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e1877bec-add9-4223-acd2-ff15509772d3 | how-to-control-hydrodynamic-force-on-fluidic | 2304.11526 | null | https://arxiv.org/abs/2304.11526v1 | https://arxiv.org/pdf/2304.11526v1.pdf | How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning | Deep reinforcement learning (DRL) for fluidic pinball, three individually rotating cylinders in the uniform flow arranged in an equilaterally triangular configuration, can learn the efficient flow control strategies due to the validity of self-learning and data-driven state estimation for complex fluid dynamic problems... | ['Dixia Fan', 'Zhiyang Jin', 'Hui Xiang', 'Yue Wang', 'Haodong Feng'] | 2023-04-23 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-4.38724667e-01 -1.46921009e-01 -1.46410570e-01 3.43988746e-01
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-5.56900740e-01 5.19152582e-01 2.15039268e-01 -4.14616227... | [4.470613479614258, 2.1580824851989746] |
bfc1fa12-c70a-4d14-919c-da5c58a96a46 | semi-supervised-neural-architecture-search | 2002.10389 | null | https://arxiv.org/abs/2002.10389v4 | https://arxiv.org/pdf/2002.10389v4.pdf | Semi-Supervised Neural Architecture Search | Neural architecture search (NAS) relies on a good controller to generate better architectures or predict the accuracy of given architectures. However, training the controller requires both abundant and high-quality pairs of architectures and their accuracy, while it is costly to evaluate an architecture and obtain its ... | ['Tie-Yan Liu', 'Rui Wang', 'Renqian Luo', 'Enhong Chen', 'Xu Tan', 'Tao Qin'] | 2020-02-24 | null | http://proceedings.neurips.cc/paper/2020/hash/77305c2f862ad1d353f55bf38e5a5183-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/77305c2f862ad1d353f55bf38e5a5183-Paper.pdf | neurips-2020-12 | ['natural-language-transduction'] | ['natural-language-processing'] | [-1.01552382e-01 -2.61107057e-01 -2.12366104e-01 -4.86407220e-01
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2.34605744e-01 8.29253614e-01 2.34258816e-01 -1.38362333... | [8.675314903259277, 3.288219928741455] |
1a8d9444-bd87-4a96-81d8-57e54eb85e65 | typo-robust-representation-learning-for-dense | 2306.10348 | null | https://arxiv.org/abs/2306.10348v1 | https://arxiv.org/pdf/2306.10348v1.pdf | Typo-Robust Representation Learning for Dense Retrieval | Dense retrieval is a basic building block of information retrieval applications. One of the main challenges of dense retrieval in real-world settings is the handling of queries containing misspelled words. A popular approach for handling misspelled queries is minimizing the representations discrepancy between misspelle... | ['Sarana Nutanong', 'Ekapol Chuangsuwanich', 'Can Udomcharoenchaikit', 'Peerat Limkonchotiwat', 'Wuttikorn Ponwitayarat', 'Panuthep Tasawong'] | 2023-06-17 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-1.73960757e-02 -5.43889344e-01 -3.01312417e-01 -1.95142210e-01
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3.41168553e-01 7.41867721e-01 3.87971699e-01 -5.14549494... | [11.44036865234375, 7.665599346160889] |
3b284a61-4f58-4e37-8d2d-6fc0ccd89913 | harnessing-the-power-of-text-image | 2304.10249 | null | https://arxiv.org/abs/2304.10249v1 | https://arxiv.org/pdf/2304.10249v1.pdf | Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation | As growing usage of social media websites in the recent decades, the amount of news articles spreading online rapidly, resulting in an unprecedented scale of potentially fraudulent information. Although a plenty of studies have applied the supervised machine learning approaches to detect such content, the lack of gold ... | ['Siwei Lyu', 'Xi Wu', 'Jinrong Hu', 'Bin Zhu', 'Shu Hu', 'Xin Wang', 'Peng Zheng', 'Hao Chen'] | 2023-04-19 | null | null | null | null | ['misinformation', 'self-learning'] | ['miscellaneous', 'natural-language-processing'] | [ 2.99816132e-01 1.45844713e-01 -3.18520665e-01 -1.83460951e-01
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a4eff907-37a8-46a5-be2e-047c93952667 | convabuse-data-analysis-and-benchmarks-for | 2109.09483 | null | https://arxiv.org/abs/2109.09483v1 | https://arxiv.org/pdf/2109.09483v1.pdf | ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI | We present the first English corpus study on abusive language towards three conversational AI systems gathered "in the wild": an open-domain social bot, a rule-based chatbot, and a task-based system. To account for the complexity of the task, we take a more `nuanced' approach where our ConvAI dataset reflects fine-grai... | ['Verena Rieser', 'Gavin Abercrombie', 'Amanda Cercas Curry'] | 2021-09-20 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-2.26991296e-01 4.55452502e-01 7.11056963e-02 -3.61987174e-01
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b8f60c6a-8d58-4a7f-8e4d-f8d0fa0bc0b4 | d2df2wod-learning-object-proposals-for-weakly | 2212.01376 | null | https://arxiv.org/abs/2212.01376v1 | https://arxiv.org/pdf/2212.01376v1.pdf | D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation | Weakly-supervised object detection (WSOD) models attempt to leverage image-level annotations in lieu of accurate but costly-to-obtain object localization labels. This oftentimes leads to substandard object detection and localization at inference time. To tackle this issue, we propose D2DF2WOD, a Dual-Domain Fully-to-We... | ['Vladimir Pavlovic', 'Ricardo Guerrero', 'Yuting Wang'] | 2022-12-02 | null | null | null | null | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 2.34965533e-01 1.29389480e-01 -3.25541198e-01 -3.61674964e-01
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1.96895406e-01 6.42897785e-01 9.69215274e-01 2.00249314... | [9.390867233276367, 1.3655011653900146] |
99fe3a34-de2b-4a80-901d-1d6048b43747 | towards-remote-fault-detection-by-analyzing | 2209.15498 | null | https://arxiv.org/abs/2209.15498v1 | https://arxiv.org/pdf/2209.15498v1.pdf | Towards remote fault detection by analyzing communication priorities | The ability to detect faults is an important safety feature for event-based multi-agent systems. In most existing algorithms, each agent tries to detect faults by checking its own behavior. But what if one agent becomes unable to recognize misbehavior, for example due to failure in its onboard fault detection? To impro... | ['Sebastian Trimpe', 'Dominik Baumann', 'Alexander Gräfe'] | 2022-09-30 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.16738945e-01 2.21569031e-01 1.68854877e-01 -3.46486382e-02
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-7.14652538e-01 6.05581224e-01 1.10129833e+00 -1.27090052... | [5.41487979888916, 2.5037519931793213] |
a0ef5363-865e-4f27-a36b-611a022052d5 | domain-based-punjabi-text-document-clustering | null | null | https://aclanthology.org/C12-3049 | https://aclanthology.org/C12-3049.pdf | Domain Based Punjabi Text Document Clustering | null | ['Saurabh Sharma', 'Vishal Gupta'] | 2012-12-01 | domain-based-punjabi-text-document-clustering-1 | https://aclanthology.org/C12-3049 | https://aclanthology.org/C12-3049.pdf | coling-2012-12 | ['text-clustering'] | ['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... | [-7.339441299438477, 3.840492010116577] |
8ec90d0d-4f35-4fd7-bc1c-7705358529c0 | a-recommender-system-for-equitable-public-art | 2207.14367 | null | https://arxiv.org/abs/2207.14367v1 | https://arxiv.org/pdf/2207.14367v1.pdf | A Recommender System for Equitable Public Art Curation and Installation | The placement of art in public spaces can have a significant impact on who feels a sense of belonging. In cities, public art communicates whose interests and culture are being favored. In this paper, we propose a graph matching approach with local constraints to build a curatorial tool for selecting public art in a way... | ['Dina Deitsch', 'Abiy Tasissa', 'Anna Haensch'] | 2022-07-28 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 2.81537890e-01 2.93443352e-02 -5.51341414e-01 -4.47395593e-01
-5.65328121e-01 -4.84777540e-01 3.26193273e-01 2.70230234e-01
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-5.81932306e-01 -1.11048865e+00 -2.90370643e-01 -3.06281149e-01
6.00966871e-01 4.12450880e-01 -2.70559222e-01 -3.59448865... | [8.563907623291016, 5.4099578857421875] |
89aa1f4c-0d71-4e1f-84d4-ef72ee2f96ff | multi-garment-net-learning-to-dress-3d-people | 1908.06903 | null | https://arxiv.org/abs/1908.06903v2 | https://arxiv.org/pdf/1908.06903v2.pdf | Multi-Garment Net: Learning to Dress 3D People from Images | We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video. Several experiments demonstrate that this representation allows higher level of control when compared to single mesh or voxel representations of shape. Our model allo... | ['Gerard Pons-Moll', 'Christian Theobalt', 'Bharat Lal Bhatnagar', 'Garvita Tiwari'] | 2019-08-19 | multi-garment-net-learning-to-dress-3d-people-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Bhatnagar_Multi-Garment_Net_Learning_to_Dress_3D_People_From_Images_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Bhatnagar_Multi-Garment_Net_Learning_to_Dress_3D_People_From_Images_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-shape-reconstruction-from-a-single-2d'] | ['computer-vision'] | [ 2.08456382e-01 -3.35053578e-02 1.65922135e-01 -6.39134228e-01
-2.84355938e-01 -6.14072800e-01 2.12854251e-01 -3.40954542e-01
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-1.20674498e-01 8.80114257e-01 3.13060224e-01 -3.15719783... | [7.127345561981201, -1.2438609600067139] |
014c15ac-930f-489e-a9de-283a3af616c9 | learning-local-complex-features-using | 2007.05643 | null | https://arxiv.org/abs/2007.05643v2 | https://arxiv.org/pdf/2007.05643v2.pdf | Learning Local Complex Features using Randomized Neural Networks for Texture Analysis | Texture is a visual attribute largely used in many problems of image analysis. Currently, many methods that use learning techniques have been proposed for texture discrimination, achieving improved performance over previous handcrafted methods. In this paper, we present a new approach that combines a learning technique... | ['Jarbas Joaci de Mesquita Sá Junior', 'Leonardo F. S. Scabini', 'Lucas C. Ribas', 'Odemir M. Bruno'] | 2020-07-10 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 3.41195315e-01 -1.82746723e-01 -3.14488798e-01 -3.13538164e-01
5.32920621e-02 -5.52415252e-02 6.02748752e-01 1.80759355e-01
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-2.84456104e-01 -1.16291702e+00 -2.89964557e-01 -9.73408222e-01
-1.80291101e-01 4.66695994e-01 4.97098565e-01 -2.67896235... | [10.29839038848877, -0.42996010184288025] |
9824b5b2-fced-4ba2-8074-1687027150c5 | an-algorithmic-framework-for-the-optimization | 2303.12797 | null | https://arxiv.org/abs/2303.12797v1 | https://arxiv.org/pdf/2303.12797v1.pdf | An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters | In this paper, we propose an algorithmic framework to automatically generate efficient deep neural networks and optimize their associated hyperparameters. The framework is based on evolving directed acyclic graphs (DAGs), defining a more flexible search space than the existing ones in the literature. It allows mixtures... | ['Gilles Cabriel', 'Sandra Claudel', 'El-Ghazali Talbi', 'Julie Keisler'] | 2023-02-27 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.93336323e-01 1.17841721e-01 1.36798888e-01 -2.71258593e-01
1.83085173e-01 -4.78585273e-01 7.73387551e-01 -2.15470530e-02
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-4.04203147e-01 6.42934024e-01 2.98639715e-01 -4.06758338... | [8.278223037719727, 3.2424302101135254] |
66dacbf3-71ba-4b27-a70d-bca58e3d49a9 | quantile-filtered-imitation-learning | 2112.00950 | null | https://arxiv.org/abs/2112.00950v1 | https://arxiv.org/pdf/2112.00950v1.pdf | Quantile Filtered Imitation Learning | We introduce quantile filtered imitation learning (QFIL), a novel policy improvement operator designed for offline reinforcement learning. QFIL performs policy improvement by running imitation learning on a filtered version of the offline dataset. The filtering process removes $ s,a $ pairs whose estimated Q values fal... | ['Joan Bruna', 'Rajesh Ranganath', 'William F. Whitney', 'David Brandfonbrener'] | 2021-12-02 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.76597103e-01 2.86142290e-01 -7.78789043e-01 -1.95513859e-01
-1.06249642e+00 -8.46281230e-01 5.61913490e-01 1.57983556e-01
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-5.38437903e-01 3.20840359e-01 1.87934801e-01 -1.12573057... | [4.051209449768066, 2.2500553131103516] |
dc8bfdc8-0461-4b1a-ac53-91e880cbe38c | multivariate-regression-with-calibration | null | null | http://papers.nips.cc/paper/5630-multivariate-regression-with-calibration | http://papers.nips.cc/paper/5630-multivariate-regression-with-calibration.pdf | Multivariate Regression with Calibration | We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an imp... | ['Tuo Zhao', 'Lie Wang', 'Han Liu'] | 2014-12-01 | null | null | null | neurips-2014-12 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [-8.11656192e-03 -2.14582868e-02 -1.31186366e-01 -2.03029007e-01
-1.39738154e+00 -2.03047946e-01 6.56857863e-02 -2.05946550e-01
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-2.45664984e-01 5.01218796e-01 -1.45549074e-01 1.03077926... | [7.054462432861328, 4.407219886779785] |
580a4927-929c-41de-b1b7-94714c7d8241 | deep-unsupervised-image-hashing-by-maximizing | 2012.12334 | null | https://arxiv.org/abs/2012.12334v1 | https://arxiv.org/pdf/2012.12334v1.pdf | Deep Unsupervised Image Hashing by Maximizing Bit Entropy | Unsupervised hashing is important for indexing huge image or video collections without having expensive annotations available. Hashing aims to learn short binary codes for compact storage and efficient semantic retrieval. We propose an unsupervised deep hashing layer called Bi-half Net that maximizes entropy of the bin... | ['Jan van Gemert', 'Yunqiang Li'] | 2020-12-22 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [-6.72674999e-02 1.11247012e-02 -5.15386641e-01 -6.30066395e-01
-1.16862607e+00 -3.57217520e-01 4.54839855e-01 3.72202754e-01
-9.85598743e-01 7.94525862e-01 2.57678300e-01 5.98054416e-02
-4.14053053e-02 -6.27230048e-01 -1.03236079e+00 -8.22381437e-01
-4.46963131e-01 5.90476930e-01 1.34550676e-01 3.42669696... | [11.245474815368652, 0.9640119075775146] |
36fd39d6-86eb-418d-938a-8962a657e52b | speechmatrix-a-large-scale-mined-corpus-of-1 | 2211.04508 | null | https://arxiv.org/abs/2211.04508v1 | https://arxiv.org/pdf/2211.04508v1.pdf | SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations | We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments in 136 language pairs with a total of 418 thousand hours of speech. To evaluate the quality of this parallel speech, we train bilingual spee... | ['Holger Schwenk', 'Benoît Sagot', 'Juan Pino', 'Changhan Wang', 'Vedanuj Goswani', 'Ann Lee', 'Jingfei Du', 'Ning Dong', 'Hongyu Gong', 'Paul-Ambroise Duquenne'] | 2022-11-08 | null | null | null | arxiv-2022-10 | ['speech-to-speech-translation'] | ['speech'] | [-4.68200222e-02 3.57836694e-01 -3.19270819e-01 -4.81695235e-01
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1.25601426e-01 1.04491711e+00 -1.58999547e-01 -5.85847080... | [14.410694122314453, 7.220067977905273] |
86da367d-c636-462e-8745-ababaece95ab | deepclue-enhanced-image-clustering-via-multi | 2206.00359 | null | https://arxiv.org/abs/2206.00359v1 | https://arxiv.org/pdf/2206.00359v1.pdf | DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks | Deep clustering has recently emerged as a promising technique for complex image clustering. Despite the significant progress, previous deep clustering works mostly tend to construct the final clustering by utilizing a single layer of representation, e.g., by performing $K$-means on the last fully-connected layer or by ... | ['Jian-Huang Lai', 'Chang-Dong Wang', 'Xiangji Chen', 'Ding-Hua Chen', 'Dong Huang'] | 2022-06-01 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-1.59717843e-01 -4.19368967e-02 2.29691997e-01 -3.56454134e-01
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-2.13016972e-01 6.04256272e-01 -2.10448913e-02 1.74185932... | [9.121519088745117, 3.2436153888702393] |
7d3dcac8-c249-43cb-ab02-d003d685cfcb | possibility-before-utility-learning-and-using-1 | 2203.12686 | null | https://arxiv.org/abs/2203.12686v1 | https://arxiv.org/pdf/2203.12686v1.pdf | Possibility Before Utility: Learning And Using Hierarchical Affordances | Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of every high-level action in existence, but instead only consider ones they deem possible in the first place. By focusing only on what is feas... | ['Fei Sha', 'Shariq Iqbal', 'Robby Costales'] | 2022-03-23 | possibility-before-utility-learning-and-using | https://openreview.net/forum?id=7b4zxUnrO2N | https://openreview.net/pdf?id=7b4zxUnrO2N | iclr-2022-4 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.77592659e-01 3.41580063e-01 -2.70781606e-01 -5.94998859e-02
-4.41021502e-01 -7.71523595e-01 7.21201956e-01 4.04169589e-01
-5.23290217e-01 8.47709239e-01 2.17246100e-01 -4.27358687e-01
-5.15069962e-01 -9.40210342e-01 -6.11836016e-01 -4.64626074e-01
-3.28708231e-01 6.18102312e-01 4.63233918e-01 -6.23426139... | [4.1276679039001465, 1.3767915964126587] |
f6e9c26c-d3e4-4c1a-b11c-f556c3fd20da | unsupervised-domain-attention-adaptation | 2007.09344 | null | https://arxiv.org/abs/2007.09344v1 | https://arxiv.org/pdf/2007.09344v1.pdf | Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition | Caricature attributes provide distinctive facial features to help research in Psychology and Neuroscience. However, unlike the facial photo attribute datasets that have a quantity of annotated images, the annotations of caricature attributes are rare. To facility the research in attribute learning of caricatures, we pr... | ['Zheng Gu', 'Yang Gao', 'Wen Ji', 'Jing Huo', 'Kelei He'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/429_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530018.pdf | eccv-2020-8 | ['caricature'] | ['computer-vision'] | [ 1.17042236e-01 4.96076494e-02 -2.91819870e-01 -9.32776809e-01
-7.96270192e-01 -3.34932595e-01 5.57110965e-01 -2.37597749e-01
-1.47189423e-01 4.58820790e-01 1.37106925e-01 4.49298471e-01
3.40701193e-02 -4.69341516e-01 -8.33510637e-01 -6.85706913e-01
4.97537345e-01 5.71417212e-01 -2.36495018e-01 1.92982003... | [13.415716171264648, 0.7379297018051147] |
29ef8ab5-a1fe-4957-81b5-20189b2d31f3 | word-order-sensitive-embedding | null | null | https://aclanthology.org/W16-4910 | https://aclanthology.org/W16-4910.pdf | Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection | This paper discusses how to adapt two new word embedding features to build a more efficient Chinese Grammatical Error Diagnosis (CGED) systems to assist Chinese foreign learners (CFLs) in improving their written essays. The major idea is to apply word order sensitive Word2Vec approaches including (1) structured skip-gr... | ['Yih-Ru Wang', 'Yuan-Fu Liao', 'Chin-Kui Lin', 'Wei-Chieh Chou'] | 2016-12-01 | null | null | null | ws-2016-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-5.46671510e-01 -4.27791536e-01 9.96021032e-02 -1.47631228e-01
-7.18740642e-01 -3.72565299e-01 4.25184667e-01 5.41787386e-01
-1.10138643e+00 1.01294315e+00 4.77966845e-01 -7.52676547e-01
2.74511762e-02 -6.67307496e-01 -1.59376413e-01 -2.82483429e-01
2.36227810e-01 2.23005727e-01 3.64368796e-01 -6.61065578... | [11.045891761779785, 10.79604721069336] |
371c111c-adc4-4f85-868b-86a4acd4b9b5 | learnable-graph-convolutional-network-and | 2211.09155 | null | https://arxiv.org/abs/2211.09155v1 | https://arxiv.org/pdf/2211.09155v1.pdf | Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning | In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms. However, given multi-view data, there is limited work for learning discriminative node relationships and graph information simultaneously via graph convolutional networ... | ['Shiping Wang', 'Claudia Plant', 'Wenzhong Guo', 'Jie Yao', 'Lele Fu', 'Zhaoliang Chen'] | 2022-11-16 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 1.51886910e-01 2.28304446e-01 -2.19334468e-01 -5.39613485e-01
-4.84298021e-01 -2.75651544e-01 5.78723133e-01 1.14765413e-01
8.34823996e-02 5.39166152e-01 3.29823017e-01 2.03630120e-01
-4.19983566e-01 -9.19444799e-01 -6.03443027e-01 -7.12229729e-01
-9.17280316e-02 2.26002365e-01 -1.39648840e-01 -9.97015908... | [7.469359874725342, 6.054861545562744] |
e6ddf494-bb78-4f17-a39e-4d93018009f5 | multiclass-spectral-feature-scaling-method | 1910.07174 | null | https://arxiv.org/abs/1910.07174v1 | https://arxiv.org/pdf/1910.07174v1.pdf | Multiclass spectral feature scaling method for dimensionality reduction | Irregular features disrupt the desired classification. In this paper, we consider aggressively modifying scales of features in the original space according to the label information to form well-separated clusters in low-dimensional space. The proposed method exploits spectral clustering to derive scaling factors that a... | ['Akira Imakura', 'Xiucai Ye', 'Tetsuya Sakurai', 'Momo Matsuda', 'Keiichi Morikuni'] | 2019-10-16 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 5.80605939e-02 2.27976013e-02 -1.42790809e-01 -4.09292549e-01
-7.31715798e-01 -8.40336800e-01 4.06120300e-01 -2.67450988e-01
-3.85663033e-01 5.20660937e-01 8.19700137e-02 9.19504687e-02
-7.06979394e-01 -1.88370645e-01 -1.45497382e-01 -1.12205970e+00
-1.80287659e-01 3.76537710e-01 -1.79961115e-01 1.59893468... | [7.785927772521973, 4.3043212890625] |
74a6c08b-5610-446f-b622-a0316657a3d8 | implicit-u-net-for-volumetric-medical-image | 2206.15217 | null | https://arxiv.org/abs/2206.15217v1 | https://arxiv.org/pdf/2206.15217v1.pdf | Implicit U-Net for volumetric medical image segmentation | U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose the Implicit U-Net architecture that adapts the efficient Implicit Representation paradigm to supervised image segmentation tasks. By combinin... | ['Giacomo Tarroni', 'Sergio Naval Marimont'] | 2022-06-30 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 6.92222953e-01 1.03869939e+00 -5.17984092e-01 -5.00551939e-01
-9.91141498e-01 -2.02772208e-03 1.28541857e-01 3.55923414e-01
-4.44969237e-01 5.62847257e-01 1.21798575e-01 -6.78223372e-01
1.74036235e-01 -9.08398986e-01 -9.71605182e-01 -2.67398119e-01
-2.42879465e-01 4.86523181e-01 3.48338902e-01 1.96231097... | [14.556987762451172, -2.6047840118408203] |
4a474dd9-c60a-47e8-b6fc-15f95abb2000 | interactive-learning-with-corrective-feedback | 1810.00466 | null | http://arxiv.org/abs/1810.00466v1 | http://arxiv.org/pdf/1810.00466v1.pdf | Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks | Deep Reinforcement Learning (DRL) has become a powerful strategy to solve
complex decision making problems based on Deep Neural Networks (DNNs). However,
it is highly data demanding, so unfeasible in physical systems for most
applications. In this work, we approach an alternative Interactive Machine
Learning (IML) stra... | ['Javier Ruiz-del-Solar', 'Rodrigo Pérez-Dattari', 'Carlos Celemin', 'Jens Kober'] | 2018-09-30 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-7.02819973e-02 5.36976874e-01 2.10857868e-01 1.13216855e-01
2.05677658e-01 -3.83378834e-01 6.76675260e-01 1.35371566e-01
-8.76408696e-01 9.61295545e-01 -5.01819193e-01 -4.88629818e-01
-3.58732432e-01 -7.56586194e-01 -5.86220205e-01 -7.99648821e-01
-1.54524475e-01 6.33872926e-01 6.07839167e-01 -6.14208102... | [4.259618282318115, 1.7542214393615723] |
51b8af86-161d-4a13-9ddc-492ec9b2c679 | interact-interaction-network-inference-from | 1801.03011 | null | http://arxiv.org/abs/1801.03011v3 | http://arxiv.org/pdf/1801.03011v3.pdf | INtERAcT: Interaction Network Inference from Vector Representations of Words | In recent years, the number of biomedical publications has steadfastly grown,
resulting in a rich source of untapped new knowledge. Most biomedical facts are
however not readily available, but buried in the form of unstructured text, and
hence their exploitation requires the time-consuming manual curation of
published ... | [] | 2018-04-16 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 5.34392715e-01 -2.76989583e-02 -1.08986296e-01 -2.28934243e-01
-5.41554272e-01 -7.62857795e-01 5.34736693e-01 1.17450130e+00
-6.71110928e-01 1.15887320e+00 1.67326242e-01 -3.28124195e-01
-4.55192506e-01 -7.74420500e-01 -5.38921237e-01 -8.77642155e-01
-4.08372562e-03 7.18568563e-01 2.96015263e-01 -1.44038945... | [8.330918312072754, 8.558834075927734] |
8c57bed5-7c68-4bf9-bce8-c3b38acef80b | improving-continual-relation-extraction-by | 2305.06620 | null | https://arxiv.org/abs/2305.06620v1 | https://arxiv.org/pdf/2305.06620v1.pdf | Improving Continual Relation Extraction by Distinguishing Analogous Semantics | Continual relation extraction (RE) aims to learn constantly emerging relations while avoiding forgetting the learned relations. Existing works store a small number of typical samples to re-train the model for alleviating forgetting. However, repeatedly replaying these samples may cause the overfitting problem. We condu... | ['Wei Hu', 'Yuanning Cui', 'Wenzheng Zhao'] | 2023-05-11 | null | null | null | null | ['relation-extraction', 'continual-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.68823709e-02 2.83808947e-01 -6.25719726e-01 -3.10465962e-01
-1.83256105e-01 -1.07488692e-01 4.63415772e-01 2.26114035e-01
-3.80611271e-01 9.89244223e-01 1.03029914e-01 -3.84621650e-01
-4.96515781e-01 -9.16379750e-01 -4.94246453e-01 -3.32727611e-01
-9.79441963e-03 4.49130803e-01 2.99105257e-01 -4.24037009... | [9.17829418182373, 8.531557083129883] |
6d6c2f28-199c-4f0a-bd56-6c7e2b7fea5e | deep-reinforcement-learning-for-on-line | 2009.10321 | null | https://arxiv.org/abs/2009.10321v1 | https://arxiv.org/pdf/2009.10321v1.pdf | Deep Reinforcement Learning for On-line Dialogue State Tracking | Dialogue state tracking (DST) is a crucial module in dialogue management. It is usually cast as a supervised training problem, which is not convenient for on-line optimization. In this paper, a novel companion teaching based deep reinforcement learning (DRL) framework for on-line DST optimization is proposed. To the be... | ['Xiang Zhou', 'Zhi Chen', 'Kai Yu', 'Lu Chen'] | 2020-09-22 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-3.68923217e-01 3.21246088e-01 -9.64549035e-02 -3.87115061e-01
-3.77824068e-01 -5.09457946e-01 7.30161369e-01 3.17035288e-01
-5.43165743e-01 8.65097225e-01 3.23471129e-01 -5.37531853e-01
4.64337543e-02 -5.34666359e-01 -5.77648692e-02 -3.68723005e-01
2.99710575e-02 6.82530642e-01 4.57409501e-01 -9.36877429... | [13.07951831817627, 8.028826713562012] |
52c1c168-19a5-4a96-a242-dd89a6af5137 | masked-pre-training-of-transformers-for | 2304.07434 | null | https://arxiv.org/abs/2304.07434v1 | https://arxiv.org/pdf/2304.07434v1.pdf | Masked Pre-Training of Transformers for Histology Image Analysis | In digital pathology, whole slide images (WSIs) are widely used for applications such as cancer diagnosis and prognosis prediction. Visual transformer models have recently emerged as a promising method for encoding large regions of WSIs while preserving spatial relationships among patches. However, due to the large num... | ['Saeed Hassanpour', 'Arief A. Suriawinata', 'Liesbeth Hondelink', 'Shuai Jiang'] | 2023-04-14 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 4.87950116e-01 4.77018237e-01 -4.14768338e-01 -1.77482635e-01
-1.17651200e+00 -2.63106376e-01 3.96714866e-01 4.76811200e-01
-3.17001939e-01 5.05587459e-01 3.01385909e-01 -2.22982943e-01
-9.71003622e-02 -5.67879558e-01 -6.34322822e-01 -1.01845467e+00
1.18499786e-01 3.04661423e-01 6.08573258e-01 9.05938596... | [15.068456649780273, -2.8323702812194824] |
72610613-7f17-4bfc-b5ec-4267a31ea022 | hiporank-incorporating-hierarchical-and | 2005.00513 | null | https://arxiv.org/abs/2005.00513v2 | https://arxiv.org/pdf/2005.00513v2.pdf | Discourse-Aware Unsupervised Summarization of Long Scientific Documents | We propose an unsupervised graph-based ranking model for extractive summarization of long scientific documents. Our method assumes a two-level hierarchical graph representation of the source document, and exploits asymmetrical positional cues to determine sentence importance. Results on the PubMed and arXiv datasets sh... | ['Andrei Mircea', 'Jackie C. K. Cheung', 'Yue Dong'] | 2020-05-01 | null | null | null | null | ['unsupervised-extractive-summarization'] | ['natural-language-processing'] | [ 3.66770267e-01 9.35814440e-01 -6.56399846e-01 -3.33956808e-01
-1.30815101e+00 -7.55535364e-01 8.56245816e-01 1.28375936e+00
-3.81456554e-01 9.97528195e-01 1.37407827e+00 -2.94180036e-01
-4.18570131e-01 -4.82079327e-01 -6.97789133e-01 -3.16107750e-01
-2.24018112e-01 5.84349632e-01 1.92036465e-01 -2.26186842... | [12.455068588256836, 9.470772743225098] |
58fcd758-82ba-4133-81e4-dc56ad4bbf3e | spatio-temporal-transformer-network-for-video | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Tae_Hyun_Kim_Spatio-temporal_Transformer_Network_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Tae_Hyun_Kim_Spatio-temporal_Transformer_Network_ECCV_2018_paper.pdf | Spatio-temporal Transformer Network for Video Restoration | State-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information. However, these networks typically consider only a pair of consecutive frames and hence are not capable of capturing long-range temporal dependencies and fall short of establishing correspondences acros... | ['Tae Hyun Kim', 'Michael Hirsch', 'Mehdi S. M. Sajjadi', 'Bernhard Scholkopf'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-restoration'] | ['computer-vision'] | [ 3.72890085e-02 -5.60861290e-01 -2.35890925e-01 -2.50487924e-02
-1.82094261e-01 -3.33632916e-01 4.98474628e-01 -4.05405700e-01
-3.32563639e-01 9.91048932e-01 3.63754690e-01 5.30231511e-04
-1.75464943e-01 -7.05728590e-01 -5.51344573e-01 -5.22182882e-01
-2.13238373e-01 -1.54513121e-01 5.67773521e-01 -3.44314761... | [10.908451080322266, -1.78080415725708] |
186e22c5-ddb4-4deb-a6c0-6d755b22fb04 | deep-video-generation-prediction-and | 1711.08682 | null | http://arxiv.org/abs/1711.08682v3 | http://arxiv.org/pdf/1711.08682v3.pdf | Deep Video Generation, Prediction and Completion of Human Action Sequences | Current deep learning results on video generation are limited while there are
only a few first results on video prediction and no relevant significant
results on video completion. This is due to the severe ill-posedness inherent
in these three problems. In this paper, we focus on human action videos, and
propose a gene... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Haoye Cai', 'Chunyan Bai'] | 2017-11-23 | deep-video-generation-prediction-and-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Chunyan_Bai_Deep_Video_Generation_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Chunyan_Bai_Deep_Video_Generation_ECCV_2018_paper.pdf | eccv-2018-9 | ['human-action-generation'] | ['computer-vision'] | [ 5.29989481e-01 1.62876040e-01 2.28826385e-02 3.85892019e-02
-7.72463977e-01 -2.95427144e-01 6.16629779e-01 -6.53455555e-01
-2.62431920e-01 7.71204174e-01 3.09926450e-01 -4.15019654e-02
2.89796501e-01 -5.78713536e-01 -9.77526546e-01 -6.08709753e-01
-3.54532413e-02 2.76393771e-01 2.82614321e-01 -4.07783687... | [10.756230354309082, -0.6050357222557068] |
e958a8f7-a2ac-40ac-bb1e-d47ca2241eca | meta-learning-adversarial-domain-adaptation | 2107.12262 | null | https://arxiv.org/abs/2107.12262v1 | https://arxiv.org/pdf/2107.12262v1.pdf | Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification | Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the exploitation of lexical features and their distributional signatures on training data, while neglecting to strengthen the model's ability to... | ['Aoying Zhou', 'Ming Gao', 'Minghui Qiu', 'Dongxiang Zhang', 'Zeqiu Fan', 'Chengcheng Han'] | 2021-07-26 | null | https://aclanthology.org/2021.findings-acl.145 | https://aclanthology.org/2021.findings-acl.145.pdf | findings-acl-2021-8 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 5.77505790e-02 -2.02293515e-01 -3.01928341e-01 -4.08312023e-01
-5.96661568e-01 -6.94637373e-02 8.38545203e-01 2.47907013e-01
-6.63698196e-01 8.62622440e-01 1.64815813e-01 2.19644129e-01
9.21493769e-03 -8.40220094e-01 -2.84866393e-01 -6.70630276e-01
2.30997294e-01 2.03551605e-01 3.43205959e-01 -4.63513255... | [10.198762893676758, 3.521707057952881] |
34f87b13-c613-402a-93b8-08c3c1c7cc10 | traditional-methods-in-edge-corner-and | 2208.07714 | null | https://arxiv.org/abs/2208.07714v1 | https://arxiv.org/pdf/2208.07714v1.pdf | Traditional methods in Edge, Corner and Boundary detection | This is a review paper of traditional approaches for edge, corner, and boundary detection methods. There are many real-world applications of edge, corner, and boundary detection methods. For instance, in medical image analysis, edge detectors are used to extract the features from the given image. In modern innovations ... | ['Sai Pavan Tadem'] | 2022-08-12 | null | null | null | null | ['edge-detection', 'boundary-detection'] | ['computer-vision', 'computer-vision'] | [ 9.03196484e-02 -4.30089712e-01 -2.84719616e-01 -8.38462710e-02
-6.04562052e-02 -3.16874534e-01 1.28903553e-01 1.83915168e-01
-6.02713883e-01 4.42442745e-01 -1.55425310e-01 -3.32590163e-01
1.11691065e-01 -5.34688711e-01 -8.85958672e-02 -5.75980783e-01
-1.01791777e-01 2.09637750e-02 8.68545890e-01 -6.49644202... | [9.137112617492676, -1.3715037107467651] |
2fcc176d-1199-47e7-ab76-7570752e06fa | detecting-melanoma-fairly-skin-tone-detection | 2202.02832 | null | https://arxiv.org/abs/2202.02832v4 | https://arxiv.org/pdf/2202.02832v4.pdf | Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification | Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident performance disparities between differing skin tones should be addressed before widespread deployment. In this work, we propose an efficient yet effective algorithm for automatic... | ['Amir Atapour-Abarghouei', 'Peter J. Bevan'] | 2022-02-06 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 1.15856826e+00 1.55760139e-01 -4.90063667e-01 -3.78829300e-01
-1.10166979e+00 -4.59596574e-01 5.38190484e-01 -8.30754191e-02
-5.88308811e-01 4.97280657e-01 3.53063226e-01 -3.82359058e-01
-6.90992698e-02 -4.01896685e-01 8.37359801e-02 -8.61082256e-01
2.38239855e-01 -3.26075912e-01 2.00520083e-01 -1.64300799... | [15.672615051269531, -2.946049213409424] |
1a196919-7dc8-4d14-968e-4ee0efcf856c | memory-aware-curriculum-federated-learning | 2107.02504 | null | https://arxiv.org/abs/2107.02504v2 | https://arxiv.org/pdf/2107.02504v2.pdf | Memory-aware curriculum federated learning for breast cancer classification | For early breast cancer detection, regular screening with mammography imaging is recommended. Routinary examinations result in datasets with a predominant amount of negative samples. A potential solution to such class-imbalance is joining forces across multiple institutions. Developing a collaborative computer-aided di... | ['Gemma Piella', 'Diana Mateus', 'Miguel A. González Ballester', 'Mickael Tardy', 'Amelia Jiménez-Sánchez'] | 2021-07-06 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 5.47008030e-02 -2.21168855e-03 -5.51550567e-01 -4.37142134e-01
-9.43328023e-01 -6.25648975e-01 2.71687005e-02 4.24078763e-01
-4.95210886e-01 7.31781483e-01 -4.55574803e-02 -5.94362557e-01
-3.00939322e-01 -1.01433253e+00 -8.72294545e-01 -1.01327264e+00
3.56793739e-02 4.86738354e-01 1.18323527e-02 5.28556518... | [6.088781356811523, 6.451897621154785] |
7ef6e1c7-3a85-47bf-81aa-5a73479b34c5 | self-concordant-analysis-of-frank-wolfe-1 | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/2292-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2292-Paper.pdf | Self-concordant analysis of Frank-Wolfe algorithm | Projection-free optimization via different variants of the Frank-Wolfe (FW) method has become one of the cornerstones in optimization for machine learning since in many cases the linear minimization oracle is much cheaper to implement than projections and some sparsity needs to be preserved. In a number of applications... | ['Kamil Safin', 'Mathias Staudigl', 'Petr Ostroukhov', 'Shimrit Shtern', 'Pavel Dvurechenskii'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/2292-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2292-Paper.pdf | icml-2020-1 | ['quantum-state-tomography'] | ['medical'] | [ 1.89796403e-01 4.02295947e-01 -1.47562504e-01 -2.92214960e-01
-8.71632516e-01 -6.34620130e-01 3.45278740e-01 -4.86428104e-02
-6.90265596e-01 1.01737344e+00 -5.97652346e-02 -4.92020428e-01
-2.47300357e-01 -6.62040353e-01 -9.31407452e-01 -1.05606186e+00
6.27309084e-02 5.70731401e-01 2.43904721e-02 -1.97628900... | [6.592894554138184, 4.54079008102417] |
32aeb8ac-7445-47c6-8282-57e3a4de5b9f | guided-deep-decoder-unsupervised-image-pair | 2007.11766 | null | https://arxiv.org/abs/2007.11766v1 | https://arxiv.org/pdf/2007.11766v1.pdf | Guided Deep Decoder: Unsupervised Image Pair Fusion | The fusion of input and guidance images that have a tradeoff in their information (e.g., hyperspectral and RGB image fusion or pansharpening) can be interpreted as one general problem. However, previous studies applied a task-specific handcrafted prior and did not address the problems with a unified approach. To addres... | ['wei he', 'Naoto Yokoya', 'Danfeng Hong', 'Tatsumi Uezato'] | 2020-07-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4749_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510086.pdf | eccv-2020-8 | ['pansharpening'] | ['computer-vision'] | [ 7.17157781e-01 1.19945090e-02 1.61851183e-01 -5.16142309e-01
-7.37111390e-01 -1.95627376e-01 5.50234497e-01 -3.06511104e-01
-4.19839263e-01 4.02074218e-01 2.99267769e-02 -9.76867899e-02
-6.35371953e-02 -9.23858166e-01 -6.45297229e-01 -1.07605135e+00
6.07500613e-01 -1.58457950e-01 1.70654729e-01 -3.29775900... | [10.450265884399414, -1.8429944515228271] |
edda8296-8cd1-45a4-a2f1-82ed06fc4b79 | airborne-lidar-point-cloud-classification | 2004.09057 | null | https://arxiv.org/abs/2004.09057v1 | https://arxiv.org/pdf/2004.09057v1.pdf | Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network | Airborne light detection and ranging (LiDAR) plays an increasingly significant role in urban planning, topographic mapping, environmental monitoring, power line detection and other fields thanks to its capability to quickly acquire large-scale and high-precision ground information. To achieve point cloud classification... | ['Congcong Wen', 'Xiaojing Yao', 'Xiang Li', 'Tianhe Chi', 'Ling Peng'] | 2020-04-20 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [-1.22124888e-01 -4.02524322e-01 2.69117132e-02 -4.90410060e-01
-5.15718818e-01 -2.29378074e-01 4.66436088e-01 2.67478168e-01
-7.38597959e-02 3.86036754e-01 -2.88898051e-01 -6.39820755e-01
-2.72234976e-01 -1.26822102e+00 -7.58049309e-01 -4.89295900e-01
-1.61048889e-01 4.33856070e-01 -3.23387049e-02 -1.13866121... | [7.974843978881836, -3.44391131401062] |
2924a0e1-6699-43c0-ada2-be072771d967 | turn-level-dialog-evaluation-with-dialog | 2011.06395 | null | https://arxiv.org/abs/2011.06395v1 | https://arxiv.org/pdf/2011.06395v1.pdf | Turn-level Dialog Evaluation with Dialog-level Weak Signals for Bot-Human Hybrid Customer Service Systems | We developed a machine learning approach that quantifies multiple aspects of the success or values in Customer Service contacts, at anytime during the interaction. Specifically, the value/reward function regarding to the turn-level behaviors across human agents, chatbots and other hybrid dialog systems is characterized... | ['Ruofeng Wen'] | 2020-10-25 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-1.58723965e-01 4.01795268e-01 -3.45261246e-01 -9.68880594e-01
-9.94580388e-01 -7.09306419e-01 7.11989939e-01 1.48891807e-01
-2.91840404e-01 1.01376450e+00 3.00334126e-01 -2.23680690e-01
-6.04959950e-02 -6.80471897e-01 1.24029279e-01 -5.88692188e-01
1.51266247e-01 1.36436141e+00 1.74707353e-01 -7.13578701... | [12.818747520446777, 7.981148719787598] |
5f09fb0f-1f11-4725-a167-cff12bc9cfd9 | decoupling-features-and-coordinates-for-few | 1911.11534 | null | https://arxiv.org/abs/1911.11534v4 | https://arxiv.org/pdf/1911.11534v4.pdf | Decoupling Features and Coordinates for Few-shot RGB Relocalization | Cross-scene model adaption is crucial for camera relocalization in real scenarios. It is often preferable that a pre-learned model can be fast adapted to a novel scene with as few training samples as possible. The existing state-of-the-art approaches, however, can hardly support such few-shot scene adaption due to the ... | ['Songyin Wu', 'Shanghang Zhang', 'Yixin Zhuang', 'Siyan Dong', 'Kai Xu', 'Baoquan Chen'] | 2019-11-26 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [-1.00697996e-02 -2.95875043e-01 -7.59347007e-02 -4.06347483e-01
-5.48870206e-01 -5.04569352e-01 4.82462853e-01 -3.06943357e-01
-3.43050510e-01 5.21027565e-01 -3.84383649e-02 2.35818312e-01
4.65080738e-02 -3.73904765e-01 -7.45029211e-01 -6.57141924e-01
5.79415381e-01 3.48103106e-01 5.57944357e-01 -2.09403753... | [8.060348510742188, -2.3362889289855957] |
6a25ebab-f5e3-460e-8aab-64cf1d8785d0 | backdoor-attack-with-sparse-and-invisible | 2306.06209 | null | https://arxiv.org/abs/2306.06209v1 | https://arxiv.org/pdf/2306.06209v1.pdf | Backdoor Attack with Sparse and Invisible Trigger | Deep neural networks (DNNs) are vulnerable to backdoor attacks, where the adversary manipulates a small portion of training data such that the victim model predicts normally on the benign samples but classifies the triggered samples as the target class. The backdoor attack is an emerging yet threatening training-phase ... | ['Qian Wang', 'Shu-Tao Xia', 'Xueluan Gong', 'Yiming Li', 'Yinghua Gao'] | 2023-05-11 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 2.13233948e-01 -7.71291628e-02 -2.93952286e-01 3.12923267e-02
-3.24609250e-01 -1.10263622e+00 4.96086478e-01 -3.36330771e-01
-2.34071508e-01 6.16286933e-01 -2.64416575e-01 -8.40022445e-01
-4.74652983e-02 -7.97295570e-01 -9.19865251e-01 -9.47084963e-01
-5.24838343e-02 -1.30760670e-01 2.69718111e-01 -1.70596182... | [5.713866710662842, 7.706539154052734] |
acf101cc-da87-43de-aa73-967bfafc674c | synergy-between-3dmm-and-3d-landmarks-for | 2110.09772 | null | https://arxiv.org/abs/2110.09772v2 | https://arxiv.org/pdf/2110.09772v2.pdf | Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry | This work studies learning from a synergy process of 3D Morphable Models (3DMM) and 3D facial landmarks to predict complete 3D facial geometry, including 3D alignment, face orientation, and 3D face modeling. Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks. 3D landmarks can be e... | ['Ulrich Neumann', 'Qiangeng Xu', 'Cho-Ying Wu'] | 2021-10-19 | null | null | null | null | ['3d-face-reconstruction', '3d-face-modeling', 'head-pose-estimation', 'face-alignment'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-3.95375192e-01 3.08670402e-01 -4.38079000e-01 -6.11012399e-01
-6.05729342e-01 -3.70610267e-01 4.62564379e-01 -3.23566109e-01
2.32398674e-01 5.52190654e-02 4.69592392e-01 -1.05798729e-01
-2.40883231e-02 -6.57500029e-01 -7.57242560e-01 -1.67097434e-01
-3.19647968e-01 6.08298659e-01 -3.39962512e-01 -1.22648954... | [13.26046085357666, 0.12581495940685272] |
a224e8be-b610-4026-9efd-3d19d7cb3890 | embedded-graph-theory | 1709.04710 | null | http://arxiv.org/abs/1709.04710v1 | http://arxiv.org/pdf/1709.04710v1.pdf | Embedded-Graph Theory | In this paper, we propose a new type of graph, denoted as "embedded-graph",
and its theory, which employs a distributed representation to describe the
relations on the graph edges. Embedded-graphs can express linguistic and
complicated relations, which cannot be expressed by the existing edge-graphs or
weighted-graphs.... | ['Atsushi Yokoyama'] | 2017-09-14 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-7.82457143e-02 2.44907275e-01 -1.21318094e-01 -4.08031225e-01
1.84376121e-01 -5.33567727e-01 4.59491611e-01 4.34996873e-01
-7.44302422e-02 2.19197959e-01 2.20226273e-01 -2.80790538e-01
-5.18324673e-01 -1.26681983e+00 7.57768983e-03 -3.42015356e-01
-4.54997063e-01 1.79120302e-01 5.20996451e-01 -6.52433872... | [7.313633918762207, 5.829979419708252] |
0b979a77-5354-47a7-986f-755637c1f7ce | clips-stylometry-investigation-csi-corpus-a | null | null | https://aclanthology.org/L14-1001 | https://aclanthology.org/L14-1001.pdf | CLiPS Stylometry Investigation (CSI) corpus: A Dutch corpus for the detection of age, gender, personality, sentiment and deception in text | We present the CLiPS Stylometry Investigation (CSI) corpus, a new Dutch corpus containing reviews and essays written by university students. It is designed to serve multiple purposes: detection of age, gender, authorship, personality, sentiment, deception, topic and genre. Another major advantage is its planned yearly ... | ['Walter Daelemans', 'Ben Verhoeven'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['deception-detection'] | ['miscellaneous'] | [-1.99571162e-01 2.05754399e-01 -2.19778836e-01 -2.51652330e-01
-6.01858318e-01 -7.15117812e-01 9.19761837e-01 4.32854265e-01
-5.90673268e-01 8.66035104e-01 1.21753216e-01 -3.39196652e-01
1.78552836e-01 -1.92401066e-01 -1.29722238e-01 -4.52649981e-01
3.97814631e-01 3.74154866e-01 -1.53913125e-01 -6.52405396... | [8.342473030090332, 10.437302589416504] |
1ae7e657-fa64-43a4-8661-63dcb9eb67e1 | automatic-3d-registration-of-dental-cbct-and | 2305.10132 | null | https://arxiv.org/abs/2305.10132v2 | https://arxiv.org/pdf/2305.10132v2.pdf | Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection images | This paper presents a fully automatic registration method of dental cone-beam computed tomography (CBCT) and face scan data. It can be used for a digital platform of 3D jaw-teeth-face models in a variety of applications, including 3D digital treatment planning and orthognathic surgery. Difficulties in accurately mergin... | ['Kiwan Jeon', 'Jin Keun Seo', 'Sang-Hwy Lee', 'Chang Min Hyun', 'Hyoung Suk Park'] | 2023-05-17 | null | null | null | null | ['facial-landmark-detection'] | ['computer-vision'] | [ 1.15686111e-01 2.86843896e-01 5.30360034e-04 -4.01517600e-01
-9.21241522e-01 3.66327651e-02 2.68752158e-01 1.33190006e-01
-4.53483939e-01 2.77757049e-02 -1.60985604e-01 -1.83866292e-01
-3.47893685e-01 -8.25236678e-01 -2.92751193e-01 -6.10098183e-01
5.60281500e-02 1.08169806e+00 3.90570283e-01 -1.08763248... | [13.773301124572754, -2.2109780311584473] |
d02cd3b8-fb7a-4950-8e3f-6df05011c7c3 | intelligible-lip-to-speech-synthesis-with | 2305.19603 | null | https://arxiv.org/abs/2305.19603v1 | https://arxiv.org/pdf/2305.19603v1.pdf | Intelligible Lip-to-Speech Synthesis with Speech Units | In this paper, we propose a novel Lip-to-Speech synthesis (L2S) framework, for synthesizing intelligible speech from a silent lip movement video. Specifically, to complement the insufficient supervisory signal of the previous L2S model, we propose to use quantized self-supervised speech representations, named speech un... | ['Yong Man Ro', 'Minsu Kim', 'Jeongsoo Choi'] | 2023-05-31 | null | null | null | null | ['lip-to-speech-synthesis', 'speech-synthesis'] | ['computer-vision', 'speech'] | [ 5.10435164e-01 4.08232749e-01 -4.62500393e-01 -1.37341440e-01
-9.43728149e-01 -2.72817284e-01 4.52839881e-01 -5.18050373e-01
2.51458019e-01 8.87718558e-01 5.19788086e-01 -3.77350986e-01
3.95807475e-01 -2.78729498e-01 -6.08640254e-01 -6.79965317e-01
7.04712212e-01 -4.12593126e-01 1.11064233e-01 4.09378894... | [14.547938346862793, 5.40910005569458] |
a7626a4e-521f-4c39-99ad-27bc0b9a85a8 | distributional-reinforcement-learning-for-2 | null | null | https://openreview.net/forum?id=19drPzGV691 | https://openreview.net/pdf?id=19drPzGV691 | Distributional Reinforcement Learning for Risk-Sensitive Policies | We address the problem of learning a risk-sensitive policy based on the CVaR risk measure using distributional reinforcement learning. In particular, we show that applying the distributional Bellman optimality operator with respect to a risk-based action-selection strategy overestimates the dynamic, Markovian CVaR. The... | ['Ilyas Malik', 'Shiau Hong Lim'] | 2021-01-01 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.87728003e-01 3.80285203e-01 -3.81972551e-01 -2.62064874e-01
-1.15593481e+00 -7.22187817e-01 5.54802716e-01 2.39102557e-01
-1.06655848e+00 1.11468387e+00 6.16233088e-02 -5.03963351e-01
-4.48194355e-01 -8.30079138e-01 -5.54544151e-01 -9.06517625e-01
-2.92113394e-01 6.14615917e-01 -2.22489517e-02 -7.07884580... | [4.222787380218506, 2.5596389770507812] |
e41a3608-c0ca-4272-8a88-96dd1d2d7413 | how-do-human-users-teach-a-continual-learning | 2307.00123 | null | https://arxiv.org/abs/2307.00123v1 | https://arxiv.org/pdf/2307.00123v1.pdf | How Do Human Users Teach a Continual Learning Robot in Repeated Interactions? | Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has be... | ['Chrystopher L. Nehaniv', 'Kerstin Dautenhahn', 'Patrick Holthaus', 'Zachary De Francesco', 'Jainish Mehta', 'Ali Ayub'] | 2023-06-30 | null | null | null | null | ['continual-learning'] | ['methodology'] | [-3.95936668e-01 2.62363821e-01 -1.41276166e-01 -4.06444550e-01
-1.98199973e-01 -7.03236163e-01 3.74649048e-01 1.73757210e-01
-5.28355420e-01 6.22645557e-01 -2.67833099e-02 -4.93342906e-01
-7.36448765e-01 -2.42634371e-01 -8.84914994e-01 -1.93461314e-01
-4.25831914e-01 6.45682335e-01 8.86338353e-02 -4.61982459... | [4.498420238494873, 1.0794414281845093] |
b6b8ec19-a370-4719-bf04-c7a938be50a5 | unsupervised-meta-learning-via-few-shot-1 | 2303.00996 | null | https://arxiv.org/abs/2303.00996v1 | https://arxiv.org/pdf/2303.00996v1.pdf | Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning | Unsupervised meta-learning aims to learn generalizable knowledge across a distribution of tasks constructed from unlabeled data. Here, the main challenge is how to construct diverse tasks for meta-learning without label information; recent works have proposed to create, e.g., pseudo-labeling via pretrained representati... | ['Jinwoo Shin', 'Hankook Lee', 'Huiwon Jang'] | 2023-03-02 | unsupervised-meta-learning-via-few-shot | https://openreview.net/forum?id=i0Fgim2tB0i | https://openreview.net/pdf?id=i0Fgim2tB0i | 6th-workshop-on-meta-learning-at-neurips-2022 | ['cross-domain-few-shot'] | ['computer-vision'] | [ 4.15560097e-01 -6.20204769e-03 -4.06556994e-01 -3.95002425e-01
-1.03261244e+00 -2.71444827e-01 7.72313058e-01 -2.08794072e-01
-2.03469485e-01 1.04688060e+00 2.71045178e-01 2.08671302e-01
2.71601416e-03 -6.45092785e-01 -7.34738469e-01 -7.21669316e-01
6.70817792e-01 7.25997031e-01 1.00827720e-02 -9.28118005... | [9.95498275756836, 3.0043439865112305] |
bdbeac6d-cf06-4f9f-a751-a1482bb8ccf7 | unsupervised-brain-lesion-segmentation-from | 1811.09655 | null | http://arxiv.org/abs/1811.09655v1 | http://arxiv.org/pdf/1811.09655v1.pdf | Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder | Lesions that appear hyperintense in both Fluid Attenuated Inversion Recovery
(FLAIR) and T2-weighted magnetic resonance images (MRIs) of the human brain are
common in the brains of the elderly population and may be caused by ischemia or
demyelination. Lesions are biomarkers for various neurodegenerative diseases,
makin... | ['Lotta M. Ellingsen', 'Vilmundur Gudnason', 'Hans E. Atlason', 'Sigurdur Sigurdsson', 'Askell Love'] | 2018-11-23 | null | null | null | null | ['brain-lesion-segmentation-from-mri'] | ['medical'] | [ 3.75253171e-01 -9.59881693e-02 3.04102525e-02 -4.31253165e-01
-4.91743356e-01 -2.53856272e-01 3.44804525e-01 1.61278397e-01
-8.20811868e-01 8.64453554e-01 7.72628486e-02 -1.78460523e-01
-1.33398309e-01 -6.04586601e-01 -4.03873503e-01 -7.97623515e-01
-3.41016293e-01 8.53468597e-01 5.40291250e-01 1.67602643... | [14.162829399108887, -2.122345209121704] |
11275abe-2e50-4272-8749-820135635b9c | improving-opinion-based-question-answering | 2306.07499 | null | https://arxiv.org/abs/2306.07499v1 | https://arxiv.org/pdf/2306.07499v1.pdf | Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite | Label error is a ubiquitous problem in annotated data. Large amounts of label error substantially degrades the quality of deep learning models. Existing methods to tackle the label error problem largely focus on the classification task, and either rely on task specific architecture or require non-trivial additional com... | ['Pranab Mohanty', 'Gagan Aneja', 'Nikita Bhalla', 'Hanwen Zha', 'Debojeet Chatterjee', 'Stanislav Peshterliev', 'Shashank Jain', 'Ahmed K. Mohamed', 'Xiao Yang'] | 2023-06-13 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.30658704e-01 2.48142824e-01 -5.25022708e-02 -4.72984463e-01
-1.40722644e+00 -6.19275510e-01 4.08881307e-01 5.43617189e-01
-6.57962620e-01 7.31223881e-01 -1.41080990e-01 -3.23397189e-01
-1.67728946e-01 -3.92973363e-01 -1.01670754e+00 -3.85060698e-01
4.32756692e-01 6.63220584e-01 1.65553302e-01 7.73016810... | [9.295044898986816, 4.300482273101807] |
405b7341-f1f2-4f1a-b44d-5ecea7b7650a | background-subtraction-with-real-time | 1811.10020 | null | http://arxiv.org/abs/1811.10020v2 | http://arxiv.org/pdf/1811.10020v2.pdf | Background Subtraction with Real-time Semantic Segmentation | Accurate and fast foreground object extraction is very important for object
tracking and recognition in video surveillance. Although many background
subtraction (BGS) methods have been proposed in the recent past, it is still
regarded as a tough problem due to the variety of challenging situations that
occur in real-wo... | ['Arjan Kuijper', 'Xiang Chen', 'Ming Zhu', 'Michael Goesele', 'Dongdong Zeng'] | 2018-11-25 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 5.24979651e-01 -4.33813930e-01 1.00065991e-01 -4.18489158e-01
-4.86382365e-01 -2.81048656e-01 2.09998146e-01 1.46854162e-01
-4.89650458e-01 4.94307280e-01 -7.19667912e-01 -5.36514223e-01
1.04585022e-01 -1.05676353e+00 -6.67520881e-01 -9.90876436e-01
-4.61529978e-02 2.70972639e-01 1.05546570e+00 2.92866840... | [8.998948097229004, -0.5708959698677063] |
24009884-caf8-4be4-b38d-1ed5c84ad098 | heterogeneous-domain-adaptation-for-iot | 2301.09801 | null | https://arxiv.org/abs/2301.09801v1 | https://arxiv.org/pdf/2301.09801v1.pdf | Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach | Data scarcity hinders the usability of data-dependent algorithms when tackling IoT intrusion detection (IID). To address this, we utilise the data rich network intrusion detection (NID) domain to facilitate more accurate intrusion detection for IID domains. In this paper, a Geometric Graph Alignment (GGA) approach is l... | ['Chengzhong Xu', 'Kejiang Ye', 'Yang Wang', 'Hao Dai', 'Jiashu Wu'] | 2023-01-24 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 3.88989002e-01 -1.03799857e-01 -2.76833922e-01 -2.82644808e-01
1.88552842e-01 -6.88414872e-01 6.94625318e-01 5.03983974e-01
-4.01095971e-02 3.78421694e-01 -2.97077298e-01 -5.16596138e-01
-6.91402495e-01 -1.19899476e+00 -1.24170586e-01 -4.30186927e-01
-1.95671692e-01 6.56417489e-01 2.56375998e-01 -2.91749775... | [6.576463222503662, 5.881117820739746] |
5014e810-6cba-4b7f-af64-3fec601e6baa | small-sample-hyperspectral-image | null | null | https://doi.org/10.3390/s23052499 | https://www.mdpi.com/1424-8220/23/5/2499 | Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering | In recent years, different deep learning frameworks were introduced for hyperspectral image (HSI) classification. However, the proposed network models have a higher model complexity, and do not provide high classification accuracy if few-shot learning is used. This paper presents an HSI classification method that combi... | ['Dmitry Uchaev', 'Denis Uchaev'] | 2023-02-23 | null | null | null | sensors-2023-2 | ['few-shot-image-classification', 'dimensionality-reduction'] | ['computer-vision', 'methodology'] | [ 5.98682046e-01 -4.85331982e-01 2.25979201e-02 -2.62877107e-01
-4.29553092e-01 -1.48852959e-01 3.89407486e-01 1.78337712e-02
-3.47262859e-01 8.20840359e-01 -3.62887457e-02 1.16578288e-01
-8.06328118e-01 -1.36134636e+00 -1.16420142e-01 -1.23061550e+00
-1.51703626e-01 -1.41730011e-01 1.04601823e-01 -7.54065886... | [9.910750389099121, -1.5504357814788818] |
5a9ebd7c-3656-483d-bf6a-1a76be4e9ee6 | simvtp-simple-video-text-pre-training-with | 2212.03490 | null | https://arxiv.org/abs/2212.03490v1 | https://arxiv.org/pdf/2212.03490v1.pdf | SimVTP: Simple Video Text Pre-training with Masked Autoencoders | This paper presents SimVTP: a Simple Video-Text Pretraining framework via masked autoencoders. We randomly mask out the spatial-temporal tubes of input video and the word tokens of input text and then feed them into a unified autencoder to reconstruct the missing pixels and words. Our SimVTP has several properties: 1) ... | ['Xiu Li', 'Yin Shan', 'Tianyu Yang', 'Yue Ma'] | 2022-12-07 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 1.57446519e-01 -2.63356678e-02 -3.16909075e-01 2.45312825e-02
-6.73705101e-01 -5.38028181e-01 6.85996056e-01 -5.69960594e-01
-5.71039855e-01 2.35814139e-01 2.34503895e-01 -4.11101341e-01
4.39192593e-01 -5.60607612e-01 -1.16262507e+00 -6.39906168e-01
2.05341443e-01 2.65587389e-01 3.90471965e-01 -1.59492388... | [9.842999458312988, 0.8859527111053467] |
aa1fa858-0e9b-495d-96e6-a26213392124 | deep-neural-network-and-data-augmentation | 1903.00389 | null | http://arxiv.org/abs/1903.00389v1 | http://arxiv.org/pdf/1903.00389v1.pdf | Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets | A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly... | ['Viktor Varkarakis', 'Shabab Bazrafkan', 'Peter Corcoran'] | 2019-03-01 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 4.63037133e-01 7.63910711e-01 -2.98643172e-01 -4.31164593e-01
-4.10853833e-01 -3.25565398e-01 2.62108892e-01 -2.43021473e-01
-1.88861310e-01 4.35150653e-01 -1.39794692e-01 -5.21754682e-01
-6.91451356e-02 -2.65110791e-01 -5.14971852e-01 -4.08054888e-01
7.66224414e-02 5.31518519e-01 -1.75891563e-01 -1.71553731... | [3.743356943130493, -3.6324100494384766] |
fb5a5cb9-6568-4e97-8862-7c2185554511 | modelling-depth-for-nonparametric-foreground | 1609.09240 | null | http://arxiv.org/abs/1609.09240v1 | http://arxiv.org/pdf/1609.09240v1.pdf | Modelling depth for nonparametric foreground segmentation using RGBD devices | The problem of detecting changes in a scene and segmenting the foreground
from background is still challenging, despite previous work. Moreover, new RGBD
capturing devices include depth cues, which could be incorporated to improve
foreground segmentation. In this work, we present a new nonparametric approach
where a un... | ['Antoni Jaume-i-Capó', 'Gabriel Moyà-Alcover', 'Ahmed Elgammal', 'Javier Varona'] | 2016-09-29 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 6.04390025e-01 -9.88291875e-02 -1.91030711e-01 -3.98804933e-01
-6.15553856e-01 -5.08161902e-01 3.58439684e-01 -1.43272594e-01
-3.78043890e-01 7.98116505e-01 -3.73345643e-01 -2.96044517e-02
-6.92257434e-02 -6.25994086e-01 -4.93807077e-01 -1.04989207e+00
3.14769685e-01 3.22840989e-01 1.10151589e+00 4.65614110... | [8.814130783081055, -1.0674192905426025] |
cd568d8e-96c6-44cd-8a61-6649d81f197d | an-exploration-of-neural-radiance-field-scene | 2210.12268 | null | https://arxiv.org/abs/2210.12268v1 | https://arxiv.org/pdf/2210.12268v1.pdf | An Exploration of Neural Radiance Field Scene Reconstruction: Synthetic, Real-world and Dynamic Scenes | This project presents an exploration into 3D scene reconstruction of synthetic and real-world scenes using Neural Radiance Field (NeRF) approaches. We primarily take advantage of the reduction in training and rendering time of neural graphic primitives multi-resolution hash encoding, to reconstruct static video game sc... | ['Zheng Xin Yong', 'Wasiwasi Mgonzo', 'Tuluhan Akbulut', 'Benedict Quartey'] | 2022-10-21 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 4.36419219e-01 -4.24460739e-01 7.39379764e-01 -4.44361985e-01
-5.56678832e-01 -4.28419709e-01 7.42538095e-01 -2.87207901e-01
-3.40697110e-01 5.58292985e-01 2.43739560e-01 -4.51376677e-01
-1.45184295e-02 -1.37965703e+00 -6.20902181e-01 -2.82818884e-01
-3.19741756e-01 4.20303583e-01 4.45211440e-01 -3.97565365... | [9.346010208129883, -2.984679698944092] |
8990b0f8-d3a4-4dd1-899c-12a4ab8e661e | proteinbert-a-universal-deep-learning-model | null | null | https://doi.org/10.1093/bioinformatics/btac020 | https://academic.oup.com/bioinformatics/article-pdf/38/8/2102/49009610/btac020.pdf | ProteinBERT: a universal deep-learning model of protein sequence and function | Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for text analysis. We introduce ProteinBERT, a deep language model specifically desi... | ['Michal Linial', 'Nadav Rappoport', 'Yam Peleg', 'Dan Ofer', 'Nadav Brandes'] | 2022-02-10 | null | null | null | bioinformatics-volume-38-issue-8-2022-2 | ['protein-secondary-structure-prediction', 'protein-structure-prediction'] | ['medical', 'miscellaneous'] | [ 1.61591753e-01 -8.53687990e-03 -5.65726817e-01 -7.76847422e-01
-8.71352315e-01 -5.43956876e-01 3.33747685e-01 6.51365459e-01
-5.37955642e-01 9.66792643e-01 1.49836183e-01 -3.36144000e-01
2.76596934e-01 -4.41875488e-01 -9.99115527e-01 -7.56908417e-01
-2.76483834e-01 8.98330629e-01 -8.32514081e-04 -7.88672417... | [4.710461139678955, 5.698302745819092] |
e1ba7b01-0300-4cf6-a8c6-49df9eed6e38 | ai-enabled-prediction-of-esports-player | 2012.03491 | null | https://arxiv.org/abs/2012.03491v2 | https://arxiv.org/pdf/2012.03491v2.pdf | AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors | The emerging progress of eSports lacks the tools for ensuring high-quality analytics and training in Pro and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected t... | ['Anton Stepanov', 'Andrey Somov', 'Evgeny Burnaev', 'Anton Smerdov'] | 2020-12-07 | null | null | null | null | ['sensor-modeling', 'skills-evaluation', 'skills-assessment', 'fps-games'] | ['computer-vision', 'computer-vision', 'computer-vision', 'playing-games'] | [-2.18913123e-01 3.08542456e-02 2.16937270e-02 2.20356137e-02
-3.89883637e-01 -3.50645453e-01 -2.63950199e-01 3.03354356e-02
-7.31301427e-01 5.01573980e-01 -1.54787973e-01 2.35600516e-01
-5.94696522e-01 -7.07368493e-01 -1.91844940e-01 -6.06121898e-01
-1.48028024e-02 3.00268769e-01 2.37218723e-01 -5.15699446... | [6.826514720916748, 0.39057713747024536] |
5bd49e9b-a20e-44b1-bf42-5cd3b252d1e4 | keep-the-conversation-going-fixing-162-out-of | 2304.00385 | null | https://arxiv.org/abs/2304.00385v1 | https://arxiv.org/pdf/2304.00385v1.pdf | Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT | Automated Program Repair (APR) aims to automatically generate patches for buggy programs. Recent APR work has been focused on leveraging modern Large Language Models (LLMs) to directly generate patches for APR. Such LLM-based APR tools work by first constructing an input prompt built using the original buggy code and t... | ['Lingming Zhang', 'Chunqiu Steven Xia'] | 2023-04-01 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-6.85829297e-02 6.93114460e-01 9.24221873e-02 -4.48006168e-02
-1.40688407e+00 -6.87457740e-01 4.58383895e-02 4.26908791e-01
4.44169939e-01 8.21732223e-01 -1.30143389e-01 -7.42362440e-01
1.53759256e-01 -9.08698797e-01 -9.91028130e-01 -1.72552288e-01
-1.83445543e-01 4.94158864e-01 6.13629699e-01 -4.09872681... | [7.640715599060059, 7.714723110198975] |
d8b4f71a-abc7-4859-99e9-743c5eac87d9 | identifying-training-stop-point-with-noisy | 2012.13435 | null | https://arxiv.org/abs/2012.13435v2 | https://arxiv.org/pdf/2012.13435v2.pdf | Identifying Training Stop Point with Noisy Labeled Data | Training deep neural networks (DNNs) with noisy labels is a challenging problem due to over-parameterization. DNNs tend to essentially fit on clean samples at a higher rate in the initial stages, and later fit on the noisy samples at a relatively lower rate. Thus, with a noisy dataset, the test accuracy increases initi... | ['Ganesh Sankaranarayanan', 'Babak Namazi', 'Venkat Devarajan', 'Sree Ram Kamabattula'] | 2020-12-24 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 9.08181295e-02 -2.66445458e-01 3.73362191e-02 -4.84586090e-01
-8.18354905e-01 -5.09427369e-01 1.61881104e-01 2.31094025e-02
-6.40393615e-01 9.14191127e-01 -6.93447709e-01 -3.62098187e-01
-3.11563700e-01 -7.22096741e-01 -7.62828946e-01 -9.73165631e-01
2.42353946e-01 5.23355722e-01 2.95392096e-01 2.14844167... | [9.262702941894531, 3.8676228523254395] |
7754214c-cdaf-4a87-9fda-0bbe9e837175 | education-to-skill-mapping-using-hierarchical | null | null | https://www.mdpi.com/2076-3417/11/13/5868 | https://www.mdpi.com/2076-3417/11/13/5868/pdf | Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network | Skills gained from vocational or higher education form an essential component of country’s economy, determining the structure of the national labor force. Therefore, knowledge on how people’s education converts to jobs enables data-driven choices concerning human resources within an ever-changing job market. Moreover, ... | ['Linas Petkevičius', 'Vilija Kuodytė'] | 2021-06-24 | null | null | null | applied-sciences-2021-6 | ['occupation-prediction'] | ['natural-language-processing'] | [ 1.18857883e-01 5.55099808e-02 -3.38045388e-01 -3.17278177e-01
-3.66990753e-02 -3.00827920e-01 5.08431494e-01 5.10342717e-01
-5.99404633e-01 8.05857599e-01 6.01585329e-01 -5.07977307e-01
-7.57053673e-01 -1.15621769e+00 -4.19818103e-01 -5.31926811e-01
3.75989407e-01 6.93096817e-01 -4.31495905e-01 -2.91694701... | [9.737175941467285, 9.45209789276123] |
7479199e-a8f3-4de2-bcd8-e20464d3a738 | greedy-layer-pruning-decreasing-inference | 2105.14839 | null | https://arxiv.org/abs/2105.14839v2 | https://arxiv.org/pdf/2105.14839v2.pdf | Greedy-layer Pruning: Speeding up Transformer Models for Natural Language Processing | Fine-tuning transformer models after unsupervised pre-training reaches a very high performance on many different natural language processing tasks. Unfortunately, transformers suffer from long inference times which greatly increases costs in production. One possible solution is to use knowledge distillation, which solv... | ['Antonio Rodriguez-Sanchez', 'Stefan Engl', 'Sebastian Stabinger', 'David Peer'] | 2021-05-31 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.28901370e-03 3.35938901e-01 -2.27350220e-01 -2.92326719e-01
-6.65349126e-01 -5.07892609e-01 3.57871264e-01 4.30840582e-01
-6.29311502e-01 6.81287885e-01 -2.24178731e-02 -6.51599050e-01
-2.70784795e-01 -9.93082345e-01 -6.43035114e-01 -4.52861667e-01
2.86207646e-01 8.62964094e-01 6.73863649e-01 1.45544317... | [8.775311470031738, 3.5990819931030273] |
73780f12-eece-4f78-a806-c57573af5051 | higher-order-integration-of-hierarchical | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Cai_Higher-Order_Integration_of_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Cai_Higher-Order_Integration_of_ICCV_2017_paper.pdf | Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization | The success of fine-grained visual categorization (FGVC) extremely relies on the modeling of appearance and interactions of various semantic parts. This makes FGVC very challenging because: (i) part annotation and detection require expert guidance and are very expensive; (ii) parts are of different sizes; and (iii) the... | ['WangMeng Zuo', 'Sijia Cai', 'Lei Zhang'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-1.87146813e-01 -2.15299904e-01 -1.27381191e-01 -3.87054622e-01
-5.05575716e-01 -5.92873454e-01 4.14076000e-01 4.29417133e-01
-2.80652434e-01 2.68962830e-01 1.81308985e-01 1.39679924e-01
4.09762450e-02 -7.64775574e-01 -7.62185693e-01 -6.57047093e-01
-1.47651255e-01 -2.27273498e-02 7.64620483e-01 -2.33372953... | [9.62134838104248, 1.9685039520263672] |
27f4cb7e-3e06-4b4b-83fa-043d2351d85f | everlight-indoor-outdoor-editable-hdr | 2304.13207 | null | https://arxiv.org/abs/2304.13207v1 | https://arxiv.org/pdf/2304.13207v1.pdf | EverLight: Indoor-Outdoor Editable HDR Lighting Estimation | Because of the diversity in lighting environments, existing illumination estimation techniques have been designed explicitly on indoor or outdoor environments. Methods have focused specifically on capturing accurate energy (e.g., through parametric lighting models), which emphasizes shading and strong cast shadows; or ... | ['Jean-François Lalonde', 'Jonathan Eisenmann', 'Yannick Hold-Geoffroy', 'Mohammad Reza Karimi Dastjerdi'] | 2023-04-26 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 5.50499558e-01 -2.13372171e-01 3.95654738e-01 -2.46092856e-01
-3.22231829e-01 -6.33178055e-01 8.68138552e-01 -4.43753600e-01
2.46808097e-01 6.70886338e-01 3.05888683e-01 -1.44575849e-01
2.32726589e-01 -1.06199026e+00 -4.32315677e-01 -6.08033597e-01
2.92539626e-01 2.90672183e-02 5.74908443e-02 -4.27264869... | [9.748034477233887, -3.056702136993408] |
2c49b5e0-21a0-4d4f-af5e-0b2d238b82fd | multi-armed-bandit-learning-on-a-graph | 2209.09419 | null | https://arxiv.org/abs/2209.09419v4 | https://arxiv.org/pdf/2209.09419v4.pdf | Multi-armed Bandit Learning on a Graph | The multi-armed bandit(MAB) problem is a simple yet powerful framework that has been extensively studied in the context of decision-making under uncertainty. In many real-world applications, such as robotic applications, selecting an arm corresponds to a physical action that constrains the choices of the next available... | ['Na Li', 'Kasper Johansson', 'Tianpeng Zhang'] | 2022-09-20 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 6.07446842e-02 5.68835318e-01 -8.24392140e-01 -9.72291231e-02
-7.31268406e-01 -7.39667892e-01 1.20627895e-01 2.87003636e-01
-6.49111927e-01 1.10001373e+00 -3.29066545e-01 -6.05572522e-01
-8.99616241e-01 -1.06801295e+00 -1.21265733e+00 -9.08784807e-01
-6.61821425e-01 9.94688392e-01 3.60750267e-03 -1.36552095... | [4.489352226257324, 3.15429949760437] |
e91c9335-9bb0-4aeb-8580-655e558e5463 | methods-for-spoken-language-identification | null | null | http://cs229.stanford.edu/proj2017/final-reports/5239784.pdf | http://cs229.stanford.edu/proj2017/final-reports/5239784.pdf | Methods for Spoken Language Identification | In this paper, we explore several machine learning techniques for classifying spoken language. In particular, we construct algorithms which utilize various spectral features derived from English and Mandarin Chinese phone call audio to predict the language to
which the phone call belongs. We investigate multiple featu... | ['Justin Pyron', 'Andrew Deveau', 'Julien Boussard'] | 2017-12-16 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-1.40394002e-01 -6.61326408e-01 -2.82936394e-01 -6.07156336e-01
-1.06128323e+00 -6.05281711e-01 5.54255247e-01 -1.63451150e-01
-4.25334364e-01 6.40915692e-01 2.63845026e-01 -3.71892840e-01
2.74136905e-02 -3.79389524e-01 1.25065312e-01 -5.27681470e-01
-1.07603550e-01 2.37864792e-01 8.75064358e-02 -1.12722732... | [14.684094429016113, 5.975832462310791] |
a6859e4c-b357-4832-aaf8-bdcc9a53aff0 | vocalset-a-singing-voice-dataset | null | null | http://ismir2018.ircam.fr/pages/events-main-program.html | http://ismir2018.ircam.fr/doc/pdfs/114_Paper.pdf | VocalSet: A Singing Voice Dataset | We present VocalSet, a singing voice dataset of a capella singing. Existing singing voice datasets either do not capture a large range of vocal techniques, have very few singers, or are single-pitch and devoid of musical context. VocalSet captures not only a range of vowels, but also a diverse set of voices on many dif... | ['Bryan Pardo', 'Alison Wahl', 'Prem Seetharaman', 'Julia Wilkins'] | 2018-09-25 | null | null | null | international-society-for-music-information | ['singer-identification', 'vocal-technique-classification'] | ['music', 'music'] | [-3.94937098e-02 -6.58532798e-01 -4.34879698e-02 -1.98776528e-01
-7.22211063e-01 -1.44657028e+00 3.26878846e-01 -3.75881076e-01
1.22281045e-01 9.20313373e-02 2.21016333e-01 -7.15868473e-02
-1.37328148e-01 -2.49630645e-01 -1.56798586e-01 -5.16154885e-01
-2.19389409e-01 3.14680457e-01 -1.61071837e-01 -3.53575468... | [15.490644454956055, 6.014828681945801] |
3dcf909c-27ac-4afe-b823-34dcabebc35f | qtrojan-a-circuit-backdoor-against-quantum | 2302.08090 | null | https://arxiv.org/abs/2302.08090v1 | https://arxiv.org/pdf/2302.08090v1.pdf | QTrojan: A Circuit Backdoor Against Quantum Neural Networks | We propose a circuit-level backdoor attack, \textit{QTrojan}, against Quantum Neural Networks (QNNs) in this paper. QTrojan is implemented by few quantum gates inserted into the variational quantum circuit of the victim QNN. QTrojan is much stealthier than a prior Data-Poisoning-based Backdoor Attack (DPBA), since it d... | ['Fan Chen', 'Martin Swany', 'Lei Jiang', 'Cheng Chu'] | 2023-02-16 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 3.77077937e-01 4.76540834e-01 5.34431124e-03 2.58948773e-01
-6.16067588e-01 -8.66241455e-01 5.11937797e-01 1.40641602e-02
-7.21358657e-01 8.85354757e-01 -5.00650167e-01 -8.61458242e-01
1.72815755e-01 -1.18304729e+00 -1.10387528e+00 -1.23378062e+00
2.85850227e-01 -1.96105093e-01 3.30593169e-01 -4.84669715... | [5.574103355407715, 5.159966468811035] |
9b0e7240-c2a1-4b9b-bb70-f8847bf9d285 | augmenting-librispeech-with-french | 1802.03142 | null | http://arxiv.org/abs/1802.03142v1 | http://arxiv.org/pdf/1802.03142v1.pdf | Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation | Recent works in spoken language translation (SLT) have attempted to build
end-to-end speech-to-text translation without using source language
transcription during learning or decoding. However, while large quantities of
parallel texts (such as Europarl, OpenSubtitles) are available for training
machine translation syst... | ['Laurent Besacier', 'Ali Can Kocabiyikoglu', 'Olivier Kraif'] | 2018-02-09 | augmenting-librispeech-with-french-1 | https://aclanthology.org/L18-1001 | https://aclanthology.org/L18-1001.pdf | lrec-2018-5 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.58761546e-01 2.69666556e-02 -7.42387921e-02 -5.23119748e-01
-1.78512192e+00 -7.66979992e-01 5.58569193e-01 -3.16879936e-02
-4.94529009e-01 9.32113588e-01 5.86721003e-01 -6.44511521e-01
3.03579926e-01 -2.61767119e-01 -6.20438576e-01 -3.91135812e-01
2.08860070e-01 9.18106616e-01 -1.16948346e-02 -5.72794974... | [14.40749740600586, 7.165937900543213] |
779922d8-fb7f-4a30-bb09-7edb842be626 | secure-and-robust-mimo-transceiver-for | 2012.05829 | null | https://arxiv.org/abs/2012.05829v1 | https://arxiv.org/pdf/2012.05829v1.pdf | Secure and Robust MIMO Transceiver for Multicast Mission Critical Communications | Mission-critical communications (MCC) involve all communications between people in charge of the safety of the civil society. MCC have unique requirements that include improved reliability, security and group communication support. In this paper, we propose a secure and robust Multiple-Input-Multiple-Output (MIMO) tran... | ['Marceau Coupechoux', 'Deepa Jagyasi'] | 2020-12-10 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 3.50986749e-01 3.16209316e-01 2.86579937e-01 8.71082693e-02
-8.69330883e-01 -6.63745046e-01 7.81830177e-02 4.83671017e-02
-5.33423722e-01 9.64009166e-01 -1.15899049e-01 -6.83902025e-01
-9.83734667e-01 -5.12973785e-01 -3.22376162e-01 -1.53221023e+00
-5.28149962e-01 -4.77567583e-01 -8.41316432e-02 -2.43428260... | [6.15503454208374, 1.4308875799179077] |
53da3792-ac08-44c4-a4b7-573065ca76e3 | z-bert-a-a-zero-shot-pipeline-for-unknown | 2208.07084 | null | https://arxiv.org/abs/2208.07084v2 | https://arxiv.org/pdf/2208.07084v2.pdf | Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection | Intent discovery is a fundamental task in NLP, and it is increasingly relevant for a variety of industrial applications (Quarteroni 2018). The main challenge resides in the need to identify from input utterances novel unseen in-tents. Herein, we propose Z-BERT-A, a two-stage method for intent discovery relying on a Tra... | ['Matteo Manica', 'Pier Francesco Piazza', 'Dimitrios Christofidellis', 'Daniele Comi'] | 2022-08-15 | null | null | null | null | ['intent-discovery', 'intent-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.61762762e-01 1.91884503e-01 -6.16931878e-02 -4.64769065e-01
-1.05087757e+00 -6.48968816e-01 5.80923557e-01 8.55209753e-02
1.82373181e-03 4.65997934e-01 2.66922861e-01 -4.74968493e-01
-6.88341558e-02 -6.08208001e-01 -5.68072677e-01 -3.56491357e-01
1.40664786e-01 8.63950372e-01 -1.38689712e-01 -3.42098475... | [12.236568450927734, 7.586358070373535] |
b01a0664-bd38-4508-9f1c-ce94051c52a8 | timeline-extraction-using-distant-supervision | null | null | https://aclanthology.org/D16-1200 | https://aclanthology.org/D16-1200.pdf | Timeline extraction using distant supervision and joint inference | null | ['Savelie Cornegruta', 'Andreas Vlachos'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['temporal-information-extraction'] | ['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... | [-7.432382106781006, 3.664335250854492] |
ea73f335-be66-468f-b63e-a6947b53b041 | tps-attention-enhanced-thin-plate-spline-for | 2305.05322 | null | https://arxiv.org/abs/2305.05322v1 | https://arxiv.org/pdf/2305.05322v1.pdf | TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition | Text irregularities pose significant challenges to scene text recognizers. Thin-Plate Spline (TPS)-based rectification is widely regarded as an effective means to deal with them. Currently, the calculation of TPS transformation parameters purely depends on the quality of regressed text borders. It ignores the text cont... | ['Yu-Gang Jiang', 'Hongtao Xie', 'Jinfeng Bai', 'Zhineng Chen', 'Tianlun Zheng'] | 2023-05-09 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.67756301e-01 -2.54463702e-01 5.40230237e-02 -3.03373903e-01
-9.78801370e-01 -3.06317389e-01 5.51390231e-01 -2.38314644e-01
-2.66597658e-01 1.34360284e-01 2.27811843e-01 -2.04738542e-01
4.49803144e-01 -7.07848310e-01 -8.52286041e-01 -8.62802446e-01
9.92267966e-01 3.96208644e-01 5.48220932e-01 -1.38695449... | [11.982915878295898, 2.1447219848632812] |
878752fd-c519-44e0-9802-ad088861b54f | learned-spectral-super-resolution | 1703.09470 | null | http://arxiv.org/abs/1703.09470v1 | http://arxiv.org/pdf/1703.09470v1.pdf | Learned Spectral Super-Resolution | We describe a novel method for blind, single-image spectral super-resolution.
While conventional super-resolution aims to increase the spatial resolution of
an input image, our goal is to spectrally enhance the input, i.e., generate an
image with the same spatial resolution, but a greatly increased number of
narrow (hy... | ['Konrad Schindler', 'Silvano Galliani', 'Emmanuel Baltsavias', 'Charis Lanaras', 'Dimitrios Marmanis'] | 2017-03-28 | null | null | null | null | ['spectral-super-resolution'] | ['computer-vision'] | [ 9.78059292e-01 -2.32779786e-01 1.29153341e-01 -1.91843525e-01
-7.76339352e-01 -6.41680419e-01 2.59057134e-01 -5.31232595e-01
-3.60764146e-01 1.02639318e+00 2.69907236e-01 -1.22507803e-01
-3.09514821e-01 -1.11984932e+00 -7.23836124e-01 -1.05421662e+00
1.81465939e-01 1.44651487e-01 2.63993945e-02 -4.36801016... | [10.225908279418945, -2.0316128730773926] |
b853246d-4b08-4014-8a48-b673bfcb02d9 | gauche-a-library-for-gaussian-processes-in | 2212.04450 | null | https://arxiv.org/abs/2212.04450v2 | https://arxiv.org/pdf/2212.04450v2.pdf | GAUCHE: A Library for Gaussian Processes in Chemistry | We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations, however, is nontrivia... | ['Alán Aspuru-Guzik', 'Bingqing Cheng', 'Felix Strieth-Kalthoff', 'Saudamini Chaurasia', 'Johannes Durholt', 'Chengzhi Guo', 'Jacob Moss', 'Alex Chan', 'Anthony Bourached', 'Simon Frieder', 'Gregory Kell', 'Austin Tripp', 'Julius Schwartz', 'Aryan Deshwal', 'Arian Jamasb', 'Yuanqi Du', 'Gary Tom', 'Samuel Stanton', 'Ji... | 2022-12-06 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.34482697e-01 -3.67041454e-02 4.10410911e-02 -1.31606847e-01
-8.14713955e-01 -9.92856562e-01 9.86232936e-01 7.17588305e-01
-2.91759312e-01 9.57156956e-01 -6.28367960e-02 -8.18424761e-01
-3.32370639e-01 -8.75693858e-01 -6.34682834e-01 -1.15006804e+00
-1.88035190e-01 6.88601971e-01 -3.25290523e-02 4.21142042... | [6.3898539543151855, 4.293345928192139] |
5936a411-78e1-4ead-bdb0-6217904a8505 | a-dense-cnn-approach-for-skin-lesion | 1807.06416 | null | http://arxiv.org/abs/1807.06416v2 | http://arxiv.org/pdf/1807.06416v2.pdf | A Dense CNN approach for skin lesion classification | This article presents a Deep CNN, based on the DenseNet architecture jointly
with a highly discriminating learning methodology, in order to classify seven
kinds of skin lesions: Melanoma, Melanocytic nevus, Basal cell carcinoma,
Actinic keratosis / Bowen's disease, Benign keratosis, Dermatofibroma, Vascular
lesion. In ... | ['Pierluigi Carcagnì', 'Cosimo Distante', 'Andrea Cuna'] | 2018-07-17 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 3.79956990e-01 1.29343435e-01 -1.40774667e-01 -1.06549725e-01
-1.81210503e-01 -2.44567111e-01 9.07805800e-01 -1.06182888e-01
-6.16512299e-01 9.52125847e-01 2.73101270e-01 -4.69326913e-01
-3.70397955e-01 -5.30230343e-01 -2.39836469e-01 -8.10329378e-01
-1.01766303e-01 1.99088052e-01 -2.71775424e-02 -5.45277409... | [15.700451850891113, -3.0154011249542236] |
fbbd1562-d2c2-4af5-acfa-b6716f151a2e | predicting-grammaticality-on-an-ordinal-scale | null | null | https://aclanthology.info/papers/P14-2029/p14-2029 | https://www.aclweb.org/anthology/P14-2029 | Predicting Grammaticality on an Ordinal Scale | null | ['Melissa Lopez', 'Nitin Madnani', 'Matthew Mulholland', 'Joel Tetreault', 'Aoife Cahill', 'Michael Heilman'] | 2014-06-01 | null | https://aclanthology.org/P14-2029 | https://aclanthology.org/P14-2029.pdf | acl-2014-6 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5392177104949951, 15.869220733642578] |
2bfa51b4-4a96-40ab-a53d-0d7c10af1472 | a-dive-into-sam-prior-in-image-restoration | 2305.13620 | null | https://arxiv.org/abs/2305.13620v1 | https://arxiv.org/pdf/2305.13620v1.pdf | A Dive into SAM Prior in Image Restoration | The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-quality (HQ) image from its degraded low-quality (LQ) observation. Multiple HQ solutions may correspond to an LQ input in this poorly posed problem, creating an ambiguous solution space. This motivates the investigation and... | ['Zhiwei Xiong', 'Zhihe Lu', 'Jiawang Bai', 'Zeyu Xiao'] | 2023-05-23 | null | null | null | null | ['color-image-denoising', 'image-super-resolution', 'image-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.10321403e-01 -1.42013952e-01 1.26851067e-01 -1.47200912e-01
-1.02686584e+00 -4.80976939e-01 3.73240888e-01 -3.02607059e-01
-2.80407637e-01 4.17854041e-01 2.83429742e-01 -1.28735334e-01
-1.95059270e-01 -6.60199940e-01 -7.82660484e-01 -8.76563430e-01
4.74521697e-01 -2.07521573e-01 1.12446815e-01 -3.53203118... | [10.912443161010742, -2.4700136184692383] |
fe5c210b-b7fe-4776-829a-4060d6a1a2da | evcenternet-uncertainty-estimation-for-object | 2303.03037 | null | https://arxiv.org/abs/2303.03037v1 | https://arxiv.org/pdf/2303.03037v1.pdf | EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning | Uncertainty estimation is crucial in safety-critical settings such as automated driving as it provides valuable information for several downstream tasks including high-level decision-making and path planning. In this work, we propose EvCenterNet, a novel uncertainty-aware 2D object detection framework utilizing evident... | ['Abhinav Valada', 'Chih-Hong Cheng', 'Wolfram Burgard', 'Paulo L. J. Drews-Jr', 'Kshitij Sirohi', 'Monish R. Nallapareddy'] | 2023-03-06 | null | null | null | null | ['2d-object-detection'] | ['computer-vision'] | [-4.82665747e-02 2.32016966e-01 -2.74307638e-01 -6.67193651e-01
-1.25873423e+00 -2.63060451e-01 6.60396874e-01 4.02769536e-01
-2.29787380e-01 6.72830582e-01 -1.21504873e-01 -4.67124462e-01
-5.82212031e-01 -5.74339271e-01 -8.66810322e-01 -6.47648990e-01
-1.96023509e-02 6.02190793e-01 3.36174846e-01 1.77457631... | [7.684136390686035, -1.2045000791549683] |
4e88607c-4773-400f-b3f3-54af4e792cd0 | deep-transfer-learning-for-single-channel | 1904.05945 | null | https://arxiv.org/abs/1904.05945v2 | https://arxiv.org/pdf/1904.05945v2.pdf | Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch | Many sleep studies suffer from the problem of insufficient data to fully utilize deep neural networks as different labs use different recordings set ups, leading to the need of training automated algorithms on rather small databases, whereas large annotated databases are around but cannot be directly included into thes... | ['Alfred Mertins', 'Oliver Y. Chén', 'Philipp Koch', 'Huy Phan', 'Maarten De Vos'] | 2019-04-11 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 1.18274547e-01 6.31235167e-02 -1.20971039e-01 -5.18514454e-01
-5.30492485e-01 -2.71162957e-01 -7.57005624e-03 -1.97832078e-01
-8.13465655e-01 9.97264028e-01 1.21871538e-01 -2.71076504e-02
1.00348383e-01 -5.91693878e-01 -7.36879826e-01 -6.55376732e-01
2.13264391e-01 4.12482768e-01 4.06055570e-01 -1.59534574... | [13.452412605285645, 3.5177111625671387] |
d39eba28-2727-47ba-9c9a-192ee5875260 | words-with-consistent-diachronic-usage | null | null | https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.12963 | https://onlinelibrary.wiley.com/doi/epdf/10.1111/cogs.12963 | Words with Consistent Diachronic Usage Patterns are Learned Earlier: A Computational Analysis Using Temporally Aligned Word Embeddings | In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data-driven, scalable way, which is grounded in language use. We show a unique and reliable relation between measures of language change and age of acquisition (AoA) while controlling for f... | ['Marco Marelli', 'Federico Bianchi', 'Giovanni Cassani'] | 2021-04-20 | null | null | null | cognitive-science-2021-4 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-3.88519824e-01 -4.27134007e-01 -4.72137839e-01 -1.81544468e-01
-5.63062727e-02 -7.21130908e-01 1.07665956e+00 8.24834108e-01
-1.04933012e+00 3.41154605e-01 1.24587119e+00 -2.61331320e-01
-2.64807314e-01 -1.06286263e+00 -5.68311036e-01 -4.71771628e-01
-3.25680561e-02 5.32553159e-02 -1.51007175e-01 -4.96807247... | [10.189831733703613, 8.995393753051758] |
c8fcea18-5540-4b5e-9410-b1fb9b42763d | seizure-prediction-with-long-term-ieeg | 2201.04137 | null | https://arxiv.org/abs/2201.04137v1 | https://arxiv.org/pdf/2201.04137v1.pdf | Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity? | Repeated epileptic seizures impair around 65 million people worldwide and a successful prediction of seizures could significantly help patients suffering from refractory epilepsy. For two dogs with yearlong intracranial electroencephalography (iEEG) recordings, we studied the influence of time series nonstationarity on... | ['Ronald Tetzlaff', 'Jens Müller', 'Matthias Eberlein', 'Hongliu Yang'] | 2022-01-11 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.63547739e-01 -3.68537247e-01 1.73090756e-01 -2.55755752e-01
-3.71552467e-01 -2.93727219e-01 6.05894327e-01 5.25940731e-02
-2.54230380e-01 1.08108878e+00 7.37298802e-02 -4.09219772e-01
-5.78065634e-01 -1.15993761e-01 -5.40680110e-01 -8.22575331e-01
-9.93308187e-01 3.24314892e-01 1.28300279e-01 -1.45206422... | [13.230660438537598, 3.5234899520874023] |
052d3e35-f35e-42e7-a3e2-9e712179d647 | noise-injected-consistency-training-and | null | null | https://aclanthology.org/2022.coling-1.561 | https://aclanthology.org/2022.coling-1.561.pdf | Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization | Labeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical applications. This limitation can be overcome by semi-supervised approaches: consistency-training and ... | ['JianXin Li', 'Hongdong Zhu', 'Weifeng Jiang', 'Junnan Liu', 'Qianren Mao', 'Yiming Wang'] | null | null | null | null | coling-2022-10 | ['extractive-summarization'] | ['natural-language-processing'] | [ 3.64163399e-01 3.59219193e-01 -7.98021376e-01 -3.75499666e-01
-8.66515160e-01 -4.02909189e-01 4.42549199e-01 3.48005086e-01
-3.51369083e-01 1.10436893e+00 2.24767089e-01 -4.35624039e-03
1.75289378e-01 -5.87014854e-01 -4.57840502e-01 -6.09674156e-01
6.07435524e-01 5.00564814e-01 1.30328014e-01 1.75842568... | [9.554106712341309, 4.086165428161621] |
d6f5828e-ca71-4e1c-ae10-739a53d8f036 | improved-algorithms-for-multi-period-multi | 2301.13791 | null | https://arxiv.org/abs/2301.13791v2 | https://arxiv.org/pdf/2301.13791v2.pdf | Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback | We consider the linear contextual multi-class multi-period packing problem (LMMP) where the goal is to pack items such that the total vector of consumption is below a given budget vector and the total value is as large as possible. We consider the setting where the reward and the consumption vector associated with each... | ['Assaf Zeevi', 'Garud Iyengar', 'Wonyoung Kim'] | 2023-01-31 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 4.05477434e-02 1.29111171e-01 -9.57428098e-01 -1.07726946e-01
-8.03786397e-01 -8.06286871e-01 -4.08397853e-01 4.45581406e-01
-5.11736631e-01 1.11568558e+00 -1.63098559e-01 -4.37600046e-01
-6.92530215e-01 -7.65532672e-01 -1.32648432e+00 -1.02858555e+00
-2.17698440e-01 6.46755099e-01 -1.71390474e-01 4.78972128... | [4.570013046264648, 3.3292136192321777] |
4b913be5-b635-440a-8999-751d17d0c1da | explicit-visual-prompting-for-universal | 2305.18476 | null | https://arxiv.org/abs/2305.18476v1 | https://arxiv.org/pdf/2305.18476v1.pdf | Explicit Visual Prompting for Universal Foreground Segmentations | Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works have typically relied on domain-specific solutions to address accuracy and robustness issues in those... | ['Xiaodong Cun', 'Chi-Man Pun', 'Xi Shen', 'Weihuang Liu'] | 2023-05-29 | null | null | null | null | ['image-manipulation-detection', 'defocus-blur-detection', 'visual-prompting', 'camouflaged-object-segmentation', 'foreground-segmentation', 'shadow-detection', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.03496355e-01 -2.09639043e-01 -2.42403038e-02 -4.60039526e-01
-7.33770788e-01 -6.57864034e-01 6.56143725e-01 -2.58025080e-01
-4.56275046e-01 5.67486882e-01 -1.95657797e-02 -2.12711677e-01
6.54059574e-02 -2.34910652e-01 -8.89642060e-01 -1.07266283e+00
4.07545358e-01 -5.89919537e-02 7.91294754e-01 2.96463907... | [9.729228019714355, 0.24048329889774323] |
b19adaba-4570-4885-9a6e-9c21a7469531 | scaling-up-the-self-optimization-model-by | 2211.01698 | null | https://arxiv.org/abs/2211.01698v1 | https://arxiv.org/pdf/2211.01698v1.pdf | Scaling up the self-optimization model by means of on-the-fly computation of weights | The Self-Optimization (SO) model is a useful computational model for investigating self-organization in "soft" Artificial life (ALife) as it has been shown to be general enough to model various complex adaptive systems. So far, existing work has been done on relatively small network sizes, precluding the investigation ... | ['Tom Froese', 'Werner Koch', 'Natalya Weber'] | 2022-11-03 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.02617937e-01 3.14705342e-01 3.14798206e-01 -2.67373826e-02
4.37164783e-01 -5.13871908e-01 4.84593838e-01 3.71740192e-01
-5.90670586e-01 8.86410415e-01 -3.10158879e-01 -5.83165765e-01
-5.59775710e-01 -9.51261461e-01 -3.83693874e-01 -9.50795531e-01
-8.86582077e-01 5.57645380e-01 5.62383354e-01 -6.81417048... | [6.096345901489258, 4.588033199310303] |
01cd4691-6750-4f0c-b28e-68a53d3cd0f3 | heuristic-modularity-maximization-algorithms | 2302.14698 | null | https://arxiv.org/abs/2302.14698v3 | https://arxiv.org/pdf/2302.14698v3.pdf | Heuristic Modularity Maximization Algorithms for Community Detection Rarely Return an Optimal Partition or Anything Similar | Community detection is a fundamental problem in computational sciences with extensive applications in various fields. The most commonly used methods are the algorithms designed to maximize modularity over different partitions of the network nodes. Using 80 real and random networks from a wide range of contexts, we inve... | ['Hriday Chheda', 'Mahdi Mostajabdaveh', 'Samin Aref'] | 2023-02-28 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 1.82995841e-01 2.76121587e-01 -1.65626749e-01 2.73813665e-01
-9.54106599e-02 -9.51153100e-01 1.32090986e-01 4.48784977e-01
-1.72680721e-01 6.21139526e-01 -2.15263620e-01 -4.66029108e-01
-7.69170761e-01 -1.16579926e+00 -1.74446911e-01 -4.63217020e-01
-8.83120954e-01 8.13943863e-01 5.19744873e-01 -2.22936030... | [6.946511745452881, 5.266814231872559] |
d77936e5-c408-4b20-8d21-1e6e314c8d97 | occcasnet-occlusion-aware-cascade-cost-volume | 2305.17710 | null | https://arxiv.org/abs/2305.17710v1 | https://arxiv.org/pdf/2305.17710v1.pdf | OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation | Light field (LF) depth estimation is a crucial task with numerous practical applications. However, mainstream methods based on the multi-view stereo (MVS) are resource-intensive and time-consuming as they need to construct a finer cost volume. To address this issue and achieve a better trade-off between accuracy and ef... | ['Guanghui Wang', 'Yingqian Wang', 'Xuechun Wang', 'Fuqing Duan', 'Wentao Chao'] | 2023-05-28 | null | null | null | null | ['disparity-estimation'] | ['computer-vision'] | [ 1.49131015e-01 -4.54854220e-01 -3.51699628e-02 -4.90860194e-01
-6.65776491e-01 -2.93058217e-01 2.25902140e-01 -2.79498845e-01
-2.12250441e-01 6.98485017e-01 2.73548424e-01 -1.02526201e-02
9.82953794e-03 -9.43366110e-01 -5.46152472e-01 -7.76104629e-01
6.05702400e-01 -1.89162567e-02 4.71829057e-01 7.30819181... | [9.310364723205566, -2.477752447128296] |
0b29d9d4-8bca-424c-9b97-35ffebb9f991 | emaq-expected-max-q-learning-operator-for | 2007.11091 | null | https://arxiv.org/abs/2007.11091v2 | https://arxiv.org/pdf/2007.11091v2.pdf | EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL | Off-policy reinforcement learning holds the promise of sample-efficient learning of decision-making policies by leveraging past experience. However, in the offline RL setting -- where a fixed collection of interactions are provided and no further interactions are allowed -- it has been shown that standard off-policy RL... | ['Seyed Kamyar Seyed Ghasemipour', 'Dale Schuurmans', 'Shixiang Shane Gu'] | 2020-07-21 | null | https://openreview.net/forum?id=B8fp0LVMHa | https://openreview.net/pdf?id=B8fp0LVMHa | null | ['d4rl'] | ['robots'] | [-6.59069642e-02 3.21088284e-01 -7.40084410e-01 2.25905352e-03
-8.97299886e-01 -7.58993387e-01 5.34472525e-01 -1.54951811e-02
-7.17617333e-01 1.08261216e+00 1.25377253e-01 -6.69785619e-01
-3.14555645e-01 -4.06066626e-01 -8.91693056e-01 -8.86081457e-01
-2.29115501e-01 4.78600889e-01 -6.99611902e-02 -3.92289221... | [4.10744047164917, 2.2327263355255127] |
2949ed86-4c9c-4b4f-9323-6bdef96c23cd | abstractive-sentence-summarization-with | null | null | https://aclanthology.org/N16-1012 | https://aclanthology.org/N16-1012.pdf | Abstractive Sentence Summarization with Attentive Recurrent Neural Networks | null | ['er M.', 'Michael Auli', 'Alex Rush', 'Sumit Chopra'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['summarization', 'abstractive-sentence-summarization'] | ['natural-language-processing', '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... | [-7.354478359222412, 3.6406850814819336] |
26f4693a-ea68-4cdd-a242-1e62786c22cf | contrastive-principal-component-analysis | 1709.06716 | null | http://arxiv.org/abs/1709.06716v2 | http://arxiv.org/pdf/1709.06716v2.pdf | Contrastive Principal Component Analysis | We present a new technique called contrastive principal component analysis
(cPCA) that is designed to discover low-dimensional structure that is unique to
a dataset, or enriched in one dataset relative to other data. The technique is
a generalization of standard PCA, for the setting where multiple datasets are
availabl... | ['Martin J. Zhang', 'Abubakar Abid', 'James Zou', 'Vivek K. Bagaria'] | 2017-09-20 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 4.35183555e-01 -2.33373135e-01 -3.17800134e-01 -3.12784284e-01
-8.66378844e-01 -6.17395699e-01 4.59116757e-01 -2.03753728e-03
5.52862138e-02 3.09861094e-01 7.45580316e-01 -1.60172701e-01
-8.29920411e-01 -2.40093663e-01 -3.46924305e-01 -1.15873861e+00
-6.05556250e-01 4.82036293e-01 -2.08395630e-01 1.91874012... | [7.440646648406982, 4.618546009063721] |
4614bc3a-7101-4fd1-abbb-2e83bae8f395 | learning-what-and-where-unsupervised | 2205.13349 | null | https://arxiv.org/abs/2205.13349v4 | https://arxiv.org/pdf/2205.13349v4.pdf | Learning What and Where: Disentangling Location and Identity Tracking Without Supervision | Our brain can almost effortlessly decompose visual data streams into background and salient objects. Moreover, it can anticipate object motion and interactions, which are crucial abilities for conceptual planning and reasoning. Recent object reasoning datasets, such as CATER, have revealed fundamental shortcomings of c... | ['Martin V. Butz', 'Jannik Thümmel', 'Matthias Karlbauer', 'Tobias Menge', 'Sebastian Otte', 'Manuel Traub'] | 2022-05-26 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [ 1.78233892e-01 5.25157666e-03 -4.94112998e-01 -2.04825908e-01
-1.01038657e-01 -5.62900126e-01 8.27705204e-01 2.55363345e-01
-5.68486631e-01 1.88780740e-01 5.34227192e-01 -5.76444454e-02
-4.25558805e-01 -6.48805499e-01 -7.74106920e-01 -7.11930394e-01
-1.63211510e-01 6.11192107e-01 3.76076430e-01 1.10633075... | [9.953917503356934, 0.8956162333488464] |
0e451d69-dc90-454f-9ea5-0bce514a713e | distributionally-robust-neural-networks | null | null | https://openreview.net/forum?id=ryxGuJrFvS | https://openreview.net/pdf?id=ryxGuJrFvS | Distributionally Robust Neural Networks | Overparameterized neural networks can be highly accurate on average on an i.i.d. test set, yet consistently fail on atypical groups of the data (e.g., by learning spurious correlations that hold on average but not in such groups). Distributionally robust optimization (DRO) allows us to learn models that instead minimiz... | ['Pang Wei Koh*', 'Shiori Sagawa*', 'Tatsunori B. Hashimoto', 'Percy Liang'] | 2020-05-01 | null | null | null | iclr-2020-1 | ['l2-regularization'] | ['methodology'] | [ 7.25033656e-02 2.98123330e-01 -3.40267271e-01 -6.92098737e-01
-1.29303622e+00 -4.56816256e-01 3.19653869e-01 -9.20305029e-02
-8.03960860e-01 9.26747382e-01 -1.68387070e-01 -3.70952517e-01
-3.88689399e-01 -5.06829619e-01 -1.06119120e+00 -9.09517527e-01
-1.92831159e-01 4.55074400e-01 -1.91582412e-01 1.13024130... | [8.091060638427734, 3.909313201904297] |
e7615352-d1ee-475e-853d-f89d7eee9830 | semantic-operator-prediction-and-applications | 2301.00399 | null | https://arxiv.org/abs/2301.00399v1 | https://arxiv.org/pdf/2301.00399v1.pdf | Semantic Operator Prediction and Applications | In the present paper, semantic parsing challenges are briefly introduced and QDMR formalism in semantic parsing is implemented using sequence to sequence model with attention but uses only part of speech(POS) as a representation of words of a sentence to make the training as simple and as fast as possible and also avoi... | ['Farshad Noravesh'] | 2023-01-01 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.52992648e-01 9.93722856e-01 -5.66780530e-02 -6.97922885e-01
-3.24164748e-01 -5.77815950e-01 2.92963743e-01 3.37053806e-01
-4.95498121e-01 5.91922998e-01 4.64066744e-01 -6.37430668e-01
-4.74345055e-04 -9.21328664e-01 -6.52425647e-01 -2.20962241e-01
3.39931041e-01 4.95097041e-01 4.16341126e-01 -4.50788945... | [10.42581844329834, 9.296927452087402] |
54704740-07c3-4cdb-88d7-5902c91be461 | when-differential-privacy-meets-graph-neural | 2006.05535 | null | https://arxiv.org/abs/2006.05535v9 | https://arxiv.org/pdf/2006.05535v9.pdf | Locally Private Graph Neural Networks | Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techn... | ['Daniel Gatica-Perez', 'Sina Sajadmanesh'] | 2020-06-09 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 2.20197514e-01 5.63241959e-01 -1.76660001e-01 -5.71006656e-01
-5.31105816e-01 -5.91181397e-01 2.00130582e-01 2.09620968e-01
-2.13521019e-01 6.46395326e-01 -4.38042395e-02 -2.98421860e-01
-9.20606926e-02 -1.26304924e+00 -7.95587301e-01 -9.78881121e-01
-2.09980577e-01 -2.38838866e-02 -2.46506393e-01 1.90644011... | [6.024110317230225, 6.963780403137207] |
b4d62276-2b9a-41be-9a83-49c32079c8a6 | learning-from-ordered-sets-and-applications | 1408.0043 | null | http://arxiv.org/abs/1408.0043v1 | http://arxiv.org/pdf/1408.0043v1.pdf | Learning From Ordered Sets and Applications in Collaborative Ranking | Ranking over sets arise when users choose between groups of items. For
example, a group may be of those movies deemed $5$ stars to them, or a
customized tour package. It turns out, to model this data type properly, we
need to investigate the general combinatorics problem of partitioning a set and
ordering the subsets. ... | ['Svetha Venkatesh', 'Truyen Tran', 'Dinh Phung'] | 2014-07-31 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [ 9.89491716e-02 -1.31393477e-01 -2.64622837e-01 -5.49437404e-01
-1.02294481e+00 -6.87814355e-01 2.84377247e-01 1.89378679e-01
-6.17732167e-01 6.84817910e-01 -8.00865665e-02 -5.53229332e-01
-6.50716662e-01 -9.92339432e-01 -7.70247161e-01 -7.73534358e-01
-3.67056549e-01 1.08391929e+00 -1.80584472e-02 2.52595335... | [4.781757831573486, 3.39837908744812] |
b962f168-b1be-43f9-be3d-31fc70c699c1 | micro-expression-generation-with-thin-plate | null | null | https://dl.acm.org/doi/abs/10.1145/3503161.3551609 | https://dl.acm.org/doi/abs/10.1145/3503161.3551609 | Micro Expression Generation with Thin-plate Spline Motion Model and Face Parsing | Micro-expression generation aims at transfering the expression from the driving videos to the source images, which can be viewed as a motion transfer task. Recently, several works have been proposed to tackle this problem and achieve great performance. However, due to the intrinsic complexity of the face motion and dif... | ['Qiang Ling', 'Fang Gao', 'Peng He', 'Zhongpeng Cai', 'Guochen Xie', 'Jun Yu'] | 2022-10-10 | null | null | null | mm-22-proceedings-of-the-30th-acm | ['face-parsing'] | ['computer-vision'] | [-1.41302660e-01 -5.48002534e-02 -2.39751358e-02 -6.50491834e-01
-6.04542136e-01 -2.52876222e-01 3.31437796e-01 -1.00876236e+00
-2.38667890e-01 5.73188245e-01 2.75346905e-01 2.01742634e-01
2.29945794e-01 -3.43601644e-01 -7.57408500e-01 -8.51344764e-01
1.74073026e-01 4.57874723e-02 1.16645070e-02 -2.73870707... | [13.001837730407715, -0.19633445143699646] |
cf13b975-dcf7-4837-bd07-5f7bd250edf8 | recurrent-dynamic-embedding-for-video-object | 2205.03761 | null | https://arxiv.org/abs/2205.03761v1 | https://arxiv.org/pdf/2205.03761v1.pdf | Recurrent Dynamic Embedding for Video Object Segmentation | Space-time memory (STM) based video object segmentation (VOS) networks usually keep increasing memory bank every several frames, which shows excellent performance. However, 1) the hardware cannot withstand the ever-increasing memory requirements as the video length increases. 2) Storing lots of information inevitably i... | ['Dong Liu', 'Pan Pan', 'Bang Zhang', 'Zhiwei Xiong', 'Li Hu', 'Mingxing Li'] | 2022-05-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Recurrent_Dynamic_Embedding_for_Video_Object_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Recurrent_Dynamic_Embedding_for_Video_Object_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-video-object-segmentation'] | ['computer-vision'] | [-1.37517396e-02 -2.22846597e-01 -1.60641402e-01 -8.91869515e-02
-3.06720316e-01 -9.57702845e-02 -6.19665980e-02 -2.38331288e-01
-4.81553823e-01 4.65180784e-01 3.03320028e-02 -1.83495685e-01
2.10004240e-01 -8.28043103e-01 -8.35720181e-01 -7.93174326e-01
3.57367665e-01 -2.57768154e-01 8.29355597e-01 2.41523460... | [9.19150161743164, -0.07980822026729584] |
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