.32271665e-01
-5.73184133e-01 7.40508795e-01 2.19694644e-01 -8.63916159... | [9.187468528747559, 7.16771125793457] |
cb5a210c-1246-417d-8638-b86f409b8eb5 | hierarchical-adaptive-voxel-guided-sampling | 2305.14306 | null | https://arxiv.org/abs/2305.14306v1 | https://arxiv.org/pdf/2305.14306v1.pdf | Hierarchical Adaptive Voxel-guided Sampling for Real-time Applications in Large-scale Point Clouds | While point-based neural architectures have demonstrated their efficacy, the time-consuming sampler currently prevents them from performing real-time reasoning on scene-level point clouds. Existing methods attempt to overcome this issue by using random sampling strategy instead of the commonly-adopted farthest point sa... | ['Haoyao Chen', 'Xiao Liu', 'Junyuan Ouyang'] | 2023-05-23 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [-1.51265010e-01 -2.11987734e-01 8.19019042e-03 -1.96392953e-01
-8.97964120e-01 -4.19358641e-01 2.59183347e-01 3.03007308e-02
-2.80958623e-01 3.93234074e-01 -5.34032226e-01 -4.07072753e-01
1.01438046e-01 -1.29208767e+00 -9.73594129e-01 -7.41817415e-01
-1.32408105e-02 7.05888748e-01 7.09245145e-01 9.85428109... | [7.990493297576904, -3.334444522857666] |
53f14f5a-da3a-4539-9935-129dd6b915b4 | learning-to-generate-better-than-your-llm | 2306.11816 | null | https://arxiv.org/abs/2306.11816v1 | https://arxiv.org/pdf/2306.11816v1.pdf | Learning to Generate Better Than Your LLM | Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large Language Models (LLMs) for conditional text generation. In particular, recent LLMs such as ChatGPT and GPT-4 can engage in fluent conversations with users by incorporating RL and feedback from humans. Inspired by learning-to-search alg... | ['Wen Sun', 'Dipendra Misra', 'Rajkumar Ramamurthy', 'Kiante Brantley', 'Jonathan D. Chang'] | 2023-06-20 | null | null | null | null | ['text-generation', 'conditional-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.03957891e-01 5.20652056e-01 -5.56363344e-01 5.74628357e-03
-1.44891560e+00 -7.01166630e-01 1.04996979e+00 -1.01316236e-01
-4.44619268e-01 1.06222141e+00 6.51483297e-01 -7.09447205e-01
2.53937542e-01 -7.81610668e-01 -7.22448349e-01 -2.95191705e-01
1.60232738e-01 1.00762188e+00 -2.88750470e-01 -6.37452304... | [11.802301406860352, 8.725769996643066] |
b6544a0a-a796-4bca-bea2-53ba7e0270f1 | knowledge-driven-self-supervised | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chang_Knowledge-Driven_Self-Supervised_Representation_Learning_for_Facial_Action_Unit_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chang_Knowledge-Driven_Self-Supervised_Representation_Learning_for_Facial_Action_Unit_Recognition_CVPR_2022_paper.pdf | Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition | Facial action unit (AU) recognition is formulated as a supervised learning problem by recent works. However, the complex labeling process makes it challenging to provide AU annotations for large amounts of facial images. To remedy this, we utilize AU labeling rules defined by the Facial Action Coding System (FACS) ... | ['Shangfei Wang', 'Yanan Chang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['facial-action-unit-detection'] | ['computer-vision'] | [ 6.67370081e-01 4.80975091e-01 -7.71570325e-01 -8.17139268e-01
-8.35703015e-01 -2.92808741e-01 4.95968908e-01 -5.01734197e-01
2.26478562e-01 2.28274211e-01 4.49625224e-01 3.91329497e-01
2.35154018e-01 -7.88321555e-01 -6.76933885e-01 -9.01703537e-01
2.26403028e-02 1.05174743e-01 -2.91787654e-01 5.16482592... | [13.606512069702148, 1.5053863525390625] |
e01d9f05-9401-44ba-948e-d264f05ad472 | multi-evidence-filtering-and-fusion-for-multi | 1802.09129 | null | http://arxiv.org/abs/1802.09129v1 | http://arxiv.org/pdf/1802.09129v1.pdf | Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning | Supervised object detection and semantic segmentation require object or even
pixel level annotations. When there exist image level labels only, it is
challenging for weakly supervised algorithms to achieve accurate predictions.
The accuracy achieved by top weakly supervised algorithms is still
significantly lower than ... | ['Weifeng Ge', 'Yizhou Yu', 'Sibei Yang'] | 2018-02-26 | multi-evidence-filtering-and-fusion-for-multi-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Ge_Multi-Evidence_Filtering_and_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Ge_Multi-Evidence_Filtering_and_CVPR_2018_paper.pdf | cvpr-2018-6 | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.64179265e-01 -2.81468406e-02 -4.04267460e-01 -6.78950548e-01
-1.17735255e+00 -7.95444429e-01 2.33994484e-01 2.59085029e-01
-6.99361622e-01 5.12748301e-01 -5.92940390e-01 -3.77182811e-02
2.92367309e-01 -5.83801091e-01 -1.09057212e+00 -8.39879155e-01
4.70968932e-01 8.74347866e-01 7.96735227e-01 5.58328748... | [9.48409366607666, 0.6148332357406616] |
a90cb0ef-d629-43bd-978c-3051029b9261 | patchaugment-local-neighborhood-augmentation | null | null | https://openaccess.thecvf.com/content/ICCV2021W/DLGC/html/Sheshappanavar_PatchAugment_Local_Neighborhood_Augmentation_in_Point_Cloud_Classification_ICCVW_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021W/DLGC/papers/Sheshappanavar_PatchAugment_Local_Neighborhood_Augmentation_in_Point_Cloud_Classification_ICCVW_2021_paper.pdf | PatchAugment: Local Neighborhood Augmentation in Point Cloud Classification | Recent deep neural network models trained on smaller and less diverse datasets use data augmentation to alleviate limitations such as overfitting, reduced robustness, and lower generalization. Methods using 3D datasets are among the most common to use data augmentation techniques such as random point drop, scaling, tra... | ['Chandra Kambhamettu', 'Vinit Veerendraveer Singh', 'Shivanand Venkanna Sheshappanavar'] | 2021-10-19 | null | null | null | iccv-workshops-2021-10 | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-1.78082108e-01 4.89627756e-02 1.35584120e-02 -3.71009499e-01
-1.31871058e-02 -4.10006493e-01 5.69285512e-01 2.87203074e-01
-3.14575613e-01 2.06578404e-01 -1.40511289e-01 -3.72249573e-01
2.72206128e-01 -1.07173800e+00 -1.02240622e+00 -3.27145159e-01
-1.60185516e-01 7.19026446e-01 3.87263149e-01 -4.62866783... | [7.918700695037842, -3.5599892139434814] |
aaa06808-d77a-4168-a061-21e718e478a5 | development-of-a-rule-based-lemmatization | 2210.16006 | null | https://arxiv.org/abs/2210.16006v1 | https://arxiv.org/pdf/2210.16006v1.pdf | Development of a rule-based lemmatization algorithm through Finite State Machine for Uzbek language | Lemmatization is one of the core concepts in natural language processing, thus creating a lemmatization tool is an important task. This paper discusses the construction of a lemmatization algorithm for the Uzbek language. The main purpose of the work is to remove affixes of words in the Uzbek language by means of the f... | ['Ogabek Sobirov', 'Maksud Sharipov'] | 2022-10-28 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [ 1.52874649e-01 2.50602990e-01 -7.99954087e-02 -3.16620231e-01
3.24700736e-02 -8.07075858e-01 7.72159278e-01 4.24990773e-01
-6.44809842e-01 7.04937935e-01 4.97354835e-01 -7.53049016e-01
-1.59783915e-01 -1.06953287e+00 -3.23283821e-01 -4.90212709e-01
1.79102853e-01 7.31944919e-01 2.62948661e-03 -5.65685272... | [10.372941970825195, 10.201835632324219] |
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