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f5800eb1-c4d2-46b7-a126-1269a1fe54a7 | msdc-exploiting-multi-state-power-consumption | 2302.05565 | null | https://arxiv.org/abs/2302.05565v1 | https://arxiv.org/pdf/2302.05565v1.pdf | MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model | Non-intrusive load monitoring (NILM) aims to decompose aggregated electrical usage signal into appliance-specific power consumption and it amounts to a classical example of blind source separation tasks. Leveraging recent progress on deep learning techniques, we design a new neural NILM model Multi-State Dual CNN (MSDC... | ['Liehuang Zhu', 'Bakh Khoussainov', 'Yiwei Liu', 'Yang Chen', 'Zijian Zhang', 'Jiamou Liu', 'Jialing He'] | 2023-02-11 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 3.53452325e-01 1.27088368e-01 -8.23947072e-01 -4.98874784e-01
-8.25184464e-01 -3.37035656e-01 5.59190750e-01 -3.09382647e-01
2.02590451e-01 6.38244390e-01 3.19457710e-01 -5.17136574e-01
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2.94914003e-02 2.72252500e-01 -4.48586076e-01 1.66158482... | [16.072616577148438, 7.586677551269531] |
83783b47-40ee-4f0c-a161-b12c0239b2d7 | distributional-reinforcement-learning-for-3 | 2011.0184 | null | https://arxiv.org/abs/2011.01840v1 | https://arxiv.org/pdf/2011.01840v1.pdf | Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV | In this paper, a novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed to enhance multi-user downlink transmissions over millimeter wave (mmWave) frequencies. In order to maximize the downlink sum-rate, the optimal precoding matrix (at the base station) ... | ['Mehdi Bennis', 'Walid Saad', 'Qianqian Zhang'] | 2020-11-03 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.05417508e-01 3.76198798e-01 3.16429943e-01 4.57283296e-02
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-4.97593224e-01 -1.12434857e-01 -8.08343410e-01 -1.59173906... | [6.141955852508545, 1.369336485862732] |
c4d1d96d-bbb7-40ae-80de-e3ec0a2fa5a3 | ustep-structuration-des-logs-en-flux-gr-a-ce | 2304.12331 | null | https://arxiv.org/abs/2304.12331v1 | https://arxiv.org/pdf/2304.12331v1.pdf | USTEP: Structuration des logs en flux gr{â}ce {à} un arbre de recherche {é}volutif | Logs record valuable system information at runtime. They are widely used by data-driven approaches for development and monitoring purposes. Parsing log messages to structure their format is a classic preliminary step for log-mining tasks. As they appear upstream, parsing operations can become a processing time bottlene... | ['Mar Callau-Zori', 'Raja Chiky', 'Arthur Vervaet'] | 2023-04-24 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [-1.56262413e-01 -4.58144784e-01 -5.50116599e-01 -4.59895045e-01
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-6.30889416e-01 4.48919892e-01 8.71001363e-01 -5.65549545... | [7.976958751678467, 6.853243827819824] |
a733936b-c089-4672-85a3-655e6c173ebb | cheap-and-quick-efficient-vision-language | 2305.15023 | null | https://arxiv.org/abs/2305.15023v2 | https://arxiv.org/pdf/2305.15023v2.pdf | Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models | Recently, growing interest has been aroused in extending the multimodal capability of large language models (LLMs), e.g., vision-language (VL) learning, which is regarded as the next milestone of artificial general intelligence. However, existing solutions are prohibitively expensive, which not only need to optimize ex... | ['Rongrong Ji', 'Xiaoshuai Sun', 'Shengxin Chen', 'Tianhe Ren', 'Yiyi Zhou', 'Gen Luo'] | 2023-05-24 | null | null | null | null | ['chatbot', 'science-question-answering', 'chatbot'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-1.95287526e-01 2.61646807e-02 -7.56647959e-02 -2.76607007e-01
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6.27656460e-01 4.03851062e-01 3.49463783e-02 -2.32048735... | [10.774001121520996, 1.4639867544174194] |
a1fbc783-9cd2-4979-a508-f3ab23552366 | investigation-of-network-architecture-for | 2212.10724 | null | https://arxiv.org/abs/2212.10724v1 | https://arxiv.org/pdf/2212.10724v1.pdf | Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation | Inspired by the recent success of Transformers for Natural Language Processing and vision Transformer for Computer Vision, many researchers in the medical imaging community have flocked to Transformer-based networks for various main stream medical tasks such as classification, segmentation, and estimation. In this stud... | ['Quanzheng Li', 'Kuang Gong', 'Se-In Jang', 'Junyu Chen', 'Ye Li'] | 2022-12-21 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 5.61539046e-02 3.01110744e-01 -1.66804329e-01 -2.91571647e-01
-5.90160668e-01 -1.22013621e-01 2.93985158e-01 3.02871346e-01
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-8.27497244e-02 7.53365517e-01 3.75526965e-01 -1.16306096... | [14.539372444152832, -2.473653793334961] |
7f823ee4-e19c-4fdf-86f5-ff5c014f431d | hyperthumbnail-real-time-6k-image-rescaling | 2304.01064 | null | https://arxiv.org/abs/2304.01064v2 | https://arxiv.org/pdf/2304.01064v2.pdf | Real-time 6K Image Rescaling with Rate-distortion Optimization | Contemporary image rescaling aims at embedding a high-resolution (HR) image into a low-resolution (LR) thumbnail image that contains embedded information for HR image reconstruction. Unlike traditional image super-resolution, this enables high-fidelity HR image restoration faithful to the original one, given the embedd... | ['Qifeng Chen', 'Ying-Cong Chen', 'Ka Leong Cheng', 'Xin Yang', 'Chenyang Qi'] | 2023-04-03 | real-time-6k-image-rescaling-with-rate | http://openaccess.thecvf.com//content/CVPR2023/html/Qi_Real-Time_6K_Image_Rescaling_With_Rate-Distortion_Optimization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qi_Real-Time_6K_Image_Rescaling_With_Rate-Distortion_Optimization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-super-resolution'] | ['computer-vision'] | [ 1.02156460e+00 7.02178627e-02 -3.53880674e-01 -1.04227560e-02
-1.19645607e+00 -2.62099206e-01 2.58113354e-01 -2.91578501e-01
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2.02140033e-01 -3.78316253e-01 5.70456460e-02 -1.76948443... | [10.962095260620117, -2.1103968620300293] |
76fa3385-8ab6-409c-a583-ae68ffcf002e | event-causality-identification-via-generation | null | null | https://aclanthology.org/2022.starsem-1.28 | https://aclanthology.org/2022.starsem-1.28.pdf | Event Causality Identification via Generation of Important Context Words | An important problem of Information Extraction involves Event Causality Identification (ECI) that seeks to identify causal relation between pairs of event mentions. Prior models for ECI have mainly solved the problem using the classification framework that does not explore prediction/generation of important context wor... | ['Thien Nguyen', 'Minh Nguyen', 'Hieu Man'] | null | null | null | null | sem-naacl-2022-7 | ['event-causality-identification'] | ['natural-language-processing'] | [ 4.98204499e-01 3.55423659e-01 -3.41357142e-01 -3.82585347e-01
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-2.91195154e-01 3.75212729e-01 1.21606246e-01 1.86462849... | [9.120917320251465, 9.075896263122559] |
a0392c17-4291-4033-a847-cfa958d94d4f | timestamping-documents-and-beliefs | 2106.14622 | null | https://arxiv.org/abs/2106.14622v1 | https://arxiv.org/pdf/2106.14622v1.pdf | Timestamping Documents and Beliefs | Most of the textual information available to us are temporally variable. In a world where information is dynamic, time-stamping them is a very important task. Documents are a good source of information and are used for many tasks like, sentiment analysis, classification of reviews etc. The knowledge of creation date of... | ['Swayambhu Nath Ray'] | 2021-06-09 | null | null | null | null | ['document-dating'] | ['natural-language-processing'] | [-1.72916338e-01 -4.85977903e-02 -5.20404339e-01 -5.70334792e-01
-3.35497051e-01 -6.61824048e-01 1.30572987e+00 7.06647635e-01
-4.15351361e-01 5.79691350e-01 4.18398976e-01 -3.43326539e-01
-2.52971381e-01 -9.89219904e-01 -8.09032261e-01 -3.15834165e-01
-1.73118398e-01 6.76666021e-01 3.20726871e-01 -3.77341986... | [12.21107292175293, 9.347225189208984] |
512cafff-63e3-4317-96e8-65a63fd2c8a0 | a-large-dataset-to-train-convolutional | 1512.02134 | null | http://arxiv.org/abs/1512.02134v1 | http://arxiv.org/pdf/1512.02134v1.pdf | A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation | Recent work has shown that optical flow estimation can be formulated as a
supervised learning task and can be successfully solved with convolutional
networks. Training of the so-called FlowNet was enabled by a large
synthetically generated dataset. The present paper extends the concept of
optical flow estimation via co... | ['Philip Häusser', 'Thomas Brox', 'Nikolaus Mayer', 'Alexey Dosovitskiy', 'Philipp Fischer', 'Eddy Ilg', 'Daniel Cremers'] | 2015-12-07 | a-large-dataset-to-train-convolutional-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Mayer_A_Large_Dataset_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Mayer_A_Large_Dataset_CVPR_2016_paper.pdf | cvpr-2016-6 | ['scene-flow-estimation'] | ['computer-vision'] | [ 0.25646833 -0.27890474 0.03063365 -0.37212718 -0.15884508 -0.4949053
0.5940998 -0.6686021 -0.3995022 1.0092087 0.08679529 -0.21955419
0.3988814 -0.8026942 -0.91988677 -0.18070114 -0.22527999 0.08991061
0.38235542 -0.10608427 0.4046022 0.56988037 -1.8412501 0.32052934
0.8542095 1.0403056 0.1... | [8.747321128845215, -1.916761875152588] |
a8c8ef71-1354-4460-96ff-ad7527ef31df | morphte-injecting-morphology-in-tensorized | 2210.15379 | null | https://arxiv.org/abs/2210.15379v1 | https://arxiv.org/pdf/2210.15379v1.pdf | MorphTE: Injecting Morphology in Tensorized Embeddings | In the era of deep learning, word embeddings are essential when dealing with text tasks. However, storing and accessing these embeddings requires a large amount of space. This is not conducive to the deployment of these models on resource-limited devices. Combining the powerful compression capability of tensor products... | ['Benyou Wang', 'Xiuqing Lu', 'Sunzhu Li', 'Peng Zhang', 'Guobing Gan'] | 2022-10-27 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [-1.82532027e-01 -1.83549136e-01 -2.99574226e-01 -4.38624993e-02
-1.68646395e-01 -4.50911701e-01 3.31259102e-01 4.17388856e-01
-8.12550902e-01 1.07320160e-01 3.62179518e-01 -7.96225548e-01
2.02972203e-01 -1.07407331e+00 -5.00094771e-01 -5.52023172e-01
-2.80035585e-01 3.42349380e-01 -3.54638398e-02 -3.08000058... | [10.628108978271484, 8.697815895080566] |
92e04f11-cfdb-4ba2-9afb-4eb01bc15aec | multi-centroid-task-descriptor-for-dynamic | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cai_Multi-Centroid_Task_Descriptor_for_Dynamic_Class_Incremental_Inference_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cai_Multi-Centroid_Task_Descriptor_for_Dynamic_Class_Incremental_Inference_CVPR_2023_paper.pdf | Multi-Centroid Task Descriptor for Dynamic Class Incremental Inference | Incremental learning could be roughly divided into two categories, i.e., class- and task-incremental learning. The main difference is whether the task ID is given during evaluation. In this paper, we show this task information is indeed a strong prior knowledge, which will bring significant improvement over class-i... | ['Yuan Xie', 'Chengjie Wang', 'Guannan Jiang', 'Yanyun Qu', 'Xin Tan', 'Zhizhong Zhang', 'Tenghao Cai'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 8.33086371e-02 -1.01990774e-01 -4.84541148e-01 -5.63409269e-01
-9.34666991e-01 -7.57438779e-01 6.31240547e-01 -3.24044563e-02
-6.14687800e-01 4.55162138e-01 6.05434701e-02 -9.80290677e-03
-1.99786931e-01 -2.41692692e-01 -8.13295603e-01 -8.43531072e-01
2.77281404e-01 6.81642175e-01 2.82199383e-01 1.89882591... | [9.632356643676758, 2.9282469749450684] |
72974bb9-2fcd-4c33-91f5-196a75faf18e | completer-incomplete-multi-view-clustering | null | null | http://pengxi.me/wp-content/uploads/2021/03/2021CVPR-completer.pdf | http://pengxi.me/wp-content/uploads/2021/03/2021CVPR-completer.pdf | COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction | In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data. To this end, we propose a novel objective that incorpor... | ['Xi Peng', 'Jiancheng Lv', 'Boyun Li', 'Zitao Liu', 'Yuanbiao Gou', 'Yijie Lin'] | 2021-03-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lin_COMPLETER_Incomplete_Multi-View_Clustering_via_Contrastive_Prediction_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lin_COMPLETER_Incomplete_Multi-View_Clustering_via_Contrastive_Prediction_CVPR_2021_paper.pdf | cvpr-2021-1 | ['incomplete-multi-view-clustering', 'multi-view-learning'] | ['computer-vision', 'computer-vision'] | [ 1.03308529e-01 -4.54239286e-02 -5.30157149e-01 -4.52575266e-01
-1.10065639e+00 -5.52628875e-01 2.93388188e-01 -3.06247383e-01
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-4.16776448e-01 -3.37390274e-01 -5.36054611e-01 -9.58447576e-01
2.55504578e-01 6.16248012e-01 -3.98080856e-01 2.46587381... | [8.378715515136719, 4.541141033172607] |
ce2135e9-64e2-46f1-a9f0-07ab50d7434b | attacking-perceptual-similarity-metrics-1 | 2305.0884 | null | https://arxiv.org/abs/2305.08840v1 | https://arxiv.org/pdf/2305.08840v1.pdf | Attacking Perceptual Similarity Metrics | Perceptual similarity metrics have progressively become more correlated with human judgments on perceptual similarity; however, despite recent advances, the addition of an imperceptible distortion can still compromise these metrics. In our study, we systematically examine the robustness of these metrics to imperceptibl... | ['Feng Liu', 'Abhijay Ghildyal'] | 2023-05-15 | attacking-perceptual-similarity-metrics | https://openreview.net/forum?id=VUcI0pKic8l | https://openreview.net/pdf?id=VUcI0pKic8l | null | ['experimental-design'] | ['methodology'] | [ 7.00060368e-01 -2.67304033e-01 4.26035672e-01 -1.78721279e-01
-9.24602330e-01 -1.23465383e+00 7.40209818e-01 7.44182020e-02
-4.50300157e-01 4.78674054e-01 7.27177486e-02 -4.91664737e-01
-5.54427914e-02 -6.30869269e-01 -6.33573532e-01 -6.12212658e-01
-3.58900994e-01 -4.49068099e-01 2.49642834e-01 -3.25823963... | [5.597940921783447, 7.848174571990967] |
979e1d65-c43f-4f2c-a514-ce9a83a5fd67 | lazier-than-lazy-greedy | 1409.7938 | null | http://arxiv.org/abs/1409.7938v3 | http://arxiv.org/pdf/1409.7938v3.pdf | Lazier Than Lazy Greedy | Is it possible to maximize a monotone submodular function faster than the
widely used lazy greedy algorithm (also known as accelerated greedy), both in
theory and practice? In this paper, we develop the first linear-time algorithm
for maximizing a general monotone submodular function subject to a cardinality
constraint... | ['Amin Karbasi', 'Baharan Mirzasoleiman', 'Ashwinkumar Badanidiyuru', 'Andreas Krause', 'Jan Vondrak'] | 2014-09-28 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [-1.19052399e-02 4.14428562e-01 -3.82530361e-01 -3.52915555e-01
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-6.43224776e-01 -8.32042634e-01 -1.01559865e+00 -8.22760820e-01
-5.56499422e-01 9.66566980e-01 -1.37940437e-01 1.98290229... | [6.594685077667236, 4.924299716949463] |
4770029a-654d-41b7-9ce2-6c460d5ed68a | analyzing-berts-knowledge-of-hypernymy-via | null | null | https://aclanthology.org/2021.blackboxnlp-1.20 | https://aclanthology.org/2021.blackboxnlp-1.20.pdf | Analyzing BERT’s Knowledge of Hypernymy via Prompting | The high performance of large pretrained language models (LLMs) such as BERT on NLP tasks has prompted questions about BERT’s linguistic capabilities, and how they differ from humans’. In this paper, we approach this question by examining BERT’s knowledge of lexical semantic relations. We focus on hypernymy, the “is-a”... | ['David Mareček', 'Michael Hanna'] | null | null | null | null | emnlp-blackboxnlp-2021-11 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 9.41114686e-03 6.95130765e-01 -4.58568364e-01 -2.91922599e-01
5.74735142e-02 -7.10010767e-01 7.82019913e-01 5.76614559e-01
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-3.94502968e-01 -1.15233529e+00 -3.19277853e-01 -1.69169486e-01
-3.55059020e-02 1.06258869e+00 2.08456770e-01 -6.95772529... | [10.040850639343262, 8.770288467407227] |
dc2c9655-7cb4-4500-850b-ee5444dddb61 | learning-goal-conditioned-value-functions | null | null | https://openreview.net/forum?id=BkesGnCcFX | https://openreview.net/pdf?id=BkesGnCcFX | Learning Goal-Conditioned Value Functions with one-step Path rewards rather than Goal-Rewards | Multi-goal reinforcement learning (MGRL) addresses tasks where the desired goal state can change for every trial. State-of-the-art algorithms model these problems such that the reward formulation depends on the goals, to associate them with high reward. This dependence introduces additional goal reward resampling steps... | ['Jeffrey M. Siskind', 'Jason J. Corso', 'Shurjo Banerjee', 'Vikas Dhiman'] | null | null | null | null | iclr-2019-5 | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-7.51862302e-02 2.36643910e-01 -4.80100334e-01 -1.26310736e-01
-9.61305976e-01 -6.20721817e-01 6.37744963e-01 3.78435493e-01
-1.06440866e+00 1.38172400e+00 2.03825846e-01 -2.37233892e-01
-3.74373972e-01 -8.98107708e-01 -6.35393322e-01 -8.84648860e-01
-4.17546064e-01 6.53318822e-01 2.07054690e-01 -5.35219252... | [4.031910419464111, 1.7652043104171753] |
b6ad1d03-c13a-4263-b9c5-721b3107b9d9 | distantly-supervised-aspect-clustering-and | null | null | https://aclanthology.org/2022.naacl-industry.12 | https://aclanthology.org/2022.naacl-industry.12.pdf | Distantly Supervised Aspect Clustering And Naming For E-Commerce Reviews | Product aspect extraction from reviews is a critical task for e-commerce services to understand customer preferences and pain points. While aspect phrases extraction and sentiment analysis have received a lot of attention, clustering of aspect phrases and assigning human readable names to clusters in e-commerce reviews... | ['Anirban Majumdar', 'Deepak Gupta', 'Aniket Chakrabarti', 'Prateek Sircar'] | null | null | null | null | naacl-acl-2022-7 | ['aspect-extraction'] | ['natural-language-processing'] | [-1.78971179e-02 9.32568312e-02 -3.02654505e-01 -6.33722007e-01
-9.39817786e-01 -8.90503645e-01 3.80362660e-01 2.47747198e-01
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1.83846936e-01 -7.76494622e-01 -3.91437411e-01 -5.97319186e-01
4.11532074e-01 9.36053097e-01 -1.41916284e-02 -3.94695312... | [11.350112915039062, 6.6887006759643555] |
fe0cc738-fed8-43cc-8a4f-8ebfbbe817a7 | cognito-automated-feature-engineering-for | null | null | https://ieeexplore.ieee.org/abstract/document/7836821/ | https://ieeexplore.ieee.org/abstract/document/7836821/ | Cognito: Automated Feature Engineering for Supervised Learning | Feature engineering involves constructing novel features from given data with the goal of improving predictive learning performance. Feature engineering is predominantly a human-intensive and time consuming step that is central to the data science workflow. In this paper, we present a novel system called "Cognito", tha... | ['Horst Samulowitz', 'Deepak Turaga', 'Udayan Khurana', 'Srinivasan Parthasrathy'] | 2016-01-01 | null | null | null | icdmw-2016-2016-1 | ['automated-feature-engineering'] | ['methodology'] | [ 2.70260125e-01 -2.89323539e-01 -9.00160968e-02 -6.07423723e-01
-5.36461473e-01 -3.80650073e-01 4.84705448e-01 3.70361596e-01
-2.04899848e-01 6.25753224e-01 -3.12349677e-01 -2.58134842e-01
-5.43975770e-01 -8.85897756e-01 -3.17747653e-01 -4.82604861e-01
-2.41228968e-01 9.62227702e-01 8.75785276e-02 1.18768491... | [8.30702018737793, 4.603633880615234] |
37841b7c-2e3a-4c0b-919e-d5f49bc206a5 | targeted-extraction-of-temporal-facts-from | 2203.11054 | null | https://arxiv.org/abs/2203.11054v1 | https://arxiv.org/pdf/2203.11054v1.pdf | Targeted Extraction of Temporal Facts from Textual Resources for Improved Temporal Question Answering over Knowledge Bases | Knowledge Base Question Answering (KBQA) systems have the goal of answering complex natural language questions by reasoning over relevant facts retrieved from Knowledge Bases (KB). One of the major challenges faced by these systems is their inability to retrieve all relevant facts due to factors such as incomplete KB a... | ['L Venkata Subramaniam', 'Hima Karanam', 'Shajith Ikbal', 'Dinesh Khandelwal', 'Sumit Neelam', 'Udit Sharma', 'Nithish Kannen'] | 2022-03-21 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.35687926e-01 4.87137347e-01 1.04337400e-02 -2.30000794e-01
-1.15034306e+00 -8.51814747e-01 5.63199043e-01 6.17688894e-01
-3.84054393e-01 1.41385448e+00 1.27706632e-01 -5.11592686e-01
-6.40259385e-01 -1.26133955e+00 -6.48190439e-01 -6.74134959e-03
-1.31658152e-01 6.85580790e-01 1.21739209e+00 -8.28283608... | [10.421568870544434, 7.961282253265381] |
3ef69213-b2f8-4a69-ba74-a50e0ec8e002 | the-transformative-potential-of-machine | 2303.15832 | null | https://arxiv.org/abs/2303.15832v2 | https://arxiv.org/pdf/2303.15832v2.pdf | The transformative potential of machine learning for experiments in fluid mechanics | The field of machine learning has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the original big-data disciplines. This perspective will highlight several aspects of experimental fluid mechanics that stand to benefit from progress... | ['Beverley J. McKeon', 'Steven L. Brunton', 'Ricardo Vinuesa'] | 2023-03-28 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-1.89267442e-01 -4.84660417e-01 -1.58336014e-02 1.48018315e-01
-3.75925660e-01 -6.06390573e-02 6.19168460e-01 2.38489762e-01
-4.71875131e-01 9.98201489e-01 8.41920525e-02 -3.65424186e-01
-3.05977434e-01 -6.53664410e-01 -7.87551343e-01 -7.61536181e-01
-6.23602331e-01 5.10977209e-01 -1.29979461e-01 -1.31898150... | [6.373892307281494, 3.568662166595459] |
a322dded-2a29-4e6c-a2d1-44bc086298da | scapegoat-generation-for-privacy-protection | 2303.0293 | null | https://arxiv.org/abs/2303.02930v1 | https://arxiv.org/pdf/2303.02930v1.pdf | Scapegoat Generation for Privacy Protection from Deepfake | To protect privacy and prevent malicious use of deepfake, current studies propose methods that interfere with the generation process, such as detection and destruction approaches. However, these methods suffer from sub-optimal generalization performance to unseen models and add undesirable noise to the original image. ... | ['Shigeo Morishima', 'Hirokatsu Kataoka', 'Hideki Tsunashima', 'Mariko Isogawa', 'Yoshihiro Fukuhara', 'Gido Kato'] | 2023-03-06 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 3.72752100e-01 5.86858690e-01 2.61929572e-01 -1.23722911e-01
-1.80533469e-01 -9.29936528e-01 6.01941407e-01 -5.85846007e-01
-1.48875102e-01 5.95387697e-01 5.41306697e-02 1.65972468e-02
1.38791040e-01 -9.56118643e-01 -8.08171034e-01 -6.87000215e-01
5.56949258e-01 -1.59603700e-01 -1.54018953e-01 -8.54399875... | [12.71765422821045, 0.816485583782196] |
5cea2f44-a590-4dbb-b041-b25fe0c432c6 | decision-making-under-miscalibration | 2203.09852 | null | https://arxiv.org/abs/2203.09852v1 | https://arxiv.org/pdf/2203.09852v1.pdf | Decision-Making under Miscalibration | ML-based predictions are used to inform consequential decisions about individuals. How should we use predictions (e.g., risk of heart attack) to inform downstream binary classification decisions (e.g., undergoing a medical procedure)? When the risk estimates are perfectly calibrated, the answer is well understood: a cl... | ['Gal Yona', 'Guy N. Rothblum'] | 2022-03-18 | null | null | null | null | ['medical-procedure'] | ['medical'] | [ 5.55221140e-01 8.51074576e-01 -6.44334316e-01 -6.29677117e-01
-8.67788196e-01 -4.21136111e-01 -2.14259654e-01 5.54578006e-01
-5.73344886e-01 1.16204977e+00 9.83187854e-02 -8.23838532e-01
-5.76431513e-01 -8.54352117e-01 -8.32594275e-01 -6.98871017e-01
-2.80266464e-01 4.52040404e-01 -5.48071623e-01 1.20968044... | [8.174915313720703, 5.270421981811523] |
896f2293-d803-4631-9e0f-b409857707dd | oscar-data-driven-operational-space-control | 2110.00704 | null | https://arxiv.org/abs/2110.00704v1 | https://arxiv.org/pdf/2110.00704v1.pdf | OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation | Learning performant robot manipulation policies can be challenging due to high-dimensional continuous actions and complex physics-based dynamics. This can be alleviated through intelligent choice of action space. Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. Nonet... | ['Yuke Zhu', 'Anima Anandkumar', 'Viktor Makoviychuk', 'Josiah Wong'] | 2021-10-02 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.09156653e-01 -1.34211257e-01 -5.04549146e-01 1.37712270e-01
-7.72154570e-01 -8.08362007e-01 5.96804261e-01 -1.82405099e-01
-3.29781443e-01 7.55619228e-01 1.04365088e-01 -1.62176624e-01
-5.20169377e-01 -1.23580799e-01 -9.15339172e-01 -5.94525456e-01
-2.87763327e-01 6.92911088e-01 4.88172054e-01 -6.81666374... | [4.48192834854126, 1.3637778759002686] |
d583783c-5b29-45cf-9bc2-213195292b08 | from-rewriting-to-remembering-common-ground-1 | 2204.0393 | null | https://arxiv.org/abs/2204.03930v1 | https://arxiv.org/pdf/2204.03930v1.pdf | From Rewriting to Remembering: Common Ground for Conversational QA Models | In conversational QA, models have to leverage information in previous turns to answer upcoming questions. Current approaches, such as Question Rewriting, struggle to extract relevant information as the conversation unwinds. We introduce the Common Ground (CG), an approach to accumulate conversational information as it ... | ['Adrià De Gispert', 'Bill Byrne', 'Gianni Barlacchi', 'Xiaoyu Shen', 'Marco del Tredici'] | 2022-04-08 | null | https://aclanthology.org/2022.nlp4convai-1.7 | https://aclanthology.org/2022.nlp4convai-1.7.pdf | nlp4convai-acl-2022-5 | ['question-rewriting'] | ['natural-language-processing'] | [ 3.87847155e-01 9.42674756e-01 1.95892411e-04 -4.53297883e-01
-1.52479613e+00 -1.04283583e+00 9.45136428e-01 3.49312156e-01
-6.21986389e-02 1.19464827e+00 9.50913548e-01 -6.62162185e-01
-8.56079385e-02 -7.37433732e-01 -3.19454908e-01 1.10044694e-02
1.02569714e-01 1.03004074e+00 4.54340249e-01 -1.20003784... | [12.125896453857422, 8.006229400634766] |
fd0cff62-6b93-4ef1-809a-efc7ade7b790 | a-23-mw-data-centre-is-all-you-need | 2203.17265 | null | https://arxiv.org/abs/2203.17265v1 | https://arxiv.org/pdf/2203.17265v1.pdf | A 23 MW data centre is all you need | The field of machine learning has achieved striking progress in recent years, witnessing breakthrough results on language modelling, protein folding and nitpickingly fine-grained dog breed classification. Some even succeeded at playing computer games and board games, a feat both of engineering and of setting their empl... | ['João F. Henriques', 'Dylan Campbell', 'Samuel Albanie'] | 2022-03-31 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 2.63384014e-01 1.64476603e-01 -1.88474849e-01 -4.47252244e-01
-5.80761969e-01 -4.90687758e-01 5.28771162e-01 2.57396430e-01
-7.78431892e-01 7.58791208e-01 -8.01123828e-02 -9.75969195e-01
-1.81577802e-01 -5.86575747e-01 -3.97833556e-01 -2.65694112e-01
-8.89043063e-02 3.77240360e-01 -1.46057829e-01 -6.37217283... | [9.01220989227295, 6.490387916564941] |
8c6ea4b1-e0a9-4c9e-9128-3f7d45bdd31e | releasing-inequlity-phenomena-in-l-infty | 2305.09305 | null | https://arxiv.org/abs/2305.09305v2 | https://arxiv.org/pdf/2305.09305v2.pdf | Releasing Inequality Phenomena in $L_{\infty}$-Adversarial Training via Input Gradient Distillation | Since adversarial examples appeared and showed the catastrophic degradation they brought to DNN, many adversarial defense methods have been devised, among which adversarial training is considered the most effective. However, a recent work showed the inequality phenomena in $l_{\infty}$-adversarial training and revealed... | ['Xiaohua Xie', 'Junhao Dong', 'Junxi Chen'] | 2023-05-16 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.00047606e-01 5.38490951e-01 2.30806202e-01 -3.35393697e-02
-5.02423108e-01 -6.60672605e-01 4.37583297e-01 -5.78066945e-01
-4.77999777e-01 1.04486167e+00 -1.85001001e-01 -5.07111669e-01
6.58225566e-02 -8.68893266e-01 -1.08455312e+00 -9.00343657e-01
-1.17692076e-01 -1.75689697e-01 3.52249473e-01 -4.54193741... | [5.564581394195557, 7.9494500160217285] |
b2bbac02-ef25-4729-9468-b469eb725c25 | the-metaverse-survey-trends-novel-pipeline | 2304.0924 | null | https://arxiv.org/abs/2304.09240v1 | https://arxiv.org/pdf/2304.09240v1.pdf | The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions | The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields ... | ['Mohsen Guizani', 'Ernesto Damiani', 'Zbigniew Dziong', 'Chamseddine Talhi', 'Jamal Bentahar', 'Rabeb Mizouni', 'Omar Abdel Wahab', 'Hadi Otrok', 'Azzam Mourad', 'Mohamad Ajaj', 'Osama Wehbi', 'Mario Chahoud', 'Sarhad Arisdakessian', 'Mohamad Wazzeh', 'Mouhamad Arafeh', 'Ahmad Hammoud', 'Hani Sami'] | 2023-04-18 | null | null | null | null | ['business-ethics', 'culture'] | ['miscellaneous', 'speech'] | [-3.57610822e-01 5.37124276e-02 -5.66605568e-01 3.35358858e-01
-1.57887731e-02 -1.13674474e+00 6.67489052e-01 -2.36908227e-01
-3.28987278e-02 5.98327875e-01 2.50261635e-01 -8.65826309e-01
7.01504424e-02 -8.75759661e-01 -4.49299872e-01 -2.75300413e-01
7.84042925e-02 -2.24625781e-01 -1.99262463e-02 -6.86094165... | [9.06573486328125, 6.541296482086182] |
fdf58c3c-682c-41c3-97ab-9f4efed2647a | crossvqa-scalably-generating-benchmarks-for | null | null | https://aclanthology.org/2021.emnlp-main.164 | https://aclanthology.org/2021.emnlp-main.164.pdf | CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization | One challenge in evaluating visual question answering (VQA) models in the cross-dataset adaptation setting is that the distribution shifts are multi-modal, making it difficult to identify if it is the shifts in visual or language features that play a key role. In this paper, we propose a semi-automatic framework for ge... | ['Radu Soricut', 'Song-Chun Zhu', 'Piyush Sharma', 'Boqing Gong', 'Soravit Changpinyo', 'Arjun Akula'] | null | null | null | null | emnlp-2021-11 | ['question-answer-generation'] | ['natural-language-processing'] | [ 1.30848080e-01 6.62124436e-03 2.82367676e-01 -4.82817262e-01
-1.28174078e+00 -1.23132658e+00 6.43133163e-01 -1.85131654e-02
-1.13943927e-01 4.44493711e-01 2.43030056e-01 -4.68902946e-01
7.88011216e-03 -5.23439229e-01 -8.03462505e-01 -4.23238456e-01
3.89691204e-01 7.33657479e-01 1.76819474e-01 -5.03645241... | [10.85538101196289, 1.8963358402252197] |
a367195c-67dc-4be1-8065-21a5882193ab | artificial-intelligence-moral-agent-as-adam | 2305.11519 | null | https://arxiv.org/abs/2305.11519v1 | https://arxiv.org/pdf/2305.11519v1.pdf | Artificial intelligence moral agent as Adam Smith's impartial spectator | Adam Smith developed a version of moral philosophy where better decisions are made by interrogating an impartial spectator within us. We discuss the possibility of using an external non-human-based substitute tool that would augment our internal mental processes and play the role of the impartial spectator. Such tool w... | ['Nikodem Tomczak'] | 2023-05-19 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-1.17224693e-01 7.54765332e-01 1.38278361e-02 -2.99830109e-01
2.47151315e-01 -7.69246042e-01 7.92209446e-01 6.47395104e-02
-6.98721707e-01 9.47526038e-01 7.58163691e-01 -3.25329393e-01
-1.29228488e-01 -7.41751611e-01 -1.39456421e-01 -6.36394083e-01
5.47393858e-01 3.93673927e-01 -3.11184227e-01 -6.15965128... | [9.035667419433594, 6.2300543785095215] |
479a3a58-0c01-4cd3-bf78-6e08d4474eea | abstractive-text-summarization-using-the-brio | 2305.13696 | null | https://arxiv.org/abs/2305.13696v1 | https://arxiv.org/pdf/2305.13696v1.pdf | Abstractive Text Summarization Using the BRIO Training Paradigm | Summary sentences produced by abstractive summarization models may be coherent and comprehensive, but they lack control and rely heavily on reference summaries. The BRIO training paradigm assumes a non-deterministic distribution to reduce the model's dependence on reference summaries, and improve model performance duri... | ['Jugal Kalita', 'Khang Thua Pham', 'Thieu Gia Doan', 'Khang Nhut Lam'] | 2023-05-23 | null | null | null | null | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.75608054e-01 2.67835021e-01 -4.23985600e-01 -4.45398539e-01
-1.04759824e+00 -4.73585755e-01 6.82860494e-01 4.59124863e-01
-4.05855745e-01 9.68738854e-01 1.01765001e+00 -2.71479458e-01
2.54611254e-01 -7.42649555e-01 -7.00573802e-01 -1.37055144e-01
1.25360668e-01 4.89146382e-01 6.31824955e-02 -2.14341298... | [12.54194450378418, 9.506134033203125] |
b456ca5c-353b-45a4-af70-bec2e7f2b549 | semantic-discriminative-mixup-for | 2206.06629 | null | https://arxiv.org/abs/2206.06629v1 | https://arxiv.org/pdf/2206.06629v1.pdf | Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition | It is expensive and time-consuming to collect sufficient labeled data to build human activity recognition (HAR) models. Training on existing data often makes the model biased towards the distribution of the training data, thus the model might perform terribly on test data with different distributions. Although existing... | ['Xin Qin', 'Chunyu Hu', 'Sinno Jialin Pan', 'Yiqiang Chen', 'Jindong Wang', 'Wang Lu'] | 2022-06-14 | null | null | null | null | ['cross-domain-activity-recognition'] | ['computer-vision'] | [ 1.01550473e-02 -2.25725651e-01 -4.01797116e-01 -5.79488218e-01
-9.92514312e-01 -4.19089019e-01 3.41952890e-01 -1.04991250e-01
-2.79217452e-01 1.08329308e+00 1.60872489e-01 1.39132112e-01
6.14938736e-02 -7.58273005e-01 -6.55260384e-01 -5.30271828e-01
3.91491085e-01 6.51165903e-01 3.49603206e-01 1.17365517... | [8.03231143951416, 1.06674063205719] |
d0afcc62-0930-4b87-9903-71d9feddf1ec | scan2cap-context-aware-dense-captioning-in | 2012.02206 | null | https://arxiv.org/abs/2012.02206v1 | https://arxiv.org/pdf/2012.02206v1.pdf | Scan2Cap: Context-aware Dense Captioning in RGB-D Scans | We introduce the task of dense captioning in 3D scans from commodity RGB-D sensors. As input, we assume a point cloud of a 3D scene; the expected output is the bounding boxes along with the descriptions for the underlying objects. To address the 3D object detection and description problems, we propose Scan2Cap, an end-... | ['Angel X. Chang', 'Matthias Nießner', 'Ali Gholami', 'Dave Zhenyu Chen'] | 2020-12-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Scan2Cap_Context-Aware_Dense_Captioning_in_RGB-D_Scans_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Scan2Cap_Context-Aware_Dense_Captioning_in_RGB-D_Scans_CVPR_2021_paper.pdf | cvpr-2021-1 | ['dense-captioning'] | ['computer-vision'] | [ 1.43612355e-01 5.23153424e-01 -1.25121893e-02 -7.08972812e-01
-9.42995131e-01 -6.93960845e-01 6.39480114e-01 2.29784340e-01
3.65330428e-02 1.22941233e-01 4.73280877e-01 -1.89986937e-02
1.73608080e-01 -5.31432092e-01 -1.27804017e+00 -2.31950462e-01
-1.97494566e-01 9.01695192e-01 2.98784554e-01 1.68337613... | [8.239520072937012, -3.2197132110595703] |
3c26ad95-f07c-43f5-8140-e67755db76ae | large-selective-kernel-network-for-remote | 2303.0903 | null | https://arxiv.org/abs/2303.09030v2 | https://arxiv.org/pdf/2303.09030v2.pdf | Large Selective Kernel Network for Remote Sensing Object Detection | Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge presented in remote sensing scenarios. Such prior knowledge can be useful because tiny remote sensing objects may be mistakenly detected without... | ['Xiang Li', 'Jian Yang', 'Ming-Ming Cheng', 'Zhaohui Zheng', 'Qibin Hou', 'YuXuan Li'] | 2023-03-16 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.41391465e-01 -3.36963058e-01 -8.77873227e-02 -4.18259889e-01
-5.43625653e-01 -7.77588129e-01 6.15730286e-01 1.11218914e-01
-5.82847238e-01 5.08421004e-01 1.90134980e-02 -4.74518031e-01
-4.41946447e-01 -9.36493695e-01 -6.52919054e-01 -6.60245180e-01
-4.38389897e-01 1.78242996e-01 4.62028682e-01 -1.96339004... | [9.062548637390137, -0.8962389230728149] |
e13fec07-3855-4309-a0b4-62414f2c3525 | deep-rectangling-for-image-stitching-a | 2203.03831 | null | https://arxiv.org/abs/2203.03831v4 | https://arxiv.org/pdf/2203.03831v4.pdf | Deep Rectangling for Image Stitching: A Learning Baseline | Stitched images provide a wide field-of-view (FoV) but suffer from unpleasant irregular boundaries. To deal with this problem, existing image rectangling methods devote to searching an initial mesh and optimizing a target mesh to form the mesh deformation in two stages. Then rectangular images can be generated by warpi... | ['Yao Zhao', 'Shuaicheng Liu', 'Kang Liao', 'Chunyu Lin', 'Lang Nie'] | 2022-03-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Nie_Deep_Rectangling_for_Image_Stitching_A_Learning_Baseline_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Nie_Deep_Rectangling_for_Image_Stitching_A_Learning_Baseline_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-stitching'] | ['computer-vision'] | [ 6.72406137e-01 7.86636919e-02 -6.11909553e-02 -1.11090019e-01
-3.95983011e-01 -3.64637643e-01 4.38355625e-01 -2.71648228e-01
4.09473106e-02 5.46713769e-01 4.24592942e-02 1.46145090e-01
-6.27319049e-03 -8.45368862e-01 -9.02159572e-01 -7.50854731e-01
3.84157658e-01 2.10463867e-01 1.60078958e-01 -2.82865256... | [9.377681732177734, -2.3122763633728027] |
0f6b1771-d002-4621-81eb-7f820f31934d | the-impact-of-extraneous-variables-on-the | 1904.01125 | null | http://arxiv.org/abs/1904.01125v1 | http://arxiv.org/pdf/1904.01125v1.pdf | The Impact of Extraneous Variables on the Performance of Recurrent Neural Network Models in Clinical Tasks | Electronic Medical Records (EMR) are a rich source of patient information,
including measurements reflecting physiologic signs and administered therapies.
Identifying which variables are useful in predicting clinical outcomes can be
challenging. Advanced algorithms such as deep neural networks were designed to
process ... | ['Eugene Laksana', 'David Ledbetter', 'Cameron Carlin', 'Randall Wetzel', 'Melissa Aczon', 'Long Ho'] | 2019-04-01 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 2.30323046e-01 -1.27364293e-01 -2.26198304e-02 -2.88318247e-01
-3.67896527e-01 -2.21687004e-01 2.86541253e-01 4.86452758e-01
-6.08740449e-01 8.67250144e-01 4.99487907e-01 -6.50152266e-01
-5.77705503e-01 -7.63472617e-01 -3.87140870e-01 -5.80713630e-01
-3.51543844e-01 7.70476043e-01 -5.70693731e-01 3.91500331... | [7.978903770446777, 6.181511878967285] |
89e4f490-d671-4cac-ad8e-90f170c03279 | distance-based-hyperspherical-classification | 2107.02067 | null | https://arxiv.org/abs/2107.02067v3 | https://arxiv.org/pdf/2107.02067v3.pdf | Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation | Vision systems trained in closed-world scenarios fail when presented with new environmental conditions, new data distributions, and novel classes at deployment time. How to move towards open-world learning is a long-standing research question. The existing solutions mainly focus on specific aspects of the problem (sing... | ['Tatiana Tommasi', 'Barbara Caputo', 'Francesco Cappio Borlino', 'Silvia Bucci'] | 2021-07-05 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 1.93705276e-01 -1.63860664e-01 -1.04070678e-01 -3.01847577e-01
-6.71716809e-01 -8.37111175e-01 7.61545777e-01 -1.04119234e-01
-5.93485653e-01 9.38034832e-01 -4.64589000e-01 2.21733555e-01
-2.37323925e-01 -6.68604493e-01 -6.89055681e-01 -1.00680745e+00
-3.39736007e-02 9.45185244e-01 4.22707140e-01 -1.30872428... | [9.824564933776855, 2.1976022720336914] |
c4e4ce07-29dd-4fec-aba2-4d5b7bd22dfa | zero-shot-object-counting | 2303.02001 | null | https://arxiv.org/abs/2303.02001v2 | https://arxiv.org/pdf/2303.02001v2.pdf | Zero-shot Object Counting | Class-agnostic object counting aims to count object instances of an arbitrary class at test time. It is challenging but also enables many potential applications. Current methods require human-annotated exemplars as inputs which are often unavailable for novel categories, especially for autonomous systems. Thus, we prop... | ['Dimitris Samaras', 'Viresh Ranjan', 'Vu Nguyen', 'Hieu Le', 'Jingyi Xu'] | 2023-03-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Zero-Shot_Object_Counting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Zero-Shot_Object_Counting_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-counting'] | ['computer-vision'] | [ 1.50416121e-02 -3.90795857e-01 -1.19722292e-01 -3.49558294e-01
-7.08647370e-01 -6.21009052e-01 4.89581287e-01 5.43445289e-01
-5.89944959e-01 6.40598893e-01 -4.11345452e-01 1.18415453e-01
8.86451174e-03 -1.15063918e+00 -6.90622568e-01 -4.37222779e-01
1.52814329e-01 9.67011571e-01 7.15112329e-01 3.04524541... | [8.99374008178711, 0.5420737266540527] |
0287449c-6356-47c0-9d53-deb771fe7449 | safe-reinforcement-learning-using-data-driven | 2211.11027 | null | https://arxiv.org/abs/2211.11027v1 | https://arxiv.org/pdf/2211.11027v1.pdf | Safe Reinforcement Learning using Data-Driven Predictive Control | Reinforcement learning (RL) algorithms can achieve state-of-the-art performance in decision-making and continuous control tasks. However, applying RL algorithms on safety-critical systems still needs to be well justified due to the exploration nature of many RL algorithms, especially when the model of the robot and the... | ['Karl H. Johansson', 'Hazem M. Abbas', 'M. Watheq El-Kharashi', 'Amr Alanwar', 'Mahmoud Selim'] | 2022-11-20 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-1.09294876e-01 4.64704692e-01 -2.89469719e-01 9.09464732e-02
-3.75290930e-01 -5.91843963e-01 6.27151370e-01 2.86151975e-01
-7.60555446e-01 1.06367850e+00 -3.32082868e-01 -7.64946699e-01
-6.64154530e-01 -9.27489877e-01 -1.04946172e+00 -8.73249531e-01
-4.67566967e-01 5.31210899e-01 5.66476107e-01 -6.46740735... | [4.737216949462891, 1.8641114234924316] |
1f270e54-ecd7-4d20-b568-7916f30556ef | conditional-cross-design-synthesis-estimators | 2109.13288 | null | https://arxiv.org/abs/2109.13288v1 | https://arxiv.org/pdf/2109.13288v1.pdf | Conditional Cross-Design Synthesis Estimators for Generalizability in Medicaid | While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population when the t... | ['Sherri Rose', 'Jacob Wallace', 'Tim Layton', 'Irina Degtiar'] | 2021-09-27 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [ 3.19697708e-01 3.38031173e-01 -1.51036906e+00 -5.78278482e-01
-8.41523409e-01 -2.13651985e-01 3.19452852e-01 3.96073610e-01
-4.96844262e-01 1.08407915e+00 1.07117593e+00 -9.41466451e-01
-4.29549992e-01 -9.21768010e-01 -7.67806530e-01 -6.71228915e-02
-1.39142200e-01 3.01741302e-01 -5.29184580e-01 4.68407094... | [7.974704742431641, 5.394338130950928] |
e20d4d80-040c-4872-94b8-14cedb481b62 | on-evaluating-adversarial-robustness-of-large | 2305.16934 | null | https://arxiv.org/abs/2305.16934v1 | https://arxiv.org/pdf/2305.16934v1.pdf | On Evaluating Adversarial Robustness of Large Vision-Language Models | Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented performance in response generation, especially with visual inputs, enabling more creative and adaptable interaction than large language models such as ChatGPT. Nonetheless, multimodal generation exacerbates safety concerns, since adversaries ... | ['Min Lin', 'Ngai-Man Cheung', 'Chongxuan Li', 'Xiao Yang', 'Chao Du', 'Tianyu Pang', 'Yunqing Zhao'] | 2023-05-26 | null | null | null | null | ['response-generation', 'multimodal-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.38265812e-02 1.87199846e-01 1.29021183e-01 1.79578252e-02
-1.04101396e+00 -1.31887197e+00 8.62875700e-01 -5.82644224e-01
-4.48415637e-01 5.11546373e-01 1.00780167e-01 -6.99078858e-01
3.97525549e-01 -7.35543489e-01 -8.62049222e-01 -2.85548031e-01
1.11712843e-01 3.21412385e-01 -2.01697379e-01 -4.15138870... | [5.897946834564209, 7.947128772735596] |
350febaf-ce3a-4f05-9f96-bdb4e26b583d | isbnet-a-3d-point-cloud-instance-segmentation | 2303.00246 | null | https://arxiv.org/abs/2303.00246v2 | https://arxiv.org/pdf/2303.00246v2.pdf | ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution | Existing 3D instance segmentation methods are predominated by the bottom-up design -- manually fine-tuned algorithm to group points into clusters followed by a refinement network. However, by relying on the quality of the clusters, these methods generate susceptible results when (1) nearby objects with the same semanti... | ['Khoi Nguyen', 'Binh-Son Hua', 'Tuan Duc Ngo'] | 2023-03-01 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ngo_ISBNet_A_3D_Point_Cloud_Instance_Segmentation_Network_With_Instance-Aware_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ngo_ISBNet_A_3D_Point_Cloud_Instance_Segmentation_Network_With_Instance-Aware_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-8.52403715e-02 5.89954592e-02 -3.00795764e-01 -4.97528881e-01
-8.93698871e-01 -7.18639374e-01 6.01850033e-01 -1.47939958e-02
-3.96598816e-01 1.75650120e-01 -1.49096817e-01 -1.73664719e-01
9.80804395e-03 -8.94587159e-01 -1.05788314e+00 -4.83502775e-01
-9.11654308e-02 6.88372254e-01 1.02157438e+00 2.86047887... | [8.00167179107666, -3.1968297958374023] |
75cdcb07-b5a9-484d-91ec-e4e8b7d56432 | autoformalization-with-large-language-models | 2205.12615 | null | https://arxiv.org/abs/2205.12615v1 | https://arxiv.org/pdf/2205.12615v1.pdf | Autoformalization with Large Language Models | Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence. While the long-term goal of autoformalization seemed... | ['Christian Szegedy', 'Mateja Jamnik', 'Charles Staats', 'Markus N. Rabe', 'Wenda Li', 'Albert Q. Jiang', 'Yuhuai Wu'] | 2022-05-25 | null | null | null | null | ['program-synthesis', 'automated-theorem-proving', 'automated-theorem-proving'] | ['computer-code', 'miscellaneous', 'reasoning'] | [ 1.28274113e-01 6.46641910e-01 -1.66014984e-01 -3.52275640e-01
-8.75832558e-01 -7.11546600e-01 8.09665799e-01 1.33507550e-01
3.55265774e-02 9.34532225e-01 -2.83900172e-01 -1.25342059e+00
-1.05317466e-01 -9.82419312e-01 -1.35914004e+00 1.68311015e-01
-4.86192346e-01 4.07794178e-01 2.92205065e-02 -2.95252144... | [8.913480758666992, 7.07528829574585] |
7517b96a-4498-45c9-8e88-069bfe182a7e | learning-selective-communication-for-multi | 2109.05413 | null | https://arxiv.org/abs/2109.05413v2 | https://arxiv.org/pdf/2109.05413v2.pdf | Learning Selective Communication for Multi-Agent Path Finding | Learning communication via deep reinforcement learning (RL) or imitation learning (IL) has recently been shown to be an effective way to solve Multi-Agent Path Finding (MAPF). However, existing communication based MAPF solvers focus on broadcast communication, where an agent broadcasts its message to all other or prede... | ['Jia Pan', 'Yudong Luo', 'Ziyuan Ma'] | 2021-09-12 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-6.48525655e-02 3.43140960e-01 -3.87067348e-01 -6.98426589e-02
-3.65962356e-01 -4.22424167e-01 7.34849453e-01 5.35935581e-01
-6.31474435e-01 1.32018793e+00 1.05390340e-01 -2.26361319e-01
-4.58624661e-01 -1.14865077e+00 -4.28863585e-01 -1.03607154e+00
-5.40608108e-01 9.13526356e-01 3.76173824e-01 -3.75494182... | [3.786374568939209, 1.9764885902404785] |
66bd98dd-6058-4ee9-8d21-926568f22c0f | graph-based-multi-view-fusion-and-local | 2207.04081 | null | https://arxiv.org/abs/2207.04081v1 | https://arxiv.org/pdf/2207.04081v1.pdf | Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification | Speaker identification (SID) in the household scenario (e.g., for smart speakers) is an important but challenging problem due to limited number of labeled (enrollment) utterances, confusable voices, and demographic imbalances. Conventional speaker recognition systems generalize from a large random sample of speakers, c... | ['Andreas Stolcke', 'Venkatesh Ravichandran', 'Yixiong Meng', 'Long Chen'] | 2022-07-08 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.57086715e-01 9.41658467e-02 -1.44498155e-01 -1.03409612e+00
-1.09805655e+00 -5.57957530e-01 1.83326378e-01 1.32880256e-01
2.95154989e-01 3.77039373e-01 6.60650551e-01 -1.31924655e-02
1.53372124e-01 -4.81854796e-01 -2.72454500e-01 -6.49081826e-01
2.43298598e-02 5.82659006e-01 -2.50371695e-01 -1.52373925... | [14.300603866577148, 6.089914321899414] |
ab63bc6f-6e9a-4b88-9191-74f608959fea | task-oriented-clustering-for-dialogues | null | null | https://aclanthology.org/2021.findings-emnlp.368 | https://aclanthology.org/2021.findings-emnlp.368.pdf | Task-Oriented Clustering for Dialogues | A reliable clustering algorithm for task-oriented dialogues can help developer analysis and define dialogue tasks efficiently. It is challenging to directly apply prior normal text clustering algorithms for task-oriented dialogues, due to the inherent differences between them, such as coreference, omission and diversit... | ['Xiaojie Wang', 'Caixia Yuan', 'Wei Wu', 'Huixing Jiang', 'Shuyu Lei', 'Hengtong Lu', 'Chenxu Lv'] | null | null | null | null | findings-emnlp-2021-11 | ['text-clustering'] | ['natural-language-processing'] | [-8.06384534e-02 4.01805282e-01 3.03288624e-02 -7.14508593e-01
-8.63800704e-01 -7.06235290e-01 6.84043884e-01 -1.24435678e-01
3.92040201e-02 3.35759491e-01 7.08050370e-01 -2.12145030e-01
-2.80657522e-02 2.37245690e-02 2.62966931e-01 -4.18308854e-01
2.91305155e-01 1.00272262e+00 -1.42385453e-01 -5.93919516... | [12.663139343261719, 7.782310962677002] |
2a03e575-ee87-4c83-a8a9-294e1ad75bf7 | distilling-translations-with-visual-awareness | 1906.07701 | null | https://arxiv.org/abs/1906.07701v1 | https://arxiv.org/pdf/1906.07701v1.pdf | Distilling Translations with Visual Awareness | Previous work on multimodal machine translation has shown that visual information is only needed in very specific cases, for example in the presence of ambiguous words where the textual context is not sufficient. As a consequence, models tend to learn to ignore this information. We propose a translate-and-refine approa... | ['Lucia Specia', 'Julia Ive', 'Pranava Madhyastha'] | 2019-06-18 | distilling-translations-with-visual-awareness-1 | https://aclanthology.org/P19-1653 | https://aclanthology.org/P19-1653.pdf | acl-2019-7 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.20722449e-01 1.75569326e-01 -5.47617499e-04 -3.11909735e-01
-8.83591592e-01 -8.84397447e-01 9.77536201e-01 3.00045878e-01
-4.62379158e-01 9.80900824e-01 1.80032447e-01 -7.08862245e-01
5.17529011e-01 -4.65705812e-01 -7.51863241e-01 -5.17790496e-01
5.70895910e-01 7.25543022e-01 1.30835995e-01 -3.36855233... | [11.445738792419434, 1.4403221607208252] |
86b5f27f-272d-4fa5-8b12-9ad8945e6acf | acr-loss-adaptive-coordinate-based-regression | 2203.15835 | null | https://arxiv.org/abs/2203.15835v2 | https://arxiv.org/pdf/2203.15835v2.pdf | ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment | Although deep neural networks have achieved reasonable accuracy in solving face alignment, it is still a challenging task, specifically when we deal with facial images, under occlusion, or extreme head poses. Heatmap-based Regression (HBR) and Coordinate-based Regression (CBR) are among the two mainly used methods for ... | ['Mohammad H. Mahoor', 'Ali Pourramezan Fard'] | 2022-03-29 | null | null | null | null | ['face-alignment', 'facial-landmark-detection'] | ['computer-vision', 'computer-vision'] | [-2.38975823e-01 8.15789104e-02 -1.36258885e-01 -7.17952609e-01
-6.38560593e-01 8.74299109e-02 3.00263584e-01 -5.06369919e-02
-3.70547056e-01 2.17712864e-01 7.60429054e-02 3.62577379e-01
-2.95884252e-01 -5.57212889e-01 -5.61240852e-01 -8.05283368e-01
2.11269632e-01 5.46212733e-01 -1.35864556e-01 -3.65156472... | [13.466218948364258, 0.4248078167438507] |
827787b5-495c-4f53-838e-365dfaae1479 | learning-cross-lingual-word-embeddings-via | null | null | https://aclanthology.org/P15-2093 | https://aclanthology.org/P15-2093.pdf | Learning Cross-lingual Word Embeddings via Matrix Co-factorization | null | ['Yang Liu', 'Tianze Shi', 'Maosong Sun', 'Zhiyuan Liu'] | 2015-07-01 | learning-cross-lingual-word-embeddings-via-1 | https://aclanthology.org/P15-2093 | https://aclanthology.org/P15-2093.pdf | ijcnlp-2015-7 | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.241291046142578, 3.901024103164673] |
4ab66947-3525-42df-849f-83a53ac18c9b | node-classification-on-graphs-with-few-shot | 2007.02914 | null | https://arxiv.org/abs/2007.02914v2 | https://arxiv.org/pdf/2007.02914v2.pdf | Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding | We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier. The study of this problem is instructive and corresponds to many... | ['Xiaohong Guan', 'Lin Lan', 'Xuefeng Du', 'Pinghui Wang', 'Kaikai Song', 'Jing Tao'] | 2020-07-06 | null | http://proceedings.neurips.cc/paper/2020/hash/c055dcc749c2632fd4dd806301f05ba6-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/c055dcc749c2632fd4dd806301f05ba6-Paper.pdf | neurips-2020-12 | ['graph-structure-learning'] | ['graphs'] | [ 3.10823262e-01 5.57390511e-01 -5.43782115e-01 -1.92807391e-01
-1.35035500e-01 -9.14787054e-02 6.18021190e-01 3.80613416e-01
-6.35816306e-02 4.86981928e-01 -1.42364502e-01 -1.11998603e-01
-3.04810166e-01 -1.28531063e+00 -3.32405984e-01 -7.80937850e-01
-2.51358211e-01 2.23580271e-01 3.79902720e-01 -4.63668257... | [7.378446102142334, 6.178509712219238] |
2cbb7a0d-0746-43b5-9b92-fd029a39b093 | a-robust-multimodal-remote-sensing-image | 2202.13347 | null | https://arxiv.org/abs/2202.13347v1 | https://arxiv.org/pdf/2202.13347v1.pdf | A Robust Multimodal Remote Sensing Image Registration Method and System Using Steerable Filters with First- and Second-order Gradients | Co-registration of multimodal remote sensing images is still an ongoing challenge because of nonlinear radiometric differences (NRD) and significant geometric distortions (e.g., scale and rotation changes) between these images. In this paper, a robust matching method based on the Steerable filters is proposed consistin... | ['Guo Zhang', 'Qizhi Xu', 'Chao Yang', 'Tengfeng Tang', 'Bai Zhu', 'Yuanxin Ye'] | 2022-02-27 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 2.90376246e-01 -6.29509568e-01 3.78324836e-01 -2.46639714e-01
-6.66006386e-01 -2.38308132e-01 6.44823790e-01 -8.16955343e-02
-5.26548088e-01 2.92398512e-01 6.21765889e-02 1.41854763e-01
-4.57733780e-01 -9.71946776e-01 -1.71056598e-01 -9.43843722e-01
-2.79094595e-02 9.05904099e-02 3.07756811e-01 -5.08623540... | [10.295793533325195, -1.83228600025177] |
f531ce87-fd60-4835-81ee-9f50da0235ab | saliency-based-multiple-region-of-interest | 2209.03656 | null | https://arxiv.org/abs/2209.03656v1 | https://arxiv.org/pdf/2209.03656v1.pdf | Saliency-based Multiple Region of Interest Detection from a Single 360° image | 360{\deg} images are informative -- it contains omnidirectional visual information around the camera. However, the areas that cover a 360{\deg} image is much larger than the human's field of view, therefore important information in different view directions is easily overlooked. To tackle this issue, we propose a metho... | ['Kiyoharu Aizawa', 'Satoshi Ikehata', 'Yuuki Sawabe'] | 2022-09-08 | null | null | null | null | ['saliency-prediction'] | ['computer-vision'] | [ 2.13553995e-01 3.31590503e-01 -2.34500214e-01 -3.78500253e-01
-3.79960001e-01 -4.15001184e-01 1.91148013e-01 8.93843845e-02
-1.85616076e-01 4.92879212e-01 5.16652107e-01 -1.59226716e-01
-2.10116595e-01 -6.11714184e-01 -7.45398879e-01 -5.98714292e-01
4.04138654e-01 -1.94314599e-01 7.93753028e-01 -2.16237009... | [9.766566276550293, -0.4847685396671295] |
cd3a7f86-b968-423f-967c-99f342e9bde3 | joint-event-detection-and-description-in | 1802.1025 | null | http://arxiv.org/abs/1802.10250v3 | http://arxiv.org/pdf/1802.10250v3.pdf | Joint Event Detection and Description in Continuous Video Streams | Dense video captioning is a fine-grained video understanding task that
involves two sub-problems: localizing distinct events in a long video stream,
and generating captions for the localized events. We propose the Joint Event
Detection and Description Network (JEDDi-Net), which solves the dense video
captioning task in... | ['Vasili Ramanishka', 'Kate Saenko', 'Leonid Sigal', 'Huijuan Xu', 'Boyang Li'] | 2018-02-28 | null | null | null | null | ['dense-captioning', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 2.33787894e-01 -2.51494590e-02 -2.82056034e-01 -4.92705941e-01
-1.15524912e+00 -4.70769405e-01 7.00290501e-01 9.98687744e-02
-2.23856717e-01 7.12400317e-01 1.04432392e+00 4.03575569e-01
4.45718378e-01 -3.83564949e-01 -1.18335021e+00 -2.26697430e-01
-4.75353509e-01 4.56393361e-01 4.30628389e-01 1.94629893... | [10.47044563293457, 0.6698200702667236] |
899c4283-a93d-41b6-a639-4059c9386be1 | uniparma-semeval-2021-task-5-toxic-spans | 2103.09645 | null | https://arxiv.org/abs/2103.09645v2 | https://arxiv.org/pdf/2103.09645v2.pdf | UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words Model | With the ever-increasing availability of digital information, toxic content is also on the rise. Therefore, the detection of this type of language is of paramount importance. We tackle this problem utilizing a combination of a state-of-the-art pre-trained language model (CharacterBERT) and a traditional bag-of-words te... | ['Andrea Prati', 'Leonardo Rossi', 'Akbar Karimi'] | 2021-03-17 | null | https://aclanthology.org/2021.semeval-1.25 | https://aclanthology.org/2021.semeval-1.25.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 7.66694322e-02 -3.07923943e-01 -1.27494290e-01 -2.10150005e-03
-6.37781024e-01 -6.36427999e-01 8.67690682e-01 6.67671919e-01
-1.15788138e+00 5.65348923e-01 2.59337544e-01 -3.52408290e-01
2.64141887e-01 -8.46745312e-01 -5.20656168e-01 -6.20700896e-01
2.92885989e-01 5.98372519e-01 4.23379928e-01 -4.03725773... | [9.59294605255127, 10.0322904586792] |
d00379c7-84fd-4048-8857-688791c67d66 | orchnet-a-robust-global-feature-aggregation | 2303.00477 | null | https://arxiv.org/abs/2303.00477v1 | https://arxiv.org/pdf/2303.00477v1.pdf | ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards | Robust and reliable place recognition and loop closure detection in agricultural environments is still an open problem. In particular, orchards are a difficult case study due to structural similarity across the entire field. In this work, we address the place recognition problem in orchards resorting to 3D LiDAR data, ... | ['U. J. Nunes', 'C. Premebida', 'C. Liu', 'M. J. Coombes', 'P. Conde', 'L. Garrote', 'T. Barros'] | 2023-03-01 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-1.00865178e-01 -3.86972755e-01 -1.58137009e-01 -2.03119814e-01
-6.39794588e-01 -8.13904226e-01 6.57552719e-01 6.08590961e-01
-4.36274320e-01 5.01366615e-01 -3.34915876e-01 -2.39126459e-01
-3.96046132e-01 -1.08045435e+00 -7.66804516e-01 -6.80139959e-01
-2.09292203e-01 1.63936540e-01 4.40328211e-01 -2.09694088... | [7.555147647857666, -2.002532720565796] |
fabbd9c1-62c6-4011-9f38-2c4e6b8c8292 | mucpad-a-multi-domain-chinese-predicate | 2205.06703 | null | https://arxiv.org/abs/2205.06703v1 | https://arxiv.org/pdf/2205.06703v1.pdf | MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset | During the past decade, neural network models have made tremendous progress on in-domain semantic role labeling (SRL). However, performance drops dramatically under the out-of-domain setting. In order to facilitate research on cross-domain SRL, this paper presents MuCPAD, a multi-domain Chinese predicate-argument datas... | ['Min Zhang', 'Zhenghua Li', 'Qingrong Xia', 'Chen Gong', 'Haoping Yang', 'Yahui Liu'] | 2022-05-13 | null | https://aclanthology.org/2022.naacl-main.123 | https://aclanthology.org/2022.naacl-main.123.pdf | naacl-2022-7 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 4.94264036e-01 3.95318210e-01 -7.64250219e-01 -6.23027205e-01
-7.70137668e-01 -9.73516285e-01 4.47649002e-01 2.96298206e-01
-5.51628530e-01 1.26902616e+00 6.95210397e-01 -2.42732778e-01
-1.63635641e-01 -6.07461095e-01 -5.88779032e-01 -1.69967934e-01
3.95548999e-01 5.99550724e-01 4.81192589e-01 -4.04378116... | [10.254103660583496, 9.321586608886719] |
a9332e96-ffb2-4b33-a7d1-806056aa3e51 | personalization-disentanglement-for-federated | 2306.0357 | null | https://arxiv.org/abs/2306.03570v1 | https://arxiv.org/pdf/2306.03570v1.pdf | Personalization Disentanglement for Federated Learning | Personalized federated learning (PFL) jointly trains a variety of local models through balancing between knowledge sharing across clients and model personalization per client. This paper addresses PFL via explicit disentangling latent representations into two parts to capture the shared knowledge and client-specific pe... | ['Guodong Long', 'Peng Yan'] | 2023-06-06 | null | null | null | null | ['personalized-federated-learning', 'disentanglement'] | ['methodology', 'methodology'] | [-4.80145276e-01 2.56862849e-01 -6.30212605e-01 -5.27755380e-01
-7.98423529e-01 -5.97290695e-01 7.29156256e-01 -5.88897645e-01
-4.55190316e-02 7.97793329e-01 8.46182942e-01 -2.99584139e-02
-2.82033682e-01 -7.71415412e-01 -6.07552946e-01 -8.89475286e-01
2.38726616e-01 6.09451115e-01 -3.44952136e-01 2.17347685... | [5.825687408447266, 6.287322998046875] |
783fb678-6f93-4421-a35d-35b09987a196 | information-prebuilt-recurrent-reconstruction | 2112.05755 | null | https://arxiv.org/abs/2112.05755v4 | https://arxiv.org/pdf/2112.05755v4.pdf | Information Prebuilt Recurrent Reconstruction Network for Video Super-Resolution | The video super-resolution (VSR) method based on the recurrent convolutional network has strong temporal modeling capability for video sequences. However, the temporal receptive field of different recurrent units in the unidirectional recurrent network is unbalanced. Earlier reconstruction frames receive less spatio-te... | ['Ming Yu', 'Shuyun Wang', 'Gang Yan', 'Yingchun Guo', 'Cuihong Xue'] | 2021-12-10 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 1.85214639e-01 -2.14088380e-01 -1.99256435e-01 -4.28871587e-02
-3.41613084e-01 1.88497994e-02 2.55935550e-01 -7.30577648e-01
-1.56757861e-01 6.54502094e-01 6.01830423e-01 5.88507392e-03
1.26145735e-01 -7.02684164e-01 -5.37352681e-01 -8.99414062e-01
2.36314952e-01 -3.50557536e-01 7.52111256e-01 -2.66509235... | [11.046378135681152, -1.8380577564239502] |
97cb6c22-0e56-4206-9d38-bd09fe4aff13 | deepilluminance-contextual-illuminance | 1905.04791 | null | https://arxiv.org/abs/1905.04791v2 | https://arxiv.org/pdf/1905.04791v2.pdf | DeepIlluminance: Contextual Illuminance Estimation via Deep Neural Networks | Computational color constancy refers to the estimation of the scene illumination and makes the perceived color relatively stable under varying illumination. In the past few years, deep Convolutional Neural Networks (CNNs) have delivered superior performance in illuminant estimation. Several representative methods formu... | ['Jun Zhang', 'Meng Wang', 'Tong Zheng', 'Shengping Zhang'] | 2019-05-12 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 4.42577481e-01 -6.68902874e-01 -2.14611813e-02 -4.69377041e-01
-4.66489226e-01 -3.57048750e-01 2.76966363e-01 -1.86703220e-01
-1.61797866e-01 7.49888957e-01 2.21338272e-02 1.26108944e-01
2.46660396e-01 -6.76952362e-01 -8.41190755e-01 -1.14731431e+00
4.61393893e-01 -3.43806893e-01 1.23559423e-01 4.48473580... | [10.522788047790527, -2.5752880573272705] |
1b9ff439-0ef3-4495-93f6-11414a5ce2c9 | a-neuro-symbolic-approach-for-enhanced-human | 2304.1174 | null | https://arxiv.org/abs/2304.11740v1 | https://arxiv.org/pdf/2304.11740v1.pdf | A Neuro-Symbolic Approach for Enhanced Human Motion Prediction | Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for... | ['Nicola Bellotto', 'Marc Hanheide', 'Luca Castri', 'Sariah Mghames'] | 2023-04-23 | null | null | null | null | ['motion-prediction', 'time-series-prediction', 'human-motion-prediction'] | ['computer-vision', 'time-series', 'time-series'] | [ 1.93709671e-01 1.81111798e-01 9.05358791e-02 -9.00405571e-02
1.62072092e-01 -2.01573759e-01 1.24473071e+00 1.39789507e-01
-5.70751727e-01 6.48924291e-01 5.55348516e-01 -3.04860890e-01
-2.93783873e-01 -6.65536046e-01 -3.95829558e-01 -5.20439267e-01
-4.94572580e-01 3.32506180e-01 8.23019803e-01 -5.81104338... | [7.089297771453857, 0.20028288662433624] |
b5fb3d9a-4e52-4dbc-8723-07ae1af5ab3c | data-augmentation-imbalance-for-imbalanced | 2004.13628 | null | https://arxiv.org/abs/2004.13628v3 | https://arxiv.org/pdf/2004.13628v3.pdf | Data Augmentation Imbalance For Imbalanced Attribute Classification | Pedestrian attribute recognition is an important multi-label classification problem. Although the convolutional neural networks are prominent in learning discriminative features from images, the data imbalance in multi-label setting for fine-grained tasks remains an open problem. In this paper, we propose a new re-samp... | ['ShengMei Shen', 'Fanhua Shang', 'Pan Zhou', 'Yang Hu', 'Xiaying Bai'] | 2020-04-19 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 1.27569884e-01 -8.01555291e-02 -5.19153774e-01 -8.59461308e-01
-7.02855170e-01 -2.13793725e-01 2.61233509e-01 2.64380604e-01
-4.77137744e-01 1.08441854e+00 1.40922800e-01 8.55697244e-02
7.41812065e-02 -8.69148612e-01 -7.47420907e-01 -9.77020919e-01
3.89772594e-01 7.47058988e-01 -1.40276879e-01 1.62379846... | [14.312019348144531, 1.0243993997573853] |
9dea9adf-dc54-4f65-93ef-54a21c382db8 | visual-speech-aware-perceptual-3d-facial | 2207.11094 | null | https://arxiv.org/abs/2207.11094v1 | https://arxiv.org/pdf/2207.11094v1.pdf | Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos | The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial imag... | ['Petros Maragos', 'Anastasios Roussos', 'Athanasios Katsamanis', 'Foivos Paraperas-Papantoniou', 'George Retsinas', 'Panagiotis P. Filntisis'] | 2022-07-22 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.85041994e-02 1.90924957e-01 -9.51398239e-02 -2.93793708e-01
-6.48471594e-01 -4.11143988e-01 6.13701880e-01 -1.41314596e-01
-3.18501741e-01 4.60626394e-01 2.65776604e-01 1.23832077e-01
1.43272236e-01 -2.37139806e-01 -7.92291880e-01 -7.52926946e-01
2.09279642e-01 1.94902942e-01 -1.87051386e-01 -9.40691009... | [13.188892364501953, -0.30633479356765747] |
2d1a450b-9bd9-4eab-a023-2d82bda2b7b7 | bilingually-guided-monolingual-dependency | null | null | https://aclanthology.org/P13-1105 | https://aclanthology.org/P13-1105.pdf | Bilingually-Guided Monolingual Dependency Grammar Induction | null | ['Yajuan L{\\"u}', 'Qun Liu', 'Kai Liu', 'Wenbin Jiang'] | 2013-08-01 | null | null | null | acl-2013-8 | ['dependency-grammar-induction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3634443283081055, 3.8467299938201904] |
586b6928-c052-4e5b-ae3a-a49391ca4f4c | video-highlights-detection-and-summarization-1 | 1708.0221 | null | http://arxiv.org/abs/1708.02210v1 | http://arxiv.org/pdf/1708.02210v1.pdf | Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowd-sourced Time-Sync Comments | With the prevalence of video sharing, there are increasing demands for
automatic video digestion such as highlight detection. Recently, platforms with
crowdsourced time-sync video comments have emerged worldwide, providing a good
opportunity for highlight detection. However, this task is non-trivial: (1)
time-sync comm... | ['Chaomei Chen', 'Qing Ping'] | 2017-08-07 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 1.67067535e-02 -5.40188670e-01 -4.21630323e-01 1.50417969e-01
-8.91708910e-01 -7.17913449e-01 5.51676095e-01 7.75470316e-01
-2.96782374e-01 5.85256517e-01 7.33345330e-01 4.23143566e-01
3.32322001e-01 -3.29966217e-01 -3.91821653e-01 -3.19621533e-01
-2.27036431e-01 -1.02254532e-01 6.80063009e-01 -1.02562860... | [10.097009658813477, 0.4384283721446991] |
ff9d7062-65f3-4dd9-b807-9263e1f16dca | h4vdm-h-264-video-device-matching | 2210.11549 | null | https://arxiv.org/abs/2210.11549v1 | https://arxiv.org/pdf/2210.11549v1.pdf | H4VDM: H.264 Video Device Matching | Methods that can determine if two given video sequences are captured by the same device (e.g., mobile telephone or digital camera) can be used in many forensics tasks. In this paper we refer to this as "video device matching". In open-set video forensics scenarios it is easier to determine if two video sequences were c... | ['Edward J. Delp', 'Stefano Tubaro', 'Paolo Bestagini', 'Ziyue Xiang'] | 2022-10-20 | null | null | null | null | ['video-forensics'] | ['computer-vision'] | [ 5.33708513e-01 -5.28099835e-01 -1.13443211e-01 1.02994025e-01
-8.43963683e-01 -1.10492694e+00 2.45906994e-01 3.05163693e-02
-1.22703180e-01 3.18098009e-01 -1.35376856e-01 -6.15179002e-01
1.61170319e-01 -5.43932319e-01 -9.85895216e-01 -5.90208650e-01
1.22422338e-01 -1.14030354e-01 5.82272589e-01 6.06771648... | [12.408952713012695, 1.0055556297302246] |
7e52db81-e2b4-4ed1-8735-c8a00243f4cc | text2chart-a-multi-staged-chart-generator | 2104.04584 | null | https://arxiv.org/abs/2104.04584v1 | https://arxiv.org/pdf/2104.04584v1.pdf | Text2Chart: A Multi-Staged Chart Generator from Natural Language Text | Generation of scientific visualization from analytical natural language text is a challenging task. In this paper, we propose Text2Chart, a multi-staged chart generator method. Text2Chart takes natural language text as input and produce visualization as two-dimensional charts. Text2Chart approaches the problem in three... | ['Swakkhar Shatabda', 'Md. Saddam Hossain Mukta', 'Farhana Meem', 'Tamim Bin Zakir', 'Riyasaat Ahmed Rahul', 'Annysha Huzzat', 'Hasin Kawsar Jahan', 'Md. Mahinur Rashid'] | 2021-04-09 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 2.14530155e-02 2.60994345e-01 -1.26684438e-02 -2.64631808e-01
-6.15372241e-01 -4.85089332e-01 8.72181535e-01 4.63472724e-01
8.52584168e-02 6.93430841e-01 3.42987776e-01 -6.39005780e-01
9.83362943e-02 -9.62920070e-01 -6.33098423e-01 -4.64201003e-01
-2.36661389e-01 4.96735036e-01 -1.93000078e-01 2.88191557... | [11.320622444152832, 2.0398199558258057] |
5a807448-d3db-46db-a1cb-9733ce90a6cb | a-curriculum-domain-adaptation-approach-to | 1812.09953 | null | http://arxiv.org/abs/1812.09953v3 | http://arxiv.org/pdf/1812.09953v3.pdf | A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes | During the last half decade, convolutional neural networks (CNNs) have
triumphed over semantic segmentation, which is one of the core tasks in many
applications such as autonomous driving and augmented reality. However, to
train CNNs requires a considerable amount of data, which is difficult to
collect and laborious to... | ['Hassan Foroosh', 'Philip David', 'Yang Zhang', 'Boqing Gong'] | 2018-12-24 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 4.13802266e-01 2.85687506e-01 -2.51731962e-01 -8.08511972e-01
-6.02316141e-01 -6.49488389e-01 4.90670860e-01 -1.96034297e-01
-5.21501243e-01 7.83998489e-01 -1.28765747e-01 -2.77656704e-01
2.73233682e-01 -8.99386525e-01 -1.12972665e+00 -4.89930451e-01
3.05457801e-01 7.72175550e-01 5.57515919e-01 -1.59297749... | [9.641218185424805, 0.7312387228012085] |
b69d4f4a-12ce-403a-8eaf-04e11b48b9e8 | stemm-self-learning-with-speech-text-manifold | 2203.10426 | null | https://arxiv.org/abs/2203.10426v1 | https://arxiv.org/pdf/2203.10426v1.pdf | STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation | How to learn a better speech representation for end-to-end speech-to-text translation (ST) with limited labeled data? Existing techniques often attempt to transfer powerful machine translation (MT) capabilities to ST, but neglect the representation discrepancy across modalities. In this paper, we propose the Speech-TEx... | ['Mingxuan Wang', 'Yang Feng', 'Lei LI', 'Rong Ye', 'Qingkai Fang'] | 2022-03-20 | null | https://aclanthology.org/2022.acl-long.486 | https://aclanthology.org/2022.acl-long.486.pdf | acl-2022-5 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 5.79823852e-01 1.23317994e-01 -5.51403821e-01 -5.62367141e-01
-1.54448736e+00 -6.74055457e-01 1.04405057e+00 -7.26373553e-01
1.22928591e-02 5.47703445e-01 8.84013295e-01 -7.95009017e-01
7.69186556e-01 -1.08816341e-01 -7.51307189e-01 -5.36604047e-01
7.37256229e-01 8.75193000e-01 -3.18890423e-01 -3.89809459... | [14.504891395568848, 7.225879192352295] |
9a1b0252-e5b6-4bcc-b24f-6300195bac29 | end-to-end-neural-bridging-resolution | null | null | https://aclanthology.org/2022.coling-1.64 | https://aclanthology.org/2022.coling-1.64.pdf | End-to-End Neural Bridging Resolution | The state of bridging resolution research is rather unsatisfactory: not only are state-of-the-art resolvers evaluated in unrealistic settings, but the neural models underlying these resolvers are weaker than those used for entity coreference resolution. In light of these problems, we evaluate bridging resolvers in an e... | ['Vincent Ng', 'Yufang Hou', 'Hideo Kobayashi'] | null | null | null | null | coling-2022-10 | ['coreference-resolution'] | ['natural-language-processing'] | [ 2.50816911e-01 7.47154236e-01 -5.79000354e-01 -4.18965250e-01
-1.11086893e+00 -5.96203387e-01 4.82653379e-01 2.45257184e-01
-6.25435174e-01 1.19246256e+00 8.26192677e-01 -4.12872881e-01
-2.79407889e-01 -6.41187072e-01 -9.14256573e-01 3.24131250e-02
8.28515664e-02 1.10918427e+00 2.52842963e-01 -6.39059067... | [9.32451057434082, 9.51655387878418] |
d4c22668-8a15-4490-b127-84cf1f010382 | reinforcement-learning-with-analogical | 1712.1007 | null | http://arxiv.org/abs/1712.10070v1 | http://arxiv.org/pdf/1712.10070v1.pdf | Reinforcement Learning with Analogical Similarity to Guide Schema Induction and Attention | Research in analogical reasoning suggests that higher-order cognitive
functions such as abstract reasoning, far transfer, and creativity are founded
on recognizing structural similarities among relational systems. Here we
integrate theories of analogy with the computational framework of reinforcement
learning (RL). We ... | ['Matt Jones', 'James M. Foster'] | 2017-12-28 | null | null | null | null | ['analogical-similarity'] | ['reasoning'] | [-1.13393798e-01 2.24721938e-01 -1.03673056e-01 -2.18396991e-01
4.87242728e-01 -5.63164592e-01 7.14963794e-01 3.32825512e-01
-2.30517164e-01 5.49780488e-01 1.59514174e-01 -4.13701981e-01
-8.11584711e-01 -1.22727728e+00 -4.98218983e-01 -4.82248031e-02
-9.85984504e-02 2.57577062e-01 -4.64668348e-02 -8.65040898... | [10.598616600036621, 2.3063225746154785] |
32817920-0c95-47a4-8b9c-6f366a52d69b | pag-net-progressive-attention-guided-depth | 1911.09878 | null | https://arxiv.org/abs/1911.09878v1 | https://arxiv.org/pdf/1911.09878v1.pdf | PAG-Net: Progressive Attention Guided Depth Super-resolution Network | In this paper, we propose a novel method for the challenging problem of guided depth map super-resolution, called PAGNet. It is based on residual dense networks and involves the attention mechanism to suppress the texture copying problem arises due to improper guidance by RGB images. The attention module mainly involve... | ['Sankaraganesh Jonna', 'Arpit Bansal', 'Rajiv R. Sahay'] | 2019-11-22 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 6.27858758e-01 5.41414201e-01 2.27736875e-01 -1.94401562e-01
-4.42654163e-01 3.50200266e-01 4.96834338e-01 -5.76718748e-01
-3.44481319e-01 8.54070365e-01 7.35937953e-01 3.24734837e-01
-1.69503763e-02 -1.26346219e+00 -6.02056384e-01 -6.13812983e-01
2.67257720e-01 3.17808509e-01 7.73631573e-01 -4.66777146... | [9.873373031616211, -2.4120566844940186] |
4c5b11ab-8591-46dc-86b7-9ab84d62fef3 | chordmixer-a-scalable-neural-attention-model | 2206.05852 | null | https://arxiv.org/abs/2206.05852v2 | https://arxiv.org/pdf/2206.05852v2.pdf | ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths | Sequential data naturally have different lengths in many domains, with some very long sequences. As an important modeling tool, neural attention should capture long-range interaction in such sequences. However, most existing neural attention models admit only short sequences, or they have to employ chunking or padding ... | ['Zhirong Yang', 'Lei Cheng', 'Tong Yu', 'Ruslan Khalitov'] | 2022-06-12 | null | null | null | null | ['document-classification', 'long-range-modeling'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.89181823e-01 5.23357876e-02 -4.56501395e-01 -3.03350449e-01
-4.78251994e-01 -5.33227384e-01 4.71444190e-01 -4.46299836e-02
-5.58059454e-01 6.69139206e-01 2.88344443e-01 -4.29638028e-01
1.02256713e-02 -5.24482131e-01 -9.70434010e-01 -9.67240632e-01
-2.00720772e-01 6.64053559e-01 2.77419865e-01 -1.16143383... | [10.807540893554688, 6.863028049468994] |
2cda35db-be90-43a5-8d1e-3b74134a854a | multiple-manifolds-metric-learning-with | 1805.11918 | null | http://arxiv.org/abs/1805.11918v1 | http://arxiv.org/pdf/1805.11918v1.pdf | Multiple Manifolds Metric Learning with Application to Image Set Classification | In image set classification, a considerable advance has been made by modeling
the original image sets by second order statistics or linear subspace, which
typically lie on the Riemannian manifold. Specifically, they are Symmetric
Positive Definite (SPD) manifold and Grassmann manifold respectively, and some
algorithms ... | ['Kai-Xuan Chen', 'Xiao-Jun Wu', 'Rui Wang', 'Josef Kittler'] | 2018-05-30 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 1.32938363e-02 -2.48514667e-01 -2.87257023e-02 -5.75802863e-01
-3.67397904e-01 -3.47927451e-01 5.80534339e-01 -5.39860010e-01
-2.65607595e-01 1.84802458e-01 -6.97234720e-02 -7.88514391e-02
-4.46675658e-01 -3.62682611e-01 -1.42208695e-01 -9.67759609e-01
-1.87489450e-01 -2.13397592e-01 -1.15244955e-01 1.69516150... | [7.9232707023620605, 4.077700138092041] |
b41ccf89-afd1-4164-9104-84f4916f8acc | dynamic-bandits-with-an-auto-regressive | 2210.16386 | null | https://arxiv.org/abs/2210.16386v2 | https://arxiv.org/pdf/2210.16386v2.pdf | Dynamic Bandits with an Auto-Regressive Temporal Structure | Multi-armed bandit (MAB) problems are mainly studied under two extreme settings known as stochastic and adversarial. These two settings, however, do not capture realistic environments such as search engines and marketing and advertising, in which rewards stochastically change in time. Motivated by that, we introduce an... | ['Djallel Bouneffouf', 'Negin Golrezaei', 'Qinyi Chen'] | 2022-10-28 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 1.31531417e-01 -2.45831907e-02 -5.63934326e-01 -1.42809704e-01
-7.07958579e-01 -1.18278193e+00 4.95464593e-01 8.93203095e-02
-7.23948598e-01 1.11127996e+00 -2.90079862e-02 -5.26070893e-01
-5.57675362e-01 -8.20367038e-01 -1.14846051e+00 -8.68912876e-01
-2.66569614e-01 6.05735421e-01 -1.06708203e-02 -2.31494203... | [4.504827499389648, 3.2714035511016846] |
dfc51034-97cf-4c60-8fad-547fdd931116 | timeline-summarization-based-on-event-graph | null | null | https://aclanthology.org/2021.emnlp-main.519 | https://aclanthology.org/2021.emnlp-main.519.pdf | Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport | Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged. Previous methods generally generate summaries separately for each date after they determine the key dates of events. These methods overlook the events’ intra-structures (arguments) a... | ['Kathleen McKeown', 'Heng Ji', 'Tian Gao', 'Lingfei Wu', 'Mo Yu', 'Tengfei Ma', 'Manling Li'] | null | null | null | null | emnlp-2021-11 | ['timeline-summarization'] | ['natural-language-processing'] | [ 2.45648965e-01 2.95679599e-01 -4.20494378e-01 -3.00687432e-01
-8.68945062e-01 -7.03796387e-01 1.02021706e+00 1.24605155e+00
-1.72619164e-01 6.28361821e-01 1.38080335e+00 1.73845425e-01
-2.79533893e-01 -9.64406788e-01 -8.54723573e-01 -2.90761501e-01
-6.82585120e-01 5.14880419e-01 5.13597310e-01 2.29355264... | [12.570459365844727, 9.515050888061523] |
188a45be-49dd-43ad-96ce-5c8ca0f1dbcd | hand-segmentation-for-hand-object-interaction | 1603.02345 | null | http://arxiv.org/abs/1603.02345v3 | http://arxiv.org/pdf/1603.02345v3.pdf | Hand Segmentation for Hand-Object Interaction from Depth map | Hand segmentation for hand-object interaction is a necessary preprocessing
step in many applications such as augmented reality, medical application, and
human-robot interaction. However, typical methods are based on color
information which is not robust to objects with skin color, skin pigment
difference, and light con... | ['Hung-Shuo Tai', 'Kar-Han Tan', 'Truong Q. Nguyen', 'Byeongkeun Kang', 'Nan Jiang', 'Daniel Tretter'] | 2016-03-08 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 3.17493618e-01 -3.04726064e-01 4.28539664e-02 -1.58910915e-01
-2.58467346e-02 -5.68072319e-01 2.28187561e-01 -1.83056921e-01
-5.21567822e-01 6.95514202e-01 -2.37598553e-01 -2.90454179e-01
3.44975293e-02 -6.16303384e-01 -6.46435171e-02 -6.40437543e-01
4.01708513e-01 6.96916580e-01 7.39093721e-01 1.47498056... | [6.558807849884033, -0.5581557750701904] |
9ceaf47c-dca9-4a59-9f15-f2a89523ecc8 | cose-co-sentence-conditioned-generative | null | null | https://openreview.net/forum?id=1yieqYLUIXj | https://openreview.net/pdf?id=1yieqYLUIXj | CoSe-Co: Sentence Conditioned Generative CommonSense Contextualizer for Language Models | Pre-trained Language Models (PTLMs) have been shown to perform well on natural language reasoning tasks requiring commonsense. Prior work has leveraged structured commonsense present in knowledge graphs (KGs) to assist PTLMs. Some of these methods use KGs as separate static modules which limits knowledge coverage since... | ['Balaji Krishnamurthy', 'Jivat Neet Kaur', 'Sumit Bhatia', 'Milan Aggarwal', 'Rachit Bansal'] | 2021-09-03 | null | null | null | akbc-workshop-cskb-2021-10 | ['novel-concepts'] | ['reasoning'] | [ 6.17135108e-01 5.85343063e-01 3.55212204e-02 -3.57125700e-01
-9.92549896e-01 -7.49146521e-01 8.82046223e-01 2.30272368e-01
-3.91290456e-01 8.94481182e-01 6.67683780e-01 -4.92325485e-01
-1.22694127e-01 -1.05065978e+00 -8.92968535e-01 -2.16430739e-01
3.88236970e-01 7.98905373e-01 2.80884832e-01 -7.27409840... | [10.427983283996582, 8.056236267089844] |
d68ac372-a764-427c-bdfc-5b0434249378 | an-entity-guided-text-summarization-framework | 2302.03205 | null | https://arxiv.org/abs/2302.03205v1 | https://arxiv.org/pdf/2302.03205v1.pdf | An entity-guided text summarization framework with relational heterogeneous graph neural network | Two crucial issues for text summarization to generate faithful summaries are to make use of knowledge beyond text and to make use of cross-sentence relations in text. Intuitive ways for the two issues are Knowledge Graph (KG) and Graph Neural Network (GNN) respectively. Entities are semantic units in text and in KG. Th... | ['Jingqiang Chen'] | 2023-02-07 | null | null | null | null | ['entity-embeddings'] | ['methodology'] | [ 1.14466861e-01 7.57762372e-01 -4.59212929e-01 -2.07086325e-01
-5.01184106e-01 -1.71267882e-01 2.97533780e-01 5.83749354e-01
-5.00052810e-01 7.09822536e-01 1.23693788e+00 -5.35928197e-02
-1.34948090e-01 -1.33982623e+00 -7.30639756e-01 -2.61934727e-01
-2.18182504e-01 2.93100715e-01 1.75706461e-01 -4.84120011... | [12.575108528137207, 9.536314010620117] |
87a27118-d0a5-46a9-bd4b-9e1524a79ea9 | high-performance-offline-handwritten-chinese | 1505.04925 | null | http://arxiv.org/abs/1505.04925v1 | http://arxiv.org/pdf/1505.04925v1.pdf | High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps | Just like its great success in solving many computer vision problems, the
convolutional neural networks (CNN) provided new end-to-end approach to
handwritten Chinese character recognition (HCCR) with very promising results in
recent years. However, previous CNNs so far proposed for HCCR were neither deep
enough nor sli... | ['Lianwen Jin', 'Zhuoyao Zhong', 'Zecheng Xie'] | 2015-05-19 | null | null | null | null | ['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character'] | ['computer-vision', 'natural-language-processing'] | [-3.23518276e-01 -5.16946316e-01 1.00391224e-01 -3.61676633e-01
-4.27918106e-01 -3.81071121e-01 5.18133700e-01 -4.24368560e-01
-6.98908985e-01 6.32242858e-01 -2.72616819e-02 -4.75823343e-01
1.53680460e-03 -8.05537283e-01 -6.38481259e-01 -5.73947310e-01
-2.92238835e-02 2.46728316e-01 2.05282673e-01 -6.00529373... | [11.777482986450195, 2.58612322807312] |
c1c5921c-9eec-4ecb-bac1-e9366fbadba6 | multi-temporal-land-cover-classification-with | 1802.0208 | null | http://arxiv.org/abs/1802.02080v4 | http://arxiv.org/pdf/1802.02080v4.pdf | Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders | Earth observation (EO) sensors deliver data with daily or weekly temporal
resolution. Most land use and land cover (LULC) approaches, however, expect
cloud-free and mono-temporal observations. The increasing temporal capabilities
of today's sensors enables the use of temporal, along with spectral and spatial
features. ... | ['Marco Körner', 'Marc Rußwurm'] | 2018-02-06 | multi-temporal-land-cover-classification-with-1 | https://www.mdpi.com/2220-9964/7/4/129 | https://www.mdpi.com/2220-9964/7/4/129 | international-journal-of-geo-information-2018 | ['unet-segmentation'] | ['computer-vision'] | [ 5.06613135e-01 -5.18326819e-01 1.48168936e-01 -5.00908554e-01
-3.75095546e-01 -7.21255004e-01 6.27628744e-01 1.32747322e-01
-3.90686303e-01 6.60113990e-01 -1.64128706e-01 -8.02643239e-01
1.38183296e-01 -1.14100444e+00 -6.45080447e-01 -7.50764906e-01
-4.24348444e-01 -2.33145341e-01 -1.84974223e-02 -5.44637561... | [9.598726272583008, -1.6166291236877441] |
f1969ed7-84ed-49c3-bcd7-59adc99b564b | on-gibbs-sampling-architecture-for-labeled | 2306.15135 | null | https://arxiv.org/abs/2306.15135v1 | https://arxiv.org/pdf/2306.15135v1.pdf | On Gibbs Sampling Architecture for Labeled Random Finite Sets Multi-Object Tracking | Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled random finite sets literature, Gibbs sampling procedures have recently been applied to efficiently trunc... | ['Pramod K. Varshney', 'Donald J. Bucci Jr.', 'Anthony Trezza'] | 2023-06-27 | null | null | null | null | ['object-tracking', 'multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 5.55542648e-01 -1.35449454e-01 -4.11300845e-02 -4.83977765e-01
-1.04147053e+00 -1.03686035e-01 6.42038286e-01 4.15484458e-02
-4.11750585e-01 1.07112610e+00 -1.65959284e-01 -1.34266168e-01
7.83370957e-02 -1.07023203e+00 -3.59894991e-01 -7.84276426e-01
-1.83365852e-01 7.75531828e-01 4.70304757e-01 5.86994886... | [6.7456769943237305, 3.939074993133545] |
46175c77-5f53-4b60-b0a1-907a1a02de51 | trig-transformer-based-text-recognizer-with | 2111.08314 | null | https://arxiv.org/abs/2111.08314v1 | https://arxiv.org/pdf/2111.08314v1.pdf | TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance | Scene text recognition (STR) is an important bridge between images and text, attracting abundant research attention. While convolutional neural networks (CNNS) have achieved remarkable progress in this task, most of the existing works need an extra module (context modeling module) to help CNN to capture global dependen... | ['Shugong Xu', 'Runze Ma', 'Zhiwei Jia', 'Yue Tao'] | 2021-11-16 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.99934518e-01 -3.81688565e-01 -1.87001318e-01 -5.98954618e-01
-2.54395515e-01 -6.44437969e-02 7.43521512e-01 -1.63974985e-01
-3.43789876e-01 9.62426066e-02 2.37895250e-01 -1.90325037e-01
3.35844427e-01 -7.43892014e-01 -6.64220929e-01 -7.00367093e-01
9.40429807e-01 1.11677133e-01 4.32053894e-01 -1.01288088... | [11.855624198913574, 2.160369873046875] |
86e3a04d-ef47-4a1c-a4c1-0b0df7ed18db | diagnostic-test-accuracy-dta-of-artificial | 2306.07999 | null | https://arxiv.org/abs/2306.07999v2 | https://arxiv.org/pdf/2306.07999v2.pdf | Artificial intelligence in digital pathology: a diagnostic test accuracy systematic review and meta-analysis | Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology images for diagnostic purposes have rapidly increased in number in recent years. The aim of this work is to provide an overview of the diag... | ['Deborah D Stocken', 'Emily L Clarke', 'Darren Treanor', 'Henschel Freduah-Agyemang', 'Caroline Cartlidge', 'Gillian Matthews', 'Charlotte Jennings', 'Clare McGenity'] | 2023-06-13 | null | null | null | null | ['whole-slide-images', 'specificity'] | ['computer-vision', 'natural-language-processing'] | [ 4.91206616e-01 -3.61913294e-02 -7.21156240e-01 3.12904306e-02
-1.15733755e+00 -6.14352345e-01 2.46920273e-01 3.94019544e-01
-5.57012200e-01 4.70189989e-01 -1.16105941e-04 -6.38741136e-01
-7.02531099e-01 -4.90367502e-01 -4.54049021e-01 -1.00288594e+00
-1.21259451e-01 7.19788194e-01 1.23541161e-01 7.26910710... | [15.14658260345459, -3.051274299621582] |
37924a1a-c547-49d2-8002-6de0ee7ac7a5 | heart-murmur-detection-from-phonocardiogram | null | null | https://www.medrxiv.org/content/10.1101/2022.08.11.22278688v1 | https://www.medrxiv.org/content/10.1101/2022.08.11.22278688v1.full.pdf | Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022 | Objective Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment, especially in resource-constrained environments. However, experts are needed to interpret the heart sound recordings, limiting the accessibili... | ['Gari D. Clifford', 'Ali Bahrami Rad', 'Reza Sameni', 'Miguel T. Coimbra', 'Sandra Mattos', 'ASHISH SHARMA', 'Nadi Sadr', 'Erick A. Perez Alday', 'Annie Gu', 'Francesco Renna', 'Jorge Oliveira', 'Andoni Elola', 'Yashar Kiarashi', 'Matthew A. Reyna'] | 2022-08-16 | null | null | null | computing-in-cardiology-2022-8 | ['predict-clinical-outcome', 'classify-murmurs'] | ['time-series', 'time-series'] | [ 3.90641928e-01 1.23395629e-01 2.65379310e-01 -2.22786695e-01
-1.00994229e+00 -7.24453628e-01 -5.42005599e-01 5.80357671e-01
-1.06582545e-01 4.47682500e-01 3.24105650e-01 -7.82435954e-01
-4.90068167e-01 -2.91330457e-01 -2.65299708e-01 -2.07308218e-01
-3.85140032e-01 6.23857439e-01 1.95919931e-01 5.76783538... | [14.301526069641113, 3.2638609409332275] |
5844b90a-7c35-4d52-bd64-30c4491aa22e | protoinfomax-prototypical-networks-with | 2108.12229 | null | https://arxiv.org/abs/2108.12229v5 | https://arxiv.org/pdf/2108.12229v5.pdf | ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection | The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications. For example, intent classification in dialogue systems. The reason is that the inclusion of unsupported OOD inputs may lead to catastrophic failure of systems. However, it remains an empirical question ... | ['Mykola Pechenizkiy', 'Vlado Menkovski', 'Meng Fang', "Iftitahu Ni'mah"] | 2021-08-27 | null | https://aclanthology.org/2021.findings-emnlp.138 | https://aclanthology.org/2021.findings-emnlp.138.pdf | findings-emnlp-2021-11 | ['zero-shot-out-of-domain-detection', 'few-shot-text-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.60249972e-01 6.79970741e-01 -1.99766174e-01 -7.97576487e-01
-2.67200500e-01 -4.50047404e-01 8.44948947e-01 1.68407559e-01
-6.00383162e-01 8.35951149e-01 2.74284631e-01 -6.79161966e-01
2.06480492e-02 -5.00986993e-01 -2.02620968e-01 4.48157340e-02
9.59591940e-02 7.19219923e-01 -1.99051318e-03 -3.11245978... | [12.665203094482422, 7.91787576675415] |
dd8fa725-8ca7-4d6d-91a5-d5253950d0d0 | exploring-the-compositional-generalization-in | 2306.0448 | null | https://arxiv.org/abs/2306.04480v1 | https://arxiv.org/pdf/2306.04480v1.pdf | Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing | In the context-dependent Text-to-SQL task, the generated SQL statements are refined iteratively based on the user input utterance from each interaction. The input text from each interaction can be viewed as component modifications to the previous SQL statements, which could be further extracted as the modification patt... | ['Lijie Wen', 'Yawen Yang', 'Fukun Ma', 'Shuang Li', 'Xuming Hu', 'Wei Liu', 'Aiwei Liu'] | 2023-05-29 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 4.21045184e-01 2.01212108e-01 -6.32558614e-02 -1.07323670e+00
-6.86149180e-01 -7.39742339e-01 5.59328079e-01 1.15828171e-01
-1.21232565e-03 4.13982987e-01 4.69301552e-01 -4.53194112e-01
-7.67830312e-02 -7.79377401e-01 -8.35020304e-01 -1.78376976e-02
7.79839382e-02 6.34171188e-01 5.27088642e-01 -6.72257245... | [9.861727714538574, 7.85487174987793] |
64ff71b5-3dfc-492e-baeb-8ba7d999280e | deep-learning-for-direction-of-arrival | 2007.13824 | null | https://arxiv.org/abs/2007.13824v3 | https://arxiv.org/pdf/2007.13824v3.pdf | Deep Learning for DOA Estimation in MIMO Radar Systems via Emulation of Large Antenna Arrays | We present a MUSIC-based Direction of Arrival (DOA) estimation strategy using small antenna arrays, via employing deep learning for reconstructing the signals of a virtual large antenna array. Not only does the proposed strategy deliver significantly better performance than simply plugging the incoming signals into MUS... | ['Udaya Sampath K. P. Miriya Thanthrige', 'Aydin Sezgin', 'Aya Mostafa Ahmed', 'Aly El Gamal'] | 2020-07-27 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-7.14667365e-02 -3.20084453e-01 5.59410930e-01 5.19877747e-02
-1.06587899e+00 -9.04924273e-01 2.96359450e-01 -2.66471297e-01
-3.21657002e-01 4.56642479e-01 4.77483809e-01 -5.15082955e-01
-6.06430233e-01 -5.75422466e-01 -5.11368632e-01 -1.08831692e+00
-4.02213037e-01 1.11340396e-01 -1.32966697e-01 3.92832085... | [6.487947940826416, 1.327072262763977] |
707d544a-7930-48b8-8217-780fa7fd20f4 | tiled-sparse-coding-in-eigenspaces-for-the | 2106.14724 | null | https://arxiv.org/abs/2106.14724v1 | https://arxiv.org/pdf/2106.14724v1.pdf | Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray images | The ongoing crisis of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world. According to the World Health Organization (WHO), 4 million people have died due to this disease, whereas there have been more than 180 million confirmed cases of COVID-19. The collapse of the health system in many countries h... | ['Juan M Gorriz', 'Javier Ramírez', 'Andrés Ortiz', 'Juan E. Arco'] | 2021-06-28 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.60967189e-01 -4.35750514e-01 1.32710814e-01 1.19325355e-01
-6.00623369e-01 -3.34375739e-01 4.07740057e-01 2.31287137e-01
-4.38007444e-01 5.00006855e-01 3.00184369e-01 -8.70394781e-02
-2.13164449e-01 -5.84260345e-01 -2.83368260e-01 -1.04325843e+00
-8.97049308e-02 7.21808970e-01 -1.35968357e-01 2.73841262... | [15.55225944519043, -1.6935322284698486] |
70999a78-0d3a-4de7-b20e-853f0ca88da5 | instantaneous-physiological-estimation-using | 2202.12368 | null | https://arxiv.org/abs/2202.12368v1 | https://arxiv.org/pdf/2202.12368v1.pdf | Instantaneous Physiological Estimation using Video Transformers | Video-based physiological signal estimation has been limited primarily to predicting episodic scores in windowed intervals. While these intermittent values are useful, they provide an incomplete picture of patients' physiological status and may lead to late detection of critical conditions. We propose a video Transform... | ['Laszlo A. Jeni', 'Conrad S. Tucker', 'Ananyananda Dasari', 'Ambareesh Revanur'] | 2022-02-24 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 1.01934627e-01 -1.58251271e-01 -8.36609378e-02 -5.37978888e-01
-6.68524683e-01 -1.11648612e-01 1.33821729e-03 7.82772526e-02
-2.76459336e-01 9.07930255e-01 2.99050122e-01 2.08757833e-01
1.90916210e-01 -7.17472583e-02 -1.46410376e-01 -7.73066342e-01
-4.28526551e-01 -1.76715747e-01 -3.36172253e-01 2.97653824... | [13.88534164428711, 2.84633469581604] |
a351fbdf-5c06-4ec5-af05-b7b73a3a5aff | neuri-diversifying-dnn-generation-via | 2302.02261 | null | https://arxiv.org/abs/2302.02261v1 | https://arxiv.org/pdf/2302.02261v1.pdf | NeuRI: Diversifying DNN Generation via Inductive Rule Inference | Deep Learning (DL) is prevalently used in various industries to improve decision-making and automate processes, driven by the ever-evolving DL libraries and compilers. The correctness of DL systems is crucial for trust in DL applications. As such, the recent wave of research has been studying the automated synthesis of... | ['Lingming Zhang', 'Yuyao Wang', 'Jinjun Peng', 'Jiawei Liu'] | 2023-02-04 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-1.86605096e-01 4.60874697e-04 -6.52150869e-01 -1.35768488e-01
-6.98058188e-01 -7.79974997e-01 1.66765288e-01 -1.52367324e-01
3.57172549e-01 6.22205496e-01 -1.59736171e-01 -1.09130490e+00
1.71341479e-01 -9.20643628e-01 -1.10564923e+00 1.70086354e-01
-2.28125840e-01 2.66487122e-01 4.95118529e-01 -1.55740023... | [7.691824436187744, 7.644417762756348] |
46ff50dd-ca09-4b4f-bc2e-8bd39a159b37 | compositional-probabilistic-and-causal | 2304.08278 | null | https://arxiv.org/abs/2304.08278v1 | https://arxiv.org/pdf/2304.08278v1.pdf | Compositional Probabilistic and Causal Inference using Tractable Circuit Models | Probabilistic circuits (PCs) are a class of tractable probabilistic models, which admit efficient inference routines depending on their structural properties. In this paper, we introduce md-vtrees, a novel structural formulation of (marginal) determinism in structured decomposable PCs, which generalizes previously prop... | ['Marta Kwiatkowska', 'Benjie Wang'] | 2023-04-17 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 4.37313378e-01 5.51621556e-01 -4.34281468e-01 -3.68806124e-01
-7.92640269e-01 -1.11358154e+00 6.58412695e-01 -5.81467040e-02
3.20810169e-01 9.05732870e-01 1.63823292e-01 -1.13633704e+00
-7.42317140e-01 -1.33136392e+00 -1.07923388e+00 -8.17396104e-01
-4.33777511e-01 9.46442425e-01 4.05361027e-01 1.93633899... | [7.488500118255615, 5.176295280456543] |
77887e05-923f-461b-80f2-efe4267221d1 | evaluating-performance-of-an-adult | 2005.08766 | null | https://arxiv.org/abs/2005.08766v1 | https://arxiv.org/pdf/2005.08766v1.pdf | Evaluating Performance of an Adult Pornography Classifier for Child Sexual Abuse Detection | The information technology revolution has facilitated reaching pornographic material for everyone, including minors who are the most vulnerable in case they were abused. Accuracy and time performance are features desired by forensic tools oriented to child sexual abuse detection, whose main components may rely on image... | ['Javier Velasco-Mata', 'Francisco Jañez-Martino', 'Roberto A. Vasco-Carofilis', 'Mhd Wesam Al-Nabki', 'Eduardo Fidalgo'] | 2020-05-18 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-1.68933511e-01 -2.46300638e-01 -2.87450552e-01 -2.52815306e-01
-2.68723428e-01 -4.87882465e-01 -3.48795392e-02 7.08798110e-01
-7.38314211e-01 3.07673633e-01 -4.29269940e-01 -5.34860551e-01
-2.29900554e-02 -8.54559481e-01 -5.48541665e-01 -5.17512441e-01
-3.41740549e-01 2.30458498e-01 -7.58250477e-03 3.49158645... | [12.814279556274414, 1.0264443159103394] |
96fd10a9-c6c7-47ed-ac21-134f748add22 | robust-video-background-identification-by | 1903.02232 | null | http://arxiv.org/abs/1903.02232v1 | http://arxiv.org/pdf/1903.02232v1.pdf | Robust Video Background Identification by Dominant Rigid Motion Estimation | The ability to identify the static background in videos captured by a moving
camera is an important pre-requisite for many video applications (e.g. video
stabilization, stitching, and segmentation). Existing methods usually face
difficulties when the foreground objects occupy a larger area than the
background in the im... | ['Nianjuan Jiang', 'Xun Xu', 'Loong Fah Cheong', 'Kaimo Lin', 'Jiangbo Lu'] | 2019-03-06 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 1.18823841e-01 -5.19128144e-01 -1.29837483e-01 1.32615164e-01
-4.90173757e-01 -7.87823737e-01 3.44539076e-01 -1.59603000e-01
-4.26293731e-01 5.89579523e-01 -2.54398733e-01 -1.29096776e-01
2.95200020e-01 -5.42102098e-01 -6.57243311e-01 -1.07969189e+00
-5.23612974e-03 3.02418292e-01 9.82392550e-01 1.48073956... | [8.894689559936523, -0.8621174097061157] |
08885234-13fa-4205-a3ac-3a57c14b344c | stochastic-distributed-optimization-under | 2304.07504 | null | https://arxiv.org/abs/2304.07504v1 | https://arxiv.org/pdf/2304.07504v1.pdf | Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis | We study finite-sum distributed optimization problems with $n$-clients under popular $\delta$-similarity condition and $\mu$-strong convexity. We propose two new algorithms: SVRS and AccSVRS motivated by previous works. The non-accelerated SVRS method combines the techniques of gradient-sliding and variance reduction, ... | ['Zhihua Zhang', 'Haishan Ye', 'Yuze Han', 'Dachao Lin'] | 2023-04-15 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 9.18116793e-02 -1.48117468e-01 -5.27017303e-02 -3.21146876e-01
-1.39553726e+00 -4.18924451e-01 -5.07466733e-01 4.84818704e-02
-7.57548153e-01 1.09605479e+00 -2.33199358e-01 -8.11265647e-01
-6.55948997e-01 -8.42578232e-01 -6.33164048e-01 -1.00469851e+00
-7.72774756e-01 2.32531279e-01 -9.86346155e-02 -4.48169470... | [6.323436260223389, 4.59801721572876] |
80cad6e2-aa61-4baf-8c08-406ed0fef065 | exemplar-based-video-colorization-with-long | 2303.15081 | null | https://arxiv.org/abs/2303.15081v1 | https://arxiv.org/pdf/2303.15081v1.pdf | Exemplar-based Video Colorization with Long-term Spatiotemporal Dependency | Exemplar-based video colorization is an essential technique for applications like old movie restoration. Although recent methods perform well in still scenes or scenes with regular movement, they always lack robustness in moving scenes due to their weak ability in modeling long-term dependency both spatially and tempor... | ['Yue Zhang', 'Jiatong Han', 'Yu Zhang', 'Mingdao Wang', 'Xianlin Zhang', 'Xueming Li', 'Siqi Chen'] | 2023-03-27 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-1.89170927e-01 -7.57428229e-01 -1.46347880e-01 -1.80265129e-01
2.15544701e-02 -2.92394340e-01 4.80649352e-01 -2.93547183e-01
-2.30684623e-01 7.14751720e-01 2.42663622e-01 8.45710002e-03
1.53424349e-02 -8.14752042e-01 -8.37472320e-01 -6.94283903e-01
-6.90514818e-02 -4.11610544e-01 7.57178664e-01 -4.26709026... | [11.062002182006836, -1.1892063617706299] |
13aed26d-91a7-4b48-970d-75afbd4fbdc8 | translation-scale-and-rotation-cross-modal | 2209.13801 | null | https://arxiv.org/abs/2209.13801v1 | https://arxiv.org/pdf/2209.13801v1.pdf | Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection | Integrating multispectral data in object detection, especially visible and infrared images, has received great attention in recent years. Since visible (RGB) and infrared (IR) images can provide complementary information to handle light variations, the paired images are used in many fields, such as multispectral pedest... | ['Xingxing Wei', 'Yinyan Wang', 'Maoxun Yuan'] | 2022-09-28 | null | null | null | null | ['pedestrian-detection', 'object-detection-in-aerial-images'] | ['computer-vision', 'computer-vision'] | [ 2.92768478e-01 -7.76382387e-01 1.23645112e-01 -7.04043508e-02
-7.22364247e-01 -6.74153686e-01 4.62088078e-01 -2.67236739e-01
-4.46075439e-01 1.99784234e-01 6.67787809e-03 -1.76358506e-01
-1.93025393e-03 -6.55108035e-01 -4.80152786e-01 -9.36418414e-01
5.38092613e-01 -3.08190465e-01 4.02109146e-01 -5.33562005... | [9.763856887817383, -1.3968803882598877] |
5f2d590b-1fc2-450c-83a6-23b8b2141318 | out-of-domain-intent-detection-considering | 2305.03237 | null | https://arxiv.org/abs/2305.03237v1 | https://arxiv.org/pdf/2305.03237v1.pdf | Out-of-Domain Intent Detection Considering Multi-turn Dialogue Contexts | Out-of-Domain (OOD) intent detection is vital for practical dialogue systems, and it usually requires considering multi-turn dialogue contexts. However, most previous OOD intent detection approaches are limited to single dialogue turns. In this paper, we introduce a context-aware OOD intent detection (Caro) framework t... | ['Yongbin Li', 'Fei Huang', 'Binyuan Hui', 'Yinhe Zheng', 'Hao Lang'] | 2023-05-05 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [ 3.04467320e-01 4.91898149e-01 -3.25134277e-01 -5.55055439e-01
-8.04953098e-01 -6.63300395e-01 8.24638784e-01 7.21807629e-02
-3.07393372e-01 4.66445923e-01 6.23349547e-01 -3.39873016e-01
5.29503345e-01 -2.18607992e-01 -7.67847598e-02 -1.74934313e-01
2.82802850e-01 5.67315161e-01 3.18311378e-02 -5.53241134... | [12.663859367370605, 7.836772441864014] |
47cd8347-ef83-4755-bd8a-5174bc488e57 | a-novel-distributed-representation-of-news | 2005.11706 | null | https://arxiv.org/abs/2005.11706v2 | https://arxiv.org/pdf/2005.11706v2.pdf | A Novel Distributed Representation of News (DRNews) for Stock Market Predictions | In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and po... | ['Ye Ma', 'Peiwan Wang', 'Lu Zong'] | 2020-05-24 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-8.44648123e-01 2.28190362e-01 -6.83244824e-01 -1.74902275e-01
-2.92036712e-01 -3.55390221e-01 1.20344174e+00 3.14335942e-01
-3.06008905e-01 7.10414529e-01 1.50874829e+00 -3.49377126e-01
1.68082956e-02 -1.26537490e+00 -5.58118880e-01 -2.50309706e-01
-1.66570410e-01 2.57413387e-01 1.67856902e-01 -5.71156204... | [4.403802871704102, 4.2868971824646] |
3ec6ef3f-a7bb-4e21-b15e-5386263b0c84 | maevi-motion-aware-event-based-video-frame | 2303.02025 | null | https://arxiv.org/abs/2303.02025v1 | https://arxiv.org/pdf/2303.02025v1.pdf | MAEVI: Motion Aware Event-Based Video Frame Interpolation | Utilization of event-based cameras is expected to improve the visual quality of video frame interpolation solutions. We introduce a learning-based method to exploit moving region boundaries in a video sequence to increase the overall interpolation quality.Event cameras allow us to determine moving areas precisely; and ... | ['A. Aydin Alatan', 'Onur Selim Kılıç', 'Ahmet Akman'] | 2023-03-03 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 6.81626722e-02 -4.28064793e-01 -1.60936534e-01 -2.96069413e-01
-5.48358798e-01 -3.16055976e-02 2.74963826e-01 6.75756717e-03
-3.74003202e-01 1.10258329e+00 7.13581890e-02 -3.32296104e-03
9.84086543e-02 -5.14708936e-01 -7.36986816e-01 -7.90830672e-01
-4.56226796e-01 -6.24943554e-01 6.23053730e-01 2.48938799... | [10.996702194213867, -1.7297922372817993] |
b14aa7f0-287f-4888-ab89-0c27f93e9ce0 | content-based-detection-of-temporal-metadata | 2103.04736 | null | https://arxiv.org/abs/2103.04736v2 | https://arxiv.org/pdf/2103.04736v2.pdf | Content-Aware Detection of Temporal Metadata Manipulation | Most pictures shared online are accompanied by temporal metadata (i.e., the day and time they were taken), which makes it possible to associate an image content with real-world events. Maliciously manipulating this metadata can convey a distorted version of reality. In this work, we present the emerging problem of dete... | ['Nathan Jacobs', 'Anderson Rocha', 'Fernanda A. Andaló', 'Scott Workman', 'Tawfiq Salem', 'Rafael Padilha'] | 2021-03-08 | content-aware-detection-of-temporal-metadata | https://rafaspadilha.github.io/publication/padilha22content/ | https://rafaspadilha.github.io/publication/padilha22content/ | ieee-transactions-on-information-forensics-8 | ['temporal-metadata-manipulation-detection'] | ['computer-vision'] | [ 4.53363955e-01 -1.70824915e-01 -2.53732443e-01 -2.49520555e-01
-6.57343268e-01 -9.38799083e-01 8.14590156e-01 3.00445914e-01
-3.73323262e-01 5.27814567e-01 -1.86883304e-02 -2.84059137e-01
1.76933587e-01 -7.60402322e-01 -1.07492459e+00 -5.99553049e-01
-3.48714262e-01 -1.75296962e-01 4.33647335e-01 3.07547212... | [12.341078758239746, 1.0217957496643066] |
9010852d-bb3f-40b5-9ab2-f5a1539b9f3d | image-smoothing-algorithm-based-on-gradient | null | null | https://ieeexplore.ieee.org/document/9117646 | https://ieeexplore.ieee.org/document/9117646 | Image Smoothing Algorithm Based on Gradient Analysis | In this paper image smoothing algorithm based on gradient analysis is proposed. Our algorithm uses filtering and to achieve edge-preserving smoothing it uses two components of gradient vectors: their magnitudes (or lengths) and directions. Our method discriminates between two types of boundaries in given neighborhood: ... | ['Ilia Moiseev', 'Vladimir Gudkov'] | 2020-06-16 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 2.10471243e-01 -1.36731789e-01 1.06901936e-01 -3.91176194e-01
2.16546580e-01 -5.73349595e-01 5.97710073e-01 6.34272873e-01
-6.52095914e-01 9.15585279e-01 5.53923249e-01 -2.74501443e-01
-1.40349194e-01 -1.09884286e+00 -2.61210978e-01 -6.09505773e-01
-2.09981635e-01 -1.04272023e-01 9.04225826e-01 -4.17628944... | [11.006725311279297, -2.49760365486145] |
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