paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
534afc1e-7dfa-4d01-96bf-caa131a84217 | adaptive-modeling-of-satellite-derived | 2306.08501 | null | https://arxiv.org/abs/2306.08501v1 | https://arxiv.org/pdf/2306.08501v1.pdf | Adaptive Modeling of Satellite-Derived Nighttime Lights Time-Series for Tracking Urban Change Processes Using Machine Learning | Remotely sensed nighttime lights (NTL) uniquely capture urban change processes that are important to human and ecological well-being, such as urbanization, socio-political conflicts and displacement, impacts from disasters, holidays, and changes in daily human patterns of movement. Though several NTL products are globa... | ['Eleanor C. Stokes', 'Srija Chakraborty'] | 2023-06-14 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 2.38749478e-02 -5.64245820e-01 -2.73597926e-01 -4.17134970e-01
-2.94224888e-01 -5.86445451e-01 9.06134844e-01 4.45655733e-01
-1.98074669e-01 7.56106496e-01 5.92621148e-01 -6.64616466e-01
-1.59539133e-01 -1.23392320e+00 -3.19796711e-01 -7.18520105e-01
-1.77042469e-01 7.71863312e-02 -5.72040975e-02 -3.97689879... | [9.431089401245117, -1.3093488216400146] |
ea48c47b-7dff-41d8-abed-a8f3547f2aa6 | learning-to-predict-scene-level-implicit-3d-1 | 2306.08671 | null | https://arxiv.org/abs/2306.08671v1 | https://arxiv.org/pdf/2306.08671v1.pdf | Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data | We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data. At test time, our system maps a previously unseen RGB image to a 3D reconstruction of a scene via implicit functions. While implicit functions for 3D reconstruction have often been tied to meshes, ... | ['David F. Fouhey', 'Justin Johnson', 'Linyi Jin', 'Nilesh Kulkarni'] | 2023-06-14 | learning-to-predict-scene-level-implicit-3d | http://openaccess.thecvf.com//content/CVPR2023/html/Kulkarni_Learning_To_Predict_Scene-Level_Implicit_3D_From_Posed_RGBD_Data_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kulkarni_Learning_To_Predict_Scene-Level_Implicit_3D_From_Posed_RGBD_Data_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 2.97535509e-01 4.45164353e-01 -4.41268049e-02 -5.32085180e-01
-7.86627352e-01 -5.34118533e-01 3.70353371e-01 -3.12210768e-01
-3.35852019e-02 4.59431618e-01 2.67625093e-01 -3.38301569e-01
2.27476254e-01 -8.66997898e-01 -1.31051862e+00 -1.56414688e-01
1.04813501e-01 7.53281891e-01 2.05750108e-01 -1.78566664... | [8.486034393310547, -2.818328857421875] |
cab6c556-f0fe-4706-8890-4a33997e2af6 | fine-tuning-pre-trained-language-model-with | 2010.07835 | null | https://arxiv.org/abs/2010.07835v3 | https://arxiv.org/pdf/2010.07835v3.pdf | Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach | Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fine-tuning stage. We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data. This problem is ... | ['Chao Zhang', 'Tuo Zhao', 'Wendi Ren', 'Haoming Jiang', 'Simiao Zuo', 'Yue Yu'] | 2020-10-15 | null | https://aclanthology.org/2021.naacl-main.84 | https://aclanthology.org/2021.naacl-main.84.pdf | naacl-2021-4 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 3.48224938e-01 -4.82234769e-02 -5.47187030e-01 -6.85189545e-01
-1.07883918e+00 -5.89314818e-01 6.38305724e-01 3.05607349e-01
-8.92018557e-01 7.91072309e-01 3.70298594e-01 -2.67165244e-01
3.10692728e-01 -3.45601916e-01 -6.88462973e-01 -4.09409940e-01
1.92359298e-01 4.80137825e-01 1.68521985e-01 -4.03334737... | [10.826324462890625, 8.385525703430176] |
53187776-e9e9-46fb-a534-cbce68de90bf | selective-token-generation-for-few-shot-1 | 2209.08206 | null | https://arxiv.org/abs/2209.08206v1 | https://arxiv.org/pdf/2209.08206v1.pdf | Selective Token Generation for Few-shot Natural Language Generation | Natural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. Among them, additive learning that incorporates a task-specific adapter on top of the fixed large-scale PLM has b... | ['Sungwoong Kim', 'Eun-Sol Kim', 'Taehwan Kwon', 'DaeJin Jo'] | 2022-09-17 | null | https://aclanthology.org/2022.coling-1.510 | https://aclanthology.org/2022.coling-1.510.pdf | coling-2022-10 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 5.31446457e-01 3.05206954e-01 -3.23611110e-01 -9.37053263e-02
-1.04150569e+00 -1.42318189e-01 7.42040098e-01 1.59006059e-01
-4.13490117e-01 1.01029706e+00 2.92520493e-01 -1.26150295e-01
1.62556112e-01 -1.04525661e+00 -7.23232806e-01 -8.18167865e-01
4.26820457e-01 4.78759646e-01 2.31537625e-01 -4.69877511... | [11.818882942199707, 8.911764144897461] |
38cae7ac-ff66-4399-ad71-8d1bca484b34 | integrated-face-analytics-networks-through | 1711.06055 | null | http://arxiv.org/abs/1711.06055v1 | http://arxiv.org/pdf/1711.06055v1.pdf | Integrated Face Analytics Networks through Cross-Dataset Hybrid Training | Face analytics benefits many multimedia applications. It consists of a number
of tasks, such as facial emotion recognition and face parsing, and most
existing approaches generally treat these tasks independently, which limits
their deployment in real scenarios. In this paper we propose an integrated Face
Analytics Netw... | ['Jian Zhao', 'Terence Sim', 'Jianshu Li', 'Fang Zhao', 'Shengtao Xiao', 'Jiashi Feng', 'Shuicheng Yan', 'Jianan Li'] | 2017-11-16 | null | null | null | null | ['facial-emotion-recognition', 'face-parsing'] | ['computer-vision', 'computer-vision'] | [-3.16181383e-03 1.62464440e-01 6.26178160e-02 -7.64236033e-01
-6.65443778e-01 -4.16327983e-01 4.02466595e-01 -2.32325837e-01
-4.10774708e-01 2.06479028e-01 -2.32997254e-01 9.07532871e-02
-3.68366390e-02 -4.10477430e-01 -8.09586048e-01 -6.19716525e-01
1.01901628e-02 5.02769828e-01 5.75830089e-03 1.03576720... | [13.455460548400879, 0.7850288152694702] |
47790e2d-a071-42a5-97b7-58d7263d7d92 | a-hierarchical-game-theoretic-decision-making | 2303.16641 | null | https://arxiv.org/abs/2303.16641v1 | https://arxiv.org/pdf/2303.16641v1.pdf | A Hierarchical Game-Theoretic Decision-Making for Cooperative Multi-Agent Systems Under the Presence of Adversarial Agents | Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS d... | ['Ramviyas Parasuraman', 'Qin Yang'] | 2023-03-28 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-4.44846630e-01 6.25523806e-01 3.79155606e-01 3.20609897e-01
-1.48597792e-01 -3.38602781e-01 5.97839653e-01 6.65108413e-02
-5.26005030e-01 1.02265394e+00 -2.79683560e-01 -6.25729486e-02
-9.71458435e-01 -9.27951932e-01 3.00384723e-02 -6.55453920e-01
-8.42592955e-01 7.02908754e-01 5.76290190e-01 -1.09706628... | [3.7276811599731445, 1.8770866394042969] |
b3edeb0a-ce24-4926-85a9-664d36951bac | learning-geometry-disentangled-representation | 2012.10921 | null | https://arxiv.org/abs/2012.10921v3 | https://arxiv.org/pdf/2012.10921v3.pdf | Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud | In 2D image processing, some attempts decompose images into high and low frequency components for describing edge and smooth parts respectively. Similarly, the contour and flat area of 3D objects, such as the boundary and seat area of a chair, describe different but also complementary geometries. However, such investig... | ['Yu Qiao', 'Xiaojuan Qi', 'Mingye Xu', 'Zhipeng Zhou', 'Junhao Zhang', 'Mutian Xu'] | 2020-12-20 | null | null | null | null | ['3d-object-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.77212745e-01 -6.32360280e-02 -1.78194568e-01 -4.01345998e-01
-6.09230638e-01 -7.99125493e-01 5.54605722e-01 7.90402368e-02
1.25368059e-01 1.90802976e-01 8.00588951e-02 -3.74429189e-02
-2.45020226e-01 -7.75362611e-01 -7.87094891e-01 -8.36804032e-01
1.74062833e-01 5.06714046e-01 8.13017860e-02 -3.10171545... | [8.015222549438477, -3.475912094116211] |
b191549f-ed45-4d15-8f90-fb9dd7bc2395 | learning-attribute-structure-co-evolutions-in | 2007.13004 | null | https://arxiv.org/abs/2007.13004v1 | https://arxiv.org/pdf/2007.13004v1.pdf | Learning Attribute-Structure Co-Evolutions in Dynamic Graphs | Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the nodes. We find that real dynamic attributed graphs exhibit complex co-evolution of node attributes and graph structure. Learning node embeddin... | ['Meng Jiang', 'Yihong Ma', 'Zhihan Zhang', 'Tianwen Jiang', 'Daheng Wang', 'Tong Zhao', 'Nitesh V. Chawla'] | 2020-07-25 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-3.13580066e-01 4.54229027e-01 -2.90984094e-01 -3.64140332e-01
6.39551520e-01 -4.78224874e-01 9.77435529e-01 6.48891866e-01
-6.82147518e-02 6.28060400e-01 2.65644401e-01 -1.60336271e-01
-4.49858785e-01 -1.47824204e+00 -6.77609682e-01 -3.98461998e-01
-9.80896592e-01 1.00682461e+00 3.49711806e-01 -5.22550285... | [7.172128677368164, 6.10564661026001] |
9874106c-e210-47df-82ce-b07c60dbde9a | open-vocabulary-object-detection-via-scene | 2307.03339 | null | https://arxiv.org/abs/2307.03339v1 | https://arxiv.org/pdf/2307.03339v1.pdf | Open-Vocabulary Object Detection via Scene Graph Discovery | In recent years, open-vocabulary (OV) object detection has attracted increasing research attention. Unlike traditional detection, which only recognizes fixed-category objects, OV detection aims to detect objects in an open category set. Previous works often leverage vision-language (VL) training data (e.g., referring g... | ['Jianfei Cai', 'Munawar Hayat', 'Hengcan Shi'] | 2023-07-07 | null | null | null | null | ['open-vocabulary-object-detection', 'scene-graph-generation', 'object-detection', 'object-localization', 'graph-generation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'graphs'] | [ 9.97729599e-02 1.07305437e-01 -2.31172815e-01 -3.27713937e-01
-4.53500301e-01 -4.52712178e-01 6.55053973e-01 2.76564747e-01
-1.64484203e-01 3.41833732e-03 2.51319587e-01 -2.42799848e-01
2.02594727e-01 -9.95218933e-01 -9.52515483e-01 -4.30327028e-01
2.99397260e-01 5.67295253e-02 3.95939440e-01 -9.83588845... | [10.345049858093262, 1.5066074132919312] |
f687f3f0-fed8-4610-b193-9f9847613d89 | sequence-learning-with-rnns-for-medical | 1811.11523 | null | http://arxiv.org/abs/1811.11523v2 | http://arxiv.org/pdf/1811.11523v2.pdf | Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts | In this work, we consider the medical concept normalization problem, i.e.,
the problem of mapping a disease mention in free-form text to a concept in a
controlled vocabulary, usually to the standard thesaurus in the Unified Medical
Language System (UMLS). This task is challenging since medical terminology is
very diffe... | ['Elena Tutubalina', 'Zulfat Miftahutdinov', 'Sergey Nikolenko', 'Valentin Malykh'] | 2018-11-28 | null | null | null | null | ['medical-concept-normalization'] | ['medical'] | [ 9.04866874e-01 4.49071467e-01 -4.30802077e-01 -4.47662711e-01
-9.59858239e-01 -1.90700769e-01 4.07913864e-01 8.05650830e-01
-1.08856916e+00 6.67476773e-01 6.48363054e-01 -4.67170149e-01
3.40105779e-02 -7.95646608e-01 -4.65916842e-01 -4.62517887e-01
2.30951816e-01 6.38064206e-01 -1.78041235e-01 -6.21541739... | [8.517056465148926, 8.68575668334961] |
ec153fe1-5ba7-486c-8fb1-a2e34fd71550 | comparision-of-adversarial-and-non | 2211.00731 | null | https://arxiv.org/abs/2211.00731v1 | https://arxiv.org/pdf/2211.00731v1.pdf | Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models | Algorithmic music composition is a way of composing musical pieces with minimal to no human intervention. While recurrent neural networks are traditionally applied to many sequence-to-sequence prediction tasks, including successful implementations of music composition, their standard supervised learning approach based ... | ['Johan Pieter de Villiers', 'Anna Sergeevna Bosman', "Moseli Mots'oehli"] | 2022-11-01 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 5.22248805e-01 2.30553299e-01 2.87953824e-01 -8.18413496e-02
-6.49452150e-01 -9.06887710e-01 5.30195355e-01 -6.16451144e-01
-6.81299493e-02 8.05663884e-01 3.95393759e-01 2.09677871e-02
-9.17593092e-02 -7.80472934e-01 -5.87540150e-01 -7.37453043e-01
1.35374188e-01 6.28622472e-01 -2.79379785e-01 -6.36531651... | [16.01044464111328, 5.5565009117126465] |
0a34c4fb-8015-4379-80c9-f342db3f70d8 | temporally-coherent-video-anonymization | 2106.02328 | null | https://arxiv.org/abs/2106.02328v1 | https://arxiv.org/pdf/2106.02328v1.pdf | Temporally coherent video anonymization through GAN inpainting | This work tackles the problem of temporally coherent face anonymization in natural video streams.We propose JaGAN, a two-stage system starting with detecting and masking out faces with black image patches in all individual frames of the video. The second stage leverages a privacy-preserving Video Generative Adversarial... | ['Torsten Schön', 'Marcel Wasserer', 'Raphael Mitsch', 'Georg Göri', 'Patrick Blies', 'Thangapavithraa Balaji'] | 2021-06-04 | null | null | null | null | ['face-anonymization'] | ['computer-vision'] | [ 5.82132339e-01 1.00044094e-01 4.27669212e-02 -2.17766941e-01
-6.07510507e-01 -8.89331162e-01 5.21500051e-01 -5.97576559e-01
-1.59283295e-01 7.56512523e-01 2.37333074e-01 -4.65754420e-02
2.66736627e-01 -4.81814593e-01 -9.40562904e-01 -6.68568432e-01
-3.26353490e-01 -2.31146991e-01 -2.42130250e-01 1.67865261... | [12.726192474365234, 0.20487245917320251] |
3612baeb-19c6-4ee3-b739-4d6af377e3b6 | damp-doubly-aligned-multilingual-parser-for | 2212.08054 | null | https://arxiv.org/abs/2212.08054v2 | https://arxiv.org/pdf/2212.08054v2.pdf | DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue | Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands. However, prior work has demonstrated that semantic parsing is a difficult multilingual transfer task with low transfer efficiency compared to other tasks. In global markets such as India and Latin America,... | ['Rushin Shah', 'Diyi Yang', 'Rahul Goel', 'Eric Zhu', 'Fei Liu', 'Christopher Hidey', 'William Held'] | 2022-12-15 | null | null | null | null | ['semantic-parsing', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.16873789e-02 3.31462830e-01 -1.38675943e-01 -6.10789061e-01
-1.36831665e+00 -1.19112086e+00 3.98465604e-01 -2.59344041e-01
-8.98790836e-01 1.02289581e+00 2.89896607e-01 -9.23266590e-01
4.91052926e-01 -5.09069085e-01 -1.01521277e+00 -1.45596802e-01
4.00576055e-01 9.51844990e-01 -9.92324576e-02 -6.47863090... | [10.875717163085938, 9.41396427154541] |
9fc3f6ba-6a8e-4401-af47-2ab6b339bf33 | adversarial-capsule-networks-for-romanian | 2306.07845 | null | https://arxiv.org/abs/2306.07845v1 | https://arxiv.org/pdf/2306.07845v1.pdf | Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis | Satire detection and sentiment analysis are intensively explored natural language processing (NLP) tasks that study the identification of the satirical tone from texts and extracting sentiments in relationship with their targets. In languages with fewer research resources, an alternative is to produce artificial exampl... | ['Florin Pop', 'Dumitru-Clementin Cercel', 'Andrei-Marius Avram', 'Răzvan-Alexandru Smădu', 'Sebastian-Vasile Echim'] | 2023-06-13 | null | null | null | null | ['sentiment-analysis', 'satire-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.21917057e-01 -1.37027279e-01 -1.22813463e-01 -1.26378655e-01
-5.41954875e-01 -7.80690849e-01 7.13167489e-01 -1.44889683e-01
-5.52324712e-01 7.73366630e-01 5.35801232e-01 -1.87024072e-01
4.68175143e-01 -7.06265628e-01 -5.19562542e-01 -7.24974573e-01
1.30934089e-01 5.33847734e-02 -3.94756526e-01 -8.48957181... | [11.200509071350098, 7.033242225646973] |
39659789-5ef7-46e2-a95c-eec7c603f701 | image-moment-invariants-to-rotational-motion | 2303.14566 | null | https://arxiv.org/abs/2303.14566v1 | https://arxiv.org/pdf/2303.14566v1.pdf | Image Moment Invariants to Rotational Motion Blur | Rotational motion blur caused by the circular motion of the camera or/and object is common in life. Identifying objects from images affected by rotational motion blur is challenging because this image degradation severely impacts image quality. Therefore, it is meaningful to develop image invariant features under rotat... | ['Guoying Zhao', 'Hongxiang Hao', 'Hanlin Mo'] | 2023-03-25 | null | null | null | null | ['template-matching', 'handwritten-digit-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.01267457e-01 -8.36638272e-01 -8.31386482e-04 -2.69509673e-01
-1.44274965e-01 -5.45186818e-01 4.72005695e-01 -3.83809686e-01
-3.45734894e-01 6.22943342e-01 1.89807639e-02 -7.80446380e-02
-4.32864040e-01 -1.03761517e-01 -4.41345006e-01 -8.36562335e-01
-2.47160513e-02 -2.40170389e-01 2.50243992e-01 1.99098006... | [11.608992576599121, -2.752147674560547] |
b9148382-5bae-49be-b710-6d4818d21408 | acdmsr-accelerated-conditional-diffusion | 2307.00781 | null | https://arxiv.org/abs/2307.00781v1 | https://arxiv.org/pdf/2307.00781v1.pdf | ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution | Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demonstrated that iteratively refining pure Gaussian noise using a U-Net architecture trained on denoising at various noise levels can yield sati... | ['Yanning Zhang', 'In So Kweon', 'Yu Zhu', 'Jinqiu Sun', 'Kang Zhang', 'Pham Xuan Trung', 'Axi Niu'] | 2023-07-03 | null | null | null | null | ['image-super-resolution', 'image-to-image-translation', 'super-resolution', 'image-to-image-translation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 6.18452132e-01 -1.25861457e-02 -3.05397548e-02 -6.88816234e-02
-1.02492619e+00 -6.84307888e-02 7.46376395e-01 -5.71060836e-01
-2.86321938e-01 8.28598082e-01 5.40987730e-01 5.22100441e-02
-1.11794747e-01 -9.91050720e-01 -6.22753322e-01 -6.93518162e-01
2.75808871e-01 -3.63913216e-02 3.83084297e-01 -3.99513990... | [11.203714370727539, -1.9890614748001099] |
6dbf7e65-606a-4047-826a-f8171ea28777 | csi-based-outdoor-localization-for-massive | 1806.07447 | null | http://arxiv.org/abs/1806.07447v1 | http://arxiv.org/pdf/1806.07447v1.pdf | CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach | We report on experimental results on the use of a learning-based approach to
infer the location of a mobile user of a cellular network within a cell, for a
5G-type Massive multiple input, multiple output (MIMO) system. We describe how
the sample spatial covariance matrix computed from the CSI can be used as the
input t... | ['Luis García Ordóñez', 'Li Bojie', 'Paul Ferrand', 'Alexis Decurninge', 'Zhang Wei', 'Maxime Guillaud', 'He Gaoning'] | 2018-06-19 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [-4.78757918e-01 2.22628668e-01 -1.40690550e-01 -1.40931100e-01
-8.50985885e-01 -3.33346844e-01 4.57400046e-02 -1.02026656e-01
1.00965217e-01 1.22235703e+00 -2.31436133e-01 -1.03777635e+00
-5.41489005e-01 -5.10589302e-01 -5.28075397e-01 -9.79968309e-01
-1.06131816e+00 4.25910711e-01 -4.49277997e-01 3.13448668... | [6.114151954650879, 1.5997627973556519] |
ccb7835b-589b-47c1-8dbc-0b06e168de46 | lmscnet-lightweight-multiscale-3d-semantic | 2008.10559 | null | https://arxiv.org/abs/2008.10559v2 | https://arxiv.org/pdf/2008.10559v2.pdf | LMSCNet: Lightweight Multiscale 3D Semantic Completion | We introduce a new approach for multiscale 3Dsemantic scene completion from voxelized sparse 3D LiDAR scans. As opposed to the literature, we use a 2D UNet backbone with comprehensive multiscale skip connections to enhance feature flow, along with 3D segmentation heads. On the SemanticKITTI benchmark, our method perfor... | ['Anne Verroust-Blondet', 'Luis Roldão', 'Raoul de Charette'] | 2020-08-24 | null | null | null | null | ['3d-semantic-scene-completion', '3d-semantic-scene-completion-from-a-single'] | ['computer-vision', 'computer-vision'] | [ 1.47500157e-01 1.50257617e-01 -2.42289871e-01 -3.38729799e-01
-8.48023117e-01 -4.45968390e-01 4.45689023e-01 -7.89295062e-02
-5.14419615e-01 4.13501441e-01 2.03787386e-01 -3.82441968e-01
6.22842135e-03 -7.66964674e-01 -8.16572249e-01 -6.26411522e-03
-3.06560714e-02 8.48103464e-01 5.46870768e-01 -8.62633958... | [8.276215553283691, -2.83524489402771] |
3019bfc4-4188-4faa-ac54-b7e25ca652d1 | detection-of-abnormal-behavior-with-self | 2107.06530 | null | https://arxiv.org/abs/2107.06530v1 | https://arxiv.org/pdf/2107.06530v1.pdf | Detection of Abnormal Behavior with Self-Supervised Gaze Estimation | Due to the recent outbreak of COVID-19, many classes, exams, and meetings have been conducted non-face-to-face. However, the foundation for video conferencing solutions is still insufficient. So this technology has become an important issue. In particular, these technologies are essential for non-face-to-face testing, ... | ['Seong-Whan Lee', 'Suneung-Kim'] | 2021-07-14 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 7.69542605e-02 4.33923490e-03 2.32778579e-01 -6.05147243e-01
-2.15439647e-01 -3.43022309e-02 6.03454262e-02 -1.41885668e-01
-3.05656910e-01 7.08095312e-01 -2.37687930e-01 -5.12840748e-02
-3.75641882e-01 -3.02625716e-01 -5.79174936e-01 -7.94411719e-01
-4.36055548e-02 -3.22409086e-02 2.02611789e-01 -1.52347624... | [14.024275779724121, 0.2446908950805664] |
ac190f95-90f6-4865-a7f3-e7a620c7997e | robust-environment-perception-for-automated | 2206.03943 | null | https://arxiv.org/abs/2206.03943v1 | https://arxiv.org/pdf/2206.03943v1.pdf | Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection | The RGB complementary metal-oxidesemiconductor (CMOS) sensor works within the visible light spectrum. Therefore it is very sensitive to environmental light conditions. On the contrary, a long-wave infrared (LWIR) sensor operating in 8-14 micro meter spectral band, functions independent of visible light. In this paper, ... | ['Lutz Eckstein', 'Laurent Kloeker', 'Christian Mayr', 'Ali Kariminezhad', 'Mohsen Vadidar'] | 2022-06-08 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 7.27828920e-01 -2.60877550e-01 2.47231568e-03 -3.02919716e-01
-5.84674001e-01 -4.30649251e-01 4.42976713e-01 -1.66561127e-01
-7.67599523e-01 4.48550433e-01 -1.19229808e-01 -4.22068238e-01
4.29288238e-01 -9.85521019e-01 -6.39234960e-01 -9.75768209e-01
6.15217626e-01 -4.72786814e-01 3.76397610e-01 -2.55035609... | [9.466604232788086, -1.3364198207855225] |
61941fd6-569e-42b5-b98f-c57b0d0ec140 | wikicoder-learning-to-write-knowledge-powered | 2303.08574 | null | https://arxiv.org/abs/2303.08574v1 | https://arxiv.org/pdf/2303.08574v1.pdf | WikiCoder: Learning to Write Knowledge-Powered Code | We tackle the problem of automatic generation of computer programs from a few pairs of input-output examples. The starting point of this work is the observation that in many applications a solution program must use external knowledge not present in the examples: we call such programs knowledge-powered since they can re... | ['Gaëtan Margueritte', 'Nathanaël Fijalkow', 'Théo Matricon'] | 2023-03-15 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 5.78234568e-02 6.04340672e-01 -3.38428259e-01 -1.22765735e-01
-4.01296705e-01 -7.40455806e-01 6.34893775e-01 4.07351494e-01
-2.72818625e-01 7.16070294e-01 -9.83153805e-02 -6.01804614e-01
-2.00784475e-01 -1.28363204e+00 -9.27270532e-01 1.75377697e-01
1.85024753e-01 4.93669331e-01 7.39088416e-01 -5.59704900... | [8.27916145324707, 7.448019027709961] |
76eb9cbd-8e1c-457e-9711-33172003e134 | spatr-mocap-3d-human-action-recognition-based | 2306.17574 | null | https://arxiv.org/abs/2306.17574v1 | https://arxiv.org/pdf/2306.17574v1.pdf | SpATr: MoCap 3D Human Action Recognition based on Spiral Auto-encoder and Transformer Network | Recent advancements in technology have expanded the possibilities of human action recognition by leveraging 3D data, which offers a richer representation of actions through the inclusion of depth information, enabling more accurate analysis of spatial and temporal characteristics. However, 3D human action recognition i... | ['Lahoucine Ballihi', 'Hamza Bouzid'] | 2023-06-30 | null | null | null | null | ['action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.68564969e-01 -4.35206205e-01 -2.40387544e-02 9.06328931e-02
-1.31413981e-01 -3.57618153e-01 6.28752887e-01 -3.67787510e-01
-4.97249871e-01 3.11529905e-01 2.29566500e-01 -1.11818239e-01
-4.51569036e-02 -8.28773320e-01 -6.87171221e-01 -6.98570371e-01
-2.55540252e-01 4.21464026e-01 3.82968903e-01 -1.45525441... | [7.842621326446533, 0.3511888086795807] |
644651ab-247d-445f-ab05-5ea5b0c48d21 | benchmarking-joint-lexical-and-syntactic | null | null | https://aclanthology.org/W17-1725 | https://aclanthology.org/W17-1725.pdf | Benchmarking Joint Lexical and Syntactic Analysis on Multiword-Rich Data | This article evaluates the extension of a dependency parser that performs joint syntactic analysis and multiword expression identification. We show that, given sufficient training data, the parser benefits from explicit multiword information and improves overall labeled accuracy score in eight of the ten evaluation cas... | ["H{\\'e}ctor Martinez Alonso", 'Matthieu Constant'] | 2017-04-01 | null | null | null | ws-2017-4 | ['lexical-analysis'] | ['natural-language-processing'] | [ 8.06342252e-03 2.45404050e-01 -6.48838103e-01 -1.02979970e+00
-1.37894952e+00 -7.90519655e-01 1.03756785e-01 3.13002527e-01
-7.40386248e-01 1.10988283e+00 3.66996795e-01 -3.94059956e-01
3.04381430e-01 -2.18875036e-01 -9.61769596e-02 -3.22580367e-01
-2.29096785e-01 2.70211667e-01 1.83890879e-01 -2.70857930... | [10.333008766174316, 9.77993106842041] |
bdaded89-1d9c-4b9f-97a9-1132e76382a8 | rethinking-the-defocus-blur-detection-problem | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1182_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550613.pdf | Rethinking the Defocus Blur Detection Problem and A Real-Time Deep DBD Model | Defocus blur detection (DBD) is a classical low level vision task. It has recently attracted attention focusing on designing complex convolutional neural networks (CNN) which make full use of both low level features and high level semantic information. The heavy networks used in these methods lead to low processing spe... | ['Junchi Yan', 'Ning Zhang'] | null | null | null | null | eccv-2020-8 | ['defocus-blur-detection'] | ['computer-vision'] | [ 4.14278388e-01 -8.51627067e-02 3.83061878e-02 -3.41915488e-01
2.91389555e-01 8.15282390e-03 5.19833028e-01 -6.64879307e-02
-4.04513478e-01 7.10607648e-01 1.56838268e-01 -2.14885905e-01
-4.37834084e-01 -5.23210943e-01 -5.70712149e-01 -6.53564811e-01
9.94455889e-02 -2.15605900e-01 4.92148936e-01 -1.08272247... | [11.238838195800781, -2.6792984008789062] |
9ad31758-6d56-47c8-8cec-af9a596f039e | projection-inpainting-using-partial | 2005.00762 | null | https://arxiv.org/abs/2005.00762v1 | https://arxiv.org/pdf/2005.00762v1.pdf | Projection Inpainting Using Partial Convolution for Metal Artifact Reduction | In computer tomography, due to the presence of metal implants in the patient body, reconstructed images will suffer from metal artifacts. In order to reduce metal artifacts, metals are typically removed in projection images. Therefore, the metal corrupted projection areas need to be inpainted. For deep learning inpaint... | ['Yixing Huang', 'Lin Yuan', 'Andreas Maier'] | 2020-05-02 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 3.95427942e-01 1.87687695e-01 2.65688062e-01 -1.58532307e-01
-3.76255423e-01 1.38984606e-01 -7.69857736e-03 -1.78229198e-01
-3.84422481e-01 8.60682309e-01 2.62205362e-01 -1.16419271e-02
2.47672707e-01 -1.04829907e+00 -8.79400909e-01 -6.32851481e-01
6.34619415e-01 -2.26015057e-02 3.44679326e-01 -1.22969300... | [13.499804496765137, -2.5542221069335938] |
a746ffa7-1544-4e67-9ce5-f4e0b5bdbe5a | a-heat-jarrow-morton-framework-for-energy | 2305.01485 | null | https://arxiv.org/abs/2305.01485v2 | https://arxiv.org/pdf/2305.01485v2.pdf | A Heath-Jarrow-Morton framework for energy markets: a pragmatic approach | In this article we discuss the application of the Heath-Jarrow-Morton framework Heath et al. [26] to energy markets. The goal of the article is to give a detailed overview of the topic, focusing on practical aspects rather than on theory, which has been widely studied in literature. This work aims to be a guide for pra... | ['Edoardo Santilli', 'Matteo Gardini'] | 2023-05-02 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-5.55122614e-01 -1.93590328e-01 -3.36276330e-02 1.46988437e-01
-4.25620943e-01 -1.03875089e+00 1.00234711e+00 -3.74150485e-01
-1.10684946e-01 6.27363384e-01 7.57200047e-02 -7.28861153e-01
-4.63406265e-01 -8.95492733e-01 -1.94520783e-02 -7.61725903e-01
-2.61596859e-01 5.36945820e-01 -4.01358694e-01 -1.62448660... | [5.285648345947266, 3.9043285846710205] |
abb42e2a-ba6f-47c8-80d2-7b79ddfb1c8d | active-learning-strategies-for-weakly | 2207.12112 | null | https://arxiv.org/abs/2207.12112v1 | https://arxiv.org/pdf/2207.12112v1.pdf | Active Learning Strategies for Weakly-supervised Object Detection | Object detectors trained with weak annotations are affordable alternatives to fully-supervised counterparts. However, there is still a significant performance gap between them. We propose to narrow this gap by fine-tuning a base pre-trained weakly-supervised detector with a few fully-annotated samples automatically sel... | ['Jean Ponce', 'Patrick Pérez', 'Andrei Bursuc', 'Spyros Gidaris', 'Oriane Siméoni', 'Huy V. Vo'] | 2022-07-25 | null | null | null | null | ['weakly-supervised-object-detection'] | ['computer-vision'] | [-2.66192462e-02 5.02648413e-01 -6.15786731e-01 -3.29419762e-01
-1.42272067e+00 -7.52455413e-01 5.54451287e-01 2.33138025e-01
-9.48382735e-01 3.91454011e-01 6.86531365e-02 5.98298721e-02
4.20205891e-01 -3.70134860e-01 -7.34568655e-01 -7.99857318e-01
1.12389095e-01 5.73893368e-01 8.22995424e-01 3.05895925... | [9.243380546569824, 1.2538433074951172] |
1962c496-b2ae-4e51-b3b8-9afcdd1bb18c | structural-break-detection-in-quantile | 2302.05193 | null | https://arxiv.org/abs/2302.05193v1 | https://arxiv.org/pdf/2302.05193v1.pdf | Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates | We propose an econometric environment for structural break detection in nonstationary quantile predictive regressions. We establish the limit distributions for a class of Wald and fluctuation type statistics based on both the ordinary least squares estimator and the endogenous instrumental regression estimator proposed... | ['Christis Katsouris'] | 2023-02-10 | null | null | null | null | ['unity'] | ['computer-vision'] | [-1.88819617e-01 -3.84518981e-01 -6.74156368e-01 -1.85714468e-01
-1.04091358e+00 -8.47188473e-01 4.43135947e-01 3.06249224e-02
3.18608806e-02 1.09514165e+00 1.53837860e-01 -9.82025504e-01
-9.46194887e-01 -6.66799784e-01 -4.56832886e-01 -8.64086866e-01
-3.56890172e-01 2.63221741e-01 -2.54381090e-01 1.60320252... | [6.369168758392334, 4.201462268829346] |
ae34e19d-1e2e-4e3c-b359-34a6bf8a07b5 | planning-in-stochastic-environments-with-a | null | null | https://openreview.net/forum?id=X6D9bAHhBQ1 | https://openreview.net/pdf?id=X6D9bAHhBQ1 | Planning in Stochastic Environments with a Learned Model | Model-based reinforcement learning has proven highly successful. However, learning a model in isolation from its use during planning is problematic in complex environments. To date, the most effective techniques have instead combined value-equivalent model learning with powerful tree-search methods. This approach is ex... | ['David Silver', 'Thomas K Hubert', 'Sherjil Ozair', 'Julian Schrittwieser', 'Ioannis Antonoglou'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['game-of-go', 'board-games', '2048'] | ['playing-games', 'playing-games', 'playing-games'] | [ 6.41980991e-02 1.38891011e-01 -1.89738184e-01 1.34741277e-01
-8.23646903e-01 -6.82660341e-01 8.60418737e-01 1.20835572e-01
-7.25804627e-01 1.27423310e+00 -1.36780873e-01 -3.88459861e-01
-3.94229323e-01 -8.48976731e-01 -5.06939709e-01 -6.14634395e-01
-4.92827207e-01 1.02890074e+00 4.93751943e-01 -4.29888666... | [3.921502113342285, 1.6609264612197876] |
bc44cb0c-a428-4078-b50d-731035b304a2 | communication-efficient-robust-federated-1 | 2206.05558 | null | https://arxiv.org/abs/2206.05558v1 | https://arxiv.org/pdf/2206.05558v1.pdf | Communication-Efficient Robust Federated Learning with Noisy Labels | Federated learning (FL) is a promising privacy-preserving machine learning paradigm over distributed located data. In FL, the data is kept locally by each user. This protects the user privacy, but also makes the server difficult to verify data quality, especially if the data are correctly labeled. Training with corrupt... | ['Heng Huang', 'Jian Pei', 'Junyi Li'] | 2022-06-11 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [-1.22632131e-01 -8.15136209e-02 -2.99415171e-01 -5.40233552e-01
-1.15572608e+00 -9.10034001e-01 2.43731260e-01 2.04469115e-01
-4.65941548e-01 8.25724125e-01 6.76123286e-03 -3.35852265e-01
-2.28507996e-01 -5.93992829e-01 -7.95567334e-01 -1.28312349e+00
-5.01687564e-02 2.18808651e-01 -4.13480908e-01 3.02645862... | [5.870334148406982, 6.373134136199951] |
a83aa72a-2cf0-4735-a0b8-fde49ffae25d | improving-point-cloud-semantic-segmentation | 2009.10569 | null | https://arxiv.org/abs/2009.10569v3 | https://arxiv.org/pdf/2009.10569v3.pdf | Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection | Point cloud semantic segmentation plays an essential role in autonomous driving, providing vital information about drivable surfaces and nearby objects that can aid higher level tasks such as path planning and collision avoidance. While current 3D semantic segmentation networks focus on convolutional architectures that... | ['Luc van Gool', 'Ozan Unal', 'Dengxin Dai'] | 2020-09-22 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 7.96603635e-02 1.70362785e-01 -9.68482122e-02 -7.84090102e-01
-8.33408713e-01 -7.36560166e-01 6.50840521e-01 2.54749686e-01
-4.98755872e-01 -1.33545890e-01 -3.68341416e-01 -5.20640373e-01
1.94591954e-01 -8.22343886e-01 -8.68795395e-01 -2.92767763e-01
-9.94610637e-02 8.89936924e-01 1.01674867e+00 -6.14107788... | [7.7774763107299805, -2.6534321308135986] |
64b971c1-9988-41e9-8d58-e301bd9fe8c1 | multi-spectral-class-center-network-for-face | 2305.10794 | null | https://arxiv.org/abs/2305.10794v1 | https://arxiv.org/pdf/2305.10794v1.pdf | Multi-spectral Class Center Network for Face Manipulation Detection and Localization | As Deepfake contents continue to proliferate on the internet, advancing face manipulation forensics has become a pressing issue. To combat this emerging threat, previous methods mainly focus on studying how to distinguish authentic and manipulated face images. Despite impressive, image-level classification lacks explai... | ['Nenghai Yu', 'Honggang Hu', 'Bin Liu', 'Yue Wu', 'Wanyi Zhuang', 'Zhenchao Jin', 'Zhentao Tan', 'Qi Chu', 'Changtao Miao'] | 2023-05-18 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 3.85736346e-01 -5.51779449e-01 -2.67010003e-01 -2.80016929e-01
-6.26933157e-01 -4.91730422e-01 5.37039042e-01 -1.63238332e-01
1.67361051e-01 3.31540495e-01 8.03908557e-02 1.13013551e-01
-3.00343454e-01 -8.47122967e-01 -5.06560922e-01 -7.84863710e-01
-6.69977739e-02 -6.02617919e-01 -9.37120989e-02 -1.83845133... | [12.764852523803711, 1.07510244846344] |
2710b07a-df8f-45cc-803f-d3074d00680a | a-cautionary-tale-on-fitting-decision-trees | 2110.09626 | null | https://arxiv.org/abs/2110.09626v1 | https://arxiv.org/pdf/2110.09626v1.pdf | A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds | Decision trees are important both as interpretable models amenable to high-stakes decision-making, and as building blocks of ensemble methods such as random forests and gradient boosting. Their statistical properties, however, are not well understood. The most cited prior works have focused on deriving pointwise consis... | ['Bin Yu', 'Abhineet Agarwal', 'Yan Shuo Tan'] | 2021-10-18 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 6.16554379e-01 3.82766843e-01 -4.91360486e-01 -6.21418357e-01
-8.68654013e-01 -6.52512074e-01 4.55760241e-01 9.30878818e-02
3.68045568e-02 9.71881747e-01 2.98408329e-01 -5.88227034e-01
-3.95598650e-01 -8.81173253e-01 -6.20168269e-01 -9.17134345e-01
-9.38773453e-02 6.02514207e-01 -2.70519167e-01 1.75687790... | [8.11760425567627, 4.756628513336182] |
5f1ec1d7-bc7c-41c2-aa89-9c025f1971a4 | ae-net-adjoint-enhancement-network-for | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9835116 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9835116 | AE-Net:Adjoint Enhancement Network for Efficient Action Recognition in Video Understanding | Action recognition in video understanding is a challenging task, largely because of the complexity and difficulty in
temporal modeling, making it suffer from motion information loss
and misalignment of temporal attention in spatial dimensions. To
overcome these difficulties, we propose a novel temporal modeling
met... | ['Wenqian Wang and Nanjun Li', 'Faliang Chang', 'Chunsheng Liu', 'Bin Wang'] | 2022-07-21 | null | null | null | tmm-2022-7 | ['video-understanding'] | ['computer-vision'] | [-4.81258556e-02 -4.33137506e-01 -2.95751207e-02 -1.76333591e-01
-2.17706352e-01 -1.06163330e-01 5.34556031e-01 -6.77161038e-01
-4.78109926e-01 3.41603339e-01 7.19883323e-01 -1.37974545e-02
-2.28184357e-01 -5.98044038e-01 -4.52505827e-01 -9.37824488e-01
-2.18315154e-01 -4.73213583e-01 6.52378559e-01 -2.68529862... | [8.619685173034668, 0.4687407612800598] |
0cf096fc-b2a9-4582-81c8-a60180714038 | mask-conditioned-latent-diffusion-for | 2304.05233 | null | https://arxiv.org/abs/2304.05233v1 | https://arxiv.org/pdf/2304.05233v1.pdf | Mask-conditioned latent diffusion for generating gastrointestinal polyp images | In order to take advantage of AI solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in the medical field and the requirement of getting aid from experts for the time-consuming and costly medical data annotation process. In com... | ['Vajira Thambawita', 'Michael A. Riegler', 'Pål Halvorsen', 'Sravanthi Parasa', 'Zahra Sepasdar', 'Leila Mozaffari', 'Roman Macháček'] | 2023-04-11 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 5.91649532e-01 6.30139768e-01 1.42611936e-01 -1.81315109e-01
-8.16731632e-01 -4.66121525e-01 5.85060656e-01 -3.29913110e-01
-4.20120329e-01 8.30427229e-01 1.30511463e-01 -2.09485471e-01
4.19628263e-01 -8.38418186e-01 -8.62843812e-01 -6.98542416e-01
9.88951623e-02 3.13596874e-01 -7.52199849e-04 1.61955222... | [14.187469482421875, -1.9212177991867065] |
3974c409-4107-4637-9340-fc364f428039 | adapting-membership-inference-attacks-to-gnn | 2110.08760 | null | https://arxiv.org/abs/2110.08760v1 | https://arxiv.org/pdf/2110.08760v1.pdf | Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications | Graph Neural Networks (GNNs) are widely adopted to analyse non-Euclidean data, such as chemical networks, brain networks, and social networks, modelling complex relationships and interdependency between objects. Recently, Membership Inference Attack (MIA) against GNNs raises severe privacy concerns, where training data... | ['Xingliang Yuan', 'Shirui Pan', 'Xiangwen Yang', 'Bang Wu'] | 2021-10-17 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 5.43465734e-01 3.08562785e-01 -1.37906857e-02 -1.49095789e-01
-5.32733202e-02 -9.37482536e-01 4.46304381e-01 4.59777445e-01
-3.07349056e-01 8.16841424e-01 -6.66871369e-01 -8.03852439e-01
-2.16339558e-01 -1.25484788e+00 -1.01007414e+00 -6.81598306e-01
-6.12700939e-01 1.68580309e-01 3.51239234e-01 8.86705518... | [6.035338878631592, 7.281796932220459] |
353fa992-7760-4fac-846a-8205e9b63edb | a-survey-of-applications-of-artificial | 2107.06179 | null | https://arxiv.org/abs/2107.06179v2 | https://arxiv.org/pdf/2107.06179v2.pdf | Application of artificial intelligence techniques for automated detection of myocardial infarction: A review | Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to... | ['U Rajendra Acharya', 'Ru-San Tan', 'Hui Wen Loh', 'Edris Hassannatajjeloudari', 'Mitra Akbari Kohnehshari', 'Samiyeh Khosravi', 'Tahereh Tamadon', 'Amir Mosavi', 'Danial Sharifrazi', 'Roohallah Alizadehsani', 'Sahar Khanjani Shirkharkolaie', 'Zeynab Kiani Zadegan', 'Amir Mashmool', 'Issa Nodehi', 'Sanaz Mojrian', 'Ja... | 2021-07-05 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 4.41397339e-01 -3.40421647e-01 -2.53109783e-01 -4.25517336e-02
-4.61414844e-01 -2.48707980e-01 -3.27069849e-01 3.19991767e-01
-5.62074721e-01 8.80231619e-01 -2.54419029e-01 -5.89033306e-01
-8.07277560e-02 -6.84475541e-01 -8.72602761e-02 -6.14860296e-01
-4.91783321e-01 3.25254619e-01 -2.81541675e-01 1.10629395... | [14.310415267944336, 3.270939350128174] |
f15fdea0-e174-4c19-820c-87c78c44e82a | look-remember-and-reason-visual-reasoning | 2306.17778 | null | https://arxiv.org/abs/2306.17778v1 | https://arxiv.org/pdf/2306.17778v1.pdf | Look, Remember and Reason: Visual Reasoning with Grounded Rationales | Large language models have recently shown human level performance on a variety of reasoning tasks. However, the ability of these models to perform complex visual reasoning has not been studied in detail yet. A key challenge in many visual reasoning tasks is that the visual information needs to be tightly integrated in ... | ['Roland Memisevic', 'Pulkit Madan', 'Reza Pourreza', 'Mingu Lee', 'Sunny Panchal', 'Apratim Bhattacharyya'] | 2023-06-30 | null | null | null | null | ['object-recognition', 'visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'reasoning'] | [-1.16045333e-01 1.32602826e-01 4.88499813e-02 -3.47012997e-01
-4.63467181e-01 -8.80709946e-01 7.98929036e-01 3.10673594e-01
-3.07360023e-01 1.89916089e-01 2.35418305e-01 -7.73890913e-01
6.88467845e-02 -5.93784690e-01 -7.20791161e-01 -1.88684851e-01
2.77267724e-01 5.98266482e-01 4.61776853e-01 -4.05725330... | [10.821497917175293, 1.9917234182357788] |
6f9ed357-08ea-44b7-b7e5-e9be3a6a1817 | a-hierarchical-pose-based-approach-to-complex | 1606.04992 | null | http://arxiv.org/abs/1606.04992v1 | http://arxiv.org/pdf/1606.04992v1.pdf | A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets | In this paper, we introduce a new hierarchical model for human action
recognition using body joint locations. Our model can categorize complex
actions in videos, and perform spatio-temporal annotations of the atomic
actions that compose the complex action being performed.That is, for each
atomic action, the model gener... | ['Ivan Lillo', 'Juan Carlos Niebles', 'Alvaro Soto'] | 2016-06-15 | a-hierarchical-pose-based-approach-to-complex-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Lillo_A_Hierarchical_Pose-Based_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Lillo_A_Hierarchical_Pose-Based_CVPR_2016_paper.pdf | cvpr-2016-6 | ['action-understanding'] | ['computer-vision'] | [ 3.12842757e-01 2.78983712e-01 -5.46038568e-01 -2.05312863e-01
-5.31391382e-01 -4.14073527e-01 6.87485874e-01 -1.45455480e-01
-1.92422062e-01 5.28880417e-01 9.67589915e-01 4.29822475e-01
6.23052381e-02 -2.53151059e-01 -7.60393918e-01 -6.04664624e-01
-5.32478154e-01 6.61521554e-01 6.34801447e-01 -6.67491853... | [8.120256423950195, 0.45924386382102966] |
395c747f-f5e7-406d-b40b-c07d8b44eb68 | hierarchical-strategies-for-cooperative-multi | 2212.07397 | null | https://arxiv.org/abs/2212.07397v1 | https://arxiv.org/pdf/2212.07397v1.pdf | Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning | Adequate strategizing of agents behaviors is essential to solving cooperative MARL problems. One intuitively beneficial yet uncommon method in this domain is predicting agents future behaviors and planning accordingly. Leveraging this point, we propose a two-level hierarchical architecture that combines a novel informa... | ['Ammar Fayad', 'Majd Ibrahim'] | 2022-12-14 | null | null | null | null | ['starcraft'] | ['playing-games'] | [-3.83388132e-01 3.26709241e-01 -3.84271950e-01 -4.33033891e-02
-4.61527675e-01 -4.84231532e-01 5.71999490e-01 -1.50995031e-01
-4.89262700e-01 9.56010640e-01 5.82306720e-02 -3.71395618e-01
-6.63644671e-01 -9.26792026e-01 -8.34877968e-01 -6.77776992e-01
-7.11710572e-01 8.15940142e-01 6.08429983e-02 -6.37556374... | [3.7915091514587402, 1.8117890357971191] |
6cdbe47a-db5d-467b-a22c-073449645a8c | ppt-token-pruned-pose-transformer-for | 2209.08194 | null | https://arxiv.org/abs/2209.08194v1 | https://arxiv.org/pdf/2209.08194v1.pdf | PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation | Recently, the vision transformer and its variants have played an increasingly important role in both monocular and multi-view human pose estimation. Considering image patches as tokens, transformers can model the global dependencies within the entire image or across images from other views. However, global attention is... | ['Xiaohui Xie', 'Hao Tang', 'Xiangyi Yan', 'Xingwei Liu', 'Liangjian Chen', 'Deying Kong', 'Yifei Chen', 'Zhe Wang', 'Haoyu Ma'] | 2022-09-16 | null | null | null | null | ['3d-human-pose-estimation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.49640650e-01 -3.31393480e-01 1.20476313e-01 -1.22923128e-01
-8.04226995e-01 -2.67733842e-01 2.37976119e-01 -4.10840720e-01
-3.64936382e-01 4.90226865e-01 1.71220288e-01 5.24122179e-01
2.14701846e-01 -4.12649751e-01 -7.60209203e-01 -5.65235376e-01
3.10614705e-01 5.00144541e-01 6.99993551e-01 -1.71721995... | [7.174656867980957, -0.8458951711654663] |
4b3b98a3-b07b-4054-b2ba-b42b5fcad70d | enhancing-unsupervised-sentence-similarity | null | null | https://aclanthology.org/R19-1115 | https://aclanthology.org/R19-1115.pdf | Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations | Calculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. All modern state of the art STS methods rely on word embeddings one way or another. The recently introduced contextualised wor... | ['Tharindu Ranasinghe', 'Constantin Orasan', 'Ruslan Mitkov'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['contextualised-word-representations'] | ['natural-language-processing'] | [ 2.73197651e-01 -5.97313568e-02 -3.58713299e-01 -1.10157885e-01
-4.08981532e-01 -3.54114503e-01 1.22555685e+00 1.27857697e+00
-1.18084812e+00 5.15555084e-01 1.10696328e+00 -2.04382360e-01
-4.76685047e-01 -6.85984433e-01 2.75883198e-01 -3.07187825e-01
1.88411660e-02 6.40244424e-01 6.78091705e-01 -6.60164654... | [10.499785423278809, 8.675131797790527] |
9199d65c-0a27-4767-9766-da566bd225b5 | biobart-pretraining-and-evaluation-of-a | 2204.03905 | null | https://arxiv.org/abs/2204.03905v2 | https://arxiv.org/pdf/2204.03905v2.pdf | BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model | Pretrained language models have served as important backbones for natural language processing. Recently, in-domain pretraining has been shown to benefit various domain-specific downstream tasks. In the biomedical domain, natural language generation (NLG) tasks are of critical importance, while understudied. Approaching... | ['Sheng Yu', 'Yutao Xie', 'Jiaxing Zhang', 'Ruyi Gan', 'Zheng Yuan', 'Hongyi Yuan'] | 2022-04-08 | null | https://aclanthology.org/2022.bionlp-1.9 | https://aclanthology.org/2022.bionlp-1.9.pdf | bionlp-acl-2022-5 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 6.52407229e-01 7.47035801e-01 -8.68935660e-02 -5.02705693e-01
-1.24612761e+00 -4.96807098e-01 6.78798079e-01 2.91138202e-01
-4.59250748e-01 1.32223058e+00 9.23721313e-01 -5.73461235e-01
8.29135776e-02 -6.94005668e-01 -7.47577548e-01 -4.13677394e-01
3.96394283e-02 8.19367170e-01 -3.10729563e-01 -5.11180758... | [8.677449226379395, 8.752117156982422] |
42bd5446-76e4-4404-ab6c-5f10621ad08a | vidlankd-improving-language-understanding-via | 2107.02681 | null | https://arxiv.org/abs/2107.02681v2 | https://arxiv.org/pdf/2107.02681v2.pdf | VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer | Since visual perception can give rich information beyond text descriptions for world understanding, there has been increasing interest in leveraging visual grounding for language learning. Recently, vokenization (Tan and Bansal, 2020) has attracted attention by using the predictions of a text-to-image retrieval model a... | ['Mohit Bansal', 'Hao Tan', 'Jaemin Cho', 'Zineng Tang'] | 2021-07-06 | null | http://proceedings.neurips.cc/paper/2021/hash/ccdf3864e2fa9089f9eca4fc7a48ea0a-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ccdf3864e2fa9089f9eca4fc7a48ea0a-Paper.pdf | neurips-2021-12 | ['video-grounding'] | ['computer-vision'] | [-2.02129722e-01 1.87045261e-01 -4.56625998e-01 -4.22630310e-01
-7.36016631e-01 -6.86871827e-01 6.61590695e-01 -2.77680196e-02
-4.36739057e-01 4.22875106e-01 2.86449224e-01 -5.20874381e-01
5.55302985e-02 -5.85208058e-01 -9.89895463e-01 -2.84572542e-01
3.51191223e-01 5.35747826e-01 2.10849330e-01 -1.80581123... | [10.738512992858887, 1.689260721206665] |
e2f93982-ba9f-4094-b90e-19c880036e49 | biorex-improving-biomedical-relation | 2306.11189 | null | https://arxiv.org/abs/2306.11189v1 | https://arxiv.org/pdf/2306.11189v1.pdf | BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets | Biomedical relation extraction (RE) is the task of automatically identifying and characterizing relations between biomedical concepts from free text. RE is a central task in biomedical natural language processing (NLP) research and plays a critical role in many downstream applications, such as literature-based discover... | ['Zhiyong Lu', 'Qingyu Chen', 'Ling Luo', 'Chih-Hsuan Wei', 'Po-Ting Lai'] | 2023-06-19 | null | null | null | null | ['graph-construction', 'multi-task-learning', 'transfer-learning', 'relation-extraction'] | ['graphs', 'methodology', 'miscellaneous', 'natural-language-processing'] | [ 3.75786573e-01 3.24078314e-02 -4.48164850e-01 -1.03105359e-01
-1.22312665e+00 -5.09677708e-01 5.05361497e-01 8.50042045e-01
-4.84779000e-01 1.16465807e+00 1.02114551e-01 -6.00803077e-01
-4.84523177e-01 -5.39634466e-01 -6.99525654e-01 -6.43524885e-01
-1.61340386e-01 7.69057274e-01 9.85922106e-03 -6.58310279... | [8.47409725189209, 8.732340812683105] |
505794dd-8a2c-4ac3-9cfb-53c50c210aa6 | hcr-net-a-deep-learning-based-script | 2108.06663 | null | https://arxiv.org/abs/2108.06663v3 | https://arxiv.org/pdf/2108.06663v3.pdf | HCR-Net: A deep learning based script independent handwritten character recognition network | Despite being studied extensively for a few decades, handwritten character recognition (HCR) is still considered a challenging learning problem in pattern recognition, and there is very limited research on script independent models. This is mainly because of similarity in structure of characters, different handwriting ... | ['Anuj Sharma', 'Sukhdeep Singh', 'Vinod Kumar Chauhan'] | 2021-08-15 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [-2.32967660e-02 -7.56865859e-01 -1.22142985e-01 -3.61251831e-01
-4.51430142e-01 -6.26531005e-01 5.36800683e-01 -4.31393385e-01
-5.55497289e-01 7.95632124e-01 -9.04280022e-02 -3.35498184e-01
-8.11532885e-02 -6.82706892e-01 -5.90626776e-01 -8.10631812e-01
8.51376653e-02 5.65380454e-01 3.16278517e-01 -3.92913401... | [11.884960174560547, 2.6029372215270996] |
665cc9c3-6c87-4e67-ab84-ba9676aaa95a | image-captioning-with-unseen-objects | 1908.00047 | null | https://arxiv.org/abs/1908.00047v1 | https://arxiv.org/pdf/1908.00047v1.pdf | Image Captioning with Unseen Objects | Image caption generation is a long standing and challenging problem at the intersection of computer vision and natural language processing. A number of recently proposed approaches utilize a fully supervised object recognition model within the captioning approach. Such models, however, tend to generate sentences which ... | ['Nazli Ikizler-Cinbis', 'Ramazan Gokberk Cinbis', 'Berkan Demirel'] | 2019-07-31 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 9.40455496e-01 6.32708430e-01 1.45243220e-02 -3.65669489e-01
-9.90036964e-01 -3.82572234e-01 8.66290748e-01 -1.07406996e-01
-2.55161494e-01 8.00968289e-01 -5.47440313e-02 -1.04178965e-01
3.90026033e-01 -7.87804782e-01 -1.16789949e+00 -5.89902520e-01
5.18574655e-01 6.44073308e-01 2.52329886e-01 -9.42254625... | [10.936067581176758, 1.0347152948379517] |
374cba95-d958-4497-90c2-bbb1d14a1d05 | functional-nanomaterials-design-in-the | 2108.13171 | null | https://arxiv.org/abs/2108.13171v1 | https://arxiv.org/pdf/2108.13171v1.pdf | Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models | Machine-learning (ML) techniques have revolutionized a host of research fields of chemical and materials science with accelerated, high-efficiency discoveries in design, synthesis, manufacturing, characterization and application of novel functional materials, especially at the nanometre scale. The reason is the time ef... | ['Zhexu Xi'] | 2021-08-16 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [ 5.33878684e-01 -5.20517826e-01 -5.59945822e-01 -6.11285083e-02
-4.91139978e-01 -5.48006535e-01 4.79793847e-01 6.07309759e-01
-1.86457112e-01 1.28473961e+00 -1.77336738e-01 -3.34920138e-01
-2.31749237e-01 -1.10073090e+00 -5.30763745e-01 -1.18777144e+00
-6.24788590e-02 4.36220765e-01 1.99067160e-01 -1.88598216... | [5.1822381019592285, 5.468028545379639] |
e49d2903-3d9b-44ae-aa24-56c846d01cb4 | nlpositionality-characterizing-design-biases | 2306.01943 | null | https://arxiv.org/abs/2306.01943v1 | https://arxiv.org/pdf/2306.01943v1.pdf | NLPositionality: Characterizing Design Biases of Datasets and Models | Design biases in NLP systems, such as performance differences for different populations, often stem from their creator's positionality, i.e., views and lived experiences shaped by identity and background. Despite the prevalence and risks of design biases, they are hard to quantify because researcher, system, and datase... | ['Maarten Sap', 'Katharina Reinecke', 'Ronan Le Bras', 'Jenny T. Liang', 'Sebastin Santy'] | 2023-06-02 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-7.68992901e-02 3.36371183e-01 -3.96744847e-01 -5.77161491e-01
-5.52444518e-01 -1.07053864e+00 6.16138458e-01 2.55565524e-01
-5.40234745e-01 4.67005759e-01 1.08001685e+00 -2.73974866e-01
9.42897424e-03 -3.53833064e-02 -5.39133132e-01 -1.84570059e-01
6.14870965e-01 2.06949994e-01 -6.36656821e-01 2.78092802... | [9.245162010192871, 10.010473251342773] |
7a1d52ef-3de9-434d-83e3-1d78deb581b0 | conditional-diffusion-feature-refinement-for | 2305.03614 | null | https://arxiv.org/abs/2305.03614v2 | https://arxiv.org/pdf/2305.03614v2.pdf | Conditional Diffusion Feature Refinement for Continuous Sign Language Recognition | In this work, we are dedicated to leveraging the denoising diffusion models' success and formulating feature refinement as the autoencoder-formed diffusion process, which is a mask-and-predict scheme. The state-of-the-art CSLR framework consists of a spatial module, a visual module, a sequence module, and a sequence le... | ['ShengYong Chen', 'Tiantian Yuan', 'Yuxi Zhou', 'Qing Guo', 'Wanli Xue', 'Leming Guo'] | 2023-05-05 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 6.33511007e-01 1.75869502e-02 4.41925488e-02 -3.73723030e-01
-4.81228530e-01 -1.93426609e-02 7.27441847e-01 -3.80380958e-01
-3.25214595e-01 4.14172620e-01 2.87443727e-01 -5.87741286e-02
7.33374357e-02 -6.87220871e-01 -7.75352836e-01 -1.04575586e+00
2.79181480e-01 7.31712505e-02 1.19547307e-01 -1.76772520... | [11.150444030761719, -1.218219518661499] |
1987f947-d262-4273-abed-68177a2d2cce | joint-passage-ranking-for-diverse-multi | 2104.08445 | null | https://arxiv.org/abs/2104.08445v2 | https://arxiv.org/pdf/2104.08445v2.pdf | Joint Passage Ranking for Diverse Multi-Answer Retrieval | We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question. This task requires joint modeling of retrieved passages, as models should not repeatedly retrieve passages containing the same answer at the cost of missing a different v... | ['Hannaneh Hajishirzi', 'Kristina Toutanova', 'Ming-Wei Chang', 'Kenton Lee', 'Sewon Min'] | 2021-04-17 | null | https://aclanthology.org/2021.emnlp-main.560 | https://aclanthology.org/2021.emnlp-main.560.pdf | emnlp-2021-11 | ['passage-ranking'] | ['natural-language-processing'] | [ 1.06175505e-01 -1.39955372e-01 -2.65764028e-01 1.22371033e-01
-1.85876560e+00 -8.01696360e-01 4.93666798e-01 5.05575538e-01
-4.51583296e-01 9.47780550e-01 7.06162155e-01 -3.32711011e-01
-3.75864118e-01 -8.48778605e-01 -6.53926373e-01 -1.11611933e-01
2.82961637e-01 8.50138903e-01 5.98526657e-01 -5.61311543... | [11.47359848022461, 7.771096229553223] |
c72fb3c3-b698-4f70-b5b6-46c9865c6ec6 | add-net-an-effective-deep-learning-model-for | null | null | https://ieeexplore.ieee.org/abstract/document/9877809 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9877809 | ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans | Alzheimer's Disease (AD) is a neurological brain disorder marked by dementia and neurological dysfunction that affects memory, behavioral patterns, and reasoning. Alzheimer's disease is an incurable disease that primarily affects people over 40. Alzheimer's disease is diagnosed through a manual evaluation of a patient'... | ['Muhammad Asad', 'AHMAD MOUSTAFA', 'SYEDA FIZZAH JILLANI', 'Muhammad Aslam', 'SAQIB MAHMOOD', 'MUI-ZZUD-DIN', 'GULNAZ AHMED', 'SHAHID ZIKRIA', 'MIAN MUHAMMAD SADIQ FAREED'] | 2022-09-19 | null | null | null | journal-2022-9 | ['alzheimer-s-disease-detection'] | ['medical'] | [-1.01880215e-01 -4.16285684e-03 -4.23271023e-02 -4.87020522e-01
-2.12703809e-01 7.94409513e-02 2.94744194e-01 1.40883967e-01
-7.11957991e-01 8.88796031e-01 -2.04975829e-01 -1.51761904e-01
-1.92544505e-01 -8.73302460e-01 -1.45433858e-01 -5.55521727e-01
-6.16362870e-01 6.04835808e-01 4.83851314e-01 -6.15729077... | [14.166129112243652, -1.7507214546203613] |
b3b71e28-2f8f-4389-92ae-0fa228b145bb | can-gamification-reduce-the-burden-of-self | 2302.03616 | null | https://arxiv.org/abs/2302.03616v2 | https://arxiv.org/pdf/2302.03616v2.pdf | Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load | The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement. We conduct a study to explore the impact of gamification on self-reporting. Our approach involves the creation of a system to assess co... | ['Aneta Lisowska', 'Maciej Malawski', 'Arkadiusz Sitek', 'Tomasz Trzciński', 'Rosmary Blanco', 'M. Patrycja Lelujko', 'Maciej Kuś', 'Ryszard Pręcikowski', 'Sylwia Marek', 'Paulina Adamczyk', 'Michal K. Grzeszczyk'] | 2023-02-07 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 2.01480202e-02 3.48165512e-01 -2.78456300e-01 -4.42881554e-01
-5.01380026e-01 -3.40501040e-01 4.60513793e-02 -1.17584892e-01
-5.58526635e-01 5.29602110e-01 3.29377592e-01 -2.41164997e-01
2.07449004e-01 -7.01339781e-01 2.66869128e-01 -1.61894009e-01
1.34954244e-01 -4.43578139e-02 -2.54228503e-01 9.51596200... | [13.653636932373047, 3.133635997772217] |
ceea79f0-5c9f-44e5-b526-7a667c6563ea | improving-patent-mining-and-relevance | 2105.03979 | null | https://arxiv.org/abs/2105.03979v2 | https://arxiv.org/pdf/2105.03979v2.pdf | Improving Patent Mining and Relevance Classification using Transformers | Patent analysis and mining are time-consuming and costly processes for companies, but nevertheless essential if they are willing to remain competitive. To face the overload induced by numerous patents, the idea is to automatically filter them, bringing only few to read to experts. This paper reports a successful applic... | ['Binbin Xu', 'Sylvie Ranwez', 'Walter Vermeiren', 'Théo Ding'] | 2021-05-09 | null | null | null | null | ['classification'] | ['methodology'] | [ 2.52591133e-01 5.79754375e-02 -3.14343333e-01 -2.18171731e-01
-6.70564532e-01 -7.84650743e-01 3.24595690e-01 3.46559912e-01
-6.42996311e-01 8.12642694e-01 -3.05201203e-01 -9.12747979e-01
-4.26457018e-01 -8.63253057e-01 -5.98211527e-01 -1.35629326e-01
1.49541825e-01 5.07669747e-01 3.18506733e-02 -8.65013450... | [9.86742115020752, 8.188393592834473] |
dfe63395-3457-4a5e-81f6-9ef245fda7fa | ask2transformers-zero-shot-domain-labelling-1 | null | null | https://aclanthology.org/2021.gwc-1.6 | https://aclanthology.org/2021.gwc-1.6.pdf | Ask2Transformers: Zero-Shot Domain labelling with Pretrained Language Models | In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, the system is not restricted to use a particular set of domain labels. We exploit the knowledge encoded within different off-the-shelf pre-tr... | ['German Rigau', 'Oscar Sainz'] | null | null | null | null | eacl-gwc-2021-1 | ['domain-labelling'] | ['natural-language-processing'] | [ 1.05611585e-01 4.08288166e-02 -5.41941881e-01 -6.20445907e-01
-3.96326125e-01 -6.19562447e-01 7.32581437e-01 4.00188297e-01
-9.75023091e-01 8.84219229e-01 -1.02732129e-01 -1.79072097e-01
5.70961982e-02 -9.12268400e-01 4.95789805e-03 -1.12894788e-01
3.57770056e-01 8.82821798e-01 5.84559262e-01 -6.78857982... | [10.47866439819336, 8.810250282287598] |
0d02a7e0-bcee-426d-8fec-c8d9d4d28140 | probabilistic-3d-segmentation-for-aleatoric | 2305.00950 | null | https://arxiv.org/abs/2305.00950v1 | https://arxiv.org/pdf/2305.00950v1.pdf | Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data | Uncertainty quantification in medical images has become an essential addition to segmentation models for practical application in the real world. Although there are valuable developments in accurate uncertainty quantification methods using 2D images and slices of 3D volumes, in clinical practice, the complete 3D volume... | ['Fons van der Sommen', 'Peter H. N. de With', 'Amaan M. M. Valiuddin', 'Christiaan G. A. Viviers'] | 2023-05-01 | null | null | null | null | ['medical-procedure', 'lung-nodule-segmentation'] | ['medical', 'medical'] | [-3.71934064e-02 4.03455585e-01 1.05837788e-02 -4.21380192e-01
-9.43688214e-01 -5.56074619e-01 4.81145084e-01 3.95478934e-01
-5.38718402e-01 6.99699342e-01 1.31180674e-01 -2.40461066e-01
-6.39656186e-01 -5.42121708e-01 -4.41312224e-01 -7.95795143e-01
-1.05678573e-01 1.08554053e+00 2.59780318e-01 4.42540556... | [14.3719482421875, -2.1162331104278564] |
65afcf36-b986-4378-8656-f8b78022f248 | neural-network-accelerator-for-quantum | 2208.02645 | null | https://arxiv.org/abs/2208.02645v2 | https://arxiv.org/pdf/2208.02645v2.pdf | Neural network accelerator for quantum control | Efficient quantum control is necessary for practical quantum computing implementations with current technologies. Conventional algorithms for determining optimal control parameters are computationally expensive, largely excluding them from use outside of the simulation. Existing hardware solutions structured as lookup ... | ['Farah Fahim', 'Luca Carloni', 'Gabriel N. Perdue', 'Nhan Tran', 'Giuseppe Di Guglielmo', 'A. Barış Özgüler', 'David Xu'] | 2022-08-04 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 1.28068626e-01 -1.55254379e-01 -2.47241095e-01 -4.05899972e-01
-5.95214665e-01 -6.22843266e-01 3.14613134e-01 5.81510544e-01
-6.02843165e-01 8.95416737e-01 -5.88352561e-01 -1.05079365e+00
2.33964577e-01 -1.02650404e+00 -7.30307341e-01 -6.52872503e-01
-2.14408070e-01 5.27014315e-01 2.05424607e-01 -3.12375158... | [5.582597255706787, 4.923130989074707] |
8e856cd3-063e-42bd-a060-5e7d43489a8e | visual-speech-recognition-aligning | 1710.01292 | null | http://arxiv.org/abs/1710.01292v1 | http://arxiv.org/pdf/1710.01292v1.pdf | Visual speech recognition: aligning terminologies for better understanding | We are at an exciting time for machine lipreading. Traditional research
stemmed from the adaptation of audio recognition systems. But now, the computer
vision community is also participating. This joining of two previously
disparate areas with different perspectives on computer lipreading is creating
opportunities for ... | ['Helen L. Bear', 'Sarah Taylor'] | 2017-10-03 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 3.11552227e-01 4.61906083e-02 -4.08671737e-01 -2.78166711e-01
-9.98773396e-01 -3.91142607e-01 5.58494151e-01 3.62074040e-02
-5.21207035e-01 4.15553212e-01 9.76622581e-01 -4.06309605e-01
1.21074751e-01 1.97840575e-02 -2.86061734e-01 -2.28175074e-01
5.26119471e-01 2.64838934e-02 8.63860250e-02 1.24089316... | [14.282767295837402, 4.962830066680908] |
151eb1fd-4a1d-450f-bb94-88114367269f | collaborative-and-distributed-bayesian | 2306.14348 | null | https://arxiv.org/abs/2306.14348v1 | https://arxiv.org/pdf/2306.14348v1.pdf | Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design | Optimal design is a critical yet challenging task within many applications. This challenge arises from the need for extensive trial and error, often done through simulations or running field experiments. Fortunately, sequential optimal design, also referred to as Bayesian optimization when using surrogates with a Bayes... | ['Blake N. Johnson', 'Kevin Edgar', 'Zhenghao Zai', 'Yang Liu', 'Albert S. Berahas', 'Raed Al Kontar', 'Xubo Yue'] | 2023-06-25 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-9.77956578e-02 -1.60372213e-01 -3.32335502e-01 -3.44912708e-01
-8.57280910e-01 -6.38082743e-01 1.04415379e-01 -1.26073271e-01
-3.66613179e-01 7.32315898e-01 1.24363415e-01 -4.80849177e-01
-7.02126980e-01 -6.35975122e-01 -7.08190322e-01 -8.40104401e-01
-6.22749180e-02 6.53824389e-01 -2.88420856e-01 2.57712543... | [4.893179893493652, 3.4494359493255615] |
8e61c68e-92c3-4d09-801b-c3809f87ac2a | stain-normalized-breast-histopathology-image | 2201.00957 | null | https://arxiv.org/abs/2201.00957v1 | https://arxiv.org/pdf/2201.00957v1.pdf | Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection | Computer assisted diagnosis in digital pathology is becoming ubiquitous as it can provide more efficient and objective healthcare diagnostics. Recent advances have shown that the convolutional Neural Network (CNN) architectures, a well-established deep learning paradigm, can be used to design a Computer Aided Diagnosti... | ['Arnav Bhavsar', 'Shivsubramani Krishnamoorthy', 'Suganthi S. S', 'Sruthi Krishna'] | 2022-01-04 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.12451711e-01 2.74973810e-01 1.30578503e-01 -1.83599219e-01
-6.54849768e-01 -3.81981999e-01 2.95591921e-01 2.99787879e-01
-6.58198655e-01 5.90462863e-01 -1.87114149e-01 -8.43282461e-01
-3.18138480e-01 -7.43822575e-01 -5.05492628e-01 -9.90795255e-01
-3.60327542e-01 5.25837600e-01 2.48642430e-01 -1.25193745... | [15.184981346130371, -2.9449448585510254] |
89620684-f9ac-41e3-96b2-4dec1aaea261 | share-a-system-for-hierarchical-assistive | 2105.08185 | null | https://arxiv.org/abs/2105.08185v2 | https://arxiv.org/pdf/2105.08185v2.pdf | SHARE: a System for Hierarchical Assistive Recipe Editing | The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models. To help them, we propose the task of controllable recipe editing: adapt a base recipe to satisfy a user-specified dietary constraint. This task is challenging, and cannot be adequatel... | ['Julian McAuley', 'Jianmo Ni', 'Yufei Li', 'Shuyang Li'] | 2021-05-17 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [ 2.60025471e-01 3.79900455e-01 -2.02212408e-01 -5.52507818e-01
-4.52131957e-01 -9.42313850e-01 2.05590382e-01 4.12023544e-01
1.00751974e-01 4.99625921e-01 8.58814716e-01 -8.17214027e-02
1.62210673e-01 -9.43727732e-01 -8.43337297e-01 -1.25052303e-01
2.09212929e-01 5.96594870e-01 -4.04000580e-01 -7.49490440... | [11.509529113769531, 4.547859191894531] |
aa374b35-ae87-44d4-99e4-4ed2cfed332e | word-sense-disambiguation-of-french | null | null | https://aclanthology.org/2022.textgraphs-1.8 | https://aclanthology.org/2022.textgraphs-1.8.pdf | Word Sense Disambiguation of French Lexicographical Examples Using Lexical Networks | This paper focuses on the task of word sense disambiguation (WSD) on lexicographic examples relying on the French Lexical Network (fr-LN). For this purpose, we exploit the lexical and relational properties of the network, that we integrated in a feedforward neural WSD model on top of pretrained French BERT embeddings. ... | ['Mathieu Constant', 'Sandrine Ollinger', 'Aman Sinha'] | null | null | null | null | coling-textgraphs-2022-10 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-9.40683112e-02 2.72909433e-01 -2.70216942e-01 -3.21722776e-01
-1.10712629e-02 -6.14889920e-01 8.13702583e-01 5.67959130e-01
-1.09965670e+00 7.92571664e-01 7.69238234e-01 -4.43880945e-01
-2.74100304e-01 -9.70047891e-01 -2.07027301e-01 -3.70599441e-02
-6.59079924e-02 3.85079652e-01 1.37663737e-01 -8.29242289... | [10.537095069885254, 9.386603355407715] |
10c84895-5443-4e01-a30d-7191fd22f442 | trimming-the-sail-a-second-order-learning | 2002.06878 | null | http://arxiv.org/abs/2002.06878v1 | http://arxiv.org/pdf/2002.06878v1.pdf | Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction | Nowadays, machine learning methods have been widely used in stock prediction.
Traditional approaches assume an identical data distribution, under which a
learned model on the training data is fixed and applied directly in the test
data. Although such assumption has made traditional machine learning techniques
succeed i... | [] | 2020-02-17 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-5.47021925e-01 -6.28623903e-01 -7.05086768e-01 -2.93619305e-01
1.14591956e-01 -6.84060633e-01 6.14915848e-01 -1.00179002e-01
-3.15999359e-01 9.73185539e-01 -3.32223415e-01 -5.68917513e-01
-5.45716658e-02 -1.21264815e+00 -6.22931480e-01 -6.99392259e-01
-2.26649418e-01 5.23856997e-01 4.90192831e-01 -3.04281712... | [4.499125957489014, 4.186222076416016] |
dac06c87-d6c1-44fb-927e-052ae03d96e9 | large-context-conversational-representation | 2102.08147 | null | https://arxiv.org/abs/2102.08147v1 | https://arxiv.org/pdf/2102.08147v1.pdf | Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents | This paper presents a novel self-supervised learning method for handling conversational documents consisting of transcribed text of human-to-human conversations. One of the key technologies for understanding conversational documents is utterance-level sequential labeling, where labels are estimated from the documents i... | ['Shota Orihashi', 'Tomohiro Tanaka', 'Akihiko Takashima', 'Mana Ihori', 'Naoki Makishima', 'Ryo Masumura'] | 2021-02-16 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 5.75769663e-01 2.79253304e-01 -1.56569645e-01 -7.96645105e-01
-1.00428903e+00 -6.32762074e-01 6.75886452e-01 2.76253253e-01
-2.67403036e-01 5.32244146e-01 5.02156019e-01 -2.01929346e-01
2.96597242e-01 -4.26839054e-01 -2.33359724e-01 -5.68665087e-01
1.42938539e-01 6.81748271e-01 1.26379162e-01 -7.91085213... | [12.613600730895996, 7.58472204208374] |
c892551d-9a48-493b-aec2-3a35607fd935 | laplace-redux-effortless-bayesian-deep | 2106.14806 | null | https://arxiv.org/abs/2106.14806v3 | https://arxiv.org/pdf/2106.14806v3.pdf | Laplace Redux -- Effortless Bayesian Deep Learning | Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection. The Laplace approximation (LA) is a classic, and arguably the simplest family of approximations for the in... | ['Philipp Hennig', 'Matthias Bauer', 'Runa Eschenhagen', 'Alexander Immer', 'Agustinus Kristiadi', 'Erik Daxberger'] | 2021-06-28 | laplace-redux-effortless-bayesian-deep-1 | http://proceedings.neurips.cc/paper/2021/hash/a7c9585703d275249f30a088cebba0ad-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a7c9585703d275249f30a088cebba0ad-Paper.pdf | neurips-2021-12 | ['misconceptions'] | ['miscellaneous'] | [-1.95319295e-01 5.36134802e-02 1.77595124e-01 -5.21418393e-01
-9.52996790e-01 -5.36663532e-01 7.06503570e-01 5.36192618e-02
-6.04771197e-01 9.54221010e-01 -1.74217373e-01 -5.78860700e-01
-4.02235925e-01 -4.77144480e-01 -8.55758429e-01 -9.59411979e-01
-1.71794802e-01 5.10463893e-01 3.02510440e-01 1.71941444... | [7.271490097045898, 3.830002546310425] |
d9afdba1-043c-4bf0-a941-05687f63c1e3 | carla-bsp-a-simulated-dataset-with | 2305.00204 | null | https://arxiv.org/abs/2305.00204v1 | https://arxiv.org/pdf/2305.00204v1.pdf | CARLA-BSP: a simulated dataset with pedestrians | We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestrian detection, autoencoding, pose estimation, and pose lifting. We also showcase baseline results. For more information, visit https://projec... | ['Muhammad Naveed Riaz', 'Antonio M. López', 'Maciej Wielgosz'] | 2023-04-29 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [-2.58278847e-01 -7.23435879e-02 4.25813347e-01 -4.49328840e-01
-6.43780768e-01 -7.63617754e-01 9.20434535e-01 -3.21756035e-01
-3.99056882e-01 8.32769930e-01 3.28912079e-01 -7.47329518e-02
7.03354657e-01 -8.36229384e-01 -1.11120808e+00 -4.89396125e-01
-2.43712559e-01 3.45520765e-01 1.99543819e-01 -2.36876398... | [7.696247100830078, -0.8630622029304504] |
1f467a41-b227-4cd0-a95c-7676b028549d | pseudo-pair-based-self-similarity-learning | 2207.13035 | null | https://arxiv.org/abs/2207.13035v1 | https://arxiv.org/pdf/2207.13035v1.pdf | Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification | Person re-identification (re-ID) is of great importance to video surveillance systems by estimating the similarity between a pair of cross-camera person shorts. Current methods for estimating such similarity require a large number of labeled samples for supervised training. In this paper, we present a pseudo-pair based... | ['Jialie Shen', 'Mohammed Bennamoun', 'Farid Boussaid', 'ZongYuan Ge', 'Dapeng Chen', 'Wenying Zhang', 'Deyin Liu', 'Lin Wu'] | 2022-07-09 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.14666808e-01 -3.88958871e-01 -9.59428251e-02 -7.28894889e-01
-5.92554390e-01 -3.84705484e-01 7.98525751e-01 1.22036979e-01
-6.03294611e-01 3.23422432e-01 3.24080288e-01 5.40569484e-01
-1.69368804e-01 -5.10435045e-01 -7.91160166e-01 -6.20902717e-01
1.40228301e-01 4.23203170e-01 2.55862474e-01 1.21476747... | [14.773555755615234, 0.9880939722061157] |
413fa7cd-b859-4c57-8109-61cd10792b9a | a-survey-on-sentiment-analysis-in-persian-a | 2104.14751 | null | https://arxiv.org/abs/2104.14751v1 | https://arxiv.org/pdf/2104.14751v1.pdf | A Survey on sentiment analysis in Persian: A Comprehensive System Perspective Covering Challenges and Advances in Resources, and Methods | Social media has been remarkably grown during the past few years. Nowadays, posting messages on social media websites has become one of the most popular Internet activities. The vast amount of user-generated content has made social media the most extensive data source of public opinion. Sentiment analysis is one of the... | ['MohammadReza Valavi', 'Zeinab Rajabi'] | 2021-04-30 | null | null | null | null | ['persian-sentiment-anlysis'] | ['natural-language-processing'] | [-2.00353786e-01 -6.46272674e-02 -4.42169815e-01 -3.86252493e-01
-2.44855002e-01 -6.71700180e-01 4.60741013e-01 5.80610633e-01
-7.86492825e-01 6.93885565e-01 9.14003551e-02 -1.40852019e-01
2.38236934e-02 -6.77441359e-01 2.98946142e-01 -4.72425222e-01
2.23375291e-01 4.66377437e-01 -2.13008612e-01 -9.79929030... | [11.060519218444824, 6.917701721191406] |
bdd2db54-d68c-4c62-ba4a-c05687add30f | multilayer-graph-contrastive-clustering | 2112.14021 | null | https://arxiv.org/abs/2112.14021v1 | https://arxiv.org/pdf/2112.14021v1.pdf | Multilayer Graph Contrastive Clustering Network | Multilayer graph has garnered plenty of research attention in many areas due to their high utility in modeling interdependent systems. However, clustering of multilayer graph, which aims at dividing the graph nodes into categories or communities, is still at a nascent stage. Existing methods are often limited to exploi... | ['Xixu He', 'Wenbo Xu', 'Ling Tian', 'Zhao Kang', 'Liang Liu'] | 2021-12-28 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.78201839e-01 4.31481376e-02 -1.84558198e-01 -3.09972495e-01
1.58570260e-01 -1.84314653e-01 7.41244733e-01 4.93479192e-01
4.42876294e-02 2.03355327e-01 2.62263805e-01 -1.17248222e-01
-3.43426079e-01 -8.47332537e-01 -3.23348433e-01 -7.66532004e-01
-2.27645114e-01 2.12858588e-01 2.50358373e-01 -2.90382858... | [7.329298973083496, 6.025482177734375] |
f863bacb-be8e-493f-8e13-4cf4fb8e86bf | beyond-labels-empowering-human-with-natural | 2305.12710 | null | https://arxiv.org/abs/2305.12710v1 | https://arxiv.org/pdf/2305.12710v1.pdf | Beyond Labels: Empowering Human with Natural Language Explanations through a Novel Active-Learning Architecture | Data annotation is a costly task; thus, researchers have proposed low-scenario learning techniques like Active-Learning (AL) to support human annotators; Yet, existing AL works focus only on the label, but overlook the natural language explanation of a data point, despite that real-world humans (e.g., doctors) often ne... | ['Dakuo Wang', 'James Hendler', 'Shashank Srivastava', 'Yuxuan Lu', 'Lihong He', 'Sayan Ghosh', 'Yannis Katsis', 'Lucian Popa', 'Ishan Jindal', 'Bingsheng Yao'] | 2023-05-22 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 1.59719080e-01 1.04186797e+00 -6.29752815e-01 -8.17057133e-01
-7.47419775e-01 -1.50446698e-01 3.53757769e-01 6.61743522e-01
-1.56187579e-01 6.76573217e-01 3.90128642e-01 -5.23385167e-01
-2.09689096e-01 -5.46888530e-01 -2.76502490e-01 -2.95042276e-01
-2.32408047e-02 9.42068458e-01 1.34930713e-02 1.10254101... | [8.958307266235352, 5.86250114440918] |
8638862e-5a48-4fcf-a960-b218f53e3aa0 | conflict-aware-pseudo-labeling-via-optimal | 2209.01847 | null | https://arxiv.org/abs/2209.01847v2 | https://arxiv.org/pdf/2209.01847v2.pdf | Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment | Entity alignment aims to discover unique equivalent entity pairs with the same meaning across different knowledge graphs (KGs). Existing models have focused on projecting KGs into a latent embedding space so that inherent semantics between entities can be captured for entity alignment. However, the adverse impacts of a... | ['Jie Yin', 'Daokun Zhang', 'Qijie Ding'] | 2022-09-05 | null | null | null | null | ['entity-alignment', 'entity-embeddings', 'entity-alignment'] | ['knowledge-base', 'methodology', 'natural-language-processing'] | [-8.66925046e-02 4.56423670e-01 -5.90691209e-01 -5.92650354e-01
-5.63103318e-01 -5.62168300e-01 4.94896561e-01 4.97292459e-01
-2.75732040e-01 5.77568591e-01 3.59073997e-01 -7.21248388e-02
-4.44068789e-01 -9.83165026e-01 -9.19284225e-01 -4.27414745e-01
-1.81716811e-02 5.33315957e-01 1.25904515e-01 -3.96598037... | [8.726268768310547, 7.984524726867676] |
3df5db4d-a4d9-4841-9596-0b94808e9cb7 | a-central-asian-food-dataset-for-personalized | 2305.07257 | null | https://arxiv.org/abs/2305.07257v1 | https://arxiv.org/pdf/2305.07257v1.pdf | A Central Asian Food Dataset for Personalized Dietary Interventions, Extended Abstract | Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tediou... | ['Mei-Yen Chan', 'Huseyin Atakan Varol', 'Arman Bolatov', 'Aknur Karabay'] | 2023-05-12 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 1.75287545e-01 -2.52759397e-01 -4.26438332e-01 -4.62026596e-01
-3.83515000e-01 -5.03145874e-01 1.07758410e-01 9.73619998e-01
-3.61056209e-01 1.83806866e-01 5.97053349e-01 1.49063766e-02
4.58221853e-01 -1.15549612e+00 -6.77337348e-01 -6.22669637e-01
-2.00676098e-01 -3.28862667e-01 -2.42991909e-01 -4.84546348... | [11.556360244750977, 4.422477722167969] |
eb5ea4f0-aab0-42bd-9e92-573fc6daf0b3 | cosqa-20000-web-queries-for-code-search-and | 2105.13239 | null | https://arxiv.org/abs/2105.13239v1 | https://arxiv.org/pdf/2105.13239v1.pdf | CoSQA: 20,000+ Web Queries for Code Search and Question Answering | Finding codes given natural language query isb eneficial to the productivity of software developers. Future progress towards better semantic matching between query and code requires richer supervised training resources. To remedy this, we introduce the CoSQA dataset.It includes 20,604 labels for pairs of natural langua... | ['Nan Duan', 'Ming Zhou', 'Daxin Jiang', 'Ke Xu', 'Ming Gong', 'Linjun Shou', 'Duyu Tang', 'JunJie Huang'] | 2021-05-27 | null | https://aclanthology.org/2021.acl-long.442 | https://aclanthology.org/2021.acl-long.442.pdf | acl-2021-5 | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.02320829e-01 2.87110090e-01 -3.95927042e-01 -3.53276134e-01
-1.36351514e+00 -9.50068772e-01 2.44719967e-01 3.30835909e-01
-1.80630818e-01 2.94087231e-01 7.79683217e-02 -5.86866796e-01
3.12998772e-01 -4.59920108e-01 -8.75587344e-01 2.41425544e-01
1.52915403e-01 2.77978659e-01 4.77690816e-01 -2.02920079... | [7.547796249389648, 8.062687873840332] |
fb32f9d4-6b46-414d-8f89-05b7980982b1 | disentangling-bipartite-and-core-periphery | 1511.08830 | null | http://arxiv.org/abs/1511.08830v1 | http://arxiv.org/pdf/1511.08830v1.pdf | Disentangling bipartite and core-periphery structure in financial networks | A growing number of systems are represented as networks whose architecture
conveys significant information and determines many of their properties.
Examples of network architecture include modular, bipartite, and core-periphery
structures. However inferring the network structure is a non trivial task and
can depend som... | [] | 2015-11-25 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-7.42766112e-02 5.92815220e-01 -1.19258896e-01 -2.24880129e-01
4.07768823e-02 -7.61721969e-01 9.89902675e-01 3.26818764e-01
-1.45871369e-02 8.32580924e-01 3.57686281e-01 -5.41862071e-01
-5.98558486e-01 -8.98506403e-01 -3.04982960e-01 -8.77393305e-01
-2.94674695e-01 9.31248128e-01 4.56201822e-01 -4.20130402... | [6.984835147857666, 5.295896530151367] |
4bb13756-3029-43fb-83af-6e61cb3b25aa | learning-deep-temporal-representations-for | 1412.7522 | null | http://arxiv.org/abs/1412.7522v4 | http://arxiv.org/pdf/1412.7522v4.pdf | Learning Deep Temporal Representations for Brain Decoding | Functional magnetic resonance imaging produces high dimensional data, with a
less then ideal number of labelled samples for brain decoding tasks (predicting
brain states). In this study, we propose a new deep temporal convolutional
neural network architecture with spatial pooling for brain decoding which aims
to reduce... | ['Fatos T. Yarman Vural', 'Emre Aksan', 'Orhan Firat', 'Ilke Oztekin'] | 2014-12-23 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 3.64229143e-01 -5.95272109e-02 1.58125497e-02 -5.23754478e-01
-3.05219274e-02 -3.25416297e-01 5.73107541e-01 -3.09355985e-02
-6.09853864e-01 7.92360842e-01 4.87174332e-01 -2.41587609e-02
-4.70953256e-01 -5.39732158e-01 -4.23473209e-01 -7.84219146e-01
-6.78543687e-01 -3.46266888e-02 3.61562520e-01 1.90225929... | [12.658693313598633, 3.3963143825531006] |
eb6f603c-e60f-45bf-b49e-0ba2d8110b19 | finding-dataset-shortcuts-with-grammar | 2210.11560 | null | https://arxiv.org/abs/2210.11560v1 | https://arxiv.org/pdf/2210.11560v1.pdf | Finding Dataset Shortcuts with Grammar Induction | Many NLP datasets have been found to contain shortcuts: simple decision rules that achieve surprisingly high accuracy. However, it is difficult to discover shortcuts automatically. Prior work on automatic shortcut detection has focused on enumerating features like unigrams or bigrams, which can find only low-level shor... | ['Danqi Chen', 'Alexander Wettig', 'Dan Friedman'] | 2022-10-20 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 8.04034531e-01 6.14877880e-01 -2.36260608e-01 -8.58557403e-01
-1.01691651e+00 -6.38656676e-01 6.05031013e-01 6.73043430e-01
-8.75447392e-02 6.84797406e-01 3.02955925e-01 -3.89305919e-01
-5.95148861e-01 -4.44083303e-01 -4.47870016e-01 -5.46453297e-01
-1.00074194e-01 5.82382321e-01 2.29225591e-01 -1.81718647... | [10.901677131652832, 8.608402252197266] |
f7950636-13f5-496a-affc-485084545773 | sketching-out-the-details-sketch-based-image | null | null | https://doi.org/10.1016/j.cag.2017.12.006 | https://doi.org/10.1016/j.cag.2017.12.006 | Sketching out the Details: Sketch-based Image Retrieval using Convolutional Neural Networks with Multi-stage Regression | We propose and evaluate several deep network architectures for measuring the similarity between sketches and photographs, within the context of the sketch based image retrieval (SBIR) task. We study the ability of our networks to generalize across diverse object categories from limited training data, and explore in det... | ['J. Collomosse', 'M. Ponti', 'L. Ribeiro', 'T. Bui'] | 2017-12-01 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.24209368e-01 -5.33215582e-01 -1.43811092e-01 -6.49557769e-01
-6.53851509e-01 -7.78527379e-01 1.00786483e+00 -2.50290811e-01
-4.54819441e-01 2.34044760e-01 1.49178773e-01 -7.24977404e-02
-6.70602202e-01 -5.40806293e-01 -3.49870771e-01 -2.28156701e-01
5.10623232e-02 5.63377976e-01 -5.57115525e-02 -3.28416288... | [11.627283096313477, 0.4905932545661926] |
28fd6c92-a866-4e0a-8482-804263f9a255 | enhanced-dynamic-sign-language-recognition | null | null | https://ieeexplore.ieee.org/document/9698904 | https://ieeexplore.ieee.org/document/9698904 | Enhanced dynamic sign language recognition using slowfast networks | In this paper, we use the SlowFast Networks developed by the Facebook research team to enhance the accuracy of dynamic sign language recognition. Firstly, we prepared the Word-Level American Sign Language (WLASL) dataset so each sign can be considered an action. We used the pre-trained SLOWFAST_8×8_R50 model provided o... | ['Elsayed Hemayed', 'Ahmed Elgabry', 'Ahmed Hassan'] | 2021-12-30 | null | null | null | ieee-2021-12 | ['sign-language-recognition'] | ['computer-vision'] | [-8.66563544e-02 -2.40030989e-01 -3.35995734e-01 -2.89673746e-01
-8.32583487e-01 -4.58817124e-01 5.43189406e-01 -9.26006913e-01
-8.17312121e-01 5.09832919e-01 5.65537870e-01 -1.69407427e-01
-8.09187070e-02 -4.13836300e-01 -6.26563251e-01 -7.34395325e-01
-1.04104742e-01 4.61602390e-01 5.42791963e-01 -1.96270794... | [9.156557083129883, -6.4785943031311035] |
f704b3fc-d8cc-4257-8b8b-c1708932cb5a | pay-attention-to-the-atlas-atlas-guided-test | 2307.00676 | null | https://arxiv.org/abs/2307.00676v1 | https://arxiv.org/pdf/2307.00676v1.pdf | Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation | Convolutional neural networks (CNNs) often suffer from poor performance when tested on target data that differs from the training (source) data distribution, particularly in medical imaging applications where variations in imaging protocols across different clinical sites and scanners lead to different imaging appearan... | ['Chen Chen', 'Daniel Rueckert', 'Matthew Sinclair', 'Weitong Zhang', 'Jingjie Guo'] | 2023-07-02 | null | null | null | null | ['medical-image-segmentation', 'unsupervised-domain-adaptation'] | ['medical', 'methodology'] | [ 3.02878737e-01 5.68088032e-02 -2.29388714e-01 -9.33637679e-01
-9.77227092e-01 -6.85524106e-01 5.60653210e-02 1.23278648e-01
-7.47200668e-01 6.19561493e-01 -5.21355532e-02 -3.84939522e-01
3.84124406e-02 -3.76821041e-01 -7.39201188e-01 -8.81759763e-01
5.77766262e-02 6.89935982e-01 3.39180112e-01 2.63220519... | [14.5600004196167, -2.0592892169952393] |
728f0cf5-5ed8-4637-aaff-502d2ff95df1 | enolp-musk-smm4h22-leveraging-pre-trained | null | null | https://aclanthology.org/2022.smm4h-1.42 | https://aclanthology.org/2022.smm4h-1.42.pdf | Enolp musk@SMM4H’22 : Leveraging Pre-trained Language Models for Stance And Premise Classification | This paper covers our approaches for the Social Media Mining for Health (SMM4H) Shared Tasks 2a and 2b. Apart from the baseline architectures, we experiment with Parts of Speech (PoS), dependency parsing, and Tf-Idf features. Additionally, we perform contrastive pretraining on our best models using a supervised contras... | ['Sohan Patnaik', 'Manav Kapadnis', 'Ishan Manchanda', 'Archit Mangrulkar', 'Millon Das'] | null | null | null | null | smm4h-coling-2022-10 | ['dependency-parsing'] | ['natural-language-processing'] | [ 3.19535196e-01 8.12859654e-01 -3.34847957e-01 -5.61650336e-01
-1.31697965e+00 -1.46555498e-01 6.21112645e-01 7.53065228e-01
-8.51762950e-01 8.08718085e-01 1.68695882e-01 -4.15286273e-01
-1.53806955e-01 -4.64646965e-01 -6.65267110e-01 -4.43983108e-01
-4.66352910e-01 5.56146622e-01 3.80123407e-01 -1.30672276... | [8.54227066040039, 8.895301818847656] |
e35230b8-4776-4b8e-84ff-c4de1bf09e99 | building-multimodal-simulations-for-natural | null | null | https://aclanthology.org/E17-5006 | https://aclanthology.org/E17-5006.pdf | Building Multimodal Simulations for Natural Language | In this tutorial, we introduce a computational framework and modeling language (VoxML) for composing multimodal simulations of natural language expressions within a 3D simulation environment (VoxSim). We demonstrate how to construct voxemes, which are visual object representations of linguistic entities. We also show h... | ['Nikhil Krishnaswamy', 'James Pustejovsky'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['referring-expression-generation', 'scene-generation', 'formal-logic'] | ['computer-vision', 'computer-vision', 'reasoning'] | [ 9.95238125e-03 5.33616364e-01 9.58237946e-02 -6.12231232e-02
1.49761587e-01 -9.37532902e-01 1.43362081e+00 1.83983862e-01
-1.68212801e-01 6.01514816e-01 3.63974541e-01 -4.85787332e-01
-6.17746934e-02 -8.41534138e-01 -3.36109221e-01 -2.33680397e-01
-2.69440055e-01 3.79025877e-01 1.80493221e-01 -4.59940672... | [5.089273929595947, 0.5207586884498596] |
78cdd9b2-8c1f-4925-9b7b-ed109a284f40 | time-frequency-warped-waveforms-for-well | 2305.01113 | null | https://arxiv.org/abs/2305.01113v1 | https://arxiv.org/pdf/2305.01113v1.pdf | Time-Frequency Warped Waveforms for Well-Contained Massive Machine Type Communications | This paper proposes a novel time-frequency warped waveform for short symbols, massive machine-type communication (mMTC), and internet of things (IoT) applications. The waveform is composed of asymmetric raised cosine (RC) pulses to increase the signal containment in time and frequency domains. The waveform has low powe... | ['Sabit Ekin', 'Hakan Ali Cirpan', 'Huseyin Arslan', 'Mostafa Ibrahim'] | 2023-05-01 | null | null | null | null | ['type'] | ['speech'] | [ 6.64768696e-01 -2.69239604e-01 -4.96765167e-01 2.30415296e-02
-1.29050732e-01 -5.30215442e-01 5.80874622e-01 -3.96029890e-01
-3.36154625e-02 9.49290991e-01 1.34618014e-01 -7.26829648e-01
-6.15699947e-01 -3.28645736e-01 2.65762806e-01 -7.73794830e-01
-7.15080082e-01 -3.68935347e-01 1.05988584e-01 1.87394228... | [6.43930196762085, 1.3129463195800781] |
a0785568-8011-4815-bff5-b358c07fccfe | a-link-recognizing-disguised-faces-via-active | null | null | http://iab-rubric.org/papers/2019_BTAS_ALINK.pdf | http://iab-rubric.org/papers/2019_BTAS_ALINK.pdf | A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain Knowledge | Recent advancements in deep learning have significantly increased the capabilities of face recognition. However, face recognition in an unconstrained environment is still an active research challenge. Covariates such as pose and low resolution have received significant attention, but “disguise” is considered an onerous... | ['Mayank Vatsa', 'Richa Singh', 'Anshuman Suri'] | 2019-09-23 | null | null | null | ieee-international-conference-on-biometrics | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 2.8319436e-01 4.9551083e-03 -4.3370917e-01 -9.2907202e-01
-5.4832971e-01 -2.4215306e-01 4.9215522e-01 -5.7909673e-01
-3.3851713e-01 6.7651302e-01 -7.7780262e-02 3.1492522e-01
-4.5058063e-01 -6.5632480e-01 -6.5004957e-01 -9.5342153e-01
-1.4804359e-01 4.4884676e-01 -1.3305920e-01 9.6424609e-02
3.4943789e-02... | [13.221677780151367, 0.73390793800354] |
4bde510b-ca35-4086-8d09-29d181a7e5b2 | deep-conversational-recommender-systems-a-new | 2004.13245 | null | https://arxiv.org/abs/2004.13245v1 | https://arxiv.org/pdf/2004.13245v1.pdf | Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems | In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applications is the Conversational Recommender System (CRS). Unlike traditional recommender systems with content-based and collaborative filtering app... | ['Nguyen Lu Dang Khoa', 'Nguyen H. Tran', 'Lina Yao', 'Salma Abdalla Hamad', 'Munazza Zaib', 'Wei Emma Zhang', 'Quan Z. Sheng', 'Dai Hoang Tran'] | 2020-04-28 | null | null | null | null | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-1.31907552e-01 -8.02075714e-02 -1.18581943e-01 -5.24128199e-01
-2.53565848e-01 -3.54392409e-01 8.36902142e-01 -2.20083803e-01
-1.97233886e-01 4.96303439e-01 9.10917819e-01 -3.85290116e-01
-4.93654698e-01 -9.81598854e-01 8.60649943e-02 -5.15030384e-01
-7.36270249e-02 5.15705347e-01 -3.63980383e-02 -1.09954154... | [10.212749481201172, 5.8230133056640625] |
4db6d474-81e4-4b1a-a325-91b55c6bd1fb | one-shot-learning-for-channel-estimation-in | 2306.05759 | null | https://arxiv.org/abs/2306.05759v1 | https://arxiv.org/pdf/2306.05759v1.pdf | One-shot Learning for Channel Estimation in Massive MIMO Systems | In conventional supervised deep learning based channel estimation algorithms, a large number of training samples are required for offline training. However, in practical communication systems, it is difficult to obtain channel samples for every signal-to-noise ratio (SNR). Furthermore, the generalization ability of the... | ['Yonina C. Eldar', 'Yunlong Cai', 'Qiyu Hu', 'Kai Kang'] | 2023-06-09 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 1.56256452e-01 -2.61829495e-01 -1.22873280e-02 -3.72960120e-01
-9.32051957e-01 -2.82959454e-02 3.03074326e-02 -1.49711877e-01
-5.29574513e-01 9.11508739e-01 -3.12313467e-01 -5.30781686e-01
6.49252981e-02 -7.92768180e-01 -6.75591111e-01 -1.00850022e+00
-2.27531970e-01 -1.95373371e-01 -7.01777935e-02 3.17810923... | [6.395370960235596, 1.4813352823257446] |
dda1ef2b-27ce-4eef-998a-90302c28cc89 | sharp-shape-regularized-multidimensional | 2306.00554 | null | https://arxiv.org/abs/2306.00554v1 | https://arxiv.org/pdf/2306.00554v1.pdf | ShaRP: Shape-Regularized Multidimensional Projections | Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having a distinct visual signature - i.e., a recognizable way to arrange points in the resulting scatterplot. Such signatures are implicit conseque... | ['Michael Behrisch', 'Alexandru Telea', 'Alister Machado'] | 2023-06-01 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-2.20442861e-01 -2.03317434e-01 -1.04876637e-01 -2.66269833e-01
-1.34772509e-01 -1.06307256e+00 7.06945300e-01 3.40187848e-01
-4.56571952e-02 1.98808461e-01 4.83821154e-01 -6.25943840e-01
-6.26336932e-01 -6.28629148e-01 -4.74270545e-02 -7.44743943e-01
-4.08847153e-01 6.42603695e-01 1.89352915e-01 -9.44692791... | [7.993043899536133, 4.55886697769165] |
0ecd2587-8f5a-41ad-88c0-a8bcb8fd7c83 | multi-person-3d-pose-and-shape-estimation-via | 2210.13529 | null | https://arxiv.org/abs/2210.13529v2 | https://arxiv.org/pdf/2210.13529v2.pdf | Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement | Estimating 3D poses and shapes in the form of meshes from monocular RGB images is challenging. Obviously, it is more difficult than estimating 3D poses only in the form of skeletons or heatmaps. When interacting persons are involved, the 3D mesh reconstruction becomes more challenging due to the ambiguity introduced by... | ['Seungryul Baek', 'Mingyu Shin', 'GeonU Kim', 'Muhammad Saqlain', 'Junuk Cha'] | 2022-10-24 | null | null | null | null | ['3d-human-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-5.78956082e-02 -1.03149503e-01 4.11972463e-01 -3.89579684e-01
-7.84256279e-01 -4.21834528e-01 3.58158052e-01 -5.74590638e-02
-2.42863044e-01 5.20935178e-01 2.57099420e-01 2.83855379e-01
3.70366648e-02 -8.31489444e-01 -6.87774122e-01 -2.97473639e-01
2.90369064e-01 1.14696538e+00 4.32767838e-01 1.02993073... | [7.070488452911377, -1.114454984664917] |
d8ed1dc6-8639-4855-857d-2de08559c308 | time-series-prediction-by-multi-task-gpr-with | 2204.12085 | null | https://arxiv.org/abs/2204.12085v1 | https://arxiv.org/pdf/2204.12085v1.pdf | Time Series Prediction by Multi-task GPR with Spatiotemporal Information Transformation | Making an accurate prediction of an unknown system only from a short-term time series is difficult due to the lack of sufficient information, especially in a multi-step-ahead manner. However, a high-dimensional short-term time series contains rich dynamical information, and also becomes increasingly available in many f... | ['Luonan Chen', 'Jie Cheng', 'Xiaohu Hao', 'Peng Tao'] | 2022-04-26 | null | null | null | null | ['gpr', 'gpr', 'time-series-prediction'] | ['computer-vision', 'miscellaneous', 'time-series'] | [-1.60966478e-02 -7.30640531e-01 7.78250545e-02 -1.14254519e-01
-5.65715015e-01 -2.93338656e-01 5.78901052e-01 -6.97638988e-02
8.75412151e-02 9.59747553e-01 -1.22644797e-01 -3.26188087e-01
-7.53953695e-01 -7.86572397e-01 -3.80029589e-01 -8.94883454e-01
-4.03287619e-01 4.24133867e-01 3.68426204e-01 -3.37852985... | [6.929501533508301, 3.033017635345459] |
0749e0a7-8c9d-4583-b415-4583711dd1a9 | gett-qa-graph-embedding-based-t2t-transformer | 2303.13284 | null | https://arxiv.org/abs/2303.13284v3 | https://arxiv.org/pdf/2303.13284v3.pdf | GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering | In this work, we present an end-to-end Knowledge Graph Question Answering (KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text pre-trained language model. The model takes a question in natural language as input and produces a simpler form of the intended SPARQL query. In the simpler form, the model does... | ['Chris Biemann', 'Ricardo Usbeck', 'Pranav Ajit Nair', 'Debayan Banerjee'] | 2023-03-23 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [-2.74231791e-01 8.00162971e-01 -8.61804858e-02 -5.45876265e-01
-1.14900041e+00 -7.13427067e-01 3.40030462e-01 5.13568640e-01
-4.15875971e-01 6.26830220e-01 2.89157689e-01 -5.29058635e-01
-8.10579807e-02 -1.24140036e+00 -7.82553613e-01 -9.45412815e-02
2.16422230e-02 1.08317137e+00 4.55026060e-01 -3.97228807... | [10.4762601852417, 7.948594570159912] |
8c665a2a-8e8f-4f71-ba87-7ccacf22f2b7 | posterior-ratio-estimation-for-latent | 2002.06410 | null | https://arxiv.org/abs/2002.06410v2 | https://arxiv.org/pdf/2002.06410v2.pdf | Posterior Ratio Estimation of Latent Variables | Density Ratio Estimation has attracted attention from the machine learning community due to its ability to compare the underlying distributions of two datasets. However, in some applications, we want to compare distributions of random variables that are \emph{inferred} from observations. In this paper, we study the pro... | ['Yulong Zhang', 'Mingxuan Yi', 'Song Liu', 'Mladen Kolar'] | 2020-02-15 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-4.36702892e-02 2.69848271e-04 -3.80496502e-01 -4.02522147e-01
-6.74557626e-01 -2.58111715e-01 4.23091680e-01 6.91315159e-02
-2.53670692e-01 1.07571244e+00 -3.12657952e-01 -2.88183928e-01
-2.24100217e-01 -7.82632411e-01 -6.29634738e-01 -7.83021033e-01
7.72798955e-02 6.10651851e-01 -1.50434345e-01 5.35238981... | [7.192014694213867, 4.0993266105651855] |
409c3ec9-9964-4a30-a0ef-e408cada3e61 | three-sentences-are-all-you-need-local-path | 2106.01793 | null | https://arxiv.org/abs/2106.01793v1 | https://arxiv.org/pdf/2106.01793v1.pdf | Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction | Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair. In this paper, we present an embarrassingly simple but effec... | ['Dongyan Zhao', 'Yuxuan Lai', 'Yuan Ye', 'Yansong Feng', 'Shengqi Zhu', 'Quzhe Huang'] | 2021-06-03 | null | https://aclanthology.org/2021.acl-short.126 | https://aclanthology.org/2021.acl-short.126.pdf | acl-2021-5 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-3.14469673e-02 4.17195559e-01 -3.43027502e-01 -4.65632409e-01
-1.15412271e+00 -5.08679092e-01 2.77408749e-01 6.27531290e-01
-4.59188282e-01 1.04477644e+00 2.52009243e-01 -6.43893242e-01
-5.37160374e-02 -9.40749824e-01 -6.80063128e-01 -1.42084166e-01
1.18223708e-02 5.37856102e-01 2.81361431e-01 -2.91747659... | [9.373714447021484, 8.697059631347656] |
867b92f1-ba39-4be9-8e25-3763643161ab | talking-heads-signing-avatars-and-social | null | null | https://aclanthology.org/W15-5101 | https://aclanthology.org/W15-5101.pdf | Talking Heads, Signing Avatars and Social Robots | null | ['Jonas Beskow'] | 2015-09-01 | null | null | null | ws-2015-9 | ['lipreading'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.283622741699219, 3.7357709407806396] |
1bc5e069-5292-4fd3-945f-675245735ecc | perceptual-speech-enhancement-via-generative | 1910.12620 | null | https://arxiv.org/abs/1910.12620v3 | https://arxiv.org/pdf/1910.12620v3.pdf | AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks | Automatic speech recognition (ASR) systems are of vital importance nowadays in commonplace tasks such as speech-to-text processing and language translation. This created the need for an ASR system that can operate in realistic crowded environments. Thus, speech enhancement is a valuable building block in ASR systems an... | ['Jayasankar T. Sajeev', 'Karim Armanious', 'Karim Guirguis', 'Sherif Abdulatif', 'Bin Yang'] | 2019-10-21 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 4.76459116e-01 9.64550972e-02 6.13236606e-01 -2.20900491e-01
-9.87434387e-01 -2.55107611e-01 7.96042442e-01 -1.50516063e-01
-4.29793626e-01 6.99864209e-01 4.04607475e-01 -5.50575972e-01
1.51273429e-01 -5.46268284e-01 -4.74782795e-01 -8.64498019e-01
4.21970695e-01 3.60853188e-02 3.29361064e-03 -5.89270771... | [14.963175773620605, 6.086564540863037] |
6db26fc9-225f-4d1c-8f13-c3f320beb519 | dynamic-conditional-networks-for-few-shot | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Fang_Zhao_Dynamic_Conditional_Networks_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Fang_Zhao_Dynamic_Conditional_Networks_ECCV_2018_paper.pdf | Dynamic Conditional Networks for Few-Shot Learning | This paper proposes a novel Dynamic Conditional Convolutional Network (DCCN) to handle conditional few-shot learning, i.e, only a few training samples are available for each condition. DCCN consists of dual subnets: DyConvNet contains a dynamic convolutional layer with a bank of basis filters; CondiNet predicts a set o... | ['Jian Zhao', 'Fang Zhao', 'Jiashi Feng', 'Shuicheng Yan'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['object-counting', 'phrase-grounding'] | ['computer-vision', 'natural-language-processing'] | [ 3.14952165e-01 -6.39546812e-02 -4.85195100e-01 -5.24095476e-01
-4.53101248e-01 -8.24027359e-02 6.83368266e-01 -1.79464698e-01
-6.19656563e-01 5.94738722e-01 -1.27367646e-01 1.45402968e-01
1.62535682e-01 -8.90325546e-01 -7.19896019e-01 -8.14633131e-01
3.36915731e-01 4.98280406e-01 2.82575816e-01 2.38812655... | [9.951421737670898, 2.904409170150757] |
b7577098-da51-4094-8b5f-eb2b78acf773 | uncertainty-in-extreme-multi-label | 2210.10160 | null | https://arxiv.org/abs/2210.10160v1 | https://arxiv.org/pdf/2210.10160v1.pdf | Uncertainty in Extreme Multi-label Classification | Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small label spaces and ignored eXtreme Multi-label Classification (XMC), which is an essential task in the e... | ['Hsiang-Fu Yu', 'Cho-Jui Hsieh', 'Jiong Zhong', 'Wei-Cheng Chang', 'Jyun-Yu Jiang'] | 2022-10-18 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [-8.35000202e-02 -1.47652730e-01 -3.21951620e-02 -6.84824824e-01
-1.57211292e+00 -5.55450618e-01 3.16158682e-01 3.62287372e-01
-1.30326048e-01 1.04526818e+00 -3.67067605e-01 -4.94883388e-01
-3.90441060e-01 -7.02734888e-01 -6.99725389e-01 -9.46187496e-01
1.20959960e-01 9.59449172e-01 8.79729614e-02 4.11456496... | [8.897016525268555, 4.190561771392822] |
5dbae2a8-1f5d-45ae-b8eb-2d5c6d7fb1f9 | action-improving-semi-supervised-medical | 2304.02689 | null | https://arxiv.org/abs/2304.02689v2 | https://arxiv.org/pdf/2304.02689v2.pdf | ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast | Medical data often exhibits long-tail distributions with heavy class imbalance, which naturally leads to difficulty in classifying the minority classes (i.e., boundary regions or rare objects). Recent work has significantly improved semi-supervised medical image segmentation in long-tailed scenarios by equipping them w... | ['Jasjeet S. Sekhon', 'James S. Duncan', 'Lawrence Staib', 'Yifei Min', 'Weicheng Dai', 'Chenyu You'] | 2023-04-05 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 3.85753632e-01 5.19458801e-02 -5.13927639e-01 -5.07349551e-01
-1.12574482e+00 -4.75032806e-01 1.34497344e-01 5.56752205e-01
-7.12305844e-01 5.70494056e-01 -1.44568190e-01 -2.57136524e-01
-1.69761583e-01 -5.70717573e-01 -5.43201387e-01 -1.18573737e+00
1.07639041e-02 8.59044611e-01 3.47576916e-01 2.33737364... | [14.719650268554688, -2.3124208450317383] |
6a95e8a5-0c8c-48d1-9d68-3640076a4b9d | a-predictive-model-for-the-identification-of | null | null | https://link.springer.com/article/10.1007%2Fs10586-019-02992-4 | https://link.springer.com/article/10.1007%2Fs10586-019-02992-4 | A predictive model for the identification of learning styles in MOOC environments | Massive online open course (MOOC) platform generates a large amount of data, which provides many opportunities for studying the behaviors of learners. In parallel, recent advancements in machine learning techniques and big data analysis have created new opportunities for a better understanding of how learners behave an... | ['Omar Baz', 'Ali El Mezouary', 'Brahim Hmedna'] | 2019-10-12 | null | null | null | null | ['event-data-classification', 'automatic-machine-learning-model-selection', 'clustering-algorithms-evaluation'] | ['computer-vision', 'methodology', 'methodology'] | [-6.02659583e-01 -1.67299688e-01 -4.59980726e-01 -3.36040735e-01
-3.48736316e-01 -4.27083433e-01 1.89962134e-01 5.54288805e-01
-1.44955114e-01 5.23634851e-01 2.29787394e-01 -6.49723947e-01
-5.91247916e-01 -9.84435141e-01 -6.14883482e-01 -3.51468295e-01
2.94633843e-02 -1.22678339e-01 1.25871211e-01 -3.21752429... | [10.127315521240234, 7.205688953399658] |
af2d812d-5112-44fa-8b7f-c592090d6010 | mawps-a-math-word-problem-repository | null | null | https://aclanthology.org/N16-1136 | https://aclanthology.org/N16-1136.pdf | MAWPS: A Math Word Problem Repository | null | ['Rik Koncel-Kedziorski', 'Nate Kushman', 'Subhro Roy', 'Hannaneh Hajishirzi', 'Aida Amini'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.498989105224609, 3.5796220302581787] |
0bd90b69-b02c-4ee6-8800-edcb3c2b8adc | toward-subgraph-guided-knowledge-graph | 2004.06015 | null | https://arxiv.org/abs/2004.06015v4 | https://arxiv.org/pdf/2004.06015v4.pdf | Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks | Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting which is to generate questions from a single KG triple. In this work, we focus on a more realistic setting where we aim to generate questions from a KG s... | ['Lingfei Wu', 'Yu Chen', 'Mohammed J. Zaki'] | 2020-04-13 | null | null | null | null | ['kg-to-text'] | ['natural-language-processing'] | [ 3.74878019e-01 8.77279520e-01 1.65558457e-01 -3.17471385e-01
-1.15467691e+00 -6.04335606e-01 5.97942531e-01 -9.44624543e-02
8.01333413e-03 7.07149804e-01 4.43564117e-01 -5.90737224e-01
2.14356720e-01 -1.27724469e+00 -1.13274372e+00 -2.54484564e-01
5.52292645e-01 5.83164096e-01 3.27271163e-01 -6.71479702... | [11.25779914855957, 8.120773315429688] |
cf681bab-9990-428d-b5b5-28604237038c | generalized-inter-class-loss-for-gait | 2210.06779 | null | https://arxiv.org/abs/2210.06779v1 | https://arxiv.org/pdf/2210.06779v1.pdf | Generalized Inter-class Loss for Gait Recognition | Gait recognition is a unique biometric technique that can be performed at a long distance non-cooperatively and has broad applications in public safety and intelligent traffic systems. Previous gait works focus more on minimizing the intra-class variance while ignoring the significance in constraining inter-class varia... | ['Liang Wang', 'Yan Huang', 'Hongyuan Yu', 'Weichen Yu'] | 2022-10-13 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-7.28730187e-02 -2.64397532e-01 -3.12083423e-01 -5.52034259e-01
-1.92189604e-01 -8.81425440e-02 8.22754949e-02 -1.89954892e-01
-4.28910643e-01 6.61139071e-01 -1.15061603e-01 3.17357123e-01
-5.39530694e-01 -9.29145277e-01 -1.69783548e-01 -1.09366620e+00
-2.45400429e-01 2.83375084e-01 4.31716114e-01 -1.42309204... | [14.295713424682617, 1.3435463905334473] |
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