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9a9742ab-45c1-4b00-a493-baece26f0253 | node-embedding-from-neural-hamiltonian-orbits | 2305.18965 | null | https://arxiv.org/abs/2305.18965v1 | https://arxiv.org/pdf/2305.18965v1.pdf | Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks | In the graph node embedding problem, embedding spaces can vary significantly for different data types, leading to the need for different GNN model types. In this paper, we model the embedding update of a node feature as a Hamiltonian orbit over time. Since the Hamiltonian orbits generalize the exponential maps, this ap... | ['Wee Peng Tay', 'Sijie Wang', 'Yang song', 'Kai Zhao', 'Qiyu Kang'] | 2023-05-30 | null | null | null | null | ['graph-embedding', 'link-prediction'] | ['graphs', 'graphs'] | [-4.27913696e-01 5.87152839e-01 -2.56221384e-01 5.51310778e-02
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-4.41063434e-01 5.96797645e-01 2.59378731e-01 -4.68924046... | [7.027192115783691, 5.8788628578186035] |
90a177a2-e4fb-4961-9c92-5903a5504a39 | automated-pulmonary-embolism-detection-from | 2111.05506 | null | https://arxiv.org/abs/2111.05506v1 | https://arxiv.org/pdf/2111.05506v1.pdf | Automated Pulmonary Embolism Detection from CTPA Images Using an End-to-End Convolutional Neural Network | Automated methods for detecting pulmonary embolisms (PEs) on CT pulmonary angiography (CTPA) images are of high demand. Existing methods typically employ separate steps for PE candidate detection and false positive removal, without considering the ability of the other step. As a result, most existing methods usually su... | ['Xin Yang', 'Kwang-Ting Cheng', 'Jingen Liu', 'Xiang Li', 'Xiang Wang', 'Jianchao Su', 'Yi Lin'] | 2021-11-10 | null | null | null | null | ['pulmonary-embolism-detection'] | ['medical'] | [-1.01566382e-01 4.24616560e-02 1.85681269e-01 1.40731141e-01
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7688ebe2-2be4-48cf-b3f5-12f3a2b689ea | multi-object-video-generation-from-single | 2305.03983 | null | https://arxiv.org/abs/2305.03983v2 | https://arxiv.org/pdf/2305.03983v2.pdf | Multi-object Video Generation from Single Frame Layouts | In this paper, we study video synthesis with emphasis on simplifying the generation conditions. Most existing video synthesis models or datasets are designed to address complex motions of a single object, lacking the ability of comprehensively understanding the spatio-temporal relationships among multiple objects. Besi... | ['Liang Lin', 'Hefeng Wu', 'Zhibin Liu', 'Yang Wu'] | 2023-05-06 | null | null | null | null | ['video-recognition', 'video-generation'] | ['computer-vision', 'computer-vision'] | [ 6.98727429e-01 -4.19188403e-02 -1.98914409e-01 -9.75410938e-02
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2.52181560e-01 2.11152032e-01 2.25631073e-01 -8.91035423... | [10.789131164550781, -0.543785810470581] |
c0b3ebe1-5b9d-44e3-a5ce-475a718189a9 | learning-generalisable-omni-scale | 1910.06827 | null | https://arxiv.org/abs/1910.06827v5 | https://arxiv.org/pdf/1910.06827v5.pdf | Learning Generalisable Omni-Scale Representations for Person Re-Identification | An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation. In this paper, we develop novel CNN architectures to address both challenges. First,... | ['Andrea Cavallaro', 'Yongxin Yang', 'Tao Xiang', 'Kaiyang Zhou'] | 2019-10-15 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-2.40823016e-01 -4.62574542e-01 7.68525293e-03 -7.42796183e-01
-5.46011567e-01 -6.23181522e-01 6.38518572e-01 -1.31608322e-01
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-2.10877389e-01 4.57376122e-01 -1.01531891e-03 -2.96879619... | [14.702816009521484, 0.9752671718597412] |
2f2564db-7e70-432b-a478-1f88db9adb82 | refer-itts-a-system-for-referring-in-spoken | null | null | https://aclanthology.org/W17-3509 | https://aclanthology.org/W17-3509.pdf | Refer-iTTS: A System for Referring in Spoken Installments to Objects in Real-World Images | Current referring expression generation systems mostly deliver their output as one-shot, written expressions. We present on-going work on incremental generation of spoken expressions referring to objects in real-world images. This approach extends upon previous work using the words-as-classifier model for generation. W... | ["M. Soledad L{\\'o}pez Gambino", 'Sina Zarrie{\\ss}', 'David Schlangen'] | 2017-09-01 | null | null | null | ws-2017-9 | ['referring-expression-generation'] | ['computer-vision'] | [ 4.19733405e-01 7.27003038e-01 6.07459173e-02 -8.05811524e-01
-1.18342233e+00 -5.84482551e-01 1.20429015e+00 -2.16336846e-01
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0db96baa-fd54-48fd-a891-76d1907d1f46 | eica-team-at-semeval-2017-task-3-semantic-and | null | null | https://aclanthology.org/S17-2047 | https://aclanthology.org/S17-2047.pdf | EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering | We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important group turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the ma... | ['Jian Jiang', 'Yufei Xie', 'Maoquan Wang', 'Zhao Lu', 'Jing Ma'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [-3.17372799e-01 -8.24790541e-03 -6.08530305e-02 -2.85938025e-01
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1.72137380e-01 8.92474115e-01 5.22819936e-01 -5.55283010... | [11.383204460144043, 7.999159812927246] |
19fbe579-d7fe-4bbd-88c8-de6557c0eaa6 | vren-volleyball-rally-dataset-with-expression | 2209.13846 | null | https://arxiv.org/abs/2209.13846v1 | https://arxiv.org/pdf/2209.13846v1.pdf | VREN: Volleyball Rally Dataset with Expression Notation Language | This research is intended to accomplish two goals: The first goal is to curate a large and information rich dataset that contains crucial and succinct summaries on the players' actions and positions and the back-and-forth travel patterns of the volleyball in professional and NCAA Div-I indoor volleyball games. While se... | ['Linda Petzold', 'Yuan-Fang Wang', 'Erwan Fraisse', 'Yun Zhao', 'Rhys Tracy', 'Haotian Xia'] | 2022-09-28 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-2.33808070e-01 -5.60299277e-01 -5.52738488e-01 -7.71271512e-02
-7.28347898e-01 -6.68921649e-01 1.15234137e-01 3.04529607e-01
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-3.07540059e-01 5.89471459e-01 3.70947480e-01 -8.41065705... | [6.635614395141602, 0.37314194440841675] |
b937b9fb-6b4c-4eec-93a7-3d949ba0f4a2 | design-challenges-for-entity-linking | null | null | https://aclanthology.org/Q15-1023 | https://aclanthology.org/Q15-1023.pdf | Design Challenges for Entity Linking | Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions... | ['Xiao Ling', 'Sameer Singh', 'Daniel S. Weld'] | 2015-01-01 | null | null | null | tacl-2015-1 | ['type-prediction'] | ['computer-code'] | [-4.13627326e-01 6.32521451e-01 -8.05267155e-01 -2.44251937e-01
-8.33544910e-01 -6.19076610e-01 5.55634081e-01 5.04299939e-01
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-4.59665567e-01 1.22511768e+00 2.90579557e-01 -3.47866535... | [9.402423858642578, 8.75474739074707] |
40aaad8b-dc56-4f5b-b512-e86147a077ff | conformalized-fairness-via-quantile | 2210.02015 | null | https://arxiv.org/abs/2210.02015v2 | https://arxiv.org/pdf/2210.02015v2.pdf | Conformalized Fairness via Quantile Regression | Algorithmic fairness has received increased attention in socially sensitive domains. While rich literature on mean fairness has been established, research on quantile fairness remains sparse but vital. To fulfill great needs and advocate the significance of quantile fairness, we propose a novel framework to learn a rea... | ['Bei Jiang', 'Linglong Kong', 'Wulong Liu', 'Dengdeng Yu', 'Lei Ding', 'Meichen Liu'] | 2022-10-05 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 1.61626905e-01 2.26361811e-01 -6.99761927e-01 -1.02979541e+00
-8.92576635e-01 -5.34286499e-01 2.26925477e-01 5.72659671e-01
-4.59302366e-01 1.20472252e+00 5.46214223e-01 -4.99391913e-01
-4.76227641e-01 -8.63516986e-01 -3.16151202e-01 -3.21946323e-01
-3.61827463e-01 3.71786803e-01 -3.52174073e-01 -3.01165376... | [8.892563819885254, 5.2859015464782715] |
40b066e5-8496-430b-84b6-d10470e0250c | adversarial-inference-for-multi-sentence | 1812.05634 | null | http://arxiv.org/abs/1812.05634v2 | http://arxiv.org/pdf/1812.05634v2.pdf | Adversarial Inference for Multi-Sentence Video Description | While significant progress has been made in the image captioning task, video
description is still in its infancy due to the complex nature of video data.
Generating multi-sentence descriptions for long videos is even more
challenging. Among the main issues are the fluency and coherence of the
generated descriptions, an... | ['Anna Rohrbach', 'Jae Sung Park', 'Trevor Darrell', 'Marcus Rohrbach'] | 2018-12-13 | adversarial-inference-for-multi-sentence-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Park_Adversarial_Inference_for_Multi-Sentence_Video_Description_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Park_Adversarial_Inference_for_Multi-Sentence_Video_Description_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-description'] | ['computer-vision'] | [ 3.03255886e-01 -4.69142906e-02 -1.92299187e-01 -2.23033711e-01
-9.96331871e-01 -6.28961921e-01 7.63206959e-01 -2.13104606e-01
-1.40676692e-01 9.91968751e-01 5.48189163e-01 2.62396634e-01
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3.22491705e-01 2.42015630e-01 5.78808673e-02 -1.60704717... | [10.96462345123291, 0.6095175743103027] |
61ff6a9d-3664-4f8a-888f-4af579d49a66 | hierarchical-aggregation-of-dialectal-data | null | null | https://aclanthology.org/2022.lrec-1.489 | https://aclanthology.org/2022.lrec-1.489.pdf | Hierarchical Aggregation of Dialectal Data for Arabic Dialect Identification | Arabic is a collection of dialectal variants that are historically related but significantly different. These differences can be seen across regions, countries, and even cities in the same countries. Previous work on Arabic Dialect identification has focused mainly on specific dialect levels (region, country, province,... | ['Nizar Habash', 'Houda Bouamor', 'Nurpeiis Baimukan'] | null | null | null | null | lrec-2022-6 | ['dialect-identification'] | ['natural-language-processing'] | [-7.34740555e-01 -4.10936832e-01 -9.29156095e-02 -4.98303950e-01
-7.79339612e-01 -1.29030859e+00 9.54029202e-01 4.42343354e-01
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-2.63105128e-02 -1.12840080e+00 1.95184653e-03 -3.97614717e-01
5.99531308e-02 8.05545568e-01 3.37530106e-01 -1.06416845... | [10.199626922607422, 10.711024284362793] |
cd85f778-c3b3-4a05-b5a6-07f30f58ec70 | can-domain-pre-training-help | null | null | https://aclanthology.org/2021.nlp4dh-1.14 | https://aclanthology.org/2021.nlp4dh-1.14.pdf | Can Domain Pre-training Help Interdisciplinary Researchers from Data Annotation Poverty? A Case Study of Legal Argument Mining with BERT-based Transformers | Interdisciplinary Natural Language Processing (NLP) research traditionally suffers from the requirement for costly data annotation. However, transformer frameworks with pre-training have shown their ability on many downstream tasks including digital humanities tasks with limited small datasets. Considering the fact tha... | ['Paul Nulty', 'David Lillis', 'Gechuan Zhang'] | null | null | null | null | nlp4dh-icon-2021-12 | ['argument-mining'] | ['natural-language-processing'] | [ 2.35531226e-01 6.97341621e-01 -6.04824603e-01 -2.00904325e-01
-9.96060789e-01 -8.64110053e-01 1.10848880e+00 4.96302783e-01
-3.97233963e-01 8.50890219e-01 6.77812397e-01 -1.08576119e+00
-5.48533738e-01 -8.06372225e-01 -6.58819914e-01 -1.05648793e-01
3.30019653e-01 1.01980865e+00 4.43268210e-01 -6.53596103... | [9.705301284790039, 9.414767265319824] |
582194da-f026-4eea-9280-5666f1e3a68f | one-shot-doc-snippet-detection-powering | 2209.06584 | null | https://arxiv.org/abs/2209.06584v1 | https://arxiv.org/pdf/2209.06584v1.pdf | One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text | Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text matching problem ignoring the rich layout and visual cues commonly present in structured documents, forms, etc. To that end, we ask a mostl... | ['Balaji Krishnamurthy', 'Mausoom Sarkar', 'Surgan Jandial', 'Milan Aggarwal', 'Shripad Deshmukh', 'Abhinav Java'] | 2022-09-12 | null | null | null | null | ['one-shot-object-detection', 'template-matching'] | ['computer-vision', 'computer-vision'] | [ 4.81995285e-01 -3.05893958e-01 -1.58909589e-01 -2.36648902e-01
-1.01502323e+00 -1.02422452e+00 8.54820967e-01 6.78717434e-01
-1.60216510e-01 3.57282581e-03 3.64764035e-01 -4.70003188e-01
-3.51330966e-01 -4.59001154e-01 -8.23029876e-01 -1.34249359e-01
3.02398741e-01 4.97619569e-01 6.30976737e-01 -6.67911395... | [11.548048973083496, 2.2812037467956543] |
71fb3303-68a2-42a8-8b5e-ee387ce13bb2 | semantic-and-syntactic-enhanced-aspect | 2106.03315 | null | https://arxiv.org/abs/2106.03315v1 | https://arxiv.org/pdf/2106.03315v1.pdf | Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from sentences, where each triplet includes an entity, its associated sentiment, and the opinion span explaining the reason for the sentiment. Most existing research addresses this problem in a multi-stage pipeline manner, which neglects the mutual inf... | ['Hai Jin', 'Xuanhua Shi', 'Bang Liu', 'Hong Huang', 'Zhexue Chen'] | 2021-06-07 | null | https://aclanthology.org/2021.findings-acl.128 | https://aclanthology.org/2021.findings-acl.128.pdf | findings-acl-2021-8 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 1.81466490e-01 1.14383437e-01 -1.22764066e-01 -6.02934480e-01
-4.15840030e-01 -5.14232457e-01 3.06982249e-01 4.07593548e-01
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2.17348039e-01 2.06854522e-01 1.03009155e-03 -3.29800963... | [11.519247055053711, 6.619039535522461] |
5c600441-50aa-46f0-b140-bb66842aa4b4 | draformer-differentially-reconstructed | 2206.05495 | null | https://arxiv.org/abs/2206.05495v1 | https://arxiv.org/pdf/2206.05495v1.pdf | DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting | Time-series forecasting plays an important role in many real-world scenarios, such as equipment life cycle forecasting, weather forecasting, and traffic flow forecasting. It can be observed from recent research that a variety of transformer-based models have shown remarkable results in time-series forecasting. However,... | ['Zhen Jia', 'Jie Hu', 'Tianrui Li', 'Shengdong Du', 'Benhan Li'] | 2022-06-11 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 3.29614848e-01 -6.78442955e-01 -5.48830703e-02 -2.16809273e-01
-4.51980114e-01 -4.83548939e-01 4.81084675e-01 -1.69433281e-01
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-1.19066603e-01 -5.14680803e-01 -5.96832037e-01 -9.80459988e-01
-1.71934292e-01 -1.34591945e-02 3.10114861e-01 -5.66691816... | [7.0132951736450195, 2.9655921459198] |
43a81ecf-dcae-4bdc-9694-04ed07e6dbf2 | learn-the-big-picture-representation-learning | null | null | https://aclanthology.org/2021.repl4nlp-1.15 | https://aclanthology.org/2021.repl4nlp-1.15.pdf | Learn The Big Picture: Representation Learning for Clustering | Existing supervised models for text clustering find it difficult to directly optimize for clustering results. This is because clustering is a discrete process and it is difficult to estimate meaningful gradient of any discrete function that can drive gradient based optimization algorithms. So, existing supervised clust... | ['Laura Dietz', 'Sumanta Kashyapi'] | null | null | null | null | acl-repl4nlp-2021-8 | ['text-clustering'] | ['natural-language-processing'] | [-2.93001205e-01 -1.39797181e-01 -3.39254797e-01 -8.82251799e-01
-1.19871581e+00 -5.51712096e-01 6.93450332e-01 5.60609877e-01
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-2.73236692e-01 1.16744649e+00 -1.92998409e-01 3.40348631... | [9.133063316345215, 3.2735660076141357] |
23a576a3-1565-4fef-bbb7-e625fba4f5c7 | explanatory-analysis-and-rectification-of-the | 2111.05679 | null | https://arxiv.org/abs/2111.05679v1 | https://arxiv.org/pdf/2111.05679v1.pdf | Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets | Since the onset of the COVID-19 pandemic in 2020, millions of people have succumbed to this deadly virus. Many attempts have been made to devise an automated method of testing that could detect the virus. Various researchers around the globe have proposed deep learning based methodologies to detect the COVID-19 using C... | ['Chandra Prakash', 'Yuvraj Singh Champawat', 'Amrit Raj', 'Shaanya Singh', 'Japman Singh Monga', 'Samyak Prajapati'] | 2021-11-10 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.69918942e-01 -8.62112194e-02 2.47272179e-01 -3.94114941e-01
-4.76473421e-01 -3.08650821e-01 5.49232483e-01 1.98542595e-01
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-9.25175995e-02 6.44218326e-01 3.21309626e-01 -1.38512452... | [15.561485290527344, -1.7564095258712769] |
76d2a872-9c60-4198-b317-43a3e97da3b8 | gender-prediction-in-english-hindi-code-mixed | 1806.056 | null | http://arxiv.org/abs/1806.05600v1 | http://arxiv.org/pdf/1806.05600v1.pdf | Gender Prediction in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System | The rapid expansion in the usage of social media networking sites leads to a
huge amount of unprocessed user generated data which can be used for text
mining. Author profiling is the problem of automatically determining profiling
aspects like the author's gender and age group through a text is gaining much
popularity i... | ['Ankush Khandelwal', 'Manish Shrivastava', 'Syed Sarfaraz Akhtar', 'Sahil Swami'] | 2018-06-14 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-1.79481357e-01 4.41500619e-02 -2.81474710e-01 -3.16278219e-01
-4.44905937e-01 -6.83956027e-01 7.31829703e-01 9.06027198e-01
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1.17819890e-01 -5.89544237e-01 1.03995241e-01 -3.69136006e-01
5.24015129e-02 5.31412125e-01 6.13026088e-04 -2.97963053... | [9.473895072937012, 10.343740463256836] |
4b59b1eb-742b-4dde-8bea-fa828d726c49 | counterfactual-reasoning-do-language-models | 2212.03278 | null | https://arxiv.org/abs/2212.03278v1 | https://arxiv.org/pdf/2212.03278v1.pdf | Counterfactual reasoning: Do language models need world knowledge for causal understanding? | Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on understanding of the real world. In this paper we tease these factors apart by leveraging counterfac... | ['Allyson Ettinger', 'Lang Yu', 'Jiaxuan Li'] | 2022-12-06 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 4.35587168e-02 5.93979418e-01 -2.86750495e-01 -3.43212247e-01
-6.15637124e-01 -6.65194035e-01 1.26976728e+00 3.67468059e-01
-5.55547059e-01 1.27322888e+00 1.00996780e+00 -9.35688674e-01
-2.80433059e-01 -1.08292401e+00 -1.03818440e+00 -2.01939717e-01
-3.70994151e-01 2.59503454e-01 1.37595400e-01 -4.81288671... | [9.928193092346191, 7.9254350662231445] |
ec9a9ce9-7e9f-44fd-9923-9e3fae99e126 | real-time-multi-view-3d-human-pose-estimation | 2106.14729 | null | https://arxiv.org/abs/2106.14729v1 | https://arxiv.org/pdf/2106.14729v1.pdf | Real-Time Multi-View 3D Human Pose Estimation using Semantic Feedback to Smart Edge Sensors | We present a novel method for estimation of 3D human poses from a multi-camera setup, employing distributed smart edge sensors coupled with a backend through a semantic feedback loop. 2D joint detection for each camera view is performed locally on a dedicated embedded inference processor. Only the semantic skeleton rep... | ['Sven Behnke', 'Simon Bultmann'] | 2021-06-28 | null | null | null | null | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [ 6.96436465e-02 4.16682839e-01 1.93150610e-01 -3.45134377e-01
-7.09584713e-01 -4.04144496e-01 2.86371738e-01 7.45809004e-02
-8.28832448e-01 1.85551226e-01 2.92752028e-01 7.43877828e-01
2.40523845e-01 -6.90443099e-01 -8.74750018e-01 -1.47496700e-01
1.38841523e-02 1.05160582e+00 8.43756378e-01 -1.30077332... | [7.087376117706299, -0.9466521739959717] |
1910fc05-ef52-49e6-8a3a-17962e0caac9 | an-algorithm-for-automatically-updating-a | 2009.03193 | null | https://arxiv.org/abs/2009.03193v2 | https://arxiv.org/pdf/2009.03193v2.pdf | An Algorithm for Automatically Updating a Forsyth-Edwards Notation String Without an Array Board Representation | We present an algorithm that correctly updates the Forsyth-Edwards Notation (FEN) chessboard character string after any move is made without the need for an intermediary array representation of the board. In particular, this relates to software that have to do with chess, certain chess variants and possibly even simila... | ['Azlan Iqbal'] | 2020-09-02 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 4.20795381e-01 6.57189116e-02 4.07964200e-01 -5.54905720e-02
-5.38830519e-01 -1.09432399e+00 3.63441527e-01 5.01603186e-01
-4.58192229e-01 6.64902627e-01 -3.30187529e-01 -1.07597303e+00
-6.84734210e-02 -1.01376808e+00 -5.63667774e-01 -2.21775770e-01
-1.16213292e-01 4.24054921e-01 8.17620516e-01 -5.67962408... | [8.055740356445312, 7.120926856994629] |
3221d66c-2495-4dcb-bc24-4c8f939d0b75 | learning-residual-flow-as-dynamic-motion-from | 1909.06999 | null | https://arxiv.org/abs/1909.06999v1 | https://arxiv.org/pdf/1909.06999v1.pdf | Learning Residual Flow as Dynamic Motion from Stereo Videos | We present a method for decomposing the 3D scene flow observed from a moving stereo rig into stationary scene elements and dynamic object motion. Our unsupervised learning framework jointly reasons about the camera motion, optical flow, and 3D motion of moving objects. Three cooperating networks predict stereo matching... | ['In So Kweon', 'Stephen Lin', 'Seokju Lee', 'Sunghoon Im'] | 2019-09-16 | null | null | null | null | ['stereo-matching', 'depth-and-camera-motion'] | ['computer-vision', 'computer-vision'] | [-1.70980126e-01 -2.70062834e-01 -2.95661628e-01 -2.29191944e-01
1.28016084e-01 -6.69273257e-01 6.13361061e-01 -7.00433373e-01
-2.96594292e-01 3.57355505e-01 4.08902913e-01 1.76474769e-02
1.26869991e-01 -4.75288093e-01 -5.79528809e-01 -5.24500251e-01
7.14158192e-02 6.05273545e-01 3.75547945e-01 4.39638823... | [8.61208438873291, -2.027155637741089] |
8cf84fe5-87e3-484a-8299-8ac70bd6a9f4 | visual-place-recognition-a-tutorial | 2303.03281 | null | https://arxiv.org/abs/2303.03281v1 | https://arxiv.org/pdf/2303.03281v1.pdf | Visual Place Recognition: A Tutorial | Localization is an essential capability for mobile robots. A rapidly growing field of research in this area is Visual Place Recognition (VPR), which is the ability to recognize previously seen places in the world based solely on images. This present work is the first tutorial paper on visual place recognition. It unifi... | ['Tobias Fischer', 'Michael Milford', 'Sourav Garg', 'Peer Neubert', 'Stefan Schubert'] | 2023-03-06 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 4.54792157e-02 -2.23991185e-01 -1.23977877e-01 -3.04218531e-01
-5.85912168e-01 -9.91224766e-01 6.27801061e-01 3.49448889e-01
-6.03432715e-01 3.32981348e-01 -1.37227714e-01 -5.78145027e-01
-1.18466653e-01 -5.43401361e-01 -7.64698863e-01 -3.60799700e-01
-2.23430797e-01 4.14094895e-01 4.88502711e-01 -3.65674913... | [7.500015735626221, -1.8724173307418823] |
fa7204b7-ae65-4c8b-8100-15e7f619149f | ju_nlp-at-semeval-2016-task-11-identifying | null | null | https://aclanthology.org/S16-1152 | https://aclanthology.org/S16-1152.pdf | JU\_NLP at SemEval-2016 Task 11: Identifying Complex Words in a Sentence | null | ['B', 'Niloy Mukherjee', 'Dipankar Das', 'Braja Gopal Patra', 'Sivaji yopadhyay'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['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... | [-7.303244113922119, 3.6287333965301514] |
c696fd50-8a56-44d0-aa4e-e4903704e08d | sok-explainable-machine-learning-for-computer | 2208.10605 | null | https://arxiv.org/abs/2208.10605v2 | https://arxiv.org/pdf/2208.10605v2.pdf | SoK: Explainable Machine Learning for Computer Security Applications | Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine learning (ML) pipelines. We systematize the increasingly growing (but fragmented) microcosm of studies that develop and utilize XAI methods for defensive and offensive cybersecurity tasks. We identify 3 cybersecurity stakeholders, i.e... | ['Sicco Verwer', 'Robert Baumgartner', 'Simon Dieck', 'Luca Pajola', 'Clinton Cao', 'Daniël Vos', 'Azqa Nadeem'] | 2022-08-22 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 2.33200431e-01 6.60317063e-01 -2.36619905e-01 -1.02429733e-01
-1.02811791e-01 -1.33175540e+00 7.34495580e-01 2.11386204e-01
1.93764612e-01 7.47217238e-02 1.16434887e-01 -1.60454464e+00
-4.38301653e-01 -4.06670839e-01 -6.00463986e-01 -1.13004230e-01
1.70603946e-01 1.18172586e-01 -5.60824692e-01 7.29052648... | [8.716739654541016, 6.153114318847656] |
0167121c-d59b-4923-90e5-15fc40c08d6c | hyspa-hybrid-span-generation-for-scalable | 2106.15838 | null | https://arxiv.org/abs/2106.15838v1 | https://arxiv.org/pdf/2106.15838v1.pdf | HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction | Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts. Existing approaches, such as table filling and pairwise scoring, have shown impressive performance on various information extraction tasks, but they are difficult to scale to datasets ... | ['Julia Hockenmaier', 'Heng Ji', 'Chenkai Sun', 'Liliang Ren'] | 2021-06-30 | null | https://aclanthology.org/2021.findings-acl.356 | https://aclanthology.org/2021.findings-acl.356.pdf | findings-acl-2021-8 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 4.23070669e-01 5.00163317e-01 -3.69268984e-01 -1.43802181e-01
-1.14192367e+00 -8.86093676e-01 4.63258207e-01 6.25458241e-01
-1.95548788e-01 9.23875928e-01 1.37269318e-01 -6.45634413e-01
1.01392528e-04 -1.07150710e+00 -6.75858855e-01 -6.78338856e-02
-2.17249721e-01 9.25162971e-01 3.19506079e-01 -1.23771749... | [9.429912567138672, 8.657818794250488] |
a95a6b43-ccd0-4ad4-a353-76f397d43267 | unsupervised-pansharpening-via-low-rank | 2305.10925 | null | https://arxiv.org/abs/2305.10925v1 | https://arxiv.org/pdf/2305.10925v1.pdf | Unsupervised Pansharpening via Low-rank Diffusion Model | Pansharpening is a process of merging a highresolution panchromatic (PAN) image and a low-resolution multispectral (LRMS) image to create a single high-resolution multispectral (HRMS) image. Most of the existing deep learningbased pansharpening methods have poor generalization ability and the traditional model-based pa... | ['Deyu Meng', 'Zongsheng Yue', 'Zeyu Zhu', 'Xiangyong Cao', 'Xiangyu Rui'] | 2023-05-18 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 4.07732338e-01 -6.42669022e-01 -1.13803372e-01 5.47497720e-02
-6.66237175e-01 -3.83992732e-01 4.67354864e-01 -4.49923813e-01
-2.81471163e-01 3.84466082e-01 1.24432497e-01 -3.34654003e-01
-5.12739420e-01 -1.09229827e+00 -4.83687103e-01 -1.25049353e+00
1.16477363e-01 1.64793521e-01 -2.11181082e-02 -2.23371565... | [10.195398330688477, -1.9208978414535522] |
074f0690-d17e-46bb-aaaa-a06ef5fba61b | convolutional-oriented-boundaries-from-image | 1701.04658 | null | http://arxiv.org/abs/1701.04658v2 | http://arxiv.org/pdf/1701.04658v2.pdf | Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks | We present Convolutional Oriented Boundaries (COB), which produces multiscale
oriented contours and region hierarchies starting from generic image
classification Convolutional Neural Networks (CNNs). COB is computationally
efficient, because it requires a single CNN forward pass for multi-scale
contour detection and it... | ['Luc van Gool', 'Pablo Arbeláez', 'Jordi Pont-Tuset', 'Kevis-Kokitsi Maninis'] | 2017-01-17 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 1.58572838e-01 1.33791283e-01 -2.57514138e-02 -3.73979867e-01
-9.82383847e-01 -7.90253103e-01 3.84801477e-01 3.52960110e-01
-4.87943739e-01 1.51142821e-01 -2.21937388e-01 -2.28802964e-01
5.31336963e-01 -8.75946879e-01 -7.49766946e-01 -3.02908808e-01
-3.13853383e-01 6.39019907e-01 1.38600993e+00 -1.93242535... | [9.473952293395996, 0.1971074342727661] |
e96283b6-8475-41ed-a5a4-e75ecd88f553 | discrete-simulation-optimization-for-tuning | 2201.05978 | null | https://arxiv.org/abs/2201.05978v3 | https://arxiv.org/pdf/2201.05978v3.pdf | Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters | Machine learning (ML) methods are used in most technical areas such as image recognition, product recommendation, financial analysis, medical diagnosis, and predictive maintenance. An important aspect of implementing ML methods involves controlling the learning process for the ML method so as to maximize the performanc... | ['Nomesh Bhojkumar Bolia', 'Aditya Raj Gupta', 'Shobhit Singhal', 'Varun Ramamohan'] | 2022-01-16 | null | null | null | null | ['product-recommendation', 'time-series-prediction'] | ['miscellaneous', 'time-series'] | [ 1.06510716e-02 -2.40025759e-01 -5.17668426e-01 -9.04806182e-02
-8.68646562e-01 -3.62108111e-01 2.20551819e-01 6.43622829e-03
-5.13942897e-01 6.86405361e-01 -6.78941071e-01 -4.50823605e-01
-6.67205095e-01 -7.45090783e-01 -6.19117498e-01 -9.94339883e-01
-1.74539968e-01 6.40229762e-01 -1.45946577e-01 1.27671018... | [6.6205620765686035, 3.9859941005706787] |
fe5f4c49-6cbc-4c8b-bd82-89d0a19ce392 | an-optimal-algorithm-for-finding-champions-in | 2111.13621 | null | https://arxiv.org/abs/2111.13621v4 | https://arxiv.org/pdf/2111.13621v4.pdf | An Optimal Algorithm for Finding Champions in Tournament Graphs | A tournament graph is a complete directed graph, which can be used to model a round-robin tournament between $n$ players. In this paper, we address the problem of finding a champion of the tournament, also known as Copeland winner, which is a player that wins the highest number of matches. In detail, we aim to investig... | ['Rossano Venturini', 'Roberto Trani', 'Franco Maria Nardini', 'Lorenzo Beretta'] | 2021-11-26 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 2.37949733e-02 5.37983440e-02 3.30135450e-02 -3.24000269e-01
-9.82911944e-01 -8.54283571e-01 -2.76611485e-02 4.32796091e-01
-7.18051195e-01 5.81670165e-01 -4.89764959e-01 -4.16744292e-01
-8.82443488e-01 -1.34595847e+00 -1.04958963e+00 -5.24361432e-01
-2.69808233e-01 1.32246149e+00 3.05489868e-01 -7.17898428... | [6.729135513305664, 4.97307014465332] |
163911d2-0b4c-4956-84d6-af252ddad9d8 | priorband-practical-hyperparameter | 2306.1237 | null | https://arxiv.org/abs/2306.12370v1 | https://arxiv.org/pdf/2306.12370v1.pdf | PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning | Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been developed, their incurred costs are often untenable for modern DL. Consequently, manual experimentation is still the most prevalent approach to op... | ['Frank Hutter', 'Luigi Nardi', 'Marius Lindauer', 'Maciej Janowski', 'Danny Stoll', 'Carl Hvarfner', 'Edward Bergman', 'Neeratyoy Mallik'] | 2023-06-21 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [-5.73178470e-01 1.81588620e-01 -7.09900022e-01 -3.43139589e-01
-1.15954304e+00 -8.70466173e-01 4.89134699e-01 2.39826962e-02
-5.65874100e-01 7.86228836e-01 2.53092617e-01 -4.41383302e-01
-2.44471341e-01 -3.93186897e-01 -6.98094249e-01 -5.67924678e-01
1.42438844e-01 7.67392516e-01 4.49270718e-02 -7.53398240... | [8.391520500183105, 3.804572343826294] |
c372d8d2-1684-4eda-ba2f-91e9b747fb97 | a-quantitative-study-of-nlp-approaches-to | 2305.10236 | null | https://arxiv.org/abs/2305.10236v1 | https://arxiv.org/pdf/2305.10236v1.pdf | A quantitative study of NLP approaches to question difficulty estimation | Recent years witnessed an increase in the amount of research on the task of Question Difficulty Estimation from Text QDET with Natural Language Processing (NLP) techniques, with the goal of targeting the limitations of traditional approaches to question calibration. However, almost the entirety of previous research foc... | ['Luca Benedetto'] | 2023-05-17 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [-2.41323680e-01 1.53578483e-02 -5.91716915e-02 -2.18943551e-01
-8.33310843e-01 -8.97595525e-01 7.02459395e-01 1.10049176e+00
-7.02442169e-01 6.43329084e-01 4.84752089e-01 -4.78080660e-01
-7.94517815e-01 -9.22028065e-01 -3.82563412e-01 -3.27553321e-03
3.60076398e-01 4.78107274e-01 5.26324809e-01 -7.00274587... | [11.332818984985352, 8.322988510131836] |
133393cd-61c3-4942-9951-5235147d687a | a-machine-learning-based-severity-prediction | 2203.15151 | null | https://arxiv.org/abs/2203.15151v1 | https://arxiv.org/pdf/2203.15151v1.pdf | A machine learning-based severity prediction tool for diabetic sensorimotor polyneuropathy using Michigan neuropathy screening instrumentations | Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term complication in diabetic patients associated with painful neuropathy, foot ulceration and amputation. The Michigan neuropathy screening instrument (MNSI) is one of the most common screening techniques for DSPN, however, it does not provide any... | ['Geetika Srivastava', 'Ahmad A. A Bakar', 'Sawal H. M. Ali', 'Iffat Ara', 'Syoji Kobashi', 'Mohammed Alhatou', 'Rayaz Malik', 'Muhammad E. H. Chowdhury', 'Mamun B. I. Reaz', 'Fahmida Haque'] | 2022-03-28 | null | null | null | null | ['severity-prediction', 'epidemiology'] | ['computer-vision', 'medical'] | [ 2.06199244e-01 -4.11090195e-01 -6.42826855e-01 -2.34029830e-01
-6.79942191e-01 -4.04554129e-01 -1.35996386e-01 5.83607852e-01
-7.03381777e-01 1.13202238e+00 3.71628821e-01 -3.74195367e-01
-8.97496521e-01 -7.17905819e-01 -1.95397809e-01 -4.55546558e-01
-5.72743773e-01 6.11139536e-01 1.61445603e-01 1.49424300... | [14.436646461486816, -1.7782269716262817] |
ff26442c-b0fd-4209-86af-9b8452dcca8e | contentctr-frame-level-live-streaming-click | 2306.14392 | null | https://arxiv.org/abs/2306.14392v1 | https://arxiv.org/pdf/2306.14392v1.pdf | ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer | In recent years, live streaming platforms have gained immense popularity as they allow users to broadcast their videos and interact in real-time with hosts and peers. Due to the dynamic changes of live content, accurate recommendation models are crucial for enhancing user experience. However, most previous works treat ... | ['Gaofeng Meng', 'Guorui Zhou', 'Fan Yang', 'Xiangyu Wu', 'Shiyao Wang', 'Dong Shen', 'Jiaxin Deng'] | 2023-06-26 | null | null | null | null | ['video-alignment', 'click-through-rate-prediction', 'dynamic-time-warping'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 1.98330879e-02 -8.17341447e-01 -2.46763736e-01 -2.36823887e-01
-5.83750784e-01 -6.58716321e-01 2.92975515e-01 -2.84887310e-02
-2.30608508e-01 1.86202124e-01 5.83981276e-01 5.01165614e-02
-6.08510561e-02 -4.57226157e-01 -6.41343474e-01 -6.30154669e-01
-4.28498209e-01 -3.05214435e-01 4.61754024e-01 -2.39713281... | [10.004186630249023, 0.470393568277359] |
c7341715-560b-41da-8edf-62cf57417142 | try-to-avoid-attacks-a-federated-data | 2211.01592 | null | https://arxiv.org/abs/2211.01592v1 | https://arxiv.org/pdf/2211.01592v1.pdf | Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems | Healthcare IoMT systems are becoming intelligent, miniaturized, and more integrated into daily life. As for the distributed devices in the IoMT, federated learning has become a topical area with cloud-based training procedures when meeting data security. However, the distribution of IoMT has the risk of protection from... | ['Siquan Huang', 'Leyu Shi', 'Ying Gao', 'Chong Chen'] | 2022-11-03 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-1.57053381e-01 -4.68919426e-01 -1.35305122e-01 7.88998604e-02
-2.19536379e-01 -6.68214381e-01 1.29553929e-01 2.16873765e-01
-3.36407155e-01 2.72376418e-01 1.06854014e-01 -1.63316324e-01
-3.76660675e-01 -9.20043647e-01 -2.99346507e-01 -1.17324328e+00
2.00077564e-01 3.71291161e-01 1.87725034e-02 8.48004073... | [5.81882905960083, 6.533346176147461] |
8ecdc28d-c6b8-4d09-8859-f27622436084 | covid-19-epidemiology-as-emergent-behavior-on | 2205.0215 | null | https://arxiv.org/abs/2205.02150v1 | https://arxiv.org/pdf/2205.02150v1.pdf | COVID-19 epidemiology as emergent behavior on a dynamic transmission forest | In this paper we create a compartmental, stochastic process model of SARS-CoV-2 transmission, where the process's mean and variance have distinct dynamics. The model is fit to time series data from Washington from January 2020 to March 2021 using a deterministic, biologically-motivated signal processing approach, and w... | ['Mike Famulare', 'Niket Thakkar'] | 2022-05-04 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 3.99237454e-01 -6.99861646e-02 -1.50452703e-01 -1.21274605e-01
-7.05527738e-02 -6.24883235e-01 9.29175138e-01 2.06148788e-01
-1.99582547e-01 9.60639358e-01 3.24377894e-01 -5.47073781e-01
-4.14156049e-01 -7.56712794e-01 -2.79955953e-01 -9.70779240e-01
-8.99065852e-01 9.11907375e-01 2.66245633e-01 -1.15469478... | [6.00039529800415, 4.329865455627441] |
205b3029-1e1b-4f25-bd3f-010e9b55b878 | write-like-you-synthesizing-your-cursive | null | null | https://onlinelibrary.wiley.com/doi/10.1111/cgf.142621 | https://onlinelibrary.wiley.com/doi/10.1111/cgf.142621 | Write like you: Synthesizing your cursive online Chinese handwriting via metric-based meta learning | In this paper, we propose a novel Sequence-to-Sequence model based on metric-based meta learning for the arbitrary style transfer of online Chinese handwritings. Unlike most existing methods that treat Chinese handwritings as images and are unable to reflect the human writing process, the proposed model directly handle... | ['Zhouhui Lian', 'Shusen Tang'] | 2021-06-04 | null | null | null | computer-graphics-forum-2021-6 | ['style-transfer'] | ['computer-vision'] | [ 2.97615975e-01 -1.64846450e-01 -7.28074387e-02 -4.49249774e-01
-4.16290373e-01 -7.60717273e-01 5.64013958e-01 -6.26708627e-01
-3.57809365e-01 5.99912047e-01 2.15164930e-01 -1.36478484e-01
4.42753315e-01 -7.03577101e-01 -6.63726687e-01 -6.53657854e-01
8.24077427e-01 5.81606686e-01 -8.57726112e-02 -2.28222311... | [11.616415023803711, -0.2662298381328583] |
19338519-6a7e-4be0-ae7e-7e46e308464b | 3d-instance-segmentation-of-mvs-buildings | 2112.09902 | null | https://arxiv.org/abs/2112.09902v2 | https://arxiv.org/pdf/2112.09902v2.pdf | 3D Instance Segmentation of MVS Buildings | We present a novel 3D instance segmentation framework for Multi-View Stereo (MVS) buildings in urban scenes. Unlike existing works focusing on semantic segmentation of urban scenes, the emphasis of this work lies in detecting and segmenting 3D building instances even if they are attached and embedded in a large and imp... | ['Liangliang Nan', 'Ronghua Liang', 'Shufang Lu', 'Yanghui Xu', 'Jiazhou Chen'] | 2021-12-18 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 3.93948078e-01 3.17145824e-01 4.70733374e-01 -4.88465488e-01
-7.55722284e-01 -6.71756744e-01 4.08800334e-01 -1.43663418e-02
-1.61097180e-02 3.53267044e-01 -2.47122109e-01 3.97061296e-02
1.05532847e-01 -1.23585320e+00 -7.28819191e-01 -4.63738292e-01
1.66664049e-01 8.10351789e-01 8.40446055e-01 -3.49063754... | [8.4148530960083, -2.840707540512085] |
9cec8d11-d585-4a7c-8b2b-b419536e6280 | human-interpretation-and-exploitation-of-self | 2112.05364 | null | https://arxiv.org/abs/2112.05364v2 | https://arxiv.org/pdf/2112.05364v2.pdf | Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation | The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why and how transformers work. In another vein, researchers propose new attention augmentation methods to make transformers more accurate, efficie... | ['Gabriel Murray', 'Lanjun Wang', 'Linzi Xing', 'Giuseppe Carenini', 'Wen Xiao', 'Raymond Li'] | 2021-12-10 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 3.94183010e-01 7.05647826e-01 -3.12324345e-01 -2.82774568e-01
-7.93141007e-01 -6.13630176e-01 5.19598424e-01 2.20844179e-01
6.62452206e-02 3.80450308e-01 7.49480605e-01 -4.97267514e-01
8.80606696e-02 -4.58532721e-01 -7.62021601e-01 -1.36912167e-01
3.19406599e-01 7.97375679e-01 3.65366906e-01 -6.14812225... | [11.30672836303711, 8.471853256225586] |
d27bbb47-cd49-4959-97a9-dd8c68f7ccfd | self-supervised-learning-by-estimating-twin-1 | 2110.07402 | null | https://arxiv.org/abs/2110.07402v4 | https://arxiv.org/pdf/2110.07402v4.pdf | Self-Supervised Learning by Estimating Twin Class Distributions | We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a softmax operation to produce twin class distributions of two augmented images. Without supervision, we e... | ['Hang Li', 'Huaping Liu', 'Rufeng Zhang', 'Tao Kong', 'Feng Wang'] | 2021-10-14 | self-supervised-learning-by-estimating-twin | https://openreview.net/forum?id=TLgW66V2CbP | https://openreview.net/pdf?id=TLgW66V2CbP | null | ['self-supervised-image-classification', 'semi-supervised-image-classification', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.47791225e-01 6.15678072e-01 -5.17779768e-01 -5.66672564e-01
-5.57876766e-01 -5.53626418e-01 4.99385744e-01 -1.53348461e-01
-4.45128262e-01 9.05731499e-01 1.74540043e-01 -2.47849733e-01
3.20173889e-01 -5.10971606e-01 -9.65667188e-01 -9.03773367e-01
2.67018110e-01 6.71606064e-01 -2.49821886e-01 1.38570011... | [9.477259635925293, 2.6461987495422363] |
3850e12a-18c2-4ed4-9a44-e81f297700cc | a-fully-convolutional-deep-auditory-model-for | 1612.05082 | null | http://arxiv.org/abs/1612.05082v1 | http://arxiv.org/pdf/1612.05082v1.pdf | A Fully Convolutional Deep Auditory Model for Musical Chord Recognition | Chord recognition systems depend on robust feature extraction pipelines.
While these pipelines are traditionally hand-crafted, recent advances in
end-to-end machine learning have begun to inspire researchers to explore
data-driven methods for such tasks. In this paper, we present a chord
recognition system that uses a ... | ['Filip Korzeniowski', 'Gerhard Widmer'] | 2016-12-15 | null | null | null | null | ['chord-recognition'] | ['audio'] | [ 3.73471826e-01 1.88297257e-02 3.22022825e-01 -3.74162376e-01
-9.90822196e-01 -9.88740683e-01 4.17751193e-01 1.64190054e-01
-7.03547180e-01 4.73896749e-02 2.10780367e-01 -2.19821464e-02
-1.53779849e-01 -6.50035441e-01 -3.99085343e-01 -2.23985031e-01
-4.20705527e-01 3.89235318e-01 1.82176828e-01 -3.54284644... | [15.831361770629883, 5.314194679260254] |
803b72ad-c38b-4b5b-953c-9078bac03782 | whether-and-when-does-endoscopy-domain | 2303.17636 | null | https://arxiv.org/abs/2303.17636v1 | https://arxiv.org/pdf/2303.17636v1.pdf | Whether and When does Endoscopy Domain Pretraining Make Sense? | Automated endoscopy video analysis is a challenging task in medical computer vision, with the primary objective of assisting surgeons during procedures. The difficulty arises from the complexity of surgical scenes and the lack of a sufficient amount of annotated data. In recent years, large-scale pretraining has shown ... | ['Nassir Navab', 'Tobias Czempiel', 'Ege Özsoy', 'Felix Holm', 'Dominik Batić'] | 2023-03-30 | null | null | null | null | ['action-triplet-detection', 'video-understanding', 'surgical-phase-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.37403917e-01 2.80298442e-01 -3.99613500e-01 -1.95740640e-01
-6.90291464e-01 -8.27258170e-01 2.74339318e-01 2.95454767e-02
-7.03571200e-01 2.55624145e-01 5.53860724e-01 -4.97930944e-01
9.39578488e-02 -2.91886985e-01 -7.50720024e-01 -5.48800766e-01
1.40660509e-01 3.62239145e-02 -2.12021489e-02 -1.04137622... | [14.250507354736328, -3.159609317779541] |
0643d712-7cdd-46e7-9c8b-01850a291397 | lagnet-logic-aware-graph-network-for-human | 2011.1025 | null | https://arxiv.org/abs/2011.10250v3 | https://arxiv.org/pdf/2011.10250v3.pdf | Consistency-Aware Graph Network for Human Interaction Understanding | Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU). Apart from the latter task is much more challenging, the main cause is that recent approaches learn human interactive relations via shallow graphical models, which is inadequat... | ['ShengYong Chen', 'Javen Qinfeng Shi', 'Jianhua Zhang', 'Dongyan Guo', 'Jiajun Meng', 'Zhenhua Wang'] | 2020-11-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Consistency-Aware_Graph_Network_for_Human_Interaction_Understanding_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Consistency-Aware_Graph_Network_for_Human_Interaction_Understanding_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [ 2.69125760e-01 7.01662481e-01 -3.36960196e-01 -5.41701853e-01
-9.52306613e-02 -7.53375366e-02 5.87067366e-01 2.99706552e-02
-8.12702999e-02 4.85446364e-01 3.47467542e-01 -2.91034192e-01
-2.54062027e-01 -7.32496858e-01 -8.57107699e-01 -1.61619455e-01
-1.37420237e-01 5.96775472e-01 1.34897828e-01 1.13448547... | [8.186402320861816, 0.6635504364967346] |
92e30b4e-bfba-4a9c-9ee3-cc1cbe5b32c4 | o-type-stars-stellar-parameter-estimation | 2210.12791 | null | https://arxiv.org/abs/2210.12791v2 | https://arxiv.org/pdf/2210.12791v2.pdf | O-type Stars Stellar Parameter Estimation Using Recurrent Neural Networks | In this paper, we present a deep learning system approach to estimating luminosity, effective temperature, and surface gravity of O-type stars using the optical region of the stellar spectra. In previous work, we compare a set of machine learning and deep learning algorithms in order to establish a reliable way to fit ... | ['Silvana G. Navarro', 'Celia R. Fierro-Santillán', 'Luis J. Corral', 'Miguel Flores R.'] | 2022-10-23 | null | null | null | null | ['type'] | ['speech'] | [-9.63154882e-02 -2.51265734e-01 3.72475147e-01 -1.99194565e-01
-1.51418701e-01 -2.04393774e-01 4.38753963e-01 -1.30343392e-01
-3.50303084e-01 5.01335263e-01 -5.44668734e-01 -3.40964258e-01
-4.27866936e-01 -6.52863145e-01 -3.13149065e-01 -1.06099176e+00
3.23289514e-01 6.48141623e-01 -5.78391850e-02 -1.35895804... | [7.441359043121338, 3.0807840824127197] |
6b0816ae-2232-4c3f-873e-424f68318416 | that-s-what-i-said-fully-controllable-talking | 2304.03275 | null | https://arxiv.org/abs/2304.03275v1 | https://arxiv.org/pdf/2304.03275v1.pdf | That's What I Said: Fully-Controllable Talking Face Generation | The goal of this paper is to synthesise talking faces with controllable facial motions. To achieve this goal, we propose two key ideas. The first is to establish a canonical space where every face has the same motion patterns but different identities. The second is to navigate a multimodal motion space that only repres... | ['Joon Son Chung', 'Byeong-Yeol Kim', 'Youshin Lim', 'Jihwan Park', 'Hyeongkeun Lee', 'Jong-Bin Woo', 'Kyeongha Rho', 'Youngjoon Jang'] | 2023-04-06 | null | null | null | null | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 1.10425659e-01 1.53382733e-01 -7.56678656e-02 -2.53543258e-01
-6.27251744e-01 -6.23087406e-01 6.73004925e-01 -9.55416679e-01
2.16726333e-01 3.54675978e-01 4.91661102e-01 2.18488902e-01
3.42873067e-01 -2.74471045e-01 -4.45463926e-01 -7.24406302e-01
3.60370100e-01 -1.12494767e-01 -2.84445405e-01 -1.22176528... | [13.198308944702148, -0.4148283302783966] |
b05d01d8-29f0-4ab8-9588-845a1b10a249 | forbidden-knowledge-in-machine-learning | 1911.08603 | null | https://arxiv.org/abs/1911.08603v1 | https://arxiv.org/pdf/1911.08603v1.pdf | Forbidden knowledge in machine learning -- Reflections on the limits of research and publication | Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics researc... | ['Thilo Hagendorff'] | 2019-11-19 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 4.42052871e-01 8.25887680e-01 -2.91501671e-01 6.10284284e-02
4.37766872e-03 -7.14432061e-01 8.15965652e-01 2.90483773e-01
-4.71385449e-01 9.29043889e-01 -3.23732905e-02 -8.54638696e-01
-1.11445941e-01 -7.26449192e-01 -5.66747487e-01 -9.00667846e-01
5.31391263e-01 -2.49040991e-01 8.07675440e-03 5.13431989... | [8.9616060256958, 6.5857834815979] |
14468063-0cf1-4d51-a88e-2073f2939fb5 | neural-segmental-hypergraphs-for-overlapping | 1810.01817 | null | http://arxiv.org/abs/1810.01817v1 | http://arxiv.org/pdf/1810.01817v1.pdf | Neural Segmental Hypergraphs for Overlapping Mention Recognition | In this work, we propose a novel segmental hypergraph representation to model
overlapping entity mentions that are prevalent in many practical datasets. We
show that our model built on top of such a new representation is able to
capture features and interactions that cannot be captured by previous models
while maintain... | ['Bailin Wang', 'Wei Lu'] | 2018-10-03 | neural-segmental-hypergraphs-for-overlapping-1 | https://aclanthology.org/D18-1019 | https://aclanthology.org/D18-1019.pdf | emnlp-2018-10 | ['overlapping-mention-recognition', 'nested-named-entity-recognition', 'nested-mention-recognition'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.06194541e-01 7.54021108e-01 -4.88536805e-01 -5.15650988e-01
-6.80131495e-01 -4.68549103e-01 7.18247294e-01 6.12491846e-01
-2.26236433e-01 8.85830641e-01 3.85278106e-01 -1.86273456e-01
-3.06364030e-01 -9.99719501e-01 -9.76405561e-01 -3.09104353e-01
-4.60046262e-01 8.32088053e-01 3.97368759e-01 -3.24298173... | [9.252662658691406, 8.541504859924316] |
0b0aefcf-7e57-4276-884b-e064171de618 | automatic-textual-evidence-mining-in-covid-19 | 2004.12563 | null | https://arxiv.org/abs/2004.12563v3 | https://arxiv.org/pdf/2004.12563v3.pdf | Automatic Textual Evidence Mining in COVID-19 Literature | We created this EVIDENCEMINER system for automatic textual evidence mining in COVID-19 literature. EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. It is constructed in a completely automated ... | ['Aabhas Chauhan', 'Xuan Wang', 'Yingjun Guan', 'Weili Liu', 'Jiawei Han'] | 2020-04-27 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 1.39394164e-01 3.78196687e-01 -1.16003191e+00 -2.06851382e-02
-7.34427035e-01 -7.36221731e-01 7.04957724e-01 1.48981237e+00
-5.85372686e-01 1.20807958e+00 4.62509274e-01 -6.29088640e-01
-4.66292709e-01 -6.91888571e-01 -5.59780717e-01 -1.20153986e-01
-1.49371609e-01 6.28436148e-01 2.78137058e-01 1.45424932... | [8.761971473693848, 8.660676956176758] |
ca851793-3bdb-47f8-8e1e-ac651f08ffdd | adjusted-asymmetric-accuracy-a-well-behaving | 2209.02935 | null | https://arxiv.org/abs/2209.02935v1 | https://arxiv.org/pdf/2209.02935v1.pdf | Adjusted Asymmetric Accuracy: A Well-Behaving External Cluster Validity Measure | There is no, nor will there ever be, single best clustering algorithm, but we would still like to be able to pinpoint those which are well-performing on certain task types and filter out the systematically disappointing ones. Clustering algorithms are traditionally evaluated using either internal or external validity m... | ['Marek Gagolewski'] | 2022-09-07 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [-9.89952385e-02 4.11444418e-02 -1.14232786e-01 -3.65256518e-01
-4.51675087e-01 -8.89934123e-01 4.82453346e-01 7.61329055e-01
-4.10528213e-01 6.51008964e-01 -1.32060319e-01 -3.03997368e-01
-6.42353594e-01 -8.21500242e-01 -1.45967707e-01 -1.10545444e+00
6.46145344e-02 8.48977327e-01 4.89319652e-01 1.29438251... | [7.6595845222473145, 4.515707492828369] |
f91aeeb3-a5a7-4c68-9429-97059aff38ea | temporal-dynamics-of-coordinated-online | 2301.06774 | null | https://arxiv.org/abs/2301.06774v1 | https://arxiv.org/pdf/2301.06774v1.pdf | Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence | Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordinati... | ['Stefano Cresci', 'Giovanni Da San Martino', 'Preslav Nakov', 'Mauro Conti', 'Maurizio Tesconi', 'Leonardo Nizzoli', 'Serena Tardelli'] | 2023-01-17 | null | null | null | null | ['dynamic-community-detection', 'community-detection'] | ['graphs', 'graphs'] | [-1.36463940e-01 -6.18719049e-02 -2.08454385e-01 1.54392153e-01
-3.73154203e-03 -1.15519559e+00 1.04852045e+00 7.14745820e-01
-1.38890058e-01 3.87566417e-01 4.33963716e-01 -6.72591150e-01
-3.93779814e-01 -8.00961018e-01 -3.32972467e-01 -4.56247181e-01
-6.52134418e-01 3.81250143e-01 5.50513089e-01 -4.25706863... | [7.013131618499756, 5.297226428985596] |
16fcece6-2f17-4350-8a82-9e67ec7627fa | improving-spectral-graph-convolution-for | 2112.0716 | null | https://arxiv.org/abs/2112.07160v2 | https://arxiv.org/pdf/2112.07160v2.pdf | A New Perspective on the Effects of Spectrum in Graph Neural Networks | Many improvements on GNNs can be deemed as operations on the spectrum of the underlying graph matrix, which motivates us to directly study the characteristics of the spectrum and their effects on GNN performance. By generalizing most existing GNN architectures, we show that the correlation issue caused by the $unsmooth... | ['Qiang Zhang', 'Yanming Shen', 'BaoCai Yin', 'Heng Qi', 'Rui Li', 'Mingqi Yang'] | 2021-12-14 | null | null | null | null | ['graph-property-prediction', 'graph-regression'] | ['graphs', 'graphs'] | [-1.38942584e-01 4.11965363e-02 -6.66563660e-02 1.99695351e-03
-1.05667993e-01 -6.09546721e-01 6.19312525e-01 -1.56000331e-01
-2.22709313e-01 3.03999871e-01 2.15485871e-01 -7.16584921e-01
-1.86578587e-01 -1.02619386e+00 -7.31426179e-01 -4.07715946e-01
-3.88291061e-01 -2.91630924e-01 2.51163334e-01 -6.62280798... | [6.888579368591309, 6.132213115692139] |
d1145fb2-3a83-4728-ae03-f24bdf1eb901 | transfer-learning-approach-to-bicycle-sharing | 2111.0099 | null | https://arxiv.org/abs/2111.00990v1 | https://arxiv.org/pdf/2111.00990v1.pdf | Transfer Learning Approach to Bicycle-sharing Systems' Station Location Planning using OpenStreetMap Data | Bicycle-sharing systems (BSS) have become a daily reality for many citizens of larger, wealthier cities in developed regions. However, planning the layout of bicycle-sharing stations usually requires expensive data gathering, surveying travel behavior and trip modelling followed by station layout optimization. Many sma... | ['Piotr Szymański', 'Kamil Raczycki'] | 2021-11-01 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-6.71880484e-01 2.66724080e-01 -3.06875229e-01 -5.86468466e-02
-7.04367578e-01 -6.52719080e-01 4.52591330e-01 -7.18475431e-02
-2.25029320e-01 9.59871113e-01 5.76484382e-01 -1.03601348e+00
-3.32410276e-01 -1.25715089e+00 -2.84439176e-01 -5.60483992e-01
3.30657023e-03 9.04890001e-01 1.21042743e-01 -6.29571140... | [6.1789350509643555, 1.854358434677124] |
a547866a-15e2-42b0-8c68-14a8471a3c66 | going-deeper-into-semi-supervised-person-re | 2107.11566 | null | https://arxiv.org/abs/2107.11566v1 | https://arxiv.org/pdf/2107.11566v1.pdf | Going Deeper into Semi-supervised Person Re-identification | Person re-identification is the challenging task of identifying a person across different camera views. Training a convolutional neural network (CNN) for this task requires annotating a large dataset, and hence, it involves the time-consuming manual matching of people across cameras. To reduce the need for labeled data... | ['Mahsa Baktashmotlagh', 'Feras Dayoub', 'Frederic Maire', 'Olga Moskvyak'] | 2021-07-24 | null | null | null | null | ['semi-supervised-person-re-identification'] | ['computer-vision'] | [ 8.59858915e-02 -4.03447092e-01 -1.64357364e-01 -6.40749753e-01
-3.83186996e-01 -7.66165495e-01 5.74009359e-01 -9.91195589e-02
-9.15551960e-01 5.83451152e-01 -2.94642057e-03 2.53392577e-01
3.41386557e-01 -6.17908895e-01 -6.65902793e-01 -5.26079059e-01
5.59637368e-01 6.50567889e-01 -7.67217726e-02 1.61864728... | [14.748485565185547, 1.0100929737091064] |
ec4b3b44-c483-45b8-9bb6-2e1f4b2543c2 | graph-to-sequence-learning-using-gated-graph | 1806.09835 | null | http://arxiv.org/abs/1806.09835v1 | http://arxiv.org/pdf/1806.09835v1.pdf | Graph-to-Sequence Learning using Gated Graph Neural Networks | Many NLP applications can be framed as a graph-to-sequence learning problem.
Previous work proposing neural architectures on this setting obtained promising
results compared to grammar-based approaches but still rely on linearisation
heuristics and/or standard recurrent networks to achieve the best performance.
In this... | ['Trevor Cohn', 'Gholamreza Haffari', 'Daniel Beck'] | 2018-06-26 | graph-to-sequence-learning-using-gated-graph-1 | https://aclanthology.org/P18-1026 | https://aclanthology.org/P18-1026.pdf | acl-2018-7 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 6.80024326e-01 8.48274350e-01 -3.91690910e-01 -2.28284940e-01
-9.57746744e-01 -5.03527105e-01 6.65038347e-01 -1.20681889e-01
-1.28031313e-01 7.76489615e-01 3.80163312e-01 -9.22501326e-01
3.93976629e-01 -9.56182063e-01 -9.34809685e-01 -4.08268332e-01
6.01518117e-02 8.69647622e-01 3.01880967e-02 -4.47350144... | [10.301071166992188, 8.383410453796387] |
ff48c1db-d1c8-40a7-8b2c-0c2a9926752e | faster-lead-optimization-mapper-algorithm-for | 2304.04713 | null | https://arxiv.org/abs/2304.04713v1 | https://arxiv.org/pdf/2304.04713v1.pdf | Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free Energy Perturbation | In recent years, free energy perturbation (FEP) calculations have garnered increasing attention as tools to support drug discovery. The lead optimization mapper (Lomap) was proposed as an algorithm to calculate the relative free energy between ligands efficiently. However, Lomap requires checking whether each edge in t... | ['Masahito Ohue', 'Kairi Furui'] | 2023-04-10 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 1.31592274e-01 -1.68310869e-02 5.56222871e-02 1.22885182e-01
-5.70604801e-01 -6.43512309e-01 5.47196977e-02 8.68045270e-01
-3.79145890e-01 1.13020742e+00 -3.64072382e-01 -6.09835386e-01
-1.25833124e-01 -9.69467878e-01 -7.00826347e-01 -6.59231365e-01
-1.37594253e-01 4.04724777e-01 5.15100777e-01 3.15720635... | [4.951566696166992, 5.599321365356445] |
302c9849-8058-4e9a-a247-f216314b0e9f | exploring-visual-prompts-for-whole-slide | 2303.13122 | null | https://arxiv.org/abs/2303.13122v1 | https://arxiv.org/pdf/2303.13122v1.pdf | Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning | Multiple instance learning (MIL) has emerged as a popular method for classifying histopathology whole slide images (WSIs). However, existing approaches typically rely on pre-trained models from large natural image datasets, such as ImageNet, to generate instance features, which can be sub-optimal due to the significant... | ['Hao Chen', 'Kwang-Ting Cheng', 'Lisheng Wang', 'Zhengjie ZHU', 'Zhongchen Zhao', 'Yi Lin'] | 2023-03-23 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 6.94197893e-01 1.68931052e-01 -3.43243927e-01 -4.92616594e-01
-1.36080468e+00 -4.66121405e-01 4.01963830e-01 4.17251408e-01
-5.07991493e-01 7.14083135e-01 -2.73634563e-03 -2.23898917e-01
-1.40197277e-01 -6.01514339e-01 -6.50944412e-01 -8.88651192e-01
1.90524086e-01 3.63272429e-01 2.99582839e-01 1.58392098... | [15.078362464904785, -2.763939380645752] |
11500d73-5ea1-47db-b69d-00a23888c217 | ckd-transbts-clinical-knowledge-driven-hybrid | 2207.0737 | null | https://arxiv.org/abs/2207.07370v1 | https://arxiv.org/pdf/2207.07370v1.pdf | CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation | Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain tumor diagnosis, cancer management and research purposes. With the great success of the ten-year BraTS challenges as well as the advances of CNN and Transformer algorithms, a lot of outstanding BTS models have been proposed to tackle ... | ['Chu Han', 'Zaiyi Liu', 'Guoqiang Han', 'Changhong Liang', 'Biao Huang', 'Zeyan Xu', 'Xipeng Pan', 'Bingjiang Qiu', 'Zhenwei Shi', 'Bingchao Zhao', 'Huan Lin', 'Hao Chen', 'Cheng Lu', 'Jiatai Lin', 'Jianwei Lin'] | 2022-07-15 | null | null | null | null | ['brain-tumor-segmentation', 'clinical-knowledge'] | ['medical', 'miscellaneous'] | [ 2.35391811e-01 4.28370647e-02 -1.84315190e-01 -5.04075110e-01
-1.04378939e+00 3.31485197e-02 4.38166261e-01 -2.19409347e-01
-4.69145626e-01 5.59567094e-01 2.09428340e-01 -4.91217464e-01
-1.96359634e-01 -5.91322422e-01 -4.93633419e-01 -8.32382619e-01
2.72820085e-01 4.61235255e-01 4.51068878e-01 -1.62510037... | [14.571869850158691, -2.3734941482543945] |
0f2c5361-6768-4c85-8bc9-275dfee8de54 | ranpac-random-projections-and-pre-trained | 2307.02251 | null | https://arxiv.org/abs/2307.02251v1 | https://arxiv.org/pdf/2307.02251v1.pdf | RanPAC: Random Projections and Pre-trained Models for Continual Learning | Continual learning (CL) aims to incrementally learn different tasks (such as classification) in a non-stationary data stream without forgetting old ones. Most CL works focus on tackling catastrophic forgetting under a learning-from-scratch paradigm. However, with the increasing prominence of foundation models, pre-trai... | ['Anton Van Den Hengel', 'Ehsan Abbasnejad', 'Amin Parveneh', 'Dong Gong', 'Mark D. McDonnell'] | 2023-07-05 | null | null | null | null | ['continual-learning'] | ['methodology'] | [ 2.45597139e-01 1.03455380e-01 3.71716321e-02 -7.15849325e-02
-5.05842566e-01 -3.49939555e-01 6.78271890e-01 2.18024582e-01
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-2.93190181e-02 4.17996496e-01 5.17968178e-01 -2.58211672... | [9.78907299041748, 3.4468982219696045] |
4718c9c1-a0f0-462d-ad42-48582b8144b6 | faster-riemannian-newton-type-optimization-by | 2302.11076 | null | https://arxiv.org/abs/2302.11076v1 | https://arxiv.org/pdf/2302.11076v1.pdf | Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularization | This work is on constrained large-scale non-convex optimization where the constraint set implies a manifold structure. Solving such problems is important in a multitude of fundamental machine learning tasks. Recent advances on Riemannian optimization have enabled the convenient recovery of solutions by adapting unconst... | ['Tingting Mu', 'Yian Deng'] | 2023-02-22 | null | null | null | null | ['type'] | ['speech'] | [-2.71209568e-01 -1.36296809e-01 1.55943200e-01 -2.96830922e-01
-7.93829739e-01 -4.68764931e-01 2.38595635e-01 -2.35493913e-01
-5.03546655e-01 5.89044988e-01 -6.61314800e-02 -1.41829014e-01
-4.79010403e-01 -2.89894283e-01 -4.12659347e-01 -9.19862151e-01
-2.87351817e-01 6.87867254e-02 1.46307945e-02 -2.20790282... | [7.53929328918457, 4.153052806854248] |
f77596c8-f015-4fdd-9560-9fd856d28e6d | multi-fidelity-black-box-optimization-with | null | null | https://icml.cc/Conferences/2018/Schedule?showEvent=2264 | http://proceedings.mlr.press/v80/sen18a/sen18a.pdf | Multi-Fidelity Black-Box Optimization with Hierarchical Partitions |
Motivated by settings such as hyper-parameter tuning and physical simulations, we consider the problem of black-box optimization of a function. Multi-fidelity techniques have become popular for applications where exact function evaluations are expensive, but coarse (biased) approximations are available at much low... | ['Kirthevasan Kandasamy', 'Sanjay Shakkottai', 'Rajat Sen'] | 2018-07-01 | null | null | null | icml-2018-7 | ['physical-simulations'] | ['miscellaneous'] | [ 6.44573793e-02 1.18606180e-01 -4.49688673e-01 -2.75038630e-01
-1.41923273e+00 -6.26758397e-01 2.96851099e-01 2.84171522e-01
-4.99811172e-01 1.25027037e+00 -1.54421311e-02 -1.57602951e-01
-4.82108176e-01 -5.91171563e-01 -9.56871986e-01 -8.53361905e-01
-2.25649834e-01 6.27213418e-01 -2.69676447e-01 1.63597554... | [6.705516338348389, 4.052088260650635] |
6a406546-05d3-4641-a484-117c89f64954 | knn-box-a-unified-framework-for-nearest | 2302.13574 | null | https://arxiv.org/abs/2302.13574v1 | https://arxiv.org/pdf/2302.13574v1.pdf | kNN-BOX: A Unified Framework for Nearest Neighbor Generation | Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT). In this paper, we introduce a unified framework kNN-BOX, which enables quick development and interactive analysis for this novel paradigm. kNN-BOX decompo... | ['Jiajun Chen', 'Sizhe Liu', 'Siheng Zhao', 'ShuJian Huang', 'Yunzhe Lv', 'Qianfeng Zhao', 'Wenhao Zhu'] | 2023-02-27 | null | null | null | null | ['paraphrase-generation', 'question-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 2.95810878e-01 7.33291581e-02 -3.28310490e-01 -3.78811270e-01
-9.73689914e-01 -5.91682136e-01 4.79374945e-01 -9.99857262e-02
-3.49397480e-01 9.40083265e-01 3.10105920e-01 -7.16166139e-01
1.60682470e-01 -8.08916628e-01 -8.12612534e-01 -2.67068177e-01
6.04651392e-01 6.73089921e-01 -2.73528814e-01 -6.56429410... | [11.63825798034668, 9.803398132324219] |
819d3b38-5f9c-4e65-8c2b-be965541cbe1 | semi-supervised-outlier-detection-using | null | null | https://openreview.net/forum?id=BkS3fnl0W | https://openreview.net/pdf?id=BkS3fnl0W | Semi-supervised Outlier Detection using Generative And Adversary Framework | In a conventional binary/multi-class classification task, the decision boundary is supported by data from two or more classes. However, in one-class classification task, only data from one class are available. To build an robust outlier detector using only data from a positive class, we propose a corrupted GAN(CorGAN),... | ['Matthias Schubert', 'Jindong Gu', 'Volker Tresp'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['one-class-classifier'] | ['methodology'] | [ 3.40644449e-01 2.22770169e-01 2.15491042e-01 -8.71232226e-02
-5.23033559e-01 -6.16547644e-01 6.01422131e-01 -6.61146417e-02
-2.28062853e-01 8.04417789e-01 -4.89142567e-01 -1.71624005e-01
2.75592446e-01 -1.28083098e+00 -8.02703381e-01 -1.06675911e+00
1.79032251e-01 4.44763780e-01 1.37062117e-01 2.08709463... | [7.6405487060546875, 2.3042726516723633] |
d1195e52-29a3-4bee-9061-24787013b744 | highres-net-multi-frame-super-resolution-by | null | null | https://openreview.net/forum?id=HJxJ2h4tPr | https://openreview.net/pdf?id=HJxJ2h4tPr | HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion | Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.... | ['Samira E. Kahou', 'Vincent Michalski', 'Julien Cornebise', 'Israel Goytom', 'Yoshua Bengio', 'Michel Deudon', 'Kris Sankaran', 'Zhichao Lin', 'Md Rifat Arefin', 'Alfredo Kalaitzis'] | 2020-01-01 | null | null | null | iclr-2020-1 | ['de-aliasing', 'multi-frame-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 6.27746522e-01 2.12477937e-01 2.25812435e-01 -4.85586703e-01
-1.52701259e+00 -3.92202467e-01 8.93515766e-01 -5.59921682e-01
-3.18767816e-01 8.93728137e-01 6.33415222e-01 2.01257512e-01
-2.07747310e-01 -1.07744145e+00 -8.72682214e-01 -8.31698358e-01
-3.05677980e-01 2.50242412e-01 -1.72640532e-01 -7.99583256... | [10.378314018249512, -1.9152246713638306] |
d22f69d7-e328-4a44-9169-956dbe84bcd7 | debatekg-automatic-policy-debate-case | 2307.0409 | null | https://arxiv.org/abs/2307.04090v1 | https://arxiv.org/pdf/2307.04090v1.pdf | DebateKG: Automatic Policy Debate Case Creation with Semantic Knowledge Graphs | Recent work within the Argument Mining community has shown the applicability of Natural Language Processing systems for solving problems found within competitive debate. One of the most important tasks within competitive debate is for debaters to create high quality debate cases. We show that effective debate cases can... | ['Allen Roush'] | 2023-07-09 | null | null | null | null | ['knowledge-graphs', 'argument-mining'] | ['knowledge-base', 'natural-language-processing'] | [ 1.62960421e-02 1.08131409e+00 -6.45222485e-01 -3.46603334e-01
-1.30334115e+00 -1.18958056e+00 1.06792796e+00 6.02910995e-01
-2.88289607e-01 1.14020908e+00 9.16703224e-01 -1.22931027e+00
-6.39372170e-01 -9.49597657e-01 -8.74189079e-01 1.84881892e-02
2.81428486e-01 1.11761725e+00 3.81222457e-01 -3.83451790... | [9.493098258972168, 9.537003517150879] |
1d4a79e5-9536-478c-a50b-ab06a455b20f | visual-subtitle-feature-enhanced-video | 2208.11307 | null | https://arxiv.org/abs/2208.11307v2 | https://arxiv.org/pdf/2208.11307v2.pdf | Visual Subtitle Feature Enhanced Video Outline Generation | With the tremendously increasing number of videos, there is a great demand for techniques that help people quickly navigate to the video segments they are interested in. However, current works on video understanding mainly focus on video content summarization, while little effort has been made to explore the structure ... | ['Ba Yuan', 'Zhiwei Hu', 'Guohong Fu', 'Sujian Li', 'Wenjie Li', 'Min Cao', 'Yuanhang Li', 'Tangkun Zhang', 'Jingwen Wang', 'Derui Wang', 'Wenrui Xie', 'Ziqiang Cao', 'Qi Lv'] | 2022-08-24 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 3.54001254e-01 4.00860906e-02 -3.50092709e-01 -1.58051774e-01
-6.80124164e-01 -8.48863363e-01 4.68439639e-01 1.47685427e-02
-1.44035399e-01 4.75638300e-01 4.37785000e-01 -2.50749826e-01
4.10741985e-01 -4.18023884e-01 -8.87549520e-01 -3.80865633e-01
1.82525471e-01 1.38869667e-02 2.42952541e-01 1.74266651... | [10.473539352416992, 0.5773870348930359] |
def221f4-464c-4f55-b4fa-c2011ba7b1af | embrace-opportunities-and-face-challenges | 2305.18616 | null | https://arxiv.org/abs/2305.18616v1 | https://arxiv.org/pdf/2305.18616v1.pdf | Embrace Opportunities and Face Challenges: Using ChatGPT in Undergraduate Students' Collaborative Interdisciplinary Learning | ChatGPT, launched in November 2022, has gained widespread attention from students and educators globally, with an online report by Hu (2023) stating it as the fastest-growing consumer application in history. While discussions on the use of ChatGPT in higher education are abundant, empirical studies on its impact on col... | ['Tan Lay Poh', 'Low Kin Yew', 'Preman Rajalingam', 'Annabel Chen Shen-Hsing', 'Peter Seow', 'Tianlong Zhong', 'Chenyu Hou', 'Xiuyi Fan', 'Gaoxia Zhu'] | 2023-05-23 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-2.96580583e-01 3.82498980e-01 -5.76518118e-01 1.25409871e-01
-5.95097303e-01 -8.64736736e-01 5.15309513e-01 6.34149790e-01
-2.28449464e-01 3.36202174e-01 7.09408462e-01 -9.55101788e-01
-3.73699903e-01 -7.92634964e-01 -5.55437803e-01 -3.93841147e-01
7.66349673e-01 -3.03185344e-01 1.94490850e-01 -2.95574576... | [10.205437660217285, 7.318281173706055] |
b6b6f355-2d30-4f99-b222-5b173d060de6 | causal-discovery-with-unobserved-variables-a | 2305.05281 | null | https://arxiv.org/abs/2305.05281v2 | https://arxiv.org/pdf/2305.05281v2.pdf | Causal Discovery with Unobserved Variables: A Proxy Variable Approach | Discovering causal relations from observational data is important. The existence of unobserved variables, such as latent confounders or mediators, can mislead the causal identification. To address this issue, proximal causal discovery methods proposed to adjust for the bias with the proxy of the unobserved variable. Ho... | ['Yizhou Wang', 'Yu Qiao', 'Xinwei Sun', 'Mingzhou Liu'] | 2023-05-09 | null | null | null | null | ['causal-discovery', 'causal-identification'] | ['knowledge-base', 'reasoning'] | [ 1.60295591e-01 1.93031222e-01 -8.76391768e-01 -3.90561998e-01
-4.77931589e-01 -3.62512559e-01 3.62592340e-01 1.93013430e-01
-2.60271728e-02 1.29495347e+00 4.30334836e-01 -5.40068567e-01
-4.47988153e-01 -1.18528938e+00 -8.40520382e-01 -6.72294080e-01
-2.79096395e-01 2.38734439e-01 -9.73453224e-02 3.54813904... | [7.92462682723999, 5.285148620605469] |
33f8e546-7b45-4128-b121-67036aa5d675 | 190503556 | 1905.03556 | null | https://arxiv.org/abs/1905.03556v1 | https://arxiv.org/pdf/1905.03556v1.pdf | Cycle-IR: Deep Cyclic Image Retargeting | Supervised deep learning techniques have achieved great success in various fields due to getting rid of the limitation of handcrafted representations. However, most previous image retargeting algorithms still employ fixed design principles such as using gradient map or handcrafted features to compute saliency map, whic... | ['Bo Yan', 'Xuejing Niu', 'Weimin Tan', 'Chumin Lin'] | 2019-05-09 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 2.73942560e-01 1.11721545e-01 -2.10056216e-01 -8.77146274e-02
-3.82551819e-01 -4.56860006e-01 4.46300179e-01 -3.07671428e-01
-3.36787492e-01 5.24985492e-01 2.19908446e-01 -3.80774438e-01
1.50304392e-01 -7.25917220e-01 -8.92688811e-01 -6.27032459e-01
5.59928119e-01 -2.28579566e-01 5.24594724e-01 -4.29590106... | [11.20089340209961, -0.9535713195800781] |
2923a88f-6216-4581-ae8e-a4e5eeaf39e3 | multi-epoch-learning-for-deep-click-through | 2305.19531 | null | https://arxiv.org/abs/2305.19531v1 | https://arxiv.org/pdf/2305.19531v1.pdf | Multi-Epoch Learning for Deep Click-Through Rate Prediction Models | The one-epoch overfitting phenomenon has been widely observed in industrial Click-Through Rate (CTR) applications, where the model performance experiences a significant degradation at the beginning of the second epoch. Recent advances try to understand the underlying factors behind this phenomenon through extensive exp... | ['Han Li', 'Dongying Kong', 'Jian Liang', 'Zhongxiang Fan', 'Zhaocheng Liu'] | 2023-05-31 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-3.41715477e-02 -4.36331391e-01 -2.80759126e-01 -1.80450846e-02
-4.01175082e-01 9.89170372e-03 3.86405855e-01 1.07432283e-01
-2.95531422e-01 3.13131154e-01 2.05850210e-02 -6.21422112e-01
-2.54245102e-01 -5.84616840e-01 -7.30187416e-01 -7.68529117e-01
2.21911003e-03 -1.72017328e-02 1.75792038e-01 -3.35503787... | [10.128704071044922, 5.484592914581299] |
4c090674-4cc9-4bd0-ba08-64bef6d21d73 | out-of-vocabulary-challenge-report | 2209.06717 | null | https://arxiv.org/abs/2209.06717v1 | https://arxiv.org/pdf/2209.06717v1.pdf | Out-of-Vocabulary Challenge Report | This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of pu... | ['Dimosthenis Karatzas', 'Ron Litman', 'Shai Mazor', 'Aviad Aberdam', 'Oren Nuriel', 'Ali Furkan Biten', 'Andrés Mafla', 'Sergi Garcia-Bordils'] | 2022-09-14 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 7.19536901e-01 -2.18812287e-01 -2.16248911e-02 -5.52489996e-01
-6.64213121e-01 -6.85211241e-01 1.02494621e+00 2.54604042e-01
-4.94390398e-01 2.86982685e-01 2.11147502e-01 -1.08764760e-01
4.48437303e-01 -3.91087919e-01 -8.76954019e-01 -4.52418834e-01
4.15593117e-01 8.23598862e-01 5.99624336e-01 9.74361226... | [11.954167366027832, 2.239657163619995] |
e441c20c-43c5-48e0-81cf-b45ecdde7713 | musicnn-pre-trained-convolutional-neural | 1909.06654 | null | https://arxiv.org/abs/1909.06654v1 | https://arxiv.org/pdf/1909.06654v1.pdf | musicnn: Pre-trained convolutional neural networks for music audio tagging | Pronounced as "musician", the musicnn library contains a set of pre-trained musically motivated convolutional neural networks for music audio tagging: https://github.com/jordipons/musicnn. This repository also includes some pre-trained vgg-like baselines. These models can be used as out-of-the-box music audio taggers, ... | ['Xavier Serra', 'Jordi Pons'] | 2019-09-14 | null | null | null | null | ['audio-tagging'] | ['audio'] | [-7.73507282e-02 -7.71435276e-02 -3.43848705e-01 -2.38953382e-02
-1.38784277e+00 -8.33745599e-01 1.75355181e-01 -2.79610485e-01
-3.99330705e-01 2.85562724e-01 4.59786534e-01 2.27435619e-01
-7.44789317e-02 -5.36284328e-01 -6.79232717e-01 -5.26525974e-01
-4.14038420e-01 2.86583215e-01 -2.52949214e-03 -5.90859689... | [15.801839828491211, 5.251217365264893] |
a20e611b-2c59-49b2-b2d7-269aa935eaa9 | designing-deep-convolutional-neural-networks-1 | null | null | https://ieeexplore.ieee.org/document/9870218 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9870218 | Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification | In recent years, deep Convolutional Neural Networks (CNNs) have shown great potential in malware classification. CNNs, which are originally designed for image processing, identify malware binaries visualised as images. Despite offering promising performance, these human-designed networks are very large requiring more r... | ['Vijay Varadharajan', 'Raymond Chiong', 'Nasimul Noman', 'Cornelius Paardekooper'] | 2022-07-18 | null | null | null | ieee-congress-on-evolutionary-computation-cec | ['malware-classification'] | ['miscellaneous'] | [ 7.53813311e-02 -4.16921675e-01 6.49881512e-02 -1.23666406e-01
6.48243248e-01 -5.54251075e-01 6.42494917e-01 -3.57011974e-01
-5.42149782e-01 5.83327234e-01 -6.75075471e-01 -7.75028288e-01
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-4.42308217e-01 5.92843592e-01 3.49796593e-01 -3.73632997... | [14.390206336975098, 9.66279411315918] |
a9d97bab-1f7c-47c9-92bb-44953b685f9c | multimodal-transformer-distillation-for-audio | 2210.15563 | null | https://arxiv.org/abs/2210.15563v1 | https://arxiv.org/pdf/2210.15563v1.pdf | Multimodal Transformer Distillation for Audio-Visual Synchronization | Audio-visual synchronization aims to determine whether the mouth movements and speech in the video are synchronized. VocaLiST reaches state-of-the-art performance by incorporating multimodal Transformers to model audio-visual interact information. However, it requires high computing resources, making it impractical for... | ['Jyh-Shing Roger Jang', 'Hung-Yi Lee', 'Chung-Che Wang', 'Haibin Wu', 'Xuanjun Chen'] | 2022-10-27 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [-1.56105042e-01 -5.94385900e-02 -3.13077956e-01 3.53887826e-02
-9.30877686e-01 -5.73082864e-01 6.49260283e-01 -2.29548469e-01
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3.30696166e-01 3.14948022e-01 1.53376728e-01 -9.16433260... | [14.581631660461426, 5.155244827270508] |
d18fa40a-f1ad-4b85-97f0-38c139a4f7c8 | a-hierarchical-variational-neural-uncertainty-1 | 2110.03446 | null | https://arxiv.org/abs/2110.03446v1 | https://arxiv.org/pdf/2110.03446v1.pdf | A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction | Predicting the future frames of a video is a challenging task, in part due to the underlying stochastic real-world phenomena. Prior approaches to solve this task typically estimate a latent prior characterizing this stochasticity, however do not account for the predictive uncertainty of the (deep learning) model. Such ... | ['Anoop Cherian', 'Narendra Ahuja', 'Moitreya Chatterjee'] | 2021-10-06 | a-hierarchical-variational-neural-uncertainty | http://openaccess.thecvf.com//content/ICCV2021/html/Chatterjee_A_Hierarchical_Variational_Neural_Uncertainty_Model_for_Stochastic_Video_Prediction_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chatterjee_A_Hierarchical_Variational_Neural_Uncertainty_Model_for_Stochastic_Video_Prediction_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-prediction'] | ['computer-vision'] | [ 2.88252890e-01 1.21234238e-01 -3.52808982e-02 -4.21723366e-01
-9.95840251e-01 -2.98313737e-01 7.29452372e-01 -1.29309535e-01
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a4cf25ff-1e89-4ba6-a751-ec58ae4955d7 | prototypical-classifier-for-robust-class | 2110.11553 | null | https://arxiv.org/abs/2110.11553v1 | https://arxiv.org/pdf/2110.11553v1.pdf | Prototypical Classifier for Robust Class-Imbalanced Learning | Deep neural networks have been shown to be very powerful methods for many supervised learning tasks. However, they can also easily overfit to training set biases, i.e., label noise and class imbalance. While both learning with noisy labels and class-imbalanced learning have received tremendous attention, existing works... | ['Min-Ling Zhang', 'Yu-Feng Li', 'Jiang-Xin Shi', 'Tong Wei'] | 2021-10-22 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.09074682e-01 1.72010750e-01 -4.95796859e-01 -7.58058786e-01
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2.82320380e-01 4.23121095e-01 8.79301038e-03 7.76528716... | [9.241744995117188, 3.804807186126709] |
3f1d62b3-04ac-4f0c-93ea-0fa3735bda6b | missing-modality-meets-meta-sampling-m3s-an | 2210.03428 | null | https://arxiv.org/abs/2210.03428v1 | https://arxiv.org/pdf/2210.03428v1.pdf | Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality | Multimodal sentiment analysis (MSA) is an important way of observing mental activities with the help of data captured from multiple modalities. However, due to the recording or transmission error, some modalities may include incomplete data. Most existing works that address missing modalities usually assume a particula... | ['Gaoang Wang', 'Guanhong Wang', 'Junhao Zhu', 'Minghua Yang', 'Haozhe Chi'] | 2022-10-07 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 3.88536543e-01 -2.15690613e-01 -5.75395346e-01 -5.69312572e-01
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6.97540283e-01 1.38498515e-01 -2.16492549e-01 -3.95120770... | [13.158853530883789, 5.102975368499756] |
039a378c-844f-4076-8988-288b27f3a5ff | split-learning-in-6g-edge-networks | 2306.12194 | null | https://arxiv.org/abs/2306.12194v2 | https://arxiv.org/pdf/2306.12194v2.pdf | Split Learning in 6G Edge Networks | With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence. Along this line, the proposal to incorporate federated learning into the mobile edge has gained considerable interest in recent years. However, the deployment of federated learnin... | ['Kaibin Huang', 'Xianhao Chen', 'Guanqiao Qu', 'Zheng Lin'] | 2023-06-21 | null | null | null | null | ['management', 'edge-computing'] | ['miscellaneous', 'time-series'] | [-4.65268672e-01 1.11041650e-01 -5.16887307e-01 -1.12172708e-01
-2.11242318e-01 -6.96862996e-01 -9.08239111e-02 -5.55558860e-01
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-3.46014917e-01 2.65291572e-01 -2.78671175e-01 2.10004747... | [5.949120044708252, 5.652664661407471] |
170aa451-3fab-4b6d-9374-ab732f143f07 | 3dias-3d-shape-reconstruction-with-implicit | 2108.08653 | null | https://arxiv.org/abs/2108.08653v1 | https://arxiv.org/pdf/2108.08653v1.pdf | 3DIAS: 3D Shape Reconstruction with Implicit Algebraic Surfaces | 3D Shape representation has substantial effects on 3D shape reconstruction. Primitive-based representations approximate a 3D shape mainly by a set of simple implicit primitives, but the low geometrical complexity of the primitives limits the shape resolution. Moreover, setting a sufficient number of primitives for an a... | ['Kyoung Mu Lee', 'Reyhaneh Neshatavar', 'JaeYoung Chung', 'Mohsen Yavartanoo'] | 2021-08-19 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yavartanoo_3DIAS_3D_Shape_Reconstruction_With_Implicit_Algebraic_Surfaces_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yavartanoo_3DIAS_3D_Shape_Reconstruction_With_Implicit_Algebraic_Surfaces_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-shape-representation'] | ['computer-vision'] | [-2.94760764e-02 1.26948550e-01 4.08754796e-02 -2.01664791e-01
-6.68522894e-01 -6.27722383e-01 5.95526934e-01 -1.22576065e-01
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1.26402780e-01 8.57467890e-01 2.69838810e-01 -8.29225704... | [8.643349647521973, -3.6448042392730713] |
6caa5239-725b-48a7-99a8-7ea93e6c36a8 | physiomtl-personalizing-physiological | 2203.12595 | null | https://arxiv.org/abs/2203.12595v1 | https://arxiv.org/pdf/2203.12595v1.pdf | PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression | Heart rate variability (HRV) is a practical and noninvasive measure of autonomic nervous system activity, which plays an essential role in cardiovascular health. However, using HRV to assess physiology status is challenging. Even in clinical settings, HRV is sensitive to acute stressors such as physical activity, menta... | ['Shirley You Ren', 'XuanLong Nguyen', 'Bo Li', 'Ding Zhao', 'Agni Kumar', 'Gregory Darnell', 'Jiacheng Zhu'] | 2022-03-19 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 2.24271595e-01 -3.50859284e-01 -3.43113035e-01 -4.64143455e-01
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-4.59973335e-01 4.34181422e-01 -5.59684753e-01 7.28297383... | [13.708244323730469, 3.1468279361724854] |
1a65d517-1bb0-4ddd-8848-55cdc2edf1d5 | sequential-end-to-end-network-for-efficient | 2103.10148 | null | https://arxiv.org/abs/2103.10148v1 | https://arxiv.org/pdf/2103.10148v1.pdf | Sequential End-to-end Network for Efficient Person Search | Person search aims at jointly solving Person Detection and Person Re-identification (re-ID). Existing works have designed end-to-end networks based on Faster R-CNN. However, due to the parallel structure of Faster R-CNN, the extracted features come from the low-quality proposals generated by the Region Proposal Network... | ['Duoqian Miao', 'Zhengjia Li'] | 2021-03-18 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.79423109e-01 -2.47957140e-01 9.92040038e-02 -3.44708890e-01
-4.28714722e-01 -1.41381323e-01 3.29930335e-01 -1.09693468e-01
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-1.18202250e-02 -8.70606065e-01 -3.81468922e-01 -1.86105773e-01
6.44027144e-02 7.08941221e-01 4.06027079e-01 -1.89987466... | [14.812895774841309, 0.8178457021713257] |
a035b659-2aec-479a-8b2c-fec6f94f74bd | hiding-in-plain-sight-differential-privacy | 2307.00268 | null | https://arxiv.org/abs/2307.00268v1 | https://arxiv.org/pdf/2307.00268v1.pdf | Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning | Lately, differential privacy (DP) has been introduced in cooperative multiagent reinforcement learning (CMARL) to safeguard the agents' privacy against adversarial inference during knowledge sharing. Nevertheless, we argue that the noise introduced by DP mechanisms may inadvertently give rise to a novel poisoning threa... | ['Hung La', 'Md Tamjid Hossain'] | 2023-07-01 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [-5.39688766e-02 -5.05936856e-04 1.15786292e-01 2.89893985e-01
-7.67288625e-01 -1.12830448e+00 4.10440922e-01 6.16121829e-01
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-9.81669649e-02 -8.76797616e-01 -7.63823092e-01 -9.28852916e-01
-3.29332858e-01 -2.49510616e-01 4.87740338e-02 -2.03857556... | [5.676334381103516, 7.338398456573486] |
ec236a05-b63a-4057-9f51-d8e12b130e63 | cost-sensitive-bert-for-generalisable-1 | 2003.11563 | null | https://arxiv.org/abs/2003.11563v1 | https://arxiv.org/pdf/2003.11563v1.pdf | Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data | The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed. That this task can be addressed effectively using BERT, a powerful new architecture which can be fine-tuned for text classification tasks, is not surprisi... | ['Harish Tayyar Madabushi', 'Michael Castelle', 'Elena Kochkina'] | 2020-03-16 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [ 2.13367254e-01 -1.11408994e-01 -2.54064560e-01 -5.11197627e-01
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-6.13796078e-02 6.14439905e-01 1.72266588e-02 -7.34489560... | [8.532018661499023, 10.441838264465332] |
7be64161-ac44-4c11-84fe-eea43721c4fc | discovering-implicit-discourse-relations | null | null | https://aclanthology.org/E14-1068 | https://aclanthology.org/E14-1068.pdf | Discovering Implicit Discourse Relations Through Brown Cluster Pair Representation and Coreference Patterns | null | ['Attapol Rutherford', 'Nianwen Xue'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.229660511016846, 3.6934990882873535] |
bd656032-433e-486f-ba04-e6db30b413f2 | massive-language-models-can-be-accurately | 2301.00774 | null | https://arxiv.org/abs/2301.00774v3 | https://arxiv.org/pdf/2301.00774v3.pdf | SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot | We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT, specifically designed to work efficiently and accurately ... | ['Dan Alistarh', 'Elias Frantar'] | 2023-01-02 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 1.12837411e-01 6.28502548e-01 -4.27685738e-01 -4.20475096e-01
-9.16230679e-01 -2.90602148e-01 4.35585618e-01 -1.00806490e-01
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-5.93434758e-02 1.30282295e+00 1.33674189e-01 -1.17164575... | [8.711661338806152, 3.586719036102295] |
21c7c943-3b12-415e-9769-a4f551bb421b | a-majorization-minimization-gauss-newton | 2304.1394 | null | https://arxiv.org/abs/2304.13940v1 | https://arxiv.org/pdf/2304.13940v1.pdf | A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion | In 1-bit matrix completion, the aim is to estimate an underlying low-rank matrix from a partial set of binary observations. We propose a novel method for 1-bit matrix completion called MMGN. Our method is based on the majorization-minimization (MM) principle, which yields a sequence of standard low-rank matrix completi... | ['Boaz Nadler', 'Eric C. Chi', 'Xu Han', 'Xiaoqian Liu'] | 2023-04-27 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 6.11135721e-01 -1.27811968e-01 -2.61336356e-01 -6.40584007e-02
-1.07125068e+00 -4.28204209e-01 4.63543564e-01 -1.47594333e-01
-3.84653538e-01 7.76159942e-01 4.09168333e-01 -4.34900373e-01
-2.61124969e-01 -2.23386362e-01 -6.77370310e-01 -7.02956617e-01
-4.68564034e-01 3.34631860e-01 -5.18070936e-01 -2.30791494... | [6.968489646911621, 4.662806510925293] |
ed64d0ba-7e42-408e-9522-c6981d56d4e7 | sysnoise-exploring-and-benchmarking-training | 2307.0028 | null | https://arxiv.org/abs/2307.00280v1 | https://arxiv.org/pdf/2307.00280v1.pdf | SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency | Extensive studies have shown that deep learning models are vulnerable to adversarial and natural noises, yet little is known about model robustness on noises caused by different system implementations. In this paper, we for the first time introduce SysNoise, a frequently occurred but often overlooked noise in the deep ... | ['Xianglong Liu', 'Fengwei Yu', 'Tianzi Xiao', 'Yunchen Zhang', 'Yongqiang Yao', 'Jian Hu', 'Yanfei Wang', 'Aishan Liu', 'Ruihao Gong', 'Yuhang Li', 'Yan Wang'] | 2023-07-01 | null | null | null | null | ['instance-segmentation', 'benchmarking', 'benchmarking'] | ['computer-vision', 'miscellaneous', 'robots'] | [-1.92298159e-01 -3.11744392e-01 2.89306760e-01 -2.75138468e-01
-6.82676315e-01 -1.04415846e+00 6.29501998e-01 -2.55373538e-01
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1.82075977e-01 -6.08769178e-01 -1.10897458e+00 -5.85255563e-01
2.42442358e-02 -2.61412747e-02 1.75781026e-01 -2.83289522... | [5.678770542144775, 7.7696661949157715] |
28ef6a6a-0e79-4e6b-9884-ac02820b5ffc | supervised-prototypical-contrastive-learning | 2210.08713 | null | https://arxiv.org/abs/2210.08713v2 | https://arxiv.org/pdf/2210.08713v2.pdf | Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation | Capturing emotions within a conversation plays an essential role in modern dialogue systems. However, the weak correlation between emotions and semantics brings many challenges to emotion recognition in conversation (ERC). Even semantically similar utterances, the emotion may vary drastically depending on contexts or s... | ['Songlin Hu', 'Hui Xue', 'Longtao Huang', 'Xiaohui Song'] | 2022-10-17 | null | null | null | null | ['imbalanced-classification', 'emotion-recognition-in-conversation'] | ['miscellaneous', 'natural-language-processing'] | [-1.34943843e-01 -1.76756233e-01 -2.02268571e-01 -7.97451556e-01
-6.90906167e-01 -4.13721293e-01 3.88718992e-01 2.48832285e-01
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2.17988230e-02 -3.74196917e-01 -3.45252484e-01 -6.53517842e-01
1.32953778e-01 2.02551022e-01 -2.43846461e-01 -4.79046166... | [13.037490844726562, 6.081747055053711] |
4543cbcd-6a34-41b6-962f-1a1636a43d6b | capturing-emerging-complexity-in-lenia | 2305.09378 | null | https://arxiv.org/abs/2305.09378v2 | https://arxiv.org/pdf/2305.09378v2.pdf | Capturing Emerging Complexity in Lenia | This research project investigates Lenia, an artificial life platform that simulates ecosystems of digital creatures. Lenia's ecosystem consists of simple, artificial organisms that can move, consume, grow, and reproduce. The platform is important as a tool for studying artificial life and evolution, as it provides a s... | ['Stefano Nichele', 'Aarati Shrestha', 'Sanyam Jain'] | 2023-05-16 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.32172140e-02 -3.64219218e-01 4.36517328e-01 3.51915330e-01
5.77438712e-01 -6.52706504e-01 7.05064654e-01 4.03967425e-02
-5.83434045e-01 8.32005978e-01 -1.96959808e-01 -1.49764195e-02
-3.08034271e-01 -1.07629395e+00 -6.38000309e-01 -1.08486187e+00
-5.50221980e-01 1.45176634e-01 5.56653500e-01 -3.26566398... | [5.63350248336792, 4.060884475708008] |
52c14191-bd8a-4af1-bc4d-62756e02ef3d | pseudo-labels-refinement-with-intra-camera | 2304.12634 | null | https://arxiv.org/abs/2304.12634v1 | https://arxiv.org/pdf/2304.12634v1.pdf | Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised Person Re-identification | Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels. Most clustering-based methods roughly divide image features into clusters and neglect the feature distribution noise caused by domain shifts among different cameras, leading to inevitable performance... | ['Jinjun Wang', 'Sanping Zhou. Qianxin Huang', 'Kangyi Wu', 'Pengna Li'] | 2023-04-25 | null | null | null | null | ['person-re-identification', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [-1.02348343e-01 -5.07050753e-01 6.36970848e-02 -4.91849601e-01
-5.63161731e-01 -6.36974335e-01 5.68220437e-01 7.32265264e-02
-7.39683270e-01 4.74879175e-01 2.14713901e-01 5.18777430e-01
1.06444217e-01 -3.46266657e-01 -3.68368208e-01 -6.91155910e-01
4.55955744e-01 5.06541133e-01 2.07293987e-01 4.88070011... | [14.792131423950195, 1.0468157529830933] |
1decab8c-ce02-43f7-adad-163101d72317 | from-node-to-graph-joint-reasoning-on-visual | null | null | https://ieeexplore.ieee.org/document/9706663 | https://openaccess.thecvf.com/content/WACV2022/papers/Nie_From_Node_To_Graph_Joint_Reasoning_on_Visual-Semantic_Relational_Graph_WACV_2022_paper.pdf | From Node to Graph: Joint Reasoning on Visual-Semantic Relational Graph for Zero-Shot Detection | Zero-Shot Detection (ZSD), which aims at localizing andrecognizing unseen objects in a complicated scene, usuallyleverages the visual and semantic information of individ-ual objects alone. However, scene understanding of hu-man exceeds recognizing individual objects separately: thecontextual information ... | ['Xilin Chen', 'Ruiping Wang', 'Hui Nie'] | 2022-02-15 | null | null | null | winter-conference-on-applications-of-computer-5 | ['zero-shot-object-detection'] | ['computer-vision'] | [ 6.58382080e-04 6.65444136e-02 -2.70771474e-01 -2.75271475e-01
-1.98022798e-01 -7.01165617e-01 8.92972350e-01 3.90871823e-01
-7.23466724e-02 4.08143997e-01 2.82132894e-01 -1.80026278e-01
-2.90090829e-01 -1.14464808e+00 -6.25352085e-01 -5.03514409e-01
-6.94682896e-02 2.22116902e-01 3.63863170e-01 -2.21291348... | [10.304455757141113, 1.6601642370224] |
78d1ea68-bcad-4082-9b78-a3bc528844d9 | hatemm-a-multi-modal-dataset-for-hate-video | 2305.03915 | null | https://arxiv.org/abs/2305.03915v1 | https://arxiv.org/pdf/2305.03915v1.pdf | HateMM: A Multi-Modal Dataset for Hate Video Classification | Hate speech has become one of the most significant issues in modern society, having implications in both the online and the offline world. Due to this, hate speech research has recently gained a lot of traction. However, most of the work has primarily focused on text media with relatively little work on images and even... | ['Animesh Mukherjee', 'Manish Gupta', 'Binny Mathew', 'Punyajoy Saha', 'Rohit Raj', 'Mithun Das'] | 2023-05-06 | null | null | null | null | ['video-classification', 'hate-speech-detection'] | ['computer-vision', 'natural-language-processing'] | [-1.14409029e-01 -1.83048293e-01 -1.62103549e-01 2.85166889e-01
-5.93624890e-01 -8.30279768e-01 5.00821173e-01 8.26200917e-02
-1.83425143e-01 3.65759879e-01 2.86352873e-01 1.93346977e-01
2.11688131e-01 -1.77501470e-01 -5.67564309e-01 -7.43302941e-01
3.92037071e-02 -3.32632989e-01 1.74261346e-01 -1.03310540... | [8.659400939941406, 10.576521873474121] |
5b7f945c-c4f5-48de-a728-70273cf324ee | learning-and-planning-in-complex-action | 2104.06303 | null | https://arxiv.org/abs/2104.06303v1 | https://arxiv.org/pdf/2104.06303v1.pdf | Learning and Planning in Complex Action Spaces | Many important real-world problems have action spaces that are high-dimensional, continuous or both, making full enumeration of all possible actions infeasible. Instead, only small subsets of actions can be sampled for the purpose of policy evaluation and improvement. In this paper, we propose a general framework to re... | ['David Silver', 'Simon Schmitt', 'Mohammadamin Barekatain', 'Ioannis Antonoglou', 'Julian Schrittwieser', 'Thomas Hubert'] | 2021-04-13 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 4.21740472e-01 4.94999707e-01 -4.98398483e-01 1.16983518e-01
-7.72246659e-01 -6.31587029e-01 7.43664145e-01 8.14036354e-02
-7.26617754e-01 1.43042576e+00 3.38428319e-02 -6.22232139e-01
-5.76294124e-01 -8.89715493e-01 -6.64181471e-01 -7.40992785e-01
-3.41469377e-01 7.17615604e-01 3.92197698e-01 -2.30431676... | [4.17844820022583, 1.9549046754837036] |
478d88b0-3989-423c-8481-a3cafef254d6 | an-approach-based-on-combination-of-features | 2004.11699 | null | https://arxiv.org/abs/2004.11699v1 | https://arxiv.org/pdf/2004.11699v1.pdf | An approach based on Combination of Features for automatic news retrieval | Nowadays, according to the increasingly increasing information, the importance of its presentation is also increasing. The internet has become one of the main sources of information for users and their favorite topics. It also provides access to more information. Understanding this information is very important for pro... | ['Sasan Harifi', 'Mohammad Moradi', 'Elham Ghanbari', 'Mehrdad Maeen'] | 2020-04-16 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-1.90254763e-01 -4.41615015e-01 -3.51503491e-01 -1.08725682e-01
-3.91352624e-01 -6.13112748e-01 6.38200581e-01 1.11772323e+00
-5.19145310e-01 6.32445455e-01 3.48846018e-01 1.51772320e-01
-9.02830482e-01 -1.00198507e+00 1.93540212e-02 -4.58542943e-01
1.70693528e-02 6.33446455e-01 4.18743193e-01 -4.83827412... | [10.493752479553223, 7.4236650466918945] |
98d73b06-480c-4358-b362-ed2738644578 | a-simple-framework-for-contrastive-learning | 2002.05709 | null | https://arxiv.org/abs/2002.05709v3 | https://arxiv.org/pdf/2002.05709v3.pdf | A Simple Framework for Contrastive Learning of Visual Representations | This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn use... | ['Simon Kornblith', 'Mohammad Norouzi', 'Geoffrey Hinton', 'Ting Chen'] | 2020-02-13 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6165-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6165-Paper.pdf | icml-2020-1 | ['self-supervised-image-classification', 'self-supervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 3.84483397e-01 2.34936565e-01 -5.57084441e-01 -4.10957903e-01
-5.69587231e-01 -4.17355835e-01 7.85861731e-01 1.71490699e-01
-5.66242456e-01 7.37413704e-01 2.20646068e-01 -1.73064992e-01
1.17406771e-01 -5.75235248e-01 -9.38100398e-01 -4.95120734e-01
-1.59925133e-01 4.20404106e-01 2.34736204e-01 -2.64075458... | [9.383039474487305, 2.6947383880615234] |
f3fd3baa-5cb8-46cb-911c-eeb4e9cb9dbb | grafit-learning-fine-grained-image | 2011.12982 | null | https://arxiv.org/abs/2011.12982v1 | https://arxiv.org/pdf/2011.12982v1.pdf | Grafit: Learning fine-grained image representations with coarse labels | This paper tackles the problem of learning a finer representation than the one provided by training labels. This enables fine-grained category retrieval of images in a collection annotated with coarse labels only. Our network is learned with a nearest-neighbor classifier objective, and an instance loss inspired by self... | ['Hervé Jégou', 'Matthieu Cord', 'Matthijs Douze', 'Alexandre Sablayrolles', 'Hugo Touvron'] | 2020-11-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Touvron_Grafit_Learning_Fine-Grained_Image_Representations_With_Coarse_Labels_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Touvron_Grafit_Learning_Fine-Grained_Image_Representations_With_Coarse_Labels_ICCV_2021_paper.pdf | iccv-2021-1 | ['learning-with-coarse-labels'] | ['computer-vision'] | [ 1.61255121e-01 -2.96903193e-01 -8.25279713e-01 -4.63316768e-01
-1.45844007e+00 -9.69617128e-01 9.68292952e-01 3.35068166e-01
-6.69902682e-01 6.54482007e-01 3.03338706e-01 2.24050865e-01
-5.04544079e-01 -7.28700757e-01 -8.69247317e-01 -6.16127372e-01
-4.70579602e-02 6.86508775e-01 -4.51911129e-02 4.47810799... | [9.719943046569824, 2.103008985519409] |
33527806-cb2a-4805-aacf-4cc970e61684 | untrimmednets-for-weakly-supervised-action | 1703.03329 | null | http://arxiv.org/abs/1703.03329v2 | http://arxiv.org/pdf/1703.03329v2.pdf | UntrimmedNets for Weakly Supervised Action Recognition and Detection | Current action recognition methods heavily rely on trimmed videos for model
training. However, it is expensive and time-consuming to acquire a large-scale
trimmed video dataset. This paper presents a new weakly supervised
architecture, called UntrimmedNet, which is able to directly learn action
recognition models from ... | ['Luc van Gool', 'Limin Wang', 'Yuanjun Xiong', 'Dahua Lin'] | 2017-03-09 | untrimmednets-for-weakly-supervised-action-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Wang_UntrimmedNets_for_Weakly_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_UntrimmedNets_for_Weakly_CVPR_2017_paper.pdf | cvpr-2017-7 | ['weakly-supervised-action-localization', 'weakly-supervised-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.89073122e-01 2.36952603e-02 -6.26570880e-01 -5.13591707e-01
-6.10088527e-01 -3.40289742e-01 6.98424399e-01 -4.95207846e-01
-6.13666654e-01 5.99206984e-01 2.68031478e-01 -6.59226552e-02
3.01619917e-02 -3.51940811e-01 -7.30396450e-01 -7.21624076e-01
-2.34321713e-01 3.24758887e-01 5.40259004e-01 3.42471510... | [8.472122192382812, 0.6460627317428589] |
153a6211-ecd1-4c00-9496-b1792a9bc3bb | latent-graph-attention-for-enhanced-spatial | 2307.04149 | null | https://arxiv.org/abs/2307.04149v1 | https://arxiv.org/pdf/2307.04149v1.pdf | Latent Graph Attention for Enhanced Spatial Context | Global contexts in images are quite valuable in image-to-image translation problems. Conventional attention-based and graph-based models capture the global context to a large extent, however, these are computationally expensive. Moreover, the existing approaches are limited to only learning the pairwise semantic relati... | ['Dilip K. Prasad', 'Deepak K. Gupta', 'Himanshu Buckchash', 'Yash Bhambhu', 'Ayush Singh'] | 2023-07-09 | null | null | null | null | ['optical-flow-estimation', 'image-to-image-translation', 'image-restoration', 'graph-attention', 'image-to-image-translation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'graphs', 'miscellaneous'] | [ 1.96571693e-01 1.46815181e-01 -2.16827869e-01 -1.84065700e-01
-1.16769254e-01 -2.86286652e-01 3.88544679e-01 3.17309648e-01
-3.42718482e-01 4.27958786e-01 6.67339265e-02 -1.03555374e-01
-2.52185632e-02 -1.04396510e+00 -7.43726075e-01 -6.94584370e-01
7.76806921e-02 8.84637162e-02 4.98113602e-01 -3.85059603... | [9.836669921875, -0.13856558501720428] |
fc59bfcc-e0ea-4c72-9636-4bde819b1f9c | total-text-a-comprehensive-dataset-for-scene | 1710.104 | null | http://arxiv.org/abs/1710.10400v1 | http://arxiv.org/pdf/1710.10400v1.pdf | Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition | Text in curve orientation, despite being one of the common text orientations
in real world environment, has close to zero existence in well received scene
text datasets such as ICDAR2013 and MSRA-TD500. The main motivation of
Total-Text is to fill this gap and facilitate a new research direction for the
scene text comm... | ['Chee Seng Chan', 'Chee Kheng Chng'] | 2017-10-28 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [ 1.24470308e-01 -2.74962574e-01 3.41549478e-02 -3.55521679e-01
-4.10391510e-01 -8.26050937e-01 8.75079572e-01 -8.91937762e-02
-3.08414370e-01 1.03771247e-01 1.99261695e-01 -2.95269519e-01
1.71937793e-01 -8.27691138e-01 -5.88808179e-01 -5.95371485e-01
5.62223256e-01 8.14367354e-01 6.43995464e-01 -2.62484968... | [12.061807632446289, 2.278257369995117] |
992dbc01-1620-4524-a1c2-4d0381ec9f65 | lyricsim-a-novel-dataset-and-benchmark-for | 2306.01325 | null | https://arxiv.org/abs/2306.01325v1 | https://arxiv.org/pdf/2306.01325v1.pdf | LyricSIM: A novel Dataset and Benchmark for Similarity Detection in Spanish Song LyricS | In this paper, we present a new dataset and benchmark tailored to the task of semantic similarity in song lyrics. Our dataset, originally consisting of 2775 pairs of Spanish songs, was annotated in a collective annotation experiment by 63 native annotators. After collecting and refining the data to ensure a high degree... | ['Elena González-Blanco', 'Salvador Ros', 'Víctor Fresno', 'Pedro Hernández', 'Adrián Ghajari', 'Alejandro Benito-Santos'] | 2023-06-02 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-5.84401563e-02 -3.91437829e-01 -5.91761582e-02 -5.15369594e-01
-1.13398957e+00 -1.11713541e+00 4.44006354e-01 2.79937565e-01
-6.18500948e-01 7.66909659e-01 4.36230004e-01 3.45549196e-01
1.39010116e-01 -2.91251093e-01 -3.50230068e-01 -1.76821336e-01
4.17209193e-02 7.20251918e-01 2.23668665e-01 -3.78038853... | [10.91490364074707, 9.703363418579102] |
ac998abd-08b1-45f4-8c63-53c71ac90edb | conditional-training-with-bounding-map-for | 2103.12277 | null | https://arxiv.org/abs/2103.12277v1 | https://arxiv.org/pdf/2103.12277v1.pdf | Conditional Training with Bounding Map for Universal Lesion Detection | Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by coarse-to-fine two-stage detection approaches, but such two-stage ULD methods still suffer from issues like imbalance of positive v.s. negative anchors during object p... | ['S. Kevin Zhou', 'Hu Han', 'Long Chen', 'Han Li'] | 2021-03-23 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [ 4.20984000e-01 2.59786695e-01 -5.99663079e-01 -1.01212390e-01
-1.39115560e+00 -9.70431720e-04 3.81397665e-01 4.25826967e-01
-3.90471011e-01 6.33455634e-01 1.73629578e-02 -6.11863375e-01
-4.07118537e-02 -5.97876430e-01 -4.62027609e-01 -9.95962381e-01
1.80940017e-01 5.96693575e-01 9.88152385e-01 2.03895301... | [15.068865776062012, -2.3907337188720703] |
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