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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
04004133-b6df-4c48-a8b8-af6dedff6999 | end-to-end-adversarial-shape-learning-for | 1910.06474 | null | https://arxiv.org/abs/1910.06474v1 | https://arxiv.org/pdf/1910.06474v1.pdf | End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation | Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventiona... | ['Jinzheng Cai', 'Dong Yang', 'Holger Roth', 'Lin Yang', 'Daguang Xu', 'Yingda Xia'] | 2019-10-15 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [-5.58087081e-02 4.41653430e-01 1.28481358e-01 -5.25264919e-01
-7.15744495e-01 -6.85258150e-01 1.31411314e-01 4.75140661e-01
-2.03626454e-01 3.59542668e-01 4.82288115e-02 -3.09438556e-01
1.47015154e-01 -8.64321172e-01 -9.07043993e-01 -7.25486696e-01
-1.21799745e-01 7.26839125e-01 1.56510770e-01 -1.76588565... | [14.482056617736816, -2.5530683994293213] |
ed317689-b186-46db-8278-f4bec91728fa | two-stream-convolutional-neural-network-for | 1612.07978 | null | http://arxiv.org/abs/1612.07978v1 | http://arxiv.org/pdf/1612.07978v1.pdf | Two-stream convolutional neural network for accurate RGB-D fingertip detection using depth and edge information | Accurate detection of fingertips in depth image is critical for
human-computer interaction. In this paper, we present a novel two-stream
convolutional neural network (CNN) for RGB-D fingertip detection. Firstly edge
image is extracted from raw depth image using random forest. Then the edge
information is combined with ... | ['Xinghao Chen', 'Hengkai Guo', 'Guijin Wang'] | 2016-12-23 | null | null | null | null | ['fingertip-detection'] | ['computer-vision'] | [-4.31493111e-02 -5.07471561e-01 -2.84131840e-02 -1.03307135e-01
-1.81445479e-01 -6.04346156e-01 5.22263497e-02 -4.51210976e-01
-6.99007094e-01 2.38545150e-01 -1.83043316e-01 -1.67747185e-01
7.20555112e-02 -8.04095149e-01 -4.42404956e-01 -3.76370996e-01
-3.39533016e-02 1.86639100e-01 6.08876467e-01 -7.54818469... | [6.495484352111816, -0.4573672413825989] |
94887cb5-9dc9-45c9-9712-bf45bced2d53 | implicit-acoustic-echo-cancellation-for | 2111.10639 | null | https://arxiv.org/abs/2111.10639v4 | https://arxiv.org/pdf/2111.10639v4.pdf | Implicit Acoustic Echo Cancellation for Keyword Spotting and Device-Directed Speech Detection | In many speech-enabled human-machine interaction scenarios, user speech can overlap with the device playback audio. In these instances, the performance of tasks such as keyword-spotting (KWS) and device-directed speech detection (DDD) can degrade significantly. To address this problem, we propose an implicit acoustic e... | ['Thibaud Sénéchal', 'Thomas Balestri', 'Samuele Cornell'] | 2021-11-20 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 4.63204354e-01 -1.05276860e-01 2.49797747e-01 -2.12404072e-01
-1.37804008e+00 -5.90134501e-01 4.84176636e-01 -1.02104343e-01
-4.76337433e-01 7.70882592e-02 5.27248204e-01 -6.55321538e-01
2.77406096e-01 2.82332629e-01 -7.19706535e-01 -2.99718678e-01
7.69670382e-02 -3.10253590e-01 3.44482630e-01 4.30564098... | [14.715478897094727, 6.129934787750244] |
10913376-3659-4e93-961c-cf3762ce5fde | can-bert-do-it-controller-area-network | 2210.09439 | null | https://arxiv.org/abs/2210.09439v1 | https://arxiv.org/pdf/2210.09439v1.pdf | CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model | Due to the rising number of sophisticated customer functionalities, electronic control units (ECUs) are increasingly integrated into modern automotive systems. However, the high connectivity between the in-vehicle and the external networks paves the way for hackers who could exploit in-vehicle network protocols' vulner... | ['Jean-Luc Danger', 'Hadi Ghauch', 'Maria Mushtaq', 'Natasha Alkhatib'] | 2022-10-17 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-2.36915186e-01 9.25998669e-04 -4.31661248e-01 -1.81156337e-01
-6.61714435e-01 -7.02253163e-01 4.86060917e-01 -3.41159068e-02
-4.60089058e-01 3.87214005e-01 -6.39307797e-01 -1.17853045e+00
1.51683658e-01 -9.01270092e-01 -8.93810511e-01 -5.56219161e-01
-3.68803710e-01 3.30353111e-01 6.06231809e-01 -3.70266289... | [5.250732898712158, 7.347848415374756] |
c54ff77c-3d60-45d1-8844-bb40ac501a58 | devis-making-deformable-transformers-work-for | 2207.11103 | null | https://arxiv.org/abs/2207.11103v1 | https://arxiv.org/pdf/2207.11103v1.pdf | DeVIS: Making Deformable Transformers Work for Video Instance Segmentation | Video Instance Segmentation (VIS) jointly tackles multi-object detection, tracking, and segmentation in video sequences. In the past, VIS methods mirrored the fragmentation of these subtasks in their architectural design, hence missing out on a joint solution. Transformers recently allowed to cast the entire VIS task a... | ['Laura Leal-Taixé', 'Guillem Brasó', 'Tim Meinhardt', 'Adrià Caelles'] | 2022-07-22 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.30211607e-01 -2.34137297e-01 -1.17188752e-01 -1.12997189e-01
-1.11918044e+00 -7.81370699e-01 2.48694167e-01 -2.55467892e-01
-4.69877243e-01 3.98926020e-01 -1.41268611e-01 5.19571230e-02
1.86664723e-02 -3.22232276e-01 -9.72665846e-01 -6.27326965e-01
5.87627143e-02 4.81654018e-01 9.81929779e-01 -4.93563637... | [9.11889362335205, -0.06587432324886322] |
b96da0a4-0744-4f20-9b24-f8cff4bc70be | mipt-nsu-utmn-at-semeval-2021-task-5 | 2104.04739 | null | https://arxiv.org/abs/2104.04739v1 | https://arxiv.org/pdf/2104.04739v1.pdf | MIPT-NSU-UTMN at SemEval-2021 Task 5: Ensembling Learning with Pre-trained Language Models for Toxic Spans Detection | This paper describes our system for SemEval-2021 Task 5 on Toxic Spans Detection. We developed ensemble models using BERT-based neural architectures and post-processing to combine tokens into spans. We evaluated several pre-trained language models using various ensemble techniques for toxic span identification and achi... | ['Dmitry Morozov', 'Anna Glazkova', 'Mikhail Kotyushev'] | 2021-04-10 | null | https://aclanthology.org/2021.semeval-1.124 | https://aclanthology.org/2021.semeval-1.124.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-4.83529896e-01 -4.01176691e-01 -1.77749082e-01 -1.69346869e-01
-1.40666068e+00 -4.33753222e-01 5.42140484e-01 4.05433744e-01
-6.88946724e-01 1.15560639e+00 5.43370426e-01 -1.96255818e-01
1.55104369e-01 -8.34030628e-01 -4.09506410e-01 -2.07747549e-01
-7.12636769e-01 1.47769466e-01 2.10137805e-03 -2.79773980... | [8.967960357666016, 10.652958869934082] |
846202d8-bde7-4516-812b-9a2ad536594a | isolation-forest | null | null | https://ieeexplore.ieee.org/abstract/document/4781136 | https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf?q=isolation-forest | Isolation forest | Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. This paper proposes a fundamentally different model-based method that explicitly isolates anomalies instead of profiles normal points. To our... | ['Zhi-Hua Zhou', 'Kai Ming Ting', 'Fei Tony Liu'] | 2008-12-15 | null | null | null | null | ['unsupervised-anomaly-detection-with-specified-5', 'unsupervised-anomaly-detection-with-specified-4', 'unsupervised-anomaly-detection-with-specified-7', 'unsupervised-anomaly-detection-with-specified-6', 'unsupervised-anomaly-detection-with-specified'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.25155726e-01 -1.08013069e-02 -1.00037642e-01 -2.38857046e-01
-4.22818989e-01 -4.36421573e-01 5.84732115e-01 5.48016667e-01
-1.75900981e-01 4.84066516e-01 -4.91335362e-01 -5.54465592e-01
-5.61141610e-01 -9.36397910e-01 -1.00521244e-01 -7.32687116e-01
-4.13777858e-01 9.69296277e-01 6.66931927e-01 7.28342608... | [7.551881790161133, 2.783468008041382] |
09bb1f8f-31f3-4b07-8510-5c6430be120d | the-2015-sheffield-system-for-transcription | 1512.06643 | null | http://arxiv.org/abs/1512.06643v1 | http://arxiv.org/pdf/1512.06643v1.pdf | The 2015 Sheffield System for Transcription of Multi-Genre Broadcast Media | We describe the University of Sheffield system for participation in the 2015
Multi-Genre Broadcast (MGB) challenge task of transcribing multi-genre
broadcast shows. Transcription was one of four tasks proposed in the MGB
challenge, with the aim of advancing the state of the art of automatic speech
recognition, speaker ... | ['Yu-Lan Liu', 'Mortaza Doulaty', 'Rosanna Milner', 'Raymond W. M. Ng', 'Oscar Saz', 'Thomas Hain', 'Salil Deena', 'Madina Hasan'] | 2015-12-21 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 4.37214553e-01 1.15639322e-01 5.85972607e-01 -6.65622354e-01
-1.75542796e+00 -6.63612366e-01 6.59054160e-01 7.07101077e-02
-5.14268398e-01 3.26822758e-01 6.78593099e-01 -3.13834608e-01
6.24108873e-02 1.83431283e-01 -5.38120985e-01 -8.25169861e-01
1.64479658e-01 7.81102121e-01 1.89714849e-01 -4.57978845... | [14.502267837524414, 6.531845569610596] |
659f5db1-aa74-4c2e-8247-c8f723562a84 | 3d-axial-attention-for-lung-nodule | 2012.14117 | null | https://arxiv.org/abs/2012.14117v3 | https://arxiv.org/pdf/2012.14117v3.pdf | 3D Axial-Attention for Lung Nodule Classification | Purpose: In recent years, Non-Local based methods have been successfully applied to lung nodule classification. However, these methods offer 2D attention or limited 3D attention to low-resolution feature maps. Moreover, they still depend on a convenient local filter such as convolution as full 3D attention is expensive... | ['Maxine Tan', 'Kelvin Shak', 'Mundher Al-Shabi'] | 2020-12-28 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 6.76867738e-02 4.50314343e-01 -2.08964035e-01 -1.31772861e-01
-7.15055346e-01 -2.06866845e-01 4.80912566e-01 -6.91126287e-02
-4.59310830e-01 1.57903448e-01 1.16941303e-01 -4.15832132e-01
-2.75849879e-01 -6.83226943e-01 -7.85375595e-01 -8.45539570e-01
1.47638425e-01 2.49783233e-01 5.79202294e-01 1.28703818... | [15.331622123718262, -2.182124614715576] |
b8ef4ca8-dacd-4016-8ba8-2d1c312f1519 | a-benchmark-for-systematic-generalization-in | 2003.05161 | null | https://arxiv.org/abs/2003.05161v2 | https://arxiv.org/pdf/2003.05161v2.pdf | A Benchmark for Systematic Generalization in Grounded Language Understanding | Humans easily interpret expressions that describe unfamiliar situations composed from familiar parts ("greet the pink brontosaurus by the ferris wheel"). Modern neural networks, by contrast, struggle to interpret novel compositions. In this paper, we introduce a new benchmark, gSCAN, for evaluating compositional genera... | ['Laura Ruis', 'Diane Bouchacourt', 'Marco Baroni', 'Jacob Andreas', 'Brenden M. Lake'] | 2020-03-11 | null | http://proceedings.neurips.cc/paper/2020/hash/e5a90182cc81e12ab5e72d66e0b46fe3-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e5a90182cc81e12ab5e72d66e0b46fe3-Paper.pdf | neurips-2020-12 | ['systematic-generalization'] | ['reasoning'] | [ 3.74875426e-01 2.28619024e-01 6.80018142e-02 -6.30028903e-01
-4.78265136e-01 -9.58196461e-01 1.20325172e+00 2.05435559e-01
-5.25377035e-01 7.13063061e-01 8.77597332e-01 -3.77841383e-01
-2.94540934e-02 -8.20523977e-01 -8.00726354e-01 -5.06743610e-01
8.60536471e-02 7.25567698e-01 4.63495627e-02 -8.40523124... | [9.653207778930664, 7.179845333099365] |
bfa90be2-83a0-4cfc-8ad9-b84ae59f4878 | a-copula-based-boosting-model-for-time-to | 2210.04869 | null | https://arxiv.org/abs/2210.04869v2 | https://arxiv.org/pdf/2210.04869v2.pdf | A copula-based boosting model for time-to-event prediction with dependent censoring | A characteristic feature of time-to-event data analysis is possible censoring of the event time. Most of the statistical learning methods for handling censored data are limited by the assumption of independent censoring, even if this can lead to biased predictions when the assumption does not hold. This paper introduce... | ['Arne Bang Huseby', 'Riccardo De Bin', 'Alise Danielle Midtfjord'] | 2022-10-10 | null | null | null | null | ['time-to-event-prediction'] | ['time-series'] | [-1.17743343e-01 -2.28049502e-01 -4.38388675e-01 -7.28377044e-01
-8.24632347e-01 -4.08420533e-01 6.17038310e-01 5.53942204e-01
-2.37444878e-01 1.14642167e+00 2.39225432e-01 -7.93890834e-01
-4.41209555e-01 -7.76584506e-01 -6.33630335e-01 -6.86993957e-01
-3.63187104e-01 6.40302658e-01 1.50570244e-01 3.09854536... | [7.816097259521484, 5.432304382324219] |
13463f80-faaf-4120-8326-b8bcac894e60 | traffic-signs-detection-and-recognition | 2003.03256 | null | https://arxiv.org/abs/2003.03256v1 | https://arxiv.org/pdf/2003.03256v1.pdf | Traffic Signs Detection and Recognition System using Deep Learning | With the rapid development of technology, automobiles have become an essential asset in our day-to-day lives. One of the more important researches is Traffic Signs Recognition (TSR) systems. This paper describes an approach for efficiently detecting and recognizing traffic signs in real-time, taking into account the va... | ['Magdy El-Moursy', 'Marco Magdy William', 'Keroles Khalil', 'Kerolos Gamal Alexsan', 'Pavly Salah Zaki', 'Bolis Karam Soliman'] | 2020-03-06 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [-1.23781584e-01 -7.90023685e-01 1.01329178e-01 -1.36927679e-01
-5.25986910e-01 -4.86141723e-03 5.30894041e-01 -7.48697579e-01
-5.50983310e-01 3.90844405e-01 -6.93757176e-01 -8.07195187e-01
-1.67198777e-01 -5.58513761e-01 -4.39141482e-01 -7.56089211e-01
6.81083202e-02 2.04085007e-01 9.18664515e-01 -5.82584560... | [8.06602954864502, -0.7966309189796448] |
823b811a-8c71-42ea-a04b-b181fe6d308d | flar-a-unified-prototype-framework-for-few | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Fan_FLAR_A_Unified_Prototype_Framework_for_Few-Sample_Lifelong_Active_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Fan_FLAR_A_Unified_Prototype_Framework_for_Few-Sample_Lifelong_Active_Recognition_ICCV_2021_paper.pdf | FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition | Intelligent agents with visual sensors are allowed to actively explore their observations for better recognition performance. This task is referred to as Active Recognition (AR). Currently, most methods toward AR are implemented under a fixed-category setting, which constrains their applicability in realistic scena... | ['Ying Wu', 'Wei Wei', 'Peixi Xiong', 'Lei Fan'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['scene-recognition'] | ['computer-vision'] | [ 5.24518073e-01 2.75961459e-01 -3.51546377e-01 -3.91072124e-01
-6.50750279e-01 -5.54586828e-01 7.86198437e-01 6.25222325e-02
-6.25122070e-01 8.95962715e-01 -4.08708155e-02 9.03884768e-02
-3.32433805e-02 -6.46665514e-01 -8.33924949e-01 -1.04307008e+00
7.07705542e-02 4.22208011e-01 1.91050068e-01 1.47748113... | [9.821172714233398, 3.1864731311798096] |
e75be8b7-0cd8-4e0f-8226-24e5598c5cfb | joint-voxel-and-coordinate-regression-for | 1801.09242 | null | http://arxiv.org/abs/1801.09242v1 | http://arxiv.org/pdf/1801.09242v1.pdf | Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization | 3D face shape is more expressive and viewpoint-consistent than its 2D
counterpart. However, 3D facial landmark localization in a single image is
challenging due to the ambiguous nature of landmarks under 3D perspective.
Existing approaches typically adopt a suboptimal two-step strategy, performing
2D landmark localizat... | ['Zhenan Sun', 'Qi Li', 'Hongwen Zhang'] | 2018-01-28 | null | null | null | null | ['3d-facial-landmark-localization'] | ['computer-vision'] | [-3.47870797e-01 5.43803163e-02 -9.94155556e-02 -5.31422555e-01
-8.60674858e-01 -3.03382933e-01 6.03472710e-01 -7.03822225e-02
-3.57500881e-01 1.39351472e-01 -4.08092216e-02 -6.22203154e-03
7.14300945e-02 -5.57040691e-01 -5.11392057e-01 -7.25064158e-01
-2.13206969e-02 4.86325532e-01 -5.31920567e-02 3.10841352... | [13.474387168884277, 0.3218021094799042] |
26ff491a-dc37-4c06-b3f7-c1ece6212891 | simulation-and-measurement-of-human | 2208.00837 | null | https://arxiv.org/abs/2208.00837v2 | https://arxiv.org/pdf/2208.00837v2.pdf | The feasibility of Q-band millimeter wave on hand-gesture recognition for indoor FTTR scenario | The generalization for different scenarios and dif-ferent users is an urgent problem for millimeter wave gesture recognition for indoor fiber-to-the-room (FTTR) scenario. In order to solve this problem and verify the feasibility of FTTR Q-band millimeter wave in gesture recognition, we build a real-time millimeter wave... | ['Yanbo Zhao', 'Feng Xu', 'Zhaoyang Xia', 'Yuxuan Hu'] | 2022-07-22 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.13579103e-01 -5.69716752e-01 5.31149060e-02 -5.47212243e-01
-4.02258456e-01 -2.99325496e-01 2.25402489e-01 -1.27464247e+00
-6.82133436e-01 4.37625557e-01 -6.95161298e-02 -6.60080016e-01
-5.00359774e-01 -9.59093809e-01 3.10388803e-01 -9.38148737e-01
-4.38006908e-01 9.89870504e-02 -2.08037533e-02 -9.29547772... | [6.738157272338867, 0.3492282032966614] |
160da7d1-e881-47a8-b819-4b2dcff1fd66 | image-harmonization-with-transformer | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Guo_Image_Harmonization_With_Transformer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Guo_Image_Harmonization_With_Transformer_ICCV_2021_paper.pdf | Image Harmonization With Transformer | Image harmonization, aiming to make composite images look more realistic, is an important and challenging task. The composite, synthesized by combining foreground from one image with background from another image, inevitably suffers from the issue of inharmonious appearance caused by distinct imaging conditions, i.... | ['Junyu Dong', 'Bing Zheng', 'Zhaorui Gu', 'Haiyong Zheng', 'Dongsheng Guo', 'Zonghui Guo'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['image-harmonization'] | ['computer-vision'] | [ 5.30899107e-01 -3.02757978e-01 1.91363215e-01 -1.43685535e-01
-4.46908802e-01 -3.46765578e-01 3.71617615e-01 -4.59563196e-01
-1.21524766e-01 4.83808905e-01 2.96104431e-01 -4.59409431e-02
2.78675228e-01 -5.97265661e-01 -9.52743948e-01 -9.11145389e-01
6.21768534e-01 -3.63761425e-01 1.50762200e-01 -3.66214901... | [11.223129272460938, -1.3598802089691162] |
6ad3ea78-33b1-4e30-9ff2-407aab92b8e1 | leveraging-audio-tagging-assisted-sound-event | 2304.12688 | null | https://arxiv.org/abs/2304.12688v1 | https://arxiv.org/pdf/2304.12688v1.pdf | Leveraging Audio-Tagging Assisted Sound Event Detection using Weakified Strong Labels and Frequency Dynamic Convolutions | Jointly learning from a small labeled set and a larger unlabeled set is an active research topic under semi-supervised learning (SSL). In this paper, we propose a novel SSL method based on a two-stage framework for leveraging a large unlabeled in-domain set. Stage-1 of our proposed framework focuses on audio-tagging (A... | ['Eng Siong Chng', 'Andrew Koh', 'Rohan Kumar Das', 'Tanmay Khandelwal'] | 2023-04-25 | null | null | null | null | ['audio-tagging', 'sound-event-detection'] | ['audio', 'audio'] | [ 4.31909800e-01 2.09044009e-01 -2.03605592e-02 -4.33866858e-01
-1.51827967e+00 -5.72493494e-01 5.30069619e-02 -2.07969978e-01
-4.00835663e-01 5.19392252e-01 2.33115956e-01 -1.28217295e-01
6.58603072e-01 -5.11975288e-01 -5.60822964e-01 -5.76849520e-01
1.39019117e-01 1.73339516e-01 4.67358291e-01 2.98421681... | [15.191133499145508, 5.150817394256592] |
2d6b48e5-293c-4ebe-952d-8183cda7c0f4 | from-multi-label-learning-to-cross-domain | 2207.11742 | null | https://arxiv.org/abs/2207.11742v1 | https://arxiv.org/pdf/2207.11742v1.pdf | From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach | In multi-label learning, a particular case of multi-task learning where a single data point is associated with multiple target labels, it was widely assumed in the literature that, to obtain best accuracy, the dependence among the labels should be explicitly modeled. This premise led to a proliferation of methods offer... | ['Jesse Read'] | 2022-07-24 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 5.51962733e-01 4.07904498e-02 -4.71866190e-01 -6.91806674e-01
-8.57003093e-01 -8.71863008e-01 7.94910908e-01 2.73639619e-01
-4.03778166e-01 7.16037273e-01 -6.59446279e-03 -3.51333201e-01
-5.48743904e-01 -4.56520021e-01 -6.96485877e-01 -6.18925095e-01
1.13784231e-01 7.21442997e-01 1.52525276e-01 6.64029196... | [9.295431137084961, 4.365570545196533] |
68464361-57cc-4e10-9cc3-ca196df41f10 | optimized-power-normalized-cepstral | 2109.12058 | null | https://arxiv.org/abs/2109.12058v1 | https://arxiv.org/pdf/2109.12058v1.pdf | Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification | After their introduction to robust speech recognition, power normalized cepstral coefficient (PNCC) features were successfully adopted to other tasks, including speaker verification. However, as a feature extractor with long-term operations on the power spectrogram, its temporal processing and amplitude scaling steps d... | ['Tomi Kinnunen', 'Md Sahidullah', 'Xuechen Liu'] | 2021-09-24 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.94068104e-01 -1.70492381e-01 8.20886940e-02 -3.09191585e-01
-7.85496294e-01 -4.41388756e-01 4.27992731e-01 1.99771821e-02
-4.88334537e-01 5.46636403e-01 2.64459819e-01 -3.59121203e-01
1.06312215e-01 -1.45399153e-01 -1.67454094e-01 -8.73144686e-01
-5.44796661e-02 -5.11035204e-01 -3.57944697e-01 -1.70280069... | [14.443700790405273, 6.032254219055176] |
44a9c409-445e-494f-8c01-4674b064b805 | deep-quantigraphic-image-enhancement-via | 2304.02285 | null | https://arxiv.org/abs/2304.02285v1 | https://arxiv.org/pdf/2304.02285v1.pdf | Deep Quantigraphic Image Enhancement via Comparametric Equations | Most recent methods of deep image enhancement can be generally classified into two types: decompose-and-enhance and illumination estimation-centric. The former is usually less efficient, and the latter is constrained by a strong assumption regarding image reflectance as the desired enhancement result. To alleviate this... | ['Akisato Kimura', 'Yongqing Sun', 'Xiaomeng Wu'] | 2023-04-05 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 4.96606171e-01 -2.88664043e-01 2.24874347e-01 -5.13006330e-01
-4.08520877e-01 -4.60241884e-01 4.58906502e-01 -4.63986576e-01
-7.15117335e-01 5.91263890e-01 5.82843795e-02 -1.66234329e-01
-4.78768200e-02 -9.32020009e-01 -6.72996879e-01 -1.07545471e+00
8.68848026e-01 -3.24461758e-01 1.69838406e-02 -2.20434517... | [10.720809936523438, -2.4353718757629395] |
a4a71909-e129-4b38-a4df-dc9902e6dd63 | jccs-pfgm-a-novel-circle-supervision-based | 2306.07824 | null | https://arxiv.org/abs/2306.07824v1 | https://arxiv.org/pdf/2306.07824v1.pdf | JCCS-PFGM: A Novel Circle-Supervision based Poisson Flow Generative Model for Multiphase CECT Progressive Low-Dose Reconstruction with Joint Condition | Multiphase contrast-enhanced computed tomography (CECT) scan is clinically significant to demonstrate the anatomy at different phases. In practice, such a multiphase CECT scan inherently takes longer time and deposits much more radiation dose into a patient body than a regular CT scan, and reduction of the radiation do... | ['Ge Wang', 'Daoqiang Zhang', 'Yang Chen', 'Cong Xia', 'Yuting He', 'Rongjun Ge'] | 2023-06-13 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 8.29100832e-02 -3.96028161e-01 4.98253629e-02 -4.82317545e-02
-6.41022563e-01 -2.21508935e-01 2.50726789e-01 1.52242929e-01
-2.78813630e-01 6.07492626e-01 3.66392940e-01 -4.52897996e-01
-3.10981452e-01 -7.86928475e-01 -3.75215948e-01 -9.77015376e-01
-3.79061371e-01 3.70869517e-01 6.17443919e-01 1.50975019... | [13.553671836853027, -2.520820140838623] |
67d7376a-9b8d-426b-94ff-608520fcde0e | multi-level-graph-convolutional-networks-for | 2006.01963 | null | https://arxiv.org/abs/2006.01963v1 | https://arxiv.org/pdf/2006.01963v1.pdf | Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction | Cross-platform account matching plays a significant role in social network analytics, and is beneficial for a wide range of applications. However, existing methods either heavily rely on high-quality user generated content (including user profiles) or suffer from data insufficiency problem if only focusing on network t... | ['Hongxu Chen', 'Hongzhi Yin', 'Tong Chen', 'Katarzyna Musial', 'Bogdan Gabrys', 'Xiangguo Sun'] | 2020-06-02 | null | null | null | null | ['anchor-link-prediction'] | ['graphs'] | [ 7.08070919e-02 -1.81335777e-01 -3.94264877e-01 -2.68391341e-01
6.50115311e-02 -3.48529071e-01 4.72373724e-01 3.93018037e-01
-3.55438828e-01 5.24874210e-01 1.87969014e-01 -3.01275313e-01
-5.82996070e-01 -1.10474288e+00 -1.01817302e-01 -3.80655140e-01
8.08645412e-03 5.10762334e-01 2.49542877e-01 -2.98137546... | [7.375385284423828, 6.286040782928467] |
2a9f438b-5fb0-4747-a73f-7947a3bb964f | wikineural-combined-neural-and-knowledge | null | null | https://aclanthology.org/2021.findings-emnlp.215 | https://aclanthology.org/2021.findings-emnlp.215.pdf | WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER | Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data fo... | ['Roberto Navigli', 'Francesco Cecconi', 'Niccolò Campolungo', 'Valentino Maiorca', 'Simone Tedeschi'] | null | null | null | null | findings-emnlp-2021-11 | ['multilingual-named-entity-recognition', 'multilingual-nlp'] | ['natural-language-processing', 'natural-language-processing'] | [-2.29301482e-01 -1.67126057e-03 -2.09980514e-02 -2.38624603e-01
-1.44433069e+00 -8.91319454e-01 6.95703864e-01 3.54641020e-01
-1.12906027e+00 1.32417023e+00 6.89582765e-01 -3.01681906e-01
-3.90181318e-02 -6.24590456e-01 -7.72858083e-01 -1.35738507e-01
5.88608086e-02 7.72438288e-01 -9.15601198e-03 -6.66192055... | [9.866133689880371, 9.722396850585938] |
4441e8bc-3565-4c89-940d-bb90fe344b1d | distilling-transformers-for-neural-cross | 2108.03322 | null | https://arxiv.org/abs/2108.03322v1 | https://arxiv.org/pdf/2108.03322v1.pdf | Distilling Transformers for Neural Cross-Domain Search | Pre-trained transformers have recently clinched top spots in the gamut of natural language tasks and pioneered solutions to software engineering tasks. Even information retrieval has not been immune to the charm of the transformer, though their large size and cost is generally a barrier to deployment. While there has b... | ['Neel Sundaresan', 'Dawn Drain', 'Chen Wu', 'Colin B. Clement'] | 2021-08-06 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 2.14625746e-01 4.99541275e-02 -5.02279222e-01 -2.67264575e-01
-1.37214291e+00 -7.68860638e-01 6.96348011e-01 1.17305852e-01
-3.10699493e-01 3.45837086e-01 5.37262321e-01 -9.92818475e-01
1.66728515e-02 -6.18974209e-01 -9.06965196e-01 -2.18193814e-01
-6.16538227e-02 7.40327120e-01 2.97377914e-01 -4.27977204... | [7.566506385803223, 8.04464340209961] |
e32b9a70-1ff6-488c-9d5e-68c0d8010d98 | marc-memory-by-association-and-reinforcement | 1312.2844 | null | http://arxiv.org/abs/1312.2844v1 | http://arxiv.org/pdf/1312.2844v1.pdf | mARC: Memory by Association and Reinforcement of Contexts | This paper introduces the memory by Association and Reinforcement of Contexts
(mARC). mARC is a novel data modeling technology rooted in the second
quantization formulation of quantum mechanics. It is an all-purpose incremental
and unsupervised data storage and retrieval system which can be applied to all
types of sign... | ['Patrice Descourt', 'Norbert Rimoux'] | 2013-12-10 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-1.78775296e-01 -1.92380071e-01 4.20095623e-02 -6.13608882e-02
-7.15876937e-01 -6.58402741e-01 1.01499486e+00 4.59878683e-01
-9.55859125e-01 5.79362094e-01 1.38166100e-01 -3.93925369e-01
-8.11898828e-01 -9.86682951e-01 -2.93469757e-01 -5.52826107e-01
-2.45576248e-01 7.00360239e-01 7.20899403e-01 -7.95375049... | [11.309934616088867, 7.533426761627197] |
e7b290c7-9cad-4456-b4f8-0f3560ec2549 | improving-robustness-using-joint-attention | 2005.08094 | null | https://arxiv.org/abs/2005.08094v2 | https://arxiv.org/pdf/2005.08094v2.pdf | Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images | Noisy data and the similarity in the ocular appearances caused by different ophthalmic pathologies pose significant challenges for an automated expert system to accurately detect retinal diseases. In addition, the lack of knowledge transferability and the need for unreasonably large datasets limit clinical application ... | ['Stewart Lee Zuckerbrod', 'Sharif Amit Kamran', 'Alireza Tavakkoli'] | 2020-05-16 | null | null | null | null | ['retinal-oct-disease-classification'] | ['computer-vision'] | [ 3.64966571e-01 -1.77967444e-01 1.29019201e-01 -5.95356226e-01
-5.66505849e-01 -3.30241770e-01 2.97620803e-01 -1.40470698e-01
-2.26302758e-01 6.68625474e-01 2.86907285e-01 -1.58782125e-01
-6.41821325e-01 -3.01250964e-01 -4.10795987e-01 -9.60136771e-01
9.35833901e-02 1.83342978e-01 1.84024632e-01 6.31676167... | [15.792003631591797, -3.9550223350524902] |
9d202ad0-718d-4fdd-8a02-6879d7d8709c | blind-direction-of-arrival-estimation-in | 2005.08318 | null | https://arxiv.org/abs/2005.08318v2 | https://arxiv.org/pdf/2005.08318v2.pdf | Blind Direction-of-Arrival Estimation in Acoustic Vector-Sensor Arrays via Tensor Decomposition and Kullback-Leibler Divergence Covariance Fitting | A blind Direction-of-Arrivals (DOAs) estimate of narrowband signals for Acoustic Vector-Sensor (AVS) arrays is proposed. Building upon the special structure of the signal measured by an AVS, we show that the covariance matrix of all the received signals from the array admits a natural low-rank 4-way tensor representati... | ['Amir Weiss'] | 2020-05-17 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 1.13332316e-01 -1.40557304e-01 5.44558167e-01 -1.33271396e-01
-1.08235562e+00 -7.56267011e-01 2.19920471e-01 -1.88682899e-01
-3.11702549e-01 2.31840238e-01 5.35639584e-01 -1.97223037e-01
-5.94422281e-01 -2.24300548e-01 -5.99269092e-01 -1.22971344e+00
-3.32985938e-01 1.23015232e-01 -1.59481108e-01 -5.17012551... | [6.551137924194336, 1.3878005743026733] |
dd35f4ef-2bbb-47dc-a069-a87e2a342e26 | a-ga-based-approach-for-selection-of-local | 1501.05495 | null | http://arxiv.org/abs/1501.05495v1 | http://arxiv.org/pdf/1501.05495v1.pdf | A GA Based approach for selection of local features for recognition of handwritten Bangla numerals | Soft computing approaches are mainly designed to address the real world
ill-defined, imprecisely formulated problems, combining different kind of novel
models of computation, such as neural networks, genetic algorithms (GAs.
Handwritten digit recognition is a typical example of one such problem. In the
current work we ... | ['Mahantapas Kundu', 'Ram Sarkar', 'Nibaran Das', 'Subhadip Basu', 'Mita Nasipuri', 'Punam Kumar Saha'] | 2015-01-22 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.82751673e-01 -3.46098654e-02 5.01405634e-02 -3.95438075e-01
-2.71883458e-01 -3.33009213e-01 5.81349909e-01 4.59269047e-01
-4.37196016e-01 8.55302870e-01 -8.01613033e-02 -4.75608036e-02
-5.62640667e-01 -1.09624755e+00 -1.24455974e-01 -9.64129567e-01
1.01622820e-01 6.80340350e-01 3.30199122e-01 -7.21251145... | [7.934452056884766, 3.6769635677337646] |
4c9d9be7-e933-422e-9de3-587a8b21ea27 | ran4iqa-restorative-adversarial-nets-for-no | 1712.05444 | null | http://arxiv.org/abs/1712.05444v1 | http://arxiv.org/pdf/1712.05444v1.pdf | RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment | Inspired by the free-energy brain theory, which implies that human visual
system (HVS) tends to reduce uncertainty and restore perceptual details upon
seeing a distorted image, we propose restorative adversarial net (RAN), a
GAN-based model for no-reference image quality assessment (NR-IQA). RAN, which
mimics the proce... | ['Hongyu Ren', 'Diqi Chen', 'Yizhou Wang'] | 2017-12-14 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.78014117e-01 8.72894451e-02 4.42855716e-01 -1.29945084e-01
-6.84704542e-01 -3.34024668e-01 4.79752809e-01 -3.19057226e-01
-5.30170090e-02 6.18441463e-01 5.31300783e-01 1.44936770e-01
9.67657566e-03 -9.65103269e-01 -7.93904245e-01 -1.06740212e+00
1.01187430e-01 -2.12245256e-01 1.00678764e-01 -1.52697548... | [11.617920875549316, -1.7699822187423706] |
4d513e8b-c3b4-40e8-9e76-f8397107eaf2 | mine-your-own-anatomy-revisiting-medical | 2209.13476 | null | https://arxiv.org/abs/2209.13476v5 | https://arxiv.org/pdf/2209.13476v5.pdf | Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels | Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping. However, they face three common pitfalls: (1) tailness: medical image data usually fol... | ['James S. Duncan', 'Lawrence Staib', 'David A. Clifton', 'Xiaoxiao Li', 'Xiaoran Zhang', 'Haoran Su', 'Yifei Min', 'Fenglin Liu', 'Weicheng Dai', 'Chenyu You'] | 2022-09-27 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 6.47160530e-01 4.51484323e-01 -5.50923765e-01 -6.11080408e-01
-9.57180619e-01 -5.69130003e-01 4.05890763e-01 5.21715656e-02
-4.74485099e-01 6.23657107e-01 1.31170049e-01 -3.08150500e-01
-4.97125953e-01 -3.20089072e-01 -5.64117968e-01 -7.78405190e-01
-2.33376786e-01 4.33964640e-01 2.01473117e-01 -1.61741264... | [14.724120140075684, -2.1872053146362305] |
8248d91e-2e32-4c68-ae30-5befeb5ea203 | corefud-1-0-coreference-meets-universal | null | null | https://aclanthology.org/2022.lrec-1.520 | https://aclanthology.org/2022.lrec-1.520.pdf | CorefUD 1.0: Coreference Meets Universal Dependencies | Recent advances in standardization for annotated language resources have led to successful large scale efforts, such as the Universal Dependencies (UD) project for multilingual syntactically annotated data. By comparison, the important task of coreference resolution, which clusters multiple mentions of entities in a te... | ['Daniel Zeman', 'Amir Zeldes', 'Zdeněk Žabokrtský', 'Martin Popel', 'Michal Novák', 'Anna Nedoluzhko'] | null | null | null | null | lrec-2022-6 | ['coreference-resolution'] | ['natural-language-processing'] | [-2.33835608e-01 4.22120601e-01 -5.29643536e-01 -6.13449752e-01
-1.06809533e+00 -9.99629736e-01 5.85548401e-01 6.22730553e-01
-6.62731588e-01 1.25927281e+00 8.82207215e-01 -2.31359288e-01
-2.17099383e-01 -2.88756967e-01 -2.45859325e-01 -1.43674016e-01
-1.37604978e-02 1.12708044e+00 4.45999324e-01 -5.75047851... | [9.397720336914062, 9.559342384338379] |
3cd2d009-2afd-4c20-9b4b-5d946644d92b | how-important-is-weight-symmetry-in | 1510.05067 | null | http://arxiv.org/abs/1510.05067v4 | http://arxiv.org/pdf/1510.05067v4.pdf | How Important is Weight Symmetry in Backpropagation? | Gradient backpropagation (BP) requires symmetric feedforward and feedback
connections -- the same weights must be used for forward and backward passes.
This "weight transport problem" (Grossberg 1987) is thought to be one of the
main reasons to doubt BP's biologically plausibility. Using 15 different
classification dat... | ['Joel Z. Leibo', 'Qianli Liao', 'Tomaso Poggio'] | 2015-10-17 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.97723478e-01 9.94271040e-02 6.54287413e-02 -3.52607340e-01
4.28656220e-01 -3.24735463e-01 7.84156144e-01 5.19722141e-02
-8.83527875e-01 1.01443851e+00 1.89006522e-01 -4.37349886e-01
-2.18256533e-01 -7.16716409e-01 -7.28925109e-01 -1.01439118e+00
-2.65395612e-01 1.81016415e-01 6.74429953e-01 -7.43790329... | [8.127931594848633, 3.4333786964416504] |
1dcf2052-7c38-46dc-9b44-8026ce212486 | review-guided-helpful-answer-identification | 2003.06209 | null | https://arxiv.org/abs/2003.06209v1 | https://arxiv.org/pdf/2003.06209v1.pdf | Review-guided Helpful Answer Identification in E-commerce | Product-specific community question answering platforms can greatly help address the concerns of potential customers. However, the user-provided answers on such platforms often vary a lot in their qualities. Helpfulness votes from the community can indicate the overall quality of the answer, but they are often missing.... | ['Wenxuan Zhang', 'Jing Ma', 'Yang Deng', 'Wai Lam'] | 2020-03-13 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 7.68273231e-03 1.40917391e-01 -1.20721877e-01 -6.23106718e-01
-1.01556528e+00 -5.48826814e-01 5.70630133e-01 5.26778281e-01
-2.54841805e-01 5.26476383e-01 4.07488704e-01 -2.36607984e-01
-1.72922090e-01 -7.71949708e-01 -3.52351457e-01 -5.59646666e-01
6.63715303e-01 6.29659951e-01 3.46553534e-01 -5.84326446... | [11.545486450195312, 8.049939155578613] |
28bff533-4de5-4119-b77c-e620ad742522 | learning-with-noisy-labels-revisited-a-study-1 | 2110.12088 | null | https://arxiv.org/abs/2110.12088v2 | https://arxiv.org/pdf/2110.12088v2.pdf | Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations | Existing research on learning with noisy labels mainly focuses on synthetic label noise. Synthetic noise, though has clean structures which greatly enabled statistical analyses, often fails to model real-world noise patterns. The recent literature has observed several efforts to offer real-world noisy datasets, yet the... | ['Yang Liu', 'Gang Niu', 'Tongliang Liu', 'Hao Cheng', 'Zhaowei Zhu', 'Jiaheng Wei'] | 2021-10-22 | learning-with-noisy-labels-revisited-a-study | https://openreview.net/forum?id=TBWA6PLJZQm | https://openreview.net/pdf?id=TBWA6PLJZQm | iclr-2022-4 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.94429994e-01 -3.18504840e-01 3.41834486e-01 -6.72043562e-01
-1.21910787e+00 -9.24845517e-01 6.59015179e-01 -1.89178467e-01
-7.66787291e-01 1.05637336e+00 -1.07161524e-02 -1.57183796e-01
-1.63710028e-01 -4.97845322e-01 -5.79826355e-01 -8.03404868e-01
4.80680577e-02 6.93161666e-01 -8.94398466e-02 -1.92427993... | [9.403968811035156, 3.920912265777588] |
479118c4-ab25-46f3-97cd-8c58ce3c9c26 | robust-invertible-image-steganography | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_Robust_Invertible_Image_Steganography_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_Robust_Invertible_Image_Steganography_CVPR_2022_paper.pdf | Robust Invertible Image Steganography | Image steganography aims to hide secret images into a container image, where the secret is hidden from human vision and can be restored when necessary. Previous image steganography methods are limited in hiding capacity and robustness, commonly vulnerable to distortion on container images such as Gaussian noise, Po... | ['Jian Zhang', 'Jingfen Xie', 'Yujie Hu', 'Chong Mou', 'Youmin Xu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['image-steganography'] | ['computer-vision'] | [ 8.74398828e-01 -5.84227359e-03 1.74955712e-04 2.11443469e-01
-3.58025916e-02 -2.18667015e-01 3.03098083e-01 -6.61308885e-01
-1.40084505e-01 3.24125201e-01 2.11700693e-01 -4.70576257e-01
-4.88210954e-02 -9.70483303e-01 -6.17190659e-01 -1.34724927e+00
-2.45687306e-01 -5.97018719e-01 2.45661512e-01 -5.33518851... | [4.315708637237549, 8.050440788269043] |
40529e0b-1501-48f3-8543-202604d7887d | boosting-monocular-3d-object-detection-with | 2210.16574 | null | https://arxiv.org/abs/2210.16574v1 | https://arxiv.org/pdf/2210.16574v1.pdf | Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision | Recent advances in monocular 3D detection leverage a depth estimation network explicitly as an intermediate stage of the 3D detection network. Depth map approaches yield more accurate depth to objects than other methods thanks to the depth estimation network trained on a large-scale dataset. However, depth map approach... | ['Dongsuk Kum', 'Jun Won Choi', 'Sangmin Sim', 'Sanmin Kim', 'Youngseok Kim'] | 2022-10-29 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 1.62523210e-01 3.03837121e-01 -4.17475194e-01 -5.36856174e-01
-7.30746627e-01 -3.57380152e-01 5.10344386e-01 -2.06442162e-01
-4.63329792e-01 1.27543300e-01 -3.10237348e-01 -4.73111331e-01
5.71052074e-01 -9.54630911e-01 -1.06006312e+00 -4.59614277e-01
2.02121407e-01 6.03229105e-01 8.58009160e-01 4.43778306... | [7.856112003326416, -2.612988233566284] |
d56e4718-a6f7-43ae-bf09-9a756cfd1096 | detrs-with-hybrid-matching | 2207.13080 | null | https://arxiv.org/abs/2207.13080v3 | https://arxiv.org/pdf/2207.13080v3.pdf | DETRs with Hybrid Matching | One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader visio... | ['Han Hu', 'Chao Zhang', 'Lei Sun', 'WeiHong Lin', 'Haojun Yu', 'Xiaopei Wu', 'Haodi He', 'Yuhui Yuan', 'Ding Jia'] | 2022-07-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jia_DETRs_With_Hybrid_Matching_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jia_DETRs_With_Hybrid_Matching_CVPR_2023_paper.pdf | cvpr-2023-1 | ['set-matching'] | ['computer-vision'] | [ 3.24536592e-01 -2.99387574e-01 -1.08823806e-01 -4.52789038e-01
-1.02117932e+00 -5.16472340e-01 7.21865058e-01 -2.02559620e-01
-5.24412394e-01 5.24455309e-01 -3.76845896e-01 -1.68419927e-01
-1.76289722e-01 -4.44615602e-01 -7.73113728e-01 -7.55860269e-01
1.24966629e-01 2.61124551e-01 7.51932323e-01 -1.37526635... | [9.030004501342773, 0.3464679419994354] |
3a3cd82c-f136-43ff-8ae0-4f7829cf5d28 | causal-identification-with-matrix-equations | null | null | http://proceedings.neurips.cc/paper/2021/hash/4ea06fbc83cdd0a06020c35d50e1e89a-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/4ea06fbc83cdd0a06020c35d50e1e89a-Paper.pdf | Causal Identification with Matrix Equations | Causal effect identification is concerned with determining whether a causal effect is computable from a combination of qualitative assumptions about the underlying system (e.g., a causal graph) and distributions collected from this system. Many identification algorithms exclusively rely on graphical criteria made of a ... | ['Elias Bareinboim', 'Sanghack Lee'] | 2021-12-01 | null | https://openreview.net/forum?id=kt_s_ZbYvtP | https://openreview.net/pdf?id=kt_s_ZbYvtP | neurips-2021-12 | ['causal-identification'] | ['reasoning'] | [ 3.58213603e-01 1.06283017e-01 -4.40117985e-01 -4.19591088e-03
-2.68810034e-01 -9.95567739e-01 9.06958401e-01 1.88781351e-01
1.66560456e-01 8.42996478e-01 2.76185632e-01 -6.51218474e-01
-8.61653864e-01 -8.25995505e-01 -6.99818313e-01 -4.19873565e-01
-3.70174497e-01 8.76495987e-02 -3.41609925e-01 2.70673838... | [7.858068466186523, 5.325561046600342] |
f7e767aa-67aa-4a68-8b26-ee5bc64fc2d3 | bayesian-inference-for-neighborhood-filters | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Bayesian_Inference_for_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Huang_Bayesian_Inference_for_2015_CVPR_paper.pdf | Bayesian Inference for Neighborhood Filters With Application in Denoising | Range-weighted neighborhood filters are useful and popular for their edge-preserving property and simplicity, but they are originally proposed as intuitive tools. Previous works needed to connect them to other tools or models for indirect property reasoning or parameter estimation. In this paper, we introduce a unified... | ['Chao-Tsung Huang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['color-image-denoising'] | ['computer-vision'] | [-1.99241377e-02 -2.45007128e-01 7.03671724e-02 -4.29283619e-01
-4.50862408e-01 -1.29669383e-01 4.61323351e-01 -3.58780324e-01
-3.67912591e-01 6.96189642e-01 1.29830986e-01 2.67558359e-02
-5.99636078e-01 -8.20801079e-01 -3.93482029e-01 -1.04617548e+00
2.88437843e-01 -4.43964005e-02 4.48598862e-01 3.22877839... | [11.319449424743652, -2.514735221862793] |
eb6c7b08-d26f-49fb-8da4-48fad30f78e6 | collateral-facilitation-in-humans-and | 2211.05198 | null | https://arxiv.org/abs/2211.05198v1 | https://arxiv.org/pdf/2211.05198v1.pdf | Collateral facilitation in humans and language models | Are the predictions of humans and language models affected by similar things? Research suggests that while comprehending language, humans make predictions about upcoming words, with more predictable words being processed more easily. However, evidence also shows that humans display a similar processing advantage for hi... | ['Benjamin K. Bergen', 'James A. Michaelov'] | 2022-11-09 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [ 2.38219760e-02 3.27916205e-01 5.24682887e-02 -6.46042228e-01
6.93961456e-02 -4.79512274e-01 7.32120931e-01 6.64501786e-01
-6.01059973e-01 3.48492414e-01 5.82212329e-01 -8.66741836e-01
1.99492469e-01 -7.35945165e-01 -2.46528521e-01 1.76214613e-02
3.69762369e-02 4.19460356e-01 3.90552223e-01 -5.02096474... | [10.262497901916504, 8.681357383728027] |
55c5c5fd-229e-43cb-8685-d29f1c67362a | general-audio-tagging-with-ensembling | 1810.12832 | null | http://arxiv.org/abs/1810.12832v1 | http://arxiv.org/pdf/1810.12832v1.pdf | General audio tagging with ensembling convolutional neural network and statistical features | Audio tagging aims to infer descriptive labels from audio clips. Audio
tagging is challenging due to the limited size of data and noisy labels. In
this paper, we describe our solution for the DCASE 2018 Task 2 general audio
tagging challenge. The contributions of our solution include: We investigated a
variety of convo... | ['Dezhi Wang', 'Huaimin Wang', 'Haibo Mi', 'Bo Ding', 'Kele Xu', 'Qiuqiang Kong', 'Boqing Zhu'] | 2018-10-30 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 8.89247060e-02 -9.50363576e-02 -7.06944689e-02 -2.58164138e-01
-1.57484770e+00 -7.78837740e-01 1.63536221e-01 9.32907909e-02
-4.27387118e-01 5.41354597e-01 6.13314688e-01 3.36417019e-01
1.17154412e-01 -1.38933972e-01 -5.50534844e-01 -4.86384928e-01
-3.87050271e-01 -7.08446652e-02 -1.00970313e-01 2.07172632... | [15.227123260498047, 5.112461090087891] |
90451133-e388-4b2f-a5cb-4328d81ef6d7 | ntire-2021-challenge-on-high-dynamic-range | 2106.01439 | null | https://arxiv.org/abs/2106.01439v1 | https://arxiv.org/pdf/2106.01439v1.pdf | NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results | This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021. This manuscript focuses on the newly introduced dataset, the proposed methods and their results. The challenge aims at est... | ['Radu Timofte', 'Aleš Leonardis', 'Sibi Catley-Chandar', 'Eduardo Pérez-Pellitero'] | 2021-06-02 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 8.75970006e-01 -3.20266962e-01 2.93436140e-01 -1.82887867e-01
-1.08290684e+00 -3.22085351e-01 4.77648824e-01 -3.34825426e-01
-1.51907399e-01 7.35461056e-01 2.94135779e-01 1.14544094e-01
-8.61616582e-02 -4.76730525e-01 -4.72585112e-01 -9.13676560e-01
-1.47559106e-01 -1.81731924e-01 2.20139965e-01 -3.89462858... | [11.00524616241455, -2.1685707569122314] |
91cc2db5-5bf5-4f4c-83a2-89020cfa9b97 | energy-management-for-a-dm-i-plug-in-hybrid | 2306.08823 | null | https://arxiv.org/abs/2306.08823v1 | https://arxiv.org/pdf/2306.08823v1.pdf | Energy Management for a DM-i Plug-in Hybrid Electric Vehicle via Continuous-Discrete Reinforcement Learning | Energy management strategy (EMS) is a key technology for plug-in hybrid electric vehicles (PHEVs). The energy management of PHEVs needs to output continuous variables such as engine torque, as well as discrete variables such as clutch engagement or disengagement. This type of problem is a mixed-integer programming prob... | ['Yuan Lin', 'Jinming Xu', 'Changfu Gong'] | 2023-06-15 | null | null | null | null | ['management', 'energy-management'] | ['miscellaneous', 'time-series'] | [-2.38066807e-01 1.38808146e-01 -4.76997524e-01 9.47525576e-02
-2.63672173e-01 -4.81670946e-01 1.62772581e-01 1.03385620e-01
-3.94458115e-01 1.10047925e+00 -6.48676991e-01 -2.54155219e-01
-6.89832568e-01 -9.28479075e-01 -7.02956200e-01 -1.20796907e+00
7.39564449e-02 5.11005878e-01 6.55304715e-02 -1.78535298... | [5.553229808807373, 2.288217306137085] |
64a01920-346c-41b6-9564-1bcb245de8a8 | enhanced-frame-and-event-based-simulator-and | 2112.09379 | null | https://arxiv.org/abs/2112.09379v1 | https://arxiv.org/pdf/2112.09379v1.pdf | Enhanced Frame and Event-Based Simulator and Event-Based Video Interpolation Network | Fast neuromorphic event-based vision sensors (Dynamic Vision Sensor, DVS) can be combined with slower conventional frame-based sensors to enable higher-quality inter-frame interpolation than traditional methods relying on fixed motion approximations using e.g. optical flow. In this work we present a new, advanced event... | ['Kynan Eng', 'Hyunsurk Eric Ryu', 'Paul K. J. Park', 'Chang-Woo Shin', 'Moosung Kwak', 'Minwon Seo', 'Luca Longinotti', 'Chenghan Li', 'Thomas Debrunner', 'Andreas Georgiou', 'Adam Radomski'] | 2021-12-17 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 3.49505723e-01 -2.78984576e-01 7.38442957e-01 -2.80612469e-01
-5.04188359e-01 -5.26526630e-01 3.83895993e-01 -3.89694534e-02
-9.46927667e-01 6.81756556e-01 -7.45154843e-02 2.72119045e-01
2.78036684e-01 -4.93986905e-01 -1.41123986e+00 -4.26550865e-01
9.66086388e-02 1.60447419e-01 9.83808219e-01 1.34899005... | [8.69745922088623, -1.2734531164169312] |
31644622-0056-4c1b-ad7d-72690f7f11d3 | outfit-compatibility-prediction-and-diagnosis | 1907.11496 | null | https://arxiv.org/abs/1907.11496v2 | https://arxiv.org/pdf/1907.11496v2.pdf | Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network | Existing works about fashion outfit compatibility focus on predicting the overall compatibility of a set of fashion items with their information from different modalities. However, there are few works explore how to explain the prediction, which limits the persuasiveness and effectiveness of the model. In this work, we... | ['Bo Wu', 'Yun Ye', 'Yueqi Zhong', 'Xin Wang'] | 2019-07-26 | null | null | null | null | ['fashion-compatibility-learning'] | ['computer-vision'] | [-2.88607106e-02 -2.18572810e-01 -4.35572922e-01 -9.06765521e-01
-7.52973408e-02 -2.92078972e-01 3.39825749e-01 1.19630978e-01
-9.40142572e-02 2.08278716e-01 5.67288518e-01 1.71364933e-01
-3.08610439e-01 -7.73379147e-01 -7.72728086e-01 -3.74666154e-01
4.06561017e-01 2.12518468e-01 -8.63791630e-02 -4.03667122... | [11.02662467956543, 0.09846800565719604] |
9ab69622-f061-472e-9708-cd3011425d4e | key-information-extraction-from-documents | 2106.14624 | null | https://arxiv.org/abs/2106.14624v1 | https://arxiv.org/pdf/2106.14624v1.pdf | Key Information Extraction From Documents: Evaluation And Generator | Extracting information from documents usually relies on natural language processing methods working on one-dimensional sequences of text. In some cases, for example, for the extraction of key information from semi-structured documents, such as invoice-documents, spatial and formatting information of text are crucial to... | ['Constantin Spille', 'Mirela Popa', 'Oliver Bensch'] | 2021-06-09 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 1.70409337e-01 4.65464830e-01 6.04287051e-02 -4.10603702e-01
-4.10083413e-01 -7.99755335e-01 1.00137711e+00 8.39083254e-01
-6.30850732e-01 7.72829711e-01 3.97032887e-01 -5.82319438e-01
-3.25637490e-01 -8.80086243e-01 -7.08586752e-01 -1.41948491e-01
-9.04601216e-02 6.13135934e-01 1.15851037e-01 -1.77679121... | [11.65796184539795, 2.8955605030059814] |
48b8be05-b118-4a09-aa41-a96cda900ffa | selectivity-of-protein-interactions | 2207.01572 | null | https://arxiv.org/abs/2207.01572v1 | https://arxiv.org/pdf/2207.01572v1.pdf | Selectivity of Protein Interactions Stimulated by Terahertz Signals | It has been established that Terahertz (THz) band signals can interact with biomolecules through resonant modes. Specifically, of interest here, protein activation. Our research goal is to show how directing the mechanical signaling inside protein molecules using THz signals can control changes in their structure and a... | ['Raviraj Adve', 'Andrew W. Eckford', 'Hadeel Elayan'] | 2022-07-04 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 5.99554539e-01 2.92250872e-01 -8.82119238e-02 -2.25869454e-02
-1.23916224e-01 -6.36881232e-01 1.92437232e-01 2.41925612e-01
-2.36329839e-01 7.83298314e-01 -7.62227997e-02 -2.32362803e-02
-1.85293123e-01 -1.02789807e+00 -7.88804412e-01 -1.59547913e+00
3.46734785e-02 2.11139143e-01 1.13165602e-01 -3.60741705... | [4.99808406829834, 5.105839729309082] |
cd166f5e-3f89-4c1a-8285-8116e8835264 | interactive-image-synthesis-with-panoptic | 2203.02104 | null | https://arxiv.org/abs/2203.02104v3 | https://arxiv.org/pdf/2203.02104v3.pdf | Interactive Image Synthesis with Panoptic Layout Generation | Interactive image synthesis from user-guided input is a challenging task when users wish to control the scene structure of a generated image with ease.Although remarkable progress has been made on layout-based image synthesis approaches, in order to get realistic fake image in interactive scene, existing methods requir... | ['Peng Du', 'Minfeng Zhu', 'Tao Wu', 'Bo wang'] | 2022-03-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Interactive_Image_Synthesis_With_Panoptic_Layout_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Interactive_Image_Synthesis_With_Panoptic_Layout_Generation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['layout-to-image-generation'] | ['computer-vision'] | [ 5.39151967e-01 2.76983827e-01 3.72973472e-01 6.84732571e-03
-1.60864547e-01 -1.00384474e+00 6.09445632e-01 -4.14834648e-01
2.18041852e-01 7.57550240e-01 -1.19802244e-01 -3.76404941e-01
2.04369262e-01 -1.13787007e+00 -1.10713255e+00 -7.78783798e-01
3.62576455e-01 1.60502344e-01 1.65491745e-01 -3.84099275... | [11.617511749267578, -0.5264559984207153] |
16c67933-d608-45ef-a448-dd243069ce09 | automated-essay-scoring-based-on-two-stage | 1901.07744 | null | https://arxiv.org/abs/1901.07744v2 | https://arxiv.org/pdf/1901.07744v2.pdf | Automated Essay Scoring based on Two-Stage Learning | Current state-of-art feature-engineered and end-to-end Automated Essay Score (AES) methods are proven to be unable to detect adversarial samples, e.g. the essays composed of permuted sentences and the prompt-irrelevant essays. Focusing on the problem, we develop a Two-Stage Learning Framework (TSLF) which integrates th... | ['Yaguang Zhu', 'Jiawei Liu', 'Yang Xu'] | 2019-01-23 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-1.91296469e-02 -2.60000855e-01 -3.85330198e-03 -4.12382901e-01
-1.44085872e+00 -1.10026920e+00 8.81376565e-01 -1.73118070e-01
-4.74255770e-01 8.07747722e-01 5.54787755e-01 -3.13588679e-01
2.76173074e-02 -4.60631430e-01 -5.74970961e-01 -2.01069891e-01
3.68607461e-01 3.25726539e-01 1.61592975e-01 -4.65303510... | [11.25451946258545, 9.324196815490723] |
9c5ed696-4a08-4516-ae1a-95e5dbb70d8b | scalable-neural-probabilistic-answer-set | 2306.08397 | null | https://arxiv.org/abs/2306.08397v1 | https://arxiv.org/pdf/2306.08397v1.pdf | Scalable Neural-Probabilistic Answer Set Programming | The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI. Deep Probabilistic Programming Languages (DPPLs) have been developed for probabilistic logic programming to be carried out via the probability estimations of deep neural ne... | ['Kristian Kersting', 'Devendra Singh Dhami', 'Daniel Ochs', 'Arseny Skryagin'] | 2023-06-14 | null | null | null | null | ['visual-question-answering-1', 'probabilistic-programming'] | ['computer-vision', 'methodology'] | [ 5.21136075e-02 4.89692569e-01 -8.72754082e-02 -6.63571835e-01
-8.68574500e-01 -7.60500550e-01 5.96477628e-01 1.83320045e-01
-3.65460366e-01 6.74920917e-01 -2.04980358e-01 -5.56383967e-01
-4.10092682e-01 -1.18125188e+00 -1.25975406e+00 -5.87760150e-01
-1.04055583e-01 9.11123455e-01 6.29630983e-01 2.75949035... | [8.679677963256836, 7.002292156219482] |
8337fec9-ed29-44bc-8bea-10f39df8122f | geonet-benchmarking-unsupervised-adaptation | 2303.15443 | null | https://arxiv.org/abs/2303.15443v1 | https://arxiv.org/pdf/2303.15443v1.pdf | GeoNet: Benchmarking Unsupervised Adaptation across Geographies | In recent years, several efforts have been aimed at improving the robustness of vision models to domains and environments unseen during training. An important practical problem pertains to models deployed in a new geography that is under-represented in the training dataset, posing a direct challenge to fair and inclusi... | ['Manmohan Chandraker', 'Wangdong Xu', 'Tarun Kalluri'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kalluri_GeoNet_Benchmarking_Unsupervised_Adaptation_Across_Geographies_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kalluri_GeoNet_Benchmarking_Unsupervised_Adaptation_Across_Geographies_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-recognition'] | ['computer-vision'] | [ 6.06213883e-03 -1.14464372e-01 1.61199048e-01 -4.01531219e-01
-3.70705038e-01 -7.54125297e-01 9.10644472e-01 2.98171993e-02
-6.34534478e-01 6.49562776e-01 4.11037624e-01 -2.13536054e-01
1.68350805e-02 -5.08679450e-01 -9.77140665e-01 -3.94473583e-01
1.44585609e-01 3.96100312e-01 2.90749848e-01 -3.86279672... | [9.829038619995117, 1.4682080745697021] |
58047c54-56bb-4d65-8d9f-300ae337b082 | one-for-all-unified-workload-prediction-for | 2306.01507 | null | https://arxiv.org/abs/2306.01507v1 | https://arxiv.org/pdf/2306.01507v1.pdf | One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms | Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is vital for efficient application deployment and resource provisioning. However, the heterogeneous application patterns, variable infrastructure performance, and frequent deployments in MT-ECP pose significant challenges for accurate and efficient workl... | ['Wenyu Wang', 'Cheng Zhang', 'Xiaofei Wang', 'Heng Zhang', 'Zheng Wang', 'Shaoyuan Huang'] | 2023-06-02 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-4.10276949e-01 -9.67442393e-01 -4.19593185e-01 -1.48148999e-01
-4.18704301e-01 -2.03395799e-01 -1.74509704e-01 -2.38089282e-02
3.85012329e-01 3.74826819e-01 3.70530821e-02 -2.83082604e-01
-3.60591799e-01 -5.18840134e-01 -1.56556040e-01 -5.56450844e-01
-3.87201577e-01 5.63435972e-01 5.17518520e-01 -2.21883599... | [6.983275890350342, 2.775489330291748] |
7c779bb3-7fea-4533-8bf5-4c3ed2059ddd | on-the-generalization-of-basicvsr-to-video | 2204.05308 | null | https://arxiv.org/abs/2204.05308v2 | https://arxiv.org/pdf/2204.05308v2.pdf | On the Generalization of BasicVSR++ to Video Deblurring and Denoising | The exploitation of long-term information has been a long-standing problem in video restoration. The recent BasicVSR and BasicVSR++ have shown remarkable performance in video super-resolution through long-term propagation and effective alignment. Their success has led to a question of whether they can be transferred to... | ['Chen Change Loy', 'Xiangyu Xu', 'Shangchen Zhou', 'Kelvin C. K. Chan'] | 2022-04-11 | null | null | null | null | ['video-super-resolution', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 3.43498856e-01 -4.52887714e-01 1.24892429e-03 -1.57423496e-01
-9.09720719e-01 -2.19358310e-01 4.31301862e-01 -5.23963273e-01
-2.82777399e-01 7.87761211e-01 6.95470452e-01 -1.80325061e-01
-4.03772183e-02 -4.29729909e-01 -8.04764330e-01 -6.04154587e-01
-1.09795099e-02 -3.34882557e-01 4.51782674e-01 -4.29402679... | [11.063648223876953, -2.010988473892212] |
e4cc465c-a612-4a51-a5dc-c45924f29375 | statistical-beamformer-exploiting-non | 2306.07562 | null | https://arxiv.org/abs/2306.07562v1 | https://arxiv.org/pdf/2306.07562v1.pdf | Statistical Beamformer Exploiting Non-stationarity and Sparsity with Spatially Constrained ICA for Robust Speech Recognition | In this paper, we present a statistical beamforming algorithm as a pre-processing step for robust automatic speech recognition (ASR). By modeling the target speech as a non-stationary Laplacian distribution, a mask-based statistical beamforming algorithm is proposed to exploit both its output and masked input variance ... | ['Hyung-Min Park', 'Ui-Hyeop Shin'] | 2023-06-13 | null | null | null | null | ['robust-speech-recognition', 'automatic-speech-recognition'] | ['speech', 'speech'] | [ 4.21593308e-01 -4.73201096e-01 3.49351645e-01 -2.38748401e-01
-8.39763641e-01 -5.89587510e-01 3.27594757e-01 -6.06979012e-01
-4.18915749e-01 5.61606586e-01 5.92790127e-01 -2.20126495e-01
-4.55861837e-01 -1.78259775e-01 -3.70196402e-01 -1.28003442e+00
2.81605981e-02 -2.53445238e-01 -1.42591566e-01 9.12074894... | [15.030355453491211, 5.817958831787109] |
5af3a171-8142-444e-918c-003a9567b5b9 | an-exploratory-study-on-temporally-evolving | null | null | https://aclanthology.org/2021.nlp4dh-1.21 | https://aclanthology.org/2021.nlp4dh-1.21.pdf | An Exploratory Study on Temporally Evolving Discussion around Covid-19 using Diachronic Word Embeddings | Covid 19 has seen the world go into a lock down and unconventional social situations throughout. During this time, the world saw a surge in information sharing around the pandemic and the topics shared in the time were diverse. People’s sentiments have changed during this period. Given the wide spread usage of Online S... | ['Arun Balaji Buduru', 'Ponnurangam Kumaraguru', 'Asanobu Kitamoto', 'Avinash Tulasi'] | null | null | null | null | nlp4dh-icon-2021-12 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-2.94016331e-01 2.45695025e-01 -2.09755555e-01 -2.35267013e-01
8.34515169e-02 -8.71336401e-01 1.05279732e+00 9.39942420e-01
-6.40956044e-01 6.02546155e-01 1.32200027e+00 -4.41115767e-01
-1.55215174e-01 -8.01764667e-01 -1.96693167e-02 -5.39426923e-01
-8.83827880e-02 2.65291065e-01 4.25494015e-02 -9.93568182... | [8.516326904296875, 9.802871704101562] |
f02a478e-0338-4bd7-b4b2-36de21ecff3c | weakly-supervised-named-entity-tagging-with | 2107.02282 | null | https://arxiv.org/abs/2107.02282v1 | https://arxiv.org/pdf/2107.02282v1.pdf | Weakly Supervised Named Entity Tagging with Learnable Logical Rules | We study the problem of building entity tagging systems by using a few rules as weak supervision. Previous methods mostly focus on disambiguation entity types based on contexts and expert-provided rules, while assuming entity spans are given. In this work, we propose a novel method TALLOR that bootstraps high-quality l... | ['Zhe Feng', 'Julian McAuley', 'Jingbo Shang', 'Haibo Ding', 'Jiacheng Li'] | 2021-07-05 | null | https://aclanthology.org/2021.acl-long.352 | https://aclanthology.org/2021.acl-long.352.pdf | acl-2021-5 | ['boundary-detection'] | ['computer-vision'] | [ 7.59082437e-02 7.08025396e-01 -4.93134826e-01 -5.64247072e-01
-6.63885176e-01 -8.82303536e-01 5.43410361e-01 4.41007227e-01
-5.22227705e-01 1.10027611e+00 -2.91110110e-02 -3.40030611e-01
-3.20956893e-02 -9.46280420e-01 -8.89486313e-01 -3.04912090e-01
-8.48139226e-02 9.85105872e-01 5.34575760e-01 -2.75245577... | [9.558032989501953, 8.843116760253906] |
d4c4e7f9-9fb7-48cb-bb1a-92aeae60851a | quantization-guided-jpeg-artifact-correction | 2004.09320 | null | https://arxiv.org/abs/2004.09320v2 | https://arxiv.org/pdf/2004.09320v2.pdf | Quantization Guided JPEG Artifact Correction | The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios. However, to achieve such high compression, information is lost. For aggressive quantization settings, this leads to a noticeable reduction in image quality. Artifact correction has b... | ['Ser-Nam Lim', 'Max Ehrlich', 'Abhinav Shrivastava', 'Larry Davis'] | 2020-04-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/570_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530290.pdf | eccv-2020-8 | ['jpeg-artifact-correction'] | ['computer-vision'] | [ 5.72870135e-01 -3.05792123e-01 -2.89196074e-01 -3.30078334e-01
-8.88913274e-01 -6.80735111e-02 3.58363718e-01 1.19638667e-01
-3.97171646e-01 2.82996356e-01 2.62162298e-01 -1.54786453e-01
2.39993334e-01 -7.48037636e-01 -8.22693586e-01 -4.99343544e-01
-9.74554494e-02 1.18299611e-01 3.32171440e-01 -9.34397727... | [11.386543273925781, -1.6547811031341553] |
46922323-69a4-4015-9aa4-9ea1b8e4a601 | towards-open-temporal-graph-neural-networks | 2303.15015 | null | https://arxiv.org/abs/2303.15015v2 | https://arxiv.org/pdf/2303.15015v2.pdf | Towards Open Temporal Graph Neural Networks | Graph neural networks (GNNs) for temporal graphs have recently attracted increasing attentions, where a common assumption is that the class set for nodes is closed. However, in real-world scenarios, it often faces the open set problem with the dynamically increased class set as the time passes by. This will bring two b... | ['Jun Zhou', 'Xiaolu Zhang', 'Changsheng Li', 'Kaituo Feng'] | 2023-03-27 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 2.44189397e-01 3.06551278e-01 -2.98543662e-01 5.12034446e-02
-1.24228569e-02 -8.23295474e-01 4.21084493e-01 2.84191310e-01
-1.48918748e-01 7.47179329e-01 -9.79947001e-02 -2.82998025e-01
-6.06875062e-01 -1.34751725e+00 -6.66550219e-01 -9.39019024e-01
-6.35841608e-01 6.44757032e-01 6.70423865e-01 -3.98241282... | [7.298735618591309, 6.080990791320801] |
35863e8e-c7a1-4991-81bf-eedb181e82c6 | a-health-telemonitoring-platform-based-on | 2207.13913 | null | https://arxiv.org/abs/2207.13913v2 | https://arxiv.org/pdf/2207.13913v2.pdf | A health telemonitoring platform based on data integration from different sources | The management of people with long-term or chronic illness is one of the biggest challenges for national health systems. In fact, these diseases are among the leading causes of hospitalization, especially for the elderly, and huge amount of resources required to monitor them leads to problems with sustainability of the... | ['Raimondo Schettini', 'Matteo Romanato', 'Paolo Napoletano', 'Gianluigi Ciocca'] | 2022-07-28 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-4.12536830e-01 -2.42613316e-01 -2.35183701e-01 -2.42347613e-01
-2.53899217e-01 -2.69242913e-01 -4.72281635e-01 6.15120351e-01
-3.06295216e-01 7.11992085e-01 1.49536848e-01 -6.03266299e-01
-1.80880815e-01 -8.01113367e-01 1.04235798e-01 -4.23515856e-01
-1.75519153e-01 4.54837650e-01 -2.04924587e-02 -2.16725186... | [13.733424186706543, 3.170393943786621] |
01992804-c6b3-4d65-9df7-59ef421df084 | inferring-attention-shift-ranks-of-objects | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Siris_Inferring_Attention_Shift_Ranks_of_Objects_for_Image_Saliency_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Siris_Inferring_Attention_Shift_Ranks_of_Objects_for_Image_Saliency_CVPR_2020_paper.pdf | Inferring Attention Shift Ranks of Objects for Image Saliency | Psychology studies and behavioural observation show that humans shift their attention from one location to another when viewing an image of a complex scene. This is due to the limited capacity of the human visual system in simultaneously processing multiple visual inputs. The sequential shifting of attention on objects... | [' Rynson W.H. Lau', ' Xianghua Xie', ' Gary K.L. Tam', ' Jianbo Jiao', 'Avishek Siris'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['saliency-ranking'] | ['computer-vision'] | [ 4.65109289e-01 -1.51807740e-01 -2.65314281e-01 -5.08723915e-01
-4.35334086e-01 -1.46768779e-01 4.23294872e-01 3.61903220e-01
-2.22322434e-01 1.95648000e-01 5.12790263e-01 1.15801811e-01
-1.11094065e-01 -4.72942710e-01 -7.90488362e-01 -3.08030576e-01
2.32042577e-02 1.71002090e-01 7.30709493e-01 -1.48460209... | [9.957599639892578, 0.3373660147190094] |
d73e85a8-7e67-4ee9-b7d1-1832290118f0 | learning-articulated-shape-with-keypoint | 2304.14396 | null | https://arxiv.org/abs/2304.14396v1 | https://arxiv.org/pdf/2304.14396v1.pdf | Learning Articulated Shape with Keypoint Pseudo-labels from Web Images | This paper shows that it is possible to learn models for monocular 3D reconstruction of articulated objects (e.g., horses, cows, sheep), using as few as 50-150 images labeled with 2D keypoints. Our proposed approach involves training category-specific keypoint estimators, generating 2D keypoint pseudo-labels on unlabel... | ['Dimitris Metaxas', 'Ligong Han', 'Georgios Pavlakos', 'Anastasis Stathopoulos'] | 2023-04-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Stathopoulos_Learning_Articulated_Shape_With_Keypoint_Pseudo-Labels_From_Web_Images_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Stathopoulos_Learning_Articulated_Shape_With_Keypoint_Pseudo-Labels_From_Web_Images_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [-1.86125994e-01 3.48101348e-01 -3.87703329e-01 -4.68041569e-01
-1.19588220e+00 -8.15512657e-01 5.52648008e-01 -2.34089822e-01
-3.14845383e-01 5.20452201e-01 1.83093362e-02 -8.60539228e-02
1.74450636e-01 -3.58041316e-01 -1.02176428e+00 -4.52522576e-01
2.46792706e-03 9.76848900e-01 3.30213398e-01 2.72974700... | [8.085453033447266, -2.960296392440796] |
761ed6c2-e289-45d8-b358-841cccc6a4bf | zero-shot-composed-image-retrieval-with | 2303.15247 | null | https://arxiv.org/abs/2303.15247v1 | https://arxiv.org/pdf/2303.15247v1.pdf | Zero-Shot Composed Image Retrieval with Textual Inversion | Composed Image Retrieval (CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption that describes the difference between the two images. The high effort and cost required for labeling datasets for CIR hamper the widespread usage of existing methods, as they rely on supe... | ['Alberto del Bimbo', 'Marco Bertini', 'Lorenzo Agnolucci', 'Alberto Baldrati'] | 2023-03-27 | null | null | null | null | ['zero-shot-composed-image-retrieval-zs-cir', 'composed-image-retrieval'] | ['computer-vision', 'computer-vision'] | [ 3.02055359e-01 -4.59811032e-01 -3.68666083e-01 -3.06523263e-01
-1.73106980e+00 -6.61728442e-01 8.34726155e-01 -2.46237978e-01
-3.70845228e-01 4.10023272e-01 9.13772881e-02 -9.16010737e-02
-4.28883955e-02 -3.47294688e-01 -7.41745114e-01 -6.63658559e-01
5.03090024e-01 4.98336434e-01 2.57264338e-02 -2.31096998... | [10.8616943359375, 1.1513326168060303] |
ff576dd6-9c77-470c-913f-4eaf60d675ce | neuroevolution-for-rts-micro | 1803.10288 | null | http://arxiv.org/abs/1803.10288v1 | http://arxiv.org/pdf/1803.10288v1.pdf | Neuroevolution for RTS Micro | This paper uses neuroevolution of augmenting topologies to evolve control
tactics for groups of units in real-time strategy games. In such games, players
build economies to generate armies composed of multiple types of units with
different attack and movement characteristics to combat each other. This paper
evolves neu... | ['Sushil J. Louis', 'Siming Liu', 'Aavaas Gajurel', 'Daniel J Mendez'] | 2018-03-27 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-6.09190837e-02 9.37678888e-02 4.94963139e-01 3.16028684e-01
3.32323194e-01 -8.65349889e-01 4.26920503e-01 -7.41311193e-01
-8.80439162e-01 9.36270297e-01 -4.68740761e-01 -2.64971912e-01
-2.61952907e-01 -1.14474499e+00 -3.00704390e-01 -7.23816693e-01
-4.96369630e-01 8.09886932e-01 3.45639199e-01 -1.58844316... | [3.573101043701172, 1.6348488330841064] |
de438c0c-7fdd-4fc7-86e8-a9fd1a854d0d | mahtm-a-multi-agent-framework-for | 2303.08447 | null | https://arxiv.org/abs/2303.08447v1 | https://arxiv.org/pdf/2303.08447v1.pdf | MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids | Integrating variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability. This paper proposes a multi-agent reinforcement learning framework for managing energy transactions in microgrids. Th... | ['Martin Takac', 'Yongli Zhu', 'Roberto Gutierrez', 'Nicolas Cuadrado'] | 2023-03-15 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-5.16234040e-01 -1.11944601e-01 -3.41084659e-01 2.58728832e-01
-3.73710752e-01 -6.64157450e-01 2.84434378e-01 2.66668290e-01
-6.52212277e-02 1.27655673e+00 -1.06877409e-01 -1.24321890e-03
-2.64298320e-01 -1.28588533e+00 -1.55445812e-02 -1.19664133e+00
1.63089223e-02 2.99335390e-01 -3.96612495e-01 -9.26052928... | [5.59123420715332, 2.589179039001465] |
8e7129dd-43d0-4c3f-b382-7fe705255fbc | visual-appearance-based-person-retrieval-in | 1910.14565 | null | https://arxiv.org/abs/1910.14565v1 | https://arxiv.org/pdf/1910.14565v1.pdf | Visual Appearance Based Person Retrieval in Unconstrained Environment Videos | Visual appearance-based person retrieval is a challenging problem in surveillance. It uses attributes like height, cloth color, cloth type and gender to describe a human. Such attributes are known as soft biometrics. This paper proposes person retrieval from surveillance video using height, torso cloth type, torso clot... | ['Mehul S. Raval', 'Shivansh Dave', 'Hiren Galiyawala'] | 2019-10-31 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [-2.65134007e-01 -2.83048034e-01 1.70802921e-01 -5.48964858e-01
-5.16448796e-01 -6.07190132e-01 5.21833003e-01 2.72974133e-01
-5.00557244e-01 5.82329631e-01 5.43201119e-02 4.21843886e-01
2.10322171e-01 -7.25589812e-01 -5.04806042e-01 -6.22304082e-01
-6.00128174e-02 5.44234753e-01 -2.40038875e-02 -2.10893750... | [14.578531265258789, 0.8842045664787292] |
ba3f7656-a413-44d0-8338-07c8bede20c3 | one-adapter-for-all-programming-languages | 2303.15822 | null | https://arxiv.org/abs/2303.15822v1 | https://arxiv.org/pdf/2303.15822v1.pdf | One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization | As pre-trained models automate many code intelligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study reported that multilingual fine-tuning benefits a range of tasks and models. However, we find that multilingual fine-tuning leads to performance d... | ['Xiangke Liao', 'Wei Dong', 'Shaoliang Peng', 'Wei Luo', 'Shanshan Li', 'Boxing Chen', 'Deze Wang'] | 2023-03-28 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-3.57541502e-01 -1.91240206e-01 -4.72958177e-01 -2.58376777e-01
-1.27531159e+00 -6.79015994e-01 3.60082269e-01 1.28939062e-01
-4.67343777e-01 3.80218923e-01 4.17254269e-01 -4.45075244e-01
1.50352940e-01 -2.09834963e-01 -9.83326554e-01 -1.37888983e-01
2.17295930e-01 3.91404837e-01 7.51896128e-02 -2.28364527... | [7.702566623687744, 7.906900405883789] |
9cc67a4c-d3a3-4c08-9683-c5c2cd34e9e8 | end-to-end-abstractive-summarization-for | 2004.02016 | null | https://arxiv.org/abs/2004.02016v4 | https://arxiv.org/pdf/2004.02016v4.pdf | A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining | With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties. Traditional methods of summarizing meetings depend on complex multi-step pipelines that make joint optimization intractable. Meanwhile, there are a handful of deep neural models for te... | ['Michael Zeng', 'Chenguang Zhu', 'Xuedong Huang', 'Ruochen Xu'] | 2020-04-04 | null | https://aclanthology.org/2020.findings-emnlp.19 | https://aclanthology.org/2020.findings-emnlp.19.pdf | findings-of-the-association-for-computational | ['meeting-summarization'] | ['natural-language-processing'] | [ 5.25038466e-02 2.08782494e-01 -5.17766438e-02 -6.54638469e-01
-1.13437772e+00 -5.00771284e-01 6.12618029e-01 1.70205057e-01
-2.03786999e-01 8.84705901e-01 1.07394075e+00 2.02446710e-02
3.12648296e-01 -2.92451739e-01 -2.49772489e-01 -2.42030263e-01
2.88517118e-01 4.51652408e-01 -2.42733106e-01 -4.08818156... | [12.598843574523926, 9.304451942443848] |
b1527fbc-25b7-43fe-ad17-d0bfab3f9526 | on-implicit-filter-level-sparsity-in | 1811.12495 | null | http://arxiv.org/abs/1811.12495v2 | http://arxiv.org/pdf/1811.12495v2.pdf | On Implicit Filter Level Sparsity in Convolutional Neural Networks | We investigate filter level sparsity that emerges in convolutional neural
networks (CNNs) which employ Batch Normalization and ReLU activation, and are
trained with adaptive gradient descent techniques and L2 regularization or
weight decay. We conduct an extensive experimental study casting our initial
findings into hy... | ['Christian Theobalt', 'Dushyant Mehta', 'Kwang In Kim'] | 2018-11-29 | on-implicit-filter-level-sparsity-in-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Mehta_On_Implicit_Filter_Level_Sparsity_in_Convolutional_Neural_Networks_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Mehta_On_Implicit_Filter_Level_Sparsity_in_Convolutional_Neural_Networks_CVPR_2019_paper.pdf | cvpr-2019-6 | ['l2-regularization'] | ['methodology'] | [ 3.18677276e-01 1.39970139e-01 -1.90233111e-01 -4.64362711e-01
1.09257422e-01 -4.70602423e-01 4.23993081e-01 -7.98839778e-02
-7.63702989e-01 4.59542423e-01 1.91230193e-01 -6.50269866e-01
-1.87462986e-01 -5.71799934e-01 -7.01650083e-01 -6.12271070e-01
-2.93792933e-01 -6.22267723e-01 1.56465903e-01 -1.51002511... | [8.591538429260254, 3.2407240867614746] |
645ad8ae-2038-4fcc-9bfa-9d73a316d3f2 | graph-based-selective-outlier-ensembles | 1804.06378 | null | http://arxiv.org/abs/1804.06378v1 | http://arxiv.org/pdf/1804.06378v1.pdf | Graph-based Selective Outlier Ensembles | An ensemble technique is characterized by the mechanism that generates the
components and by the mechanism that combines them. A common way to achieve the
consensus is to enable each component to equally participate in the aggregation
process. A problem with this approach is that poor components are likely to
negativel... | ['Giovanni Stilo', 'Carlotta Domeniconi', 'Hamed Sarvari'] | 2018-04-17 | null | null | null | null | ['outlier-ensembles'] | ['methodology'] | [ 1.28250882e-01 -4.95360456e-02 1.55744284e-01 -2.40668714e-01
-4.71547186e-01 -4.50066626e-01 5.38449645e-01 7.73988664e-01
-6.81794435e-02 7.32350230e-01 7.41895437e-02 1.16300620e-01
-6.01930678e-01 -1.20150769e+00 -3.55865300e-01 -9.27681565e-01
-2.58877933e-01 6.34009182e-01 3.10848743e-01 -2.86728621... | [7.669680118560791, 4.563941955566406] |
f69d11c3-0b25-4767-99f7-cacd189459ae | on-credit-assignment-in-hierarchical | 2203.03292 | null | https://arxiv.org/abs/2203.03292v1 | https://arxiv.org/pdf/2203.03292v1.pdf | On Credit Assignment in Hierarchical Reinforcement Learning | Hierarchical Reinforcement Learning (HRL) has held longstanding promise to advance reinforcement learning. Yet, it has remained a considerable challenge to develop practical algorithms that exhibit some of these promises. To improve our fundamental understanding of HRL, we investigate hierarchical credit assignment fro... | ['Aske Plaat', 'Thomas M. Moerland', 'Joery A. de Vries'] | 2022-03-07 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.41451878e-02 1.03185773e-01 -3.84897470e-01 2.50043739e-02
-6.59729004e-01 -7.91704834e-01 4.20801431e-01 2.97156692e-01
-7.28802264e-01 1.01782084e+00 -6.62249178e-02 -5.85434616e-01
-3.60810608e-01 -8.84411693e-01 -7.66388237e-01 -7.89131284e-01
-4.80342984e-01 3.85902196e-01 3.24182361e-01 -6.29169583... | [4.012010097503662, 1.711187720298767] |
f6aff2d5-5952-4595-b9cc-fcbe48270aa0 | attention2angiogan-synthesizing-fluorescein | 2007.09191 | null | https://arxiv.org/abs/2007.09191v1 | https://arxiv.org/pdf/2007.09191v1.pdf | Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images using Generative Adversarial Networks | Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters. FA also requires fluorescein dye that is injected intravenously, which might cause adverse effects ranging from nausea, vomiting to even fatal anaphylaxis. Currently, no ot... | ['Sharif Amit Kamran', 'Stewart Lee Zuckerbrod', 'Khondker Fariha Hossain', 'Alireza Tavakkoli'] | 2020-07-17 | null | null | null | null | ['fundus-to-angiography-generation'] | ['computer-vision'] | [ 1.63583577e-01 3.17068577e-01 2.75112420e-01 -1.40283167e-01
-7.57307947e-01 -8.56830537e-01 2.17460185e-01 -5.88521063e-01
-2.08122090e-01 1.07165813e+00 1.42268658e-01 -5.50825536e-01
4.32917207e-01 -7.10848927e-01 -6.66422009e-01 -7.08950222e-01
5.14730752e-01 -2.09011942e-01 5.39547242e-02 1.89703956... | [15.595406532287598, -3.772972583770752] |
5aa6675c-892c-4437-8071-70d6c69d193d | machine-translation-with-cross-lingual-word | 1912.10167 | null | https://arxiv.org/abs/1912.10167v2 | https://arxiv.org/pdf/1912.10167v2.pdf | Machine Translation with Cross-lingual Word Embeddings | Learning word embeddings using distributional information is a task that has been studied by many researchers, and a lot of studies are reported in the literature. On the contrary, less studies were done for the case of multiple languages. The idea is to focus on a single representation for a pair of languages such tha... | ['Evan Kaplan', 'Marco Berlot'] | 2019-12-10 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [-1.53401494e-01 -1.87703878e-01 -3.92003059e-01 -4.29580539e-01
-3.76094244e-02 -3.15111339e-01 7.95447171e-01 8.56054008e-01
-7.82038510e-01 5.16533077e-01 3.56966704e-01 -2.62364950e-02
1.47612885e-01 -8.93606961e-01 -2.01780066e-01 -7.05230713e-01
2.55048513e-01 2.27743387e-01 3.62392157e-01 -3.86041731... | [10.485784530639648, 8.7276029586792] |
6d78c389-ce4b-44fb-a9f7-9de3345fbe17 | the-university-of-arizona-at-semeval-2021 | null | null | https://aclanthology.org/2021.semeval-1.56 | https://aclanthology.org/2021.semeval-1.56.pdf | The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation | This paper describes our systems for negation detection and time expression recognition in SemEval 2021 Task 10, Source-Free Domain Adaptation for Semantic Processing. We show that self-training, active learning and data augmentation techniques can improve the generalization ability of the model on the unlabeled target... | ['Steven Bethard', 'Yiyun Zhao', 'Xin Su'] | 2021-08-01 | null | null | null | semeval-2021 | ['source-free-domain-adaptation', 'negation-detection'] | ['computer-vision', 'natural-language-processing'] | [ 2.67260939e-01 4.15640354e-01 -6.78180575e-01 -1.20627391e+00
-7.64830410e-01 -7.40499735e-01 4.14915830e-01 2.79486209e-01
-9.87656474e-01 9.26230669e-01 -5.66557609e-03 -2.42926225e-01
8.48831385e-02 -3.54598641e-01 -7.03260228e-02 -2.63461292e-01
-3.75912994e-01 5.80777824e-01 1.61750555e-01 -5.17573416... | [10.530580520629883, 8.029118537902832] |
102b902b-4630-4821-9ff5-a662c6f46873 | hotnas-hierarchical-optimal-transport-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_HOTNAS_Hierarchical_Optimal_Transport_for_Neural_Architecture_Search_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_HOTNAS_Hierarchical_Optimal_Transport_for_Neural_Architecture_Search_CVPR_2023_paper.pdf | HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search | Instead of searching the entire network directly, current NAS approaches increasingly search for multiple relatively small cells to reduce search costs. A major challenge is to jointly measure the similarity of cell micro-architectures and the difference in macro-architectures between different cell-based networks.... | ['Hongteng Xu', 'Yong liu', 'Jiechao Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['architecture-search', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-4.21992421e-01 -4.96645331e-01 -3.89167368e-01 1.52017117e-01
-4.26115304e-01 -6.12124860e-01 2.62702763e-01 2.63420343e-01
-1.09447822e-01 7.14009404e-01 -2.14077383e-02 -6.34253547e-02
-8.22728157e-01 -8.70221674e-01 -2.05034181e-01 -9.69617665e-01
-2.62149155e-01 7.15636909e-01 6.12507820e-01 -4.37578969... | [7.194499969482422, 5.992859363555908] |
b8a6116a-f8da-4831-b16a-5e51cd4bf576 | ontology-based-information-integration-a | 1909.13762 | null | https://arxiv.org/abs/1909.13762v1 | https://arxiv.org/pdf/1909.13762v1.pdf | Ontology Based Information Integration: A Survey | An ontology makes a special vocabulary which describes the domain of interest and the meaning of the term on that vocabulary. Based on the precision of the specification, the concept of the ontology contains several data and conceptual models. The notion of ontology has emerged into wide ranges of applications includin... | ['Farajollah Tahernezhad-Javazm', 'Maliheh Heydarpour Shahrezaei', 'Maryam Alizadeh'] | 2019-09-26 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [-3.58701013e-02 6.29108548e-02 -3.81070614e-01 -4.14500177e-01
8.46507400e-02 -6.29747808e-01 9.56018031e-01 6.95338488e-01
-3.18630189e-01 5.03061891e-01 1.65773138e-01 -2.24833302e-02
-9.72661912e-01 -1.28758717e+00 9.39437151e-02 -2.52742380e-01
1.12058528e-01 7.35079169e-01 6.52877867e-01 -7.58186460... | [9.177143096923828, 7.958730697631836] |
24a13d82-1a5a-44f5-a91d-60d5eab8290d | self-supervised-learning-of-face | 1903.01000 | null | http://arxiv.org/abs/1903.01000v1 | http://arxiv.org/pdf/1903.01000v1.pdf | Self-Supervised Learning of Face Representations for Video Face Clustering | Analyzing the story behind TV series and movies often requires understanding
who the characters are and what they are doing. With improving deep face
models, this may seem like a solved problem. However, as face detectors get
better, clustering/identification needs to be revisited to address increasing
diversity in fac... | ['Rainer Stiefelhagen', 'Vivek Sharma', 'M. Saquib Sarfraz', 'Makarand Tapaswi'] | 2019-03-03 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-1.61508337e-01 -4.32782769e-01 -8.48085657e-02 -6.00736916e-01
-4.52886701e-01 -6.17659032e-01 6.70451760e-01 -3.73080641e-01
-1.67812213e-01 2.10304424e-01 2.13311985e-02 4.32277352e-01
-1.06394731e-01 -2.78966993e-01 -6.60289347e-01 -8.73746932e-01
-4.24839258e-02 7.69753158e-01 2.60929409e-02 -1.10951355... | [13.495491981506348, 1.0624017715454102] |
a5e134e3-4884-486a-9b28-4b5d00e7ecff | learning-the-unlearnable-adversarial | 2303.15127 | null | https://arxiv.org/abs/2303.15127v1 | https://arxiv.org/pdf/2303.15127v1.pdf | Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks | Unlearnable example attacks are data poisoning techniques that can be used to safeguard public data against unauthorized use for training deep learning models. These methods add stealthy perturbations to the original image, thereby making it difficult for deep learning models to learn from these training data effective... | ['Cheng-Zhong Xu', 'Kejiang Ye', 'Juanjuan Zhao', 'Xitong Gao', 'Tianrui Qin'] | 2023-03-27 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 2.89814863e-02 1.01116158e-01 -1.33351803e-01 3.11684906e-02
-8.55691254e-01 -1.29691756e+00 5.87720513e-01 1.13810316e-01
-7.76252747e-01 8.31868112e-01 -2.04433240e-02 -7.22857058e-01
2.16541678e-01 -9.75789249e-01 -1.16050565e+00 -8.28709722e-01
-9.90243256e-02 5.58794737e-02 -4.88774665e-02 -3.71755421... | [5.716926097869873, 7.777214050292969] |
f6611886-13a6-471e-a81f-285d977d29a5 | croano-a-crowd-annotation-platform-for | null | null | https://aclanthology.org/2021.emnlp-demo.32 | https://aclanthology.org/2021.emnlp-demo.32.pdf | CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset | In this paper, we introduce CroAno, a web-based crowd annotation platform for the Chinese named entity recognition (NER). Besides some basic features for crowd annotation like fast tagging and data management, CroAno provides a systematic solution for improving label consistency of Chinese NER dataset. 1) Disagreement ... | ['Yafei Shi', 'Shengping Liu', 'Jun Zhao', 'Kang Liu', 'Jing Wan', 'Yubo Chen', 'Zhen Gan', 'Zhucong Li', 'Baoli Zhang'] | null | null | null | null | emnlp-acl-2021-11 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-5.74996352e-01 3.60492207e-02 1.69251904e-01 -4.23474848e-01
-8.86950850e-01 -8.19634497e-01 1.42207086e-01 4.55343395e-01
-7.45447040e-01 8.18873227e-01 1.57317594e-01 -1.19045310e-01
3.70466173e-01 -6.75652623e-01 -2.72856712e-01 -3.51452351e-01
4.12159950e-01 4.49368179e-01 5.37247062e-01 -7.15225637... | [9.604256629943848, 9.4447021484375] |
334e610c-c888-40fa-b7f4-4cabbb122f08 | combining-label-propagation-and-simple-models-1 | 2010.13993 | null | https://arxiv.org/abs/2010.13993v2 | https://arxiv.org/pdf/2010.13993v2.pdf | Combining Label Propagation and Simple Models Out-performs Graph Neural Networks | Graph Neural Networks (GNNs) are the predominant technique for learning over graphs. However, there is relatively little understanding of why GNNs are successful in practice and whether they are necessary for good performance. Here, we show that for many standard transductive node classification benchmarks, we can exce... | ['Austin R. Benson', 'Ser-Nam Lim', 'Abhay Singh', 'Horace He', 'Qian Huang'] | 2020-10-27 | combining-label-propagation-and-simple-models | https://openreview.net/forum?id=8E1-f3VhX1o | https://openreview.net/pdf?id=8E1-f3VhX1o | iclr-2021-1 | ['node-classification-on-non-homophilic'] | ['graphs'] | [ 2.46255621e-01 4.13322598e-01 -4.73936856e-01 -5.68233609e-01
-5.94294429e-01 -6.03669345e-01 5.65164566e-01 5.48698962e-01
-2.97167003e-01 7.71672308e-01 -9.33016539e-02 -8.32957864e-01
-1.94459215e-01 -1.00043213e+00 -9.41422641e-01 -5.56364298e-01
-3.51225525e-01 6.46935165e-01 5.37836611e-01 -2.87365377... | [6.999476432800293, 6.1305975914001465] |
18941652-a192-446b-b919-2edb1ccd7cb4 | spectnet-end-to-end-audio-signal | 2211.09352 | null | https://arxiv.org/abs/2211.09352v1 | https://arxiv.org/pdf/2211.09352v1.pdf | SpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms | Pattern recognition from audio signals is an active research topic encompassing audio tagging, acoustic scene classification, music classification, and other areas. Spectrogram and mel-frequency cepstral coefficients (MFCC) are among the most commonly used features for audio signal analysis and classification. Recently... | ['Taufiq Hasan', 'Md. Istiaq Ansari'] | 2022-11-17 | null | null | null | null | ['audio-tagging', 'sound-classification', 'scene-classification', 'music-classification'] | ['audio', 'audio', 'computer-vision', 'music'] | [ 2.09597543e-01 -4.83242005e-01 3.11695367e-01 -2.92196900e-01
-6.90700114e-01 -3.44209373e-01 -5.36271222e-02 3.36026460e-01
-5.41863382e-01 2.74928361e-01 6.69107400e-03 -6.23955913e-02
4.26875129e-02 -4.35467690e-01 -4.00551319e-01 -6.14356756e-01
-3.21090251e-01 -3.36422354e-01 2.45921850e-01 1.11916460... | [15.222073554992676, 5.2752604484558105] |
42d44a6f-021d-4a96-88e4-526f2cd8e787 | sequential-estimation-of-nonparametric | 2012.06287 | null | https://arxiv.org/abs/2012.06287v2 | https://arxiv.org/pdf/2012.06287v2.pdf | Sequential estimation of Spearman rank correlation using Hermite series estimators | In this article we describe a new Hermite series based sequential estimator for the Spearman rank correlation coefficient and provide algorithms applicable in both the stationary and non-stationary settings. To treat the non-stationary setting, we introduce a novel, exponentially weighted estimator for the Spearman ran... | ['Melvin Varughese', 'Michael Stephanou'] | 2020-12-11 | null | null | null | null | ['sequential-correlation-estimation', 'data-summarization'] | ['miscellaneous', 'miscellaneous'] | [-1.95844650e-01 -7.22153962e-01 1.74782231e-01 -2.03774273e-01
-9.42789972e-01 -5.69930553e-01 3.80656958e-01 1.80954993e-01
-3.32771689e-01 8.91512632e-01 -3.71290118e-01 -1.88697666e-01
-7.13741899e-01 -5.59319496e-01 -9.50795114e-02 -9.60591853e-01
-1.07629585e+00 4.45455760e-01 5.76111555e-01 -6.79484606... | [7.1565985679626465, 3.9749016761779785] |
7f5c91e2-571a-48f8-a6f2-9127980a27dd | weakly-supervised-segmentation-with-multi | 2007.01152 | null | https://arxiv.org/abs/2007.01152v3 | https://arxiv.org/pdf/2007.01152v3.pdf | Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates | Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge. Weakly-supervised learning can train models by relying on weaker forms of annotation, such as scribbles. Here, we learn to segment using s... | ['Gabriele Valvano', 'Sotirios A. Tsaftaris', 'Andrea Leo'] | 2020-07-02 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 5.03706157e-01 5.81321597e-01 1.29777208e-01 -3.58507901e-01
-1.32028282e+00 -8.88662398e-01 1.78270683e-01 4.88127805e-02
-5.67164719e-01 6.90406978e-01 -5.27909398e-03 -2.52846152e-01
5.26607990e-01 -5.10713935e-01 -1.13283515e+00 -8.23312044e-01
3.10591072e-01 6.10962808e-01 5.32046258e-01 -1.59744933... | [14.491504669189453, -2.0460047721862793] |
874d78fa-3c2c-4f8f-a49d-2808e65188d9 | clustering-aware-negative-sampling-for | 2305.09892 | null | https://arxiv.org/abs/2305.09892v1 | https://arxiv.org/pdf/2305.09892v1.pdf | Clustering-Aware Negative Sampling for Unsupervised Sentence Representation | Contrastive learning has been widely studied in sentence representation learning. However, earlier works mainly focus on the construction of positive examples, while in-batch samples are often simply treated as negative examples. This approach overlooks the importance of selecting appropriate negative examples, potenti... | ['Rui Wang', 'Xiaojun Quan', 'Tao Yang', 'Fanqi Wan', 'Jinghao Deng'] | 2023-05-17 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 6.35225534e-01 6.69540092e-02 -3.83970916e-01 -6.75231040e-01
-1.16663814e+00 -5.69463849e-01 7.63852119e-01 4.29436356e-01
-6.65387154e-01 6.47253931e-01 3.29053372e-01 -3.13900083e-01
1.03425831e-01 -7.24263906e-01 -4.39664781e-01 -6.10229731e-01
2.51119494e-01 4.52909589e-01 3.91848497e-02 -2.83899635... | [10.939155578613281, 8.613167762756348] |
a347ad3c-cc5c-4baa-9d97-a6120131fea6 | 3d-to-2d-distillation-for-indoor-scene | 2104.02243 | null | https://arxiv.org/abs/2104.02243v2 | https://arxiv.org/pdf/2104.02243v2.pdf | 3D-to-2D Distillation for Indoor Scene Parsing | Indoor scene semantic parsing from RGB images is very challenging due to occlusions, object distortion, and viewpoint variations. Going beyond prior works that leverage geometry information, typically paired depth maps, we present a new approach, a 3D-to-2D distillation framework, that enables us to leverage 3D feature... | ['Chi-Wing Fu', 'Xiaojuan Qi', 'Zhengzhe Liu'] | 2021-04-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_3D-to-2D_Distillation_for_Indoor_Scene_Parsing_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_3D-to-2D_Distillation_for_Indoor_Scene_Parsing_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-parsing'] | ['computer-vision'] | [ 1.21987581e-01 3.73716623e-01 1.38851598e-01 -8.44170272e-01
-5.22087991e-01 -6.69221461e-01 3.66596401e-01 -4.99899209e-01
-4.45308715e-01 4.19211447e-01 1.59249723e-01 -4.20149744e-01
2.58118331e-01 -1.14559007e+00 -1.14107704e+00 -5.41089833e-01
1.83489412e-01 6.98793307e-02 3.62156898e-01 -3.11809659... | [8.379837036132812, -2.9104576110839844] |
3c13b429-fe0d-4648-a582-4ce2f20b3f8b | evaluation-and-comparison-of-deep-learning | 2112.10390 | null | https://arxiv.org/abs/2112.10390v1 | https://arxiv.org/pdf/2112.10390v1.pdf | Evaluation and Comparison of Deep Learning Methods for Pavement Crack Identification with Visual Images | Compared with contact detection techniques, pavement crack identification with visual images via deep learning algorithms has the advantages of not being limited by the material of object to be detected, fast speed and low cost. The fundamental frameworks and typical model architectures of transfer learning (TL), encod... | ['Kai-Liang Lu'] | 2021-12-20 | null | null | null | null | ['contact-detection'] | ['robots'] | [ 1.75335124e-01 9.36294049e-02 5.15865069e-03 1.42587572e-02
-9.13905799e-01 -1.19532801e-01 5.06222481e-04 -3.10116798e-01
-1.73423752e-01 6.00069344e-01 -4.25566018e-01 -2.27513120e-01
4.27862972e-01 -1.38543522e+00 -6.67388797e-01 -9.93753970e-01
2.73726702e-01 1.93542868e-01 6.28988802e-01 -1.19278960... | [7.506360054016113, 1.5549601316452026] |
60155ccb-71f4-458e-afef-48bf920d7a1c | derivation-of-information-theoretically | 2007.14042 | null | https://arxiv.org/abs/2007.14042v1 | https://arxiv.org/pdf/2007.14042v1.pdf | Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning | We consider the theoretical problem of designing an optimal adversarial attack on a decision system that maximally degrades the achievable performance of the system as measured by the mutual information between the degraded signal and the label of interest. This problem is motivated by the existence of adversarial exam... | ['Raghu Mudumbai', 'Jirong Yi', 'Weiyu Xu'] | 2020-07-28 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 7.21836984e-01 6.54397428e-01 3.18560869e-01 -2.37737447e-01
-1.03080285e+00 -1.03514695e+00 4.17446941e-01 -5.38985729e-02
-2.20664546e-01 5.81840634e-01 1.85399633e-02 -3.81559014e-01
-2.87850112e-01 -6.91518605e-01 -9.49576557e-01 -1.13331234e+00
-3.33914399e-01 -1.05802551e-01 -2.29691461e-01 -1.28681287... | [5.6996588706970215, 7.766674041748047] |
d9c270ea-e8c2-4cda-9cd2-9e41a9838031 | quantized-radio-map-estimation-using-tensor | 2303.01770 | null | https://arxiv.org/abs/2303.01770v1 | https://arxiv.org/pdf/2303.01770v1.pdf | Quantized Radio Map Estimation Using Tensor and Deep Generative Models | Spectrum cartography (SC), also known as radio map estimation (RME), aims at crafting multi-domain (e.g., frequency and space) radio power propagation maps from limited sensor measurements. While early methods often lacked theoretical support, recent works have demonstrated that radio maps can be provably recovered usi... | ['Xiao Fu', 'Sagar Shrestha', 'Subash Timilsina'] | 2023-03-03 | null | null | null | null | ['spectrum-cartography'] | ['computer-vision'] | [ 3.99213821e-01 9.51306149e-02 -2.41617918e-01 -9.99422371e-02
-8.86064410e-01 -5.03183544e-01 5.68187475e-01 -3.11731458e-01
4.63152342e-02 9.19391811e-01 3.11247349e-01 -3.92701149e-01
-6.33068204e-01 -9.61461306e-01 -8.25734496e-01 -9.11774635e-01
-4.31087703e-01 1.53542027e-01 -1.59528121e-01 -2.11997241... | [6.39297342300415, 1.2513070106506348] |
ef5484bf-b2b9-4a16-a386-a07be08545ac | rainbow-keywords-efficient-incremental | 2203.16361 | null | https://arxiv.org/abs/2203.16361v2 | https://arxiv.org/pdf/2203.16361v2.pdf | Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting | Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. This problem will be more challenging if KWS models are further required for edge devices due to their limited memory. To alleviate such an issue, we propose a novel diversity-aware incremental learning method na... | ['Eng Siong Chng', 'Nana Hou', 'Yang Xiao'] | 2022-03-30 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 8.85887742e-02 -4.88839000e-02 -4.60125595e-01 -2.99158216e-01
-1.01916993e+00 -3.14635932e-01 3.85207027e-01 -7.19958395e-02
-4.90258425e-01 9.53026295e-01 2.19754055e-01 -4.55636412e-01
-1.59640610e-01 -3.42173427e-01 -8.45611513e-01 -5.75085938e-01
9.53053683e-02 2.15619266e-01 4.11944419e-01 6.61595836... | [9.941373825073242, 3.6127612590789795] |
09f2d25c-0db1-417d-89f7-cdb03e175a85 | noun-paraphrasing-based-on-a-variety-of | null | null | https://aclanthology.org/Y14-1073 | https://aclanthology.org/Y14-1073.pdf | Noun Paraphrasing Based on a Variety of Contexts | null | ['Tomoyuki Kajiwara', 'Kazuhide Yamamoto'] | 2014-12-01 | noun-paraphrasing-based-on-a-variety-of-1 | https://aclanthology.org/Y14-1073 | https://aclanthology.org/Y14-1073.pdf | paclic-2014-12 | ['lexical-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.387618064880371, 3.639005184173584] |
6f51d1a6-8fd3-4cd4-9d3b-156dc7dc194c | compressing-neural-network-by-tensor-network | 2305.06058 | null | https://arxiv.org/abs/2305.06058v1 | https://arxiv.org/pdf/2305.06058v1.pdf | Compressing neural network by tensor network with exponentially fewer variational parameters | Neural network (NN) designed for challenging machine learning tasks is in general a highly nonlinear mapping that contains massive variational parameters. High complexity of NN, if unbounded or unconstrained, might unpredictably cause severe issues including over-fitting, loss of generalization power, and unbearable co... | ['Shi-Ju Ran', 'Ke Li', 'Peng-Fei Zhou', 'Yong Qing'] | 2023-05-10 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.49753362e-01 4.43451367e-02 9.67874303e-02 -5.30525267e-01
-4.98618692e-01 -3.33844066e-01 -7.39778653e-02 -4.51294154e-01
-8.02080750e-01 7.02262044e-01 -1.82544142e-01 -4.23265368e-01
-2.95058370e-01 -6.28524005e-01 -9.94484603e-01 -8.92709494e-01
-5.19142151e-01 3.52684706e-01 -3.15534249e-02 -1.72880307... | [8.525885581970215, 3.090400457382202] |
939c786a-6306-49c3-8012-8e67c3506aa8 | controlling-large-language-models-to-generate | 2302.05319 | null | https://arxiv.org/abs/2302.05319v2 | https://arxiv.org/pdf/2302.05319v2.pdf | Large Language Models for Code: Security Hardening and Adversarial Testing | Large language models (LMs) are increasingly pretrained on massive codebases and used to generate code. However, LMs lack awareness of security and are found to frequently produce unsafe code. This work studies the security of LMs along two important axes: (i) security hardening, which aims to enhance LMs' reliability ... | ['Martin Vechev', 'Jingxuan He'] | 2023-02-10 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 2.88576454e-01 7.16113895e-02 -3.32786798e-01 -3.07217538e-02
-1.32511353e+00 -1.09109008e+00 3.47264916e-01 2.36021370e-01
-1.11335970e-01 4.74824637e-01 -1.60272177e-02 -1.01348913e+00
3.14912438e-01 -1.01618683e+00 -1.08467972e+00 -2.82188803e-01
-1.78970084e-01 -2.07344934e-01 1.38957709e-01 -4.97452587... | [7.047336101531982, 7.8436055183410645] |
4b22b193-f262-40d0-91f9-63fc339b58ee | neighborhood-mixture-model-for-knowledge-base | 1606.06461 | null | http://arxiv.org/abs/1606.06461v3 | http://arxiv.org/pdf/1606.06461v3.pdf | Neighborhood Mixture Model for Knowledge Base Completion | Knowledge bases are useful resources for many natural language processing
tasks, however, they are far from complete. In this paper, we define a novel
entity representation as a mixture of its neighborhood in the knowledge base
and apply this technique on TransE-a well-known embedding model for knowledge
base completio... | ['Mark Johnson', 'Lizhen Qu', 'Kairit Sirts', 'Dat Quoc Nguyen'] | 2016-06-21 | neighborhood-mixture-model-for-knowledge-base-1 | https://aclanthology.org/K16-1005 | https://aclanthology.org/K16-1005.pdf | conll-2016-8 | ['triple-classification'] | ['graphs'] | [-4.70123082e-01 1.59266785e-01 -7.56391644e-01 -9.37554613e-02
-2.67338306e-01 -3.08708400e-01 6.48561478e-01 5.26636064e-01
-6.53199196e-01 9.70210612e-01 6.46747530e-01 -2.36965373e-01
-4.43998963e-01 -1.09623981e+00 -6.05616450e-01 -2.12488547e-01
-1.36900872e-01 6.51488423e-01 3.51588219e-01 -5.66585124... | [8.860496520996094, 7.987196922302246] |
a4887d95-0479-430e-a6af-54fa466eb02c | 3d-photogrammetry-point-cloud-segmentation | null | null | https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CP.1943-5487.0000929 | https://www.researchgate.net/profile/Meida-Chen/publication/344177868_3D_Photogrammetry_Point_Cloud_Segmentation_Using_a_Model_Ensembling_Framework/links/5f592c17a6fdcc116404704d/3D-Photogrammetry-Point-Cloud-Segmentation-Using-a-Model-Ensembling-Framework.pdf | 3D photogrammetry point cloud segmentation using a model ensembling framework | The US Army is paying increased attention to the development of rapid three-dimensional (3D) reconstruction using photogrammetry and unmanned aerial vehicle (UAV) technologies for creating virtual environments and simulations in areas of interest. The ability of the intelligence community, mission commanders, and front... | ['Lucio Soibelman', 'Ryan McAlinden', 'Pratusha Bhuvana Prasad', 'Kyle McCullough', 'Andrew Feng', 'Meida Chen'] | 2020-11-01 | null | null | null | journal-of-computing-in-civil-engineering | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.75441042e-01 -3.19571495e-01 4.01841849e-01 -1.82097122e-01
-3.54836136e-01 -7.86687136e-01 4.74867851e-01 4.02614295e-01
-3.33778203e-01 5.04749954e-01 -3.62971991e-01 -7.64873445e-01
-4.73359764e-01 -1.16621637e+00 -2.37847731e-01 -1.86919838e-01
-4.47989196e-01 1.08892941e+00 2.35659219e-02 -9.01592076... | [8.37212085723877, -2.590467929840088] |
8ee070ee-2b82-4a03-ad78-70ba0522c973 | hrda-context-aware-high-resolution-domain | 2204.13132 | null | https://arxiv.org/abs/2204.13132v2 | https://arxiv.org/pdf/2204.13132v2.pdf | HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation | Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source domain (e.g. synthetic data) to the target domain (e.g. real-world data) without requiring further annotations on the target domain. This work focuses on UDA for semantic segmentation as real-world pixel-wise annotations are particularly e... | ['Luc van Gool', 'Dengxin Dai', 'Lukas Hoyer'] | 2022-04-27 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.44257265e-01 8.61912034e-03 1.31831691e-01 -3.46249878e-01
-1.20494258e+00 -6.89197540e-01 4.41522866e-01 -3.04735489e-02
-3.75633895e-01 5.97854018e-01 -4.13842648e-02 -8.34141448e-02
4.45847332e-01 -1.20763040e+00 -9.99527991e-01 -6.32061720e-01
4.12825644e-01 7.02236593e-01 6.15177333e-01 -2.31614575... | [9.71345043182373, 1.2907793521881104] |
fa7d2493-766e-454f-9467-670c4cdb24df | overview-of-abusive-and-threatening-language | 2207.06710 | null | https://arxiv.org/abs/2207.06710v1 | https://arxiv.org/pdf/2207.06710v1.pdf | Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021 | With the growth of social media platform influence, the effect of their misuse becomes more and more impactful. The importance of automatic detection of threatening and abusive language can not be overestimated. However, most of the existing studies and state-of-the-art methods focus on English as the target language, ... | ['Alexander Gelbukh', 'Oxana Vitman', 'Hamza Imam Amjad', 'Sabur Butta', 'Andrey Labunets', 'Grigori Sidorov', 'Alisa Zhila', 'Maaz Amjad'] | 2022-07-14 | null | null | null | null | ['abusive-language'] | ['natural-language-processing'] | [-4.12143350e-01 -2.42854118e-01 -7.95930773e-02 -1.54882789e-01
-8.79712760e-01 -7.01582909e-01 8.01361859e-01 3.61955792e-01
-8.70528102e-01 1.00395477e+00 5.05303741e-02 -5.84252000e-01
5.90082943e-01 -6.21682465e-01 -1.27684444e-01 -5.59768438e-01
-1.55770462e-02 3.17816883e-01 4.48129065e-02 -6.46603286... | [8.860923767089844, 10.59447956085205] |
4695e606-5f84-4cda-9839-3913b474c70b | introducing-representations-of-facial-affect | 2008.13369 | null | https://arxiv.org/abs/2008.13369v1 | https://arxiv.org/pdf/2008.13369v1.pdf | Introducing Representations of Facial Affect in Automated Multimodal Deception Detection | Automated deception detection systems can enhance health, justice, and security in society by helping humans detect deceivers in high-stakes situations across medical and legal domains, among others. This paper presents a novel analysis of the discriminative power of dimensional representations of facial affect for aut... | ['Maja J. Matarić', 'Leena Mathur'] | 2020-08-31 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [ 1.41370252e-01 1.06614962e-01 -8.25895667e-02 -8.80287111e-01
-8.18968654e-01 -7.19023943e-01 5.42851508e-01 7.63914511e-02
-4.90285575e-01 4.10484523e-01 4.28027898e-01 -1.26031220e-01
1.71959534e-01 -7.05807284e-02 -1.93955116e-02 -4.50941026e-01
-1.62822172e-01 -1.43829435e-01 -8.24549198e-01 -1.31624460... | [13.312671661376953, 2.060544967651367] |
0d01968d-7c9b-4a15-b828-9f2f4b9edeea | depth-cooperated-trimodal-network-for-video | 2202.06060 | null | https://arxiv.org/abs/2202.06060v2 | https://arxiv.org/pdf/2202.06060v2.pdf | Depth-Cooperated Trimodal Network for Video Salient Object Detection | Depth can provide useful geographical cues for salient object detection (SOD), and has been proven helpful in recent RGB-D SOD methods. However, existing video salient object detection (VSOD) methods only utilize spatiotemporal information and seldom exploit depth information for detection. In this paper, we propose a ... | ['Qijun Zhao', 'Keren Fu', 'Dingyao Min', 'Yukang Lu'] | 2022-02-12 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 4.11912240e-02 -1.77421451e-01 -2.75639236e-01 -1.36504889e-01
-5.53277612e-01 -4.41005304e-02 5.58050334e-01 -1.57845214e-01
-4.43901449e-01 4.72798139e-01 6.54074907e-01 1.58180386e-01
5.24522252e-02 -6.90108716e-01 -3.07976961e-01 -9.94332910e-01
1.94549784e-01 -3.50892544e-01 7.49894023e-01 -2.46049061... | [9.583089828491211, -0.633668839931488] |
81d383a7-d824-41ff-a1e6-34c70a66abb1 | occlusion-handling-in-generic-object | 2101.08845 | null | https://arxiv.org/abs/2101.08845v1 | https://arxiv.org/pdf/2101.08845v1.pdf | Occlusion Handling in Generic Object Detection: A Review | The significant power of deep learning networks has led to enormous development in object detection. Over the last few years, object detector frameworks have achieved tremendous success in both accuracy and efficiency. However, their ability is far from that of human beings due to several factors, occlusion being one o... | ['Zoltán Vámossy', 'Sándor Szénási', 'Kaziwa Saleh'] | 2021-01-21 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [-8.59382227e-02 -4.83888119e-01 3.47929373e-02 -3.34967941e-01
-6.46904409e-02 -2.82680929e-01 3.55685920e-01 4.21426184e-02
-4.19848412e-01 5.40909827e-01 -6.25272617e-02 -7.93175921e-02
2.63536274e-02 -7.79331386e-01 -3.14216256e-01 -5.03330290e-01
-9.43822972e-03 9.82071459e-03 8.17746639e-01 9.56701562... | [8.64079475402832, -0.4951813220977783] |
d688c7d7-fe30-4af4-b199-f76ea0f8bbf9 | basis-pursuit-denoising-via-recurrent-neural | 2305.14209 | null | https://arxiv.org/abs/2305.14209v1 | https://arxiv.org/pdf/2305.14209v1.pdf | Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography | Finding sparse solutions of underdetermined linear systems commonly requires the solving of L1 regularized least squares minimization problem, which is also known as the basis pursuit denoising (BPDN). They are computationally expensive since they cannot be solved analytically. An emerging technique known as deep unrol... | ['Xiao Xiang Zhu', 'Yilei Shi', 'Peter Jung', 'Yuanyuan Wang', 'Kun Qian'] | 2023-05-23 | null | null | null | null | ['unrolling'] | ['computer-vision'] | [ 1.60136506e-01 -8.55508670e-02 5.00215709e-01 -4.11039561e-01
-7.80444503e-01 6.54166713e-02 8.37960839e-02 -7.82184184e-01
-2.02373743e-01 8.07489455e-01 1.91572219e-01 -1.63507119e-01
-4.46270645e-01 -6.83411777e-01 -7.77247548e-01 -1.37004173e+00
-2.57774681e-01 4.12533909e-01 -3.62396479e-01 -3.98255438... | [10.609759330749512, -2.1363468170166016] |
5d4d9d08-4b99-4e6d-b14f-c357722b8265 | co-creating-a-globally-interpretable-model | 2306.13381 | null | https://arxiv.org/abs/2306.13381v1 | https://arxiv.org/pdf/2306.13381v1.pdf | Co-creating a globally interpretable model with human input | We consider an aggregated human-AI collaboration aimed at generating a joint interpretable model. The model takes the form of Boolean decision rules, where human input is provided in the form of logical conditions or as partial templates. This focus on the combined construction of a model offers a different perspective... | ['Rahul Nair'] | 2023-06-23 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 5.21484196e-01 1.04467487e+00 -1.70484483e-01 -5.47054946e-01
-3.69380832e-01 -5.97399771e-01 1.12411535e+00 2.34904423e-01
1.94535866e-01 1.07124817e+00 2.12578058e-01 -7.07628846e-01
-3.91356856e-01 -1.00141120e+00 -3.19751710e-01 -1.92195967e-01
1.74093589e-01 6.20389104e-01 -3.47012579e-02 -7.59006515... | [9.001288414001465, 6.625453948974609] |
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