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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2e34b8bd-8243-4c1f-a281-e5464e3fda7c | authorship-detection-of-sms-messages-using | 1403.1314 | null | http://arxiv.org/abs/1403.1314v1 | http://arxiv.org/pdf/1403.1314v1.pdf | Authorship detection of SMS messages using unigrams | SMS messaging is a popular media of communication. Because of its popularity
and privacy, it could be used for many illegal purposes. Additionally, since
they are part of the day to day life, SMSes can be used as evidence for many
legal disputes. Since a cellular phone might be accessible to people close to
the owner, ... | ['P. Herath', 'R. G. Ragel', 'U. Senanayake'] | 2014-03-06 | null | null | null | null | ['author-attribution'] | ['natural-language-processing'] | [ 5.11132367e-03 1.20645680e-01 -1.29650896e-02 -4.07587029e-02
-1.69269979e-01 -6.49474084e-01 1.06957078e+00 4.14880484e-01
-5.79797685e-01 1.12243676e+00 4.79713343e-02 -6.66015744e-01
-1.28143542e-02 -8.21991384e-01 -2.95487732e-01 -5.21535814e-01
2.52491504e-01 8.13517928e-01 3.98021668e-01 -3.73652309... | [9.48448371887207, 10.609443664550781] |
6de18dd4-a15c-4544-b9c6-33455650056d | layered-neural-atlases-for-consistent-video | 2109.11418 | null | https://arxiv.org/abs/2109.11418v1 | https://arxiv.org/pdf/2109.11418v1.pdf | Layered Neural Atlases for Consistent Video Editing | We present a method that decomposes, or "unwraps", an input video into a set of layered 2D atlases, each providing a unified representation of the appearance of an object (or background) over the video. For each pixel in the video, our method estimates its corresponding 2D coordinate in each of the atlases, giving us a... | ['Tali Dekel', 'Oliver Wang', 'Dolev Ofri', 'Yoni Kasten'] | 2021-09-23 | null | null | null | null | ['video-style-transfer', 'video-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 4.30353850e-01 8.51702988e-02 4.91926484e-02 -2.81142980e-01
-5.28520226e-01 -5.98363996e-01 3.50049496e-01 -9.60815847e-02
-1.73176408e-01 4.16768163e-01 5.03451983e-03 9.07731801e-02
3.70884389e-01 -5.85938036e-01 -1.14978409e+00 -7.09503651e-01
3.83951589e-02 3.96622121e-01 3.88392836e-01 1.93214953... | [9.06655502319336, -2.789391040802002] |
3f3fbf74-5997-45d5-abff-85a0fc12c02a | fine-grained-hard-negative-mining | 2301.01079 | null | https://arxiv.org/abs/2301.01079v1 | https://arxiv.org/pdf/2301.01079v1.pdf | Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset | Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task. Here, we describe a candidate deep learning solution for the Mitosis Domain Generalization Challenge 2022 (MIDOG) to address the problem of generalization for mitosis detection in images of hematoxylin-eosin-... | ['Viktor H. Koelzer', 'Maxime W. Lafarge'] | 2023-01-03 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 3.98268878e-01 4.70456988e-01 -1.70868173e-01 -3.89859766e-01
-1.24333549e+00 -6.04824603e-01 5.57618201e-01 2.95930535e-01
-8.43198836e-01 9.25654292e-01 -1.99467257e-01 -3.21618021e-01
-1.19811587e-01 -4.98145640e-01 -4.26957160e-01 -1.05511594e+00
-4.55581620e-02 1.05189204e+00 1.70901358e-01 -1.54444957... | [15.126038551330566, -3.035402774810791] |
0d752f9e-1828-4535-bb0c-e13f373db566 | coach2vec-autoencoding-the-playing-style-of | 2106.15444 | null | https://arxiv.org/abs/2106.15444v1 | https://arxiv.org/pdf/2106.15444v1.pdf | Coach2vec: autoencoding the playing style of soccer coaches | Capturing the playing style of professional soccer coaches is a complex, and yet barely explored, task in sports analytics. Nowadays, the availability of digital data describing every relevant spatio-temporal aspect of soccer matches, allows for capturing and analyzing the playing style of players, teams, and coaches i... | ['Luca Pappalardo', 'Paolo Cintia'] | 2021-06-29 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-4.32047993e-01 -5.76357126e-01 -5.11529446e-02 -6.27563819e-02
-3.27794552e-01 -6.10911548e-01 4.05666292e-01 7.27368534e-01
-7.13029623e-01 1.15146898e-01 5.61806619e-01 4.46712285e-01
-4.16950822e-01 -1.13533258e+00 -6.78363979e-01 -5.34050882e-01
-8.47932100e-02 7.50170887e-01 2.80679375e-01 -7.77892530... | [7.2382330894470215, 0.26829901337623596] |
b9d4ac20-69b6-4da7-b670-3e1619e2f460 | scaling-strategies-for-on-device-low | 2303.03005 | null | https://arxiv.org/abs/2303.03005v1 | https://arxiv.org/pdf/2303.03005v1.pdf | Scaling strategies for on-device low-complexity source separation with Conv-Tasnet | Recently, several very effective neural approaches for single-channel speech separation have been presented in the literature. However, due to the size and complexity of these models, their use on low-resource devices, e.g. for hearing aids, and earphones, is still a challenge and established solutions are not availabl... | ['Alessio Brutti', 'Daniele Falavigna', 'Francesco Paissan', 'Mohamed Nabih Ali'] | 2023-03-06 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 1.24013811e-01 -2.02765599e-01 2.18302056e-01 -1.73122257e-01
-1.09280184e-01 -1.65916592e-01 1.89722478e-01 1.95155680e-01
-8.68193924e-01 4.35637414e-01 -1.05443478e-01 -5.77676892e-01
-3.21998000e-01 -3.57521027e-01 -3.26372266e-01 -8.11719120e-01
1.61152184e-02 3.32378410e-02 4.10931826e-01 -9.23246816... | [14.84547233581543, 5.855870246887207] |
4a3d868d-1f86-4492-b896-8a47335ddc57 | chrono-at-semeval-2018-task-6-a-system-for | null | null | https://aclanthology.org/S18-1012 | https://aclanthology.org/S18-1012.pdf | Chrono at SemEval-2018 Task 6: A System for Normalizing Temporal Expressions | Temporal information extraction is a challenging task. Here we describe Chrono, a hybrid rule-based and machine learning system that identifies temporal expressions in text and normalizes them into the SCATE schema. After minor parsing logic adjustments, Chrono has emerged as the top performing system for SemEval 2018 ... | ['Luke Maffey', 'Nicholas Morgan', 'Bridget McInnes', 'Amy Olex'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['timex-normalization', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.41819069e-02 5.33334985e-02 -9.34374928e-01 -5.55625141e-01
-4.16683197e-01 -8.56423318e-01 9.62895274e-01 6.42577767e-01
-8.64833951e-01 6.17873847e-01 4.68040794e-01 -3.28662872e-01
-3.00375730e-01 -4.19551581e-01 -2.34534740e-01 -1.91540286e-01
-3.08992535e-01 6.39684498e-01 2.17830583e-01 -3.44342381... | [9.063789367675781, 9.216462135314941] |
435697de-8575-422e-8411-14d301ec503f | cycle4completion-unpaired-point-cloud | 2103.07838 | null | https://arxiv.org/abs/2103.07838v2 | https://arxiv.org/pdf/2103.07838v2.pdf | Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding | In this paper, we present a novel unpaired point cloud completion network, named Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous unpaired completion methods merely focus on the learning of geometric correspondence from incomplete shapes to complete shapes, and ignore the learning i... | ['Yu-Shen Liu', 'Wen Zheng', 'Pengfei Wan', 'Yan-Pei Cao', 'Zhizhong Han', 'Xin Wen'] | 2021-03-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wen_Cycle4Completion_Unpaired_Point_Cloud_Completion_Using_Cycle_Transformation_With_Missing_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wen_Cycle4Completion_Unpaired_Point_Cloud_Completion_Using_Cycle_Transformation_With_Missing_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 2.38636918e-02 9.14321095e-02 3.47295329e-02 -4.15730119e-01
-5.16870558e-01 -8.77108991e-01 5.68857610e-01 -4.75913912e-01
2.88919598e-01 4.16681588e-01 1.03602737e-01 -7.70990700e-02
-2.78802812e-02 -1.03734410e+00 -9.00826752e-01 -6.41273439e-01
4.59525377e-01 1.06331825e+00 -2.02713460e-02 -8.45422745... | [8.461918830871582, -3.5997531414031982] |
67cc264a-440e-42e5-bcb8-4324940b1af3 | adaptive-recursive-neural-network-for-target | null | null | https://aclanthology.org/P14-2009 | https://aclanthology.org/P14-2009.pdf | Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification | null | ['Li Dong', 'Furu Wei', 'Chuanqi Tan', 'Ming Zhou', 'Ke Xu', 'Duyu Tang'] | 2014-06-01 | null | null | null | acl-2014-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.253398418426514, 3.7227776050567627] |
4a00d5bf-a83e-4382-b6c2-91c10b3b71f6 | multi-agent-path-finding-via-tree-lstm | 2210.12933 | null | https://arxiv.org/abs/2210.12933v2 | https://arxiv.org/pdf/2210.12933v2.pdf | Multi-Agent Path Finding via Tree LSTM | In recent years, Multi-Agent Path Finding (MAPF) has attracted attention from the fields of both Operations Research (OR) and Reinforcement Learning (RL). However, in the 2021 Flatland3 Challenge, a competition on MAPF, the best RL method scored only 27.9, far less than the best OR method. This paper proposes a new RL ... | ['Xiaolong Zhu', 'Jiaxin Chen', 'Qimai Li', 'Kunjie Zhang', 'Yuhao Jiang'] | 2022-10-24 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-7.12330863e-02 4.84026849e-01 -7.06574261e-01 2.25418091e-01
-6.79943860e-01 -6.04519665e-01 6.14630342e-01 1.18961237e-01
-8.64942253e-01 1.42806876e+00 4.22934592e-01 -5.74113965e-01
-6.18026257e-01 -7.39738584e-01 -7.00458050e-01 -4.30254072e-01
-6.56842291e-01 7.78486431e-01 2.91204005e-01 -7.25920737... | [3.776365041732788, 1.6314611434936523] |
fecbe4f7-dd6b-40c0-819f-75d23c6d9f97 | robust-reinforcement-learning-objectives-for | 2305.18820 | null | https://arxiv.org/abs/2305.18820v1 | https://arxiv.org/pdf/2305.18820v1.pdf | Robust Reinforcement Learning Objectives for Sequential Recommender Systems | Attention-based sequential recommendation methods have demonstrated promising results by accurately capturing users' dynamic interests from historical interactions. In addition to generating superior user representations, recent studies have begun integrating reinforcement learning (RL) into these models. Framing seque... | ['Lili Meng', 'Dave Evans', 'Tristan Sylvain', 'Melissa Mozifian'] | 2023-05-30 | null | null | null | null | ['sequential-recommendation', 'offline-rl'] | ['miscellaneous', 'playing-games'] | [ 3.29218626e-01 -1.32112622e-01 -5.76855183e-01 -2.69762605e-01
-7.34304965e-01 -3.91314507e-01 5.35191357e-01 -1.37013923e-02
-3.29360783e-01 8.82100284e-01 4.82161969e-01 -2.88249046e-01
-4.18474078e-01 -5.49171746e-01 -5.00330269e-01 -4.46805000e-01
-3.62628371e-01 1.56449258e-01 -1.91914573e-01 -4.90716904... | [4.2249836921691895, 2.351856231689453] |
28cb714f-1687-4428-b984-333d2a4f870f | hallucinating-very-low-resolution-and | 1811.04645 | null | http://arxiv.org/abs/1811.04645v4 | http://arxiv.org/pdf/1811.04645v4.pdf | Hallucinating very low-resolution and obscured face images | Most of the face hallucination methods are designed for complete inputs. They
will not work well if the inputs are very tiny or contaminated by large
occlusion. Inspired by this fact, we propose an obscured face hallucination
network(OFHNet). The OFHNet consists of four parts: an inpainting network, an
upsampling netwo... | ['Ting Sun', 'Song Ding', 'Lianping Yang', 'Bin Shao', 'Xiangde Zhang'] | 2018-11-12 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 1.83009028e-01 4.04356688e-01 1.43546626e-01 -3.86098623e-01
-4.59068775e-01 8.13093036e-02 2.14016527e-01 -1.07883656e+00
1.17168754e-01 7.77887583e-01 5.48607528e-01 3.46176863e-01
3.12308580e-01 -8.71272445e-01 -8.35961580e-01 -6.64418876e-01
3.67953449e-01 -7.80948773e-02 -1.47595823e-01 -2.04537600... | [12.812640190124512, -0.04418937489390373] |
bd5d88eb-e0e0-481f-8707-e0ebe879191e | style-agnostic-3d-reconstruction-via | 2110.10784 | null | https://arxiv.org/abs/2110.10784v1 | https://arxiv.org/pdf/2110.10784v1.pdf | Style Agnostic 3D Reconstruction via Adversarial Style Transfer | Reconstructing the 3D geometry of an object from an image is a major challenge in computer vision. Recently introduced differentiable renderers can be leveraged to learn the 3D geometry of objects from 2D images, but those approaches require additional supervision to enable the renderer to produce an output that can be... | ['Hilde Kuehne', 'Oliver Deussen', 'Bastian Goldluecke', 'Felix Petersen'] | 2021-10-20 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.14218295e-01 2.77657151e-01 4.26082939e-01 -4.81017977e-01
-6.72580779e-01 -8.40515137e-01 6.11447096e-01 -4.19568300e-01
-2.91567773e-01 2.33746573e-01 -3.66584510e-01 -3.27894241e-01
5.26426554e-01 -8.33119154e-01 -1.34487450e+00 -5.11857331e-01
4.43569988e-01 7.70129502e-01 3.88503551e-01 -6.68101236... | [8.754859924316406, -3.0932705402374268] |
37f3cec9-cba8-40a2-bed6-019204802623 | causality-crossing-and-analyticity-in | 2102.02433 | null | https://arxiv.org/abs/2102.02433v1 | https://arxiv.org/pdf/2102.02433v1.pdf | Causality, Crossing and Analyticity in Conformal Field Theories | Analyticity and crossing properties of four point function are investigated in conformal field theories in the frameworks of Wightman axioms. A Hermitian scalar conformal field, satisfying the Wightman axioms, is considered. The crucial role of microcausality in deriving analyticity domains is discussed and domains of ... | ['Jnanadeva Maharana'] | 2021-02-04 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.39022887e-01 4.49632496e-01 3.47074509e-01 -2.91171908e-01
-4.55035940e-02 -5.72824717e-01 1.08900690e+00 -4.73106056e-01
-1.42412439e-01 6.38439834e-01 1.19122267e-01 -4.67754304e-01
-1.01344597e+00 -7.66508341e-01 -2.89208502e-01 -1.10563076e+00
-3.97921681e-01 2.03222215e-01 1.56403184e-01 -9.53036845... | [6.4196014404296875, 4.730812072753906] |
83857e67-70cb-4358-a471-8124389e43cf | optimal-alphabet-for-single-text-compression | 2201.05234 | null | https://arxiv.org/abs/2201.05234v2 | https://arxiv.org/pdf/2201.05234v2.pdf | Optimal alphabet for single text compression | A text written using symbols from a given alphabet can be compressed using the Huffman code, which minimizes the length of the encoded text. It is necessary, however, to employ a text-specific codebook, i.e. the symbol-codeword dictionary, to decode the original text. Thus, the compression performance should be evaluat... | ['Andranik Khachatryan', 'Armen E. Allahverdyan'] | 2022-01-13 | null | null | null | null | ['text-compression'] | ['natural-language-processing'] | [ 5.23396969e-01 9.60810035e-02 -3.35446209e-01 1.15733482e-02
-1.21335030e-01 -5.99635303e-01 5.97093046e-01 4.00016725e-01
-6.12864912e-01 6.90666676e-01 2.02995539e-01 -7.26760745e-01
8.66926536e-02 -9.42286670e-01 -4.24495429e-01 -8.68239999e-01
-4.32583801e-02 4.45532918e-01 1.14049934e-01 -4.49734330... | [12.156885147094727, 9.433152198791504] |
d40eadf9-bcca-4e20-b7bb-9214ce5c9e37 | lipstick-ain-t-enough-beyond-color-matching | 2104.01867 | null | https://arxiv.org/abs/2104.01867v1 | https://arxiv.org/pdf/2104.01867v1.pdf | Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer | Makeup transfer is the task of applying on a source face the makeup style from a reference image. Real-life makeups are diverse and wild, which cover not only color-changing but also patterns, such as stickers, blushes, and jewelries. However, existing works overlooked the latter components and confined makeup transfer... | ['Minh Hoai', 'Anh Tran', 'Thao Nguyen'] | 2021-04-05 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Nguyen_Lipstick_Aint_Enough_Beyond_Color_Matching_for_In-the-Wild_Makeup_Transfer_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Nguyen_Lipstick_Aint_Enough_Beyond_Color_Matching_for_In-the-Wild_Makeup_Transfer_CVPR_2021_paper.pdf | cvpr-2021-1 | ['color-manipulation', 'facial-makeup-transfer'] | ['computer-vision', 'computer-vision'] | [ 1.71168789e-01 -6.08262777e-01 1.13031492e-01 -5.21118104e-01
-2.69757509e-01 -9.03086722e-01 4.37548280e-01 -5.81022859e-01
3.14160645e-01 5.33546865e-01 -2.60649085e-01 2.42362991e-02
2.87740529e-01 -1.07141864e+00 -9.88854110e-01 -5.33846974e-01
4.13997293e-01 3.17675650e-01 2.35939905e-01 -3.84262323... | [11.504873275756836, -0.8360395431518555] |
39e50e16-0c9e-4737-a550-91049b83b558 | semhint-md-learning-from-noisy-semantic | 2303.18219 | null | https://arxiv.org/abs/2303.18219v1 | https://arxiv.org/pdf/2303.18219v1.pdf | SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation | Without ground truth supervision, self-supervised depth estimation can be trapped in a local minimum due to the gradient-locality issue of the photometric loss. In this paper, we present a framework to enhance depth by leveraging semantic segmentation to guide the network to jump out of the local minimum. Prior works h... | ['Michael C. Yip', 'Yuheng Zhi', 'Shan Lin'] | 2023-03-31 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [ 5.00573516e-01 7.11707056e-01 -3.25719625e-01 -7.76557267e-01
-1.08259940e+00 -4.55765128e-01 3.35767597e-01 1.59544542e-01
-5.13030112e-01 5.64032316e-01 1.22330971e-01 -6.06012158e-02
2.26161376e-01 -6.01757824e-01 -8.69473040e-01 -7.45004475e-01
3.30657601e-01 4.12505627e-01 3.59435081e-01 1.20734841... | [14.609488487243652, -2.1058521270751953] |
2f85a552-98bf-4ef3-9156-e35ca19b208f | r2-trans-fine-grained-visual-categorization | 2204.10095 | null | https://arxiv.org/abs/2204.10095v1 | https://arxiv.org/pdf/2204.10095v1.pdf | R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction | Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories, whose main challenge is the large intraclass diversities and subtle inter-class differences. Existing FGVC methods usually select discriminant regions found by a trained model, which is prone to neglect other potential discriminant in... | ['Xinge You', 'Shujian Yu', 'Shuo Ye', 'Yu Wang'] | 2022-04-21 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 2.66788512e-01 -6.17135882e-01 -2.64917105e-01 -4.60890621e-01
-4.78014052e-01 -5.64768910e-01 2.88906783e-01 1.73325554e-01
-2.89271176e-01 4.72017109e-01 6.86241314e-03 -6.31207451e-02
-1.05846897e-01 -6.02180183e-01 -4.17680740e-01 -9.90318060e-01
-1.01077196e-04 -7.88702816e-02 7.37410665e-01 -2.21402608... | [9.674469947814941, 1.9536728858947754] |
6cdf857b-95c8-4bf5-bece-8886547dfd7f | clause-based-discourse-segmentation-of-arabic | null | null | https://aclanthology.org/L12-1559 | https://aclanthology.org/L12-1559.pdf | Clause-based Discourse Segmentation of Arabic Texts | This paper describes a rule-based approach to segment Arabic texts into clauses. Our method relies on an extensive analysis of a large set of lexical cues as well as punctuation marks. Our analysis was carried out on two different corpus genres: news articles and elementary school textbooks. We propose a three steps se... | ['lamia hadrich belguith', 'ar', 'Isk Keskes', 'Farah Benamara'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 2.02144787e-01 8.63399133e-02 -7.13318288e-02 -3.73241633e-01
-7.07101703e-01 -9.94734228e-01 6.97336078e-01 8.15005541e-01
-6.88466311e-01 9.24159408e-01 1.10032544e-01 -6.00524008e-01
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2.68049836e-01 7.16832995e-01 6.52266741e-01 -5.27562499... | [10.385178565979004, 10.198904991149902] |
2ac59c35-841e-4cd6-852a-158e08342a44 | dissimilarity-mixture-autoencoder-for-deep | 2006.08177 | null | https://arxiv.org/abs/2006.08177v4 | https://arxiv.org/pdf/2006.08177v4.pdf | Dissimilarity Mixture Autoencoder for Deep Clustering | The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind of deep learning architecture. It internally represents a dissimilarity mixture model (DMM) that extends classical methods like K-Me... | ['Fabio A. González', 'Juan S. Lara'] | 2020-06-15 | null | null | null | null | ['image-clustering', 'text-clustering'] | ['computer-vision', 'natural-language-processing'] | [-4.14014041e-01 9.54675004e-02 1.83124349e-01 -6.97510183e-01
-3.45146298e-01 -3.71126652e-01 1.04259825e+00 6.51778430e-02
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-8.04571509e-02 8.95773530e-01 -1.92280814e-01 1.20470040... | [9.119390487670898, 3.20656418800354] |
73695af7-84c4-4e70-89f8-d8bf0378cb8e | slot-filling-for-extracting-reskilling-and | 2207.04862 | null | https://arxiv.org/abs/2207.04862v1 | https://arxiv.org/pdf/2207.04862v1.pdf | Slot Filling for Extracting Reskilling and Upskilling Options from the Web | Disturbances in the job market such as advances in science and technology, crisis and increased competition have triggered a surge in reskilling and upskilling programs. Information on suitable continuing education options is distributed across many sites, rendering the search, comparison and selection of useful progra... | ['Philipp Kuntschik', 'Alexander van Schie', 'Andreas Fraefel', 'Roger Waldvogel', 'Albert Weichselbraun'] | 2022-07-11 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [-2.02415019e-01 3.00955087e-01 -5.57578087e-01 -1.37699291e-01
-6.55164361e-01 -8.47209513e-01 5.01380265e-01 1.26649237e+00
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5.12735128e-01 7.97523320e-01 6.46366358e-01 -1.97011665... | [9.334519386291504, 8.428871154785156] |
8785887e-bac9-47c9-8b40-ffafba697f38 | a-discourse-on-metods-meta-optimized | 2202.02363 | null | https://arxiv.org/abs/2202.02363v3 | https://arxiv.org/pdf/2202.02363v3.pdf | Meta-Reinforcement Learning with Self-Modifying Networks | Deep Reinforcement Learning has demonstrated the potential of neural networks tuned with gradient descent for solving complex tasks in well-delimited environments. However, these neural systems are slow learners producing specialized agents with no mechanism to continue learning beyond their training curriculum. On the... | ['Rufin VanRullen', 'Thomas Serre', 'Mathieu Chalvidal'] | 2022-02-04 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 1.34070426e-01 4.75816391e-02 -8.00823048e-02 1.64618716e-01
6.28828168e-01 -4.23749059e-01 9.49483633e-01 -1.27413705e-01
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-5.67710698e-01 -9.48696554e-01 -8.32708001e-01 -9.72292006e-01
-2.83616066e-01 3.79288703e-01 5.56641996e-01 -1.02226508... | [4.255012035369873, 1.5443949699401855] |
b980ede0-759d-4b31-9fae-20d843560789 | video-question-answering-with-iterative-video | 2208.00934 | null | https://arxiv.org/abs/2208.00934v1 | https://arxiv.org/pdf/2208.00934v1.pdf | Video Question Answering with Iterative Video-Text Co-Tokenization | Video question answering is a challenging task that requires understanding jointly the language input, the visual information in individual video frames, as well as the temporal information about the events occurring in the video. In this paper, we propose a novel multi-stream video encoder for video question answering... | ['Anelia Angelova', 'Michael S. Ryoo', 'Weicheng Kuo', 'Kairo Morton', 'AJ Piergiovanni'] | 2022-08-01 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 1.24300167e-01 -3.98220807e-01 -1.96429327e-01 -2.62700111e-01
-1.09340358e+00 -7.27058768e-01 2.86084950e-01 9.72936600e-02
-7.30924606e-01 5.11466324e-01 3.04031640e-01 -5.69236100e-01
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-1.45480447e-02 4.49552909e-02 7.25997210e-01 -2.27236189... | [10.318291664123535, 0.9103173613548279] |
3e9209f7-1a52-401c-b77f-2d47ac521073 | latent-dynamics-networks-ldnets-learning-the | 2305.00094 | null | https://arxiv.org/abs/2305.00094v1 | https://arxiv.org/pdf/2305.00094v1.pdf | Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes | Predicting the evolution of systems that exhibit spatio-temporal dynamics in response to external stimuli is a key enabling technology fostering scientific innovation. Traditional equations-based approaches leverage first principles to yield predictions through the numerical approximation of high-dimensional systems of... | ['Alfio Quarteroni', "Luca Dede'", 'Matteo Salvador', 'Stefano Pagani', 'Francesco Regazzoni'] | 2023-04-28 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-2.64698982e-01 -2.97719270e-01 1.05302423e-01 4.35501695e-01
-3.72590333e-01 -7.44116008e-01 8.66606295e-01 -2.08023358e-02
-2.51239270e-01 7.87256181e-01 -1.95085242e-01 -2.09094584e-01
-4.74257052e-01 -8.11987460e-01 -7.02226520e-01 -1.06595910e+00
-4.34089094e-01 7.63978660e-01 1.74550042e-01 -1.09052643... | [6.541858196258545, 3.466791868209839] |
f51d2a1f-9696-486d-aa27-ed78dd718b92 | pre-train-and-plug-in-flexible-conditional | 1911.03882 | null | https://arxiv.org/abs/1911.03882v4 | https://arxiv.org/pdf/1911.03882v4.pdf | Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders | Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents. Current conditional generation models cannot handle emerging conditions due to their joint end-to-end learning fashion. When a n... | ['Yu Duan', 'Jiaxin Pei', 'Canwen Xu', 'Chenliang Li', 'Jialong Han'] | 2019-11-10 | pre-train-and-plug-in-flexible-conditional-1 | https://aclanthology.org/2020.acl-main.23 | https://aclanthology.org/2020.acl-main.23.pdf | acl-2020-6 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 2.69642055e-01 1.84039921e-01 -1.69658646e-01 -9.20782387e-02
-7.39416957e-01 -4.69688833e-01 1.05892813e+00 -2.46115506e-01
-1.12420209e-01 1.18600225e+00 3.10787439e-01 -3.35125506e-01
4.42004830e-01 -9.33229566e-01 -6.15216315e-01 -7.45627344e-01
3.69364321e-01 5.44556558e-01 7.29032904e-02 -2.21772924... | [11.89390754699707, 9.112628936767578] |
07281132-b0ca-42e6-821c-985eeca19b75 | re-2tal-rewiring-pretrained-video-backbones | 2211.14053 | null | https://arxiv.org/abs/2211.14053v2 | https://arxiv.org/pdf/2211.14053v2.pdf | Re^2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization | Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content. Given limited GPU memory, training TAL end to end (i.e., from videos to predictions) on long videos is a significant challenge. Most methods can only train on pre-extracted features without optim... | ['Bernard Ghanem', 'Karttikeya Mangalam', 'Shuming Liu', 'Chen Zhao'] | 2022-11-25 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 2.35975787e-01 -2.19252612e-02 -4.77236807e-01 -1.10609852e-01
-4.37596291e-01 -5.68824708e-01 3.96982521e-01 -5.65784752e-01
-6.41002893e-01 6.87073052e-01 3.14262033e-01 -6.49997266e-03
2.85643607e-01 -6.22895181e-01 -1.24415314e+00 -5.57101488e-01
-6.74995780e-02 1.29495293e-01 6.28390074e-01 1.92883164... | [8.904682159423828, 0.5230687856674194] |
fc715c5c-b442-4134-927e-3f120c1ebb55 | in-silico-prediction-of-mozenavir-as | 2106.05440 | null | https://arxiv.org/abs/2106.05440v1 | https://arxiv.org/pdf/2106.05440v1.pdf | In silico Prediction of Mozenavir as potential drug for SARS-CoV-2 infection via Binding Multiple Drug Targets | Since the epidemic began in November 2019, no viable medicine against SARS-CoV-2 has been discovered. The typical medication discovery strategy requires several years of rigorous research and development as well as a significant financial commitment, which is not feasible in the face of the current epidemic. Through mo... | ['Abhiav', 'Munipally Praveen Kumar', 'Swapna Gurrapu', 'Rakesh Davella', 'Estari Mamidalaa'] | 2021-06-10 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.21646665e-01 -5.11548400e-01 -2.94428229e-01 4.24303999e-03
-2.41583034e-01 -7.09987581e-01 -2.29754016e-01 3.97804677e-01
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-4.36753035e-01 5.07715285e-01 -2.62178004e-01 -1.92572266... | [4.691133975982666, 5.106047630310059] |
4f3c1497-0a8f-4955-b805-8d6d8291a60d | multi-modal-prompting-for-low-shot-temporal | 2303.11732 | null | https://arxiv.org/abs/2303.11732v1 | https://arxiv.org/pdf/2303.11732v1.pdf | Multi-modal Prompting for Low-Shot Temporal Action Localization | In this paper, we consider the problem of temporal action localization under low-shot (zero-shot & few-shot) scenario, with the goal of detecting and classifying the action instances from arbitrary categories within some untrimmed videos, even not seen at training time. We adopt a Transformer-based two-stage action loc... | ['Weidi Xie', 'Yanfeng Wang', 'Qi Tian', 'Xiaopeng Zhang', 'Ya zhang', 'Peisen Zhao', 'Zeqian Li', 'Chen Ju'] | 2023-03-21 | null | null | null | null | ['action-classification', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 4.47238415e-01 -1.53785929e-01 -5.14297664e-01 -1.51178852e-01
-7.58666039e-01 -4.57184821e-01 7.73804724e-01 -3.82511586e-01
-5.51012993e-01 5.76600611e-01 8.07154059e-01 2.63415664e-01
-3.40745747e-02 -2.20235631e-01 -5.87415218e-01 -7.29484200e-01
1.48330018e-01 1.41578913e-01 4.77364302e-01 8.49998277... | [8.666393280029297, 0.7698109745979309] |
0434d656-4a7f-4b5a-8994-bf243d982f10 | wad-cmsn-wasserstein-distance-based-cross | 2202.05465 | null | https://arxiv.org/abs/2202.05465v1 | https://arxiv.org/pdf/2202.05465v1.pdf | WAD-CMSN: Wasserstein Distance based Cross-Modal Semantic Network for Zero-Shot Sketch-Based Image Retrieval | Zero-shot sketch-based image retrieval (ZSSBIR), as a popular studied branch of computer vision, attracts wide attention recently. Unlike sketch-based image retrieval (SBIR), the main aim of ZSSBIR is to retrieve natural images given free hand-drawn sketches that may not appear during training. Previous approaches used... | ['Jia Cai', 'Zhensheng Hu', 'Guanglong Xu'] | 2022-02-11 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.14129432e-01 -3.10492605e-01 -1.47086769e-01 -1.54373556e-01
-9.45230186e-01 -3.56289804e-01 7.03906357e-01 -3.55567276e-01
-2.55635142e-01 5.40779412e-01 2.49706935e-02 2.00914338e-01
-4.91286755e-01 -6.86710060e-01 -5.05220950e-01 -7.51185000e-01
5.16729712e-01 3.24638098e-01 1.37073457e-01 -2.00231969... | [11.591269493103027, 0.71086186170578] |
3db9c74a-a868-47f7-b7a1-40af1150c2e3 | arbitrary-conditional-distributions-with-1 | null | null | https://openreview.net/forum?id=IVxAlfGNKB | https://openreview.net/pdf?id=IVxAlfGNKB | Arbitrary Conditional Distributions with Energy | Modeling distributions of covariates, or density estimation, is a core challenge in unsupervised learning. However, the majority of work only considers the joint distribution, which has limited relevance to practical situations. A more general and useful problem is arbitrary conditional density estimation, which aims t... | ['Junier Oliva', 'Ryan Strauss'] | 2021-05-21 | null | https://openreview.net/forum?id=_idcJrecij | https://openreview.net/pdf?id=_idcJrecij | neurips-2021-12 | ['arbitrary-conditional-density-estimation'] | ['methodology'] | [ 3.8509168e-02 4.1109990e-02 -2.8704467e-01 -6.6638708e-01
-1.1229237e+00 -2.1090826e-01 4.3529740e-01 4.8896998e-02
-3.4571487e-01 1.2638911e+00 -1.1504314e-01 -2.8108037e-01
-4.8710793e-01 -9.7872043e-01 -9.1990596e-01 -9.0835315e-01
-2.9985970e-01 8.6414874e-01 -3.1094959e-01 4.5021409e-01
-3.7102859e-02... | [7.2665791511535645, 4.0636515617370605] |
6403619e-cdd8-4328-852e-c7c8d7dc3115 | efficient-video-generation-on-complex | 1907.06571 | null | https://arxiv.org/abs/1907.06571v2 | https://arxiv.org/pdf/1907.06571v2.pdf | Adversarial Video Generation on Complex Datasets | Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of subs... | ['Karen Simonyan', 'Aidan Clark', 'Jeff Donahue'] | 2019-07-15 | null | https://openreview.net/forum?id=Byx91R4twB | https://openreview.net/pdf?id=Byx91R4twB | null | ['3d-character-animation-from-a-single-photo'] | ['computer-vision'] | [ 3.88408870e-01 2.18411565e-01 -2.94109941e-01 -5.30079715e-02
-1.08485103e+00 -5.11054218e-01 1.07493734e+00 -8.87841880e-01
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3.68188947e-01 -8.91583800e-01 -1.29047585e+00 -5.31174839e-01
-5.39097711e-02 5.86774945e-01 -8.61492082e-02 -2.87554681... | [10.946781158447266, -0.5887318849563599] |
8f3a1e5a-ed1c-470a-b401-536e776857ee | avoiding-confusion-between-predictors-and | 1312.5714 | null | http://arxiv.org/abs/1312.5714v2 | http://arxiv.org/pdf/1312.5714v2.pdf | Avoiding Confusion between Predictors and Inhibitors in Value Function Approximation | In reinforcement learning, the goal is to seek rewards and avoid punishments.
A simple scalar captures the value of a state or of taking an action, where
expected future rewards increase and punishments decrease this quantity.
Naturally an agent should learn to predict this quantity to take beneficial
actions, and many... | ['Thomas P. Trappenberg', 'Patrick C. Connor'] | 2013-12-19 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 2.32920542e-01 3.58272523e-01 -3.02526861e-01 -4.56472516e-01
-2.39210680e-01 -5.52705705e-01 3.71204972e-01 5.27050972e-01
-9.44432855e-01 1.25021160e+00 -1.33369908e-01 -1.14168145e-01
-2.79396117e-01 -8.02050710e-01 -6.60191536e-01 -1.00222003e+00
-2.42488444e-01 2.66391456e-01 2.17289969e-01 -5.25282979... | [4.181521892547607, 2.0895655155181885] |
c9f40663-cd24-42ab-8114-ef9a0ace056d | star-shape-prior-in-fully-convolutional | 1806.08437 | null | http://arxiv.org/abs/1806.08437v1 | http://arxiv.org/pdf/1806.08437v1.pdf | Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation | Semantic segmentation is an important preliminary step towards automatic
medical image interpretation. Recently deep convolutional neural networks have
become the first choice for the task of pixel-wise class prediction. While
incorporating prior knowledge about the structure of target objects has proven
effective in t... | ['Zahra Mirikharaji', 'Ghassan Hamarneh'] | 2018-06-21 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 7.57933557e-01 5.63660204e-01 -2.51201034e-01 -8.83937061e-01
-1.03258729e+00 -2.64825076e-01 2.49540702e-01 1.69859424e-01
-7.21357286e-01 4.23564881e-01 -7.02196434e-02 -1.47779480e-01
1.32806078e-01 -5.56518376e-01 -9.36798632e-01 -5.81369042e-01
1.26942962e-01 5.54300487e-01 3.84947181e-01 8.83232057... | [14.557394027709961, -2.2799735069274902] |
c2e9dc57-fa61-486b-800d-3a819d304151 | mm-dfn-multimodal-dynamic-fusion-network-for | 2203.02385 | null | https://arxiv.org/abs/2203.02385v1 | https://arxiv.org/pdf/2203.02385v1.pdf | MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations | Emotion Recognition in Conversations (ERC) has considerable prospects for developing empathetic machines. For multimodal ERC, it is vital to understand context and fuse modality information in conversations. Recent graph-based fusion methods generally aggregate multimodal information by exploring unimodal and cross-mod... | ['Yang Mo', 'Lianxin Jiang', 'Lingwei Wei', 'Xiaolong Hou', 'Dou Hu'] | 2022-03-04 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 3.74086574e-02 -1.08779795e-01 5.36448881e-02 -4.18122619e-01
-5.37675977e-01 -5.27571023e-01 6.63978219e-01 1.79613695e-01
-2.06024036e-01 5.10034740e-01 9.27298009e-01 1.35200843e-01
-1.44692242e-01 -6.28840506e-01 9.45643187e-02 -7.35631824e-01
4.26088989e-01 5.75939938e-02 -3.91777128e-01 -7.24811375... | [13.106938362121582, 5.353387832641602] |
b673a4ae-8ff6-4609-94c4-22e0931a0adc | hinglish-language-modeling-a-messy-code-mixed | 1912.13109 | null | https://arxiv.org/abs/1912.13109v1 | https://arxiv.org/pdf/1912.13109v1.pdf | "Hinglish" Language -- Modeling a Messy Code-Mixed Language | With a sharp rise in fluency and users of "Hinglish" in linguistically diverse country, India, it has increasingly become important to analyze social content written in this language in platforms such as Twitter, Reddit, Facebook. This project focuses on using deep learning techniques to tackle a classification problem... | ['Vivek Kumar Gupta'] | 2019-12-30 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [-3.24417472e-01 -8.87769386e-02 3.80135253e-02 -2.74788529e-01
-9.92245227e-02 -7.66740620e-01 9.59946692e-01 3.28959256e-01
-5.54205239e-01 1.00120807e+00 6.56317174e-01 -4.31160301e-01
3.96527946e-01 -5.32017469e-01 1.36319613e-02 -3.12022865e-01
1.43042162e-01 5.96457422e-01 -3.59274209e-01 -7.62859941... | [8.825118064880371, 10.600881576538086] |
31c7ba51-0b42-4a1c-9f96-813f790b42f5 | self-training-via-metric-learning-for-source | 2212.04227 | null | https://arxiv.org/abs/2212.04227v1 | https://arxiv.org/pdf/2212.04227v1.pdf | Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation | Unsupervised source-free domain adaptation methods aim to train a model to be used in the target domain utilizing the pretrained source-domain model and unlabeled target-domain data, where the source data may not be accessible due to intellectual property or privacy issues. These methods frequently utilize self-trainin... | ['Ugur Halici', 'Ibrahim Batuhan Akkaya'] | 2022-12-08 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 2.09261805e-01 1.58256337e-01 -4.49191630e-01 -7.08411038e-01
-1.09805167e+00 -3.82343888e-01 3.28987658e-01 -7.64343292e-02
-3.94519389e-01 9.30692673e-01 -2.41487294e-01 1.00090560e-02
-1.54710878e-02 -8.10046017e-01 -8.43309224e-01 -8.39178622e-01
2.26211950e-01 6.12574995e-01 3.74165326e-01 4.95654382... | [10.368896484375, 3.0999162197113037] |
68d72a48-d894-4139-a5d3-0174a2efe85e | rgb-depth-fusion-gan-for-indoor-depth | 2203.10856 | null | https://arxiv.org/abs/2203.10856v1 | https://arxiv.org/pdf/2203.10856v1.pdf | RGB-Depth Fusion GAN for Indoor Depth Completion | The raw depth image captured by the indoor depth sensor usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and limited distance range. The incomplete depth map burdens many downstream vision tasks, and a rising number of depth complet... | ['Jian Tang', 'Feifei Feng', 'Mengshi Qi', 'XIUQUAN QIAO', 'Zhiyuan Xu', 'Zhengping Che', 'Mingyuan Wang', 'Haowen Wang'] | 2022-03-21 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_RGB-Depth_Fusion_GAN_for_Indoor_Depth_Completion_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_RGB-Depth_Fusion_GAN_for_Indoor_Depth_Completion_CVPR_2022_paper.pdf | cvpr-2022-1 | ['transparent-objects', 'depth-completion'] | ['computer-vision', 'computer-vision'] | [ 4.97639686e-01 1.02305338e-01 8.37238505e-02 -7.36911595e-01
-7.20265210e-01 -1.67903587e-01 2.60503262e-01 -3.20923209e-01
-2.87383884e-01 7.92353153e-01 2.93077886e-01 -1.62417646e-02
2.06765905e-01 -1.20786154e+00 -6.12806261e-01 -7.79336393e-01
4.96158332e-01 1.23552665e-01 3.48466843e-01 -1.20861225... | [8.933799743652344, -2.537264347076416] |
3c401135-f973-40b4-9524-f06f31658fab | hop-history-enhanced-and-order-aware-pre | null | null | https://ieeexplore.ieee.org/abstract/document/10006384 | https://ieeexplore.ieee.org/abstract/document/10006384 | HOP+: History-enhanced and Order-aware Pre-training for Vision-and-Language Navigation | Recent works attempt to employ pre-training in Vision-and-Language Navigation (VLN). However, these methods neglect the importance of historical contexts or ignore predicting future actions during pre-training, limiting the learning of visual-textual correspondence and the capability of decision-making. To address thes... | ['and Qi Wu ̊', 'Peng Wang', 'Zheng Yu', 'Yicong Hong', 'Yuankai Qi', 'Yanyuan Qiao'] | 2023-03-20 | null | null | null | ieee-transactions-on-pattern-analysis-and-23 | ['vision-and-language-navigation'] | ['robots'] | [-3.20475027e-02 -3.01488072e-01 -3.56823891e-01 -3.42727661e-01
-2.70235002e-01 -3.27648908e-01 1.10057592e+00 -7.24514648e-02
-8.32697153e-01 4.47104394e-01 4.83572900e-01 -8.05665910e-01
-1.25762582e-01 -7.44838119e-01 -7.87745953e-01 -2.59669542e-01
-1.11819170e-01 4.83749688e-01 6.87650561e-01 -4.53295469... | [4.475997447967529, 0.4651850759983063] |
21f0fb4d-6fc9-4007-837f-e2747b475082 | efficiently-predicting-high-resolution-mass | 2301.11419 | null | https://arxiv.org/abs/2301.11419v1 | https://arxiv.org/pdf/2301.11419v1.pdf | Efficiently predicting high resolution mass spectra with graph neural networks | Identifying a small molecule from its mass spectrum is the primary open problem in computational metabolomics. This is typically cast as information retrieval: an unknown spectrum is matched against spectra predicted computationally from a large database of chemical structures. However, current approaches to spectrum p... | ['Thomas Butler', 'David Healey', 'Tobias Kind', 'Ernest Fraenkel', 'Stefanie Jegelka', 'Michael Murphy'] | 2023-01-26 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 8.54440868e-01 1.47684991e-01 -7.77554452e-01 -2.86612213e-01
-8.64422560e-01 -1.00982332e+00 3.20933670e-01 9.41934109e-01
-3.69341932e-02 7.73756981e-01 -1.66595101e-01 -7.09032416e-01
-1.31100282e-01 -6.85529232e-01 -8.66695166e-01 -4.63378996e-01
-3.30765039e-01 9.56374288e-01 1.93806708e-01 1.51220605... | [5.032797336578369, 5.737403869628906] |
d1d42152-8cc1-4c12-800a-27a26d58aba8 | weak-supervised-dysarthria-invariant-features | 2210.13144 | null | https://arxiv.org/abs/2210.13144v1 | https://arxiv.org/pdf/2210.13144v1.pdf | Weak-Supervised Dysarthria-invariant Features for Spoken Language Understanding using an FHVAE and Adversarial Training | The scarcity of training data and the large speaker variation in dysarthric speech lead to poor accuracy and poor speaker generalization of spoken language understanding systems for dysarthric speech. Through work on the speech features, we focus on improving the model generalization ability with limited dysarthric dat... | ['Hugo Van hamme', 'Jinzi Qi'] | 2022-10-24 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 2.97542904e-02 5.57123661e-01 2.42997229e-01 -6.17740273e-01
-1.04344225e+00 -4.81438249e-01 7.06581533e-01 -8.89562905e-01
-4.66463476e-01 6.09684169e-01 1.07945931e+00 -4.88294065e-02
3.72557789e-02 -2.50562668e-01 -3.14050704e-01 -5.05092680e-01
1.42739028e-01 5.44935048e-01 -1.73027441e-01 -6.06917620... | [14.581570625305176, 6.474009990692139] |
2405aa3b-3b21-4860-8fd8-e63f8693a77a | learning-sense-specific-word-embeddings-by | null | null | https://aclanthology.org/C14-1048 | https://aclanthology.org/C14-1048.pdf | Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources | null | ['Ting Liu', 'Jiang Guo', 'Wanxiang Che', 'Haifeng Wang'] | 2014-08-01 | learning-sense-specific-word-embeddings-by-1 | https://aclanthology.org/C14-1048 | https://aclanthology.org/C14-1048.pdf | coling-2014-8 | ['learning-word-embeddings', 'chinese-named-entity-recognition'] | ['methodology', '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... | [-7.3983001708984375, 3.7880756855010986] |
12e9deee-93ca-468b-8280-e1d09aa6e97a | umuteam-lt-edi-acl2022-detecting-homophobic | null | null | https://aclanthology.org/2022.ltedi-1.16 | https://aclanthology.org/2022.ltedi-1.16.pdf | UMUTeam@LT-EDI-ACL2022: Detecting homophobic and transphobic comments in Tamil | This working-notes are about the participation of the UMUTeam in a LT-EDI shared task concerning the identification of homophobic and transphobic comments in YouTube. These comments are written in English, which has high availability to machine-learning resources; Tamil, which has fewer resources; and a transliteration... | ['Rafael Valencia-García', 'Camilo Caparros-Laiz', 'José García-Díaz'] | null | null | null | null | ltedi-acl-2022-5 | ['transliteration'] | ['natural-language-processing'] | [-2.00432301e-01 8.69365782e-02 -3.19513887e-01 -3.14485073e-01
-9.37800229e-01 -4.81673151e-01 4.09613699e-01 4.13206756e-01
-1.00103331e+00 6.35359049e-01 5.78981876e-01 -5.50671577e-01
-2.10369706e-01 -4.65899169e-01 -1.71227604e-01 -5.55623136e-02
2.04729453e-01 4.29563314e-01 -1.15982458e-01 -3.69836926... | [8.966151237487793, 10.672106742858887] |
78379d78-a44c-42bc-8a5a-ce93d675c1b2 | transductive-learning-for-zero-shot-object | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Rahman_Transductive_Learning_for_Zero-Shot_Object_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Rahman_Transductive_Learning_for_Zero-Shot_Object_Detection_ICCV_2019_paper.pdf | Transductive Learning for Zero-Shot Object Detection | Zero-shot object detection (ZSD) is a relatively unexplored research problem as compared to the conventional zero-shot recognition task. ZSD aims to detect previously unseen objects during inference. Existing ZSD works suffer from two critical issues: (a) large domain-shift between the source (seen) and target (unseen)... | [' Nick Barnes', ' Salman Khan', 'Shafin Rahman'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['zero-shot-object-detection'] | ['computer-vision'] | [ 5.79707563e-01 2.93158144e-01 -3.33344877e-01 -3.43983233e-01
-7.90027738e-01 -3.53220522e-01 8.49916875e-01 2.01273873e-01
-2.97242224e-01 8.22776496e-01 -1.83673620e-01 -1.45635130e-02
-1.69623066e-02 -9.94331777e-01 -8.12812090e-01 -6.90881014e-01
2.74653077e-01 7.81823993e-01 7.93262124e-01 -1.50414392... | [9.761503219604492, 2.2155601978302] |
ae4d9be8-d8d3-41d8-bf6b-9339f241c7b2 | a-systematic-survey-in-geometric-deep | 2306.11768 | null | https://arxiv.org/abs/2306.11768v3 | https://arxiv.org/pdf/2306.11768v3.pdf | A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design | Structure-based drug design (SBDD), which utilizes the three-dimensional geometry of proteins to identify potential drug candidates, is becoming increasingly vital in drug discovery. However, traditional methods based on physiochemical modeling and experts' domain knowledge are time-consuming and laborious. The recent ... | ['Enhong Chen', 'Qi Liu', 'Jiaxian Yan', 'Zaixi Zhang'] | 2023-06-20 | null | null | null | null | ['drug-discovery'] | ['medical'] | [-3.35245058e-02 -1.86067045e-01 -5.22547603e-01 -3.22253048e-01
-9.22200978e-01 -7.11309612e-01 1.20832641e-02 6.04609072e-01
2.28555109e-05 1.32190263e+00 2.26756781e-02 -8.88354897e-01
-2.06705481e-01 -6.71367764e-01 -9.89237249e-01 -9.45740283e-01
-1.47980839e-01 8.22747886e-01 -2.63158232e-01 -2.39477709... | [4.940145015716553, 5.805591106414795] |
6d300890-1f8d-4e0b-b0f6-100e26d41839 | sociallight-distributed-cooperation-learning | 2305.16145 | null | https://arxiv.org/abs/2305.16145v1 | https://arxiv.org/pdf/2305.16145v1.pdf | SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control | Many recent works have turned to multi-agent reinforcement learning (MARL) for adaptive traffic signal control to optimize the travel time of vehicles over large urban networks. However, achieving effective and scalable cooperation among junctions (agents) remains an open challenge, as existing methods often rely on ex... | ['Guillaume Sartoretti', 'Mehul Damani', 'Yifeng Zhang', 'Harsh Goel'] | 2023-04-20 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-4.01763260e-01 2.36736864e-01 -4.36362565e-01 -2.15184122e-01
-9.27613735e-01 -3.25553656e-01 6.82942092e-01 -9.65987798e-03
-7.12622285e-01 1.38112056e+00 6.23086579e-02 -6.06438518e-01
-2.36620858e-01 -9.68986809e-01 -7.25621045e-01 -8.92497599e-01
-3.81944865e-01 7.41211236e-01 7.15942383e-01 -6.34645343... | [5.218703269958496, 1.4901231527328491] |
57d9643b-4bcc-49b3-a125-dc4233b33aa0 | on-parsing-as-tagging | 2211.07344 | null | https://arxiv.org/abs/2211.07344v2 | https://arxiv.org/pdf/2211.07344v2.pdf | On Parsing as Tagging | There have been many proposals to reduce constituency parsing to tagging in the literature. To better understand what these approaches have in common, we cast several existing proposals into a unifying pipeline consisting of three steps: linearization, learning, and decoding. In particular, we show how to reduce tetrat... | ['Ryan Cotterell', 'Afra Amini'] | 2022-11-14 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.05393165e-01 4.19626355e-01 -3.22303355e-01 -5.83117485e-01
-1.22816145e+00 -1.23921967e+00 3.35744888e-01 3.08492690e-01
-4.58534241e-01 6.53843641e-01 6.78839803e-01 -8.43094051e-01
4.02983874e-01 -5.40089965e-01 -5.82633972e-01 -1.71802193e-01
2.86831737e-01 5.65580606e-01 3.62329364e-01 -3.14395159... | [10.372177124023438, 9.806989669799805] |
5ade4b63-71f7-4716-b8e8-ac44be8ea869 | a-closer-look-at-few-shot-3d-point-cloud | 2303.18210 | null | https://arxiv.org/abs/2303.18210v1 | https://arxiv.org/pdf/2303.18210v1.pdf | A Closer Look at Few-Shot 3D Point Cloud Classification | In recent years, research on few-shot learning (FSL) has been fast-growing in the 2D image domain due to the less requirement for labeled training data and greater generalization for novel classes. However, its application in 3D point cloud data is relatively under-explored. Not only need to distinguish unseen classes ... | ['Tao Chen', 'Bo Zhang', 'Hongyuan Zhu', 'Chuangguan Ye'] | 2023-03-31 | null | null | null | null | ['3d-point-cloud-classification', 'few-shot-3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-7.87301585e-02 -2.38933668e-01 -3.24815631e-01 -3.62061143e-01
-7.00555444e-01 -3.35163176e-01 6.55120075e-01 1.29211709e-01
-3.43283266e-03 2.35442072e-01 -2.73845464e-01 -1.74618766e-01
-1.90248013e-01 -8.42079043e-01 -6.19367898e-01 -5.50165057e-01
-2.17497736e-01 4.16739255e-01 7.01576829e-01 -2.08531424... | [7.965387344360352, -3.2819056510925293] |
6eea4111-bbb7-478a-8284-dff564321963 | simple-applications-of-bert-for-ad-hoc | 1903.10972 | null | http://arxiv.org/abs/1903.10972v1 | http://arxiv.org/pdf/1903.10972v1.pdf | Simple Applications of BERT for Ad Hoc Document Retrieval | Following recent successes in applying BERT to question answering, we explore
simple applications to ad hoc document retrieval. This required confronting the
challenge posed by documents that are typically longer than the length of input
BERT was designed to handle. We address this issue by applying inference on
senten... | ['Wei Yang', 'Haotian Zhang', 'Jimmy Lin'] | 2019-03-26 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 1.87558591e-01 2.13285331e-02 -1.72329191e-02 -5.69629014e-01
-1.42283940e+00 -8.55263889e-01 7.80730486e-01 4.79796082e-01
-1.01292253e+00 8.77151072e-01 4.25582051e-01 -5.88331461e-01
-5.96200287e-01 -7.91865766e-01 -6.45014703e-01 -1.64657563e-01
-4.43379939e-01 7.54318357e-01 5.71968377e-01 -5.37971377... | [11.295520782470703, 8.0549898147583] |
30a2c9cb-68b7-4198-b945-38702be6ab0c | learning-sparse-causal-models-is-not-np-hard | 1309.6824 | null | http://arxiv.org/abs/1309.6824v1 | http://arxiv.org/pdf/1309.6824v1.pdf | Learning Sparse Causal Models is not NP-hard | This paper shows that causal model discovery is not an NP-hard problem, in
the sense that for sparse graphs bounded by node degree k the sound and
complete causal model can be obtained in worst case order N^{2(k+2)}
independence tests, even when latent variables and selection bias may be
present. We present a modificat... | ['Joris Mooij', 'Tom Heskes', 'Tom Claassen'] | 2013-09-26 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 4.86032695e-01 8.89167786e-01 -6.31112039e-01 -3.53471935e-01
-5.69532633e-01 -7.01010227e-01 3.76170576e-01 -6.96815476e-02
4.88427607e-03 1.45774698e+00 9.63316634e-02 -6.45515978e-01
-1.23156381e+00 -1.00918531e+00 -8.43647778e-01 -8.98843706e-01
-8.61647666e-01 1.33516371e+00 4.30013448e-01 4.20337290... | [7.695193290710449, 5.279510974884033] |
4d366686-36f0-43c2-9498-681c4a8e4a2f | multilingual-text-to-speech-synthesis-for | 2305.15749 | null | https://arxiv.org/abs/2305.15749v1 | https://arxiv.org/pdf/2305.15749v1.pdf | Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration | This work aims to build a multilingual text-to-speech (TTS) synthesis system for ten lower-resourced Turkic languages: Azerbaijani, Bashkir, Kazakh, Kyrgyz, Sakha, Tatar, Turkish, Turkmen, Uyghur, and Uzbek. We specifically target the zero-shot learning scenario, where a TTS model trained using the data of one language... | ['Yerbolat Khassanov', 'Saida Mussakhojayeva', 'Rustem Yeshpanov'] | 2023-05-25 | null | null | null | null | ['transliteration', 'text-to-speech-synthesis', 'speech-synthesis'] | ['natural-language-processing', 'speech', 'speech'] | [-1.93077564e-01 1.11080967e-01 2.41934732e-01 -3.02740514e-01
-1.09489632e+00 -9.04625595e-01 8.16896021e-01 -6.92095399e-01
-2.68003613e-01 8.95677388e-01 1.64801329e-01 -7.81813323e-01
4.34325218e-01 -3.16706896e-01 -5.04100025e-01 -6.33914411e-01
3.97322983e-01 8.90335858e-01 -7.97011778e-02 -4.33240175... | [14.512297630310059, 7.051295757293701] |
d6cb044a-5daf-4a3c-a5b4-96f20cc40f6f | increasing-trustworthiness-of-deep-neural | 2007.01472 | null | https://arxiv.org/abs/2007.01472v1 | https://arxiv.org/pdf/2007.01472v1.pdf | Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring | Inference accuracy of deep neural networks (DNNs) is a crucial performance metric, but can vary greatly in practice subject to actual test datasets and is typically unknown due to the lack of ground truth labels. This has raised significant concerns with trustworthiness of DNNs, especially in safety-critical applicatio... | ['Zhihui Shao', 'Shaolei Ren', 'Jianyi Yang'] | 2020-07-03 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 2.29881734e-01 1.27864763e-01 -1.31635368e-01 -8.13538849e-01
-9.17477846e-01 -6.66657269e-01 4.60904509e-01 -2.29326814e-01
-8.32207203e-01 9.30711865e-01 -6.40545547e-01 -7.05221534e-01
1.23517692e-01 -8.17407131e-01 -1.28594220e+00 -6.99970603e-01
8.88647884e-02 4.46495622e-01 5.31274736e-01 4.57967550... | [5.658976078033447, 7.603898525238037] |
1647bf0d-aa97-4920-82bd-0d3db8d1f251 | m-genseg-domain-adaptation-for-target | 2212.07276 | null | https://arxiv.org/abs/2212.07276v1 | https://arxiv.org/pdf/2212.07276v1.pdf | M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision | Automated medical image segmentation using deep neural networks typically requires substantial supervised training. However, these models fail to generalize well across different imaging modalities. This shortcoming, amplified by the limited availability of annotated data, has been hampering the deployment of such meth... | ['Samuel Kadoury', 'Eugene Vorontsov', "Malo Alefsen de Boisredon d'Assier"] | 2022-12-14 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 8.57923865e-01 2.64697462e-01 -3.42894375e-01 -6.02898955e-01
-1.38696241e+00 -6.51915193e-01 6.08108103e-01 -1.59464985e-01
-6.49945796e-01 8.39655936e-01 8.16762596e-02 -1.85868219e-01
2.81927347e-01 -3.01489711e-01 -5.80516338e-01 -1.07513440e+00
3.19346875e-01 5.27363062e-01 2.98472494e-01 9.78487134... | [14.558452606201172, -2.101393222808838] |
4f86edd1-96a5-4563-8e10-c4483499896c | a-new-level-set-based-protocol-for-accurate | 1505.03093 | null | http://arxiv.org/abs/1505.03093v1 | http://arxiv.org/pdf/1505.03093v1.pdf | A new Level-set based Protocol for Accurate Bone Segmentation from CT Imaging | In this work it is proposed a medical image segmentation pipeline for
accurate bone segmentation from CT imaging. It is a two-step methodology, with
a pre-segmentation step and a segmentation refinement step. First, the user
performs a rough segmenting of the desired region of interest. Next, a fully
automatic refineme... | ['Manuel Pinheiro', 'J. L. Alves'] | 2015-05-12 | null | null | null | null | ['image-deconvolution', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [ 5.79517365e-01 3.10554355e-01 5.84077299e-01 -3.14327925e-01
-8.18548858e-01 6.48792312e-02 9.20756906e-02 5.47020316e-01
-8.97828937e-01 5.17471254e-01 -2.78011054e-01 -1.78585857e-01
-5.15289009e-02 -9.50596333e-01 -3.34108353e-01 -5.64411044e-01
1.80611163e-01 1.24552178e+00 8.21033180e-01 -1.14959981... | [14.031861305236816, -2.669954538345337] |
05431761-488a-460a-a7e6-dce96e89f31e | the-emotion-is-not-one-hot-encoding-learning | 2206.07359 | null | https://arxiv.org/abs/2206.07359v2 | https://arxiv.org/pdf/2206.07359v2.pdf | The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in Conversation | In emotion recognition in conversation (ERC), the emotion of the current utterance is predicted by considering the previous context, which can be utilized in many natural language processing tasks. Although multiple emotions can coexist in a given sentence, most previous approaches take the perspective of a classificat... | ['Joosung Lee'] | 2022-06-15 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.74691880e-01 -2.04195172e-01 -1.29189074e-01 -9.37170327e-01
-6.94494963e-01 -4.61732209e-01 2.64189839e-01 3.20698798e-01
-4.00112629e-01 5.79790950e-01 2.19160795e-01 8.59492868e-02
5.34861028e-01 -6.21192157e-01 -1.71972737e-01 -6.96915746e-01
1.86819613e-01 7.20378458e-02 -1.61396742e-01 -9.23587903... | [13.137177467346191, 5.88057804107666] |
14958995-d7cb-43e6-b993-a85e30f93ef4 | disparate-censorship-undertesting-a-source-of | 2208.01127 | null | https://arxiv.org/abs/2208.01127v1 | https://arxiv.org/pdf/2208.01127v1.pdf | Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning | As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, ... | ['Jenna Wiens', 'Michael W. Sjoding', 'Trenton Chang'] | 2022-08-01 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 3.64122391e-01 2.73850173e-01 -9.15261626e-01 -8.49463284e-01
-6.33311927e-01 -9.07284677e-01 2.70014375e-01 8.42839897e-01
-3.11793417e-01 7.10715652e-01 5.80247939e-01 -1.05967224e+00
-2.99223810e-01 -4.40957725e-01 -5.87381482e-01 -3.64248157e-01
-1.27153397e-01 6.91694736e-01 -6.54599488e-01 6.76381767... | [8.046419143676758, 5.513938903808594] |
f2f471d4-4e97-4813-91d9-9ed67f490c69 | slicenstitch-continuous-cp-decomposition-of | 2102.11517 | null | https://arxiv.org/abs/2102.11517v2 | https://arxiv.org/pdf/2102.11517v2.pdf | SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams | Consider traffic data (i.e., triplets in the form of source-destination-timestamp) that grow over time. Tensors (i.e., multi-dimensional arrays) with a time mode are widely used for modeling and analyzing such multi-aspect data streams. In such tensors, however, new entries are added only once per period, which is ofte... | ['Kijung Shin', 'Dongjin Lee', 'Inkyu Park', 'Taehyung Kwon'] | 2021-02-23 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-3.07567030e-01 -8.07771564e-01 4.68525216e-02 -1.59019232e-01
-1.19522989e-01 -8.75737786e-01 3.90251070e-01 2.14589700e-01
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-4.00889009e-01 -8.26482236e-01 -5.03974438e-01 -6.72343314e-01
-6.05547249e-01 9.29878056e-01 3.71297300e-01 -1.83140412... | [7.206669330596924, 2.9109768867492676] |
0e3e83b9-5f4c-44f3-a73c-caef017361d7 | efficient-gpt-model-pre-training-using-tensor | 2306.02697 | null | https://arxiv.org/abs/2306.02697v1 | https://arxiv.org/pdf/2306.02697v1.pdf | Efficient GPT Model Pre-training using Tensor Train Matrix Representation | Large-scale transformer models have shown remarkable performance in language modelling tasks. However, such models feature billions of parameters, leading to difficulties in their deployment and prohibitive training costs from scratch. To reduce the number of the parameters in the GPT-2 architecture, we replace the mat... | ['Alexander Panchenko', 'Ivan Oseledets', 'Julia Gusak', 'Georgii Novikov', 'Viktoriia Chekalina'] | 2023-06-05 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [-6.34804368e-03 3.66913944e-01 9.66043174e-02 -3.31022203e-01
-5.90645909e-01 -5.02637267e-01 5.17984271e-01 1.25282386e-03
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-1.90698117e-01 6.83810532e-01 1.55541286e-01 -2.93648630... | [10.800053596496582, 7.578328609466553] |
0f77c132-bc81-47b8-aa71-0722bfab053d | gemini-controlling-the-sentence-level-writing | 2304.03548 | null | https://arxiv.org/abs/2304.03548v1 | https://arxiv.org/pdf/2304.03548v1.pdf | GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization | Human experts write summaries using different techniques, including rewriting a sentence in the document or fusing multiple sentences to generate a summary sentence. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that in... | ['Yue Zhang', 'Zebin Ou', 'Guangsheng Bao'] | 2023-04-07 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.80798095e-01 6.25557527e-02 -1.59135610e-01 -5.97594917e-01
-9.09725606e-01 -1.04476130e+00 7.86006927e-01 -6.50077267e-03
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8.96466732e-01 4.25075561e-01 8.31101388e-02 -3.44565332... | [12.209059715270996, 9.243310928344727] |
f3299660-6140-4155-bc2e-cac29e65a705 | arena-rosnav-2-0-a-development-and | 2302.10023 | null | https://arxiv.org/abs/2302.10023v1 | https://arxiv.org/pdf/2302.10023v1.pdf | Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments | Following up on our previous works, in this paper, we present Arena-Rosnav 2.0 an extension to our previous works Arena-Bench and Arena-Rosnav, which adds a variety of additional modules for developing and benchmarking robotic navigation approaches. The platform is fundamentally restructured and provides unified APIs t... | ['Jens Lambrecht', 'Boris Meinardus', 'Teham Bhuiyan', 'Tuan Anh Le', 'Jacek Kmiecik', 'Huajian Zeng', 'Reyk Carstens', 'Linh Kästner'] | 2023-02-20 | null | null | null | null | ['robot-navigation'] | ['robots'] | [-7.77615964e-01 5.17958552e-02 2.26492658e-01 -2.88619041e-01
-5.50729513e-01 -1.09849000e+00 5.16655922e-01 -5.14739752e-02
-4.95015800e-01 8.50392401e-01 2.12321743e-01 -8.65143597e-01
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-3.10888886e-01 5.12040734e-01 7.80705690e-01 -1.13854516... | [4.577202320098877, 0.9560837745666504] |
fe6b633f-c7bc-4fa1-95f0-ae1620fcca1f | neural-wave-functions-for-superfluids | 2305.06989 | null | https://arxiv.org/abs/2305.06989v2 | https://arxiv.org/pdf/2305.06989v2.pdf | Neural Wave Functions for Superfluids | Understanding superfluidity remains a major goal of condensed matter physics. Here we tackle this challenge utilizing the recently developed Fermionic neural network (FermiNet) wave function Ansatz for variational Monte Carlo calculations. We study the unitary Fermi gas, a system with strong, short-range, two-body inte... | ['James S. Spencer', 'David Pfau', 'Johannes Knolle', 'W. M. C. Foulkes', 'Gino Cassella', 'Halvard Sutterud', 'Wan Tong Lou'] | 2023-05-11 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 1.25550792e-01 -2.61427790e-01 1.78541988e-01 -2.70085752e-01
-7.78174624e-02 -4.95525658e-01 8.15174937e-01 -4.84936565e-01
-5.20488739e-01 1.20108211e+00 -1.18959032e-01 -5.63214600e-01
-1.70643419e-01 -9.00694013e-01 -5.25838494e-01 -1.36604941e+00
-3.04132879e-01 6.99164271e-01 3.19206476e-01 -8.87903154... | [5.448435306549072, 5.068500518798828] |
2a131007-7475-4172-ac0d-d4dba3635cd8 | effects-of-differential-privacy-and-data | 1911.09777 | null | https://arxiv.org/abs/1911.09777v1 | https://arxiv.org/pdf/1911.09777v1.pdf | Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability | Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evaluation system, called MPLens, with three unique contributions. First, through MPLens, we demonstrate how membership inference attack methods ... | ['Stacey Truex', 'Mehmet Emre Gursoy', 'Lei Yu', 'Ling Liu', 'Wenqi Wei'] | 2019-11-21 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 2.67199814e-01 3.02099347e-01 -1.11516669e-01 -4.93155926e-01
-9.09339428e-01 -1.15240717e+00 5.68831980e-01 9.13169086e-02
-3.21614921e-01 7.03657746e-01 -1.44906104e-01 -5.72725117e-01
-2.29453862e-01 -8.75168085e-01 -1.00605357e+00 -7.37023890e-01
-2.93084800e-01 2.60377735e-01 -3.07825178e-01 1.50835454... | [5.937662601470947, 7.08242130279541] |
b427c1b5-d185-4fda-8969-bd03338c3bec | unsupervised-artifact-detection-for-whole | null | null | https://openreview.net/forum?id=j9JTX5IwPC | https://openreview.net/pdf?id=j9JTX5IwPC | Unsupervised Artifact Detection for Whole Slide Images of Prostate Biopsies | High-quality image digitisation of histological slides is essential for digital pathology to facilitate diagnosis and to develop reliable computer-aided assistance systems. Currently, image quality control (QC) to identify artefacts that result from slide preparation, staining, or scanning is mainly conducted manually,... | ['Arto Järvinen', 'Andrew Janowczyk', 'Kristian Eurén', 'Yijiang Chen', 'Nadieh Khalili', 'Walter de Back', 'Amit Suveer'] | 2021-09-29 | null | null | null | iclr-2022 | ['one-class-classification'] | ['miscellaneous'] | [ 6.80342436e-01 1.83418527e-01 4.99972522e-01 -3.27702463e-01
-1.47304666e+00 -7.04043150e-01 6.31186485e-01 8.33216608e-01
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-1.21353425e-01 4.30976331e-01 2.21566617e-01 2.93264091... | [15.099752426147461, -2.9673826694488525] |
608ce8e3-8537-417f-a798-d954e7970413 | towards-controllable-agent-in-moba-games-with | 2112.08093 | null | https://arxiv.org/abs/2112.08093v1 | https://arxiv.org/pdf/2112.08093v1.pdf | Towards Controllable Agent in MOBA Games with Generative Modeling | We propose novel methods to develop action controllable agent that behaves like a human and has the ability to align with human players in Multiplayer Online Battle Arena (MOBA) games. By modeling the control problem as an action generation process, we devise a deep latent alignment neural network model for training ag... | ['Shubao Zhang'] | 2021-12-15 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [-0.02820425 0.3135816 -0.03295505 -0.07626566 -0.41386008 -0.46079046
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-0.1241602 -0.48642048 0.13734731 0.07001867 -0.9812556 -0.07907677
0.71911126 0.6162148 0.1... | [3.7143454551696777, 1.5431870222091675] |
426549e2-69c4-4fb1-b781-4f33979b8c79 | always-strengthen-your-strengths-a-drift | 2304.09062 | null | https://arxiv.org/abs/2304.09062v1 | https://arxiv.org/pdf/2304.09062v1.pdf | Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction | Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data observed by the CTR model typically arrives as a stream. Streaming data has the characteristic that the underlying distribution drifts over ... | ['Jingping Shao', 'Jinghe Hu', 'Zhangang Lin', 'Xiwei Zhao', 'Fei Teng', 'Congcong Liu'] | 2023-04-17 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 1.05333827e-01 -5.01962304e-01 -3.40382308e-01 -2.30932117e-01
-3.12096685e-01 -4.34641898e-01 2.26398572e-01 2.78104931e-01
-1.31397784e-01 8.88997853e-01 -2.97602981e-01 -3.04250509e-01
-1.17835984e-01 -6.97503984e-01 -8.36512506e-01 -8.22889090e-01
-2.08463356e-01 5.98753095e-01 5.79225421e-01 -3.67396206... | [7.5717082023620605, 3.0970537662506104] |
2637487f-7dc3-457e-a78e-8b65a089b5b7 | a-warm-start-and-a-clean-crawled-corpus-a-1 | null | null | https://aclanthology.org/2022.lrec-1.464 | https://aclanthology.org/2022.lrec-1.464.pdf | A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models | We train several language models for Icelandic, including IceBERT, that achieve state-of-the-art performance in a variety of downstream tasks, including part-of-speech tagging, named entity recognition, grammatical error detection and constituency parsing. To train the models we introduce a new corpus of Icelandic text... | ['Hafsteinn Einarsson', 'Vilhjalmur THorsteinsson', 'Haukur Jónsson', 'Svanhvít Lilja Ingólfsdóttir', 'Pétur Orri Ragnarsson', 'Haukur Barri Símonarson', 'Vésteinn Snæbjarnarson'] | null | null | null | null | lrec-2022-6 | ['grammatical-error-detection', 'constituency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [-1.69976711e-01 3.27215165e-01 -1.04377523e-01 -4.01048034e-01
-1.64801908e+00 -1.07701027e+00 6.04299903e-01 3.42840940e-01
-7.68315017e-01 9.25647676e-01 6.31425321e-01 -5.97006083e-01
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-2.60226503e-02 9.52541590e-01 1.94153674e-02 -4.77193117... | [10.471439361572266, 9.838172912597656] |
9e8e6d6f-1f60-4fa5-99f0-c6403810c6cf | occlusion-aware-networks-for-3d-human-pose | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Cheng_Occlusion-Aware_Networks_for_3D_Human_Pose_Estimation_in_Video_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Cheng_Occlusion-Aware_Networks_for_3D_Human_Pose_Estimation_in_Video_ICCV_2019_paper.pdf | Occlusion-Aware Networks for 3D Human Pose Estimation in Video | Occlusion is a key problem in 3D human pose estimation from a monocular video. To address this problem, we introduce an occlusion-aware deep-learning framework. By employing estimated 2D confidence heatmaps of keypoints and an optical-flow consistency constraint, we filter out the unreliable estimations of occluded key... | [' Robby T. Tan', ' Wending Yan', ' Bo Wang', ' Bo Yang', 'Yu Cheng'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-4.77414489e-01 4.37594540e-02 -2.87788749e-01 -3.00235361e-01
-2.55783349e-01 -3.71380627e-01 3.02439302e-01 -3.50262821e-01
-6.14069700e-01 6.70713186e-01 1.67171448e-01 3.13723207e-01
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-2.05809236e-01 5.90979874e-01 3.08860481e-01 1.12886086... | [7.002685070037842, -0.9564274549484253] |
a0723f9b-c645-4fd3-990e-7c0bf233f349 | group-activity-recognition-using-joint | null | null | http://www.mva-org.jp/Proceedings/2021/papers/O1-2-2.pdf | http://www.mva-org.jp/Proceedings/2021/papers/O1-2-2.pdf | Group Activity Recognition Using Joint Learning of Individual Action Recognition and People Grouping | This paper proposes joint learning of individual action recognition and people grouping for improving group activity recognition. By sharing the information between two similar tasks (i.e., individual action recognition and people grouping) through joint learning, errors of these two tasks are mutually corrected. This ... | ['Norimichi Ukita', 'Kohei Sendo', 'Chihiro Nakatani'] | 2021-07-17 | null | null | null | mva-2021-7 | ['group-activity-recognition'] | ['computer-vision'] | [ 4.10004586e-01 -9.16270763e-02 -3.95650864e-01 -2.68072307e-01
-7.70660460e-01 5.90235516e-02 7.61419117e-01 -2.97861844e-01
-5.42398989e-01 8.87118638e-01 4.09321457e-01 2.49957055e-01
-5.47620766e-02 -4.78819579e-01 -3.05242538e-01 -8.42470288e-01
-9.18195918e-02 2.47692510e-01 2.17743382e-01 4.44036245... | [8.163925170898438, 0.6534997224807739] |
f9df5c85-d408-48f8-b663-7662d789f71a | a-survey-of-surface-defect-detection-of | 2203.05733 | null | https://arxiv.org/abs/2203.05733v1 | https://arxiv.org/pdf/2203.05733v1.pdf | A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data | The surface defect detection method based on visual perception has been widely used in industrial quality inspection. Because defect data are not easy to obtain and the annotation of a large number of defect data will waste a lot of manpower and material resources. Therefore, this paper reviews the methods of surface d... | ['Li Chen', 'Qifan Jin'] | 2022-03-11 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 4.99608368e-01 5.33498004e-02 9.70301703e-02 -3.00787151e-01
-2.91265994e-01 -2.13011187e-02 7.18613043e-02 4.34533775e-01
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-7.27255642e-02 -1.05273068e+00 1.81179624e-02 -6.45690084e-01
-7.62973651e-02 7.09263384e-01 4.02058661e-01 -2.27638736... | [7.408583641052246, 1.8989673852920532] |
1d80f23f-d08e-4cc8-af80-5120361508c5 | virtual-screening-of-plant-metabolites | 2005.11254 | null | https://arxiv.org/abs/2005.11254v1 | https://arxiv.org/pdf/2005.11254v1.pdf | Virtual Screening of Plant Metabolites against Main protease, RNA-dependent RNA polymerase and Spike protein of SARS-CoV-2: Therapeutics option of COVID-19 | Covid-19, a serious respiratory complications caused by SARS-CoV-2 has become one of the global threat to human healthcare system. The present study evaluated the possibility of plant originated approved 117 therapeutics against the main protease protein (MPP), RNA-dependent RNA polymerase (RdRp) and spike protein (S) ... | ['Mahmudul Hasan', 'Farhana Rumzum Bhuiyan', 'Sabbir Howlader', 'Tasfia Saiyara Shammi', 'Aklima Begum', 'Topu Raihan', 'Abdus Shukur Imran', 'Kazi Faizul Azim', 'Md Sorwer Alam Parvez'] | 2020-05-22 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.29046381e-01 -2.95212537e-01 -1.52283132e-01 3.14410180e-01
-2.04209968e-01 -8.43307674e-01 3.61892246e-02 4.85226661e-01
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-9.77828056e-02 -3.64372224e-01 -3.88095737e-01 -1.07957780e+00
-1.52760684e-01 6.25433251e-02 -7.39154220e-02 -3.16975892... | [4.665122032165527, 5.097651958465576] |
12945276-aebe-4aa4-a325-60c35c937c5c | dynaeval-unifying-turn-and-dialogue-level | 2106.01112 | null | https://arxiv.org/abs/2106.01112v3 | https://arxiv.org/pdf/2106.01112v3.pdf | DynaEval: Unifying Turn and Dialogue Level Evaluation | A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are focused very much on the turn-level quality, while ignoring such dynamics. To this end, we propose DynaEval, a unified automatic evaluation... | ['Haizhou Li', 'Grandee Lee', 'Thomas Friedrichs', 'Yan Zhang', "Luis Fernando D'Haro", 'Yiming Chen', 'Chen Zhang'] | 2021-06-02 | null | https://aclanthology.org/2021.acl-long.441 | https://aclanthology.org/2021.acl-long.441.pdf | acl-2021-5 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-3.85759205e-01 4.50607061e-01 6.37905672e-02 -6.73822165e-01
-5.47447026e-01 -6.83164239e-01 9.99234617e-01 2.23008201e-01
-1.10462740e-01 5.56735933e-01 6.70873761e-01 -6.53975382e-02
2.00282544e-01 -7.20259488e-01 8.81924778e-02 -4.41870570e-01
1.03802420e-02 7.22013652e-01 -4.74616028e-02 -1.03331995... | [12.726475715637207, 8.143411636352539] |
2059603a-8649-4846-b2c7-332c11a4acd6 | systematic-generalization-for-predictive | 2102.05602 | null | https://arxiv.org/abs/2102.05602v2 | https://arxiv.org/pdf/2102.05602v2.pdf | Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting Models | Systematic generalization aims to evaluate reasoning about novel combinations from known components, an intrinsic property of human cognition. In this work, we study systematic generalization of NNs in forecasting future time series of dependent variables in a dynamical system, conditioned on past time series of depend... | ['Prathosh A. P', 'Pankaj Malhotra', 'Gantavya Bhatt', 'Hritik Bansal'] | 2021-02-10 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 1.12915024e-01 1.60395861e-01 -1.74547747e-01 -5.04716337e-01
5.38228601e-02 -8.41088057e-01 1.02186894e+00 -5.07965349e-02
-1.28028886e-02 1.07315254e+00 1.61006972e-01 -6.02665186e-01
-5.67099035e-01 -1.03193843e+00 -8.45795512e-01 -7.78998196e-01
-6.07915998e-01 5.66262841e-01 -8.22974294e-02 -6.17770195... | [7.082828521728516, 3.182791233062744] |
59927c91-ab5c-4844-8456-8784b9bc0eeb | confidence-guided-stereo-3d-object-detection | 2003.05505 | null | https://arxiv.org/abs/2003.05505v1 | https://arxiv.org/pdf/2003.05505v1.pdf | Confidence Guided Stereo 3D Object Detection with Split Depth Estimation | Accurate and reliable 3D object detection is vital to safe autonomous driving. Despite recent developments, the performance gap between stereo-based methods and LiDAR-based methods is still considerable. Accurate depth estimation is crucial to the performance of stereo-based 3D object detection methods, particularly fo... | ['Jason Ku', 'Steven L. Waslander', 'Chengyao Li'] | 2020-03-11 | null | null | null | null | ['3d-object-detection-from-stereo-images'] | ['computer-vision'] | [ 1.62261903e-01 -1.24970727e-01 -2.48634331e-02 -3.87884736e-01
-8.53229821e-01 -4.27335560e-01 6.24862254e-01 2.24118352e-01
-6.19474888e-01 2.46833935e-01 -3.45795512e-01 -4.44024652e-01
5.48975408e-01 -6.85941458e-01 -8.42897058e-01 -6.83292031e-01
3.07004601e-01 6.50765598e-01 1.30857146e+00 2.48726934... | [7.773555755615234, -2.5683035850524902] |
3437ab95-6d13-4763-8282-2225a5dc94aa | multi-task-adversarial-network-for | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_Multi-Task_Adversarial_Network_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Multi-Task_Adversarial_Network_CVPR_2018_paper.pdf | Multi-Task Adversarial Network for Disentangled Feature Learning | We address the problem of image feature learning for the applications where multiple factors exist in the image generation process and only some factors are of our interest. We present a novel multi-task adversarial network based on an encoder-discriminator-generator architecture. The encoder extracts a disentangled fe... | ['Zhaowen Wang', 'Ian Wassell', 'Yang Liu', 'Hailin Jin'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['font-recognition'] | ['computer-vision'] | [ 3.53379846e-01 2.09323838e-01 -4.03364860e-02 -3.23812395e-01
-8.32282603e-01 -7.10236073e-01 5.47320724e-01 -9.71561015e-01
-2.15855107e-01 7.14483202e-01 1.98196307e-01 1.36252612e-01
-1.01066409e-02 -4.89835709e-01 -1.09795928e+00 -1.14255071e+00
8.33013952e-02 3.99572253e-01 -3.93826902e-01 -1.21955447... | [12.807409286499023, 0.0918397456407547] |
f36ec4c1-c06d-4d61-8fc9-8d1b6d2f1961 | geometric-algebra-based-embeddings-for | 2202.09464 | null | https://arxiv.org/abs/2202.09464v3 | https://arxiv.org/pdf/2202.09464v3.pdf | Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion | Recent years, Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a Knowledge Graph (KG) into a geometric space and thus have gained increasing attentions. In addition, many recent Knowledge Graphs involve evolving data, e.g., the fact (... | ['Jens Lehmann', 'Yung-Yu Chen', 'Mojtaba Nayyeri', 'Chengjin Xu'] | 2022-02-18 | null | null | null | null | ['knowledge-graph-embeddings', 'temporal-knowledge-graph-completion', 'knowledge-graph-embeddings'] | ['graphs', 'knowledge-base', 'methodology'] | [-6.32212043e-01 -1.33742481e-01 -5.61099291e-01 -3.30774374e-02
7.79775158e-02 -4.77406770e-01 7.01210201e-01 6.02102220e-01
-2.65082300e-01 6.59704924e-01 1.11300997e-01 -5.15292585e-01
-8.45385611e-01 -1.22132838e+00 -7.10491538e-01 -4.24878985e-01
-8.11093628e-01 5.08199155e-01 3.09994280e-01 -4.25250798... | [8.557193756103516, 7.905128002166748] |
969eb610-1528-47ff-8cf0-02098cfc8d42 | pix2nerf-unsupervised-conditional-p-gan-for-1 | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cai_Pix2NeRF_Unsupervised_Conditional_p-GAN_for_Single_Image_to_Neural_Radiance_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cai_Pix2NeRF_Unsupervised_Conditional_p-GAN_for_Single_Image_to_Neural_Radiance_CVPR_2022_paper.pdf | Pix2NeRF: Unsupervised Conditional p-GAN for Single Image to Neural Radiance Fields Translation | We propose a pipeline to generate Neural Radiance Fields (NeRF) of an object or a scene of a specific class, conditioned on a single input image. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. Our method is based on... | ['Luc van Gool', 'Dengxin Dai', 'Anton Obukhov', 'Shengqu Cai'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 5.62690318e-01 2.38277674e-01 2.42791638e-01 -3.79224688e-01
-1.05954814e+00 -6.31754100e-01 9.12171006e-01 -7.55784452e-01
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4.31548566e-01 -1.07243037e+00 -1.23399580e+00 -6.88003242e-01
6.51861131e-01 6.58396542e-01 -2.37102769e-02 -9.67564210... | [9.260529518127441, -3.132289409637451] |
574b3131-688e-43b6-95fa-3aefc15d9dc1 | self-distillation-amplifies-regularization-in | 2002.05715 | null | https://arxiv.org/abs/2002.05715v3 | https://arxiv.org/pdf/2002.05715v3.pdf | Self-Distillation Amplifies Regularization in Hilbert Space | Knowledge distillation introduced in the deep learning context is a method to transfer knowledge from one architecture to another. In particular, when the architectures are identical, this is called self-distillation. The idea is to feed in predictions of the trained model as new target values for retraining (and itera... | ['Peter L. Bartlett', 'Mehrdad Farajtabar', 'Hossein Mobahi'] | 2020-02-13 | null | http://proceedings.neurips.cc/paper/2020/hash/2288f691b58edecadcc9a8691762b4fd-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/2288f691b58edecadcc9a8691762b4fd-Paper.pdf | neurips-2020-12 | ['l2-regularization'] | ['methodology'] | [ 8.27898011e-02 5.80908358e-01 1.46277443e-01 -2.63079226e-01
-2.23636240e-01 -4.73907173e-01 6.02776408e-01 -1.23156987e-01
-5.88469803e-01 1.08726501e+00 -3.31575908e-02 -2.91526020e-01
6.79824501e-02 -7.18692958e-01 -1.03671944e+00 -9.28793371e-01
-2.63846572e-02 4.31089312e-01 3.57757173e-02 -3.06278169... | [7.830048084259033, 3.563427686691284] |
2b000fb1-b7a1-4d28-92e7-9ba9b5b0b421 | real-or-fake-spoofing-state-of-the-art-face | 1911.05351 | null | https://arxiv.org/abs/1911.05351v4 | https://arxiv.org/pdf/1911.05351v4.pdf | GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection | The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse. Such concerns hav... | ['Hugo Proença', 'Ruben Vera-Rodriguez', 'João C. Neves', 'Vasco Lopes', 'Ruben Tolosana', 'Julian Fierrez'] | 2019-11-13 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 7.26974070e-01 2.54502714e-01 3.43739808e-01 -1.64082069e-02
-2.76918828e-01 -3.61842662e-01 8.06637287e-01 -8.07935596e-01
3.30912229e-03 6.77558362e-01 -3.78586724e-02 3.09954910e-03
6.14044443e-02 -8.68825972e-01 -9.54978108e-01 -8.36284816e-01
-1.20336123e-01 -4.00814749e-02 -2.02014104e-01 -6.92625880... | [12.620269775390625, 1.077177882194519] |
3cbaf27a-44f7-459d-9512-7c1ba8e59fe2 | joint-2d-3d-multi-task-learning-on-cityscapes | 2304.00971 | null | https://arxiv.org/abs/2304.00971v3 | https://arxiv.org/pdf/2304.00971v3.pdf | Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth Estimation | This report serves as a supplementary document for TaskPrompter, detailing its implementation on a new joint 2D-3D multi-task learning benchmark based on Cityscapes-3D. TaskPrompter presents an innovative multi-task prompting framework that unifies the learning of (i) task-generic representations, (ii) task-specific re... | ['Dan Xu', 'Hanrong Ye'] | 2023-04-03 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [ 1.69809029e-01 4.74364683e-02 -2.11484641e-01 -3.84299517e-01
-1.14966261e+00 -5.96614838e-01 9.79216158e-01 -1.23158775e-01
-4.25748259e-01 1.68412611e-01 -1.84852734e-01 -4.35324252e-01
-2.07211003e-02 -4.51513052e-01 -6.40439272e-01 -7.00488985e-01
5.78909479e-02 7.60896385e-01 4.48250473e-01 -1.18292250... | [8.133723258972168, -1.7041726112365723] |
42458c88-c3e8-4850-9192-e92f94a55fed | fewer-is-more-efficient-object-detection-in | 2212.13136 | null | https://arxiv.org/abs/2212.13136v2 | https://arxiv.org/pdf/2212.13136v2.pdf | Fewer is More: Efficient Object Detection in Large Aerial Images | Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not. This paradigm, although effective, is inefficient because the detectors have to go through all p... | ['Junwei Han', 'Ke Li', 'Shicheng Miao', 'Qingyang Li', 'Gong Cheng', 'Xingxing Xie'] | 2022-12-26 | null | null | null | null | ['video-object-detection'] | ['computer-vision'] | [ 1.28312796e-01 -4.25088435e-01 -1.28075048e-01 -1.83705352e-02
-3.51219475e-01 -5.87357819e-01 8.63044895e-03 -1.03684865e-01
-4.70125616e-01 2.16282338e-01 -4.36358988e-01 -1.92766607e-01
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-2.53095925e-01 -1.15659349e-01 8.27088296e-01 2.30854517... | [8.761281967163086, -0.7196851372718811] |
b25a92b5-000e-4cdc-81e5-71c67050b1ed | unsupervised-feature-learning-with-emergent | 2307.01421 | null | https://arxiv.org/abs/2307.01421v1 | https://arxiv.org/pdf/2307.01421v1.pdf | Unsupervised Feature Learning with Emergent Data-Driven Prototypicality | Given an image set without any labels, our goal is to train a model that maps each image to a point in a feature space such that, not only proximity indicates visual similarity, but where it is located directly encodes how prototypical the image is according to the dataset. Our key insight is to perform unsupervised fe... | ['Stella X. Yu', 'Yubei Chen', 'Youren Zhang', 'Yunhui Guo'] | 2023-07-04 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 1.11559674e-01 1.23096608e-01 1.88468188e-01 -3.45059931e-01
-2.59356678e-01 -8.95122170e-01 6.63215339e-01 4.29091454e-01
-5.24338722e-01 3.53665799e-01 1.04925767e-01 1.28731072e-01
-6.29457712e-01 -8.77052844e-01 -7.67965972e-01 -1.12440920e+00
-1.40664861e-01 7.69145429e-01 1.62276775e-01 -1.24198370... | [9.166852951049805, 2.983635663986206] |
efc883ed-6c46-4259-b5e8-9322ae27669e | neural-group-recommendation-based-on-a | 2303.07001 | null | https://arxiv.org/abs/2303.07001v1 | https://arxiv.org/pdf/2303.07001v1.pdf | Neural Group Recommendation Based on a Probabilistic Semantic Aggregation | Recommendation to groups of users is a challenging subfield of recommendation systems. Its key concept is how and where to make the aggregation of each set of user information into an individual entity, such as a ranked recommendation list, a virtual user, or a multi-hot input vector encoding. This paper proposes an in... | ['Jesús Bobadilla', 'Fernando Ortega', 'Raúl Lara-Cabrera', 'Jorge Dueñas-Lerín'] | 2023-03-13 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 1.38151452e-01 6.88474625e-02 -2.48317942e-01 -9.23527896e-01
-3.31191152e-01 -4.75090832e-01 6.61943734e-01 3.28407884e-01
-4.48141247e-01 4.65383112e-01 4.05291975e-01 -4.33878005e-01
-3.52163345e-01 -1.09028447e+00 -6.76543057e-01 -4.79319781e-01
9.50552449e-02 6.33672237e-01 2.54576474e-01 -1.40405595... | [10.141571998596191, 5.702173709869385] |
d705caf7-573e-48f4-967c-71cbdb72f3b7 | occlusion-aware-instance-segmentation-via | 2208.04438 | null | https://arxiv.org/abs/2208.04438v2 | https://arxiv.org/pdf/2208.04438v2.pdf | Occlusion-Aware Instance Segmentation via BiLayer Network Architectures | Segmenting highly-overlapping image objects is challenging, because there is typically no distinction between real object contours and occlusion boundaries on images. Unlike previous instance segmentation methods, we model image formation as a composition of two overlapping layers, and propose Bilayer Convolutional Net... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Lei Ke'] | 2022-08-08 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.06581795e-01 4.03167188e-01 -2.90070623e-01 -2.43268251e-01
-4.70498443e-01 -6.83928967e-01 4.96408999e-01 -4.51663956e-02
-2.20546365e-01 3.82923961e-01 -3.21021885e-01 -2.44349048e-01
5.06377667e-02 -7.20647693e-01 -1.06822371e+00 -5.14028311e-01
-6.98387623e-02 4.83998448e-01 8.99393737e-01 -1.26429228... | [9.455224990844727, 0.18913201987743378] |
bdf3343a-c9c2-48d1-8ce7-85994be49041 | edge-aided-sensor-data-sharing-in-vehicular | 2206.08882 | null | https://arxiv.org/abs/2206.08882v1 | https://arxiv.org/pdf/2206.08882v1.pdf | Edge-Aided Sensor Data Sharing in Vehicular Communication Networks | Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and schemes for dissemination and fusion of sensor data have been developed. It is common to these schemes that measurement errors of the sensors im... | ['Andreas Festag', 'Alois Knoll', 'Numan Senel', 'Anupama Hegde', 'Rui Song'] | 2022-06-17 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 1.10959843e-01 1.49578020e-01 -5.20700812e-02 -3.49310666e-01
-4.49822336e-01 -5.30942857e-01 4.79583174e-01 4.86960143e-01
-6.06818020e-01 8.37538719e-01 -2.59011954e-01 -3.37854028e-01
-7.62410164e-02 -9.89825904e-01 -8.80140424e-01 -9.92211461e-01
-1.60022542e-01 -9.70669463e-02 8.86885762e-01 -1.51270509... | [5.716083526611328, 1.5058810710906982] |
c3081861-ec50-4e28-863b-b66d47214528 | gradient-informed-quality-diversity-for-the | 2306.05138 | null | https://arxiv.org/abs/2306.05138v1 | https://arxiv.org/pdf/2306.05138v1.pdf | Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces | Quality Diversity (QD) algorithms have been proposed to search for a large collection of both diverse and high-performing solutions instead of a single set of local optima. While early QD algorithms view the objective and descriptor functions as black-box functions, novel tools have been introduced to use gradient info... | ['Antoine Cully', 'Thomas Pierrot', 'Jérémie Dona', 'Guillaume Richard', 'Raphael Boige'] | 2023-06-08 | null | null | null | null | ['drug-discovery', 'protein-design'] | ['medical', 'medical'] | [ 2.98866838e-01 -3.88865888e-01 -2.07117841e-01 -2.53691733e-01
-1.34749591e+00 -8.95011127e-01 4.58126426e-01 1.12698726e-01
-2.97298789e-01 1.06566751e+00 2.98006713e-01 -1.10296853e-01
-4.62389380e-01 -4.64080751e-01 -8.01961482e-01 -9.74253058e-01
6.07799888e-02 6.60054684e-01 -2.19531171e-03 -1.70893863... | [4.934123992919922, 5.43230676651001] |
e6b4d683-9304-41ed-b200-b996d740f5cc | cardiac-arrhythmia-detection-from-ecg | 1801.10033 | null | http://arxiv.org/abs/1801.10033v1 | http://arxiv.org/pdf/1801.10033v1.pdf | Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks | Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder
associated with deadly and debilitating consequences including heart failure,
stroke, poor mental health, reduced quality of life and death. Having an
automatic system that diagnoses various types of cardiac arrhythmias would
assist cardiologists to... | ['Masun Nabhan Homsi', 'Philip Warrick'] | 2018-01-30 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 4.08083975e-01 -1.36710539e-01 3.17825302e-02 -4.43816841e-01
-5.07109463e-01 -3.98386717e-01 -1.26432374e-01 3.75032991e-01
-5.87291479e-01 1.22500038e+00 -1.49691552e-01 -5.34300566e-01
-3.56406093e-01 -5.59513688e-01 -3.29671472e-01 -6.34332657e-01
-6.08342826e-01 2.63076782e-01 -3.56133491e-01 3.70359004... | [14.304343223571777, 3.292480945587158] |
0ecd3d50-4f64-4c36-aaa6-b50e434c4c14 | generalization-algorithm-of-multimodal-pre | 2302.10315 | null | https://arxiv.org/abs/2302.10315v1 | https://arxiv.org/pdf/2302.10315v1.pdf | Generalization algorithm of multimodal pre-training model based on graph-text self-supervised training | Recently, a large number of studies have shown that the introduction of visual information can effectively improve the effect of neural machine translation (NMT). Its effectiveness largely depends on the availability of a large number of bilingual parallel sentence pairs and manual image annotation. The lack of images ... | ['Fuxianghua', 'Longzi', 'Tangzhenhao', 'Zhangxiaobing'] | 2023-02-16 | null | null | null | null | ['nmt', 'multimodal-machine-translation'] | ['computer-code', 'natural-language-processing'] | [ 2.76375562e-01 -1.65792421e-01 -2.57154375e-01 -2.28824317e-01
-1.06454849e+00 -7.41456270e-01 6.19995415e-01 -2.99925089e-01
-6.53791130e-01 7.47774243e-01 1.49083748e-01 -4.98405993e-01
5.58842957e-01 -4.15153414e-01 -1.06826079e+00 -5.90602517e-01
5.81194162e-01 5.21580160e-01 2.09148601e-01 -2.10730329... | [11.4533052444458, 1.4707562923431396] |
f673b0ad-358c-4ca1-9237-959d2a8cb2d7 | an-integrated-multi-time-scale-modeling-for | 1905.02616 | null | https://arxiv.org/abs/1905.02616v2 | https://arxiv.org/pdf/1905.02616v2.pdf | An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning | For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher ... | ['Sakshi Mishra', 'Praveen Palanisamy'] | 2019-05-07 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [-3.42208035e-02 -3.63060236e-01 2.43408427e-01 -2.71478087e-01
-3.51399451e-01 -6.96512401e-01 6.19846880e-01 3.26630101e-02
2.81340986e-01 9.07020748e-01 -5.35120964e-02 -7.54131317e-01
-2.85322487e-01 -9.84182596e-01 -3.58237684e-01 -8.37889910e-01
-1.34009004e-01 -3.09517443e-01 -4.60588247e-01 -3.11517924... | [6.236677646636963, 2.7851836681365967] |
ca54bf93-e330-4a73-9c81-cd15feb4c7b4 | temporal-point-cloud-completion-with-pose | 2202.03084 | null | https://arxiv.org/abs/2202.03084v1 | https://arxiv.org/pdf/2202.03084v1.pdf | Temporal Point Cloud Completion with Pose Disturbance | Point clouds collected by real-world sensors are always unaligned and sparse, which makes it hard to reconstruct the complete shape of object from a single frame of data. In this work, we manage to provide complete point clouds from sparse input with pose disturbance by limited translation and rotation. We also use tem... | ['Shaojie Shen', 'Xiaozhi Chen', 'Peiliang Li', 'Lingyun Xu', 'Jieqi Shi'] | 2022-02-07 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 6.08074442e-02 -1.14157982e-01 6.41847774e-02 -2.61982650e-01
-4.43799853e-01 -6.97163999e-01 5.24894476e-01 -4.38536853e-02
-2.46754318e-01 3.42526436e-01 -1.38798356e-01 -9.00044367e-02
9.02861804e-02 -7.02866375e-01 -1.08076799e+00 -3.56980413e-01
2.10361425e-02 7.69777358e-01 4.81305867e-01 -1.17624477... | [8.30034065246582, -3.2644920349121094] |
428437a2-47d4-44e6-bb23-afcd10c16155 | fast-algorithm-for-overcomplete-order-3 | 2202.06442 | null | https://arxiv.org/abs/2202.06442v2 | https://arxiv.org/pdf/2202.06442v2.pdf | Fast algorithm for overcomplete order-3 tensor decomposition | We develop the first fast spectral algorithm to decompose a random third-order tensor over $\mathbb{R}^d$ of rank up to $O(d^{3/2}/\text{polylog}(d))$. Our algorithm only involves simple linear algebra operations and can recover all components in time $O(d^{6.05})$ under the current matrix multiplication time. Prior to... | ['David Steurer', 'Stefan Tiegel', 'Chih-Hung Liu', "Tommaso d'Orsi", 'Jingqiu Ding'] | 2022-02-14 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 7.62534514e-02 -8.38782359e-03 1.79603055e-01 -3.69633436e-02
-6.98293447e-01 -1.06218743e+00 1.03845224e-01 1.34636521e-01
-4.69670743e-01 2.29531154e-01 -2.97171623e-03 -9.57641423e-01
-4.18892622e-01 -7.73822844e-01 -8.39627862e-01 -6.67019248e-01
-1.06807208e+00 4.61162925e-01 2.90450454e-01 -5.49510777... | [6.625377178192139, 4.783371925354004] |
e78f0b12-c1d6-4ceb-9ffc-546ab26d3601 | lightweight-real-time-semantic-segmentation | 2302.10484 | null | https://arxiv.org/abs/2302.10484v1 | https://arxiv.org/pdf/2302.10484v1.pdf | Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN | In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation. Although CNN models have very impressive performance, the ability to capture global representation is still insufficient, which results in suboptimal results. Recently, Transformer achieved huge success in NLP task... | ['Dong Yue', 'Jian Yang', 'Huimin Lu', 'Guangwei Gao', 'Juncheng Li', 'Guoan Xu'] | 2023-02-21 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [-1.80663764e-02 -2.46464625e-01 -2.61999130e-01 -3.82276684e-01
-4.27791327e-01 -3.01357746e-01 2.49824330e-01 -1.45601884e-01
-6.58895671e-01 3.88645262e-01 -2.43020296e-01 -4.44902271e-01
2.08103716e-01 -8.85887921e-01 -7.39967942e-01 -6.41081452e-01
1.43911596e-02 -3.67993978e-03 4.64866132e-01 -1.31426632... | [9.32580280303955, -0.41749945282936096] |
89840b12-6e25-4c69-8894-85c99da285f2 | computing-a-partition-function-of-a | 2305.17526 | null | https://arxiv.org/abs/2305.17526v1 | https://arxiv.org/pdf/2305.17526v1.pdf | Computing a partition function of a generalized pattern-based energy over a semiring | Valued constraint satisfaction problems with ordered variables (VCSPO) are a special case of Valued CSPs in which variables are totally ordered and soft constraints are imposed on tuples of variables that do not violate the order. We study a restriction of VCSPO, in which soft constraints are imposed on a segment of ad... | ['Rustem Takhanov'] | 2023-05-27 | null | null | null | null | ['structured-prediction'] | ['methodology'] | [ 5.04321635e-01 6.17607415e-01 -1.38101116e-01 -1.64995119e-01
-4.98335958e-01 -8.08832586e-01 -7.90491924e-02 4.43100005e-01
-5.58060884e-01 9.72429991e-01 -7.46034801e-01 -6.58420920e-01
-7.31456339e-01 -1.27353239e+00 -5.77094316e-01 -6.70489788e-01
-7.15763867e-01 7.98824131e-01 5.10001719e-01 -5.40685773... | [6.458448886871338, 4.632206439971924] |
68c122d5-2180-4da6-840f-8bea2d28394f | linguistically-driven-multi-task-pre-training | 2201.08070 | null | https://arxiv.org/abs/2201.08070v1 | https://arxiv.org/pdf/2201.08070v1.pdf | Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine Translation | In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for language pairs involving Japanese as the source or target language, and English-specific sequence to sequence (ENSS) for language pairs involv... | ['Sadao Kurohashi', 'Chenhui Chu', 'Zhuoyuan Mao'] | 2022-01-20 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 1.11318618e-01 -2.55948484e-01 -3.91801029e-01 -3.52175772e-01
-1.27368224e+00 -6.59266233e-01 4.10531968e-01 -2.97520697e-01
-6.29526138e-01 1.24492574e+00 4.60517615e-01 -1.01949382e+00
4.00823921e-01 -2.00860590e-01 -7.83501744e-01 -3.28334212e-01
3.25282156e-01 6.49013400e-01 -6.61385879e-02 -6.77220523... | [11.629557609558105, 10.32180404663086] |
202e7aa1-23e9-41be-8712-ea4c3d8bf176 | gist-distributed-training-for-large-scale | 2102.10424 | null | https://arxiv.org/abs/2102.10424v4 | https://arxiv.org/pdf/2102.10424v4.pdf | GIST: Distributed Training for Large-Scale Graph Convolutional Networks | The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the number of model parameters. Although some work has explored training on large-scale graphs (e.g., GraphSAGE, ClusterGCN, etc.), we pioneer effi... | ['Anastasios Kyrillidis', 'Santiago Segarra', 'Artun Bayer', 'Chen Dun', 'Arindam Chowdhury', 'Jingkang Yang', 'Cameron R. Wolfe'] | 2021-02-20 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-9.42158923e-02 2.80770630e-01 -1.70251027e-01 3.05675529e-02
-2.96290278e-01 -6.70322955e-01 3.36250097e-01 1.31468162e-01
-2.28858963e-01 4.58508462e-01 -3.80068094e-01 -8.99887741e-01
-1.78553015e-01 -1.18296671e+00 -8.43164146e-01 -6.34322226e-01
-5.06600976e-01 8.88261139e-01 3.37493658e-01 -2.53905705... | [6.97326135635376, 5.837663650512695] |
2fd25b3c-ff21-4b2b-99b7-419e58877a8b | graphfit-learning-multi-scale-graph | 2207.11484 | null | https://arxiv.org/abs/2207.11484v1 | https://arxiv.org/pdf/2207.11484v1.pdf | GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation | We propose a precise and efficient normal estimation method that can deal with noise and nonuniform density for unstructured 3D point clouds. Unlike existing approaches that directly take patches and ignore the local neighborhood relationships, which make them susceptible to challenging regions such as sharp edges, we ... | ['Gang Xiong', 'Fei-Yue Wang', 'Zhen Shen', 'Dong-Ming Yan', 'Huaiyu Wu', 'Mingyang Zhao', 'Keqiang Li'] | 2022-07-23 | null | null | null | null | ['surface-normals-estimation'] | ['computer-vision'] | [-3.81833613e-01 -2.60442287e-01 1.13002084e-01 -3.43613416e-01
-2.88304090e-01 -1.76583648e-01 3.93595546e-01 3.98101091e-01
-2.44015381e-01 2.43148625e-01 -9.98389274e-02 6.14649020e-02
-4.45450172e-02 -1.20885324e+00 -8.08019102e-01 -5.87946177e-01
-5.90469949e-02 4.72194105e-01 5.51353753e-01 -8.03256705... | [7.996511936187744, -3.5471701622009277] |
1fd4bdaa-2d30-4434-83c5-3fbe77b48a2c | unifying-cross-lingual-semantic-role-labeling | null | null | https://aclanthology.org/2021.naacl-main.31 | https://aclanthology.org/2021.naacl-main.31.pdf | Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources | While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vall... | ['Roberto Navigli', 'Andrea Bacciu', 'Simone Conia'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['semantic-role-labeling'] | ['natural-language-processing'] | [-3.90850492e-02 8.95529762e-02 -5.25950432e-01 -5.22290707e-01
-9.33549881e-01 -1.11601985e+00 7.63250768e-01 4.99400765e-01
-7.31568336e-01 9.13159966e-01 3.93228382e-01 -4.49493200e-01
-3.06461118e-02 -5.32985449e-01 -4.33987111e-01 -1.78016663e-01
5.68160832e-01 7.76097834e-01 2.68576711e-01 -5.65989017... | [10.411953926086426, 9.550265312194824] |
93cecec7-471b-4328-b780-e6785c115f59 | progressively-generating-better-initial | 2203.16051 | null | https://arxiv.org/abs/2203.16051v1 | https://arxiv.org/pdf/2203.16051v1.pdf | Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction | This paper presents a high-quality human motion prediction method that accurately predicts future human poses given observed ones. Our method is based on the observation that a good initial guess of the future poses is very helpful in improving the forecasting accuracy. This motivates us to propose a novel two-stage pr... | ['Guiqing Li', 'Qing Zhang', 'Chengjiang Long', 'Yongwei Nie', 'Tiezheng Ma'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ma_Progressively_Generating_Better_Initial_Guesses_Towards_Next_Stages_for_High-Quality_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ma_Progressively_Generating_Better_Initial_Guesses_Towards_Next_Stages_for_High-Quality_CVPR_2022_paper.pdf | cvpr-2022-1 | ['human-pose-forecasting'] | ['computer-vision'] | [-4.74056005e-02 3.16227019e-01 -2.76903391e-01 -2.42498085e-01
-3.59878719e-01 -9.82743502e-03 5.51512539e-01 -7.01185465e-02
-4.76676673e-01 4.30400997e-01 5.26783884e-01 -8.88542272e-03
3.28409880e-01 -7.92554915e-01 -7.08667755e-01 -3.88400763e-01
-3.54147166e-01 3.86109620e-01 7.88710535e-01 -3.14872801... | [7.2687788009643555, -0.19213685393333435] |
5a703cf4-690a-40e8-9e3a-43c28d9e6c89 | trust-an-accurate-and-end-to-end-table | 2208.14687 | null | https://arxiv.org/abs/2208.14687v1 | https://arxiv.org/pdf/2208.14687v1.pdf | TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers | Table structure recognition is a crucial part of document image analysis domain. Its difficulty lies in the need to parse the physical coordinates and logical indices of each cell at the same time. However, the existing methods are difficult to achieve both these goals, especially when the table splitting lines are blu... | ['Jingdong Wang', 'Jingtuo Liu', 'Kun Yao', 'Zhihui Wang', 'Haojie Li', 'Chengquan Zhang', 'Pengyuan Lv', 'Yuechen Yu', 'Zengyuan Guo'] | 2022-08-31 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [-3.03421486e-02 -4.44911838e-01 -3.29414248e-01 -2.08238527e-01
-1.10590410e+00 -6.87292635e-01 5.88795356e-02 5.29809356e-01
1.93330366e-02 5.16781926e-01 -2.72845477e-02 -4.59278315e-01
-5.62836155e-02 -1.09853053e+00 -8.93665016e-01 -8.55381846e-01
4.07122038e-02 7.05803990e-01 3.57456625e-01 -2.32795715... | [11.711078643798828, 3.0591719150543213] |
fe6eb47d-9611-42cb-bd5e-fae44a20580f | spice-semantic-pseudo-labeling-for-image | 2103.09382 | null | https://arxiv.org/abs/2103.09382v3 | https://arxiv.org/pdf/2103.09382v3.pdf | SPICE: Semantic Pseudo-labeling for Image Clustering | The similarity among samples and the discrepancy between clusters are two crucial aspects of image clustering. However, current deep clustering methods suffer from the inaccurate estimation of either feature similarity or semantic discrepancy. In this paper, we present a Semantic Pseudo-labeling-based Image ClustEring ... | ['Hongming Shan', 'Ge Wang', 'Chuang Niu'] | 2021-03-17 | spice-semantic-pseudo-labeling-for-image-1 | https://arxiv.org/abs/2103.09382 | https://arxiv.org/pdf/2103.09382 | null | ['image-clustering'] | ['computer-vision'] | [-1.62031367e-01 -1.36803463e-01 -1.19181097e-01 -8.27200294e-01
-1.11265087e+00 -2.92401731e-01 3.89330000e-01 6.35766014e-02
-4.67783600e-01 6.50714636e-02 -1.23744942e-01 1.77240059e-01
-1.64591651e-02 -4.04974610e-01 -6.02758169e-01 -1.04789937e+00
-9.95274186e-02 6.96582615e-01 2.08384886e-01 4.23880160... | [9.212972640991211, 3.3017261028289795] |
50e8a8b2-d076-40f2-bb7d-937b50128349 | chatgpt-for-zero-shot-dialogue-state-tracking | 2306.01386 | null | https://arxiv.org/abs/2306.01386v1 | https://arxiv.org/pdf/2306.01386v1.pdf | ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity? | Recent research on dialogue state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. However, performance gains heavily depend on aggressive data augmentation and fine-tuning of ever larger language model based architectures. In contrast, general purpose language models,... | ['Milica Gašić', 'Carel van Niekerk', 'Hsien-Chin Lin', 'Christian Geishauser', 'Shutong Feng', 'Renato Vukovic', 'Benjamin Ruppik', 'Nurul Lubis', 'Michael Heck'] | 2023-06-02 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 1.25561878e-02 5.10720789e-01 -4.43639576e-01 -4.58648682e-01
-5.71311593e-01 -5.77477872e-01 1.03977871e+00 -2.44495608e-02
-4.99297291e-01 7.67891049e-01 3.22623372e-01 -5.69150150e-01
4.28707629e-01 -2.46259883e-01 -3.92990261e-02 -7.29082618e-04
-1.92725405e-01 1.00555038e+00 5.33587039e-01 -9.08546329... | [12.817146301269531, 7.8970489501953125] |
5cb7b1e9-55ef-45c6-a2ac-b8b2606ab433 | bert-embeddings-can-track-context-in | 2104.06529 | null | https://arxiv.org/abs/2104.06529v1 | https://arxiv.org/pdf/2104.06529v1.pdf | BERT Embeddings Can Track Context in Conversational Search | The use of conversational assistants to search for information is becoming increasingly more popular among the general public, pushing the research towards more advanced and sophisticated techniques. In the last few years, in particular, the interest in conversational search is increasing, not only because of the gener... | ['Joao Magalhaes', 'David Semedo', 'Rafael Ferreira'] | 2021-04-13 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.15454279e-01 8.68894756e-02 2.79105425e-01 -4.89589602e-01
-2.28147417e-01 -5.13220489e-01 1.17353666e+00 5.86133897e-01
-8.19139361e-01 4.23724622e-01 8.06024492e-01 -8.08070004e-02
-1.97929874e-01 -9.56459224e-01 1.27923399e-01 -2.28817701e-01
9.32128206e-02 7.76306093e-01 4.50472444e-01 -8.61901581... | [12.162328720092773, 7.840281009674072] |
0c1e430f-4ffd-4d8e-86a0-43957a9d3501 | estimating-and-assessing-differential | 2212.10653 | null | https://arxiv.org/abs/2212.10653v2 | https://arxiv.org/pdf/2212.10653v2.pdf | Estimating and Assessing Differential Equation Models with Time-Course Data | Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy and some components of the system may not be o... | ['S. C. Kou', 'Shihao Yang', 'Samuel W. K. Wong'] | 2022-12-20 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 2.49725163e-01 -4.77070242e-01 2.46846855e-01 2.08333895e-01
-6.18547857e-01 -8.96429420e-01 6.65676177e-01 4.57630247e-01
-2.08772734e-01 9.23174500e-01 -4.49168712e-01 -4.60117489e-01
-5.61013579e-01 -1.80799335e-01 -3.95498544e-01 -1.09604442e+00
-2.72266537e-01 5.18378317e-01 2.15681177e-02 1.58617973... | [6.422399997711182, 4.063368320465088] |
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