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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4af09dc2-089d-4fe8-8711-42c52639837c | deepe-a-deep-neural-network-for-knowledge | 2211.0462 | null | https://arxiv.org/abs/2211.04620v1 | https://arxiv.org/pdf/2211.04620v1.pdf | DeepE: a deep neural network for knowledge graph embedding | Recently, neural network based methods have shown their power in learning more expressive features on the task of knowledge graph embedding (KGE). However, the performance of deep methods often falls behind the shallow ones on simple graphs. One possible reason is that deep models are difficult to train, while shallow ... | ['Ding Ziqi', 'Yin Chang', 'Huang Shujian', 'Shen Si', 'Zhu Danhao'] | 2022-11-09 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-5.01565754e-01 4.59791929e-01 -4.71084535e-01 -2.59728104e-01
-9.09354091e-02 -3.86223972e-01 3.61683398e-01 5.50338849e-02
-2.44598582e-01 6.87907934e-01 3.23032945e-01 -5.65081537e-01
-3.47878754e-01 -1.20522094e+00 -1.04422724e+00 -4.64680672e-01
-6.71429396e-01 4.93847907e-01 3.23683977e-01 -4.44978237... | [8.634379386901855, 7.779576778411865] |
5aedaa28-9819-492e-b78e-3a42e478f468 | similarity-preserving-adversarial-graph | 2306.13854 | null | https://arxiv.org/abs/2306.13854v1 | https://arxiv.org/pdf/2306.13854v1.pdf | Similarity Preserving Adversarial Graph Contrastive Learning | Recent works demonstrate that GNN models are vulnerable to adversarial attacks, which refer to imperceptible perturbation on the graph structure and node features. Among various GNN models, graph contrastive learning (GCL) based methods specifically suffer from adversarial attacks due to their inherent design that high... | ['Chanyoung Park', 'Kanghoon Yoon', 'Yeonjun In'] | 2023-06-24 | null | null | null | null | ['adversarial-robustness', 'contrastive-learning', 'contrastive-learning'] | ['adversarial', 'computer-vision', 'methodology'] | [ 1.27234414e-01 8.70701745e-02 -2.89384872e-02 1.41200155e-01
-5.74721038e-01 -1.01550782e+00 6.90360844e-01 -5.71133606e-02
4.25298549e-02 5.06583869e-01 9.84144397e-04 -4.43024069e-01
5.86247295e-02 -1.04288948e+00 -8.60042393e-01 -9.33790326e-01
-2.52702624e-01 9.86982696e-03 1.46356121e-01 -4.48948056... | [6.201286315917969, 7.271685600280762] |
bb5104d1-6ad1-4e18-85e9-12cb882f715b | on-deep-speaker-embeddings-for-text | 1804.1008 | null | http://arxiv.org/abs/1804.10080v1 | http://arxiv.org/pdf/1804.10080v1.pdf | On deep speaker embeddings for text-independent speaker recognition | We investigate deep neural network performance in the textindependent speaker
recognition task. We demonstrate that using angular softmax activation at the
last classification layer of a classification neural network instead of a
simple softmax activation allows to train a more generalized discriminative
speaker embedd... | ['Vadim Shchemelinin', 'Sergey Novoselov', 'Andrey Shulipa', 'Alexandr Kozlov', 'Ivan Kremnev'] | 2018-04-26 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [ 1.01998240e-01 -1.72598228e-01 1.63182020e-01 -9.57312346e-01
-9.27459180e-01 -4.88575667e-01 5.97297430e-01 -4.23647821e-01
-7.31584489e-01 3.70164573e-01 3.94513518e-01 -2.29880765e-01
2.00555727e-01 -2.09112674e-01 -3.79817337e-01 -1.03621984e+00
6.10521361e-02 2.18874574e-01 -3.02482307e-01 -5.96819967... | [14.357931137084961, 6.074621200561523] |
8d461abd-ff95-4c13-9cdf-e2a4fa5cc967 | destseg-segmentation-guided-denoising-student | 2211.11317 | null | https://arxiv.org/abs/2211.11317v2 | https://arxiv.org/pdf/2211.11317v2.pdf | DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection | Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this challenge. However, previous works based on S-T only empirically applied constraints on normal data... | ['Ting Chen', 'Jiulong Shan', 'Ping Huang', 'Xi Li', 'Shiyu Li', 'Xuan Zhang'] | 2022-11-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['one-class-classification'] | ['miscellaneous'] | [ 5.81898689e-01 2.18337670e-01 1.17472634e-01 -5.33429742e-01
-1.05809844e+00 -1.32051095e-01 1.54092252e-01 2.46071443e-01
-3.29829872e-01 1.71977490e-01 -6.55626893e-01 -1.88551009e-01
1.59183443e-01 -6.95157826e-01 -1.02008021e+00 -9.78678942e-01
2.31800854e-01 -3.23937014e-02 6.78372383e-01 1.81036696... | [7.591489315032959, 2.0016024112701416] |
5c7c087c-0ab7-49be-a00b-b7194cf0ca59 | a-probabilistic-logic-based-commonsense | 2211.16822 | null | https://arxiv.org/abs/2211.16822v2 | https://arxiv.org/pdf/2211.16822v2.pdf | A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoods | Commonsense knowledge-graphs (CKGs) are important resources towards building machines that can 'reason' on text or environmental inputs and make inferences beyond perception. While current CKGs encode world knowledge for a large number of concepts and have been effectively utilized for incorporating commonsense in neur... | ['Kenneth Kwok', 'Dongkyu Choi', 'Liu Yan', 'Shantanu Jaiswal'] | 2022-11-30 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 2.25803137e-01 7.04890966e-01 -3.16004723e-01 -4.43001151e-01
-2.25911975e-01 -6.47042453e-01 6.64381981e-01 6.99309945e-01
-1.93815202e-01 7.31318712e-01 5.29742181e-01 -4.84702051e-01
-5.03689170e-01 -1.42022347e+00 -6.84714437e-01 -1.90807104e-01
1.88185230e-01 5.76799154e-01 4.57868814e-01 -6.41501009... | [9.923569679260254, 8.087930679321289] |
5f2b16d9-f212-4326-8df9-9c9bf3d9486c | black-box-coreset-variational-inference | 2211.02377 | null | https://arxiv.org/abs/2211.02377v2 | https://arxiv.org/pdf/2211.02377v2.pdf | Black-box Coreset Variational Inference | Recent advances in coreset methods have shown that a selection of representative datapoints can replace massive volumes of data for Bayesian inference, preserving the relevant statistical information and significantly accelerating subsequent downstream tasks. Existing variational coreset constructions rely on either se... | ['Theofanis Karaletsos', 'Hippolyt Ritter', 'Dionysis Manousakas'] | 2022-11-04 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 4.14186209e-01 3.41932565e-01 -4.42902029e-01 -3.74053955e-01
-1.16525936e+00 -4.63907689e-01 8.92745137e-01 1.20267138e-01
-3.56132537e-01 1.08337688e+00 2.27966890e-01 -1.79219633e-01
-5.32230556e-01 -6.82671964e-01 -1.03050351e+00 -8.78697455e-01
1.49868965e-01 1.16700947e+00 -3.67339812e-02 6.59648836... | [6.965846538543701, 3.9168848991394043] |
b6166c78-1582-4787-88c4-28357caa2d83 | pricure-privacy-preserving-collaborative | 2102.09751 | null | https://arxiv.org/abs/2102.09751v1 | https://arxiv.org/pdf/2102.09751v1.pdf | PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting | When multiple parties that deal with private data aim for a collaborative prediction task such as medical image classification, they are often constrained by data protection regulations and lack of trust among collaborating parties. If done in a privacy-preserving manner, predictive analytics can benefit from the colle... | ['Birhanu Eshete', 'Ismat Jarin'] | 2021-02-19 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.09374303e-01 4.39138979e-01 6.59865141e-02 -8.09557736e-01
-1.20334506e+00 -1.38386607e+00 7.73672760e-02 2.84359902e-01
-4.94105428e-01 5.83254755e-01 -1.30889982e-01 -5.47181487e-01
1.85510833e-02 -8.89030516e-01 -8.26015294e-01 -1.07014859e+00
-2.36120284e-01 2.68685877e-01 -3.94414589e-02 3.48293662... | [5.856761932373047, 6.745932102203369] |
c2848340-de93-4559-98f8-9183c4cbabd0 | does-local-pruning-offer-task-specific-models | null | null | https://aclanthology.org/2021.ranlp-srw.17 | https://aclanthology.org/2021.ranlp-srw.17.pdf | Does local pruning offer task-specific models to learn effectively ? | The need to deploy large-scale pre-trained models on edge devices under limited computational resources has led to substantial research to compress these large models. However, less attention has been given to compress the task-specific models. In this work, we investigate the different methods of unstructured pruning ... | ['Mohna Chakraborty', 'Abhishek Kumar Mishra'] | null | null | null | null | ranlp-2021-9 | ['aspect-extraction', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.22357142e-01 3.98440838e-01 -1.32374376e-01 -3.87050509e-01
-5.23964167e-01 -1.03110164e-01 4.04562265e-01 9.36965793e-02
-4.62630004e-01 3.04896325e-01 2.26551414e-01 -5.87822616e-01
6.10386580e-02 -8.08754802e-01 -8.10828567e-01 -3.26953143e-01
3.02333832e-01 1.54413953e-01 -4.62755933e-02 -7.63568506... | [8.719606399536133, 3.484445571899414] |
f8e4a838-dfb7-4409-bebf-f07301327f6b | multi-view-vision-prompt-fusion-network-can | 2304.10224 | null | https://arxiv.org/abs/2304.10224v1 | https://arxiv.org/pdf/2304.10224v1.pdf | Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning? | Point cloud based 3D deep model has wide applications in many applications such as autonomous driving, house robot, and so on. Inspired by the recent prompt learning in natural language processing, this work proposes a novel Multi-view Vision-Prompt Fusion Network (MvNet) for few-shot 3D point cloud classification. MvN... | ['Hongyuan Zhu', 'Tao Chen', 'Xin Chen', 'Bo Zhang', 'Baopu Li', 'Haoyang Peng'] | 2023-04-20 | null | null | null | null | ['3d-point-cloud-classification', 'few-shot-3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.92093188e-01 -2.88271099e-01 -1.50102243e-01 -5.75263917e-01
-9.56106186e-01 -3.86973590e-01 7.65415907e-01 -1.21580012e-01
-3.21972906e-03 -1.76942989e-01 -1.72331035e-01 -8.94512460e-02
1.41569793e-01 -8.76712620e-01 -7.65368938e-01 -6.04247093e-01
5.03259718e-01 5.38773358e-01 5.54920733e-01 -4.78298306... | [8.097006797790527, -3.256427049636841] |
89598b92-cce0-4bf5-8b7b-95b28b088cdc | symmetry-as-a-representation-of-intuitive | 2206.02019 | null | https://arxiv.org/abs/2206.02019v1 | https://arxiv.org/pdf/2206.02019v1.pdf | Symmetry as a Representation of Intuitive Geometry? | Recognition of geometrical patterns seems to be an important aspect of human intelligence. Geometric pattern recognition is used in many intelligence tests, including Dehaene's odd-one-out test of Core Geometry (CG)) based on intuitive geometrical concepts (Dehaene et al., 2006). Earlier work has developed a symmetry-b... | ['Ashok Goel', 'Snejana Shegheva', 'Wangcheng Xu'] | 2022-06-04 | null | null | null | null | ['odd-one-out'] | ['reasoning'] | [-3.34071010e-01 4.01577264e-01 4.65542346e-01 -4.26840305e-01
2.13267908e-01 -6.72698200e-01 4.68699753e-01 5.42764544e-01
-4.04006511e-01 3.95971745e-01 1.56594336e-01 -7.10857511e-01
-9.07684326e-01 -1.11864996e+00 -3.22378457e-01 2.03796998e-02
-3.15712035e-01 8.09716582e-01 2.70372480e-01 -5.72870076... | [9.521374702453613, 7.173366069793701] |
b0db9676-5177-4386-ba6b-05e886519b32 | explaining-deep-neural-networks-for-point | 2207.12984 | null | https://arxiv.org/abs/2207.12984v1 | https://arxiv.org/pdf/2207.12984v1.pdf | Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations | Explaining decisions made by deep neural networks is a rapidly advancing research topic. In recent years, several approaches have attempted to provide visual explanations of decisions made by neural networks designed for structured 2D image input data. In this paper, we propose a novel approach to generate coarse visua... | ['Nicolas Schönborn', 'Muhammad Sarmad', 'Jawad Tayyub'] | 2022-07-26 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-1.27464935e-01 7.61492431e-01 3.13235447e-02 -7.64875591e-01
2.11619571e-01 -5.31380177e-01 8.55348229e-01 3.47226292e-01
-5.31902304e-03 3.47625732e-01 2.24277973e-01 -5.77469528e-01
-2.41643906e-01 -5.47266245e-01 -6.62419438e-01 -3.96457344e-01
-3.86264548e-02 6.36014998e-01 1.43783092e-01 -2.91852862... | [8.896790504455566, 5.32360315322876] |
c5a7a8cb-b5ef-4917-82f5-96c4700b884f | mockingbird-defending-against-deep-learning | 1902.06626 | null | https://arxiv.org/abs/1902.06626v3 | https://arxiv.org/pdf/1902.06626v3.pdf | Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces | Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity even when the traffic is protected by encryption, a VPN, or an anonymity system like Tor. Leveraging a deep-learning classifier, a WF attacker can gain over 98% accuracy on Tor traff... | ['Matthew Wright', 'Nate Mathews', 'Mohammad Saidur Rahman', 'Mohsen Imani'] | 2019-02-18 | null | null | null | null | ['website-fingerprinting-defense'] | ['adversarial'] | [ 1.30657151e-01 -4.54617739e-02 -3.01766038e-01 -1.39268180e-02
-9.75763083e-01 -1.34828663e+00 5.78234017e-01 -1.94752336e-01
-2.69955009e-01 6.58453524e-01 -2.97054470e-01 -1.12622964e+00
-8.79217163e-02 -1.22015584e+00 -8.15605998e-01 -4.35919672e-01
-3.61527234e-01 4.31683004e-01 6.51200831e-01 -1.61401063... | [5.574222564697266, 7.436434268951416] |
c9abe1b0-5f09-409e-bc60-048536ad63a1 | reflection-removal-using-ghosting-cues | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Shih_Reflection_Removal_Using_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Shih_Reflection_Removal_Using_2015_CVPR_paper.pdf | Reflection Removal Using Ghosting Cues | Photographs taken through glass windows often contain both the desired scene and undesired reflections. Separating the reflection and transmission layers is an important but ill-posed problem that has both aesthetic and practical applications. In this work, we introduce the use of ghosting cues that introduce asymmetry... | ['William T. Freeman', 'Fredo Durand', 'YiChang Shih', 'Dilip Krishnan'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['reflection-removal'] | ['computer-vision'] | [ 8.63912046e-01 1.28670752e-01 7.04143524e-01 -1.00837238e-02
-4.19502199e-01 -3.68434161e-01 3.22698534e-01 -3.12193692e-01
-9.79098007e-02 2.74784952e-01 3.62717479e-01 -7.82385245e-02
1.23597249e-01 -6.69876933e-01 -6.30589008e-01 -9.53049242e-01
1.91027582e-01 -3.89828950e-01 3.95958245e-01 -3.57691720... | [10.37840461730957, -2.77344012260437] |
2004b293-5cea-46cd-8a48-f06fcfc77a94 | l2-regularization-versus-batch-and-weight | 1706.0535 | null | http://arxiv.org/abs/1706.05350v1 | http://arxiv.org/pdf/1706.05350v1.pdf | L2 Regularization versus Batch and Weight Normalization | Batch Normalization is a commonly used trick to improve the training of deep
neural networks. These neural networks use L2 regularization, also called
weight decay, ostensibly to prevent overfitting. However, we show that L2
regularization has no regularizing effect when combined with normalization.
Instead, regulariza... | ['Twan van Laarhoven'] | 2017-06-16 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-1.49074584e-01 1.99745357e-01 -2.64696509e-01 -4.00775671e-01
-1.71002895e-01 -4.00039911e-01 5.78058898e-01 -1.21841229e-01
-9.95422304e-01 8.39450896e-01 4.08496447e-02 -3.99106413e-01
2.38610417e-01 -6.59067273e-01 -7.08952606e-01 -9.86042738e-01
3.09955388e-01 -2.25184187e-01 3.41822088e-01 -2.52369076... | [8.348031997680664, 3.488467216491699] |
1f51a567-771b-4b62-9871-164b3abd1728 | uima-ruta-workbench-rule-based-text | null | null | https://aclanthology.org/C14-2007 | https://aclanthology.org/C14-2007.pdf | UIMA Ruta Workbench: Rule-based Text Annotation | null | ['Frank Puppe', 'Philip-Daniel Beck', 'Martin Toepfer', 'Georg Fette', 'Peter Kluegl'] | 2014-08-01 | uima-ruta-workbench-rule-based-text-1 | https://aclanthology.org/C14-2007 | https://aclanthology.org/C14-2007.pdf | coling-2014-8 | ['text-annotation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.274023532867432, 3.739532470703125] |
3f763683-5272-4f5c-9e4b-c4c34da99286 | tutorial-on-agent-based-models-in-netlogo | 1808.09499 | null | http://arxiv.org/abs/1808.09499v1 | http://arxiv.org/pdf/1808.09499v1.pdf | Tutorial on agent-based models in NetLogo applied to immunology and virology | This tutorial introduces participants to the design and implementation of an
agent-based model using NetLogo through one of two different projects:
modelling T cell movement within a lymph node or modelling the progress of a
viral infection in an in vitro cell culture monolayer. Each project is broken
into a series of ... | [] | 2018-08-28 | null | null | null | null | ['virology'] | ['miscellaneous'] | [-2.41577595e-01 3.00499558e-01 -1.42237991e-01 -2.77743824e-02
2.88898021e-01 -6.47409081e-01 6.83301449e-01 3.46828073e-01
-2.65707225e-01 8.29507589e-01 -7.65461698e-02 -5.49124360e-01
4.73874472e-02 -6.91954255e-01 -1.95216890e-02 -3.86674821e-01
-4.01612729e-01 7.38679469e-01 4.92465496e-01 -2.52983898... | [5.924588680267334, 4.399750709533691] |
b3fe13c5-9ef6-4cfe-a635-fb6fa74da38a | end-to-end-text-dependent-speaker | 1509.08062 | null | http://arxiv.org/abs/1509.08062v1 | http://arxiv.org/pdf/1509.08062v1.pdf | End-to-End Text-Dependent Speaker Verification | In this paper we present a data-driven, integrated approach to speaker
verification, which maps a test utterance and a few reference utterances
directly to a single score for verification and jointly optimizes the system's
components using the same evaluation protocol and metric as at test time. Such
an approach will r... | ['Samy Bengio', 'Noam Shazeer', 'Ignacio Moreno', 'Georg Heigold'] | 2015-09-27 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [-1.94006283e-02 1.20869465e-01 8.34672749e-02 -1.05245638e+00
-1.28198373e+00 -7.01595306e-01 6.02389097e-01 -2.20997512e-01
-3.52167636e-01 4.90074903e-01 8.90459865e-02 -6.71982110e-01
1.81013748e-01 -9.50217322e-02 -4.12043154e-01 -5.01253784e-01
9.84387770e-02 7.69446135e-01 4.21741679e-02 -4.53350306... | [14.358512878417969, 6.342684745788574] |
43042317-f3f3-46d8-9674-7657b99f1a09 | finalmlp-an-enhanced-two-stream-mlp-model-for-1 | 2304.00902 | null | https://arxiv.org/abs/2304.00902v3 | https://arxiv.org/pdf/2304.00902v3.pdf | FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction | Click-through rate (CTR) prediction is one of the fundamental tasks for online advertising and recommendation. While multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, it has been widely recognized that applying a vanilla MLP network alone is inefficient in learning multiplicati... | ['Zhenhua Dong', 'Yuru Li', 'Guohao Cai', 'Liangcai Su', 'Jieming Zhu', 'Kelong Mao'] | 2023-04-03 | finalmlp-an-enhanced-two-stream-mlp-model-for | https://arxiv.org/abs/2304.00902 | https://arxiv.org/pdf/2304.00902 | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 1.04166307e-01 -2.31318176e-01 -6.40039980e-01 -6.07118428e-01
-5.44228315e-01 -2.10333869e-01 7.31217563e-01 2.09143251e-01
-2.87441611e-01 4.28647578e-01 1.14515886e-01 -5.42309046e-01
1.64967459e-02 -8.61492395e-01 -7.31743693e-01 -5.36934495e-01
-2.05602959e-01 1.72592252e-01 3.28299075e-01 -4.81238693... | [10.150558471679688, 5.471412658691406] |
4fc18b03-a61d-44b0-a63d-56c39fec8cc6 | latent-aspect-detection-from-online | 2204.06964 | null | https://arxiv.org/abs/2204.06964v1 | https://arxiv.org/pdf/2204.06964v1.pdf | Latent Aspect Detection from Online Unsolicited Customer Reviews | Within the context of review analytics, aspects are the features of products and services at which customers target their opinions and sentiments. Aspect detection helps product owners and service providers to identify shortcomings and prioritize customers' needs, and hence, maintain revenues and mitigate customer chur... | ['Hossein Fani', 'Arash Mansouri', 'Mohammad Forouhesh'] | 2022-04-14 | null | null | null | null | ['latent-aspect-detection', 'hidden-aspect-detection', 'aspect-category-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.84834117e-01 2.31935784e-01 -8.16412032e-01 -6.27689600e-01
-7.04846084e-01 -4.74485606e-01 7.23595798e-01 4.27616119e-01
6.47716690e-03 2.67950684e-01 4.11279440e-01 -3.29956591e-01
-1.34225050e-02 -9.34480011e-01 -2.46560469e-01 -8.72174561e-01
4.28270876e-01 6.75354600e-01 -3.32029969e-01 -1.56575218... | [11.362396240234375, 6.698551177978516] |
dc567886-53d4-4306-82fb-e18bdd995c10 | adapt-vision-language-navigation-with | 2205.15509 | null | https://arxiv.org/abs/2205.15509v1 | https://arxiv.org/pdf/2205.15509v1.pdf | ADAPT: Vision-Language Navigation with Modality-Aligned Action Prompts | Vision-Language Navigation (VLN) is a challenging task that requires an embodied agent to perform action-level modality alignment, i.e., make instruction-asked actions sequentially in complex visual environments. Most existing VLN agents learn the instruction-path data directly and cannot sufficiently explore action-le... | ['Xiaodan Liang', 'Jianzhuang Liu', 'Xiwen Liang', 'Zicong Chen', 'Yi Zhu', 'Bingqian Lin'] | 2022-05-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lin_ADAPT_Vision-Language_Navigation_With_Modality-Aligned_Action_Prompts_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lin_ADAPT_Vision-Language_Navigation_With_Modality-Aligned_Action_Prompts_CVPR_2022_paper.pdf | cvpr-2022-1 | ['vision-language-navigation'] | ['computer-vision'] | [ 4.70301718e-01 -1.21179529e-01 -4.61841971e-01 -5.02229929e-01
-7.60635793e-01 -2.82119036e-01 8.07738006e-01 -3.58614326e-01
-6.95883334e-01 3.06590706e-01 5.56257248e-01 -3.17799896e-01
7.13646039e-02 -3.96545619e-01 -1.01990235e+00 -7.88372219e-01
4.03234124e-01 2.31274545e-01 2.48242468e-01 -3.44291359... | [4.451443195343018, 0.4729261100292206] |
2b4f897d-ad0b-4d72-8f3f-1ce1a292681c | subdivision-based-mesh-convolution-networks | 2106.02285 | null | https://arxiv.org/abs/2106.02285v2 | https://arxiv.org/pdf/2106.02285v2.pdf | Subdivision-Based Mesh Convolution Networks | Convolutional neural networks (CNNs) have made great breakthroughs in 2D computer vision. However, their irregular structure makes it hard to harness the potential of CNNs directly on meshes. A subdivision surface provides a hierarchical multi-resolution structure, in which each face in a closed 2-manifold triangle mes... | ['Ralph R. Martin', 'Tai-Jiang Mu', 'Jiahui Huang', 'Jun-Xiong Cai', 'Meng-Hao Guo', 'Zheng-Ning Liu', 'Shi-Min Hu'] | 2021-06-04 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [-4.37008850e-02 1.97601527e-01 -4.99956161e-02 -4.80150729e-02
-1.04991831e-02 -3.03680122e-01 5.08605361e-01 3.58882956e-02
-7.62348399e-02 4.03451502e-01 -2.25744441e-01 -1.86715648e-01
2.29979381e-01 -1.44292045e+00 -7.82231033e-01 -4.25685823e-01
-3.73677671e-01 1.75307408e-01 3.45106423e-01 -2.96575755... | [8.227859497070312, -3.716794013977051] |
11ad861c-a4cb-4605-aab1-ab1273000c9b | let-s-do-a-thought-experiment-using | 2306.14308 | null | https://arxiv.org/abs/2306.14308v1 | https://arxiv.org/pdf/2306.14308v1.pdf | Let's Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning | Language models still struggle on moral reasoning, despite their impressive performance in many other tasks. In particular, the Moral Scenarios task in MMLU (Multi-task Language Understanding) is among the worst performing tasks for many language models, including GPT-3. In this work, we propose a new prompting framewo... | ['Jilin Chen', 'Alex Beutel', 'Ahmad Beirami', 'Swaroop Mishra', 'Xiao Ma'] | 2023-06-25 | null | null | null | null | ['multi-task-language-understanding', 'moral-scenarios'] | ['methodology', 'miscellaneous'] | [-1.16228787e-02 1.02701521e+00 -1.63436517e-01 -3.38398188e-01
-1.00849950e+00 -2.90553927e-01 8.84090841e-01 1.84755057e-01
-4.86513048e-01 9.29566920e-01 6.47130251e-01 -6.02568567e-01
1.06784329e-01 -6.84472919e-01 -6.74329996e-01 -4.33917552e-01
5.66558242e-01 5.93924224e-01 -2.06444710e-01 -5.00188470... | [10.114322662353516, 7.581653118133545] |
3e225fb4-67f6-4af7-9788-0cc1dbf533f2 | novel-fuzzy-approach-to-antimicrobial-peptide | null | null | https://openreview.net/forum?id=x0tzOYvapDl | https://openreview.net/pdf?id=x0tzOYvapDl | Novel fuzzy approach to Antimicrobial Peptide Activity Prediction: A tale of limited and imbalanced data that models won’t hear | Antimicrobial peptides have gained immense attention in recent years due to their potential for developing novel antibacterial medicines, next-generation anti-cancer treatment regimes, etc. Owing to the significant cost and time required for wet lab-based AMP screening, researchers have framed the task as an ML problem... | ['Vinay Kumar', 'Rahul Upadhyay', 'Aviral Chharia'] | 2021-09-24 | null | null | null | neurips-workshop-ai4scien-2021-12 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 7.60009885e-01 -3.31113994e-01 -3.59585971e-01 -2.60367453e-01
-6.50931001e-01 -4.13417846e-01 2.66595572e-01 6.06164515e-01
-4.34610665e-01 1.29989970e+00 -3.36398005e-01 -5.34404874e-01
-2.06926554e-01 -5.05975664e-01 -7.58407474e-01 -8.96118164e-01
-6.79508820e-02 8.74850810e-01 -3.77291143e-02 2.41419859... | [4.949558258056641, 5.469276428222656] |
217ae64c-47a1-47ac-9a27-2fd58c4cdb38 | offline-pre-trained-multi-agent-decision-1 | 2112.02845 | null | https://arxiv.org/abs/2112.02845v3 | https://arxiv.org/pdf/2112.02845v3.pdf | Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks | Offline reinforcement learning leverages previously-collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent reinforcement learning (MARL) tasks, given the increased interactions among agents and with the enviroment. Yet, in... | ['Bo Xu', 'Jun Wang', 'Haifeng Zhang', 'Ying Wen', 'Weinan Zhang', 'Xiyun Li', 'Chenyang Le', 'Yaodong Yang', 'Muning Wen', 'Linghui Meng'] | 2021-12-06 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-2.07521096e-01 -2.16576532e-01 -2.90923983e-01 1.85956299e-01
-7.34553099e-01 -9.40175414e-01 9.16602969e-01 1.31139889e-01
-8.47691715e-01 8.78892899e-01 8.92540216e-02 -4.39299107e-01
-3.35986108e-01 -5.66906393e-01 -8.91270757e-01 -5.11873841e-01
-5.41248620e-01 9.22269106e-01 2.96079189e-01 -7.28202343... | [4.0161662101745605, 1.7144389152526855] |
b91d2af0-acbf-4d36-8387-0a8f6a213786 | pose-robust-face-recognition-via-deep | 1803.00839 | null | http://arxiv.org/abs/1803.00839v1 | http://arxiv.org/pdf/1803.00839v1.pdf | Pose-Robust Face Recognition via Deep Residual Equivariant Mapping | Face recognition achieves exceptional success thanks to the emergence of deep
learning. However, many contemporary face recognition models still perform
relatively poor in processing profile faces compared to frontal faces. A key
reason is that the number of frontal and profile training faces are highly
imbalanced - th... | ['Kaidi Cao', 'Yu Rong', 'Chen Change Loy', 'Xiaoou Tang', 'Cheng Li'] | 2018-03-02 | pose-robust-face-recognition-via-deep-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Cao_Pose-Robust_Face_Recognition_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Cao_Pose-Robust_Face_Recognition_CVPR_2018_paper.pdf | cvpr-2018-6 | ['robust-face-recognition'] | ['computer-vision'] | [ 2.75663853e-01 8.76833051e-02 -1.20825227e-02 -6.98722422e-01
-3.35221916e-01 -4.47790325e-01 5.34501314e-01 -7.56430268e-01
4.43134364e-03 4.24016893e-01 1.61882728e-01 6.91093132e-02
-1.14304356e-01 -6.76509202e-01 -7.96985805e-01 -7.65176058e-01
1.38400108e-01 3.79310548e-01 -4.29878086e-01 -3.48397136... | [13.177936553955078, 0.5296312570571899] |
717b81fc-c3c3-491c-b5c2-7eaa3819849e | venn-diagram-multi-label-class-interpretation | 2305.01044 | null | https://arxiv.org/abs/2305.01044v2 | https://arxiv.org/pdf/2305.01044v2.pdf | Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer with Color and Sharpness Enhancement | DFU is a severe complication of diabetes that can lead to amputation of the lower limb if not treated properly. Inspired by the 2021 Diabetic Foot Ulcer Grand Challenge, researchers designed automated multi-class classification of DFU, including infection, ischaemia, both of these conditions, and none of these conditio... | ['Md Kamrul Hasan', 'Moi Hoon Yap', 'Md Mahamudul Hasan'] | 2023-05-01 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 5.11137486e-01 -3.73039931e-01 -2.16784239e-01 -7.21534342e-02
-5.59221029e-01 -4.17086720e-01 2.08961070e-01 3.28967124e-01
-3.76240700e-01 1.04085541e+00 1.77318886e-01 -4.15443748e-01
-3.87180716e-01 -8.45909655e-01 -5.11671007e-01 -7.26652861e-01
-2.68092323e-02 -1.33977145e-01 -3.43382582e-02 -5.27185909... | [15.74895191192627, -3.678082227706909] |
19a2473d-7114-4a46-93f7-cef1deab0972 | dadin-domain-adversarial-deep-interest | 2305.12058 | null | https://arxiv.org/abs/2305.12058v1 | https://arxiv.org/pdf/2305.12058v1.pdf | DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems | Click-Through Rate (CTR) prediction is one of the main tasks of the recommendation system, which is conducted by a user for different items to give the recommendation results. Cross-domain CTR prediction models have been proposed to overcome problems of data sparsity, long tail distribution of user-item interactions, a... | ['Yinghao Chen', 'Ri Su', 'Feng Liu', 'Shaojie Zhao', 'Muzhou Hou', 'Menglin Kong'] | 2023-05-20 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-2.24549398e-01 -4.58363295e-01 -3.82092804e-01 -3.62589329e-01
-7.02817202e-01 -6.73891127e-01 6.38384759e-01 -2.44288191e-01
-4.98217016e-01 7.86459684e-01 2.14060515e-01 -1.49176568e-01
-1.76836833e-01 -8.12392294e-01 -6.77347600e-01 -5.24250329e-01
2.97783837e-02 6.37646973e-01 3.16129357e-01 -5.07643878... | [10.103577613830566, 5.4336347579956055] |
ce187a49-685a-4148-8a30-9830716dcc24 | towards-robust-neural-retrieval-with-source | null | null | https://aclanthology.org/2022.coling-1.89 | https://aclanthology.org/2022.coling-1.89.pdf | Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning | Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines. Many practical use cases of such models, however, may involve previously unseen target domains. In this paper, we propose to improve the out-of-domain generalizatio... | ['Avirup Sil', 'Heng Ji', 'Vittorio Castelli', 'Martin Franz', 'Md Arafat Sultan', 'Vikas Yadav', 'Revanth Gangi Reddy'] | null | null | null | null | coling-2022-10 | ['passage-retrieval'] | ['natural-language-processing'] | [ 4.30209666e-01 -1.71562448e-01 -2.93806404e-01 -2.54320651e-01
-1.60180247e+00 -1.03146493e+00 9.89169836e-01 9.04122442e-02
-8.29265654e-01 1.01602471e+00 3.32955211e-01 -4.01486754e-01
-2.15196293e-02 -7.46829271e-01 -9.94649708e-01 -1.89380065e-01
7.28089586e-02 8.84139299e-01 2.34947592e-01 -8.61017764... | [11.445073127746582, 7.8019914627075195] |
a28a0f20-8aa4-472c-bc4e-8e13f88d198a | ferv39k-a-large-scale-multi-scene-dataset-for | 2203.09463 | null | https://arxiv.org/abs/2203.09463v2 | https://arxiv.org/pdf/2203.09463v2.pdf | FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos | Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether performances of existing methods remain satisfactory in real-world application-oriented scenes. For example, the "Happy" expression with... | ['Wenqiang Zhang', 'Weifeng Ge', 'Wei zhang', 'Shuyong Gao', 'Zhongying Liu', 'Yiwen Huang', 'Yixuan Sun', 'Yan Wang'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_FERV39k_A_Large-Scale_Multi-Scene_Dataset_for_Facial_Expression_Recognition_in_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_FERV39k_A_Large-Scale_Multi-Scene_Dataset_for_Facial_Expression_Recognition_in_CVPR_2022_paper.pdf | cvpr-2022-1 | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.75771528e-01 -4.24786210e-01 -6.06887303e-02 -8.76365125e-01
-7.34661937e-01 -4.93199080e-01 3.88776451e-01 -4.42489177e-01
-3.74757946e-01 6.36378706e-01 2.07358047e-01 3.52150947e-01
3.90842669e-02 -1.90162718e-01 -4.94567245e-01 -7.20468402e-01
-1.63157374e-01 -1.30493701e-01 -4.51846272e-02 -4.09411281... | [13.578207015991211, 1.8637337684631348] |
5a160376-0746-4874-9b6a-47fc23f92b4a | ukp-square-v2-explainability-and-adversarial | 2208.09316 | null | https://arxiv.org/abs/2208.09316v3 | https://arxiv.org/pdf/2208.09316v3.pdf | UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA | Question Answering (QA) systems are increasingly deployed in applications where they support real-world decisions. However, state-of-the-art models rely on deep neural networks, which are difficult to interpret by humans. Inherently interpretable models or post hoc explainability methods can help users to comprehend ho... | ['Leonardo F. R. Ribeiro', 'Sewin Tariverdian', 'Tim Baumgärtner', 'Haritz Puerto', 'Iryna Gurevych', 'Hossain Shaikh Saadi', 'Kexin Wang', 'Hao Zhang', 'Rachneet Sachdeva'] | 2022-08-19 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 9.41063836e-02 8.47310185e-01 -8.28407332e-02 -6.25648975e-01
-4.09823805e-01 -7.00625420e-01 5.13481379e-01 4.48574513e-01
1.57781541e-01 4.32949483e-01 8.21120739e-02 -7.79607892e-01
8.38692933e-02 -8.98006618e-01 -9.80669856e-01 5.92687912e-02
2.60605991e-01 5.15190363e-01 2.55616933e-01 -3.47721457... | [8.743130683898926, 6.006319522857666] |
1250641d-2a9e-487d-be47-c5cd74ddb9d6 | decour-a-corpus-of-deceptive-statements-in | null | null | https://aclanthology.org/L12-1188 | https://aclanthology.org/L12-1188.pdf | DeCour: a corpus of DEceptive statements in Italian COURts | In criminal proceedings, sometimes it is not easy to evaluate the sincerity of oral testimonies. DECOUR - DEception in COURt corpus - has been built with the aim of training models suitable to discriminate, from a stylometric point of view, between sincere and deceptive statements. DECOUR is a collection of hearings he... | ['Massimo Poesio', 'Tommaso Fornaciari'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['deception-detection'] | ['miscellaneous'] | [ 1.75273582e-01 3.57479483e-01 1.61175981e-01 -4.12626475e-01
-8.57450902e-01 -8.04951072e-01 8.17427754e-01 2.56238371e-01
-3.26368034e-01 9.64580715e-01 3.52388889e-01 -5.34059167e-01
-2.46874735e-01 -3.24399531e-01 -2.86928415e-01 -6.91415191e-01
5.72013676e-01 7.04363763e-01 -1.85467750e-01 -1.72558334... | [8.236205101013184, 10.405970573425293] |
a574e221-6417-4059-bea9-6cf39c54ffb9 | keycld-learning-constrained-lagrangian | 2206.1103 | null | https://arxiv.org/abs/2206.11030v1 | https://arxiv.org/pdf/2206.11030v1.pdf | KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images | We present KeyCLD, a framework to learn Lagrangian dynamics from images. Learned keypoints represent semantic landmarks in images and can directly represent state dynamics. Interpreting this state as Cartesian coordinates coupled with explicit holonomic constraints, allows expressing the dynamics with a constrained Lag... | ['Guillaume Crevecoeur', 'Francis wyffels', 'Jeroen Taets', 'Rembert Daems'] | 2022-06-22 | null | null | null | null | ['acrobot'] | ['playing-games'] | [-5.23639321e-01 -5.52244000e-02 -2.61592060e-01 9.45282802e-02
-2.97937393e-01 -9.05961275e-01 7.53019452e-01 -4.54092115e-01
-3.92658114e-01 6.76401496e-01 -1.01560935e-01 -1.16150580e-01
9.08365007e-03 -4.18391824e-01 -8.07162821e-01 -6.30558789e-01
-4.29419100e-01 3.66932064e-01 -8.43747482e-02 -2.44980559... | [7.872371196746826, -0.3629912734031677] |
923516d0-d176-4548-84a0-2c2297c6fb3a | banglahatebert-bert-for-abusive-language | null | null | https://aclanthology.org/2022.restup-1.2 | https://aclanthology.org/2022.restup-1.2.pdf | BanglaHateBERT: BERT for Abusive Language Detection in Bengali | This paper introduces BanglaHateBERT, a retrained BERT model for abusive language detection in Bengali. The model was trained with a large-scale Bengali offensive, abusive, and hateful corpus that we have collected from different sources and made available to the public. Furthermore, we have collected and manually anno... | ['Mourad Oussalah', 'Nabil Arhab', 'Mainul Haque', 'Md Saroar Jahan'] | null | null | null | null | restup-lrec-2022-6 | ['abusive-language'] | ['natural-language-processing'] | [-3.50084484e-01 -7.93855563e-02 5.02137095e-02 -2.58175969e-01
-1.14804089e+00 -9.67614174e-01 5.74427485e-01 -2.39427775e-01
-7.13739872e-01 8.38533401e-01 5.41639924e-01 -7.37392232e-02
1.65586069e-01 -3.41438204e-01 -3.89053375e-01 -5.52132964e-01
-3.94541658e-02 7.06502199e-01 2.23530114e-01 -9.72297609... | [8.794191360473633, 10.603121757507324] |
bf941f6e-9dfa-4334-a277-113d739e3e77 | paracolorizer-realistic-image-colorization | 2208.08295 | null | https://arxiv.org/abs/2208.08295v1 | https://arxiv.org/pdf/2208.08295v1.pdf | ParaColorizer: Realistic Image Colorization using Parallel Generative Networks | Grayscale image colorization is a fascinating application of AI for information restoration. The inherently ill-posed nature of the problem makes it even more challenging since the outputs could be multi-modal. The learning-based methods currently in use produce acceptable results for straightforward cases but usually ... | ['Sanjay Singh', 'Sumeet Saurav', 'Abeer Banerjee', 'Himanshu Kumar'] | 2022-08-17 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 6.70238435e-01 -1.90481409e-01 2.59943247e-01 -1.96186870e-01
-1.27537131e+00 -6.26270533e-01 7.99257338e-01 -1.87902525e-01
-1.50555909e-01 6.48810446e-01 -7.08967373e-02 -1.71412170e-01
1.46013111e-01 -7.25761831e-01 -8.89629245e-01 -1.13561785e+00
4.58461195e-01 3.63957226e-01 2.62173504e-01 -1.29678726... | [11.297557830810547, -1.1676820516586304] |
300a2bb1-d905-4f47-8739-95b6701e81d4 | qmix-monotonic-value-function-factorisation | 1803.11485 | null | http://arxiv.org/abs/1803.11485v2 | http://arxiv.org/pdf/1803.11485v2.pdf | QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning | In many real-world settings, a team of agents must coordinate their behaviour
while acting in a decentralised way. At the same time, it is often possible to
train the agents in a centralised fashion in a simulated or laboratory setting,
where global state information is available and communication constraints are
lifte... | ['Mikayel Samvelyan', 'Shimon Whiteson', 'Jakob Foerster', 'Tabish Rashid', 'Gregory Farquhar', 'Christian Schroeder de Witt'] | 2018-03-30 | qmix-monotonic-value-function-factorisation-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2389 | http://proceedings.mlr.press/v80/rashid18a/rashid18a.pdf | icml-2018-7 | ['smac-1'] | ['playing-games'] | [-3.35715741e-01 2.62894064e-01 -4.59349573e-01 -1.89715419e-02
-5.94677746e-01 -8.43533754e-01 8.58800113e-01 1.62726879e-01
-9.82012987e-01 1.23430955e+00 6.86835945e-02 -1.46704912e-01
-5.62317550e-01 -5.08021832e-01 -7.27349997e-01 -9.68548357e-01
-5.27376413e-01 9.81984496e-01 1.18970633e-01 -4.31252539... | [3.846000909805298, 2.0662529468536377] |
fb33eb13-b7f3-468a-ab0d-9bd33dab86f9 | daccord-un-jeu-de-donnees-pour-la-detection | null | null | http://talnarchives.atala.org/TALN/TALN-2023/459882.html | http://talnarchives.atala.org/TALN/TALN-2023/459882.pdf | DACCORD : un jeu de données pour la Détection Automatique d'énonCés COntRaDictoires en français | La tâche de détection automatique de contradictions logiques entre énoncés en TALN est une tâche de classification binaire, où chaque paire de phrases reçoit une étiquette selon que les deux phrases se contredisent ou non. Elle peut être utilisée afin de lutter contre la désinformation. Dans cet article, nous présenton... | ['Simon Robillard', 'Richard Moot', 'Maximos Skandalis'] | 2023-06-08 | null | null | null | actes-de-18e-conference-en-recherche-d | ['sentence-pair-classification'] | ['natural-language-processing'] | [-1.40026540e-01 -2.09278494e-01 1.44577399e-01 -3.89322817e-01
-2.64675200e-01 -9.98145163e-01 1.01906335e+00 7.37256825e-01
-2.36865178e-01 1.13457012e+00 2.42401391e-01 -8.36805880e-01
-1.07031494e-01 -7.63960242e-01 -7.61711419e-01 -3.30126435e-01
-5.85253425e-02 5.83061457e-01 -1.60828307e-01 -9.80707109... | [14.098211288452148, 13.312843322753906] |
f3f06fff-bcc4-4333-bb94-3dc70baf2239 | backdoor-attacks-against-incremental-learners | 2305.18384 | null | https://arxiv.org/abs/2305.18384v1 | https://arxiv.org/pdf/2305.18384v1.pdf | Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study | Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series. However, the adversarial robustness of incremental learners has not been widely verified, leaving potential security risks. Specifically, for poi... | ['Xiangyang Ji', 'Junjun Jiang', 'Deming Zhai', 'Xianming Liu', 'Yiqi Zhong'] | 2023-05-28 | null | null | null | null | ['data-poisoning', 'backdoor-attack', 'adversarial-robustness', 'incremental-learning'] | ['adversarial', 'adversarial', 'adversarial', 'methodology'] | [-3.28271389e-02 -1.14932559e-01 -8.98169577e-02 3.06345582e-01
-8.03779125e-01 -1.16942167e+00 2.29560599e-01 4.23057228e-01
-5.06266952e-01 7.92456508e-01 -4.77805197e-01 -7.18619585e-01
-3.64552140e-01 -7.82767355e-01 -1.22330403e+00 -8.34645569e-01
-8.43231499e-01 -2.39526391e-01 3.87903810e-01 -2.17069551... | [5.72032356262207, 7.586806297302246] |
457b89d5-a2a5-47aa-a762-2378f20e9ee6 | customized-attention-mechanism-for-relation | null | null | https://aclanthology.org/Y18-1067 | https://aclanthology.org/Y18-1067.pdf | Customized Attention Mechanism for Relation Classification | null | ['Hongyin Zan', 'Lijuan Zhou', 'Yang Wen', 'Shirong Shen'] | null | null | null | null | paclic-2018-12 | ['relation-classification'] | ['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... | [-7.293085098266602, 3.7988967895507812] |
b8ada2a3-f6fe-48aa-9bc7-ac945f64ae56 | learning-logic-rules-for-document-level-1 | 2111.05407 | null | https://arxiv.org/abs/2111.05407v1 | https://arxiv.org/pdf/2111.05407v1.pdf | Learning Logic Rules for Document-level Relation Extraction | Document-level relation extraction aims to identify relations between entities in a whole document. Prior efforts to capture long-range dependencies have relied heavily on implicitly powerful representations learned through (graph) neural networks, which makes the model less transparent. To tackle this challenge, in th... | ['Lei LI', 'Yong Yu', 'Weinan Zhang', 'Hao Zhou', 'Lin Qiu', 'Jiangtao Feng', 'Changzhi Sun', 'Dongyu Ru'] | 2021-11-09 | learning-logic-rules-for-document-level | https://aclanthology.org/2021.emnlp-main.95 | https://aclanthology.org/2021.emnlp-main.95.pdf | emnlp-2021-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 2.36287460e-01 8.73527169e-01 -6.49095416e-01 -5.32820046e-01
-5.45728266e-01 -5.41083932e-01 6.56323791e-01 3.88851047e-01
5.70088774e-02 7.19367266e-01 2.62250155e-01 -5.91770828e-01
-1.54724628e-01 -1.36176360e+00 -8.87288749e-01 3.29607017e-02
-1.59300685e-01 7.21140563e-01 4.74690199e-02 1.09059321... | [9.293800354003906, 8.54039478302002] |
33418b9e-fa7b-41f5-b23e-b8bdab91a4f6 | person-re-identification-via-recurrent | 1701.06351 | null | http://arxiv.org/abs/1701.06351v1 | http://arxiv.org/pdf/1701.06351v1.pdf | Person Re-Identification via Recurrent Feature Aggregation | We address the person re-identification problem by effectively exploiting a
globally discriminative feature representation from a sequence of tracked human
regions/patches. This is in contrast to previous person re-id works, which rely
on either single frame based person to person patch matching, or graph based
sequenc... | ['Bingbing Ni', 'Zhichao Song', 'Yichao Yan', 'Chao Ma', 'Yan Yan', 'Xiaokang Yang'] | 2017-01-23 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 4.46887523e-01 -4.23830420e-01 8.23950544e-02 -2.62960225e-01
-5.39811313e-01 -2.32548237e-01 8.58915687e-01 1.86793014e-01
-8.21164548e-01 7.21033633e-01 4.65389699e-01 5.47410071e-01
-9.23253149e-02 -7.74838090e-01 -5.66391170e-01 -5.45761943e-01
-3.26602936e-01 4.62687582e-01 -2.77967099e-02 -7.28047565... | [14.687019348144531, 0.9705786108970642] |
563df64a-ffe4-4eae-a8c5-41d9927dff10 | open-world-weakly-supervised-object | 2304.08271 | null | https://arxiv.org/abs/2304.08271v2 | https://arxiv.org/pdf/2304.08271v2.pdf | Open-World Weakly-Supervised Object Localization | While remarkable success has been achieved in weakly-supervised object localization (WSOL), current frameworks are not capable of locating objects of novel categories in open-world settings. To address this issue, we are the first to introduce a new weakly-supervised object localization task called OWSOL (Open-World We... | ['Mike Zheng Shou', 'Linlin Shen', 'Haozhe Liu', 'Yuexiang Li', 'Zhaochuan Luo', 'Jinheng Xie'] | 2023-04-17 | null | null | null | null | ['object-localization', 'weakly-supervised-object-localization'] | ['computer-vision', 'computer-vision'] | [-6.71586022e-02 -5.46582900e-02 -5.48532844e-01 -4.82334852e-01
-1.14396405e+00 -8.20398271e-01 5.56693256e-01 1.05049431e-01
-5.52759826e-01 6.20612144e-01 -4.47328873e-02 4.05832455e-02
6.98817000e-02 -4.38946277e-01 -9.14098084e-01 -7.52715766e-01
9.09484401e-02 4.06863540e-01 3.97799581e-01 2.37374678... | [9.622206687927246, 1.6521114110946655] |
810ea8f0-f9d7-4b19-85d9-21a397a9178e | a-comprehensive-survey-of-image-augmentation | 2205.01491 | null | https://arxiv.org/abs/2205.01491v2 | https://arxiv.org/pdf/2205.01491v2.pdf | A Comprehensive Survey of Image Augmentation Techniques for Deep Learning | Deep learning has been achieving decent performance in computer vision requiring a large volume of images, however, collecting images is expensive and difficult in many scenarios. To alleviate this issue, many image augmentation algorithms have been proposed as effective and efficient strategies. Understanding current ... | ['Dong Sun Park', 'Alvaro Fuentes', 'Sook Yoon', 'Mingle Xu'] | 2022-05-03 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 0.6221214 0.22223897 -0.53224826 -0.28402048 -0.2129071 -0.22528484
0.63886666 -0.11954515 -0.5494259 0.43313617 0.0847432 -0.42950955
-0.02127301 -0.6278446 -0.5413502 -1.007332 -0.02481792 0.29224652
-0.25533375 -0.19875641 0.3832044 0.667251 -1.7734636 0.02374342
0.8827142 0.9542797 0.... | [9.414812088012695, 2.148986339569092] |
03f3b9df-128b-4001-99db-4c8c37591972 | mediapipe-and-cnns-for-real-time-asl-gesture | 2305.05296 | null | https://arxiv.org/abs/2305.05296v3 | https://arxiv.org/pdf/2305.05296v3.pdf | Mediapipe and CNNs for Real-Time ASL Gesture Recognition | This research paper describes a realtime system for identifying American Sign Language (ASL) movements that employs modern computer vision and machine learning approaches. The suggested method makes use of the Mediapipe library for feature extraction and a Convolutional Neural Network (CNN) for ASL gesture classificati... | ['Ayush Sinha', 'Ashutosh Bajpai', 'Rupesh Kumar'] | 2023-05-09 | null | null | null | null | ['sign-language-recognition', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [-8.60301331e-02 -4.10790980e-01 -4.38915998e-01 2.02984530e-02
-5.29092669e-01 -3.86693567e-01 1.73307434e-01 -6.20760441e-01
-8.85861158e-01 2.03764379e-01 4.68213737e-01 -3.76106471e-01
-6.41148090e-02 -4.05807197e-01 5.62341623e-02 -5.64945519e-01
-1.23233870e-01 1.53353110e-01 4.77873951e-01 -2.16016740... | [9.06098461151123, -6.364945411682129] |
99052d22-009d-4252-b543-203f5ec868be | deep-learning-based-image-retrieval-in-the | 2107.03648 | null | https://arxiv.org/abs/2107.03648v1 | https://arxiv.org/pdf/2107.03648v1.pdf | Deep Learning Based Image Retrieval in the JPEG Compressed Domain | Content-based image retrieval (CBIR) systems on pixel domain use low-level features, such as colour, texture and shape, to retrieve images. In this context, two types of image representations i.e. local and global image features have been studied in the literature. Extracting these features from pixel images and compar... | ['Mohammed Javed', 'Bulla Rajesh', 'Shrikant Temburwar'] | 2021-07-08 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 4.57269371e-01 -7.58449018e-01 -1.77526593e-01 -5.45918107e-01
-1.14315569e+00 -3.72740775e-01 4.74841207e-01 4.85249519e-01
-5.86474597e-01 4.46809620e-01 -2.32101545e-01 9.20859128e-02
-5.06094813e-01 -1.16209078e+00 -2.49400839e-01 -7.29460120e-01
-1.02542294e-02 -7.93383494e-02 4.27603245e-01 -2.36247748... | [10.75968074798584, -0.04826076328754425] |
869a5ee7-cfe9-4104-ac5a-83038206e0d8 | variables-effecting-photomosaic | 1611.03318 | null | http://arxiv.org/abs/1611.03318v1 | http://arxiv.org/pdf/1611.03318v1.pdf | Variables effecting photomosaic reconstruction and ortho-rectification from aerial survey datasets | Unmanned aerial vehicles now make it possible to obtain high quality aerial
imagery at a low cost, but processing those images into a single, useful entity
is neither simple nor seamless. Specifically, there are factors that must be
addressed when merging multiple images into a single coherent one. While
ortho-rectific... | ['Jonathan Byrne', 'Debra Laefer'] | 2016-11-10 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 6.07603133e-01 -3.55401844e-01 1.38920397e-01 -3.63124877e-01
-4.72306043e-01 -6.34485662e-01 3.08745325e-01 -2.48795040e-02
-3.81016552e-01 6.84770525e-01 1.66776091e-01 -5.42237222e-01
-3.61393124e-01 -6.12621009e-01 -1.67306677e-01 -6.28317475e-01
1.04336359e-01 4.92142923e-02 1.69957116e-01 -4.94803846... | [8.780449867248535, -2.2285192012786865] |
2195fddc-f6f3-4617-9903-6b76168af9b7 | learning-realistic-patterns-from-unrealistic | 2009.10007 | null | https://arxiv.org/abs/2009.10007v2 | https://arxiv.org/pdf/2009.10007v2.pdf | Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization | Good training data is a prerequisite to develop useful ML applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical studies). This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from ... | ['Lars Aakerøy', 'Britt Øverland', 'Knut Liestøl', 'Mohan Kankanhalli', 'Stein Kristiansen', 'Sigurd Steinshamn', 'Konstantinos Nikolaidis', 'Vera Goebel', 'Thomas Plagemann', 'Harriet Akre', 'Gunn Marit Traaen'] | 2020-09-21 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 4.06278610e-01 5.32416880e-01 -7.01086894e-02 -6.56004667e-01
-5.41939914e-01 -6.73260331e-01 1.53030500e-01 8.36755410e-02
-7.45705783e-01 1.18068767e+00 1.54272899e-01 -9.62496325e-02
1.19667880e-01 -6.04283452e-01 -8.70643616e-01 -7.74204195e-01
1.62245825e-01 2.45851159e-01 -3.63039285e-01 2.42315084... | [6.3506693840026855, 6.780429840087891] |
0d8612b0-972c-4a24-a8c6-4bf0f9c428e7 | a-bag-of-visual-words-model-for-medical-image | 2007.09464 | null | https://arxiv.org/abs/2007.09464v1 | https://arxiv.org/pdf/2007.09464v1.pdf | A Bag of Visual Words Model for Medical Image Retrieval | Medical Image Retrieval is a challenging field in Visual information retrieval, due to the multi-dimensional and multi-modal context of the underlying content. Traditional models often fail to take the intrinsic characteristics of data into consideration, and have thus achieved limited accuracy when applied to medical ... | ['Sowmya Kamath S', 'Karthik K'] | 2020-07-18 | null | null | null | null | ['medical-image-retrieval', 'content-based-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'computer-vision', 'medical'] | [ 3.99085730e-01 -4.29753631e-01 -6.90985441e-01 -2.45578527e-01
-1.04264033e+00 -4.02844191e-01 6.70500815e-01 7.43975639e-01
-4.71235424e-01 2.40554959e-01 2.17705131e-01 -7.92425722e-02
-4.34785068e-01 -6.20696008e-01 -2.32159696e-03 -8.93661737e-01
5.11075882e-03 3.07346553e-01 1.32638872e-01 3.60927056... | [14.341203689575195, -1.521701693534851] |
95c66a95-fc7f-4d4c-9eff-33cdee269f5a | a-multimodal-method-based-on-cross-attention | 2305.14142 | null | https://arxiv.org/abs/2305.14142v1 | https://arxiv.org/pdf/2305.14142v1.pdf | A multimodal method based on cross-attention and convolution for postoperative infection diagnosis | Postoperative infection diagnosis is a common and serious complication that generally poses a high diagnostic challenge. This study focuses on PJI, a type of postoperative infection. X-ray examination is an imaging examination for suspected PJI patients that can evaluate joint prostheses and adjacent tissues, and detec... | ['Hongwei Shi', 'Xianjie Liu'] | 2023-05-23 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-1.04999980e-02 -2.84418106e-01 -2.34719396e-01 2.24799767e-01
-7.55788028e-01 6.91542253e-02 3.24021339e-01 -6.75382689e-02
-4.49134648e-01 8.03998590e-01 3.34160447e-01 -2.05923602e-01
-3.39011043e-01 -5.52825272e-01 -8.72281790e-02 -8.99145544e-01
-1.72863245e-01 6.58680797e-01 3.36180744e-03 3.17219764... | [15.081761360168457, -1.9213231801986694] |
70af6ae7-86c4-4b3d-8712-62344751de6f | single-image-deraining-using-scale-aware | 1712.0683 | null | http://arxiv.org/abs/1712.06830v1 | http://arxiv.org/pdf/1712.06830v1.pdf | Single Image Deraining using Scale-Aware Multi-Stage Recurrent Network | Given a single input rainy image, our goal is to visually remove rain streaks
and the veiling effect caused by scattering and transmission of rain streaks
and rain droplets. We are particularly concerned with heavy rain, where rain
streaks of various sizes and directions can overlap each other and the veiling
effect re... | ['Loong-Fah Cheong', 'Robby T. Tan', 'Ruoteng Li'] | 2017-12-19 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 7.11018369e-02 -4.74182874e-01 7.18927383e-01 -3.88806373e-01
-7.11250864e-03 -5.59961915e-01 1.50709748e-01 -3.31236073e-03
-1.31143495e-01 7.07441628e-01 9.53334793e-02 -1.80561036e-01
2.15937510e-01 -1.05721462e+00 -6.82973623e-01 -9.28122401e-01
-5.99807203e-01 1.06638700e-01 6.72309101e-01 -6.49217963... | [10.90368938446045, -3.25933575630188] |
47e58bc5-85c2-40e6-8add-213e2bc88e17 | a-dual-branch-self-supervised-representation | 2303.11019 | null | https://arxiv.org/abs/2303.11019v1 | https://arxiv.org/pdf/2303.11019v1.pdf | A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images | Supervised deep learning methods have achieved considerable success in medical image analysis, owing to the availability of large-scale and well-annotated datasets. However, creating such datasets for whole slide images (WSIs) in histopathology is a challenging task due to their gigapixel size. In recent years, self-su... | ['Jinman Kim', 'Euijoon Ahn', 'Hao Wang'] | 2023-03-20 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 3.18931133e-01 6.68466790e-04 -3.33077490e-01 -6.00076795e-01
-1.40819907e+00 -4.46558774e-01 5.63954234e-01 2.67319739e-01
-4.86145020e-01 5.20037711e-01 1.35096103e-01 -1.35837361e-01
-3.24485958e-01 -7.09022045e-01 -5.90513408e-01 -1.08257008e+00
1.64321139e-01 3.22730601e-01 5.00139117e-01 4.36718855... | [15.074057579040527, -2.837649345397949] |
a732c99c-ab14-4951-a8ae-cd547b673525 | imbalanced-classification-in-medical-imaging | 2210.12234 | null | https://arxiv.org/abs/2210.12234v2 | https://arxiv.org/pdf/2210.12234v2.pdf | Imbalanced Classification in Medical Imaging via Regrouping | We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification. This new idea is dramatically different from popular loss reweighting and class resampling methods. Our preliminary result on imbalanced medical image cl... | ['Ju Sun', 'Ying Cui', 'Rui Zhang', 'Yash Travadi', 'Le Peng'] | 2022-10-21 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.21374238e-01 3.96165371e-01 -8.46271217e-01 -7.42938697e-01
-8.51747870e-01 -1.77824497e-01 4.68865365e-01 9.14134383e-01
-5.55833101e-01 1.04514813e+00 3.62902850e-01 -3.04324925e-01
-2.20488198e-03 -9.95740354e-01 -2.95519352e-01 -6.55515611e-01
-5.87520823e-02 2.64756292e-01 1.95249647e-01 -9.35244411... | [8.84736156463623, 4.192966461181641] |
c979c9da-6aa3-49b0-b65c-0b1df39ec2ba | bev-locator-an-end-to-end-visual-semantic | 2211.14927 | null | https://arxiv.org/abs/2211.14927v1 | https://arxiv.org/pdf/2211.14927v1.pdf | BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images | Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual sema... | ['Stefan Poslad', 'Liang Li', 'Tao Peng', 'Wenqiang Zhou', 'Meng Xu', 'Zhihuang Zhang'] | 2022-11-27 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-4.99634564e-01 -2.48638373e-02 -2.19679505e-01 -8.09447050e-01
-8.25255871e-01 -9.80524480e-01 5.58717966e-01 -3.18120956e-01
-4.29134488e-01 3.42714250e-01 -1.66047752e-01 -1.14314064e-01
8.55670031e-03 -7.08941102e-01 -1.15312183e+00 -3.46219778e-01
3.40367854e-01 4.53012764e-01 4.25138116e-01 -2.97430933... | [7.736841678619385, -2.012509346008301] |
0497a41b-ddea-43e5-b442-ff55eb1425a0 | drugst-one-a-plug-and-play-solution-for | 2305.15453 | null | https://arxiv.org/abs/2305.15453v2 | https://arxiv.org/pdf/2305.15453v2.pdf | Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing | In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these t... | ['Suryadipto Sarkar', 'Julio Saez-Rodriguez', 'Sepideh Sadegh', 'Miles Mee', 'Noel Malod-Dognin', 'Mohamed Helmy', 'Jan Baumbach', 'Olga Zolotareva', 'Ruisheng Wang', 'Ugur Turhan', 'Nico Trummer', 'Ron Shamir', 'Gideon Shaked', 'Nataša Pržulj', 'Dexter Pratt', 'Julian M. Poschenrieder', 'Rudolf T. Pillich', 'Alexander... | 2023-05-24 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 6.40228838e-02 -2.49145746e-01 -5.66343427e-01 1.03690803e-01
-6.75100759e-02 -7.77524114e-01 1.14943497e-01 6.96623504e-01
6.10746071e-02 8.80631566e-01 -3.16788375e-01 -1.15093005e+00
-3.22691351e-01 -7.46295035e-01 -2.50248760e-01 -5.80114543e-01
-1.15896203e-01 5.78575313e-01 6.10553063e-02 -9.21851695... | [5.240604877471924, 5.722117900848389] |
b6b431db-61c5-41cb-8f6c-f476fe65749c | improving-named-entity-recognition-by-jointly | 1807.06683 | null | http://arxiv.org/abs/1807.06683v1 | http://arxiv.org/pdf/1807.06683v1.pdf | Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags | Previous studies have shown that linguistic features of a word such as
possession, genitive or other grammatical cases can be employed in word
representations of a named entity recognition (NER) tagger to improve the
performance for morphologically rich languages. However, these taggers require
external morphological d... | ['Tunga Güngör', 'Suzan Üsküdarlı', 'Onur Güngör'] | 2018-07-17 | null | null | null | null | ['morphological-disambiguation'] | ['natural-language-processing'] | [-2.72945493e-01 -7.03823715e-02 -1.94935516e-01 -3.93523455e-01
-9.13659215e-01 -1.14116120e+00 4.52775210e-01 6.44541204e-01
-9.84539092e-01 7.64307976e-01 2.68388838e-01 -8.10810447e-01
5.64911515e-02 -8.82706404e-01 -3.61153305e-01 -2.88744539e-01
5.32331737e-03 4.36557412e-01 2.35555157e-01 -2.54443735... | [10.308575630187988, 10.120442390441895] |
6b60f966-d1b6-4fb4-9699-2a843d47eb67 | selfact-personalized-activity-recognition | 2304.0953 | null | https://arxiv.org/abs/2304.09530v1 | https://arxiv.org/pdf/2304.09530v1.pdf | SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning | Supervised Deep Learning (DL) models are currently the leading approach for sensor-based Human Activity Recognition (HAR) on wearable and mobile devices. However, training them requires large amounts of labeled data whose collection is often time-consuming, expensive, and error-prone. At the same time, due to the intra... | ['Claudio Bettini', 'Samuele Valente', 'Gabriele Civitarese', 'Luca Arrotta'] | 2023-04-19 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 1.89514428e-01 7.81234652e-02 -7.50093520e-01 -6.24821603e-01
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-5.40119350e-01 6.84235215e-01 4.77771521e-01 2.78490931e-01
-7.38406647e-03 -7.84748673e-01 -6.98219359e-01 -5.45979440e-01
-1.24581933e-01 4.95251983e-01 2.01786056e-01 3.17244709... | [7.810839653015137, 1.186751365661621] |
c22e3e67-17f1-4811-b43f-75a96ab72ecf | knowledge-refined-denoising-network-for | 2304.14987 | null | https://arxiv.org/abs/2304.14987v1 | https://arxiv.org/pdf/2304.14987v1.pdf | Knowledge-refined Denoising Network for Robust Recommendation | Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing knowledge-aware recommendation methods directly perform information propagation on KG and user-item bipartite graph, ignoring the impacts of \te... | ['Yunjun Gao', 'Yujia Hu', 'Lu Chen', 'YUREN MAO', 'Yuntao Du', 'Xinjun Zhu'] | 2023-04-28 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [-2.76578903e-01 -9.27661285e-02 -3.61911118e-01 -1.61865383e-01
-2.11898193e-01 -3.20818216e-01 1.43847853e-01 -1.58694416e-01
-1.06960520e-01 7.62645483e-01 3.43931764e-01 -8.60273167e-02
-1.02403378e+00 -8.91685367e-01 -8.11993241e-01 -7.21538186e-01
2.19682187e-01 3.27490032e-01 1.93472177e-01 -2.10793123... | [10.221772193908691, 5.623153209686279] |
18b4ef7e-46c4-4d5e-92b3-b0f11afed8bd | combining-rules-and-embeddings-via-neuro | 2109.09566 | null | https://arxiv.org/abs/2109.09566v1 | https://arxiv.org/pdf/2109.09566v1.pdf | Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion | Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings. In particular, rule-based KBC has led to interpretable rules while being comparable in performance with graph embeddings. Even within rule-based KBC, ... | ['Alexander Gray', 'Salim Roukos', 'Francois Luus', 'Pavan Kapanipathi', 'Ibrahim Abdelaziz', 'Breno W. S. R. Carvalho', 'Prithviraj Sen'] | 2021-09-16 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 4.70179208e-02 5.39760292e-01 -3.74523014e-01 -1.25495046e-01
-1.20040238e-01 -5.94527543e-01 8.72158766e-01 5.72945178e-01
-2.07808658e-01 9.67926383e-01 3.40368509e-01 -6.89977288e-01
-8.31980646e-01 -1.24755752e+00 -8.55516016e-01 -3.77526879e-01
-3.67326647e-01 9.28358197e-01 4.93905246e-01 -6.81326926... | [8.848576545715332, 7.579674243927002] |
b084ee29-cc8d-45df-81b6-4b2023b126ba | exploiting-global-and-local-attentions-for | 2104.08126 | null | https://arxiv.org/abs/2104.08126v1 | https://arxiv.org/pdf/2104.08126v1.pdf | Exploiting Global and Local Attentions for Heavy Rain Removal on Single Images | Heavy rain removal from a single image is the task of simultaneously eliminating rain streaks and fog, which can dramatically degrade the quality of captured images. Most existing rain removal methods do not generalize well for the heavy rain case. In this work, we propose a novel network architecture consisting of thr... | ['Munchurl Kim', 'Juan Luis Gonzalez', 'Dac Tung Vu'] | 2021-04-16 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 3.30557883e-01 -2.88046598e-01 6.57985151e-01 -3.37588459e-01
-4.73527312e-01 -3.92618746e-01 1.57470390e-01 -5.73416531e-01
-1.80569053e-01 1.06742454e+00 5.19610271e-02 -1.18873894e-01
1.16604351e-01 -7.98135817e-01 -8.38089287e-01 -1.24237359e+00
-2.62080640e-01 -5.03198624e-01 1.33296743e-01 -5.61671197... | [10.924043655395508, -3.2454419136047363] |
c2a59c61-6fb3-4ba6-9a58-358922d1568f | action-parsing-using-context-features | 2205.10008 | null | https://arxiv.org/abs/2205.10008v1 | https://arxiv.org/pdf/2205.10008v1.pdf | Action parsing using context features | We propose an action parsing algorithm to parse a video sequence containing an unknown number of actions into its action segments. We argue that context information, particularly the temporal information about other actions in the video sequence, is valuable for action segmentation. The proposed parsing algorithm tempo... | ['Nagita Mehrseresht'] | 2022-05-20 | null | null | null | null | ['action-segmentation', 'action-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 7.50804007e-01 -2.14138087e-02 -7.83240318e-01 -4.78530884e-01
-7.65040338e-01 -5.59594989e-01 2.73386002e-01 2.38174498e-01
-5.23627818e-01 5.47120214e-01 3.08333904e-01 8.86340216e-02
-1.34170689e-02 -4.55561459e-01 -3.41091454e-01 -7.18873680e-01
-4.16361153e-01 2.75610566e-01 9.84185874e-01 3.49237949... | [8.354475975036621, 0.4639228582382202] |
ed631da5-f2eb-4f93-905d-57a16d4a155d | a-lightweight-deep-learning-based-cloud | 2105.00967 | null | https://arxiv.org/abs/2105.00967v1 | https://arxiv.org/pdf/2105.00967v1.pdf | A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features | Clouds are a very important factor in the availability of optical remote sensing images. Recently, deep learning-based cloud detection methods have surpassed classical methods based on rules and physical models of clouds. However, most of these deep models are very large which limits their applicability and explainabil... | ['Matthieu Molinier', 'Xiao Xiang Zhu', 'Lichao Mou', 'Shaojie Luo', 'Canliang Jian', 'Zhongwen Hu', 'Zhaocong Wu', 'Jun Li'] | 2021-04-29 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 2.34911814e-01 -8.01365435e-01 2.75864869e-01 -3.77317905e-01
-6.33179426e-01 -4.32553500e-01 2.94016808e-01 -1.24618471e-01
-4.21317905e-01 4.45289969e-01 -2.98870593e-01 -5.02242386e-01
-1.25325322e-01 -1.13942337e+00 -5.06300092e-01 -1.07062018e+00
-2.12492466e-01 -2.86055684e-01 2.38696113e-01 1.65612774... | [9.809216499328613, -1.7036465406417847] |
70c29f84-affa-4a96-bb5d-124526a97cb3 | incorporating-textual-evidence-in-visual | 1911.09334 | null | https://arxiv.org/abs/1911.09334v1 | https://arxiv.org/pdf/1911.09334v1.pdf | Incorporating Textual Evidence in Visual Storytelling | Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation. Motivated by human storytelling process which recalls stories for familiar images, we exploit textual evidence from similar images to help generate co... | ['Tianyi Li', 'Sujian Li'] | 2019-11-21 | incorporating-textual-evidence-in-visual-1 | https://aclanthology.org/W19-8102 | https://aclanthology.org/W19-8102.pdf | ws-2019-11 | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.86889464e-01 -1.28020823e-01 -2.80473918e-01 -3.25858027e-01
-8.36372256e-01 -4.79940861e-01 1.06713748e+00 -1.97364643e-01
-2.38628939e-01 8.98157358e-01 1.08687603e+00 -9.70746279e-02
2.80189335e-01 -5.69132984e-01 -1.07858884e+00 -1.96929559e-01
-9.96837951e-03 -2.31903512e-02 1.57796979e-01 -3.40491772... | [11.180033683776855, 0.7452965974807739] |
7c9badd7-6a40-4298-8b05-b9840b118f7f | humble-teacher-and-eager-student-dual-network | 2011.12498 | null | https://arxiv.org/abs/2011.12498v4 | https://arxiv.org/pdf/2011.12498v4.pdf | An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation | Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images under different augmentations. However, when applied to pose estimation, ... | ['Yizhou Wang', 'Wenjun Zeng', 'Chunyu Wang', 'Rongchang Xie'] | 2020-11-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xie_An_Empirical_Study_of_the_Collapsing_Problem_in_Semi-Supervised_2D_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xie_An_Empirical_Study_of_the_Collapsing_Problem_in_Semi-Supervised_2D_ICCV_2021_paper.pdf | iccv-2021-1 | ['semi-supervised-human-pose-estimation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.83792317e-01 5.95304668e-01 -4.55120564e-01 -7.10265100e-01
-7.29041100e-01 -4.47355598e-01 2.31077433e-01 -2.78457761e-01
-2.93012649e-01 9.92565095e-01 -1.28824905e-01 3.75147760e-02
3.79030585e-01 -4.32375520e-01 -1.13808191e+00 -8.82128477e-01
3.92999619e-01 8.40828180e-01 3.41460317e-01 -1.45621505... | [9.409756660461426, 1.3979594707489014] |
a4b097b1-2b72-4a89-9541-2b3ecd97956a | a-deep-transfer-learning-method-for-cross | null | null | https://aclanthology.org/2022.lrec-1.330 | https://aclanthology.org/2022.lrec-1.330.pdf | A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference | Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), has been one of the central tasks in Artificial Intelligence (AI) and Natural Language Processing (NLP). RTE between the two pieces of texts is a crucial problem, and it adds further challenges when involving two different languages, ... | ['Asif Ekbal', 'Tanik Saikh', 'Baban Gain', 'Arkadipta De', 'Dibyanayan Bandyopadhyay'] | null | null | null | null | lrec-2022-6 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-2.04106439e-02 -4.11929097e-03 -2.97352895e-02 -4.89193022e-01
-1.21726859e+00 -7.23688900e-01 7.39487469e-01 1.81909144e-01
-8.75595689e-01 8.96939039e-01 8.30565467e-02 -6.97903395e-01
-1.05662830e-01 -4.90720600e-01 -9.27712262e-01 -2.76247621e-01
1.48271918e-01 8.07199240e-01 -1.54674783e-01 -2.40048066... | [10.976458549499512, 9.679033279418945] |
e11b0897-2238-48b3-96d3-e40f6df82207 | pix2vox-context-aware-3d-reconstruction-from | 1901.11153 | null | https://arxiv.org/abs/1901.11153v2 | https://arxiv.org/pdf/1901.11153v2.pdf | Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images | Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. ... | ['Hongxun Yao', 'Shangchen Zhou', 'Xiaoshuai Sun', 'Haozhe Xie', 'Shengping Zhang'] | 2019-01-31 | pix2vox-context-aware-3d-reconstruction-from-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Xie_Pix2Vox_Context-Aware_3D_Reconstruction_From_Single_and_Multi-View_Images_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Xie_Pix2Vox_Context-Aware_3D_Reconstruction_From_Single_and_Multi-View_Images_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 1.71545539e-02 -1.84455022e-01 3.95364724e-02 -4.24600303e-01
-7.69069016e-01 -2.16941416e-01 2.47158930e-01 -4.36699092e-01
-7.89695531e-02 4.24611330e-01 2.69458622e-01 8.22538584e-02
-1.22510009e-01 -1.08251429e+00 -1.02972341e+00 -6.54097140e-01
5.86503327e-01 5.16048431e-01 1.26415715e-01 -2.38017678... | [8.357426643371582, -3.3598337173461914] |
1a352704-cbde-4ddb-b2eb-d7c749c7d1e5 | coding-kendalls-shape-trajectories-for-3d | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Tanfous_Coding_Kendalls_Shape_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Tanfous_Coding_Kendalls_Shape_CVPR_2018_paper.pdf | Coding Kendall's Shape Trajectories for 3D Action Recognition | Suitable shape representations as well as their temporal evolution, termed trajectories, often lie to non-linear manifolds. This puts an additional constraint (i.e., non-linearity) in using conventional machine learning techniques for the purpose of classification, event detection, prediction, etc. This paper accommoda... | ['Hassen Drira', 'Boulbaba Ben Amor', 'Amor Ben Tanfous'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.90697417e-01 -2.61739135e-01 -2.51626045e-01 -1.29137695e-01
-2.82450557e-01 -3.47866714e-01 6.34511471e-01 -6.59862012e-02
-1.67132929e-01 3.75681639e-01 2.72608340e-01 1.82665125e-01
-3.50625902e-01 -5.26662886e-01 -4.66978431e-01 -8.70125413e-01
-1.66991219e-01 1.52558252e-01 1.50252178e-01 4.18477431... | [7.896546363830566, 3.908677101135254] |
8237992a-d30e-4c53-91a0-ee2fc4ea93a0 | indic-punct-an-automatic-punctuation | 2203.16825 | null | https://arxiv.org/abs/2203.16825v1 | https://arxiv.org/pdf/2203.16825v1.pdf | indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languages | Automatic Speech Recognition (ASR) generates text which is most of the times devoid of any punctuation. Absence of punctuation is text can affect readability. Also, down stream NLP tasks such as sentiment analysis, machine translation, greatly benefit by having punctuation and sentence boundary information. We present ... | ['Vivek Raghavan', 'Harveen Singh Chadha', 'Priyanshi Shah', 'Rishabh Gaur', 'Ankur Dhuriya', 'Neeraj Chhimwal', 'Anirudh Gupta'] | 2022-03-31 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 5.08396745e-01 2.65319765e-01 2.65354365e-01 -3.70102197e-01
-6.66112363e-01 -9.57974255e-01 7.28687882e-01 2.60974020e-01
-2.79596090e-01 1.24499226e+00 5.17633080e-01 -8.41684222e-01
1.75698310e-01 -5.41534066e-01 -3.27246785e-01 -2.57887840e-01
1.05138525e-01 3.16459805e-01 1.70282170e-01 -7.65141606... | [14.184814453125, 7.177365303039551] |
b3fe1935-d7e1-4c11-85a8-9b03bf459ce6 | robust-preference-learning-for-storytelling | 2210.07792 | null | https://arxiv.org/abs/2210.07792v2 | https://arxiv.org/pdf/2210.07792v2.pdf | Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning | Controlled automated story generation seeks to generate natural language stories satisfying constraints from natural language critiques or preferences. Existing methods to control for story preference utilize prompt engineering which is labor intensive and often inconsistent. They may also use logit-manipulation method... | ['Mark Riedl', 'Spencer Frazier', 'Ian Yang', 'Anbang Ye', 'Michael Pieler', 'Shahbuland Matiana', 'Alexander Havrilla', 'Louis Castricato'] | 2022-10-14 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 5.31837285e-01 5.15783310e-01 -1.90504029e-01 -5.32405972e-01
-1.41806710e+00 -8.88675511e-01 9.56510425e-01 2.26422548e-02
-1.96289703e-01 1.04433036e+00 7.66089022e-01 -1.43568113e-01
1.63719848e-01 -7.87587404e-01 -7.59313047e-01 -2.27702737e-01
3.24526995e-01 8.59772086e-01 -1.95551783e-01 -3.04528147... | [11.660300254821777, 8.867547988891602] |
ecbe894d-1915-404f-a6c2-0ed2ce0b433c | nerfool-uncovering-the-vulnerability-of | 2306.06359 | null | https://arxiv.org/abs/2306.06359v1 | https://arxiv.org/pdf/2306.06359v1.pdf | NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations | Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and thus the possibility of instant rendering on new scenes. While adversarial robustness is essential for real-world applications, little study... | ['Yingyan Lin', 'Shunyao Zhang', 'Shang Wu', 'Souvik Kundu', 'Ye Yuan', 'Yonggan Fu'] | 2023-06-10 | null | null | null | null | ['adversarial-robustness', 'novel-view-synthesis'] | ['adversarial', 'computer-vision'] | [ 1.81860402e-01 -3.04153282e-02 2.05820948e-01 -2.57919699e-01
-4.05374140e-01 -1.14164734e+00 4.02141690e-01 -3.21987510e-01
-6.13111332e-02 4.60172057e-01 6.62419796e-02 -6.62659585e-01
-2.24033445e-01 -1.04722583e+00 -1.12851501e+00 -4.80384856e-01
-3.10700715e-01 -4.69894081e-01 1.50114343e-01 -7.09008157... | [5.467708110809326, 7.983709335327148] |
84e79e1f-5c97-407c-8c23-f19589c97788 | identifying-the-key-components-in-resnet-50 | 2110.1416 | null | https://arxiv.org/abs/2110.14160v2 | https://arxiv.org/pdf/2110.14160v2.pdf | Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation | Although deep learning based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on the prediction performance. The training setting includes various interdependent components, such as... | ['Xiaoying Tang', 'Roger Tam', 'Junyan Lyu', 'Pujin Cheng', 'Li Lin', 'Yijin Huang'] | 2021-10-27 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-7.26499110e-02 7.94796459e-03 -3.04100096e-01 -6.33217931e-01
-7.26149917e-01 -1.89282224e-01 2.30358168e-01 -3.98016460e-02
-4.41490650e-01 7.19126821e-01 2.08312467e-01 -5.28357983e-01
-2.62979388e-01 -7.04054177e-01 -5.62387764e-01 -7.94344544e-01
1.40358210e-01 1.50132984e-01 -2.79673329e-03 -6.90462813... | [15.79311466217041, -3.9603824615478516] |
75d20efb-5568-4968-97ec-84b839a93e53 | dense-pixel-to-pixel-harmonization-via | 2303.01681 | null | https://arxiv.org/abs/2303.01681v1 | https://arxiv.org/pdf/2303.01681v1.pdf | Dense Pixel-to-Pixel Harmonization via Continuous Image Representation | High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing. However, due to the high memory costs, existing dense pixel-to-pixel harmonization methods are mainly focusing on processing low-resolution (LR) images. Some recent works resort to com... | ['Zhenwei Shi', 'Keyan Chen', 'Zhengxia Zou', 'Yilan Zhang', 'Jianqi Chen'] | 2023-03-03 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 5.14877379e-01 -2.13145241e-01 5.05544767e-02 -1.43144295e-01
-5.90465307e-01 5.00092432e-02 3.84632438e-01 -3.97405654e-01
-3.79725724e-01 7.36468613e-01 -6.98329136e-02 -9.14513320e-02
-1.90524191e-01 -9.99841213e-01 -7.78467417e-01 -9.02982712e-01
5.70455909e-01 -2.71002769e-01 8.19222406e-02 -3.16528320... | [10.885327339172363, -2.0950839519500732] |
a57eef98-3dac-451e-b515-5bad5f47c890 | a-framework-for-building-closed-domain-chat | 1910.13826 | null | https://arxiv.org/abs/1910.13826v4 | https://arxiv.org/pdf/1910.13826v4.pdf | A Framework for Building Closed-Domain Chat Dialogue Systems | This paper presents HRIChat, a framework for developing closed-domain chat dialogue systems. Being able to engage in chat dialogues has been found effective for improving communication between humans and dialogue systems. This paper focuses on closed-domain systems because they would be useful when combined with task-o... | ['Mikio Nakano', 'Kazunori Komatani'] | 2019-10-30 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-3.43226761e-01 8.07292581e-01 1.14408962e-01 -6.22571290e-01
-4.10102814e-01 -8.21905077e-01 7.26530910e-01 4.08812314e-01
-7.68417343e-02 9.12408352e-01 6.30774319e-01 -5.03858030e-01
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-9.58557129e-02 8.64165187e-01 4.20240700e-01 -1.02567661... | [12.945575714111328, 7.958697319030762] |
ee9d4716-1283-41d4-bfb6-ad679d96f507 | eval-explainable-video-anomaly-localization | 2212.079 | null | https://arxiv.org/abs/2212.07900v1 | https://arxiv.org/pdf/2212.07900v1.pdf | EVAL: Explainable Video Anomaly Localization | We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes. We first learn general representations of objects and their motions (using deep networks) and then use these representations to build a high-level, location-dependent... | ['Erik Learned-Miller', 'Michael J. Jones', 'Ashish Singh'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Singh_EVAL_Explainable_Video_Anomaly_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Singh_EVAL_Explainable_Video_Anomaly_Localization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-anomaly-detection'] | ['computer-vision'] | [-1.07805550e-01 -3.00977647e-01 3.84400506e-03 -3.98611754e-01
-3.98687333e-01 -3.84485334e-01 4.69561785e-01 4.24780339e-01
5.37884608e-03 1.62057459e-01 1.80576354e-01 -3.58768404e-01
1.93693534e-01 -5.84982574e-01 -1.17534733e+00 -5.38068950e-01
-5.40809035e-01 1.45023078e-01 5.90214312e-01 2.13599205... | [7.855406284332275, 1.5518860816955566] |
d31d2465-3a71-4498-bde2-03e1263669f0 | teamtat-a-collaborative-text-annotation-tool | 2004.11894 | null | https://arxiv.org/abs/2004.11894v1 | https://arxiv.org/pdf/2004.11894v1.pdf | TeamTat: a collaborative text annotation tool | Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing te... | ['Dongseop Kwon', 'Sun Kim', 'Rezarta Islamaj', 'Zhiyong Lu'] | 2020-04-24 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 3.27216804e-01 3.82041521e-02 -3.85110945e-01 -4.50592905e-01
-1.32048154e+00 -9.45450962e-01 -2.42244974e-01 9.46910918e-01
-4.25227553e-01 8.06470513e-01 5.18447869e-02 -5.64645886e-01
-1.70450896e-01 -3.07552516e-01 -1.67131469e-01 -2.91461170e-01
5.63778758e-01 8.87418449e-01 -3.91411558e-02 2.66244084... | [8.82741641998291, 8.731945037841797] |
49877157-72c8-46b4-aa4a-f041ecc6b72e | cross-domain-labeled-lda-for-cross-domain | 1809.0582 | null | http://arxiv.org/abs/1809.05820v1 | http://arxiv.org/pdf/1809.05820v1.pdf | Cross-Domain Labeled LDA for Cross-Domain Text Classification | Cross-domain text classification aims at building a classifier for a target
domain which leverages data from both source and target domain. One promising
idea is to minimize the feature distribution differences of the two domains.
Most existing studies explicitly minimize such differences by an exact
alignment mechanis... | ['Baoyu Jing', 'Deqing Wang', 'Chenwei Lu', 'Fuzhen Zhuang', 'Cheng Niu'] | 2018-09-16 | null | null | null | null | ['cross-domain-text-classification'] | ['natural-language-processing'] | [-6.47412986e-02 -4.08665789e-03 -4.88191456e-01 -8.54474604e-01
-6.10096693e-01 -5.83758771e-01 9.64668155e-01 3.86014163e-01
-1.33542225e-01 5.79744458e-01 4.32230473e-01 1.25816450e-01
-1.19459383e-01 -7.69825280e-01 -4.00064319e-01 -5.64119220e-01
2.89026618e-01 5.25867522e-01 3.52396756e-01 -2.30364710... | [11.094862937927246, 6.53739595413208] |
aa87b3cb-9701-4cfa-ba41-6cc319b7b667 | event-temporal-relation-extraction-with | 2302.04985 | null | https://arxiv.org/abs/2302.04985v1 | https://arxiv.org/pdf/2302.04985v1.pdf | Event Temporal Relation Extraction with Bayesian Translational Model | Existing models to extract temporal relations between events lack a principled method to incorporate external knowledge. In this study, we introduce Bayesian-Trans, a Bayesian learning-based method that models the temporal relation representations as latent variables and infers their values via Bayesian inference and t... | ['Yulan He', 'Gabriele Pergola', 'Xingwei Tan'] | 2023-02-10 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 1.70547634e-01 2.88242579e-01 -4.54777867e-01 -5.34157276e-01
-8.01741421e-01 -5.30299783e-01 7.51510978e-01 3.66953999e-01
-3.46810430e-01 1.03504395e+00 2.79903799e-01 -1.17228091e-01
-5.98906040e-01 -8.68354559e-01 -6.76480711e-01 -5.69280028e-01
-3.45271826e-01 4.35295761e-01 4.15787399e-01 2.32883066... | [7.112652778625488, 3.918391466140747] |
46b93c35-c2bf-4f52-b0af-c62ca4b465f9 | predicting-lexical-complexity-in-english | 2102.08773 | null | https://arxiv.org/abs/2102.08773v2 | https://arxiv.org/pdf/2102.08773v2.pdf | Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset | Identifying words which may cause difficulty for a reader is an essential step in most lexical text simplification systems prior to lexical substitution and can also be used for assessing the readability of a text. This task is commonly referred to as Complex Word Identification (CWI) and is often modelled as a supervi... | ['Marcos Zampieri', 'Richard Evans', 'Matthew Shardlow'] | 2021-02-17 | null | null | null | null | ['lexical-complexity-prediction', 'complex-word-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.44856650e-01 2.07880080e-01 -2.09797516e-01 -4.17557597e-01
-5.16815901e-01 -7.02694654e-01 4.36226457e-01 8.71118009e-01
-8.76120687e-01 5.21542788e-01 4.96941686e-01 -4.67922777e-01
-2.76429683e-01 -5.36838531e-01 -7.91776851e-02 2.88489852e-02
5.45498073e-01 7.50543892e-01 -8.78860243e-03 -5.24557233... | [10.806625366210938, 10.366775512695312] |
581799cd-7ee4-4f6e-975c-e6391383b8d5 | self-supervised-point-cloud-completion-via | 2111.10701 | null | https://arxiv.org/abs/2111.10701v1 | https://arxiv.org/pdf/2111.10701v1.pdf | Self-Supervised Point Cloud Completion via Inpainting | When navigating in urban environments, many of the objects that need to be tracked and avoided are heavily occluded. Planning and tracking using these partial scans can be challenging. The aim of this work is to learn to complete these partial point clouds, giving us a full understanding of the object's geometry using ... | ['David Held', 'Arpit Jangid', 'Brian Okorn', 'Himangi Mittal'] | 2021-11-21 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 1.00650124e-01 2.37063408e-01 -3.07553951e-02 -5.45849860e-01
-7.49226451e-01 -8.20849419e-01 4.88815010e-01 5.07236496e-02
-4.60528880e-01 9.04148638e-01 -2.05887526e-01 -1.80741191e-01
1.51467964e-01 -1.00691569e+00 -1.23739445e+00 -3.03873807e-01
7.59065598e-02 1.31284273e+00 4.80898112e-01 -1.10296451... | [8.212786674499512, -2.8897125720977783] |
62c8711c-b95e-4b24-84db-cd6e399e181d | sparse-neural-additive-model-interpretable | 2202.12482 | null | https://arxiv.org/abs/2202.12482v1 | https://arxiv.org/pdf/2202.12482v1.pdf | Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity | Interpretable machine learning has demonstrated impressive performance while preserving explainability. In particular, neural additive models (NAM) offer the interpretability to the black-box deep learning and achieve state-of-the-art accuracy among the large family of generalized additive models. In order to empower N... | ['Ian J. Barnett', 'Pratik Chaudhari', 'Zhiqi Bu', 'Shiyun Xu'] | 2022-02-25 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.67499804e-01 5.01600027e-01 -5.05959868e-01 -4.07512307e-01
-8.30357254e-01 -1.74259871e-01 3.99342477e-02 -4.61118221e-01
1.67562768e-01 1.03487885e+00 1.36569530e-01 1.34281628e-02
-8.04221451e-01 -6.26803637e-01 -1.35962307e+00 -9.15940344e-01
-1.82801500e-01 5.06328881e-01 -8.03494096e-01 -3.73253934... | [8.135977745056152, 4.326070785522461] |
3a1b9ea9-3993-4009-a8be-8e835fec720b | character-centric-storytelling | 1909.07863 | null | https://arxiv.org/abs/1909.07863v3 | https://arxiv.org/pdf/1909.07863v3.pdf | Character-Centric Storytelling | Sequential vision-to-language or visual storytelling has recently been one of the areas of focus in computer vision and language modeling domains. Though existing models generate narratives that read subjectively well, there could be cases when these models miss out on generating stories that account and address all pr... | ['Jorma Laaksonen', 'Aditya Surikuchi'] | 2019-09-17 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.98472553e-01 3.90093476e-01 -1.72113910e-01 -3.51374418e-01
-3.38908046e-01 -5.83493769e-01 1.28355563e+00 -2.24817753e-01
-2.05563366e-01 8.66220534e-01 7.83242643e-01 -1.57837942e-01
3.11398774e-01 -6.96945488e-01 -7.57116497e-01 -2.46511415e-01
3.29930961e-01 4.76346284e-01 2.67583758e-01 -1.23396955... | [11.155591011047363, 0.765836775302887] |
17cb48f0-d3ed-41ff-88b6-eaed8d2fed9e | multiple-document-datasets-pre-training | 2012.14163 | null | https://arxiv.org/abs/2012.14163v2 | https://arxiv.org/pdf/2012.14163v2.pdf | Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks | In this paper, we introduce a fully convolutional network for the document layout analysis task. While state-of-the-art methods are using models pre-trained on natural scene images, our method Doc-UFCN relies on a U-shaped model trained from scratch for detecting objects from historical documents. We consider the line ... | ['Thierry Paquet', 'Christopher Kermorvant', 'Mélodie Boillet'] | 2020-12-28 | null | null | null | null | ['document-layout-analysis', 'line-detection'] | ['computer-vision', 'computer-vision'] | [ 2.06176743e-01 -1.03946254e-01 7.58345127e-02 -3.97601426e-01
-4.82953429e-01 -7.37316906e-01 7.17368186e-01 1.96670353e-01
-4.38047677e-01 2.38586068e-01 -1.55670062e-01 -5.25046527e-01
3.69949006e-02 -8.29215229e-01 -1.03075230e+00 -8.81897882e-02
1.50690996e-03 5.28000116e-01 6.08355403e-01 3.20083229... | [11.735086441040039, 2.609957695007324] |
d0fd6fc0-0bba-494a-8e7e-03340d99337b | facial-emotion-recognition-with-noisy-multi | 2010.09849 | null | https://arxiv.org/abs/2010.09849v2 | https://arxiv.org/pdf/2010.09849v2.pdf | Facial Emotion Recognition with Noisy Multi-task Annotations | Human emotions can be inferred from facial expressions. However, the annotations of facial expressions are often highly noisy in common emotion coding models, including categorical and dimensional ones. To reduce human labelling effort on multi-task labels, we introduce a new problem of facial emotion recognition with ... | ['Luc van Gool', 'Danda Pani Paudel', 'Zhiwu Huang', 'Siwei Zhang'] | 2020-10-19 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 3.36182155e-02 -3.75485495e-02 3.50895137e-01 -7.65154302e-01
-9.72141922e-01 -3.37436140e-01 2.60437191e-01 -5.97197354e-01
-4.17327404e-01 9.30666506e-01 3.62689272e-02 5.08915782e-01
1.19529039e-01 -1.96818158e-01 -4.74968463e-01 -1.00340486e+00
1.72382683e-01 2.88177282e-01 -7.51442730e-01 -1.51003286... | [13.671229362487793, 1.6158407926559448] |
a4140cbf-2603-422e-9dfc-80841557c368 | explicit-interaction-network-for-aspect | 2106.11148 | null | https://arxiv.org/abs/2106.11148v2 | https://arxiv.org/pdf/2106.11148v2.pdf | Explicit Interaction Network for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) aims to recognize targets, their sentiment polarities and opinions explaining the sentiment from a sentence. ASTE could be naturally divided into 3 atom subtasks, namely target detection, opinion detection and sentiment classification. We argue that the proper subtask combinat... | ['Zhifang Sui', 'Baobao Chang', 'Runxin Xu', 'Damai Dai', 'Tianyu Liu', 'Peiyi Wang'] | 2021-06-21 | null | null | null | null | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 4.05056447e-01 -1.79362416e-01 -3.33601743e-01 -7.12449193e-01
-8.85901630e-01 -9.57900584e-01 4.49135989e-01 1.96946174e-01
4.83539030e-02 5.80200732e-01 5.16537845e-01 -3.30696046e-01
2.72832096e-01 -5.29566467e-01 -4.85585809e-01 -4.99131262e-01
3.20716232e-01 2.58314997e-01 -1.15866475e-02 -6.64140403... | [11.477004051208496, 6.64941930770874] |
65f0f444-335a-4441-8f31-3ecf937dc569 | unsupervised-opinion-summarisation-in-the | 2211.14923 | null | https://arxiv.org/abs/2211.14923v1 | https://arxiv.org/pdf/2211.14923v1.pdf | Unsupervised Opinion Summarisation in the Wasserstein Space | Opinion summarisation synthesises opinions expressed in a group of documents discussing the same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media posts. Such posts are noisy and have unpredictable structure, posing additional challenges for the construction ... | ['Maria Liakata', 'Rob Procter', 'Adam Tsakalidis', 'Iman Munire Bilal', 'Jiayu Song'] | 2022-11-27 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 2.06004232e-01 4.19780105e-01 -5.15023433e-02 -4.19307202e-01
-9.39854205e-01 -5.17513871e-01 8.46630156e-01 7.33306766e-01
-1.72774270e-01 8.29419076e-01 1.05567575e+00 7.84055889e-02
2.79703319e-01 -5.02871275e-01 -4.92555022e-01 -1.00442243e+00
3.59324783e-01 5.45119464e-01 -2.74580922e-02 -2.30441168... | [12.49982738494873, 9.357665061950684] |
c975c367-1620-468d-bc17-84733b07efdb | dc-former-diverse-and-compact-transformer-for | 2302.14335 | null | https://arxiv.org/abs/2302.14335v1 | https://arxiv.org/pdf/2302.14335v1.pdf | DC-Former: Diverse and Compact Transformer for Person Re-Identification | In person re-identification (re-ID) task, it is still challenging to learn discriminative representation by deep learning, due to limited data. Generally speaking, the model will get better performance when increasing the amount of data. The addition of similar classes strengthens the ability of the classifier to ident... | ['Wei Chu', 'Yuan Cheng', 'Ruobing Zheng', 'Jianan Zhao', 'Furong Xu', 'Meng Wang', 'Cheng Zou', 'Wen Li'] | 2023-02-28 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-3.28314215e-01 -4.55395728e-01 -2.39601597e-01 -4.02641892e-01
-1.43604353e-01 -1.90603346e-01 5.51906407e-01 -1.22445181e-01
-4.31670398e-01 4.14426953e-01 5.92495084e-01 3.94418776e-01
-7.71343559e-02 -5.52557170e-01 -3.33209276e-01 -8.54015946e-01
3.38003576e-01 2.50873804e-01 1.53695241e-01 1.75432023... | [14.718685150146484, 1.03248131275177] |
9e6fffbb-0331-40ce-9257-f3b82dbd3ab5 | insmos-instance-aware-moving-object | 2303.03909 | null | https://arxiv.org/abs/2303.03909v1 | https://arxiv.org/pdf/2303.03909v1.pdf | InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data | Identifying moving objects is a crucial capability for autonomous navigation, consistent map generation, and future trajectory prediction of objects. In this paper, we propose a novel network that addresses the challenge of segmenting moving objects in 3D LiDAR scans. Our approach not only predicts point-wise moving la... | ['Xieyuanli Chen', 'Zhiqiang Zheng', 'Huimin Lu', 'Ruibin Guo', 'Chenghao Shi', 'Neng Wang'] | 2023-03-07 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 9.73354280e-02 -5.30859888e-01 -2.43216932e-01 -5.52177310e-01
-7.74758458e-01 -4.54869360e-01 4.88845766e-01 -1.41226202e-01
-5.64685404e-01 3.39570791e-01 -2.40129113e-01 -2.72775203e-01
-1.76708207e-01 -1.11288142e+00 -9.47554231e-01 -4.22422051e-01
-2.99247205e-01 8.01246583e-01 9.44234788e-01 7.56076872... | [8.123213768005371, -2.506901741027832] |
3f999c0f-b744-4cde-ad75-b2ceae5f8fc8 | optimizing-prediction-of-mgmt-promoter | 2201.04416 | null | https://arxiv.org/abs/2201.04416v2 | https://arxiv.org/pdf/2201.04416v2.pdf | Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning | Glioblastoma Multiforme (GBM) is a malignant brain cancer forming around 48% of al brain and Central Nervous System (CNS) cancers. It is estimated that annually over 13,000 deaths occur in the US due to GBM, making it crucial to have early diagnosis systems that can lead to predictable and effective treatment. The most... | ['Sauman Das'] | 2022-01-12 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 5.05813181e-01 2.34133661e-01 -1.35657743e-01 -2.31869012e-01
-9.97175455e-01 -1.45341158e-01 6.22201085e-01 3.42525125e-01
-8.94347310e-01 1.08495057e+00 2.79524893e-01 -6.37346685e-01
2.66243428e-01 -7.33891070e-01 -2.98179746e-01 -1.11114466e+00
5.15351109e-02 6.43069506e-01 1.99879751e-01 9.95849539... | [14.728407859802246, -2.55237078666687] |
ec08210c-3738-48a6-b7eb-8a7307bcbb5f | disentangling-task-relations-for-few-shot | 2211.08588 | null | https://arxiv.org/abs/2211.08588v1 | https://arxiv.org/pdf/2211.08588v1.pdf | Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering | Few-Shot Text Classification (FSTC) imitates humans to learn a new text classifier efficiently with only few examples, by leveraging prior knowledge from historical tasks. However, most prior works assume that all the tasks are sampled from a single data source, which cannot adapt to real-world scenarios where tasks ar... | ['Yu Zhang', 'Ying WEI', 'Zheng Li', 'Juan Zha'] | 2022-11-16 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.34564239e-01 -2.66107053e-01 -1.20911062e-01 -5.42326033e-01
-3.89519423e-01 -4.47613209e-01 7.05263138e-01 2.89060503e-01
-3.87279630e-01 6.28296733e-01 3.59642595e-01 6.26688376e-02
-3.66135001e-01 -3.38442117e-01 -3.49138319e-01 -7.04337180e-01
2.35775486e-01 7.09072411e-01 3.75432044e-01 1.58550870... | [10.143549919128418, 3.3650574684143066] |
31461026-ca0e-435b-bacd-d310fef6b4db | improving-code-summarization-with-block-wise | 2103.07845 | null | https://arxiv.org/abs/2103.07845v2 | https://arxiv.org/pdf/2103.07845v2.pdf | Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting | Automatic code summarization frees software developers from the heavy burden of manual commenting and benefits software development and maintenance. Abstract Syntax Tree (AST), which depicts the source code's syntactic structure, has been incorporated to guide the generation of code summaries. However, existing AST bas... | ['Rongxin Wu', 'Hui Li', 'Jianqiang Chen', 'Junqing Zhuang', 'Zhichao Ouyang', 'Chen Lin'] | 2021-03-14 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.17798078e-01 9.72737372e-02 -3.71455193e-01 -3.49443436e-01
-9.22203481e-01 -4.61901367e-01 -4.44199443e-02 4.63285416e-01
3.71756107e-01 1.98843107e-01 5.11810958e-01 -6.23802483e-01
4.25354004e-01 -5.10089517e-01 -6.54071212e-01 -2.26414949e-01
2.51389090e-02 -3.67738634e-01 1.18377633e-01 8.69566202... | [7.597941875457764, 7.956900596618652] |
709a226e-fb3b-4de6-8900-885fe72bedf3 | lift-learn-physics-informed-machine-learning | 1912.08177 | null | https://arxiv.org/abs/1912.08177v5 | https://arxiv.org/pdf/1912.08177v5.pdf | Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems | We present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system's governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting... | ['Boris Kramer', 'Karen Willcox', 'Elizabeth Qian', 'Benjamin Peherstorfer'] | 2019-12-17 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-1.06332742e-01 8.58618170e-02 1.07642800e-01 1.33205235e-01
-3.91770214e-01 -7.21723080e-01 5.78247428e-01 -3.19676906e-01
-1.23399742e-01 8.38862002e-01 -6.58686832e-02 -2.24442124e-01
-4.43008095e-01 -1.47791520e-01 -8.50385249e-01 -1.15897942e+00
-4.93183523e-01 6.88526154e-01 -1.27957642e-01 -5.50455332... | [6.493269443511963, 3.4778828620910645] |
bfce42f5-5568-4ceb-9e57-975d880b352e | multi-singer-fast-multi-singer-singing-voice-1 | 2112.10358 | null | https://arxiv.org/abs/2112.10358v1 | https://arxiv.org/pdf/2112.10358v1.pdf | Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus | High-fidelity multi-singer singing voice synthesis is challenging for neural vocoder due to the singing voice data shortage, limited singer generalization, and large computational cost. Existing open corpora could not meet requirements for high-fidelity singing voice synthesis because of the scale and quality weaknesse... | ['Zhou Zhao', 'Chenye Cui', 'Jinglin Liu', 'Yi Ren', 'Feiyang Chen', 'Rongjie Huang'] | 2021-12-20 | multi-singer-fast-multi-singer-singing-voice | https://dl.acm.org/doi/pdf/10.1145/3474085.3475437 | https://dl.acm.org/doi/pdf/10.1145/3474085.3475437 | mm-21-proceedings-of-the-29th-acm | ['audio-generation', 'singing-voice-synthesis'] | ['audio', 'speech'] | [-8.98959674e-03 -2.66447544e-01 1.10117845e-01 2.12547123e-01
-1.31161880e+00 -8.69315207e-01 -9.92192887e-03 -1.13434196e+00
1.20028973e-01 6.40088141e-01 3.40385169e-01 -7.07646236e-02
2.64091134e-01 -4.85106915e-01 -8.06960642e-01 -8.07256818e-01
4.23616260e-01 2.64237970e-01 -3.83052319e-01 -1.96272895... | [15.493611335754395, 6.173573017120361] |
c7d8b413-1cf1-4d61-b439-9c6f0a0ddcdd | exploring-sequence-to-sequence-transformer | 2211.06478 | null | https://arxiv.org/abs/2211.06478v1 | https://arxiv.org/pdf/2211.06478v1.pdf | Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting | In this paper, we present a novel approach to adapt a sequence-to-sequence Transformer-Transducer ASR system to the keyword spotting (KWS) task. We achieve this by replacing the keyword in the text transcription with a special token <kw> and training the system to detect the <kw> token in an audio stream. At inference ... | ['Quan Wang', 'Liam Fowl', 'Angelo Scorza Scarpati', 'Ignacio López Moreno', 'Guanlong Zhao', 'Beltrán Labrador'] | 2022-11-11 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 5.69744706e-01 -3.80739160e-02 -1.87923864e-01 -2.42907807e-01
-1.38509309e+00 -4.21463788e-01 3.04132253e-01 1.18986974e-02
-5.45438468e-01 2.22443178e-01 1.26787201e-01 -4.86438125e-01
7.69559741e-02 -3.94890279e-01 -4.60133046e-01 -7.32331812e-01
1.00639120e-01 9.49345157e-02 4.86611396e-01 -2.51164109... | [14.27031135559082, 6.380117416381836] |
ad2601e9-3e08-4641-afe1-89ef7397b685 | classification-and-reconstruction-of-optical | 2012.02185 | null | https://arxiv.org/abs/2012.02185v1 | https://arxiv.org/pdf/2012.02185v1.pdf | Classification and reconstruction of optical quantum states with deep neural networks | We apply deep-neural-network-based techniques to quantum state classification and reconstruction. We demonstrate high classification accuracies and reconstruction fidelities, even in the presence of noise and with little data. Using optical quantum states as examples, we first demonstrate how convolutional neural netwo... | ['Anton Frisk Kockum', 'Franco Nori', 'Carlos Sánchez Muñoz', 'Shahnawaz Ahmed'] | 2020-12-03 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.15439188e-01 2.34346688e-01 3.74481887e-01 -2.96540499e-01
-1.15663016e+00 -5.68432033e-01 6.83217704e-01 -3.97209793e-01
-5.38853168e-01 9.98463094e-01 -2.00226679e-01 -6.22450173e-01
-1.00717090e-01 -1.34665656e+00 -1.20900452e+00 -1.00720930e+00
1.25640184e-01 6.34419978e-01 -9.66545269e-02 -3.90958339... | [5.5909929275512695, 4.98147439956665] |
57b45776-fcd0-44f0-8b7d-79ec24a5b5df | 3d-meta-segmentation-neural-network | 2110.04297 | null | https://arxiv.org/abs/2110.04297v1 | https://arxiv.org/pdf/2110.04297v1.pdf | 3D Meta-Segmentation Neural Network | Though deep learning methods have shown great success in 3D point cloud part segmentation, they generally rely on a large volume of labeled training data, which makes the model suffer from unsatisfied generalization abilities to unseen classes with limited data. To address this problem, we present a novel meta-learning... | ['Yi Fang', 'Yu Hao'] | 2021-10-08 | null | null | null | null | ['3d-part-segmentation', '3d-point-cloud-part-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.62817910e-01 1.34623080e-01 -2.28805915e-01 -3.76861244e-01
-6.54480755e-01 -4.40488458e-01 3.40818614e-01 4.81219403e-02
-3.04658234e-01 1.27223045e-01 -4.16574657e-01 2.37572655e-01
1.25423297e-01 -7.79381394e-01 -8.85152698e-01 -4.77398485e-01
3.05747539e-01 1.03300798e+00 6.74869239e-01 -9.07963365... | [9.213799476623535, 0.3600652813911438] |
6890955d-11ea-433b-9a6e-7e0b0940c847 | 190910158 | 1909.10158 | null | https://arxiv.org/abs/1909.10158v2 | https://arxiv.org/pdf/1909.10158v2.pdf | Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data | A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several NLP tasks. In this work, we show that this is also the case for text gener... | ['Hamidreza Shahidi', 'Ming Li', 'Jimmy Lin'] | 2019-09-23 | two-birds-one-stone-a-simple-unified-model | https://aclanthology.org/2020.acl-main.355 | https://aclanthology.org/2020.acl-main.355.pdf | acl-2020-6 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.50658065e-01 4.94823545e-01 2.26663604e-01 -3.03065866e-01
-1.38616121e+00 -6.71857834e-01 7.73636699e-01 1.64119884e-01
-2.32477516e-01 1.34903193e+00 7.25178719e-01 -4.49974298e-01
2.45029852e-01 -1.10067785e+00 -7.72518933e-01 -4.61843908e-01
3.08400065e-01 5.35097778e-01 -3.05011660e-01 -5.00877142... | [11.791095733642578, 8.817861557006836] |
07b986f5-894f-4993-ba72-07ddfdda8a2c | bridging-the-gap-between-reality-and-ideality | 2205.05889 | null | https://arxiv.org/abs/2205.05889v1 | https://arxiv.org/pdf/2205.05889v1.pdf | Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction | Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, their realworld application performance is much frustrating. In this paper, we highlight that such the gap between reality and ideality stems... | ['Xiuwen Zhu', 'Minlong Lu', 'Hui Chen', 'Feiyu Xiong', 'Le Sun', 'Xianpei Han', 'Cheng Fu', 'Hongyu Lin', 'Tianshu Wang'] | 2022-05-12 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-1.14196703e-01 4.37248260e-01 -2.02482209e-01 -3.82480145e-01
-8.72530162e-01 -4.56343323e-01 8.09363604e-01 1.32715181e-01
-6.00778580e-01 1.03555822e+00 3.68516743e-01 -3.10495973e-01
-2.89941758e-01 -8.39292049e-01 -8.38247716e-01 -3.16878676e-01
8.74536019e-03 1.03578258e+00 -1.76800594e-01 -2.18150929... | [9.47426986694336, 8.542383193969727] |
f13b0dea-44af-4d1e-905c-2d25b3afea01 | improving-performance-of-automatic-keyword | 2211.05031 | null | https://arxiv.org/abs/2211.05031v1 | https://arxiv.org/pdf/2211.05031v1.pdf | Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness | Automatic keyword extraction (AKE) has gained more importance with the increasing amount of digital textual data that modern computing systems process. It has various applications in information retrieval (IR) and natural language processing (NLP), including text summarisation, topic analysis and document indexing. Thi... | ['Shujun Li', 'Jie Guo', 'Yang Xu', 'Jason R. C. Nurse', 'Enes Altuncu'] | 2022-11-09 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 5.69081664e-01 1.72055051e-01 -3.37725207e-02 8.67248103e-02
-1.13209176e+00 -7.95720875e-01 9.67513442e-01 8.71527553e-01
-1.00408387e+00 9.42510307e-01 5.22241771e-01 -8.68360624e-02
-5.62253058e-01 -7.26215899e-01 -3.42448086e-01 -5.20848393e-01
-8.04430470e-02 3.66754591e-01 7.38995433e-01 -3.84423077... | [10.07021427154541, 8.425080299377441] |
94d520f1-5761-4071-83c7-4861b10db14c | dr-2track-towards-real-time-visual-tracking | 2008.03912 | null | https://arxiv.org/abs/2008.03912v1 | https://arxiv.org/pdf/2008.03912v1.pdf | DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression | Visual tracking has yielded promising applications with unmanned aerial vehicle (UAV). In literature, the advanced discriminative correlation filter (DCF) type trackers generally distinguish the foreground from the background with a learned regressor which regresses the implicit circulated samples into a fixed target l... | ['Yiming Li', 'Chen Feng', 'Fangqiang Ding', 'Changhong Fu', 'Jin Jin'] | 2020-08-10 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [-8.99125859e-02 -4.08500761e-01 -1.34320766e-01 1.08819678e-01
-1.30507141e-01 -1.06202018e+00 3.74796510e-01 -3.21608156e-01
-2.78162450e-01 7.27230370e-01 -4.19436485e-01 -1.47921652e-01
1.44732550e-01 -2.34215915e-01 -4.75496203e-01 -1.03343010e+00
-3.23348850e-01 -1.56979874e-01 8.65242302e-01 6.50389940... | [6.387375831604004, -2.0656139850616455] |
15e08f9d-47f6-4245-bc7f-7fe0ba8b26d1 | scene-as-occupancy | 2306.02851 | null | https://arxiv.org/abs/2306.02851v3 | https://arxiv.org/pdf/2306.02851v3.pdf | Scene as Occupancy | Human driver can easily describe the complex traffic scene by visual system. Such an ability of precise perception is essential for driver's planning. To achieve this, a geometry-aware representation that quantizes the physical 3D scene into structured grid map with semantic labels per cell, termed as 3D Occupancy, wou... | ['Hongyang Li', 'Dahua Lin', 'Ping Luo', 'Lewei Lu', 'Yi Gu', 'Li Chen', 'Hanming Deng', 'Silei Wu', 'Tai Wang', 'Chonghao Sima', 'Wenwen Tong'] | 2023-06-05 | null | null | null | null | ['motion-planning'] | ['robots'] | [-1.67077526e-01 8.70845765e-02 -2.68408079e-02 -2.17183962e-01
-4.85572517e-01 -2.96146959e-01 7.19003081e-01 5.72793186e-02
-2.61497378e-01 2.68923521e-01 3.95710796e-01 -5.25846362e-01
-1.65673085e-02 -9.04913187e-01 -5.26157200e-01 -5.73756814e-01
1.55273601e-01 3.83934766e-01 7.13981688e-01 -4.39309001... | [8.191122055053711, -2.4984357357025146] |
64b1d415-26e1-4e10-939e-cc54f138f0e2 | 3d-instances-as-1d-kernels | 2207.07372 | null | https://arxiv.org/abs/2207.07372v2 | https://arxiv.org/pdf/2207.07372v2.pdf | 3D Instances as 1D Kernels | We introduce a 3D instance representation, termed instance kernels, where instances are represented by one-dimensional vectors that encode the semantic, positional, and shape information of 3D instances. We show that instance kernels enable easy mask inference by simply scanning kernels over the entire scenes, avoiding... | ['Weicai Zhong', 'Zhiguo Cao', 'Hao Lu', 'Shuaiyuan Du', 'Min Shi', 'Yizheng Wu'] | 2022-07-15 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 6.81834370e-02 2.34310940e-01 -9.32788402e-02 -4.25666392e-01
-6.67947650e-01 -6.82622492e-01 5.76562643e-01 1.95840493e-01
-1.82850331e-01 2.41283730e-01 -2.27081880e-01 -1.02697730e-01
-2.58546233e-01 -8.19578409e-01 -9.16982234e-01 -5.93359649e-01
-2.91944534e-01 5.64756215e-01 5.39936960e-01 2.78784275... | [8.019296646118164, -3.193833351135254] |
0b007f64-3dfe-4f0c-a0eb-090723ce90e8 | an-approach-to-human-iris-recognition-using | 2009.0588 | null | https://arxiv.org/abs/2009.05880v1 | https://arxiv.org/pdf/2009.05880v1.pdf | An approach to human iris recognition using quantitative analysis of image features and machine learning | The Iris pattern is a unique biological feature for each individual, making it a valuable and powerful tool for human identification. In this paper, an efficient framework for iris recognition is proposed in four steps. (1) Iris segmentation (using a relative total variation combined with Coarse Iris Localization), (2)... | ['Donya Khaledyan', 'Abolfazl Zargari Khuzani', 'Najmeh Mashhadi', 'Morteza Heidari'] | 2020-09-12 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 3.07131648e-01 -4.28613871e-01 -2.27433994e-01 -9.73521397e-02
-1.83712110e-01 -1.87507927e-01 5.25055945e-01 3.97694051e-01
-4.15452063e-01 4.94460583e-01 1.08624488e-01 -2.94463009e-01
-5.20071030e-01 -5.22235453e-01 1.13776848e-01 -1.14175940e+00
-2.21809253e-01 4.59432393e-01 -1.71927556e-01 3.00068706... | [3.7488532066345215, -3.6278018951416016] |
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