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cf0fc458-4033-4957-bfda-9df7a81766df | tandem3d-active-tactile-exploration-for-3d | 2209.08772 | null | https://arxiv.org/abs/2209.08772v2 | https://arxiv.org/pdf/2209.08772v2.pdf | TANDEM3D: Active Tactile Exploration for 3D Object Recognition | Tactile recognition of 3D objects remains a challenging task. Compared to 2D shapes, the complex geometry of 3D surfaces requires richer tactile signals, more dexterous actions, and more advanced encoding techniques. In this work, we propose TANDEM3D, a method that applies a co-training framework for exploration and de... | ['Matei Ciocarlie', 'Shuran Song', 'Han Lin', 'Jingxi Xu'] | 2022-09-19 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 2.27185830e-01 -1.22185327e-01 -5.30906171e-02 -1.65158883e-01
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-4.51285988e-01 5.50126910e-01 3.05553257e-01 -2.14161411... | [5.920749187469482, -0.8923181295394897] |
a3633a8a-cfeb-4df4-9220-45b1f97b4c8a | natural-language-processing-in-electronic | 2306.12834 | null | https://arxiv.org/abs/2306.12834v1 | https://arxiv.org/pdf/2306.12834v1.pdf | Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review | Background: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques ar... | ['Kathryn Turner}', 'Ph. D', 'Anthony R. Pisani', 'Prabal Datta Barua', 'Jeffrey Soar', 'Niall Higgins', 'Rajib Rana', 'Elias Hossain'] | 2023-06-22 | null | null | null | null | ['classification-1', 'transfer-learning', 'named-entity-recognition-ner', 'decision-making'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'reasoning'] | [ 1.77355587e-01 2.43136570e-01 -7.03990519e-01 -6.55942187e-02
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8.62764008e-03 5.47069728e-01 -6.59214318e-01 3.62201601... | [8.443209648132324, 8.606633186340332] |
9b47a11c-c91a-4df8-a64e-da0d65fd0231 | a-minimal-approach-for-natural-language | 2305.04082 | null | https://arxiv.org/abs/2305.04082v1 | https://arxiv.org/pdf/2305.04082v1.pdf | A Minimal Approach for Natural Language Action Space in Text-based Games | Text-based games (TGs) are language-based interactive environments for reinforcement learning. While language models (LMs) and knowledge graphs (KGs) are commonly used for handling large action space in TGs, it is unclear whether these techniques are necessary or overused. In this paper, we revisit the challenge of exp... | ['Ehsan Shareghi', 'Gholamreza Haffari', 'Shirui Pan', 'Meng Fang', 'Dongwon Kelvin Ryu'] | 2023-05-06 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-2.41406053e-01 4.38944310e-01 -1.84979588e-01 1.05875812e-01
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-4.46484089e-01 7.77985752e-01 3.46316189e-01 -7.11068451... | [3.8237357139587402, 1.433200716972351] |
52c8e111-9943-4265-bfd7-54a96c126176 | a-proximity-aware-hierarchical-clustering-of | 1703.04835 | null | http://arxiv.org/abs/1703.04835v1 | http://arxiv.org/pdf/1703.04835v1.pdf | A Proximity-Aware Hierarchical Clustering of Faces | In this paper, we propose an unsupervised face clustering algorithm called
"Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local
structure of deep representations. In the proposed method, a similarity measure
between deep features is computed by evaluating linear SVM margins. SVMs are
trained using n... | ['Jun-Cheng Chen', 'Wei-An Lin', 'Rama Chellappa'] | 2017-03-14 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-1.62603423e-01 -1.97680265e-01 -1.05232045e-01 -8.97862494e-01
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-3.23086351e-01 2.73281515e-01 -2.96099097e-01 7.97006860... | [13.364006042480469, 0.9992191791534424] |
089fc6e7-6f36-48d6-8b6b-002932d077c8 | the-afrl-ohio-state-wmt18-multimodal-system | null | null | https://aclanthology.org/W18-6440 | https://aclanthology.org/W18-6440.pdf | The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional | AFRL-Ohio State extends its usage of visual domain-driven machine translation for use as a peer with traditional machine translation systems. As a peer, it is enveloped into a system combination of neural and statistical MT systems to present a composite translation. | ['James Davis', 'S', 'Jeremy Gwinnup', 'Michael Hutt', 'Joshua vick', 'John Duselis', 'Grant Erdmann'] | 2018-10-01 | null | null | null | ws-2018-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 8.99574459e-02 2.73174822e-01 -5.27489662e-01 -5.43035626e-01
-1.38729584e+00 -7.80851424e-01 9.40902293e-01 -2.88393259e-01
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6.67414010e-01 -2.62502879e-01 -4.36458379e-01 1.05020359e-01
6.62386954e-01 1.18496275e+00 -3.30597535e-02 -5.29314756... | [11.579678535461426, 10.29758071899414] |
d442d301-0590-4155-a642-00f8568ea9db | boosting-text-to-image-diffusion-models-with | 2305.19599 | null | https://arxiv.org/abs/2305.19599v2 | https://arxiv.org/pdf/2305.19599v2.pdf | Boosting Text-to-Image Diffusion Models with Fine-Grained Semantic Rewards | Recent advances in text-to-image diffusion models have achieved remarkable success in generating high-quality, realistic images from given text prompts. However, previous methods fail to perform accurate modality alignment between text concepts and generated images due to the lack of fine-level semantic guidance that s... | ['Guansong Lu', 'Xiaodan Liang', 'Hang Xu', 'Jianhua Han', 'Zutao Jiang', 'Guian Fang'] | 2023-05-31 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.43312371e-01 1.75484255e-01 -1.36558935e-01 -2.93469220e-01
-1.01973200e+00 -4.59143460e-01 1.01507163e+00 -2.49016900e-02
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5.42509615e-01 5.00336230e-01 2.84331143e-01 -1.17448419... | [11.121505737304688, 0.6815469861030579] |
1fb34213-7e12-45d6-a367-f554536abbf6 | lae-long-tailed-age-estimation | 2110.12741 | null | https://arxiv.org/abs/2110.12741v1 | https://arxiv.org/pdf/2110.12741v1.pdf | LAE : Long-tailed Age Estimation | Facial age estimation is an important yet very challenging problem in computer vision. To improve the performance of facial age estimation, we first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with ... | ['Guodong Guo', 'Zhen Lei', 'Xibo Ma', 'Jun Wan', 'Yu Zhu', 'Zichang Tan', 'Zenghao Bao'] | 2021-10-25 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-2.78767012e-02 2.22207710e-01 -2.88712382e-01 -6.78158998e-01
-4.88643795e-01 1.23453222e-01 6.42368197e-01 -8.34562033e-02
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7.30002150e-02 4.57207859e-01 -2.48618186e-01 1.02082022... | [13.577606201171875, 0.876890242099762] |
0412673d-f496-4e86-a54f-1f6d7f7e035f | pose-is-all-you-need-the-pose-only-group | 2108.04186 | null | https://arxiv.org/abs/2108.04186v1 | https://arxiv.org/pdf/2108.04186v1.pdf | Pose is all you need: The pose only group activity recognition system (POGARS) | We introduce a novel deep learning based group activity recognition approach called the Pose Only Group Activity Recognition System (POGARS), designed to use only tracked poses of people to predict the performed group activity. In contrast to existing approaches for group activity recognition, POGARS uses 1D CNNs to le... | ['Stuart Morgan', 'Zhen He', 'Aiden Nibali', 'Haritha Thilakarathne'] | 2021-08-09 | null | null | null | null | ['group-activity-recognition', 'activity-prediction', 'activity-prediction'] | ['computer-vision', 'computer-vision', 'time-series'] | [-6.13211617e-02 -2.71146297e-01 -2.39293948e-01 -2.01857895e-01
-4.54871595e-01 -3.22007656e-01 7.49941409e-01 -4.56924066e-02
-8.45295310e-01 8.12478900e-01 3.16195190e-01 4.07527685e-01
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-4.80268329e-01 5.12361944e-01 2.39415452e-01 -5.06074633... | [8.051301956176758, 0.4886396825313568] |
1ec1673f-3d95-44d7-a09f-a04de960fd68 | domain-adversarial-reinforcement-learning-for | 1905.04094 | null | https://arxiv.org/abs/1905.04094v1 | https://arxiv.org/pdf/1905.04094v1.pdf | Domain Adversarial Reinforcement Learning for Partial Domain Adaptation | Partial domain adaptation aims to transfer knowledge from a label-rich source domain to a label-scarce target domain which relaxes the fully shared label space assumption across different domains. In this more general and practical scenario, a major challenge is how to select source instances in the shared classes acro... | ['Xinxiao wu', 'Jin Chen', 'Shenghua Gao', 'Lixin Duan'] | 2019-05-10 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 5.63285887e-01 1.94645271e-01 -4.19931561e-01 -4.47160572e-01
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9.26996991e-02 8.04366827e-01 1.89411193e-01 -1.85330167... | [10.361173629760742, 3.11637282371521] |
4d646177-176d-41b2-90e5-915cc9bd0216 | online-learning-in-the-manifold-of-low-rank | null | null | http://papers.nips.cc/paper/4084-online-learning-in-the-manifold-of-low-rank-matrices | http://papers.nips.cc/paper/4084-online-learning-in-the-manifold-of-low-rank-matrices.pdf | Online Learning in The Manifold of Low-Rank Matrices | When learning models that are represented in matrix forms, enforcing a low-rank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model. However, naive approaches for minimizing functions over the set of low-rank matrices are either prohibit... | ['Daphna Weinshall', 'Gal Chechik', 'Uri Shalit'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['multi-label-image-classification'] | ['computer-vision'] | [ 3.55572253e-01 -3.98420393e-02 -1.71837509e-01 -3.78904074e-01
-1.39778626e+00 -7.98789024e-01 6.28100336e-01 3.82092625e-01
-6.54344618e-01 3.06498975e-01 2.53601760e-01 -3.41425210e-01
-4.30965245e-01 -2.50155717e-01 -7.86559701e-01 -6.37929797e-01
-3.76247346e-01 6.77599728e-01 -1.51600376e-01 5.99089637... | [7.324477195739746, 4.473915100097656] |
c10bbaed-12d7-4ad6-9d8e-e763a8b0a455 | stmgcn-mobile-edge-computing-empowered-vessel | null | null | https://ieeexplore.ieee.org/abstract/document/9754225 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9754225 | STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network | —The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT). In maritime IoT, the spatio-temporal vessel trajectories, collected from the hybrid satellite-terrestrial automatic identification system (AIS) base station... | ['Han Yu', 'Zehui Xiong', 'Yanli Yuan', 'Jiangtian Nie', 'Maohan Liang', 'IEEE', 'Member', 'Ryan Wen Liu'] | 2022-04-08 | null | null | null | ieee-transactions-on-industrial-informatics-8 | ['trajectory-prediction', 'edge-computing'] | ['computer-vision', 'time-series'] | [-3.86036664e-01 -2.51442313e-01 9.18459787e-04 -3.06167483e-01
-7.96111524e-02 -4.05006915e-01 4.84583735e-01 1.13439046e-01
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-8.68396282e-01 -1.41059256e+00 -5.15684128e-01 -9.26503897e-01
-3.57216328e-01 2.62245804e-01 3.99101555e-01 -4.86574978... | [6.463151454925537, 2.0554120540618896] |
f085a05b-41e5-4f3b-a2c1-9a48cf2f7f6d | learning-to-embed-multi-modal-contexts-for-1 | null | null | https://aclanthology.org/2022.findings-naacl.61 | https://aclanthology.org/2022.findings-naacl.61.pdf | Learning to Embed Multi-Modal Contexts for Situated Conversational Agents | The Situated Interactive Multi-Modal Conversations (SIMMC) 2.0 aims to create virtual shopping assistants that can accept complex multi-modal inputs, i.e. visual appearances of objects and user utterances. It consists of four subtasks, multi-modal disambiguation (MM-Disamb), multi-modal coreference resolution (MM-Coref... | ['Kee-Eung Kim', 'Kangwook Lee', 'Haebin Shin', 'Youngjune Lee', 'Jinhyeon Kim', 'Yoonhyung Kim', 'Ran Han', 'Minho Park', 'Yunseon Choi', 'Oh Joon Kwon', 'Haeju Lee'] | null | null | null | null | findings-naacl-2022-7 | ['dialogue-state-tracking', 'coreference-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [-8.65826383e-02 6.38864338e-01 1.79113314e-01 -5.45782864e-01
-1.07071733e+00 -1.06571758e+00 1.10240984e+00 -9.08658206e-02
-3.75995785e-01 5.60188174e-01 5.23980916e-01 -4.20219600e-01
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5.18569708e-01 1.30232763e+00 2.61969715e-01 -6.77024961... | [10.95165729522705, 1.3672337532043457] |
3af27171-cdaa-4999-9247-dedd23e0c91c | high-throughput-high-performance-deep | 2212.10632 | null | https://arxiv.org/abs/2212.10632v1 | https://arxiv.org/pdf/2212.10632v1.pdf | High-Throughput, High-Performance Deep Learning-Driven Light Guide Plate Surface Visual Quality Inspection Tailored for Real-World Manufacturing Environments | Light guide plates are essential optical components widely used in a diverse range of applications ranging from medical lighting fixtures to back-lit TV displays. In this work, we introduce a fully-integrated, high-throughput, high-performance deep learning-driven workflow for light guide plate surface visual quality i... | ['Alexander Wong', 'Mohammad Javad Shafiee', 'Gautam Bathla', 'Mahmoud Famouri', 'Carol Xu'] | 2022-12-20 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.89047992e-01 -2.22656101e-01 5.18928528e-01 -5.98139390e-02
-5.50757170e-01 -5.21076322e-02 -1.81549475e-01 -1.37319341e-01
-1.34275749e-01 1.98545307e-01 -8.69096875e-01 -5.91869771e-01
-4.09673333e-01 -8.54520202e-01 -9.17028368e-01 -7.90614545e-01
2.84466982e-01 6.74290776e-01 6.43652380e-02 -1.84770286... | [7.435253143310547, 1.9019228219985962] |
225aa650-f2ff-45c6-b2c3-79c60f61a88d | deep-learning-for-automatic-pneumonia | 2005.13899 | null | https://arxiv.org/abs/2005.13899v1 | https://arxiv.org/pdf/2005.13899v1.pdf | Deep Learning for Automatic Pneumonia Detection | Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. Computer-aided... | ['Alexandr A. Kalinin', 'Dmytro Poplavskiy', 'Tatiana Gabruseva'] | 2020-05-28 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 3.90000999e-01 -2.09594667e-01 1.25837937e-01 -3.70515883e-02
-7.60273755e-01 -4.26936388e-01 2.55713105e-01 3.11280340e-01
-7.61518598e-01 5.02781451e-01 1.23435892e-01 -3.80598515e-01
-2.10482717e-01 -6.04078829e-01 -4.14169699e-01 -8.51656079e-01
6.18806109e-02 9.70256925e-01 5.16945004e-01 5.54778218... | [15.43396282196045, -1.8355731964111328] |
af29578c-8fca-4dbe-be14-ebd92dda2876 | unsupervised-person-re-identification-via-1 | 2004.09228 | null | https://arxiv.org/abs/2004.09228v1 | https://arxiv.org/pdf/2004.09228v1.pdf | Unsupervised Person Re-identification via Multi-label Classification | The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels. Our method starts by assigning each person image with a single-class label, t... | ['Shiliang Zhang', 'Dongkai Wang'] | 2020-04-20 | unsupervised-person-re-identification-via-3 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Unsupervised_Person_Re-Identification_via_Multi-Label_Classification_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Unsupervised_Person_Re-Identification_via_Multi-Label_Classification_CVPR_2020_paper.pdf | cvpr-2020-6 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.83656827e-01 -2.75357187e-01 -3.30575317e-01 -8.01433563e-01
-8.45798075e-01 -5.17641068e-01 6.11536086e-01 1.74693301e-01
-6.92198992e-01 6.97477639e-01 -2.82909367e-02 3.07472259e-01
-2.52496097e-02 -5.30579388e-01 -4.36695248e-01 -7.28282809e-01
2.76608199e-01 7.83688128e-01 -2.03160897e-01 4.95511174... | [14.798698425292969, 1.0595605373382568] |
c0cd9856-523c-4b90-b5c3-0ac9b1ab3a66 | attractive-or-faithful-popularity-reinforced | 2002.02095 | null | https://arxiv.org/abs/2002.02095v1 | https://arxiv.org/pdf/2002.02095v1.pdf | Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation | With the rapid proliferation of online media sources and published news, headlines have become increasingly important for attracting readers to news articles, since users may be overwhelmed with the massive information. In this paper, we generate inspired headlines that preserve the nature of news articles and catch th... | ['Wen-Chih Peng', 'Lun-Wei Ku', 'Yi-Lun Wu', 'Sung-Lin Yeh', 'Hong-Han Shuai', 'Yun-Zhu Song'] | 2020-02-06 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [-3.10171749e-02 4.61589098e-01 -3.18774253e-01 -3.93713787e-02
-1.07224500e+00 -2.07325175e-01 9.37378764e-01 2.98437297e-01
-4.71436590e-01 9.46806610e-01 6.77368879e-01 1.93615943e-01
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2.62381524e-01 4.84510928e-01 -2.34597214e-02 -6.09844327... | [12.192511558532715, 9.106755256652832] |
5ac6aea9-e6f7-4bee-ac32-df8efcaa6bba | multimodal-spontaneous-emotion-corpus-for | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf | Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis | Emotion is expressed in multiple modalities, yet most research has considered at most one or two. This stems in part from the lack of large, diverse, well-annotated, multimodal databases with which to develop and test algorithms. We present a well-annotated, multimodal, multidimensional spontaneous emotion corpus of 14... | ['Huiyuan Yang', 'Umur Ciftci', 'Shaun Canavan', 'Jeffrey F. Cohn', 'Andy Horowitz', 'Qiang Ji', 'Xing Zhang', 'Peng Liu', 'Michael Reale', 'Lijun Yin', 'Zheng Zhang', 'Yue Wu', 'Jeff M. Girard'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['action-unit-detection'] | ['computer-vision'] | [ 5.44710934e-01 -3.00514475e-02 -1.40259787e-01 -6.49362028e-01
-7.38323748e-01 -6.68743193e-01 1.61857158e-01 -8.86378437e-02
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3.39296132e-01 -1.65224552e-01 6.20932765e-02 -6.79480791e-01
-2.34975964e-01 -1.76051438e-01 -5.09665072e-01 -4.64053126... | [13.551697731018066, 2.140993356704712] |
9e08d5bb-2972-4c38-a273-28d1bab4f123 | rgi-robust-gan-inversion-for-mask-free-image | 2302.12464 | null | https://arxiv.org/abs/2302.12464v1 | https://arxiv.org/pdf/2302.12464v1.pdf | RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection | Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generator as a deep generative prior, is a promising tool for image restoration under corruptions. However, the performance of GAN-inversion can be... | ['Jianjun Shi', 'Jiulong Shan', 'Ping Huang', 'Haoping Bai', 'Meng Cao', 'Xiaoyi Gu', 'Shancong Mou'] | 2023-02-24 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 8.77593815e-01 3.04088861e-01 4.11393717e-02 1.83687553e-01
-1.00851166e+00 -5.08575618e-01 2.81010360e-01 -6.66397870e-01
1.40034273e-01 1.02394104e+00 6.95658326e-02 -1.99304819e-01
1.61800817e-01 -9.11218107e-01 -1.11583316e+00 -1.26027846e+00
6.39750957e-01 2.58342683e-01 1.90205462e-02 -2.73655087... | [11.46369743347168, -1.1611747741699219] |
45e956ff-bd57-456a-a707-5c7325c6d712 | ocelot-overlapped-cell-on-tissue-dataset-for | 2303.13110 | null | https://arxiv.org/abs/2303.13110v2 | https://arxiv.org/pdf/2303.13110v2.pdf | OCELOT: Overlapped Cell on Tissue Dataset for Histopathology | Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures and zoom in to classify cells based on their morphology and the surro... | ['Sérgio Pereira', 'Donggeun Yoo', 'Chan-Young Ock', 'Kyunghyun Paeng', 'Soo Ick Cho', 'Wonkyung Jung', 'Jinhee Lee', 'Biagio Brattoli', 'Seonwook Park', 'Jaewoong Shin', 'Aaron Valero Puche', 'Jeongun Ryu'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ryu_OCELOT_Overlapped_Cell_on_Tissue_Dataset_for_Histopathology_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ryu_OCELOT_Overlapped_Cell_on_Tissue_Dataset_for_Histopathology_CVPR_2023_paper.pdf | cvpr-2023-1 | ['whole-slide-images', 'cell-detection'] | ['computer-vision', 'computer-vision'] | [ 1.34525880e-01 -2.61057615e-01 -3.56567591e-01 2.00040624e-01
-1.25624681e+00 -6.65152729e-01 3.99447650e-01 7.35942662e-01
-5.06920159e-01 6.64010942e-01 -8.75859186e-02 -3.09383154e-01
1.55628890e-01 -4.23696131e-01 -3.91154170e-01 -1.39850223e+00
-6.10356033e-02 5.47535419e-01 1.48076847e-01 2.06280023... | [15.061299324035645, -3.063201427459717] |
5b332f8a-3bd7-4eba-8c02-8ee1bed259ee | refclip-a-universal-teacher-for-weakly | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_RefCLIP_A_Universal_Teacher_for_Weakly_Supervised_Referring_Expression_Comprehension_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_RefCLIP_A_Universal_Teacher_for_Weakly_Supervised_Referring_Expression_Comprehension_CVPR_2023_paper.pdf | RefCLIP: A Universal Teacher for Weakly Supervised Referring Expression Comprehension | Referring Expression Comprehension (REC) is a task of grounding the referent based on an expression, and its development is greatly limited by expensive instance-level annotations. Most existing weakly supervised methods are built based on two-stage detection networks, which are computationally expensive. In this p... | ['Rongrong Ji', 'Annan Shu', 'Guannan Jiang', 'Xiaoshuai Sun', 'Yiyi Zhou', 'Gen Luo', 'Lei Jin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['referring-expression', 'text-matching'] | ['computer-vision', 'natural-language-processing'] | [ 1.08469032e-01 1.30311638e-01 -6.96458578e-01 -5.28184354e-01
-1.04210281e+00 -5.92367470e-01 3.54354322e-01 1.51893139e-01
-3.51877630e-01 7.43770480e-01 1.86591268e-01 -3.64173681e-01
1.86305910e-01 -6.23139620e-01 -9.97993290e-01 -5.53802490e-01
4.46044922e-01 3.16634476e-01 2.75158674e-01 -3.54933858... | [9.53128433227539, 3.5455493927001953] |
6e3f11fb-acbe-4650-9902-ecc5be643475 | optimization-of-resource-service-composition | null | null | https://link.springer.com/chapter/10.1007/978-981-16-8048-9_18#Abs1 | https://link.springer.com/chapter/10.1007/978-981-16-8048-9_18#Abs1 | Optimization of Resource Service Composition in Cloud Manufacture Based on Improved Genetic and Ant Colony Algorithm | Aiming at resource service composition optimization under cloud manufacturing, a service composition and optimization objective function model for cloud
manufacturing resource based on quality of service was established. An improved
genetic and ant colony algorithm to solve the model was also proposed. The hybrid
al... | ['Wang Zhengcheng'] | 2022-02-01 | null | null | null | advances-in-intelligent-systems-and-computing-1 | ['service-composition'] | ['miscellaneous'] | [-2.16891214e-01 -7.06999481e-01 3.41600180e-02 1.10457718e-01
1.01596057e-01 -4.63577002e-01 -1.64569423e-01 -2.06129596e-01
-1.31124184e-01 4.30445790e-01 -5.26932478e-01 -2.59437770e-01
-7.37326026e-01 -1.17044723e+00 2.77649909e-01 -9.39321220e-01
-1.30694777e-01 1.35617459e+00 2.84293871e-02 -2.68278539... | [8.566904067993164, 6.924505233764648] |
9df70daf-3ebb-418c-8f3f-f1873c60e7a2 | a-streamlined-encoderdecoder-architecture-for | 1810.12947 | null | http://arxiv.org/abs/1810.12947v1 | http://arxiv.org/pdf/1810.12947v1.pdf | A Streamlined Encoder/Decoder Architecture for Melody Extraction | Melody extraction in polyphonic musical audio is important for music signal
processing. In this paper, we propose a novel streamlined encoder/decoder
network that is designed for the task. We make two technical contributions.
First, drawing inspiration from a state-of-the-art model for semantic
pixel-wise segmentation,... | ['Yi-Hsuan Yang', 'Tsung-Han Hsieh', 'Li Su'] | 2018-10-30 | null | null | null | null | ['melody-extraction'] | ['music'] | [ 1.73203707e-01 -1.81250513e-01 1.93756625e-01 -3.63925509e-02
-8.43708336e-01 -7.29214966e-01 -9.44988281e-02 -4.17078845e-02
-5.25008500e-01 3.82139087e-01 8.18343274e-03 5.94876967e-02
8.34578276e-02 -5.56620359e-01 -6.39833212e-01 -5.10094285e-01
-1.20626882e-01 -3.10311198e-01 3.53613287e-01 -1.71656892... | [15.723630905151367, 5.480484485626221] |
87db002b-5b1f-4508-b1b3-1d31dbf1c754 | modeling-graphs-beyond-hyperbolic-graph | 2306.14064 | null | https://arxiv.org/abs/2306.14064v1 | https://arxiv.org/pdf/2306.14064v1.pdf | Modeling Graphs Beyond Hyperbolic: Graph Neural Networks in Symmetric Positive Definite Matrices | Recent research has shown that alignment between the structure of graph data and the geometry of an embedding space is crucial for learning high-quality representations of the data. The uniform geometry of Euclidean and hyperbolic spaces allows for representing graphs with uniform geometric and topological features, su... | ['Steve Trettel', 'Diaaeldin Taha', 'Michael Strube', 'J. Maxwell Riestenberg', 'Federico Lopez', 'Wei Zhao'] | 2023-06-24 | null | null | null | null | ['graph-classification'] | ['graphs'] | [-3.86724532e-01 2.29165688e-01 8.83798748e-02 -2.09288105e-01
3.72305997e-02 -7.75621235e-01 5.63510180e-01 2.65077323e-01
4.90607247e-02 3.20317179e-01 5.30479439e-02 -6.57571375e-01
-2.27317363e-01 -1.07306278e+00 -5.16125083e-01 -6.18550122e-01
-6.39776051e-01 2.87417352e-01 -1.38347805e-01 -3.17402571... | [7.026492118835449, 6.050250053405762] |
b812a4c3-d1dc-49e0-9e9a-80a99162f514 | contextual-embedding-and-model-weighting-by | 2206.12866 | null | https://arxiv.org/abs/2206.12866v1 | https://arxiv.org/pdf/2206.12866v1.pdf | Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering | Biomedical Question Answering aims to obtain an answer to the given question from the biomedical domain. Due to its high requirement of biomedical domain knowledge, it is difficult for the model to learn domain knowledge from limited training data. We propose a contextual embedding method that combines open-domain QA m... | ['Yongping Du', 'Zhongzheng Ge', 'Zhixuan Qi', 'Jingya Yan', 'Yuxuan Lu'] | 2022-06-26 | null | null | null | null | ['unsupervised-pre-training', 'cloze-test', 'machine-reading-comprehension'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 4.88741934e-01 6.39539003e-01 -1.85932189e-01 -5.08194923e-01
-1.25567138e+00 -1.56366423e-01 1.92155302e-01 5.99989235e-01
-6.36448205e-01 1.04567730e+00 4.80770499e-01 -3.78267825e-01
-3.05589825e-01 -9.17883635e-01 -5.17128646e-01 -4.58405018e-01
2.25155950e-01 5.88828564e-01 1.38059884e-01 -2.67277688... | [8.685352325439453, 8.651269912719727] |
251764a4-6c79-4713-8b81-974ad0f3afe8 | mountnet-learning-an-inertial-sensor-mounting | 2212.11120 | null | https://arxiv.org/abs/2212.11120v1 | https://arxiv.org/pdf/2212.11120v1.pdf | MountNet: Learning an Inertial Sensor Mounting Angle with Deep Neural Networks | Finding the mounting angle of a smartphone inside a car is crucial for navigation, motion detection, activity recognition, and other applications. It is a challenging task in several aspects: (i) the mounting angle at the drive start is unknown and may differ significantly between users; (ii) the user, or bad fixture, ... | ['Barak Or', 'Areej Eweida', 'Nimrod Segol', 'Niv Sfaradi', 'Maxim Freydin'] | 2022-12-10 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 7.27226734e-02 -1.05183467e-01 -6.83493465e-02 -2.49564141e-01
-5.00237405e-01 -6.06471777e-01 2.46067926e-01 -3.07095259e-01
-3.29375207e-01 6.59813106e-01 -5.40509224e-01 -6.39068186e-01
1.08583434e-03 -5.83470881e-01 -1.10440040e+00 -6.55814946e-01
2.07309827e-01 4.81560856e-01 1.24775104e-01 -2.93382794... | [7.675172328948975, -1.802929162979126] |
f34615ac-96ff-4ce1-9691-0299befcfd1b | topic-taxonomy-expansion-via-hierarchy-aware | 2211.01981 | null | https://arxiv.org/abs/2211.01981v1 | https://arxiv.org/pdf/2211.01981v1.pdf | Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation | Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications. To dynamically incorporate new topic information, several recent studies have tried to expand (or complete) a topic taxonomy by inserting emerging topics identified in a set of new ... | ['Jiawei Han', 'Hwanjo Yu', 'Susik Yoon', 'Seonghyeon Lee', 'Jiaming Shen', 'Dongha Lee'] | 2022-10-18 | null | null | null | null | ['taxonomy-expansion'] | ['natural-language-processing'] | [ 1.45891100e-01 4.78827536e-01 -6.44919395e-01 -3.38581145e-01
-6.14866734e-01 -6.34066880e-01 8.80127609e-01 5.58640659e-01
1.05734184e-01 8.52985084e-01 7.03719437e-01 -1.01720542e-01
-2.39220083e-01 -1.06401408e+00 -2.73039639e-01 -5.16091943e-01
-5.67859858e-02 8.48809838e-01 5.54008186e-01 -1.39395684... | [10.380341529846191, 6.96053409576416] |
57227391-a8ce-43b8-a86e-502fdc48f0d9 | single-view-view-synthesis-with-self | 2304.09527 | null | https://arxiv.org/abs/2304.09527v2 | https://arxiv.org/pdf/2304.09527v2.pdf | Single-View View Synthesis with Self-Rectified Pseudo-Stereo | Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view view synthesis problem to a multi-view setting. Specifically, we leverage the reliable and explicit stereo prior to generate a pseudo-stereo vi... | ['Shengfeng He', 'Jing Qin', 'Zheng Xiong', 'Wenxi Liu', 'Hanjie Wu', 'Yang Zhou'] | 2023-04-19 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 6.37902737e-01 2.58765817e-01 -5.09864092e-02 -4.31831121e-01
-7.88132370e-01 -7.20107138e-01 5.13602972e-01 -7.89074540e-01
1.54153004e-01 7.05981553e-01 4.73508596e-01 -2.47604065e-02
1.89566314e-01 -6.66745365e-01 -1.01914477e+00 -7.37789035e-01
8.26350272e-01 3.95271868e-01 1.04369365e-01 -1.49698883... | [9.219035148620605, -3.0379080772399902] |
a4a62a54-4654-4fc2-8ef4-b20e100bab3a | simultaneous-facial-landmark-detection-pose | 1709.08130 | null | http://arxiv.org/abs/1709.08130v1 | http://arxiv.org/pdf/1709.08130v1.pdf | Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion | Facial landmark detection, head pose estimation, and facial deformation
analysis are typical facial behavior analysis tasks in computer vision. The
existing methods usually perform each task independently and sequentially,
ignoring their interactions. To tackle this problem, we propose a unified
framework for simultane... | ['Qiang Ji', 'Chao Gou', 'Yue Wu'] | 2017-09-23 | simultaneous-facial-landmark-detection-pose-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Wu_Simultaneous_Facial_Landmark_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wu_Simultaneous_Facial_Landmark_CVPR_2017_paper.pdf | cvpr-2017-7 | ['head-pose-estimation'] | ['computer-vision'] | [-3.29695702e-01 -6.60546944e-02 -2.04361379e-01 -5.99767268e-01
-5.50666869e-01 -2.99366951e-01 3.09557289e-01 -3.23380470e-01
-2.79377967e-01 2.01247349e-01 2.23186892e-02 4.16610837e-01
2.50370830e-01 -2.66066611e-01 -3.78581882e-01 -9.50737596e-01
7.42905512e-02 3.12146872e-01 4.49090786e-02 2.02635691... | [13.393013000488281, 0.3785412013530731] |
29f20f92-b3de-47fd-93fe-6415f1252bc0 | on-the-robustness-of-deep-clustering-models | 2210.01940 | null | https://arxiv.org/abs/2210.01940v1 | https://arxiv.org/pdf/2210.01940v1.pdf | On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses | Clustering models constitute a class of unsupervised machine learning methods which are used in a number of application pipelines, and play a vital role in modern data science. With recent advancements in deep learning -- deep clustering models have emerged as the current state-of-the-art over traditional clustering ap... | ['Prasant Mohapatra', 'Ashwin Sekhari', 'Anshuman Chhabra'] | 2022-10-04 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-9.17386189e-02 7.26010725e-02 3.05690706e-01 -2.20642418e-01
-7.45998383e-01 -1.07916367e+00 8.31618309e-01 -2.20939472e-01
-2.27926418e-01 -9.38138142e-02 -5.89249544e-02 -4.31772113e-01
-3.35531868e-02 -7.67218530e-01 -7.81753480e-01 -9.22295630e-01
-3.00047785e-01 6.43830240e-01 1.24200277e-01 -1.93337306... | [5.716162204742432, 7.775590419769287] |
693248ec-a543-45ea-a2af-2918bdeaf4b4 | towards-asking-clarification-questions-for | 2305.13690 | null | https://arxiv.org/abs/2305.13690v1 | https://arxiv.org/pdf/2305.13690v1.pdf | Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues | Task-oriented dialogue systems aim at providing users with task-specific services. Users of such systems often do not know all the information about the task they are trying to accomplish, requiring them to seek information about the task. To provide accurate and personalized task-oriented information seeking results, ... | ['Emine Yilmaz', 'Aldo Lipani', 'Hossein A. Rahmani', 'Yue Feng'] | 2023-05-23 | null | null | null | null | ['question-generation', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.23374188e-02 3.29866052e-01 -1.50058448e-01 -7.06825554e-01
-1.12556899e+00 -6.69279456e-01 4.06075418e-01 2.30192747e-02
-4.47574496e-01 8.15747321e-01 7.39355564e-01 -4.59584326e-01
-1.95536658e-01 -9.99302715e-02 2.11290613e-01 -1.12333059e-01
5.27613401e-01 1.01406872e+00 4.80676182e-02 -1.00450468... | [12.226594924926758, 7.853339672088623] |
d78caaea-bc4a-4218-b3b4-cea0034bb19a | human-action-segmentation-with-hierarchical | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Lu_Human_Action_Segmentation_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Lu_Human_Action_Segmentation_2015_CVPR_paper.pdf | Human Action Segmentation With Hierarchical Supervoxel Consistency | Detailed analysis of human action, such as action classification, detection and localization has received increasing attention from the community; datasets like JHMDB have made it plausible to conduct studies analyzing the impact that such deeper information has on the greater action understanding problem. However, d... | ['Jason J. Corso', 'Jiasen Lu', 'ran Xu'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['action-understanding'] | ['computer-vision'] | [ 4.53093678e-01 2.92694896e-01 -5.44703901e-01 -3.24037641e-01
-5.38232744e-01 -4.37086850e-01 6.36677921e-01 -2.63047572e-02
-5.88205397e-01 4.12812948e-01 6.71714246e-01 5.06927744e-02
2.54467964e-01 -3.49531442e-01 -6.46267235e-01 -5.28171003e-01
-7.46580809e-02 2.45572224e-01 1.01937807e+00 -8.90693665... | [8.34805965423584, 0.4580785036087036] |
ddad5ee5-fa89-4f53-9f68-3e5e60adf823 | 2d-supervised-monocular-3d-object-detection | 2306.05418 | null | https://arxiv.org/abs/2306.05418v1 | https://arxiv.org/pdf/2306.05418v1.pdf | 2D Supervised Monocular 3D Object Detection by Global-to-Local 3D Reconstruction | With the advent of the big model era, the demand for data has become more important. Especially in monocular 3D object detection, expensive manual annotations potentially limit further developments. Existing works have investigated weakly supervised algorithms with the help of LiDAR modality to generate 3D pseudo label... | ['Zhaoxiang Zhang', 'Yuntao Chen', 'Yuqi Wang', 'JiaWei He'] | 2023-06-08 | null | null | null | null | ['monocular-3d-object-detection', '3d-object-detection', '3d-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-9.63167399e-02 -7.28947520e-02 -1.82051018e-01 -3.15488070e-01
-9.12348330e-01 -5.66672742e-01 3.77939641e-01 -4.53494281e-01
-2.26339012e-01 5.00989974e-01 -2.61234522e-01 -1.31929561e-01
7.81733021e-02 -4.99527603e-01 -1.21550107e+00 -6.42907023e-01
3.02098673e-02 8.68710101e-01 5.48979461e-01 2.74664909... | [7.763980865478516, -2.70041561126709] |
e132b602-015b-4603-a2cc-c7c4fb229791 | unified-multimodal-punctuation-restoration | 2202.00468 | null | https://arxiv.org/abs/2202.00468v1 | https://arxiv.org/pdf/2202.00468v1.pdf | Unified Multimodal Punctuation Restoration Framework for Mixed-Modality Corpus | The punctuation restoration task aims to correctly punctuate the output transcriptions of automatic speech recognition systems. Previous punctuation models, either using text only or demanding the corresponding audio, tend to be constrained by real scenes, where unpunctuated sentences are a mixture of those with and wi... | ['Mingxuan Wang', 'Shanbo Cheng', 'Liwei Wu', 'Yaoming Zhu'] | 2022-01-24 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 7.91728571e-02 1.61749333e-01 -1.18614743e-02 -6.42727315e-02
-1.45525348e+00 -6.50410593e-01 5.32419384e-01 -6.04651928e-01
-1.18926084e-02 7.14183450e-01 7.18805373e-01 6.89916834e-02
3.67422283e-01 -1.48353547e-01 -9.37065244e-01 -9.71629262e-01
2.63094544e-01 2.72620380e-01 -7.52954111e-02 -6.95018098... | [14.896803855895996, 6.500770568847656] |
1e578f07-c810-4ecd-a37d-9888de796fdd | neural-amr-sequence-to-sequence-models-for | 1704.08381 | null | http://arxiv.org/abs/1704.08381v3 | http://arxiv.org/pdf/1704.08381v3.pdf | Neural AMR: Sequence-to-Sequence Models for Parsing and Generation | Sequence-to-sequence models have shown strong performance across a broad
range of applications. However, their application to parsing and generating
text usingAbstract Meaning Representation (AMR)has been limited, due to the
relatively limited amount of labeled data and the non-sequential nature of the
AMR graphs. We p... | ['Yejin Choi', 'Luke Zettlemoyer', 'Ioannis Konstas', 'Srinivasan Iyer', 'Mark Yatskar'] | 2017-04-26 | neural-amr-sequence-to-sequence-models-for-1 | https://aclanthology.org/P17-1014 | https://aclanthology.org/P17-1014.pdf | acl-2017-7 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 8.04177284e-01 7.28945196e-01 -2.92060643e-01 -5.15014648e-01
-1.33550203e+00 -9.78494644e-01 8.51986229e-01 2.57569134e-01
-2.51482457e-01 9.92712855e-01 4.28311110e-01 -8.09975207e-01
3.41184169e-01 -8.93766880e-01 -7.44553506e-01 -3.97861265e-02
5.31443715e-01 5.63049436e-01 5.84722012e-02 -4.62231696... | [10.490873336791992, 9.00589656829834] |
14c9f422-72f7-4c90-af60-614541cef495 | ddt-a-diffusion-driven-transformer-based | 2303.13397 | null | https://arxiv.org/abs/2303.13397v2 | https://arxiv.org/pdf/2303.13397v2.pdf | DDT: A Diffusion-Driven Transformer-based Framework for Human Mesh Recovery from a Video | Human mesh recovery (HMR) provides rich human body information for various real-world applications such as gaming, human-computer interaction, and virtual reality. Compared to single image-based methods, video-based methods can utilize temporal information to further improve performance by incorporating human body moti... | ['Chen Chen', 'Guo-Jun Qi', 'Ce Zheng'] | 2023-03-23 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [ 1.0906344e-01 -2.1802858e-01 -3.2909846e-01 4.2318065e-02
-3.1931037e-01 1.2388310e-01 3.2396930e-01 -4.4915456e-01
-1.2678914e-01 6.1641407e-01 4.5126826e-01 -7.2905406e-02
1.9062782e-02 -9.4653183e-01 -7.3069859e-01 -5.5303347e-01
-1.6984811e-01 3.1348670e-01 9.3671876e-01 -2.3091252e-01
-1.7421992e-02... | [7.339391708374023, -0.7727006673812866] |
854bf968-3b85-4499-b820-555b357f45ce | contrastive-representation-learning-for-cross | 2205.11438 | null | https://arxiv.org/abs/2205.11438v1 | https://arxiv.org/pdf/2205.11438v1.pdf | Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities | Identifying related entities and events within and across documents is fundamental to natural language understanding. We present an approach to entity and event coreference resolution utilizing contrastive representation learning. Earlier state-of-the-art methods have formulated this problem as a binary classification ... | ['Graham Horwood', 'Benjamin Hsu'] | 2022-05-23 | null | https://aclanthology.org/2022.naacl-main.267 | https://aclanthology.org/2022.naacl-main.267.pdf | naacl-2022-7 | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [ 2.29820669e-01 2.66257852e-01 -5.35842776e-01 -6.45665765e-01
-1.53034365e+00 -6.98428035e-01 9.22527492e-01 6.81383073e-01
-9.10553813e-01 9.38379407e-01 3.33087236e-01 -4.78672802e-01
-3.39255244e-01 -9.18293953e-01 -8.46459627e-01 -2.22968161e-01
-4.98516798e-01 1.00435412e+00 2.33329818e-01 -5.14903128... | [9.321037292480469, 9.431866645812988] |
14a2c898-0a70-4edd-923f-b65027617d5b | specializing-pre-trained-language-models-for | null | null | https://aclanthology.org/2022.findings-naacl.169 | https://aclanthology.org/2022.findings-naacl.169.pdf | Specializing Pre-trained Language Models for Better Relational Reasoning via Network Pruning | Pretrained masked language models (PLMs) were shown to be inheriting a considerable amount of relational knowledge from the source corpora. In this paper, we present an in-depth and comprehensive study concerning specializing PLMs into relational models from the perspective of network pruning. We show that it is possib... | ['Kenny Zhu', 'Siyu Ren'] | null | null | null | null | findings-naacl-2022-7 | ['relational-reasoning'] | ['natural-language-processing'] | [ 5.54909825e-01 9.27897274e-01 -2.41120324e-01 -3.16573262e-01
-1.02514969e-02 -3.81042093e-01 5.69272518e-01 2.29437649e-01
5.19077480e-02 7.38814712e-01 2.21790954e-01 -5.09439707e-01
-5.34963787e-01 -1.21513629e+00 -6.81378424e-01 -2.09896833e-01
-2.55247355e-01 5.28399944e-01 3.56938601e-01 -6.45406783... | [9.808159828186035, 7.953136920928955] |
bbb962f1-ead9-4e75-96b4-530aa93473db | cellular-automata-model-for-non-structural | 2212.00502 | null | https://arxiv.org/abs/2212.00502v1 | https://arxiv.org/pdf/2212.00502v1.pdf | Cellular Automata Model for Non-Structural Proteins Comparing Transmissibility and Pathogenesis of SARS Covid (CoV-2, CoV) and MERS Covid | Significantly higher transmissibility of SARS CoV-2 (2019) compared to SARS CoV (2003) can be attributed to mutations of structural proteins (Spike S, Nucleocapsid N, Membrane M, and Envelope E) and the role played by non-structural proteins (nsps) and accessory proteins (ORFs) for viral replication, assembly and shedd... | ['Parimal Pal Chaudhuri', 'Raju Hazari'] | 2022-11-25 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 4.12313432e-01 -4.31829602e-01 1.88444480e-01 -8.81989524e-02
6.39009103e-02 -7.15970218e-01 2.94817179e-01 3.23495179e-01
-4.75123703e-01 1.10180223e+00 1.86995640e-01 -6.51072502e-01
-5.24829216e-02 -4.04973954e-01 -4.25832123e-01 -7.83725739e-01
-4.39798027e-01 9.24022794e-01 -1.07290478e-04 -4.31681991... | [4.765488147735596, 5.135624408721924] |
443b9fb0-a02c-4156-b751-407816d646f1 | word-sense-induction-with-attentive-context | null | null | https://aclanthology.org/2021.nlp4dh-1.17 | https://aclanthology.org/2021.nlp4dh-1.17.pdf | Word Sense Induction with Attentive Context Clustering | In this paper, we present ACCWSI (Attentive Context Clustering WSI), a method for Word Sense Induction, suitable for languages with limited resources. Pretrained on a small corpus and given an ambiguous word (query word) and a set of excerpts that contain it, ACCWSI uses an attention mechanism for generating context-aw... | ['Shai Gordin', 'Amos Azaria', 'Moshe Stekel'] | null | null | null | null | nlp4dh-icon-2021-12 | ['word-sense-induction'] | ['natural-language-processing'] | [ 5.15207469e-01 5.21831997e-02 -2.96298862e-01 -3.62330049e-01
-3.53892595e-01 -8.84114444e-01 7.23346829e-01 7.04244077e-01
-9.54447925e-01 6.08502507e-01 6.34408772e-01 -4.82419312e-01
-1.24574773e-01 -6.39320910e-01 3.45472954e-02 -6.49685740e-01
-1.69253960e-01 4.80814964e-01 1.80248052e-01 -6.33838832... | [10.377334594726562, 8.987918853759766] |
61f227c6-5791-4758-975b-b396c305f081 | scale-rotation-equivariant-lie-group | 2306.06934 | null | https://arxiv.org/abs/2306.06934v1 | https://arxiv.org/pdf/2306.06934v1.pdf | Scale-Rotation-Equivariant Lie Group Convolution Neural Networks (Lie Group-CNNs) | The weight-sharing mechanism of convolutional kernels ensures translation-equivariance of convolution neural networks (CNNs). Recently, rotation-equivariance has been investigated. However, research on scale-equivariance or simultaneous scale-rotation-equivariance is insufficient. This study proposes a Lie group-CNN, w... | ['Hui Li', 'Yang Xu', 'Wei-Dong Qiao'] | 2023-06-12 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [-2.73366839e-01 -1.50553778e-01 1.03760384e-01 -6.10075533e-01
1.50080547e-01 -4.64408696e-01 3.78932863e-01 -8.52573276e-01
-5.82334220e-01 5.13565063e-01 -9.91557166e-02 -4.92625773e-01
-3.67636532e-02 -8.81676257e-01 -7.85454929e-01 -8.75320792e-01
-4.27218825e-01 -3.24905306e-01 1.65612653e-01 -4.35950249... | [8.937784194946289, 2.347343683242798] |
5e9ffb0a-f13c-45f6-bf50-e29b6992b709 | artificial-life-and-the-web-webal-comes-of | 1407.5719 | null | http://arxiv.org/abs/1407.5719v1 | http://arxiv.org/pdf/1407.5719v1.pdf | Artificial Life and the Web: WebAL Comes of Age | A brief survey is presented of the first 18 years of web-based Artificial
Life ("WebAL") research and applications, covering the period 1995-2013. The
survey is followed by a short discussion of common methodologies employed and
current technologies relevant to WebAL research. The paper concludes with a
quick look at w... | ['Tim Taylor'] | 2014-07-22 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-9.97394025e-02 4.28353012e-01 -4.67277825e-01 -9.14613083e-02
-3.90203297e-02 -7.38292217e-01 8.22932124e-01 3.95387083e-01
-2.58766174e-01 9.27536607e-01 1.62110388e-01 -4.86516565e-01
1.90472044e-02 -1.16591108e+00 -4.59580213e-01 -4.88871008e-01
-1.82783633e-01 2.20016927e-01 1.59143776e-01 -4.38202709... | [5.649632930755615, 4.152797698974609] |
939d2f8c-b116-49df-ae32-44185836d9f6 | an-automated-text-categorization-framework | 1704.01975 | null | http://arxiv.org/abs/1704.01975v2 | http://arxiv.org/pdf/1704.01975v2.pdf | An Automated Text Categorization Framework based on Hyperparameter Optimization | A great variety of text tasks such as topic or spam identification, user
profiling, and sentiment analysis can be posed as a supervised learning problem
and tackle using a text classifier. A text classifier consists of several
subprocesses, some of them are general enough to be applied to any supervised
learning proble... | ['Sabino Miranda-Jímenez', 'Eric S. Tellez', 'Mario Graff', 'Daniela Moctezuma'] | 2017-04-06 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 5.07073522e-01 1.71569660e-01 -1.82627022e-01 -5.22284746e-01
-3.92777681e-01 -5.65126538e-01 1.23151422e+00 6.89193726e-01
-6.60438538e-01 6.21355414e-01 -1.06324278e-01 -4.85428274e-01
-6.41784817e-02 -7.24883020e-01 -1.63178444e-01 -5.91436446e-01
4.15246516e-01 9.04057145e-01 2.70039767e-01 -4.66669828... | [10.33024787902832, 8.604366302490234] |
bb396081-0262-4b42-9773-95a48a1e4b7a | annotation-process-for-the-dialog-act | null | null | https://aclanthology.org/D19-5108 | https://aclanthology.org/D19-5108.pdf | Annotation Process for the Dialog Act Classification of a Taglish E-commerce Q\&A Corpus | With conversational agents or chatbots making up in quantity of replies rather than quality, the need to identify user intent has become a main concern to improve these agents. Dialog act (DA) classification tackles this concern, and while existing studies have already addressed DA classification in general contexts, n... | ['Charibeth Cheng', 'Jolene Valenzuela', 'Jan Caleb Oliver Pensica', 'Jared Rivera', 'Alfonso Secuya'] | 2019-11-01 | null | null | null | ws-2019-11 | ['dialog-act-classification'] | ['natural-language-processing'] | [-3.94998267e-02 6.25006676e-01 -3.18183340e-02 -6.92319870e-01
-6.76485956e-01 -9.70003188e-01 5.07533669e-01 2.10691169e-01
-2.99433529e-01 5.76885104e-01 9.16042686e-01 -6.23349130e-01
4.81511578e-02 -2.97798395e-01 2.87469566e-01 -5.42674422e-01
2.41983145e-01 6.86707020e-01 1.25019222e-01 -4.83112127... | [12.653841018676758, 7.825957775115967] |
bc2dd684-d562-4952-9e39-29b656e3c657 | knowledge-distillation-via-token-level | 2306.12442 | null | https://arxiv.org/abs/2306.12442v1 | https://arxiv.org/pdf/2306.12442v1.pdf | Knowledge Distillation via Token-level Relationship Graph | Knowledge distillation is a powerful technique for transferring knowledge from a pre-trained teacher model to a student model. However, the true potential of knowledge transfer has not been fully explored. Existing approaches primarily focus on distilling individual information or instance-level relationships, overlook... | ['Kun He', 'Hanpeng Liu', 'Shuoxi Zhang'] | 2023-06-20 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 1.63259372e-01 1.08348869e-01 -5.69867671e-01 -3.71580213e-01
-3.76819402e-01 -3.18718553e-01 4.76236880e-01 2.58965760e-01
-2.69031584e-01 7.89034247e-01 1.74712151e-01 -3.31864953e-01
-3.50046903e-01 -9.77464736e-01 -8.51063430e-01 -7.44162977e-01
2.08128139e-01 2.41565943e-01 2.70502090e-01 -1.01842038... | [9.510710716247559, 3.4191107749938965] |
6e4459bf-47bc-4fec-8914-63dacee1f983 | pyramid-texture-filtering | 2305.06525 | null | https://arxiv.org/abs/2305.06525v1 | https://arxiv.org/pdf/2305.06525v1.pdf | Pyramid Texture Filtering | We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation -- the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture fi... | ['Wei-Shi Zheng', 'Yongwei Nie', 'Hao Jiang', 'Qing Zhang'] | 2023-05-11 | null | null | null | null | ['image-enhancement', 'tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 6.81782603e-01 -1.71602577e-01 2.88518488e-01 1.16689101e-01
-6.43748522e-01 -2.02835053e-01 2.14465290e-01 -1.82924628e-01
2.12807462e-01 6.14547431e-01 3.72492313e-01 4.47304174e-02
-1.00676501e-02 -9.53675032e-01 -3.86954188e-01 -9.67305243e-01
1.10938577e-02 -4.71139699e-01 6.08941913e-01 -4.94600236... | [11.015384674072266, -2.3899574279785156] |
7afaa538-a8da-44fa-aa23-76244e342614 | lealla-learning-lightweight-language-agnostic | 2302.08387 | null | https://arxiv.org/abs/2302.08387v1 | https://arxiv.org/pdf/2302.08387v1.pdf | LEALLA: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation | Large-scale language-agnostic sentence embedding models such as LaBSE (Feng et al., 2022) obtain state-of-the-art performance for parallel sentence alignment. However, these large-scale models can suffer from inference speed and computation overhead. This study systematically explores learning language-agnostic sentenc... | ['Tetsuji Nakagawa', 'Zhuoyuan Mao'] | 2023-02-16 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-2.66298383e-01 -1.93557024e-01 -3.72109175e-01 -5.72068155e-01
-9.95081782e-01 -4.59351391e-01 5.96752882e-01 2.92053401e-01
-9.76609170e-01 4.92871970e-01 8.97684097e-01 -9.09092903e-01
2.92002678e-01 -5.70546687e-01 -5.85306525e-01 -1.03348374e-01
-2.45035022e-01 4.50803727e-01 -2.99900979e-01 -5.95762074... | [10.969630241394043, 8.679649353027344] |
e2c0ddc1-335b-4615-8715-156068c97d94 | exploring-the-pareto-front-of-multi-objective | 2204.05027 | null | https://arxiv.org/abs/2204.05027v1 | https://arxiv.org/pdf/2204.05027v1.pdf | Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning | Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models. Current research fo... | ['Pieter Libin', 'Ann Nowé', 'Niel Hens', 'Patrick Mannion', 'Enda Howley', 'Diederik M. Roijers', 'Steven Abrams', 'Roxana Rădulescu', 'Lander Willem', 'Conor F. Hayes', 'Mathieu Reymond'] | 2022-04-11 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 2.55633593e-01 8.98448303e-02 -6.12227665e-03 3.74507576e-01
-2.09664360e-01 -2.67805040e-01 3.52605641e-01 6.52127981e-01
-7.12118030e-01 1.02868581e+00 2.42771834e-01 -4.43493694e-01
-8.56868565e-01 -9.41036999e-01 -6.69106245e-01 -8.91155660e-01
-5.28601944e-01 1.03569746e+00 -3.29432815e-01 -3.81314933... | [6.079682350158691, 4.3769354820251465] |
37728cb2-b60b-43ab-9d09-64e9266a1b0f | multi-agent-reachability-calibration-with | 2304.00432 | null | https://arxiv.org/abs/2304.00432v1 | https://arxiv.org/pdf/2304.00432v1.pdf | Multi-Agent Reachability Calibration with Conformal Prediction | We investigate methods to provide safety assurances for autonomous agents that incorporate predictions of other, uncontrolled agents' behavior into their own trajectory planning. Given a learning-based forecasting model that predicts agents' trajectories, we introduce a method for providing probabilistic assurances on ... | ['Claire Tomlin', 'Aleksandra Faust', 'Rebecca Roelofs', 'Michael H. Lim', 'Sampada Deglurkar', 'Haotian Shen', 'Anish Muthali'] | 2023-04-02 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-4.26174939e-01 7.09849119e-01 -4.47971195e-01 -6.67373359e-01
-1.16606987e+00 -4.84327495e-01 9.12889600e-01 3.72289181e-01
-2.01523528e-01 9.86177981e-01 -1.47505160e-02 -8.65349412e-01
-2.93688774e-01 -1.08593953e+00 -8.78284216e-01 -5.00671685e-01
-8.87567580e-01 7.64034808e-01 5.76739132e-01 -1.28053188... | [4.944882392883301, 1.7771919965744019] |
c2905cdb-6801-439a-9b34-113109664d2f | the-temporal-dictionary-ensemble-tde | 2105.03841 | null | https://arxiv.org/abs/2105.03841v1 | https://arxiv.org/pdf/2105.03841v1.pdf | The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification | Using bag of words representations of time series is a popular approach to time series classification. These algorithms involve approximating and discretising windows over a series to form words, then forming a count of words over a given dictionary. Classifiers are constructed on the resulting histograms of word count... | ['Anthony Bagnall', 'Gavin Cawley', 'James Large', 'Matthew Middlehurst'] | 2021-05-09 | null | null | null | null | ['classification'] | ['methodology'] | [-2.32522652e-01 -6.07713640e-01 2.03875393e-01 -5.28175160e-02
-5.47434926e-01 -7.50399530e-01 1.03498924e+00 5.22437453e-01
-5.16848922e-01 5.62995791e-01 1.54944748e-01 -4.70795512e-01
-2.59264588e-01 -1.08104289e+00 -3.13452065e-01 -9.61418152e-01
-3.14063102e-01 5.94207346e-01 1.85446978e-01 -6.12145424... | [7.250136852264404, 3.2653262615203857] |
aca2691f-c0b8-4f09-9d04-afb78f7dbc5d | comae-single-model-hybrid-pre-training-on | 2302.06148 | null | https://arxiv.org/abs/2302.06148v1 | https://arxiv.org/pdf/2302.06148v1.pdf | CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets | Current RGB-D scene recognition approaches often train two standalone backbones for RGB and depth modalities with the same Places or ImageNet pre-training. However, the pre-trained depth network is still biased by RGB-based models which may result in a suboptimal solution. In this paper, we present a single-model self-... | ['LiMin Wang', 'Gangshan Wu', 'Sheng Guo', 'Jiange Yang'] | 2023-02-13 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 3.24195296e-01 1.38226941e-01 -1.36660263e-01 -6.15232944e-01
-9.56590652e-01 -3.59878361e-01 6.39766932e-01 -3.60092700e-01
-1.39205888e-01 4.03759152e-01 1.55202225e-01 -1.13813519e-01
2.09811151e-01 -9.63696539e-01 -9.08456147e-01 -9.42363799e-01
4.60036188e-01 3.70605648e-01 2.09908679e-01 -9.17578489... | [9.048590660095215, -1.563181757926941] |
928842bf-40de-4d9a-b721-099e4f619b99 | multi-resolution-outlier-pooling-for-sorghum | 2106.05748 | null | https://arxiv.org/abs/2106.05748v2 | https://arxiv.org/pdf/2106.05748v2.pdf | Multi-resolution Outlier Pooling for Sorghum Classification | Automated high throughput plant phenotyping involves leveraging sensors, such as RGB, thermal and hyperspectral cameras (among others), to make large scale and rapid measurements of the physical properties of plants for the purpose of better understanding the difference between crops and facilitating rapid plant breedi... | ['Abby Stylianou', 'Nadia Shakoor', 'Duke Pauli', 'Gregory Rolwes', 'Justin Dulay', 'Chao Ren'] | 2021-06-10 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.91464561e-01 -3.64878803e-01 -1.70600310e-01 -3.30091536e-01
-3.72165680e-01 -1.08174205e+00 -1.18843399e-01 5.40856659e-01
1.12218194e-01 4.26652908e-01 -2.20332116e-01 -1.78201929e-01
-2.76202828e-01 -1.18951464e+00 -5.18046260e-01 -1.03986120e+00
-7.51056522e-02 2.71923482e-01 2.65856795e-02 -2.10097939... | [9.150511741638184, -1.5550533533096313] |
4a2267e5-35ef-4433-a0ae-eba83c5d8bef | robust-facial-landmark-detection-under | 1709.08127 | null | http://arxiv.org/abs/1709.08127v1 | http://arxiv.org/pdf/1709.08127v1.pdf | Robust Facial Landmark Detection under Significant Head Poses and Occlusion | There have been tremendous improvements for facial landmark detection on
general "in-the-wild" images. However, it is still challenging to detect the
facial landmarks on images with severe occlusion and images with large head
poses (e.g. profile face). In fact, the existing algorithms usually can only
handle one of the... | ['Qiang Ji', 'Yue Wu'] | 2017-09-23 | robust-facial-landmark-detection-under-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Wu_Robust_Facial_Landmark_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Wu_Robust_Facial_Landmark_ICCV_2015_paper.pdf | iccv-2015-12 | ['occlusion-estimation'] | ['computer-vision'] | [-3.22550178e-01 -1.25198781e-01 -1.76564887e-01 -5.15174270e-01
-6.29352808e-01 -4.69147488e-02 2.70744652e-01 -1.91978812e-01
-2.57570833e-01 3.19043308e-01 1.10824823e-01 1.30398273e-01
1.95525154e-01 -2.88977951e-01 -5.56004107e-01 -9.02450264e-01
1.84449360e-01 3.99576753e-01 2.13544890e-01 -3.94495837... | [13.421154975891113, 0.4139831066131592] |
798f3518-ab6e-4d9d-8f7a-f60389c5b249 | multi-task-deep-neural-networks-for-natural | 1901.11504 | null | https://arxiv.org/abs/1901.11504v2 | https://arxiv.org/pdf/1901.11504v2.pdf | Multi-Task Deep Neural Networks for Natural Language Understanding | In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations in order to a... | ['Pengcheng He', 'Weizhu Chen', 'Xiaodong Liu', 'Jianfeng Gao'] | 2019-01-31 | multi-task-deep-neural-networks-for-natural-1 | https://aclanthology.org/P19-1441 | https://aclanthology.org/P19-1441.pdf | acl-2019-7 | ['linguistic-acceptability'] | ['natural-language-processing'] | [-3.54716118e-04 8.26393813e-02 -4.22547549e-01 -4.77579772e-01
-9.44494605e-01 -7.80395567e-01 8.65747511e-01 -1.58182576e-01
-3.40837002e-01 7.66983449e-01 2.71780670e-01 -3.29222500e-01
1.76479489e-01 -6.56172514e-01 -8.83792043e-01 -1.20215297e-01
2.93221444e-01 7.12309539e-01 9.10611227e-02 -2.91006923... | [10.797900199890137, 8.309703826904297] |
a002484e-806b-44e9-a587-351ee764fedc | harnessing-cross-lingual-features-to-improve-1 | 2112.08789 | null | https://arxiv.org/abs/2112.08789v1 | https://arxiv.org/pdf/2112.08789v1.pdf | Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages | Cognates are variants of the same lexical form across different languages; for example 'fonema' in Spanish and 'phoneme' in English are cognates, both of which mean 'a unit of sound'. The task of automatic detection of cognates among any two languages can help downstream NLP tasks such as Cross-lingual Information Retr... | ['Malhar Kulkarni', 'Gholamreza Haffari', 'Pushpak Bhattacharyya', 'Shubham Dewangan', 'Raj Dabre', 'Diptesh Kanojia'] | 2021-12-16 | harnessing-cross-lingual-features-to-improve | https://aclanthology.org/2020.coling-main.119 | https://aclanthology.org/2020.coling-main.119.pdf | coling-2020-8 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 3.16712409e-01 -3.90807241e-01 -1.23182803e-01 -1.83197841e-01
-8.81224334e-01 -1.16421342e+00 8.05421352e-01 2.20246732e-01
-5.53520560e-01 7.77216554e-01 3.69107753e-01 -1.01089299e+00
1.98685661e-01 -7.01475382e-01 -7.58016944e-01 -4.13447589e-01
3.90305035e-02 4.57374096e-01 -4.15258184e-02 -3.25402826... | [11.137060165405273, 10.148462295532227] |
45e26e1e-8a8a-413f-8db5-731371a87725 | a-two-phase-prototypical-network-model-for | null | null | https://aclanthology.org/2020.coling-main.142 | https://aclanthology.org/2020.coling-main.142.pdf | A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification | Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in open environment. To incrementally recognize the novel relations, current ... | ['Qing Li', 'Guohua Wang', 'Xiaofeng Chen', 'Yi Cai', 'Haopeng Ren'] | 2020-12-01 | null | null | null | coling-2020-8 | ['few-shot-relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing'] | [ 1.58739746e-01 2.56969452e-01 -4.35936779e-01 -5.35167992e-01
-2.05727890e-01 -2.29845524e-01 7.28969991e-01 4.07003701e-01
-3.37554008e-01 9.13292885e-01 1.40076295e-01 -3.19566309e-01
-5.73497593e-01 -8.75845373e-01 -3.56001198e-01 -5.06863058e-01
-6.11256324e-02 7.55367398e-01 3.26072663e-01 -4.32693988... | [9.152059555053711, 8.536399841308594] |
5bd83753-8d21-4032-89c6-931813f3ae57 | colnet-embedding-the-semantics-of-web-tables | 1811.01304 | null | http://arxiv.org/abs/1811.01304v2 | http://arxiv.org/pdf/1811.01304v2.pdf | ColNet: Embedding the Semantics of Web Tables for Column Type Prediction | Automatically annotating column types with knowledge base (KB) concepts is a
critical task to gain a basic understanding of web tables. Current methods rely
on either table metadata like column name or entity correspondences of cells in
the KB, and may fail to deal with growing web tables with incomplete meta
informati... | ['Ernesto Jimenez-Ruiz', 'Jiaoyan Chen', 'Charles Sutton', 'Ian Horrocks'] | 2018-11-04 | null | null | null | null | ['type-prediction', 'table-annotation', 'table-annotation', 'column-type-annotation'] | ['computer-code', 'knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-0.5693045 0.30811465 -0.70859987 -0.28095847 -0.6775895 -0.64632213
0.35796738 1.1460252 -0.47811845 1.1882137 0.46973383 -0.07820738
-0.02720024 -1.6440431 -1.2995604 0.02848728 -0.08723053 0.9684112
0.63703066 -0.5792075 -0.06671575 0.04490596 -1.4306957 1.1876118
0.9123367 1.7152072 -0.12... | [9.498955726623535, 7.925729751586914] |
280fafcb-d7bf-4adb-8bd6-8ebadae913e7 | continual-learning-with-distributed | 2211.16994 | null | https://arxiv.org/abs/2211.16994v3 | https://arxiv.org/pdf/2211.16994v3.pdf | Continual Learning with Distributed Optimization: Does CoCoA Forget? | We focus on the continual learning problem where the tasks arrive sequentially and the aim is to perform well on the newly arrived task without performance degradation on the previously seen tasks. In contrast to the continual learning literature focusing on the centralized setting, we investigate the distributed estim... | ['Anders Ahlén', 'Ayça Özçelikkale', 'Martin Hellkvist'] | 2022-11-30 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-6.79512993e-02 1.88950431e-02 1.93673745e-01 3.64952721e-02
-8.50019515e-01 -6.48679674e-01 4.72412527e-01 1.48876265e-01
-8.23294461e-01 1.08330333e+00 -2.03612089e-01 -7.40497410e-02
-6.79618180e-01 -1.42242894e-01 -9.58170831e-01 -1.07675457e+00
-2.95512140e-01 8.37909043e-01 -1.27342597e-01 4.63724136... | [4.794631004333496, 3.098980188369751] |
1a9690d1-848a-40f0-83fe-bc34bdeb3c7b | probing-the-role-of-positional-information-in-2 | 2305.10046 | null | https://arxiv.org/abs/2305.10046v1 | https://arxiv.org/pdf/2305.10046v1.pdf | Probing the Role of Positional Information in Vision-Language Models | In most Vision-Language models (VL), the understanding of the image structure is enabled by injecting the position information (PI) about objects in the image. In our case study of LXMERT, a state-of-the-art VL model, we probe the use of the PI in the representation and study its effect on Visual Question Answering. We... | ['Jindřich Libovický', 'Philipp J. Rösch'] | 2023-05-17 | probing-the-role-of-positional-information-in-1 | https://aclanthology.org/2022.findings-naacl.77 | https://aclanthology.org/2022.findings-naacl.77.pdf | findings-naacl-2022-7 | ['object-localization', 'text-matching'] | ['computer-vision', 'natural-language-processing'] | [ 3.34444463e-01 3.43729407e-01 -3.20483521e-02 -1.53727829e-01
-6.82969511e-01 -9.11563337e-01 1.03319812e+00 4.08128351e-01
-4.72056091e-01 2.31874645e-01 -2.07711048e-02 -3.29618335e-01
-1.00522935e-01 -7.46757329e-01 -1.03738236e+00 -7.67683685e-01
1.90906614e-01 6.28992617e-01 7.12583661e-01 -1.90766603... | [10.660900115966797, 1.7440789937973022] |
dae54543-ba00-4ed4-b035-807a700a3197 | 2d-convolutional-neural-networks-for-3d | 2002.12314 | null | https://arxiv.org/abs/2002.12314v1 | https://arxiv.org/pdf/2002.12314v1.pdf | 2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification | Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice. The two key challenges in developing automated methods for DBT classification are handling the variable number of slices and retaini... | ['Xin Xing', 'Xiaoqin Wang', 'Hunter Blanton', 'Nathan Jacobs', 'Yu Zhang', 'Gongbo Liang'] | 2020-02-27 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 1.99874997e-01 3.04568827e-01 -4.72790480e-01 -5.25662720e-01
-1.06983387e+00 -4.20450419e-01 3.27146351e-01 4.66819197e-01
-4.46569443e-01 2.13763312e-01 3.07000615e-02 -9.32805419e-01
4.32807431e-02 -7.33899713e-01 -5.54915309e-01 -3.89324337e-01
-2.87557989e-01 5.49225032e-01 5.56747615e-01 1.06216855... | [15.172863960266113, -2.5148568153381348] |
1705ca82-7ac8-4da8-8a83-87f91854fdd2 | pixels-regions-and-objects-multiple | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Pixels_Regions_and_Objects_Multiple_Enhancement_for_Salient_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Pixels_Regions_and_Objects_Multiple_Enhancement_for_Salient_Object_Detection_CVPR_2023_paper.pdf | Pixels, Regions, and Objects: Multiple Enhancement for Salient Object Detection | Salient object detection (SOD) aims to mimic the human visual system (HVS) and cognition mechanisms to identify and segment salient objects. However, due to the complexity of these mechanisms, current methods are not perfect. Accuracy and robustness need to be further improved, particularly in complex scenes with m... | ['Xiangjian He', 'Tianzhu Wang', 'Xin Fan', 'Ruili Wang', 'Yi Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['salient-object-detection-1'] | ['computer-vision'] | [ 2.40601629e-01 -3.84120643e-01 -6.02311790e-02 -3.55514348e-01
-5.43694735e-01 -5.51805347e-02 2.20112801e-01 2.20830008e-01
-3.87651771e-01 4.67549056e-01 2.72774786e-01 1.03800833e-01
1.41417518e-01 -6.18409634e-01 -5.47802627e-01 -7.55204737e-01
1.49649661e-02 -4.11612928e-01 7.77688503e-01 -2.60612428... | [9.707486152648926, -0.47204625606536865] |
a80f38c4-ae28-4858-98e0-edc0982ba17c | gretel-graph-contrastive-topic-enhanced | 2208.09982 | null | https://arxiv.org/abs/2208.09982v1 | https://arxiv.org/pdf/2208.09982v1.pdf | GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization | Recently, neural topic models (NTMs) have been incorporated into pre-trained language models (PLMs), to capture the global semantic information for text summarization. However, in these methods, there remain limitations in the way they capture and integrate the global semantic information. In this paper, we propose a n... | ['Sophia Ananiadou', 'Tulika Saha', 'Jimin Huang', 'Qianqian Xie'] | 2022-08-21 | null | https://aclanthology.org/2022.coling-1.546 | https://aclanthology.org/2022.coling-1.546.pdf | coling-2022-10 | ['topic-models', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.59131902e-01 6.86711729e-01 -4.98357892e-01 -3.55049342e-01
-1.18761098e+00 -2.70308666e-02 6.77585661e-01 4.88791734e-01
5.17157232e-03 6.66914403e-01 1.27013183e+00 3.99304807e-01
8.44839364e-02 -6.11872911e-01 -5.26070952e-01 -6.57117009e-01
1.72262937e-01 2.76586622e-01 2.94557214e-01 -5.06132878... | [12.602117538452148, 9.461451530456543] |
67c9a21b-2b18-4e37-801d-8d170ae9850e | intensity-aware-loss-for-dynamic-facial | 2208.10335 | null | https://arxiv.org/abs/2208.10335v1 | https://arxiv.org/pdf/2208.10335v1.pdf | Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild | Compared with the image-based static facial expression recognition (SFER) task, the dynamic facial expression recognition (DFER) task based on video sequences is closer to the natural expression recognition scene. However, DFER is often more challenging. One of the main reasons is that video sequences often contain fra... | ['Feng Zhao', 'Zhaoqing Zhu', 'Hongjing Niu', 'Hanting Li'] | 2022-08-19 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.11098462e-01 -4.73845869e-01 3.14404257e-02 -7.54397392e-01
-2.78556466e-01 -1.13279104e-01 2.13959843e-01 -5.85350573e-01
-5.27706087e-01 5.69367230e-01 6.39014095e-02 2.70798624e-01
1.84740022e-01 -5.09378970e-01 -5.80993652e-01 -1.23727822e+00
8.31565931e-02 -1.72873974e-01 -1.42629489e-01 -3.05329114... | [13.663179397583008, 1.6809594631195068] |
d1b97601-15f2-4436-bfdf-11b5f898a0b6 | clickbait-detection-via-large-language-models | 2306.09597 | null | https://arxiv.org/abs/2306.09597v1 | https://arxiv.org/pdf/2306.09597v1.pdf | Clickbait Detection via Large Language Models | Clickbait, which aims to induce users with some surprising and even thrilling headlines for increasing click-through rates, permeates almost all online content publishers, such as news portals and social media. Recently, Large Language Models (LLMs) have emerged as a powerful instrument and achieved tremendous success ... | ['Jipeng Qiang', 'Yunhao Yuan', 'Yun Li', 'Ye Wang', 'Han Wang', 'Yi Zhu'] | 2023-06-16 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-2.89683968e-01 -2.06958368e-01 -5.82059860e-01 -4.18102622e-01
-1.02937627e+00 -3.86104465e-01 7.41389692e-01 1.75824374e-01
-5.89029729e-01 7.38076389e-01 1.12078853e-01 -5.07948995e-01
1.65843610e-02 -4.92519110e-01 -6.44218206e-01 -2.33417451e-01
1.75149933e-01 5.36726356e-01 6.88004494e-01 -2.61328131... | [7.765655994415283, 9.767534255981445] |
00e88107-7816-4206-8770-c2fec3967302 | neural-network-compression-using-binarization | 2306.08960 | null | https://arxiv.org/abs/2306.08960v1 | https://arxiv.org/pdf/2306.08960v1.pdf | Neural Network Compression using Binarization and Few Full-Precision Weights | Quantization and pruning are known to be two effective Deep Neural Networks model compression methods. In this paper, we propose Automatic Prune Binarization (APB), a novel compression technique combining quantization with pruning. APB enhances the representational capability of binary networks using a few full-precisi... | ['Rossano Venturini', 'Salvatore Trani', 'Cosimo Rulli', 'Franco Maria Nardini'] | 2023-06-15 | null | null | null | null | ['neural-network-compression', 'quantization', 'model-compression', 'neural-network-compression'] | ['methodology', 'methodology', 'methodology', 'miscellaneous'] | [ 3.60475510e-01 2.16758177e-02 -2.68673003e-01 -4.52583402e-01
-3.93639594e-01 8.02976191e-02 4.23937023e-01 4.86596823e-01
-1.14406276e+00 6.93919778e-01 -1.62529483e-01 -5.82018673e-01
-5.59921503e-01 -1.00314498e+00 -8.41250300e-01 -6.94875956e-01
-1.37554631e-01 5.27356386e-01 3.47527713e-01 1.44382371... | [8.551435470581055, 3.0933854579925537] |
78126952-f733-4776-a09a-3cd1b53333ca | robustness-analysis-of-deep-learning-models | 2211.13339 | null | https://arxiv.org/abs/2211.13339v1 | https://arxiv.org/pdf/2211.13339v1.pdf | Robustness Analysis of Deep Learning Models for Population Synthesis | Deep generative models have become useful for synthetic data generation, particularly population synthesis. The models implicitly learn the probability distribution of a dataset and can draw samples from a distribution. Several models have been proposed, but their performance is only tested on a single cross-sectional ... | ['Bilal Farooq', 'Godwin Badu-Marfo', 'Daniel Opoku Mensah'] | 2022-11-23 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-0.26376113 0.47198078 -0.02748219 -0.10825006 -0.7975115 -0.51932037
1.1920979 -0.10866145 -0.3412496 1.454284 0.3446264 -0.21262188
-0.21050428 -1.541914 -0.99567765 -0.664084 0.06048175 1.0500576
-0.38542014 -0.26925278 -0.13847563 0.11891244 -1.464275 -0.06509347
1.0995114 0.39169204 -0.2... | [11.680212020874023, -0.05929701775312424] |
34726eb9-88e9-4584-8e1f-b7ed3f14a1c9 | cisco-at-semeval-2021-task-5-what-s-toxic | 2105.13959 | null | https://arxiv.org/abs/2105.13959v1 | https://arxiv.org/pdf/2105.13959v1.pdf | Cisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments | Social network platforms are generally used to share positive, constructive, and insightful content. However, in recent times, people often get exposed to objectionable content like threat, identity attacks, hate speech, insults, obscene texts, offensive remarks or bullying. Existing work on toxic speech detection focu... | ['Sonal Kumar', 'Sreyan Ghosh'] | 2021-05-28 | null | https://aclanthology.org/2021.semeval-1.29 | https://aclanthology.org/2021.semeval-1.29.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 1.50114909e-01 1.05067514e-01 7.81814680e-02 -7.84483477e-02
-1.06970739e+00 -9.51549292e-01 7.91509807e-01 7.02908993e-01
-4.43108797e-01 6.80241287e-01 4.59813327e-01 -3.10337514e-01
1.38939112e-01 -3.90471816e-01 -2.03594267e-01 -3.44127744e-01
-1.45694971e-01 2.27139786e-01 1.54573604e-01 -2.33604819... | [8.854477882385254, 10.603068351745605] |
f9a6c65c-c5df-4dc7-beea-6bdc2bba8993 | semi-supervised-time-domain-target-speaker | 2206.09072 | null | https://arxiv.org/abs/2206.09072v1 | https://arxiv.org/pdf/2206.09072v1.pdf | Semi-supervised Time Domain Target Speaker Extraction with Attention | In this work, we propose Exformer, a time-domain architecture for target speaker extraction. It consists of a pre-trained speaker embedder network and a separator network based on transformer encoder blocks. We study multiple methods to combine speaker information with the input mixture, and the resulting Exformer arch... | ['Arvindh Krishnaswamy', 'Mike Goodwin', 'Paris Smaragdis', 'Jean-Marc Valin', 'Umut Isik', 'Shrikant Venkataramani', 'Ritwik Giri', 'Zhepei Wang'] | 2022-06-18 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 1.90456644e-01 3.30964744e-01 -2.83101201e-01 -6.33072138e-01
-1.26244116e+00 -4.98427957e-01 7.98127711e-01 -7.50300825e-01
-2.00318873e-01 3.73943597e-01 5.70330024e-01 -2.49312401e-01
3.31132025e-01 -1.42257854e-01 -4.34392780e-01 -8.42569530e-01
-5.03802299e-03 2.26572812e-01 -2.62281001e-01 6.85493127... | [14.681137084960938, 6.0028791427612305] |
6a28814c-c961-438c-82cc-0f352a896b26 | neuface-realistic-3d-neural-face-rendering | 2303.14092 | null | https://arxiv.org/abs/2303.14092v2 | https://arxiv.org/pdf/2303.14092v2.pdf | NeuFace: Realistic 3D Neural Face Rendering from Multi-view Images | Realistic face rendering from multi-view images is beneficial to various computer vision and graphics applications. Due to the complex spatially-varying reflectance properties and geometry characteristics of faces, however, it remains challenging to recover 3D facial representations both faithfully and efficiently in t... | ['Di Huang', 'Hongyu Yang', 'Haiyu Zhang', 'Mingwu Zheng'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_NeuFace_Realistic_3D_Neural_Face_Rendering_From_Multi-View_Images_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_NeuFace_Realistic_3D_Neural_Face_Rendering_From_Multi-View_Images_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-rendering'] | ['computer-vision'] | [-1.00241818e-01 -2.33578503e-01 3.17100376e-01 -5.51273704e-01
-5.05283594e-01 -2.97079086e-01 4.74004000e-01 -7.53239274e-01
3.74067485e-01 5.24659991e-01 1.21673785e-01 4.52179695e-03
-2.21219569e-01 -7.07499683e-01 -5.94235778e-01 -8.34843636e-01
2.31522664e-01 2.32852161e-01 -3.49515796e-01 -1.40400991... | [12.912280082702637, -0.1755589097738266] |
de965e17-e0fe-414f-87ba-9175d0c1b3b6 | high-order-local-directional-pattern-based | 2012.06838 | null | https://arxiv.org/abs/2012.06838v1 | https://arxiv.org/pdf/2012.06838v1.pdf | High Order Local Directional Pattern Based Pyramidal Multi-structure for Robust Face Recognition | Derived from a general definition of texture in a local neighborhood, local directional pattern (LDP) encodes the directional information in the small local 3x3 neighborhood of a pixel, which may fail to extract detailed information especially during changes in the input image due to illumination variations. Therefore,... | ['Vijayan Asari', 'Almabrok Essa'] | 2020-12-12 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 4.06407952e-01 -6.82001293e-01 -1.14047751e-01 -4.55249548e-01
-1.50571927e-01 -2.38348171e-01 5.05733013e-01 -4.67718467e-02
-4.55885530e-02 6.92645550e-01 3.96571875e-01 2.61412710e-01
-5.99268794e-01 -9.07426417e-01 -4.07696456e-01 -1.22006106e+00
-1.38291657e-01 -4.15168881e-01 5.59958041e-01 6.31671697... | [10.52232551574707, -0.3697754144668579] |
7b223430-b3f7-4ca0-a26a-e114c281b708 | zeroflow-fast-zero-label-scene-flow-via | 2305.10424 | null | https://arxiv.org/abs/2305.10424v4 | https://arxiv.org/pdf/2305.10424v4.pdf | ZeroFlow: Fast Zero Label Scene Flow via Distillation | Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds for large-scale point clouds, making them unusable as computer vision primitives f... | ['James Hays', 'Deva Ramanan', 'Yang Liu', 'Dinesh Jayaraman', 'Eric Eaton', 'Ishan Khatri', 'Nathaniel Chodosh', 'Neehar Peri', 'Kyle Vedder'] | 2023-05-17 | null | null | null | null | ['open-world-object-detection', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [-1.48049057e-01 -2.66352832e-01 -1.89592138e-01 -3.04260194e-01
-8.16048205e-01 -8.25569689e-01 5.33539355e-01 7.79694319e-02
-6.00711048e-01 5.30409157e-01 -2.81757206e-01 -4.87881362e-01
2.60532260e-01 -7.45017409e-01 -7.67148674e-01 -1.79723606e-01
-3.45132887e-01 7.80082464e-01 6.23724520e-01 1.74732134... | [8.510674476623535, -2.0577948093414307] |
8fdea840-c99c-41c7-8af3-c16cab83b05b | prompting-the-hidden-talent-of-web-scale | 2305.11095 | null | https://arxiv.org/abs/2305.11095v1 | https://arxiv.org/pdf/2305.11095v1.pdf | Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization | We investigate the emergent abilities of the recently proposed web-scale speech model Whisper, by adapting it to unseen tasks with prompt engineering. We selected three tasks: audio-visual speech recognition (AVSR), code-switched speech recognition (CS-ASR), and speech translation (ST) on unseen language pairs. We desi... | ['David Harwath', 'Shinji Watanabe', 'Brian Yan', 'Puyuan Peng'] | 2023-05-18 | null | null | null | null | ['prompt-engineering', 'visual-speech-recognition', 'audio-visual-speech-recognition'] | ['natural-language-processing', 'speech', 'speech'] | [ 1.91516250e-01 -7.20273182e-02 -6.98476955e-02 -4.54963565e-01
-1.29008722e+00 -8.84034634e-01 8.44115973e-01 -2.81542569e-01
-3.25229406e-01 4.71753180e-01 6.95358336e-01 -5.85064590e-01
2.98094481e-01 8.37372523e-03 -7.42845595e-01 -5.79220116e-01
1.55361950e-01 4.40646350e-01 3.53226811e-01 -3.63773465... | [14.407244682312012, 7.062227725982666] |
af5cc3ed-9a65-40bb-8d0f-836dd7a1a7db | nerflets-local-radiance-fields-for-efficient | 2303.03361 | null | https://arxiv.org/abs/2303.03361v2 | https://arxiv.org/pdf/2303.03361v2.pdf | Nerflets: Local Radiance Fields for Efficient Structure-Aware 3D Scene Representation from 2D Supervision | We address efficient and structure-aware 3D scene representation from images. Nerflets are our key contribution -- a set of local neural radiance fields that together represent a scene. Each nerflet maintains its own spatial position, orientation, and extent, within which it contributes to panoptic, density, and radian... | ['Kyle Genova', 'Hao Su', 'Leonidas Guibas', 'Thomas Funkhouser', 'Abhijit Kundu', 'Xiaoshuai Zhang'] | 2023-03-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Nerflets_Local_Radiance_Fields_for_Efficient_Structure-Aware_3D_Scene_Representation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Nerflets_Local_Radiance_Fields_for_Efficient_Structure-Aware_3D_Scene_Representation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [ 3.03359121e-01 -2.21170917e-01 1.00244150e-01 -4.86481071e-01
-4.17530954e-01 -9.62793589e-01 5.44270992e-01 4.13604863e-02
2.74906792e-02 3.39339167e-01 2.00167015e-01 -1.15768060e-01
-1.40580982e-01 -9.44646239e-01 -8.05982828e-01 -6.42148316e-01
2.72864103e-02 5.71699619e-01 1.12684101e-01 9.51155424... | [9.144659996032715, -3.0869226455688477] |
b5fdfbdd-7984-458a-8671-fe50b13f84b8 | hitz-antidote-argumentation-driven | 2306.06029 | null | https://arxiv.org/abs/2306.06029v1 | https://arxiv.org/pdf/2306.06029v1.pdf | HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine | Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task. To work well it requires, among other factors: selecting a proper level of generality/specificity of the explanation; considering assumptions about the familiarity of the explanation beneficiary with the ... | ['Anar Yeginbergenova', 'German Rigau', 'Igor Perez-Tejedor', 'Maite Oronoz', 'Koldo Gojenola', 'Iakes Goenaga', 'Iker Garcia-Ferrero', 'Ainara Estarrona', 'Ander Berrondo', 'Aitziber Atutxa', 'Iñigo Alonso', 'Rodrigo Agerri'] | 2023-06-09 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 3.78918797e-01 1.10934842e+00 -4.80871290e-01 -4.78813320e-01
-5.11712730e-01 -2.91194409e-01 3.89197439e-01 6.80326462e-01
2.29189750e-02 8.02674770e-01 5.75036645e-01 -1.02238417e+00
-6.95442617e-01 -6.24881864e-01 -7.54839361e-01 -3.74263406e-01
3.82604837e-01 9.17792022e-01 -3.15928638e-01 -3.39587361... | [8.71905517578125, 5.809509754180908] |
93bda464-489e-46aa-8760-cc399ef3b02e | a-grammar-sparrer-for-norwegian | null | null | https://aclanthology.info/papers/W13-5640/w13-5640 | https://www.aclweb.org/anthology/W13-5640 | A Grammar Sparrer for Norwegian | null | ['Mads H. Sandøy', 'Elias Aamot', 'Tore Bruland', 'Lars Hellan'] | 2013-05-01 | null | https://aclanthology.org/W13-5640 | https://aclanthology.org/W13-5640.pdf | ws-2013-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5392173528671265, 15.869222640991211] |
d2dcecd6-e912-4d8f-ac89-f167c506f1ba | predict-anchor-links-across-social-networks | null | null | http://www.bigdatalab.ac.cn/~shenhuawei/publications/2016/ijcai-man.pdf | http://www.bigdatalab.ac.cn/~shenhuawei/publications/2016/ijcai-man.pdf | Predict Anchor Links across Social Networks via an Embedding Approach | Predicting anchor links across social networks has
important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation. One challenging problem is: whether and to what extent
we can address the anchor link prediction problem, if only structural information... | ['Hua-Wei Shen', 'Shenghua Liu', 'Xiaolong Jin', 'Tong Man', 'and Xueqi Cheng'] | 2016-06-25 | null | null | null | proceedings-of-the-twenty-fifth-international | ['anchor-link-prediction'] | ['graphs'] | [ 7.85765648e-02 3.24754030e-01 -1.15647614e+00 -2.45364487e-01
1.44184576e-02 -6.57074988e-01 5.42339623e-01 2.82139033e-01
3.11608344e-01 5.83002925e-01 3.61623257e-01 -2.29166791e-01
-7.39964664e-01 -1.04128253e+00 -3.28792006e-01 -1.29222527e-01
-7.39699662e-01 5.45589805e-01 6.30181611e-01 -2.48282775... | [7.313569068908691, 6.248383045196533] |
2465354b-fd2a-48ec-b422-9157001a33fb | imu-based-modularized-wearable-device-for | 2303.16468 | null | https://arxiv.org/abs/2303.16468v1 | https://arxiv.org/pdf/2303.16468v1.pdf | IMU-based Modularized Wearable Device for Human Motion Classification | Human motion analysis is used in many different fields and applications. Currently, existing systems either focus on one single limb or one single class of movements. Many proposed systems are designed to be used in an indoor controlled environment and must possess good technical know-how to operate. To improve mobilit... | ['Janaka Wijayakulasooriya', 'Mervyn Parakrama Ekanayake', 'Roshan Indika Godaliyadda', 'Upekha Hansanie Delay', 'Eranda Somathilake', 'Janith Bandara Senanayake', 'Kavishka Dissanayake', 'Shehan Kaushalya Senavirathna', 'Sahan Wijethunga'] | 2023-03-29 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.52717763e-01 -5.20304382e-01 -4.60983425e-01 6.53636530e-02
1.11450449e-01 -2.32600048e-01 1.27338603e-01 -1.03577100e-01
-7.50817955e-01 6.04818344e-01 2.25072876e-01 -1.71608061e-01
-5.16143501e-01 -7.18481362e-01 -1.75486013e-01 -7.66445577e-01
-1.40303120e-01 1.44907981e-01 2.82332629e-01 -2.83713847... | [7.1357221603393555, 0.5283288359642029] |
f03cbfc3-c6be-44a5-8991-6f01353a5a97 | deep-image-matting-a-comprehensive-survey | 2304.04672 | null | https://arxiv.org/abs/2304.04672v1 | https://arxiv.org/pdf/2304.04672v1.pdf | Deep Image Matting: A Comprehensive Survey | Image matting refers to extracting precise alpha matte from natural images, and it plays a critical role in various downstream applications, such as image editing. Despite being an ill-posed problem, traditional methods have been trying to solve it for decades. The emergence of deep learning has revolutionized the fiel... | ['DaCheng Tao', 'Jing Zhang', 'Jizhizi Li'] | 2023-04-10 | null | null | null | null | ['image-matting', 'referring-image-matting'] | ['computer-vision', 'computer-vision'] | [ 4.11424428e-01 -1.06113039e-01 -8.00946206e-02 -2.98377752e-01
-5.87745547e-01 -4.55797613e-01 3.79149914e-01 -2.18978092e-01
-3.30443442e-01 4.53331620e-01 2.79839244e-02 -3.38050574e-01
2.98665076e-01 -7.71870553e-01 -1.00380182e+00 -8.25446963e-01
2.60627598e-01 4.28190202e-01 -2.06720203e-01 -1.28956467... | [10.659187316894531, -0.8943882584571838] |
c51ebd41-9b8f-4f45-a460-4bcaae8537b2 | joint-modeling-of-opinion-expression | null | null | https://aclanthology.org/Q14-1039 | https://aclanthology.org/Q14-1039.pdf | Joint Modeling of Opinion Expression Extraction and Attribute Classification | In this paper, we study the problems of opinion expression extraction and expression-level polarity and intensity classification. Traditional fine-grained opinion analysis systems address these problems in isolation and thus cannot capture interactions among the textual spans of opinion expressions and their opinion-re... | ['Bishan Yang', 'Claire Cardie'] | 2014-01-01 | null | null | null | tacl-2014-1 | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [ 2.94590354e-01 2.56960420e-03 -7.44571388e-01 -7.84900486e-01
-8.68185341e-01 -9.43502426e-01 8.47227395e-01 4.99364078e-01
-3.07191432e-01 1.23148513e+00 1.84120581e-01 -5.35406649e-01
-2.05634050e-02 -8.13827097e-01 -3.23313266e-01 -7.27828205e-01
-2.97224820e-01 4.53981876e-01 3.50681283e-02 -2.54238904... | [11.450393676757812, 6.675461769104004] |
cc2acb5f-0a50-4c76-95ab-a694451330a4 | structure-matters-towards-generating | 1910.09821 | null | https://arxiv.org/abs/1910.09821v3 | https://arxiv.org/pdf/1910.09821v3.pdf | Structure Matters: Towards Generating Transferable Adversarial Images | Recent works on adversarial examples for image classification focus on directly modifying pixels with minor perturbations. The small perturbation requirement is imposed to ensure the generated adversarial examples being natural and realistic to humans, which, however, puts a curb on the attack space thus limiting the a... | ['Xiaofeng Zhang', 'Zizhan Zheng', 'Linhao Luo', 'Dan Peng'] | 2019-10-22 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 7.24007785e-01 3.19762617e-01 2.04181075e-01 -6.28511608e-02
-4.07726169e-01 -1.03828406e+00 6.99346483e-01 -5.84653839e-02
-3.64761889e-01 6.06626391e-01 -2.21567795e-01 -4.47226375e-01
-1.42218485e-01 -1.01573873e+00 -8.46716344e-01 -9.53700125e-01
-9.17517319e-02 -2.50380754e-01 4.06057239e-01 -5.79125404... | [5.6144633293151855, 7.9182658195495605] |
31a6aac8-745d-47fb-87a7-13ff2a0cf4ff | towards-better-understanding-attribution | 2205.10435 | null | https://arxiv.org/abs/2205.10435v1 | https://arxiv.org/pdf/2205.10435v1.pdf | Towards Better Understanding Attribution Methods | Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. Evaluating such methods is challenging since no ground truth ... | ['Bernt Schiele', 'Moritz Böhle', 'Sukrut Rao'] | 2022-05-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Rao_Towards_Better_Understanding_Attribution_Methods_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Rao_Towards_Better_Understanding_Attribution_Methods_CVPR_2022_paper.pdf | cvpr-2022-1 | ['explanation-fidelity-evaluation'] | ['methodology'] | [ 1.38562515e-01 1.02626599e-01 -9.74086821e-02 -6.65268838e-01
-6.19623344e-03 -4.75579083e-01 8.82649601e-01 4.10510540e-01
-5.82146168e-01 6.02183938e-01 1.11588053e-01 -3.43146712e-01
1.65382531e-02 -6.00595891e-01 -4.95186388e-01 -6.50109291e-01
2.19467074e-01 -5.74118271e-02 3.25491369e-01 2.12911621... | [9.96792984008789, 2.190460681915283] |
bf7b073a-589c-416d-bfcb-b6f278f5e234 | state-action-joint-regularized-implicit | null | null | https://openreview.net/forum?id=-7UeX2KPqs | https://openreview.net/pdf?id=-7UeX2KPqs | State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning | Offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. The lack of environmental interactions makes the policy training vulnerable to state-action pairs far from the training dataset and prone to missing rewarding actions. For training more effective age... | ['Mingyuan Zhou', 'Huangjie Zheng', 'Zhendong Wang', 'Shentao Yang'] | 2021-09-29 | null | null | null | null | ['d4rl'] | ['robots'] | [ 2.49303520e-01 2.56936729e-01 -7.37840712e-01 -3.70433509e-01
-6.06794417e-01 -8.66302967e-01 8.66053939e-01 -1.55183107e-01
-6.13300741e-01 9.82424498e-01 2.20802307e-01 -4.28272337e-01
-3.41344237e-01 -7.28003144e-01 -1.04559529e+00 -7.79627860e-01
-4.96489823e-01 4.04178143e-01 4.73372377e-02 -2.03490719... | [4.139527797698975, 1.9492058753967285] |
cd322cc1-09ed-437d-bea7-3603a3cd1cbe | knowledge-refactoring-for-program-induction | 2004.09931 | null | https://arxiv.org/abs/2004.09931v3 | https://arxiv.org/pdf/2004.09931v3.pdf | Knowledge Refactoring for Inductive Program Synthesis | Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the \textit{knowledge refactoring} problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimi... | ['Andrew Cropper', 'Tias Guns', 'Sebastijan Dumancic'] | 2020-04-21 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 5.91712952e-01 7.70389020e-01 -5.90350807e-01 -2.57258475e-01
-3.55212599e-01 -7.39683270e-01 1.64955437e-01 2.10052192e-01
-3.02732915e-01 1.07667077e+00 -1.21919826e-01 -9.75877881e-01
-3.85824502e-01 -1.17093468e+00 -1.11966407e+00 -1.21865690e-01
-8.42935145e-02 5.33776462e-01 6.72582150e-01 -3.37193370... | [8.764093399047852, 7.138303279876709] |
32da81cd-4e44-4923-a3d0-df2d566a8725 | towards-fine-grained-human-pose-transfer-with | 2005.12494 | null | https://arxiv.org/abs/2005.12494v2 | https://arxiv.org/pdf/2005.12494v2.pdf | Towards Fine-grained Human Pose Transfer with Detail Replenishing Network | Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality. For these applications, the visual realism of fine-grained appearance details is crucial for production quality and user engagement. However, existing HPT methods ofte... | ['Xian-Sheng Hua', 'Peiran Ren', 'Zhanning Gao', 'Xinfeng Zhang', 'Wen Gao', 'Chang Liu', 'Shanshe Wang', 'Lingbo Yang', 'Siwei Ma', 'Pan Wang'] | 2020-05-26 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 3.68978232e-01 -1.08404219e-01 -1.77169368e-01 -2.95180529e-01
-6.47817791e-01 -5.50246716e-01 6.92965984e-01 -2.99970329e-01
1.44799724e-01 5.49548626e-01 2.82521278e-01 9.10808966e-02
-2.85865754e-01 -7.84018576e-01 -8.28212321e-01 -6.51828349e-01
2.62803525e-01 7.08476081e-02 -7.80973360e-02 -4.56046492... | [12.002302169799805, -0.7291359901428223] |
515c8cec-333d-45ae-be35-eb75c2209bc2 | tritransnet-rgb-d-salient-object-detection | 2108.03990 | null | https://arxiv.org/abs/2108.03990v1 | https://arxiv.org/pdf/2108.03990v1.pdf | TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network | Salient object detection is the pixel-level dense prediction task which can highlight the prominent object in the scene. Recently U-Net framework is widely used, and continuous convolution and pooling operations generate multi-level features which are complementary with each other. In view of the more contribution of h... | ['Bin Tang', 'Yun Xiao', 'Zhengzheng Tu', 'YuAn Wang', 'Zhengyi Liu'] | 2021-08-09 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.10592946e-01 -1.52373701e-01 4.74359421e-03 -2.62476146e-01
-5.66755414e-01 2.03081623e-01 4.82238889e-01 -1.12268120e-01
-4.67073977e-01 3.55367452e-01 5.46133697e-01 2.32286066e-01
2.15068743e-01 -9.66605961e-01 -8.28153133e-01 -7.09926665e-01
-5.96585870e-02 -3.84552389e-01 1.00943518e+00 -3.07629943... | [9.739883422851562, -0.6843090653419495] |
79ef498c-ec7d-4b08-9b39-72d0cfad9b99 | learning-pose-grammar-to-encode-human-body | 1710.06513 | null | http://arxiv.org/abs/1710.06513v6 | http://arxiv.org/pdf/1710.06513v6.pdf | Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation | In this paper, we propose a pose grammar to tackle the problem of 3D human
pose estimation. Our model directly takes 2D pose as input and learns a
generalized 2D-3D mapping function. The proposed model consists of a base
network which efficiently captures pose-aligned features and a hierarchy of
Bi-directional RNNs (BR... | ['Hao-Shu Fang', 'Song-Chun Zhu', 'Yuanlu Xu', 'Wenguan Wang', 'Xiaobai Liu'] | 2017-10-17 | null | null | null | null | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [-1.19033292e-01 2.23011360e-01 -2.64459789e-01 -3.72021884e-01
-4.97364849e-01 -4.51982051e-01 3.49259973e-01 -3.73292357e-01
-4.87378210e-01 4.94262040e-01 4.79942560e-01 1.38147593e-01
1.33398637e-01 -5.31514585e-01 -1.01913619e+00 -1.84229314e-01
-2.72720844e-01 8.72379661e-01 1.23836458e-01 -5.74343026... | [6.965695381164551, -0.89694744348526] |
bb268bec-4cd4-446f-bce5-7af6a98e34e6 | comprehensible-counterfactual-interpretation | 2011.01223 | null | https://arxiv.org/abs/2011.01223v2 | https://arxiv.org/pdf/2011.01223v2.pdf | Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test | The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly detection, astronomy, database security and AI systems. One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test. In this paper, we tackle the problem of producing counterfactual expl... | ['Jian Pei', 'Yu Yang', 'Lingyang Chu', 'Zicun Cong'] | 2020-11-01 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.14374080e-01 3.56444746e-01 -1.97334677e-01 -4.24976230e-01
-4.40464467e-01 -8.29642653e-01 5.83759189e-01 2.97549605e-01
-7.71053135e-02 1.15200770e+00 9.78714824e-02 -1.10804272e+00
-5.87595105e-01 -6.74633026e-01 -9.11598027e-01 -3.30326736e-01
-9.34922099e-02 5.22270858e-01 2.86902279e-01 1.28329873... | [8.708343505859375, 5.67262077331543] |
6e209743-fa1d-4906-aa9f-659b226bd561 | referring-to-what-you-know-and-do-not-know | null | null | https://aclanthology.org/2020.coling-main.205 | https://aclanthology.org/2020.coling-main.205.pdf | Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities | Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data. A core micro-planning task within NLG is referring expression generation (REG), which aims to automatically generate noun phrases to refer to entities mention... | ['Fabio Alves', 'Adriana Pagano', 'Thiago castro Ferreira', 'Rossana Cunha'] | 2020-12-01 | null | null | null | coling-2020-8 | ['referring-expression-generation'] | ['computer-vision'] | [ 3.65705520e-01 1.34568012e+00 8.81868899e-02 -4.18033987e-01
-1.07121885e+00 -6.05314910e-01 1.15785182e+00 -8.80255997e-02
-1.30482867e-01 1.26244819e+00 8.90050709e-01 -2.20401019e-01
3.75338376e-01 -9.44040477e-01 -7.36287057e-01 -1.87288299e-01
2.38811046e-01 8.27392280e-01 -1.87301055e-01 -5.87939024... | [11.285527229309082, 9.061213493347168] |
b43fe87c-fb51-4f3b-80e4-699e360ad745 | multi-objective-deep-reinforcement-learning | 1610.02707 | null | http://arxiv.org/abs/1610.02707v1 | http://arxiv.org/pdf/1610.02707v1.pdf | Multi-Objective Deep Reinforcement Learning | We propose Deep Optimistic Linear Support Learning (DOL) to solve
high-dimensional multi-objective decision problems where the relative
importances of the objectives are not known a priori. Using features from the
high-dimensional inputs, DOL computes the convex coverage set containing all
potential optimal solutions o... | ['Shimon Whiteson', 'Yannis M. Assael', 'Hossam Mossalam', 'Diederik M. Roijers'] | 2016-10-09 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-2.95368701e-01 2.17745394e-01 -7.77437568e-01 -3.87936026e-01
-1.04928625e+00 -4.09206241e-01 2.58359909e-01 3.46959203e-01
-5.86033881e-01 1.48934674e+00 8.78802761e-02 -1.59076244e-01
-5.58116317e-01 -6.25493467e-01 -7.88333416e-01 -8.18977654e-01
-6.54126883e-01 1.25116849e+00 -1.63693786e-01 -2.46373981... | [4.217112064361572, 2.405066967010498] |
778ed435-81b7-4bb5-bf4e-73d451bd20c0 | asner-annotated-dataset-and-baseline-for | 2207.03422 | null | https://arxiv.org/abs/2207.03422v1 | https://arxiv.org/pdf/2207.03422v1.pdf | AsNER -- Annotated Dataset and Baseline for Assamese Named Entity recognition | We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The propos... | ['Priyankoo Sarmah', 'Sukumar Nandi', 'Dhrubajyoti Pathak'] | 2022-07-07 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-3.21324021e-01 1.91120803e-01 -9.57401991e-02 -3.61480981e-01
-8.30534101e-01 -8.50874424e-01 8.51391077e-01 3.81367773e-01
-1.37166142e+00 1.19395566e+00 7.22448409e-01 -4.24551278e-01
4.53550696e-01 -1.01780558e+00 -5.31911373e-01 -1.14822961e-01
-1.73608229e-01 7.83644438e-01 2.75494337e-01 -3.83954853... | [9.799177169799805, 9.791418075561523] |
176773c0-8e50-4130-9531-ac43a685aded | marlin-masked-autoencoder-for-facial-video | 2211.06627 | null | https://arxiv.org/abs/2211.06627v3 | https://arxiv.org/pdf/2211.06627v3.pdf | MARLIN: Masked Autoencoder for facial video Representation LearnINg | This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS). Our proposed framewor... | ['Munawar Hayat', 'Reza Haffari', 'Hamid Rezatofighi', 'Jianfei Cai', 'Abhinav Dhall', 'Kalin Stefanov', 'Shreya Ghosh', 'Zhixi Cai'] | 2022-11-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cai_MARLIN_Masked_Autoencoder_for_Facial_Video_Representation_LearnINg_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cai_MARLIN_Masked_Autoencoder_for_Facial_Video_Representation_LearnINg_CVPR_2023_paper.pdf | cvpr-2023-1 | ['facial-attribute-classification', 'lip-sync', 'facial-expression-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.39477197e-02 -2.05454580e-03 -2.31338590e-01 -4.48292583e-01
-8.98749769e-01 -4.64604080e-01 6.22877598e-01 -5.32066524e-01
-4.51311059e-02 4.79590267e-01 4.11071956e-01 4.22598034e-01
1.64551198e-01 -3.00815850e-01 -7.51369417e-01 -9.98470902e-01
-4.03164387e-01 -3.24237138e-01 -2.75537699e-01 -1.71092704... | [13.4973726272583, 1.4240798950195312] |
35b9e06e-1053-41d3-90d1-ac2306066510 | on-the-information-plane-of-autoencoders | 2005.07783 | null | https://arxiv.org/abs/2005.07783v2 | https://arxiv.org/pdf/2005.07783v2.pdf | On the Information Plane of Autoencoders | The training dynamics of hidden layers in deep learning are poorly understood in theory. Recently, the Information Plane (IP) was proposed to analyze them, which is based on the information-theoretic concept of mutual information (MI). The Information Bottleneck (IB) theory predicts that layers maximize relevant inform... | ['Pablo A. Estévez', 'Nicolás I. Tapia'] | 2020-05-15 | null | null | null | null | ['mutual-information-estimation', 'information-plane'] | ['methodology', 'methodology'] | [ 1.44717664e-01 3.92656863e-01 -4.26299088e-02 -2.65376627e-01
1.45455852e-01 -7.62166604e-02 4.90930945e-01 1.16686217e-01
-7.02308178e-01 6.49231970e-01 6.29684851e-02 -2.04985842e-01
-5.98186612e-01 -8.17454517e-01 -7.54246473e-01 -8.95653784e-01
-2.75403112e-01 4.06725496e-01 3.37152809e-01 -3.22732097... | [7.913222789764404, 3.5777554512023926] |
c637e0f9-4913-478a-8043-e605c881df13 | vision-language-pre-training-for-boosting | 2204.13867 | null | https://arxiv.org/abs/2204.13867v1 | https://arxiv.org/pdf/2204.13867v1.pdf | Vision-Language Pre-Training for Boosting Scene Text Detectors | Recently, vision-language joint representation learning has proven to be highly effective in various scenarios. In this paper, we specifically adapt vision-language joint learning for scene text detection, a task that intrinsically involves cross-modal interaction between the two modalities: vision and language, since ... | ['Cong Yao', 'Xiang Bai', 'Wenqing Cheng', 'Jun Tang', 'Zhibo Yang', 'Jianqiang Wan', 'Sibo Song'] | 2022-04-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Song_Vision-Language_Pre-Training_for_Boosting_Scene_Text_Detectors_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Song_Vision-Language_Pre-Training_for_Boosting_Scene_Text_Detectors_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-text-detection'] | ['computer-vision'] | [ 5.70360243e-01 -2.13944465e-01 8.32596645e-02 -2.52981454e-01
-9.55113173e-01 -2.36759827e-01 1.32966554e+00 2.28015289e-01
-6.11082494e-01 1.83726877e-01 4.17667896e-01 -3.90192926e-01
5.89004338e-01 -4.05223638e-01 -8.71146977e-01 -6.31749928e-01
5.19213438e-01 3.32643777e-01 3.24282229e-01 -1.18256547... | [11.69503116607666, 2.0873124599456787] |
69892cd3-75c4-4e0e-ae00-d2b42bc01dd1 | harmonic-enhancement-using-learnable-comb | 2306.00812 | null | https://arxiv.org/abs/2306.00812v1 | https://arxiv.org/pdf/2306.00812v1.pdf | Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model | With fewer feature dimensions, filter banks are often used in light-weight full-band speech enhancement models. In order to further enhance the coarse speech in the sub-band domain, it is necessary to apply a post-filtering for harmonic retrieval. The signal processing-based comb filters used in RNNoise and PercepNet h... | ['Jing Lu', 'Shenyi Song', 'Piao Ding', 'Yijian Xiao', 'Hua Gao', 'Xianjun Xia', 'Cheng Chen', 'Chao He', 'Yiqing Guo', 'Li Chen', 'Tong Lei', 'Xiaohuai Le'] | 2023-06-01 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 5.93634658e-02 -1.27717808e-01 1.58833817e-01 -1.79304168e-01
-7.25623190e-01 -1.39436901e-01 2.60837823e-01 -9.16639790e-02
-3.57274771e-01 6.88475311e-01 7.12257326e-01 4.72033862e-03
-1.33135334e-01 -7.73935616e-01 -3.19104850e-01 -6.13168120e-01
2.43358031e-01 -5.00918806e-01 7.80457035e-02 -3.73561293... | [15.003179550170898, 5.917256832122803] |
8c86e1ca-d44b-42c6-83ae-40463772b996 | viinter-view-interpolation-with-implicit | 2211.00722 | null | https://arxiv.org/abs/2211.00722v1 | https://arxiv.org/pdf/2211.00722v1.pdf | VIINTER: View Interpolation with Implicit Neural Representations of Images | We present VIINTER, a method for view interpolation by interpolating the implicit neural representation (INR) of the captured images. We leverage the learned code vector associated with each image and interpolate between these codes to achieve viewpoint transitions. We propose several techniques that significantly enha... | ['Amitabh Varshney', 'Susmija Jabbireddy', 'Brandon Yushan Feng'] | 2022-11-01 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 3.43135238e-01 -8.44077691e-02 1.00022718e-01 -3.18014741e-01
-4.25726354e-01 -8.42999995e-01 6.57653689e-01 -4.48671073e-01
1.01391703e-01 2.83164412e-01 3.34260166e-01 -2.50918627e-01
2.06339672e-01 -6.68538034e-01 -1.14650154e+00 -2.00080335e-01
2.62923300e-01 3.02329034e-01 -1.97280198e-01 -1.13240421... | [9.02921199798584, -2.951510190963745] |
bdbb84c8-de52-4b62-8fac-799d963ee8b1 | practical-privacy-filters-and-odometers-with | 2103.01379 | null | https://arxiv.org/abs/2103.01379v2 | https://arxiv.org/pdf/2103.01379v2.pdf | Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning | Differential Privacy (DP) is the leading approach to privacy preserving deep learning. As such, there are multiple efforts to provide drop-in integration of DP into popular frameworks. These efforts, which add noise to each gradient computation to make it DP, rely on composition theorems to bound the total privacy loss... | ['Mathias Lécuyer'] | 2021-03-02 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 9.71766785e-02 6.22409098e-02 -2.46331934e-02 -6.36969388e-01
-8.49226832e-01 -8.71739089e-01 7.47478530e-02 2.77589679e-01
-9.14958477e-01 5.15907228e-01 7.02778995e-03 -6.21496141e-01
5.66250049e-02 -7.89099336e-01 -7.70029128e-01 -9.20530558e-01
-4.19301502e-02 -6.86649233e-02 1.23301484e-01 1.30922258... | [5.911669731140137, 6.840139389038086] |
e511e7cc-8692-4371-9868-62a85de61372 | nimble-gnn-embedding-with-tensor-train | 2206.10581 | null | https://arxiv.org/abs/2206.10581v1 | https://arxiv.org/pdf/2206.10581v1.pdf | Nimble GNN Embedding with Tensor-Train Decomposition | This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition. We consider the scenario where (a) the graph data that lack node features, thereby requiring the learning of embeddings during training; and (b) we wish to exploit GPU ... | ['Richard Vuduc', 'George Karypis', 'Christos Faloutos', 'Israt Nisa', 'Da Zheng', 'Chunxing Yin'] | 2022-06-21 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-8.18648189e-02 2.91396052e-01 -1.11617886e-01 1.80114284e-02
-2.46290773e-01 -7.10751235e-01 4.41384315e-01 8.24186027e-01
-6.47505462e-01 3.65835220e-01 -2.87826918e-02 -9.58732247e-01
2.04102978e-01 -1.23475063e+00 -6.09976947e-01 -4.82603490e-01
-3.80066067e-01 5.73664367e-01 3.24004829e-01 -1.57516658... | [6.9736175537109375, 5.729730129241943] |
da102cfa-6c0a-46fb-8fd7-86db39080595 | what-ode-approximation-schemes-of-time-delay | 2202.13122 | null | https://arxiv.org/abs/2202.13122v5 | https://arxiv.org/pdf/2202.13122v5.pdf | What ODE-Approximation Schemes of Time-Delay Systems Reveal about Lyapunov-Krasovskii Functionals | The article proposes an approach to complete-type and related Lyapunov-Krasovskii functionals that neither requires knowledge of the delay-Lyapunov matrix function nor does it involve linear matrix inequalities. The approach is based on ordinary differential equations (ODEs) that approximate the time-delay system. The ... | ['Lutz Gröll', 'Veit Hagenmeyer', 'Tessina H. Scholl'] | 2022-02-26 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-4.83729750e-01 3.03994715e-01 -3.55301201e-02 4.84097809e-01
-6.86181068e-01 -9.38809216e-01 1.17637113e-01 7.35960007e-02
-4.14375216e-01 1.41520572e+00 -5.45069396e-01 -4.40884024e-01
-8.29849020e-02 -2.21692234e-01 -5.25574207e-01 -9.76996779e-01
8.63162577e-02 2.92189401e-02 9.82011482e-02 -6.13443553... | [5.592001438140869, 2.799112319946289] |
fd8ae9e7-df62-46b0-853d-6cc5012fa859 | audio-driven-3d-facial-animation-from-in-the | 2306.11541 | null | https://arxiv.org/abs/2306.11541v1 | https://arxiv.org/pdf/2306.11541v1.pdf | Audio-Driven 3D Facial Animation from In-the-Wild Videos | Given an arbitrary audio clip, audio-driven 3D facial animation aims to generate lifelike lip motions and facial expressions for a 3D head. Existing methods typically rely on training their models using limited public 3D datasets that contain a restricted number of audio-3D scan pairs. Consequently, their generalizatio... | ['Yu Li', 'Xuangeng Chu', 'Yunfei Liu', 'Tianke Zhang', 'Liying Lu'] | 2023-06-20 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-3.29351723e-01 4.67428602e-02 -1.89989701e-01 -4.32773232e-01
-9.33916509e-01 -5.42540789e-01 4.90425766e-01 -8.77340972e-01
2.91851699e-01 2.50051528e-01 5.57284117e-01 5.69711514e-02
3.39720279e-01 -2.33953908e-01 -4.70220834e-01 -5.15228987e-01
-4.98856008e-02 3.09888750e-01 -3.38791937e-01 -6.40991703... | [13.159504890441895, -0.40294477343559265] |
6add826c-c0ce-4b5c-8046-1e0eac539eef | biofors-a-large-biomedical-image-forensics | 2108.12961 | null | https://arxiv.org/abs/2108.12961v1 | https://arxiv.org/pdf/2108.12961v1.pdf | BioFors: A Large Biomedical Image Forensics Dataset | Research in media forensics has gained traction to combat the spread of misinformation. However, most of this research has been directed towards content generated on social media. Biomedical image forensics is a related problem, where manipulation or misuse of images reported in biomedical research documents is of seri... | ['Prem Natarajan', 'Wael AbdAlmageed', 'Soumyaroop Nandi', 'Ekraam Sabir'] | 2021-08-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Sabir_BioFors_A_Large_Biomedical_Image_Forensics_Dataset_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sabir_BioFors_A_Large_Biomedical_Image_Forensics_Dataset_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-forensics'] | ['computer-vision'] | [ 4.46462095e-01 -3.27547602e-02 1.75261289e-01 1.37482345e-01
-8.22515249e-01 -4.86647576e-01 4.27431941e-01 6.89389348e-01
-6.33732915e-01 7.14285493e-01 -1.25833556e-01 -1.96245939e-01
-3.42340604e-03 -3.62532645e-01 -7.20372677e-01 -8.33955705e-01
1.54561341e-01 3.14029127e-01 1.38119966e-01 3.30092788... | [12.413772583007812, 1.0138661861419678] |
ec3f6959-7cab-49bc-8cae-c843528920cf | kinit-classification-in-ethiopian-chants | 2201.08448 | null | https://arxiv.org/abs/2201.08448v1 | https://arxiv.org/pdf/2201.08448v1.pdf | Kinit Classification in Ethiopian Chants, Azmaris and Modern Music: A New Dataset and CNN Benchmark | In this paper, we create EMIR, the first-ever Music Information Retrieval dataset for Ethiopian music. EMIR is freely available for research purposes and contains 600 sample recordings of Orthodox Tewahedo chants, traditional Azmari songs and contemporary Ethiopian secular music. Each sample is classified by five exper... | ['Jun Feng', 'Mustafa Mhamed', 'Michael A. Berwo', 'Tigist D. Gemechu', 'Eyob Alemu', 'Yosef K. Enku', 'Eiad Almekhlafi', 'Richard Sutcliffe', 'Ephrem A. Retta'] | 2022-01-20 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [-3.37126970e-01 -7.90168941e-01 -2.05391049e-01 2.52636522e-01
-5.84396005e-01 -6.44665062e-01 2.48754531e-01 -8.19188505e-02
-5.16117752e-01 5.28595865e-01 2.82357067e-01 -1.48955435e-02
-5.92715740e-01 -5.78702807e-01 3.64020765e-02 -4.44621265e-01
-2.25726843e-01 2.53926992e-01 -2.26848125e-01 -2.77339876... | [15.879135131835938, 5.1779866218566895] |
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