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948227c4-7e7c-44b6-b5cd-5dcae61b7959 | bilingual-word-embeddings-from-non-parallel | null | null | https://aclanthology.org/P15-2118 | https://aclanthology.org/P15-2118.pdf | Bilingual Word Embeddings from Non-Parallel Document-Aligned Data Applied to Bilingual Lexicon Induction | null | ['Marie-Francine Moens', "Ivan Vuli{\\'c}"] | 2015-07-01 | bilingual-word-embeddings-from-non-parallel-1 | https://aclanthology.org/P15-2118 | https://aclanthology.org/P15-2118.pdf | ijcnlp-2015-7 | ['multilingual-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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49123d51-9f8d-4149-8155-3cd665953079 | reusable-workflows-for-gender-prediction | null | null | https://aclanthology.org/L18-1082 | https://aclanthology.org/L18-1082.pdf | Reusable workflows for gender prediction | null | ['Matej Martinc', 'Senja Pollak'] | 2018-05-01 | reusable-workflows-for-gender-prediction-1 | https://aclanthology.org/L18-1082 | https://aclanthology.org/L18-1082.pdf | lrec-2018-5 | ['gender-prediction'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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f2c16c8c-043e-46b9-a60a-bd12430cecf6 | a-knowledge-driven-generative-model-for-multi | null | null | https://aclanthology.org/2020.emnlp-main.116 | https://aclanthology.org/2020.emnlp-main.116.pdf | A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization | Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing. In this paper, we focus on Chinese medical procedure entity normalization. However, nonstandard Chinese expressions and combined procedures presen... | ['Chengqing Zong', 'Yu Zhou', 'Lu Xiang', 'Yining Wang', 'Jinghui Yan'] | null | null | null | null | emnlp-2020-11 | ['medical-procedure'] | ['medical'] | [ 5.17697573e-01 1.89525738e-01 -3.12573373e-01 -3.65885317e-01
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3.20683032e-01 4.92542326e-01 -1.71625540e-01 -1.62461087... | [8.637228965759277, 8.868586540222168] |
75a15c87-45bb-488b-bfa3-12c4288b4fa9 | learning-visual-motion-segmentation-using | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Mitrokhin_Learning_Visual_Motion_Segmentation_Using_Event_Surfaces_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Mitrokhin_Learning_Visual_Motion_Segmentation_Using_Event_Surfaces_CVPR_2020_paper.pdf | Learning Visual Motion Segmentation Using Event Surfaces | Event-based cameras have been designed for scene motion perception - their high temporal resolution and spatial data sparsity converts the scene into a volume of boundary trajectories and allows to track and analyze the evolution of the scene in time. Analyzing this data is computationally expensive, and there is subst... | [' Yiannis Aloimonos', ' Cornelia Fermuller', ' Zhiyuan Hua', 'Anton Mitrokhin'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['motion-segmentation'] | ['computer-vision'] | [ 2.10432664e-01 -3.69597644e-01 1.30455913e-02 -7.40996450e-02
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4b523ae6-6cf2-45e1-8faa-aa9ed23f8f1a | infrastructure-crack-segmentation-boundary | 2306.09196 | null | https://arxiv.org/abs/2306.09196v1 | https://arxiv.org/pdf/2306.09196v1.pdf | Infrastructure Crack Segmentation: Boundary Guidance Method and Benchmark Dataset | Cracks provide an essential indicator of infrastructure performance degradation, and achieving high-precision pixel-level crack segmentation is an issue of concern. Unlike the common research paradigms that adopt novel artificial intelligence (AI) methods directly, this paper examines the inherent characteristics of cr... | ['Yu-Hsing Wang', 'Jian Zhang', 'Wang Chen', 'Zhili He'] | 2023-06-15 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [-5.55925369e-02 -1.28326580e-01 -6.35601953e-02 -1.95039600e-01
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462d897a-c243-4a75-8f37-5dbac0feedaf | cloudattention-efficient-multi-scale | 2208.00524 | null | https://arxiv.org/abs/2208.00524v1 | https://arxiv.org/pdf/2208.00524v1.pdf | CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning | Processing 3D data efficiently has always been a challenge. Spatial operations on large-scale point clouds, stored as sparse data, require extra cost. Attracted by the success of transformers, researchers are using multi-head attention for vision tasks. However, attention calculations in transformers come with quadrati... | ['Federico Tombari', 'Benjamin Busam', 'Nassir Navab', 'Yige Wang', 'Mahdi Saleh'] | 2022-07-31 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [-2.74817813e-02 -1.14021257e-01 1.43630952e-01 -4.66299355e-01
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c58a1655-5fd4-4639-8388-cbaffa4b900c | model-based-deep-autoencoder-networks-for | 2104.08409 | null | https://arxiv.org/abs/2104.08409v1 | https://arxiv.org/pdf/2104.08409v1.pdf | Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing | Autoencoder (AEC) networks have recently emerged as a promising approach to perform unsupervised hyperspectral unmixing (HU) by associating the latent representations with the abundances, the decoder with the mixing model and the encoder with its inverse. AECs are especially appealing for nonlinear HU since they lead t... | ['Deniz Erdoğmuş', 'José Carlos Moreira Bermudez', 'Pau Closas', 'Tales Imbiriba', 'Ricardo Augusto Borsoi', 'Haoqing Li'] | 2021-04-17 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.80467820e-01 7.75136277e-02 2.11475626e-01 5.26024494e-03
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de4eefdf-d31c-4899-aacf-a7b413529e30 | skinned-motion-retargeting-with-residual | 2303.08658 | null | https://arxiv.org/abs/2303.08658v1 | https://arxiv.org/pdf/2303.08658v1.pdf | Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry | A good motion retargeting cannot be reached without reasonable consideration of source-target differences on both the skeleton and shape geometry levels. In this work, we propose a novel Residual RETargeting network (R2ET) structure, which relies on two neural modification modules, to adjust the source motions to fit t... | ['Zhigang Tu', 'Jue Wang', 'Ying Shan', 'Linchao Bao', 'Xuefei Zhe', 'Shaoli Huang', 'Fang Zhao', 'Di Kang', 'Junwu Weng', 'Jiaxu Zhang'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Skinned_Motion_Retargeting_With_Residual_Perception_of_Motion_Semantics__CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Skinned_Motion_Retargeting_With_Residual_Perception_of_Motion_Semantics__CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-retargeting'] | ['computer-vision'] | [ 2.05473602e-01 7.35496953e-02 -2.77132601e-01 -3.50019604e-01
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cc8e8037-62f6-4913-ad2e-5009b507881d | interpreting-and-generalizing-deep-learning | 2307.04569 | null | https://arxiv.org/abs/2307.04569v1 | https://arxiv.org/pdf/2307.04569v1.pdf | Interpreting and generalizing deep learning in physics-based problems with functional linear models | Although deep learning has achieved remarkable success in various scientific machine learning applications, its black-box nature poses concerns regarding interpretability and generalization capabilities beyond the training data. Interpretability is crucial and often desired in modeling physical systems. Moreover, acqui... | ['Bei Wang', 'Pania Newell', 'Lingxiao Yuan', 'Amirhossein Arzani'] | 2023-07-10 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [ 2.42994353e-01 2.68416405e-01 -4.68486771e-02 -4.95836586e-01
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e2020852-2b6c-4039-8dd6-6c89ac6246c3 | eye-tracking-guided-deep-multiple-instance | 2304.12719 | null | https://arxiv.org/abs/2304.12719v1 | https://arxiv.org/pdf/2304.12719v1.pdf | Eye tracking guided deep multiple instance learning with dual cross-attention for fundus disease detection | Deep neural networks (DNNs) have promoted the development of computer aided diagnosis (CAD) systems for fundus diseases, helping ophthalmologists reduce missed diagnosis and misdiagnosis rate. However, the majority of CAD systems are data-driven but lack of medical prior knowledge which can be performance-friendly. In ... | ['Jiang Liu', 'Mengdi Gao', 'Xiaoqing Zhang', 'Chen Tang', 'Jingqi Huang', 'Hongyang Jiang'] | 2023-04-25 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 2.03730330e-01 -7.79955909e-02 -3.13372947e-02 -2.63229579e-01
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6a4162f1-0fb8-4faa-872d-e29739423226 | leveraging-artificial-intelligence-on-binary | 2210.05103 | null | https://arxiv.org/abs/2210.05103v1 | https://arxiv.org/pdf/2210.05103v1.pdf | Leveraging Artificial Intelligence on Binary Code Comprehension | Understanding binary code is an essential but complex software engineering task for reverse engineering, malware analysis, and compiler optimization. Unlike source code, binary code has limited semantic information, which makes it challenging for human comprehension. At the same time, compiling source to binary code, o... | ['Yifan Zhang'] | 2022-10-11 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 1.96365044e-01 2.09876865e-01 -7.91348338e-01 -4.28860098e-01
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2a1fc522-e719-4248-b4b5-8d3e25ab7ddf | accurate-word-segmentation-and-pos-tagging | null | null | https://aclanthology.org/D14-1011 | https://aclanthology.org/D14-1011.pdf | Accurate Word Segmentation and POS Tagging for Japanese Microblogs: Corpus Annotation and Joint Modeling with Lexical Normalization | null | ['Masaru Kitsuregawa', 'Nobuhiro Kaji'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['lexical-normalization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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241aace5-40af-4f53-a908-4b03b4513cda | gtnet-graph-transformer-network-for-3d-point | 2305.15213 | null | https://arxiv.org/abs/2305.15213v2 | https://arxiv.org/pdf/2305.15213v2.pdf | GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation | Recently, graph-based and Transformer-based deep learning networks have demonstrated excellent performances on various point cloud tasks. Most of the existing graph methods are based on static graph, which take a fixed input to establish graph relations. Moreover, many graph methods apply maximization and averaging to ... | ['Ying He', 'Xinzhe Shi', 'Weiwei Jin', 'Qian Wang', 'Wei Zhou'] | 2023-05-24 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-4.27964061e-01 -1.70564115e-01 3.37857194e-02 -3.37092310e-01
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a7ec5dcd-5812-4857-9906-c0f7ef023693 | a-big-data-intelligence-marketplace-and | 2111.09872 | null | https://arxiv.org/abs/2111.09872v1 | https://arxiv.org/pdf/2111.09872v1.pdf | A big data intelligence marketplace and secure analytics experimentation platform for the aviation industry | The unprecedented volume, diversity and richness of aviation data that can be acquired, generated, stored, and managed provides unique capabilities for the aviation-related industries and pertains value that remains to be unlocked with the adoption of the innovative Big Data Analytics technologies. Despite the large ef... | ['Konstantinos Perakis', 'Domenico Messina', 'Fenareti Lampathaki', 'Dimitrios Alexandrou', 'Dimitrios Spyropoulos', 'Stamatis Pitsios', 'Dimitrios Miltiadou'] | 2021-11-18 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-6.23346686e-01 -2.88680315e-01 8.51048678e-02 -2.45192200e-01
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fe890059-8ea2-4a53-8971-057c6378a84c | reinforced-clarification-question-generation | 2212.10409 | null | https://arxiv.org/abs/2212.10409v3 | https://arxiv.org/pdf/2212.10409v3.pdf | ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations | Context is everything, even in commonsense moral reasoning. Changing contexts can flip the moral judgment of an action; "Lying to a friend" is wrong in general, but may be morally acceptable if it is intended to protect their life. We present ClarifyDelphi, an interactive system that learns to ask clarification questio... | ['Chandra Bhagavatula', 'Yejin Choi', 'Liwei Jiang', 'Ximing Lu', 'Vivek Srikumar', 'Jena D. Hwang', 'Valentina Pyatkin'] | 2022-12-20 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 4.05848771e-01 8.48306715e-01 1.48102000e-01 -8.34137082e-01
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d09fd09e-882e-4f93-a43c-e27e4501e193 | data-aware-low-rank-compression-for-large-nlp | null | null | https://openreview.net/forum?id=_sSHg203jSu | https://openreview.net/pdf?id=_sSHg203jSu | Data-aware Low-Rank Compression for Large NLP Models | The representations learned by large-scale NLP models such as BERT have been widely used in various tasks. However, the increasing model size of the pre-trained models also brings the efficiency challenges, including the inference speed and the model size when deploying the model on devices. Specifically, most operatio... | ['Cho-Jui Hsieh', 'Inderjit S Dhillon', 'Hsiang-Fu Yu', 'Patrick Chen'] | 2021-01-01 | null | null | null | null | ['low-rank-compression'] | ['computer-code'] | [-1.68627545e-01 3.22763398e-02 -1.85250312e-01 -3.56611103e-01
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5d85cc06-d78a-4e84-b1b8-dfec7831c5ec | how-to-make-them-stay-diverse-counterfactual | 2303.04579 | null | https://arxiv.org/abs/2303.04579v1 | https://arxiv.org/pdf/2303.04579v1.pdf | "How to make them stay?" -- Diverse Counterfactual Explanations of Employee Attrition | Employee attrition is an important and complex problem that can directly affect an organisation's competitiveness and performance. Explaining the reasons why employees leave an organisation is a key human resource management challenge due to the high costs and time required to attract and keep talented employees. Busin... | ['Andreas Gregoriades', 'André Artelt'] | 2023-03-08 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.78548855e-01 5.77249527e-01 -4.60447699e-01 -3.37935030e-01
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4.98344392e-01 6.31347775e-01 -5.40680110e-01 -2.23494828... | [8.62459945678711, 5.562601089477539] |
ce3e28f9-aef8-4abd-926a-5a773c2d0d8a | black-scholes-option-pricing-revisited | 2202.05671 | null | https://arxiv.org/abs/2202.05671v2 | https://arxiv.org/pdf/2202.05671v2.pdf | Black-Scholes-Merton Option Pricing Revisited: Did we Find a Fatal Flaw? | The option pricing formula of Black and Scholes (1973) hinges on the continuous-time self-financing condition, which is a special case of the continuous-time budget equation of Merton (1971). The self-financing condition is believed to formalize the economic concept of portfolio rebalancing without inflows or outflows ... | ['Frans J. de Weert', 'Mark Mink'] | 2022-01-09 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-5.85782230e-01 2.19170168e-01 -3.85974318e-01 -9.24223810e-02
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860f722a-4b06-4620-99ee-c7c9221380d7 | bo-muse-a-human-expert-and-ai-teaming | 2303.01684 | null | https://arxiv.org/abs/2303.01684v2 | https://arxiv.org/pdf/2303.01684v2.pdf | BO-Muse: A human expert and AI teaming framework for accelerated experimental design | In this paper we introduce BO-Muse, a new approach to human-AI teaming for the optimization of expensive black-box functions. Inspired by the intrinsic difficulty of extracting expert knowledge and distilling it back into AI models and by observations of human behavior in real-world experimental design, our algorithm l... | ['Svetha Venkatesh', 'Mahad Rashid', 'Julian Berk', 'Santu Rana', 'Hung Le', 'Majid Abdolshah', 'Shannon Ryan', 'Arun Kumar A V', 'Alistair Shilton', 'Sunil Gupta'] | 2023-03-03 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-1.19984865e-01 5.84137380e-01 1.18049853e-01 1.19984031e-01
-2.11910993e-01 -7.57890940e-01 1.83805451e-01 9.73632112e-02
-6.62465751e-01 7.55891383e-01 -1.71311602e-01 -2.03569397e-01
-4.06791508e-01 -3.38017493e-01 -6.53602123e-01 -5.03817022e-01
-2.17103466e-01 8.75172913e-01 -1.09829403e-01 -2.87955999... | [4.187417984008789, 1.726456880569458] |
517ff363-6615-4f41-9c48-cb74e5760368 | iterative-in-iterative-super-resolution | 2306.14487 | null | https://arxiv.org/abs/2306.14487v1 | https://arxiv.org/pdf/2306.14487v1.pdf | Iterative-in-Iterative Super-Resolution Biomedical Imaging Using One Real Image | Deep learning-based super-resolution models have the potential to revolutionize biomedical imaging and diagnoses by effectively tackling various challenges associated with early detection, personalized medicine, and clinical automation. However, the requirement of an extensive collection of high-resolution images prese... | ['Xun Guan', 'Jian Song', 'Shijie Deng', 'Ying Xiao', 'Benqi Zhao', 'Xinyue Wang', 'Yuanzheng Ma'] | 2023-06-26 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 6.91052496e-01 3.08855146e-01 2.31055722e-01 -3.02037716e-01
-1.16177213e+00 -1.34627381e-02 3.00341159e-01 -8.59283283e-03
-3.67061228e-01 7.49811590e-01 2.42251195e-02 1.50537446e-01
-1.74582005e-01 -7.74085879e-01 -5.06475925e-01 -9.05094683e-01
-1.03008904e-01 1.97858408e-01 1.52982816e-01 -9.75621268... | [13.660624504089355, -2.3107597827911377] |
b229d5d9-44cd-4eb6-886d-ca0654a0e336 | deep-multi-task-learning-to-recognise-subtle | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Guosheng_Hu_Deep_Multi-Task_Learning_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Guosheng_Hu_Deep_Multi-Task_Learning_ECCV_2018_paper.pdf | Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States | Facial expression recognition is a topical task. However, very little research investigates subtle expression recognition, which is important for mental activity analysis, deception detection, etc. We address subtle expression recognition through convolutional neural networks (CNNs) by developing multi-task learning (M... | ['Timothy Hospedales', 'Zhihong Zhang', 'Guosheng Hu', 'Yongxin Yang', 'Yang Hua', 'Neil Robertson', 'Zehao Yu', 'Yang Yuan', 'Li Liu', 'Fumin Shen', 'Ling Shao'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['deception-detection'] | ['miscellaneous'] | [ 1.51221350e-01 -3.02249551e-01 -2.25869343e-01 -7.86807001e-01
-7.32859075e-01 -2.01573208e-01 5.30840039e-01 -4.82427776e-01
-4.74350214e-01 5.51227629e-01 1.82136953e-01 1.85812369e-01
-3.84060070e-02 -2.80007571e-01 -3.34054440e-01 -9.95382547e-01
-1.64626703e-01 4.25508618e-02 -4.95669127e-01 -6.90324008... | [13.585808753967285, 1.805151104927063] |
0ed8efb2-87bf-46b8-9200-70be90936cbd | multipath-cnn-with-alpha-matte-inference-for | 2109.14249 | null | https://arxiv.org/abs/2109.14249v1 | https://arxiv.org/pdf/2109.14249v1.pdf | Multipath CNN with alpha matte inference for knee tissue segmentation from MRI | Precise segmentation of knee tissues from magnetic resonance imaging (MRI) is critical in quantitative imaging and diagnosis. Convolutional neural networks (CNNs), which are state of the art, have limitations owing to the lack of image-specific adaptation, such as low tissue contrasts and structural inhomogeneities, th... | ['Weitian Chen', 'Yongcheng Yao', 'Basim Azam', 'Sheheryar Khan'] | 2021-09-29 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 4.60917413e-01 3.15206409e-01 1.43041342e-01 -1.99097365e-01
-8.39865685e-01 -5.95683195e-02 1.35942593e-01 -4.93346713e-02
-4.70171899e-01 6.31964326e-01 1.80946872e-01 -5.81341572e-02
-2.94656634e-01 -5.38369536e-01 -5.98654032e-01 -8.41247201e-01
-2.00851083e-01 5.64701140e-01 5.59228539e-01 -8.48984271... | [14.383228302001953, -2.4150338172912598] |
2723689d-1670-4bee-947c-6d31f598d524 | pansharpening-by-convolutional-neural | 2111.08334 | null | https://arxiv.org/abs/2111.08334v3 | https://arxiv.org/pdf/2111.08334v3.pdf | Pansharpening by convolutional neural networks in the full resolution framework | In recent years, there has been a growing interest in deep learning-based pansharpening. Thus far, research has mainly focused on architectures. Nonetheless, model training is an equally important issue. A first problem is the absence of ground truths, unavoidable in pansharpening. This is often addressed by training n... | ['Giuseppe Scarpa', 'Giovanni Poggi', 'Antonio Mazza', 'Sergio Vitale', 'Matteo Ciotola'] | 2021-11-16 | null | null | null | null | ['satellite-image-super-resolution', 'pansharpening'] | ['computer-vision', 'computer-vision'] | [ 4.70909238e-01 -3.47913504e-01 -2.23099977e-01 -9.71666202e-02
-7.97778130e-01 -3.11340094e-01 3.55190992e-01 -1.18511997e-01
-5.33040106e-01 8.30934703e-01 -3.70104849e-01 6.15484407e-03
-5.25256872e-01 -1.29898310e+00 -5.36901236e-01 -1.12298274e+00
1.10278971e-01 4.06778008e-02 1.23686954e-01 -4.18638617... | [10.109939575195312, -1.9317055940628052] |
1a217eaf-bb69-4295-bbc7-8242c71be4f6 | gender-and-interest-targeting-for-sponsored | 1606.07189 | null | http://arxiv.org/abs/1606.07189v1 | http://arxiv.org/pdf/1606.07189v1.pdf | Gender and Interest Targeting for Sponsored Post Advertising at Tumblr | As one of the leading platforms for creative content, Tumblr offers
advertisers a unique way of creating brand identity. Advertisers can tell their
story through images, animation, text, music, video, and more, and promote that
content by sponsoring it to appear as an advertisement in the streams of Tumblr
users. In th... | ['Nemanja Djuric', 'Mihajlo Grbovic', 'Vladan Radosavljevic', 'Narayan Bhamidipati', 'Ananth Nagarajan'] | 2016-06-23 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-1.76840723e-02 2.63244927e-01 -1.12167990e+00 -7.28440046e-01
-1.20517266e+00 -6.57380462e-01 8.37058783e-01 1.03491418e-01
-2.44938582e-01 4.36860144e-01 4.65549767e-01 -8.53660479e-02
6.25934526e-02 -1.05546725e+00 -8.41837049e-01 -1.84637174e-01
-2.84624659e-02 1.00425422e+00 7.02849329e-02 -4.23854053... | [9.958996772766113, 5.861457824707031] |
25594811-40e5-462e-9b9f-ab9eb3173c48 | simultaneous-identification-of-models-and | 2305.15174 | null | https://arxiv.org/abs/2305.15174v1 | https://arxiv.org/pdf/2305.15174v1.pdf | Simultaneous identification of models and parameters of scientific simulators | Many scientific models are composed of multiple discrete components, and scien tists often make heuristic decisions about which components to include. Bayesian inference provides a mathematical framework for systematically selecting model components, but defining prior distributions over model components and developing... | ['Jakob H. Macke', 'Cornelius Schröder'] | 2023-05-24 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 3.87071341e-01 -3.12537760e-01 -8.44314098e-02 -1.52038202e-01
-7.93523967e-01 -8.63647342e-01 8.43948066e-01 8.20914656e-02
-3.17118585e-01 8.33800495e-01 -2.06400886e-01 -5.88563502e-01
-4.97496903e-01 -4.30512577e-01 -7.26016819e-01 -8.13255727e-01
-1.61108181e-01 9.77876902e-01 2.38936380e-01 1.00722440... | [6.831654071807861, 3.9442431926727295] |
1eee3c4a-5865-4ebd-82a2-8a5393e82a4c | a-faster-lighter-and-stronger-deep-learning | 2211.14864 | null | https://arxiv.org/abs/2211.14864v1 | https://arxiv.org/pdf/2211.14864v1.pdf | A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition | Visual Place Recognition is an essential component of systems for camera localization and loop closure detection, and it has attracted widespread interest in multiple domains such as computer vision, robotics and AR/VR. In this work, we propose a faster, lighter and stronger approach that can generate models with fewer... | ['Songzhi Su', 'Ze Huang', 'Rui Huang'] | 2022-11-27 | null | null | null | null | ['camera-localization', 'loop-closure-detection', 'visual-place-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.58187389e-01 -4.80067372e-01 -4.83843714e-01 -3.30874085e-01
-6.55349255e-01 -5.15799344e-01 5.38725376e-01 -2.69421432e-02
-5.93492031e-01 5.38451910e-01 3.77421714e-02 -3.10548156e-01
-2.11007252e-01 -9.07373369e-01 -1.02172971e+00 -7.02772439e-01
3.39409411e-02 4.00912128e-02 6.32076383e-01 -3.92996222... | [7.783807754516602, -1.9022700786590576] |
1447e64f-5335-4be4-90f9-a413a045d8ac | intrinsic-image-harmonization | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Guo_Intrinsic_Image_Harmonization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Guo_Intrinsic_Image_Harmonization_CVPR_2021_paper.pdf | Intrinsic Image Harmonization | Compositing an image usually inevitably suffers from inharmony problem that is mainly caused by incompatibility of foreground and background from two different images with distinct surfaces and lights, corresponding to material-dependent and light-dependent characteristics, namely, reflectance and illumination intr... | ['Bing Zheng', 'Zhaorui Gu', 'Yufeng Jiang', 'Haiyong Zheng', 'Zonghui Guo'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['image-harmonization'] | ['computer-vision'] | [ 3.31916183e-01 -3.23083192e-01 3.66188765e-01 -2.35950038e-01
-5.38490832e-01 -6.46783352e-01 2.94330835e-01 -4.61066872e-01
-6.78722486e-02 5.19941330e-01 3.32811959e-02 2.28139713e-01
8.61766338e-02 -7.61902511e-01 -8.67134988e-01 -1.17034256e+00
8.38099301e-01 -6.50127307e-02 -2.59107560e-01 -1.57480195... | [11.196016311645508, -1.3158165216445923] |
0cc03e56-c333-4672-b601-45cb7727493b | simple-question-answering-with-subgraph | 1904.04049 | null | http://arxiv.org/abs/1904.04049v1 | http://arxiv.org/pdf/1904.04049v1.pdf | Simple Question Answering with Subgraph Ranking and Joint-Scoring | Knowledge graph based simple question answering (KBSQA) is a major area of
research within question answering. Although only dealing with simple
questions, i.e., questions that can be answered through a single knowledge base
(KB) fact, this task is neither simple nor close to being solved. Targeting on
the two main ste... | ['Tagyoung Chung', 'Angeliki Metallinou', 'Anuj Goyal', 'Wenbo Zhao'] | 2019-04-04 | simple-question-answering-with-subgraph-1 | https://aclanthology.org/N19-1029 | https://aclanthology.org/N19-1029.pdf | naacl-2019-6 | ['fact-selection'] | ['natural-language-processing'] | [-4.10019308e-02 4.65302050e-01 -2.07394406e-01 -3.38367909e-01
-8.03021193e-01 -5.96937895e-01 5.21828175e-01 5.36644220e-01
-3.10404241e-01 7.31417954e-01 2.70831406e-01 -5.34617484e-01
-4.81057167e-01 -1.09834421e+00 -8.90126169e-01 -1.96224377e-01
2.07251370e-01 2.25034758e-01 7.53949761e-01 -4.77073550... | [10.503904342651367, 7.934709072113037] |
5284888c-c977-4bce-8eda-78a3ac2ec4e8 | bmn-boundary-matching-network-for-temporal | 1907.09702 | null | https://arxiv.org/abs/1907.09702v1 | https://arxiv.org/pdf/1907.09702v1.pdf | BMN: Boundary-Matching Network for Temporal Action Proposal Generation | Temporal action proposal generation is an challenging and promising task which aims to locate temporal regions in real-world videos where action or event may occur. Current bottom-up proposal generation methods can generate proposals with precise boundary, but cannot efficiently generate adequately reliable confidence ... | ['Xiao Liu', 'Tianwei Lin', 'Errui Ding', 'Shilei Wen', 'Xin Li'] | 2019-07-23 | bmn-boundary-matching-network-for-temporal-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Lin_BMN_Boundary-Matching_Network_for_Temporal_Action_Proposal_Generation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lin_BMN_Boundary-Matching_Network_for_Temporal_Action_Proposal_Generation_ICCV_2019_paper.pdf | iccv-2019-10 | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 2.03141317e-01 -9.22446847e-02 -5.74662387e-01 -1.97754085e-01
-1.10920310e+00 -2.07322780e-02 6.48842454e-01 -2.11075768e-01
-4.17371511e-01 7.30149031e-01 6.57603621e-01 2.97569185e-01
3.17702085e-01 -5.91446459e-01 -4.02844191e-01 -4.87830341e-01
-1.24639817e-01 2.55214125e-01 1.11695111e+00 1.31440058... | [8.391603469848633, 0.4504220187664032] |
fef921c3-2b92-4288-9dfc-afac9a9c4084 | conversational-semantic-parsing-using-dynamic | 2305.06164 | null | https://arxiv.org/abs/2305.06164v1 | https://arxiv.org/pdf/2305.06164v1.pdf | Conversational Semantic Parsing using Dynamic Context Graphs | In this paper we consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types. We are interested in developing models capable of interactively mapping user utterances into executable logical forms (e.g., SPARQL) in the contex... | ['Mirella Lapata', 'Parag Jain'] | 2023-05-04 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 1.30878448e-01 9.93732989e-01 -3.92058305e-02 -4.03698474e-01
-4.88339067e-01 -9.69463170e-01 5.25342107e-01 6.90527856e-01
-2.29430497e-01 6.56805098e-01 4.99983281e-01 -6.26954138e-01
2.78926194e-01 -1.18993771e+00 -7.76346862e-01 5.96134923e-02
-2.56149322e-01 8.29740584e-01 5.48456252e-01 -6.95502281... | [12.254355430603027, 8.104039192199707] |
6263e006-7437-41a4-8bfb-ac8e2e007e3a | gtr-ctrl-instrument-and-genre-conditioning | 2302.05393 | null | https://arxiv.org/abs/2302.05393v1 | https://arxiv.org/pdf/2302.05393v1.pdf | GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers | Recently, symbolic music generation with deep learning techniques has witnessed steady improvements. Most works on this topic focus on MIDI representations, but less attention has been paid to symbolic music generation using guitar tablatures (tabs) which can be used to encode multiple instruments. Tabs include informa... | ['Mathieu Barthet', 'Zack Zukowski', 'CJ Carr', 'Yu-Hua Chen', 'Adarsh Kumar', 'Pedro Sarmento'] | 2023-02-10 | null | null | null | null | ['music-generation', 'genre-classification', 'music-generation'] | ['audio', 'computer-vision', 'music'] | [ 2.36733034e-01 -3.87976259e-01 -9.29232612e-02 -1.11889370e-01
-9.17236388e-01 -1.05894637e+00 5.60825944e-01 -1.07282609e-01
-4.01031692e-03 5.42849004e-01 1.83402985e-01 -1.73519805e-01
-4.23382491e-01 -7.80191064e-01 -7.08854377e-01 -6.30716264e-01
2.00052876e-02 5.20185709e-01 -3.99233758e-01 -4.39079314... | [16.04645538330078, 5.486169338226318] |
98979816-cf37-42fb-97c0-baf1f692ec29 | multi-hop-selector-network-for-multi-turn | null | null | https://aclanthology.org/D19-1011 | https://aclanthology.org/D19-1011.pdf | Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots | Multi-turn retrieval-based conversation is an important task for building intelligent dialogue systems. Existing works mainly focus on matching candidate responses with every context utterance on multiple levels of granularity, which ignore the side effect of using excessive context information. Context utterances prov... | ['Songlin Hu', 'Mingming Li', 'Chunyuan Yuan', 'Wei Zhou', 'Shangwen Lv', 'Jizhong Han', 'Fuqing Zhu'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 2.07687601e-01 -7.69936517e-02 -2.65891433e-01 -6.59030318e-01
-1.08073843e+00 -3.68016481e-01 6.70922577e-01 7.77396094e-03
-5.24038494e-01 7.40731120e-01 8.17655802e-01 6.59475103e-02
-1.46703990e-02 -6.52630150e-01 1.49747685e-01 -5.90420246e-01
4.85052139e-01 3.74093711e-01 6.98584914e-01 -8.15931320... | [12.514565467834473, 7.794970989227295] |
27067dff-2c9f-48e4-9d6c-02c69d2b718b | road-segmentation-on-low-resolution-lidar | 2005.13102 | null | https://arxiv.org/abs/2005.13102v1 | https://arxiv.org/pdf/2005.13102v1.pdf | Road Segmentation on low resolution Lidar point clouds for autonomous vehicles | Point cloud datasets for perception tasks in the context of autonomous driving often rely on high resolution 64-layer Light Detection and Ranging (LIDAR) scanners. They are expensive to deploy on real-world autonomous driving sensor architectures which usually employ 16/32 layer LIDARs. We evaluate the effect of subsam... | ['Santiago Velasco-Forero', 'B Ravi Kiran', 'Andres Serna', 'Nagarjuna Vemuri', 'Leonardo Gigli', 'Thomas Paul', 'Beatriz Marcotegui'] | 2020-05-27 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 2.67026871e-01 -1.83526129e-02 1.06359422e-01 -6.98408067e-01
-5.02094626e-01 -3.62619400e-01 8.73726606e-01 2.36287236e-01
-7.25308716e-01 4.54908192e-01 -4.64019477e-01 -4.57765847e-01
3.94211337e-03 -1.27352798e+00 -1.08103919e+00 -2.89695263e-01
1.10662594e-01 1.03418398e+00 7.94085681e-01 -3.33544344... | [7.952734470367432, -2.7723844051361084] |
1dc7fceb-289f-49c1-97a4-58bcb12874cd | a-point-set-generation-network-for-3d-object | 1612.00603 | null | http://arxiv.org/abs/1612.00603v2 | http://arxiv.org/pdf/1612.00603v2.pdf | A Point Set Generation Network for 3D Object Reconstruction from a Single Image | Generation of 3D data by deep neural network has been attracting increasing
attention in the research community. The majority of extant works resort to
regular representations such as volumetric grids or collection of images;
however, these representations obscure the natural invariance of 3D shapes
under geometric tra... | ['Leonidas Guibas', 'Hao Su', 'Haoqiang Fan'] | 2016-12-02 | a-point-set-generation-network-for-3d-object-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Fan_A_Point_Set_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Fan_A_Point_Set_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image'] | ['computer-vision', 'computer-vision'] | [ 2.65351385e-01 2.70358622e-01 2.60173082e-01 -3.69784087e-01
-8.84559751e-01 -4.86716598e-01 7.67991126e-01 -1.03486791e-01
2.01909430e-02 5.34852743e-01 1.11773880e-02 -2.68801779e-01
-1.79458018e-02 -9.71374929e-01 -1.18080115e+00 -3.57786298e-01
1.44891858e-01 8.99916410e-01 6.91179410e-02 -2.51682281... | [8.626834869384766, -3.545947313308716] |
dfc82c93-e219-4d02-a6c3-03689a4b696e | p-transformer-towards-better-document-to | 2212.05830 | null | https://arxiv.org/abs/2212.05830v1 | https://arxiv.org/pdf/2212.05830v1.pdf | P-Transformer: Towards Better Document-to-Document Neural Machine Translation | Directly training a document-to-document (Doc2Doc) neural machine translation (NMT) via Transformer from scratch, especially on small datasets usually fails to converge. Our dedicated probing tasks show that 1) both the absolute position and relative position information gets gradually weakened or even vanished once it... | ['Min Zhang', 'Hao Yang', 'Shimin Tao', 'Jing Jiang', 'Junhui Li', 'Yachao Li'] | 2022-12-12 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.65906096e-01 1.46113455e-01 -1.61557764e-01 -2.30125025e-01
-1.25109112e+00 -7.70841599e-01 9.29812193e-01 -3.03530276e-01
-2.77139187e-01 9.14373159e-01 5.44371367e-01 -6.96005285e-01
3.23728502e-01 -6.14579916e-01 -1.08034861e+00 -7.08130836e-01
3.93245369e-01 6.78447008e-01 -1.14337578e-01 -5.54300129... | [11.724536895751953, 9.85677433013916] |
521e3808-6550-425a-b210-7c2357f290b2 | text-alignment-is-an-efficient-unified-model | 2307.02729 | null | https://arxiv.org/abs/2307.02729v1 | https://arxiv.org/pdf/2307.02729v1.pdf | Text Alignment Is An Efficient Unified Model for Massive NLP Tasks | Large language models (LLMs), typically designed as a function of next-word prediction, have excelled across extensive NLP tasks. Despite the generality, next-word prediction is often not an efficient formulation for many of the tasks, demanding an extreme scale of model parameters (10s or 100s of billions) and sometim... | ['Zhiting Hu', 'Ruichen Li', 'Yichi Yang', 'Yuheng Zha'] | 2023-07-06 | null | null | null | null | ['text-generation', 'question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.91192076e-01 9.20546576e-02 -2.53143638e-01 -3.98392320e-01
-1.43744671e+00 -7.47836113e-01 8.08225453e-01 2.36406952e-01
-4.64419365e-01 6.88602805e-01 3.49265069e-01 -8.02812040e-01
-2.40038425e-01 -5.09897292e-01 -7.24153996e-01 -1.80278033e-01
4.08091366e-01 9.03832197e-01 1.83032617e-01 -5.59305906... | [11.094555854797363, 8.494844436645508] |
85a86665-88ca-4922-ba71-1cb2c306dcb5 | federated-learning-as-variational-inference-a | 2302.04228 | null | https://arxiv.org/abs/2302.04228v1 | https://arxiv.org/pdf/2302.04228v1.pdf | Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach | The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decomposes across client loss functions. A recent alternative formulation instead treats federated learning as a distributed inference problem, whe... | ['Eric P. Xing', 'Yoon Kim', 'Andrew Gelman', 'Hongyi Wang', 'Philip Greengard', 'Han Guo'] | 2023-02-08 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-6.56808019e-01 1.39818251e-01 -7.27008209e-02 -6.81317210e-01
-1.65389538e+00 -4.71281081e-01 6.67631686e-01 -4.29300033e-02
-3.10103685e-01 7.56355464e-01 4.49457526e-01 -1.78119496e-01
-1.99546665e-01 -6.49528027e-01 -1.05614400e+00 -9.76634979e-01
-1.22076176e-01 8.41439068e-01 -2.89844751e-01 5.25770903... | [5.838528156280518, 6.302443981170654] |
05938eb2-d6f4-4999-86d2-8d0969a08ac3 | pd-quant-post-training-quantization-based-on | 2212.07048 | null | https://arxiv.org/abs/2212.07048v3 | https://arxiv.org/pdf/2212.07048v3.pdf | PD-Quant: Post-Training Quantization based on Prediction Difference Metric | Post-training quantization (PTQ) is a neural network compression technique that converts a full-precision model into a quantized model using lower-precision data types. Although it can help reduce the size and computational cost of deep neural networks, it can also introduce quantization noise and reduce prediction acc... | ['Wenyu Liu', 'Xinggang Wang', 'Dawei Yang', 'Zhihang Yuan', 'Lin Niu', 'Jiawei Liu'] | 2022-12-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [-7.85321295e-02 -2.65737236e-01 -3.81265670e-01 -4.47934031e-01
-5.44950306e-01 -1.13762915e-01 4.80029061e-02 1.50391519e-01
-8.62407446e-01 6.61889553e-01 -1.36984894e-02 -4.14187402e-01
-3.56050804e-02 -1.04160190e+00 -6.70155823e-01 -6.51955843e-01
2.46693268e-01 9.30051580e-02 3.69139135e-01 -1.96174383... | [8.598514556884766, 3.0421090126037598] |
461eabdc-2347-4924-9d59-e7da01d58dc9 | emog-synthesizing-emotive-co-speech-3d | 2306.11496 | null | https://arxiv.org/abs/2306.11496v1 | https://arxiv.org/pdf/2306.11496v1.pdf | EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model | Although previous co-speech gesture generation methods are able to synthesize motions in line with speech content, it is still not enough to handle diverse and complicated motion distribution. The key challenges are: 1) the one-to-many nature between the speech content and gestures; 2) the correlation modeling between ... | ['Jianxin Lin', 'Xin Jin', 'Bohan Li', 'Wei Zhao', 'Jinming Liu', 'Tianyu He', 'Yijun Wang', 'Lianying Yin'] | 2023-06-20 | null | null | null | null | ['gesture-generation'] | ['robots'] | [-1.28792197e-01 -2.49970675e-01 -4.21878621e-02 1.33455917e-01
-8.34755123e-01 -3.40108812e-01 8.02285492e-01 -6.44296765e-01
-9.28400531e-02 4.57074255e-01 6.90684080e-01 3.05514168e-02
-1.21138565e-01 -3.97614300e-01 -2.89085001e-01 -8.93671334e-01
1.08428188e-01 3.31133902e-01 2.30157092e-01 -3.51391584... | [5.74577522277832, -0.2007274180650711] |
ce4fba60-b2a6-4c48-9457-383f4342f3c0 | on-graph-reconstruction-via-empirical-risk | null | null | http://papers.nips.cc/paper/6588-on-graph-reconstruction-via-empirical-risk-minimization-fast-learning-rates-and-scalability | http://papers.nips.cc/paper/6588-on-graph-reconstruction-via-empirical-risk-minimization-fast-learning-rates-and-scalability.pdf | On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability | The problem of predicting connections between a set of data points finds many applications, in systems biology and social network analysis among others. This paper focuses on the \textit{graph reconstruction} problem, where the prediction rule is obtained by minimizing the average error over all n(n-1)/2 possible pairs... | ['Stephan Clémençon', 'Aurélien Bellet', 'Guillaume Papa'] | 2016-12-01 | null | null | null | neurips-2016-12 | ['graph-reconstruction'] | ['graphs'] | [ 2.50415683e-01 7.06213892e-01 -2.35861421e-01 -1.18336074e-01
-5.89890838e-01 -5.44061720e-01 4.93036866e-01 7.59887397e-01
-4.07314301e-01 9.28667724e-01 -3.45848143e-01 -3.84478360e-01
-6.11949444e-01 -9.21505094e-01 -8.94721627e-01 -7.71768630e-01
-6.92892313e-01 7.98262894e-01 3.16138208e-01 9.63292718... | [6.942177772521973, 5.341022968292236] |
01738b31-9a96-4834-ae54-6b94f7ea0079 | left-corner-transitions-on-dependency-parsing | null | null | https://aclanthology.org/C14-1202 | https://aclanthology.org/C14-1202.pdf | Left-corner Transitions on Dependency Parsing | null | ['Hiroshi Noji', 'Yusuke Miyao'] | 2014-08-01 | left-corner-transitions-on-dependency-parsing-1 | https://aclanthology.org/C14-1202 | https://aclanthology.org/C14-1202.pdf | coling-2014-8 | ['transition-based-dependency-parsing'] | ['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... | [-7.3298020362854, 3.778496265411377] |
fc0d2e6f-ebd0-4639-8db2-0ee8bc030287 | excess-risk-bound-for-deep-learning-under | 2302.07503 | null | https://arxiv.org/abs/2302.07503v1 | https://arxiv.org/pdf/2302.07503v1.pdf | Excess risk bound for deep learning under weak dependence | This paper considers deep neural networks for learning weakly dependent processes in a general framework that includes, for instance, regression estimation, time series prediction, time series classification. The $\psi$-weak dependence structure considered is quite large and covers other conditions such as mixing, asso... | ['William Kengne'] | 2023-02-15 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.05940923e-01 7.95452967e-02 -6.16393127e-02 -3.96431178e-01
-7.36003697e-01 -2.46816948e-01 2.30146617e-01 7.88183287e-02
-4.65459883e-01 1.00978029e+00 -2.03631774e-01 -3.95984113e-01
-6.61245823e-01 -9.18478072e-01 -9.98869300e-01 -1.16852915e+00
-8.29445899e-01 3.71703595e-01 -2.01820374e-01 -2.76302606... | [7.189160346984863, 3.8323276042938232] |
b1a7fb49-9cd7-40e3-b3ae-df98c30b4b46 | soft-gripping-specifying-for-trustworthiness | 2307.01159 | null | https://arxiv.org/abs/2307.01159v1 | https://arxiv.org/pdf/2307.01159v1.pdf | Soft Gripping: Specifying for Trustworthiness | Soft robotics is an emerging technology in which engineers create flexible devices for use in a variety of applications. In order to advance the wide adoption of soft robots, ensuring their trustworthiness is essential; if soft robots are not trusted, they will not be used to their full potential. In order to demonstra... | ['Shane Windsor', 'Kerstin Eder', 'Jonathan Rossiter', 'Graham Deacon', 'John Downer', 'Jonathan Ives', 'Alix J. Partridge', 'Arianna Manzini', 'Peter D. Winter', 'Greg Chance', 'Nguyen Hao Le', 'Dhaminda B. Abeywickrama'] | 2023-07-03 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-4.02152129e-02 5.02145469e-01 -3.52570936e-02 -3.68442893e-01
5.92542946e-01 -8.16349566e-01 2.25574404e-01 -2.13668749e-01
-1.78539138e-02 7.57902861e-01 -3.14475119e-01 -6.39728010e-02
-5.56194007e-01 -7.33103991e-01 -4.62239087e-01 -5.98336875e-01
1.25638142e-01 2.98461884e-01 6.02200449e-01 -5.39769769... | [4.8872904777526855, 1.1082592010498047] |
151890f4-e316-49c3-ae18-0f4127cae0d4 | open-set-multi-source-multi-target-domain | 2302.00995 | null | https://arxiv.org/abs/2302.00995v2 | https://arxiv.org/pdf/2302.00995v2.pdf | Open-Set Multi-Source Multi-Target Domain Adaptation | Single-Source Single-Target Domain Adaptation (1S1T) aims to bridge the gap between a labelled source domain and an unlabelled target domain. Despite 1S1T being a well-researched topic, they are typically not deployed to the real world. Methods like Multi-Source Domain Adaptation and Multi-Target Domain Adaptation have... | ['Ritik Agrawal', 'Muhammed Abdullah Shaikh', 'Aadhithya Iyer', 'Arihant Gaur', 'Rohit Lal'] | 2023-02-02 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 5.13348579e-01 -1.98293179e-02 -4.41584557e-01 -4.89755094e-01
-6.36730671e-01 -8.84925306e-01 6.16835356e-01 -8.98731593e-03
-7.54698068e-02 8.24312210e-01 -2.00908005e-01 -1.41408052e-02
-2.01953590e-01 -6.40920222e-01 -5.42854488e-01 -5.77794373e-01
2.88295180e-01 9.65884328e-01 6.34636819e-01 -5.17759800... | [10.337457656860352, 3.0387630462646484] |
e2ee08d5-a441-4a53-9e0b-160effee01dd | pointpwc-net-a-coarse-to-fine-network-for | 1911.12408 | null | https://arxiv.org/abs/1911.12408v2 | https://arxiv.org/pdf/1911.12408v2.pdf | PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds | We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped to a finer level, enabling the algorithm to accommodate for large motion without a prohibitive search space. We introduce novel cost volume,... | ['Li Fuxin', 'Zhuwen Li', 'Zhiyuan Wang', 'Wei Liu', 'Wenxuan Wu'] | 2019-11-27 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-2.22196728e-01 -3.53777111e-01 1.98596939e-02 -4.16276604e-01
-5.67003369e-01 -4.64880049e-01 4.73041922e-01 -1.45444423e-01
-4.66783255e-01 6.87707722e-01 2.00512931e-01 -2.21352682e-01
-7.29294717e-02 -1.08785272e+00 -9.99179006e-01 -2.87130654e-01
-2.94222534e-01 6.04052305e-01 4.64344412e-01 -9.92629528... | [8.557684898376465, -2.0580077171325684] |
300bbb1c-2f21-4865-a574-cfc5382d0e06 | small-moving-object-detection-algorithm-based | 2301.01917 | null | https://arxiv.org/abs/2301.01917v2 | https://arxiv.org/pdf/2301.01917v2.pdf | Small Moving Object Detection Algorithm in Surveillance Video Based on Motion Information | A Small Moving Object Detection algorithm Based on Motion Information (SMOD-BMI) is proposed to detect small moving objects with a low Signal-to-Noise Ratio (SNR) in surveillance video. Firstly, a ConvLSTM-PAN model structure is designed to capture suspicious small moving objects, in which the Convolutional Long and Sh... | ['Haiyan Zhong', 'Hengcao Li', 'Zexi Hua', 'Ziwei Sun'] | 2023-01-05 | null | null | null | null | ['moving-object-detection', 'small-object-detection'] | ['computer-vision', 'computer-vision'] | [ 8.82706642e-02 -5.00465512e-01 7.75807425e-02 -6.66068727e-03
-1.69457961e-02 -6.47047833e-02 1.25641987e-01 -3.77891690e-01
-6.28263116e-01 2.63293162e-02 7.25797787e-02 -1.96384430e-01
-8.63355249e-02 -8.17041278e-01 -3.87334675e-01 -1.14666378e+00
-6.09389842e-01 -3.72799963e-01 1.33467805e+00 1.60296962... | [8.753193855285645, -0.6483418941497803] |
b959879b-4f36-4cee-96c4-b320df2ab625 | segment-anything-model-sam-meets-glass-mirror | 2305.00278 | null | https://arxiv.org/abs/2305.00278v1 | https://arxiv.org/pdf/2305.00278v1.pdf | Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected | Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks. As a foundation model in the field of computer vision, SAM (Segment Anything Model) has gained attention for its impressive performance in generic object segmentation. Despite it... | ['Choong Seon Hong', 'Sung-Ho Bae', 'Seungkyu Lee', 'Yuna Jung', 'Maryam Qamar', 'Yu Qiao', 'Chaoning Zhang', 'Dongsheng Han'] | 2023-04-29 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 5.21865547e-01 1.31882191e-01 1.32130936e-01 -1.96795374e-01
-6.92758679e-01 -6.14336669e-01 2.97782689e-01 -2.37227201e-01
-2.43896753e-01 2.36351416e-01 -2.76866704e-01 -1.22511439e-01
3.16330403e-01 -5.16580760e-01 -6.95371389e-01 -6.34996712e-01
3.36554497e-01 6.61464632e-01 7.87139893e-01 -2.73513068... | [9.598631858825684, 0.35209304094314575] |
504be558-a0a6-47f7-b1de-c63ef7492ddb | ab-initio-potential-energy-surfaces-by-1 | 2110.05064 | null | https://arxiv.org/abs/2110.05064v3 | https://arxiv.org/pdf/2110.05064v3.pdf | Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions | Solving the Schr\"odinger equation is key to many quantum mechanical properties. However, an analytical solution is only tractable for single-electron systems. Recently, neural networks succeeded at modeling wave functions of many-electron systems. Together with the variational Monte-Carlo (VMC) framework, this led to ... | ['Stephan Günnemann', 'Nicholas Gao'] | 2021-10-11 | ab-initio-potential-energy-surfaces-by | https://openreview.net/forum?id=apv504XsysP | https://openreview.net/pdf?id=apv504XsysP | iclr-2022-4 | ['variational-monte-carlo'] | ['miscellaneous'] | [ 2.36184031e-01 -1.03766821e-01 -3.91049217e-03 -1.00295760e-01
-7.82795310e-01 -2.52604455e-01 3.64761055e-01 3.37951511e-01
-6.83214784e-01 1.25352287e+00 -5.28745830e-01 -7.37813532e-01
-5.53704314e-02 -1.15042925e+00 -9.24443364e-01 -9.16753173e-01
-1.96334288e-01 6.83685303e-01 -9.07665938e-02 -3.88331771... | [5.3609395027160645, 5.250645160675049] |
23fdfb5f-2b88-44c3-ab64-ef6461e5f079 | understanding-the-ability-of-deep-neural | 2101.01386 | null | https://arxiv.org/abs/2101.01386v1 | https://arxiv.org/pdf/2101.01386v1.pdf | Understanding the Ability of Deep Neural Networks to Count Connected Components in Images | Humans can count very fast by subitizing, but slow substantially as the number of objects increases. Previous studies have shown a trained deep neural network (DNN) detector can count the number of objects in an amount of time that increases slowly with the number of objects. Such a phenomenon suggests the subitizing a... | ['Murray Loew', 'Shuyue Guan'] | 2021-01-05 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-1.45134866e-01 -3.06814518e-02 4.10278663e-02 -2.61999458e-01
3.49979788e-01 -5.57834089e-01 4.95522350e-01 1.44031644e-01
-7.45396197e-01 7.09475577e-01 -2.33493730e-01 -3.04163843e-01
1.23684093e-01 -1.42302537e+00 -7.44110942e-01 -3.74819845e-01
-1.62989095e-01 1.07070661e+00 3.50625932e-01 2.61582494... | [8.914536476135254, 0.29966971278190613] |
b7f7dade-de09-43b4-aa31-72b47d0886a7 | learning-to-read-by-spelling-towards | 1809.08675 | null | http://arxiv.org/abs/1809.08675v2 | http://arxiv.org/pdf/1809.08675v2.pdf | Learning to Read by Spelling: Towards Unsupervised Text Recognition | This work presents a method for visual text recognition without using any
paired supervisory data. We formulate the text recognition task as one of
aligning the conditional distribution of strings predicted from given text
images, with lexically valid strings sampled from target corpora. This enables
fully automated, a... | ['Ankush Gupta', 'Andrew Zisserman', 'Andrea Vedaldi'] | 2018-09-23 | null | null | null | null | ['unsupervised-text-recognition'] | ['computer-vision'] | [ 1.08373284e+00 -1.43925980e-01 -2.06551462e-01 -5.00786006e-01
-6.35992348e-01 -7.87405729e-01 8.64790499e-01 1.82040080e-01
-5.28112411e-01 7.81039357e-01 -3.47819924e-02 -4.00543779e-01
2.03794297e-02 -5.58514953e-01 -9.70616281e-01 -6.25106990e-01
3.09927464e-01 8.02570462e-01 2.12091297e-01 2.72385478... | [11.819461822509766, 2.35841965675354] |
e119eace-6e56-484a-b155-2e40cdee2768 | unsupervised-audio-visual-subspace-alignment | 2102.03673 | null | https://arxiv.org/abs/2102.03673v1 | https://arxiv.org/pdf/2102.03673v1.pdf | Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception Detection | Automated systems that detect deception in high-stakes situations can enhance societal well-being across medical, social work, and legal domains. Existing models for detecting high-stakes deception in videos have been supervised, but labeled datasets to train models can rarely be collected for most real-world applicati... | ['Maja J Matarić', 'Leena Mathur'] | 2021-02-06 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [ 3.61680567e-01 2.11100932e-02 -2.94925183e-01 -5.84077060e-01
-1.17072809e+00 -5.58930635e-01 4.24911886e-01 -2.62276441e-01
-4.28579479e-01 5.98009109e-01 6.56864464e-01 -2.60427028e-01
6.60943761e-02 1.70767531e-02 -4.90669727e-01 -1.38765633e-01
2.18401924e-01 -1.50182202e-01 -3.96348149e-01 -1.64323047... | [13.266210556030273, 2.035550832748413] |
33084920-73b3-44d2-9a19-7f3f8b7c3754 | choral-collecting-humor-reaction-labels-from | null | null | https://aclanthology.org/2021.emnlp-main.364 | https://aclanthology.org/2021.emnlp-main.364.pdf | CHoRaL: Collecting Humor Reaction Labels from Millions of Social Media Users | Humor detection has gained attention in recent years due to the desire to understand user-generated content with figurative language. However, substantial individual and cultural differences in humor perception make it very difficult to collect a large-scale humor dataset with reliable humor labels. We propose CHoRaL, ... | ['Julia Hirschberg', 'Shayan Hooshmand', 'Zixiaofan Yang'] | null | null | null | null | emnlp-2021-11 | ['humor-detection'] | ['natural-language-processing'] | [-5.53648472e-01 1.05164915e-01 -3.04333363e-02 -6.67595267e-02
8.33763480e-02 -5.83521426e-01 7.86603272e-01 3.54899764e-01
-4.07521240e-02 5.67207575e-01 1.21586621e+00 7.92613775e-02
4.74654973e-01 -6.11669421e-01 2.90603161e-01 -1.06311319e-02
2.86969662e-01 3.08702499e-01 -2.08094995e-02 -6.42356098... | [8.892504692077637, 11.04920482635498] |
320da317-f067-4c17-b47d-f711dc524733 | wedge-a-multi-weather-autonomous-driving | 2305.07528 | null | https://arxiv.org/abs/2305.07528v1 | https://arxiv.org/pdf/2305.07528v1.pdf | WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models | The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E G... | ['Ketan Kotecha', 'Rahee Walambe', 'Deva Ramanan', 'Aboli Marathe'] | 2023-05-12 | null | null | null | null | ['2d-object-detection'] | ['computer-vision'] | [ 9.14025903e-02 2.12012902e-02 3.09447408e-01 -3.93110543e-01
-7.05261469e-01 -1.12942672e+00 8.05610299e-01 -2.06574097e-01
-4.48784739e-01 6.25398815e-01 7.96347558e-02 -5.37804246e-01
5.79288602e-01 -8.79551530e-01 -9.19914126e-01 -7.14753211e-01
-2.85629243e-01 2.92659283e-01 3.39305520e-01 -4.45973933... | [8.182232856750488, -1.3571802377700806] |
2d72f77d-74ac-4b80-871c-dce7e720b1c1 | creating-synthetic-datasets-for-collaborative | 2303.01297 | null | https://arxiv.org/abs/2303.01297v1 | https://arxiv.org/pdf/2303.01297v1.pdf | Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks | Research and education in machine learning needs diverse, representative, and open datasets that contain sufficient samples to handle the necessary training, validation, and testing tasks. Currently, the Recommender Systems area includes a large number of subfields in which accuracy and beyond accuracy quality measures... | ['Luis Martínez', 'Raciel Yera', 'Abraham Gutiérrez', 'Jesús Bobadilla'] | 2023-03-02 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.09674916e-01 -3.02569687e-01 -3.65103297e-02 -5.82919180e-01
-3.37997615e-01 -4.56061125e-01 5.69702089e-01 -2.03978512e-02
-2.17043400e-01 8.86423528e-01 3.23298305e-01 1.24342412e-01
-3.05334538e-01 -1.28248894e+00 -5.77985704e-01 -7.66406298e-01
2.50504196e-01 7.40191340e-01 -2.02399120e-01 -1.51957810... | [10.204936981201172, 5.518398761749268] |
a5fa9ed8-7e0e-4bbc-add5-8fad1a013ff3 | deformable-neural-radiance-fields | 2011.12948 | null | https://arxiv.org/abs/2011.12948v5 | https://arxiv.org/pdf/2011.12948v5.pdf | Nerfies: Deformable Neural Radiance Fields | We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones. Our approach augments neural radiance fields (NeRF) by optimizing an additional continuous volumetric deformation field that warps each observed point into a canonical 5D ... | ['Ricardo Martin-Brualla', 'Steven M. Seitz', 'Dan B Goldman', 'Sofien Bouaziz', 'Jonathan T. Barron', 'Utkarsh Sinha', 'Keunhong Park'] | 2020-11-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Park_Nerfies_Deformable_Neural_Radiance_Fields_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Park_Nerfies_Deformable_Neural_Radiance_Fields_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 3.64762127e-01 1.64120674e-01 4.98841256e-01 -2.54208237e-01
-7.85751164e-01 -9.38879490e-01 5.91851294e-01 -7.23020613e-01
-1.00783393e-01 5.65744340e-01 1.69684738e-01 4.46673892e-02
-5.87535203e-02 -7.20788300e-01 -1.24580741e+00 -5.28162718e-01
2.61225581e-01 4.46144104e-01 9.80201811e-02 -2.45899737... | [9.18468952178955, -2.862116575241089] |
9061e9da-6b2f-4c14-b3d0-7c74bc92b902 | real-time-visual-analysis-of-microvascular | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Liu_Real-Time_Visual_Analysis_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Liu_Real-Time_Visual_Analysis_2015_CVPR_paper.pdf | Real-Time Visual Analysis of Microvascular Blood Flow for Critical Care | Microcirculatory monitoring plays an important role in diagnosis and treatment of critical care patients. Sidestream Dark Field (SDF) imaging devices have been used to visualize and support interpretation of the micro-vascular blood flow. However, due to subsurface scattering within the tissue that embeds the capillar... | ['Chao Liu', 'Michael R. Pinsky', 'Hernando Gomez', 'Artur Dubrawski', 'Srinivasa Narasimhan', 'Brian Zuckerbraun'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['video-stabilization'] | ['computer-vision'] | [ 3.46392319e-02 -3.82997721e-01 2.95615971e-01 9.57688019e-02
1.41774088e-01 -4.58509892e-01 1.48991570e-01 6.05831385e-01
-6.26643002e-01 8.71046782e-01 1.61615312e-01 -3.21115375e-01
1.01461127e-01 -4.47120577e-01 -2.91879863e-01 -8.57605577e-01
-3.83871645e-01 1.92388549e-01 4.71216738e-01 2.49801457... | [13.794171333312988, -2.9542157649993896] |
fa3d9ebc-f895-4424-bc0e-92ebaceec00d | adanns-a-framework-for-adaptive-semantic | 2305.19435 | null | https://arxiv.org/abs/2305.19435v1 | https://arxiv.org/pdf/2305.19435v1.pdf | AdANNS: A Framework for Adaptive Semantic Search | Web-scale search systems learn an encoder to embed a given query which is then hooked into an approximate nearest neighbor search (ANNS) pipeline to retrieve similar data points. To accurately capture tail queries and data points, learned representations typically are rigid, high-dimensional vectors that are generally ... | ['Ali Farhadi', 'Prateek Jain', 'Sham Kakade', 'Qingqing Cao', 'Alan Fan', 'Sharan Ranjit S', 'Aditya Kusupati', 'Aniket Rege'] | 2023-05-30 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [-1.52580306e-01 -3.73246014e-01 -5.44246912e-01 -4.76846009e-01
-1.33294261e+00 -9.01141822e-01 4.87641007e-01 2.10037038e-01
-5.30678809e-01 1.91533174e-02 2.57219225e-01 -5.75623691e-01
-2.79090792e-01 -1.14937270e+00 -1.06736839e+00 -1.58417061e-01
1.55094057e-01 6.74209774e-01 3.14927697e-01 -4.93093342... | [8.680315017700195, 3.4382193088531494] |
776c5002-d756-4b78-afce-85a0ad56a602 | deep-cross-modal-learning-for-caricature | 1807.11688 | null | http://arxiv.org/abs/1807.11688v1 | http://arxiv.org/pdf/1807.11688v1.pdf | Deep Cross Modal Learning for Caricature Verification and Identification(CaVINet) | Learning from different modalities is a challenging task. In this paper, we
look at the challenging problem of cross modal face verification and
recognition between caricature and visual image modalities. Caricature have
exaggerations of facial features of a person. Due to the significant variations
in the caricatures,... | ['Narayanan C. Krishnan', 'Jatin Garg', 'Himanshu Tolani', 'Skand Vishwanath Peri'] | 2018-07-31 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 2.56088167e-01 -6.71422854e-02 -1.59754232e-02 -5.87415457e-01
-8.82625103e-01 -8.50814164e-01 9.59058642e-01 -5.35126567e-01
-2.15017758e-02 3.91276687e-01 1.42090321e-01 3.78927216e-02
-1.40967136e-02 -4.77721483e-01 -9.26134408e-01 -6.83471441e-01
1.15859054e-01 1.98891208e-01 -3.67813379e-01 -1.39080614... | [13.177900314331055, 0.5961452126502991] |
b762e406-c37d-4862-a588-10ed167dd31e | lys-porting-a-twitter-sentiment-analysis | null | null | https://aclanthology.org/S14-2071 | https://aclanthology.org/S14-2071.pdf | LyS: Porting a Twitter Sentiment Analysis Approach from Spanish to English | null | ["Carlos G{\\'o}mez-Rodr{\\'\\i}guez", 'David Vilares', 'Yerai Doval', 'Miguel Hermo', 'Miguel A. Alonso'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['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... | [-7.200911521911621, 3.8126556873321533] |
18f99984-9c89-4b4a-8681-460483be7d10 | pinqi-an-end-to-end-physics-informed-approach | 2306.11023 | null | https://arxiv.org/abs/2306.11023v1 | https://arxiv.org/pdf/2306.11023v1.pdf | PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction | Quantitative Magnetic Resonance Imaging (qMRI) enables the reproducible measurement of biophysical parameters in tissue. The challenge lies in solving a nonlinear, ill-posed inverse problem to obtain the desired tissue parameter maps from acquired raw data. While various learned and non-learned approaches have been pro... | ['Andreas Kofler', 'Patrick Schuenke', 'Christoph Kolbitsch', 'Felix F Zimmermann'] | 2023-06-19 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 3.56031120e-01 1.49680272e-01 2.53833562e-01 -4.80791271e-01
-1.05000877e+00 -1.93374708e-01 1.67534634e-01 -1.80903137e-01
-6.43804908e-01 8.20135117e-01 1.88469827e-01 -1.67428240e-01
-3.62472922e-01 -1.52362645e-01 -7.61723816e-01 -9.67800915e-01
-4.35883135e-01 3.24549109e-01 -1.89595036e-02 5.14988005... | [13.482513427734375, -2.4457414150238037] |
eed67c5e-4835-4b4f-9ad9-1d5739cf75f3 | model-free-generative-replay-for-lifelong | 2208.05056 | null | https://arxiv.org/abs/2208.05056v2 | https://arxiv.org/pdf/2208.05056v2.pdf | Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2 | One approach to meet the challenges of deep lifelong reinforcement learning (LRL) is careful management of the agent's learning experiences, to learn (without forgetting) and build internal meta-models (of the tasks, environments, agents, and world). Generative replay (GR) is a biologically inspired replay mechanism th... | ['Ajay Divakaran', 'Michael Piacentino', 'Indranil Sur', 'Abrar Rahman', 'Jesse Hostetler', 'Aswin Raghavan', 'Zachary Daniels'] | 2022-08-09 | null | null | null | null | ['starcraft'] | ['playing-games'] | [-1.22741237e-01 2.98441797e-02 -9.62765291e-02 -3.70991393e-03
-3.41436058e-01 -5.36039948e-01 9.28703845e-01 -1.34117901e-01
-5.91604412e-01 1.00493407e+00 6.92568421e-02 -5.82543127e-02
-2.27499038e-01 -5.70889354e-01 -1.07090616e+00 -1.06472230e+00
-3.17353249e-01 5.97383857e-01 3.02990586e-01 -3.36667806... | [4.248187065124512, 1.4791216850280762] |
bb1bf2a5-84d6-4982-8786-970534bc8286 | hdr-vdp-3-a-multi-metric-for-predicting-image | 2304.13625 | null | https://arxiv.org/abs/2304.13625v1 | https://arxiv.org/pdf/2304.13625v1.pdf | HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content | High-Dynamic-Range Visual-Difference-Predictor version 3, or HDR-VDP-3, is a visual metric that can fulfill several tasks, such as full-reference image/video quality assessment, prediction of visual differences between a pair of images, or prediction of contrast distortions. Here we present a high-level overview of the... | ['Param Hanji', 'Dounia Hammou', 'Rafal K. Mantiuk'] | 2023-04-26 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [ 2.57254034e-01 -6.17006242e-01 1.47178173e-01 -4.84843016e-01
-7.35500395e-01 -3.68301809e-01 3.37877542e-01 -1.04101703e-01
-3.24334390e-02 5.12774408e-01 3.16658229e-01 -2.47386277e-01
-1.43765539e-01 -1.82272524e-01 -2.74890333e-01 -2.87091881e-01
-4.55387622e-01 -3.64772975e-01 5.63998818e-01 -3.03114116... | [11.662485122680664, -1.8930670022964478] |
6968001f-61f2-4ff8-8613-f3e1ae19407f | visual-semantic-contrastive-alignment-for-few | 2210.11000 | null | https://arxiv.org/abs/2210.11000v1 | https://arxiv.org/pdf/2210.11000v1.pdf | Visual-Semantic Contrastive Alignment for Few-Shot Image Classification | Few-Shot learning aims to train and optimize a model that can adapt to unseen visual classes with only a few labeled examples. The existing few-shot learning (FSL) methods, heavily rely only on visual data, thus fail to capture the semantic attributes to learn a more generalized version of the visual concept from very ... | ['Ranga Rodrigo', 'Mohamed Afham'] | 2022-10-20 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.02092320e-01 1.21241860e-01 -4.47881281e-01 -5.19355774e-01
-6.24991238e-01 -5.68648994e-01 1.01375222e+00 2.02035412e-01
-4.40041631e-01 5.21193624e-01 3.52005094e-01 7.70079195e-02
1.50810421e-01 -8.05066824e-01 -8.49347770e-01 -4.19561595e-01
2.96413004e-01 2.85461724e-01 5.10396481e-01 -3.40923637... | [10.102428436279297, 2.4508845806121826] |
413249bd-6270-4f21-867d-3d9401be7a6b | cross-lingual-and-cross-domain-discourse | null | null | https://aclanthology.org/P17-2037 | https://aclanthology.org/P17-2037.pdf | Cross-lingual and cross-domain discourse segmentation of entire documents | Discourse segmentation is a crucial step in building end-to-end discourse parsers. However, discourse segmenters only exist for a few languages and domains. Typically they only detect intra-sentential segment boundaries, assuming gold standard sentence and token segmentation, and relying on high-quality syntactic parse... | ['Anders S{\\o}gaard', "Oph{\\'e}lie Lacroix", "Chlo{\\'e} Braud"] | 2017-07-01 | null | null | null | acl-2017-7 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 1.95503831e-01 7.05580175e-01 -6.27657294e-01 -4.29740459e-01
-1.35526371e+00 -1.10584867e+00 7.14256346e-01 5.16041994e-01
-6.73672318e-01 1.12658393e+00 5.00234663e-01 -5.75369060e-01
3.75290155e-01 -6.20062292e-01 -5.73700011e-01 -1.14937134e-01
3.68077531e-02 8.24713051e-01 6.87085092e-01 -3.20117682... | [10.851201057434082, 9.497427940368652] |
72f6e469-1f50-4e47-aa05-667bb18e49e3 | prompting-language-informed-distribution-for | 2305.14428 | null | https://arxiv.org/abs/2305.14428v1 | https://arxiv.org/pdf/2305.14428v1.pdf | Prompting Language-Informed Distribution for Compositional Zero-Shot Learning | The compositional zero-shot learning (CZSL) task aims to recognize unseen compositional visual concepts (i.e., sliced tomatoes), where the models are learned only from the seen compositions (i.e., sliced potatoes and red tomatoes). Thanks to the prompt tuning on large pre-trained visual language models such as CLIP, re... | ['Yu Kong', 'Heng Huang', 'Lichang Chen', 'Wentao Bao'] | 2023-05-23 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.68801266e-01 -1.50937498e-01 -4.19814587e-01 -1.40272960e-01
-6.28106892e-01 -8.14252853e-01 9.65443254e-01 -9.37484577e-02
-8.05385858e-02 1.83675736e-01 4.31854337e-01 -3.52723151e-01
2.41869748e-01 -5.90272903e-01 -7.85085618e-01 -8.51770282e-01
4.12569761e-01 3.12245637e-01 2.67368942e-01 -1.01042993... | [10.24728775024414, 2.133354663848877] |
ac7707f3-4089-4d55-9222-86590339b934 | dsnet-automatic-dermoscopic-skin-lesion | 1907.04305 | null | https://arxiv.org/abs/1907.04305v2 | https://arxiv.org/pdf/1907.04305v2.pdf | DSNet: Automatic Dermoscopic Skin Lesion Segmentation | Automatic segmentation of skin lesion is considered a crucial step in Computer Aided Diagnosis (CAD) for melanoma diagnosis. Despite its significance, skin lesion segmentation remains a challenging task due to their diverse color, texture, and indistinguishable boundaries and forms an open problem. Through this study, ... | ['Md. Kamrul Hasan', 'Prasad N. Samarakoon', 'Fakrul Islam Tushar', 'Robert Marti Marly', 'Lavsen Dahal'] | 2019-07-09 | null | null | null | null | ['melanoma-diagnosis', 'skin-lesion-segmentation'] | ['computer-vision', 'medical'] | [ 8.01394403e-01 2.35563308e-01 -8.91990885e-02 -8.68381560e-02
-8.13249588e-01 -4.65467960e-01 3.84299606e-01 4.30011190e-02
-7.39632547e-01 7.29231179e-01 -1.72980815e-01 -3.93763095e-01
-2.76793204e-02 -6.93189919e-01 -4.15790707e-01 -7.32602835e-01
2.33472824e-01 -6.00887462e-02 4.98356611e-01 -1.67214740... | [15.646245002746582, -2.9501357078552246] |
98359d97-66f9-45c8-a83c-5ab7bffecef5 | socialmapf-optimal-and-efficient-multi-agent | 2210.08390 | null | https://arxiv.org/abs/2210.08390v1 | https://arxiv.org/pdf/2210.08390v1.pdf | SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation | We propose an extension to the MAPF formulation, called SocialMAPF, to account for private incentives of agents in constrained environments such as doorways, narrow hallways, and corridor intersections. SocialMAPF is able to, for instance, accurately reason about the urgent incentive of an agent rushing to the hospital... | ['Joydeep Biswas', 'Arya Anantula', 'Rahul Maligi', 'Rohan Chandra'] | 2022-10-15 | null | null | null | null | ['multi-agent-path-finding', 'social-navigation'] | ['playing-games', 'robots'] | [-1.80600762e-01 6.61436260e-01 -6.01507068e-01 -2.02242255e-01
-4.88907784e-01 -5.28841317e-01 2.81074703e-01 3.88096154e-01
-8.66345167e-01 1.35978794e+00 1.09891288e-01 -5.28668046e-01
-1.01051891e+00 -1.18300235e+00 -4.09517288e-01 -4.51751053e-01
-7.00769126e-01 1.01034284e+00 3.24696988e-01 -5.40553093... | [4.976403713226318, 1.7523614168167114] |
d7374ff7-336d-4259-bea3-83b667ebac42 | unsupervised-object-representation-learning | 2210.12918 | null | https://arxiv.org/abs/2210.12918v2 | https://arxiv.org/pdf/2210.12918v2.pdf | Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE | In many imaging modalities, objects of interest can occur in a variety of locations and poses (i.e. are subject to translations and rotations in 2d or 3d), but the location and pose of an object does not change its semantics (i.e. the object's essence). That is, the specific location and rotation of an airplane in sate... | ['Tristan Bepler', 'Alireza Nasiri'] | 2022-10-24 | null | null | null | null | ['learning-semantic-representations'] | ['methodology'] | [ 3.61156374e-01 9.51948464e-02 2.57702749e-02 -2.98280627e-01
-5.65039992e-01 -9.06114042e-01 9.54701900e-01 -5.89450300e-01
-4.31270972e-02 5.36779881e-01 1.83423162e-01 4.49164361e-02
-2.62787312e-01 -7.24940658e-01 -1.05182028e+00 -9.99339461e-01
1.15366779e-01 1.13142622e+00 -6.14817254e-02 5.63062914... | [8.836466789245605, -3.070441484451294] |
b24e13c3-39f1-4188-8c00-5abe1f138a95 | an-empirical-study-on-challenging-math | 2306.01337 | null | https://arxiv.org/abs/2306.01337v2 | https://arxiv.org/pdf/2306.01337v2.pdf | An Empirical Study on Challenging Math Problem Solving with GPT-4 | Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural language across numerous science and engineering fields. While several prior works have investigated solving elementary mathematics using LLMs, this... | ['Qingyun Wu', 'Chi Wang', 'Richard Peng', 'Yin Tat Lee', 'Yue Wang', 'Erkang Zhu', 'Hangyu Li', 'Shaokun Zhang', 'Feiran Jia', 'Yiran Wu'] | 2023-06-02 | null | null | null | null | ['elementary-mathematics'] | ['reasoning'] | [-7.76764005e-03 5.79750016e-02 -7.84022268e-03 -2.35604495e-01
-5.03947020e-01 -5.66987634e-01 6.49373770e-01 5.16530037e-01
-1.92022443e-01 7.26874471e-01 1.78546399e-01 -5.48135996e-01
-6.15626574e-01 -1.06662965e+00 -6.22832716e-01 -2.69865960e-01
-1.00968093e-01 6.66409552e-01 2.36432403e-01 -5.14878571... | [9.741873741149902, 7.381670951843262] |
5e4209e9-1383-4b9a-bc40-336cd2a57e72 | domainstudio-fine-tuning-diffusion-models-for | 2306.14153 | null | https://arxiv.org/abs/2306.14153v1 | https://arxiv.org/pdf/2306.14153v1.pdf | DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data | Denoising diffusion probabilistic models (DDPMs) have been proven capable of synthesizing high-quality images with remarkable diversity when trained on large amounts of data. Typical diffusion models and modern large-scale conditional generative models like text-to-image generative models are vulnerable to overfitting ... | ['Jian Yuan', 'Jiansheng Chen', 'Huimin Ma', 'Jingyuan Zhu'] | 2023-06-25 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 2.19837442e-01 9.86654758e-02 -3.31031792e-02 -2.40180343e-01
-1.08515775e+00 -2.00046510e-01 9.66217160e-01 -6.62297964e-01
1.28483642e-02 1.01841617e+00 3.57765257e-01 3.78485292e-01
-4.58528101e-02 -1.28833401e+00 -6.45792961e-01 -1.01171422e+00
4.26424563e-01 6.35011733e-01 3.98465902e-01 -2.55224437... | [11.491389274597168, -0.4149489998817444] |
8419c2e9-9392-455d-94b7-e984f9203764 | mean-oriented-riesz-features-for-micro | 2005.06198 | null | https://arxiv.org/abs/2005.06198v1 | https://arxiv.org/pdf/2005.06198v1.pdf | Mean Oriented Riesz Features for Micro Expression Classification | Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second. This kind of facial expressions usually occurs in high stake situations and is considered to reflect a human's real intent. There has been some interest in micro-expression analysis, however, a great majorit... | ['Anne-Claire Legrand', 'Rémi Emonet', 'Olivier Alata', 'Hubert Konik', 'Carlos Arango Duque'] | 2020-05-13 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.64248720e-01 -1.83817253e-01 -1.57496959e-01 -4.76594985e-01
-3.43824565e-01 -2.26414829e-01 7.22578347e-01 -3.25405717e-01
-3.58374953e-01 6.97418630e-01 1.54835150e-01 5.54978609e-01
-7.22901197e-03 -5.28188944e-01 -1.96533948e-01 -1.17577541e+00
-1.18681893e-01 -2.43103355e-01 -1.06442250e-01 -5.00151515... | [13.627718925476074, 1.7915830612182617] |
5b068d61-828b-4817-807c-6278f939f766 | full-angle-quaternions-for-robustly-matching | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Liwicki_Full-Angle_Quaternions_for_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Liwicki_Full-Angle_Quaternions_for_2014_CVPR_paper.pdf | Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations | In this paper we introduce a new distance for robustly matching vectors of 3D rotations. A special representation of 3D rotations, which we coin full-angle quaternion (FAQ), allows us to express this distance as Euclidean. We apply the distance to the problems of 3D shape recognition from point clouds and 2D object tra... | ['Minh-Tri Pham', 'Maja Pantic', 'Stefanos Zafeiriou', 'Bjorn Stenger', 'Stephan Liwicki'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['3d-shape-recognition'] | ['computer-vision'] | [-3.72957230e-01 -5.23589551e-01 -3.26685667e-01 -5.83750159e-02
-6.93083763e-01 -9.13329005e-01 6.74684405e-01 -6.64143190e-02
-4.31439281e-01 7.95211196e-02 6.19929172e-02 -1.96597695e-01
6.49031810e-03 -3.74832332e-01 -7.37273455e-01 -6.75428808e-01
-2.44791508e-01 2.88542181e-01 3.92184734e-01 -2.01859623... | [7.803235054016113, -2.2867112159729004] |
d7aa82c3-9f41-45fa-8551-0de0ec6301b5 | self-supervised-image-clustering-from | 2209.11927 | null | https://arxiv.org/abs/2209.11927v1 | https://arxiv.org/pdf/2209.11927v1.pdf | Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation | Incomplete Multi-View Clustering aims to enhance clustering performance by using data from multiple modalities. Despite the fact that several approaches for studying this issue have been proposed, the following drawbacks still persist: 1) It's difficult to learn latent representations that account for complementarity y... | ['Limin Liu', 'Dongjin Guo', 'Xuewen Yang', 'Zhiwei Xu', 'Jiatai Wang'] | 2022-09-24 | null | null | null | null | ['incomplete-multi-view-clustering', 'image-clustering'] | ['computer-vision', 'computer-vision'] | [ 7.13727698e-02 -9.19869989e-02 -1.74678341e-01 -2.67435670e-01
-1.02026916e+00 -5.89899242e-01 4.46601301e-01 -5.01890182e-01
-1.11684268e-02 8.55991423e-01 3.07755977e-01 3.78273934e-01
-5.54092675e-02 -7.00899124e-01 -6.82000399e-01 -1.07463408e+00
4.79706705e-01 6.51325643e-01 -3.31261992e-01 2.07443178... | [8.46117877960205, 4.520175457000732] |
01f265b0-cbda-4a61-a8c8-56454f81ea12 | visual-relationship-detection-using-scene | 2005.08045 | null | https://arxiv.org/abs/2005.08045v1 | https://arxiv.org/pdf/2005.08045v1.pdf | Visual Relationship Detection using Scene Graphs: A Survey | Understanding a scene by decoding the visual relationships depicted in an image has been a long studied problem. While the recent advances in deep learning and the usage of deep neural networks have achieved near human accuracy on many tasks, there still exists a pretty big gap between human and machine level performan... | ['Ayush Mangal', 'Vipul', 'Aniket Agarwal'] | 2020-05-16 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.50423801e-01 1.46229401e-01 -1.48895621e-01 -5.46600223e-01
-1.56296670e-01 -4.66381341e-01 7.32959628e-01 5.06238580e-01
2.15784013e-02 4.81543958e-01 7.61422664e-02 -4.68766421e-01
-1.82994246e-01 -8.47727478e-01 -4.98304933e-01 -4.25683558e-01
-1.07192323e-01 4.31426078e-01 3.31394732e-01 -2.77076155... | [10.287991523742676, 1.56173574924469] |
5113706a-2b61-4563-8888-7300d706dba1 | raw-nav-merge-seismic-data-to-subsurface | null | null | http://proceedings.neurips.cc/paper/2021/hash/498f2c21688f6451d9f5fd09d53edda7-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/498f2c21688f6451d9f5fd09d53edda7-Paper.pdf | Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler | Traditional seismic inversion (SI) maps the hundreds of terabytes of raw-field data to subsurface properties in gigabytes. This inversion process is expensive, requiring over a year of human and computational effort. Recently, data-driven approaches equipped with Deep learning (DL) are envisioned to improve SI efficie... | ['Anshumali Shrivastava', 'Antoine Vial-Aussavy', 'Anu Chandran', 'Menal Gupta', 'Zhaozhuo Xu', 'Aditya Desai'] | 2021-12-01 | null | https://openreview.net/forum?id=HLalhDvDwrQ | https://openreview.net/pdf?id=HLalhDvDwrQ | neurips-2021-12 | ['auxiliary-learning', 'seismic-inversion'] | ['methodology', 'miscellaneous'] | [ 2.34330148e-01 -1.27987072e-01 3.52062017e-01 -2.30442628e-01
-1.19026387e+00 -1.43264920e-01 5.10076821e-01 -9.31442901e-02
-6.58375263e-01 7.65523195e-01 2.15563238e-01 -6.95329487e-01
-1.99606240e-01 -1.20880115e+00 -1.09714973e+00 -9.28580463e-01
-4.05301362e-01 8.06216419e-01 6.13454878e-01 -6.40575528... | [6.857578754425049, 2.5348434448242188] |
1261a96a-5ced-46da-b9f3-2a4372ba0c47 | a-stronger-baseline-for-multilingual-word | 1811.00586 | null | https://arxiv.org/abs/1811.00586v2 | https://arxiv.org/pdf/1811.00586v2.pdf | Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable | Word embeddings induced from local context are prevalent in NLP. A simple and effective context-based multilingual embedding learner is Levy et al. (2017)'s S-ID (sentence ID) method. Another line of work induces high-performing multilingual embeddings from concepts (Dufter et al., 2018). In this paper, we propose Co+C... | ['Hinrich Schütze', 'Philipp Dufter', 'Mengjie Zhao'] | 2018-11-01 | null | null | null | null | ['multilingual-word-embeddings'] | ['methodology'] | [-2.94389844e-01 -1.94199905e-01 -4.16980058e-01 -1.94447517e-01
-9.88806486e-01 -7.57998943e-01 1.08269536e+00 8.63822579e-01
-1.13616478e+00 8.75063300e-01 5.77459157e-01 -3.61246198e-01
2.24768505e-01 -6.25940800e-01 -4.74275678e-01 -2.50820369e-01
-2.63837159e-01 6.99569166e-01 4.29046601e-02 -4.11479652... | [10.880169868469238, 9.70950984954834] |
bd4336bf-25f9-4517-a7f7-79cee9680da6 | weakly-supervised-real-time-image-cropping | null | null | https://dl.acm.org/doi/abs/10.1145/3394171.3413824?casa_token=k8h5Xio4LfQAAAAA:pYe2xwRtzJp1gymh-FHvg8oxiYBsHaPwW4dyz98tlJXg0grhocgODGRAZ7o0HCSZ6awgOHVYFo3o | https://dl.acm.org/doi/pdf/10.1145/3394171.3413824 | Weakly Supervised Real-time Image Cropping based on Aesthetic Distributions | Image cropping is an effective tool to edit and manipulate images to achieve better aesthetic quality. Most existing cropping approaches rely on the two-step paradigm where multiple candidate cropping areas are proposed initially and the optimal cropping window is determined based on some quality criteria for these can... | ['Xiaojie Wang', 'Xujun Peng', 'Jiahui Liu', 'Peng Lu'] | 2020-10-15 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 3.69446099e-01 -2.67300218e-01 -1.40416458e-01 -1.93382636e-01
-4.06827539e-01 -2.44388953e-01 1.83941782e-01 1.00518661e-02
-2.89435714e-01 5.37554085e-01 -3.72812897e-01 3.17995846e-02
-1.10311493e-01 -1.12751079e+00 -8.12570512e-01 -9.74689722e-01
3.68586928e-01 -1.21252581e-01 5.87706149e-01 -1.82668120... | [11.243192672729492, -1.0992883443832397] |
37d01ab9-4ae8-498a-a06d-6e53905034a3 | learning-spectro-temporal-representations-of | 2103.07125 | null | https://arxiv.org/abs/2103.07125v1 | https://arxiv.org/pdf/2103.07125v1.pdf | Learning spectro-temporal representations of complex sounds with parameterized neural networks | Deep Learning models have become potential candidates for auditory neuroscience research, thanks to their recent successes on a variety of auditory tasks. Yet, these models often lack interpretability to fully understand the exact computations that have been performed. Here, we proposed a parametrized neural network la... | ['Emmanuel Dupoux', 'Anne-Catherine Bachoud-Lévi', 'Julien Karadayi', 'Rachid Riad'] | 2021-03-12 | null | null | null | null | ['sound-classification'] | ['audio'] | [-1.89287543e-01 -4.55028377e-02 4.37834591e-01 -3.95699292e-01
-4.88097131e-01 -6.15448892e-01 6.17785394e-01 1.78288043e-01
-7.13867426e-01 3.65013063e-01 3.19007725e-01 -1.84383907e-03
-5.27213037e-01 -4.35987890e-01 -3.99164677e-01 -9.32671726e-01
-6.67204797e-01 5.38004451e-02 3.87539625e-01 -1.14664339... | [15.230658531188965, 5.5165815353393555] |
f3d7a60f-2e85-4f05-8b0f-29575d9f4a03 | semantic-segmentation-with-active-semi-1 | 2210.08403 | null | https://arxiv.org/abs/2210.08403v1 | https://arxiv.org/pdf/2210.08403v1.pdf | Semantic Segmentation with Active Semi-Supervised Representation Learning | Obtaining human per-pixel labels for semantic segmentation is incredibly laborious, often making labeled dataset construction prohibitively expensive. Here, we endeavor to overcome this problem with a novel algorithm that combines semi-supervised and active learning, resulting in the ability to train an effective seman... | ['Matthew Hoffman', 'Christopher Kanan', 'Aneesh Rangnekar'] | 2022-10-16 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.85352498e-01 5.89772105e-01 -4.03150946e-01 -6.75869703e-01
-1.30884123e+00 -8.12779844e-01 3.06247413e-01 2.06853077e-01
-8.10759068e-01 6.25124812e-01 -9.61255208e-02 -1.94609061e-01
1.87588677e-01 -8.87152433e-01 -6.36884272e-01 -7.46582150e-01
2.75537550e-01 7.79826462e-01 8.01644206e-01 1.06687158... | [9.45173168182373, 0.7139582633972168] |
b144279f-f0bb-4ec5-aa13-954cbd200817 | learning-to-diversify-for-product-question | 2207.02534 | null | https://arxiv.org/abs/2207.02534v1 | https://arxiv.org/pdf/2207.02534v1.pdf | Learning to Diversify for Product Question Generation | We address the product question generation task. For a given product description, our goal is to generate questions that reflect potential user information needs that are either missing or not well covered in the description. Moreover, we wish to cover diverse user information needs that may span a multitude of product... | ['Eliyahu Kiperwasser', 'Alexander Nus', 'Yotam Eshel', 'Uriel Singer', 'Haggai Roitman'] | 2022-07-06 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 2.64818043e-01 7.03307331e-01 5.50155081e-02 -3.81698519e-01
-1.20723569e+00 -7.86938310e-01 6.00579381e-01 1.52443424e-01
8.27522278e-02 6.86012745e-01 5.94607115e-01 -1.27084360e-01
-1.18911564e-01 -9.09101665e-01 -8.60933483e-01 -1.12586789e-01
5.83375514e-01 8.26017559e-01 1.24886893e-01 -7.22586870... | [11.624471664428711, 8.542997360229492] |
6a4b39d4-9993-4981-ae45-8021878110d9 | pushing-the-limits-of-unconstrained-face | 1804.10275 | null | http://arxiv.org/abs/1804.10275v3 | http://arxiv.org/pdf/1804.10275v3.pdf | Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results | Face detection has witnessed immense progress in the last few years, with new
milestones being surpassed every year. While many challenges such as large
variations in scale, pose, appearance are successfully addressed, there still
exist several issues which are not specifically captured by existing methods or
datasets.... | ['He Zhang', 'Hajime Nada', 'Vishal M. Patel', 'Vishwanath A. Sindagi'] | 2018-04-26 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 2.07388639e-01 -5.08605480e-01 2.54090220e-01 -5.60161054e-01
-4.73696381e-01 -6.92293704e-01 6.55107439e-01 -6.58172309e-01
-1.95256904e-01 6.76622212e-01 7.44887218e-02 2.61941940e-01
1.56440318e-01 -2.42364146e-02 -2.43005037e-01 -5.74846983e-01
-2.88825303e-01 6.06494769e-02 2.56919086e-01 -8.98902267... | [13.404772758483887, 0.7397366166114807] |
07e5302a-bfe0-4f70-8546-b0df427a9da6 | approximately-optimal-binning-for-the | 2007.12463 | null | https://arxiv.org/abs/2007.12463v1 | https://arxiv.org/pdf/2007.12463v1.pdf | Approximately Optimal Binning for the Piecewise Constant Approximation of the Normalized Unexplained Variance (nUV) Dissimilarity Measure | The recently introduced Matching by Tone Mapping (MTM) dissimilarity measure enables template matching under smooth non-linear distortions and also has a well-established mathematical background. MTM operates by binning the template, but the ideal binning for a particular problem is an open question. By pointing out an... | ['György Kovács', 'Attila Fazekas'] | 2020-07-24 | null | null | null | null | ['template-matching', 'tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 6.58142447e-01 -1.43125817e-01 -3.01796436e-01 -3.55630964e-01
-7.37999380e-01 -3.16049248e-01 7.14474380e-01 7.92829618e-02
-3.91247630e-01 7.29062974e-01 1.97009757e-01 -2.88802713e-01
-7.52757370e-01 -6.76816106e-01 -3.39949697e-01 -7.11816847e-01
-3.33775520e-01 2.42942557e-01 3.03382844e-01 -1.33732513... | [11.746587753295898, -1.9286547899246216] |
2e9174f6-5402-4fd2-9da6-0fccac184f2c | stacking-of-hyperparameter-tuned-models-for | 2306.10077 | null | https://arxiv.org/abs/2306.10077v2 | https://arxiv.org/pdf/2306.10077v2.pdf | Stacking of Hyperparameter Tuned Models for Tagging Coding Problems | Coding problems are problems that require a solution in the form of a computer program. Coding problems are popular among students and professionals as it enhances their skills and career opportunities. An AI system that would help those who practice coding problems would be highly useful and there is a huge potential ... | ['P. Shunmugapriya', 'S. Lakshmana Pandian', 'Sathya Krishnan TS'] | 2023-06-16 | null | null | null | null | ['coding-problem-tagging'] | ['natural-language-processing'] | [-2.75208503e-01 1.52209714e-01 -2.69591391e-01 -4.90385711e-01
-5.16166389e-01 -4.00717288e-01 3.16681355e-01 8.74299258e-02
-6.91450313e-02 5.13802409e-01 1.09476246e-01 -7.15749979e-01
-1.32257670e-01 -5.25305033e-01 -3.64390224e-01 -1.97071090e-01
1.13571748e-01 2.85688072e-01 8.43867734e-02 -4.88151491... | [7.819547653198242, 7.703769207000732] |
a821cc37-2f18-40e7-a2b6-245f210ef14f | neural-modal-odes-integrating-physics-based | 2207.07883 | null | https://arxiv.org/abs/2207.07883v2 | https://arxiv.org/pdf/2207.07883v2.pdf | Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures | The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems (e.g., civil or mechanical structures), which are typically high-dimensional in nature. In the scope of... | ['Eleni Chatzi', 'Limin Sun', 'Kiran Bacsa', 'Xudong Jian', 'Wei Liu', 'Zhilu Lai'] | 2022-07-16 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.01812832e-01 5.41693211e-01 2.62823373e-01 1.22089334e-01
-2.81691104e-01 -4.15404826e-01 2.85484761e-01 -3.71726096e-01
1.17346108e-01 6.18238986e-01 1.97130844e-01 -1.67378321e-01
-8.30957651e-01 -7.78715014e-01 -1.11739075e+00 -1.14232481e+00
-2.70086914e-01 4.36749846e-01 -4.79053885e-01 -4.60574389... | [6.531846046447754, 3.506882429122925] |
7e6a982d-ab3c-48c2-967e-c34ef0d265d8 | on-duality-of-multiple-target-tracking-and | 1610.04542 | null | http://arxiv.org/abs/1610.04542v1 | http://arxiv.org/pdf/1610.04542v1.pdf | On Duality Of Multiple Target Tracking and Segmentation | Traditionally, object tracking and segmentation are treated as two separate
problems and solved independently. However, in this paper, we argue that
tracking and segmentation are actually closely related and solving one should
help the other. On one hand, the object track, which is a set of bounding boxes
with one boun... | ['Mubarak Shah', 'Yicong Tian'] | 2016-10-14 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 0.13550648 -0.3242838 -0.48017615 -0.16084827 -0.70013624 -0.7055364
0.10148569 0.02421554 -0.29295355 0.69014186 -0.40473014 0.09388029
0.00939117 -0.53469694 -0.73263896 -0.9406489 0.21553609 0.6236501
1.0034462 0.2983701 0.04887026 0.5127754 -1.1872845 -0.07474953
0.82458866 1.035568 0.48... | [6.442507266998291, -1.9932774305343628] |
bfbe3c1a-fe36-48e0-b7a4-08e189fc641a | more-than-you-ve-asked-for-a-comprehensive | 2302.12173 | null | https://arxiv.org/abs/2302.12173v2 | https://arxiv.org/pdf/2302.12173v2.pdf | Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection | Large Language Models (LLMs) are increasingly being integrated into various applications. The functionalities of recent LLMs can be flexibly modulated via natural language prompts. This renders them susceptible to targeted adversarial prompting, e.g., Prompt Injection (PI) attacks enable attackers to override original ... | ['Mario Fritz', 'Thorsten Holz', 'Christoph Endres', 'Shailesh Mishra', 'Sahar Abdelnabi', 'Kai Greshake'] | 2023-02-23 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 2.84684241e-01 -8.92792344e-02 -3.43679994e-01 1.38159350e-01
-6.57128990e-01 -1.52698588e+00 7.93174982e-01 -8.81785378e-02
-2.75633633e-01 -5.41324802e-02 2.15421602e-01 -1.37178934e+00
2.64526635e-01 -6.53349876e-01 -7.04315722e-01 -2.06561700e-01
-3.36311191e-01 -1.81072980e-01 5.31857193e-01 -2.48331606... | [6.131111145019531, 7.824937343597412] |
6969c115-59f4-4ee7-9148-0c0e8eec4ae5 | online-detection-of-failures-generated-by | 2101.07100 | null | https://arxiv.org/abs/2101.07100v1 | https://arxiv.org/pdf/2101.07100v1.pdf | Online detection of failures generated by storage simulator | Modern large-scale data-farms consist of hundreds of thousands of storage devices that span distributed infrastructure. Devices used in modern data centers (such as controllers, links, SSD- and HDD-disks) can fail due to hardware as well as software problems. Such failures or anomalies can be detected by monitoring the... | ['Andrey Ustyuzhanin', 'Maksim Karpov', 'Leonid Gremyachikh', 'Vladislav Belavin', 'Andrey Sapronov', 'Mikhail Hushchyn', 'Kenenbek Arzymatov'] | 2021-01-18 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-5.22125661e-01 -5.80478251e-01 -1.08664244e-01 3.87361832e-02
-1.08863413e-01 -4.30733860e-01 3.98767382e-01 4.12990957e-01
2.14234158e-01 6.63204074e-01 -7.52528965e-01 -7.85726666e-01
-2.69257396e-01 -7.60471046e-01 -7.71715403e-01 -7.18801498e-01
-7.93663681e-01 9.61144269e-01 8.97602201e-01 1.22422175... | [6.749003887176514, 2.7101778984069824] |
42feebbb-b4bd-422c-bf61-7b776f9b6926 | self-vs-self-supervised-encoding-learning-for | 2303.15993 | null | https://arxiv.org/abs/2303.15993v1 | https://arxiv.org/pdf/2303.15993v1.pdf | SELF-VS: Self-supervised Encoding Learning For Video Summarization | Despite its wide range of applications, video summarization is still held back by the scarcity of extensive datasets, largely due to the labor-intensive and costly nature of frame-level annotations. As a result, existing video summarization methods are prone to overfitting. To mitigate this challenge, we propose a nove... | ['Mahdi Eftekhari', 'Mehrdad Hosseinzadeh', 'Kave Bahraman', 'Hojjat Mokhtarabadi'] | 2023-03-28 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 5.02734542e-01 -1.80254206e-02 -4.46000427e-01 -5.64164579e-01
-9.75666285e-01 -3.60331327e-01 4.34306949e-01 1.29921600e-01
-3.24485838e-01 8.91570091e-01 7.16306925e-01 4.23027314e-02
9.04831886e-02 -6.38959408e-01 -8.94143581e-01 -3.04630667e-01
-5.62418513e-02 5.90978637e-02 2.88565189e-01 -1.29758520... | [10.245601654052734, 0.4766032099723816] |
94f9c6ce-9ca7-4d1f-b411-4b934dfda801 | joining-the-conversation-towards-language | 2305.12235 | null | https://arxiv.org/abs/2305.12235v1 | https://arxiv.org/pdf/2305.12235v1.pdf | Joining the Conversation: Towards Language Acquisition for Ad Hoc Team Play | In this paper, we propose and consider the problem of cooperative language acquisition as a particular form of the ad hoc team play problem. We then present a probabilistic model for inferring a speaker's intentions and a listener's semantics from observing communications between a team of language-users. This model bu... | ['Peter McBurney', 'Dylan Cope'] | 2023-05-20 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 3.87030959e-01 8.66821945e-01 1.16763085e-01 -4.82420474e-01
-7.10872948e-01 -6.08647585e-01 7.43551910e-01 1.64472520e-01
-4.42417055e-01 4.82644260e-01 4.13266957e-01 -3.31967115e-01
-3.33413184e-01 -4.45163965e-01 -5.18832207e-01 -7.29677260e-01
-3.72779965e-01 6.09146953e-01 2.51319349e-01 -5.23052096... | [12.577692031860352, 8.00041675567627] |
b87e37f2-bb8f-4508-9e86-130cf15b322b | topoal-an-adversarial-learning-approach-for | 2007.09084 | null | https://arxiv.org/abs/2007.09084v1 | https://arxiv.org/pdf/2007.09084v1.pdf | TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation | Most state-of-the-art approaches to road extraction from aerial images rely on a CNN trained to label road pixels as foreground and remainder of the image as background. The CNN is usually trained by minimizing pixel-wise losses, which is less than ideal to produce binary masks that preserve the road network's global c... | ['Pascal Fua', 'Mateusz Kozinski', 'Leonardo Citraro', 'Subeesh Vasu'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5687_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720222.pdf | eccv-2020-8 | ['road-segementation'] | ['computer-vision'] | [ 6.04171693e-01 5.82734883e-01 2.01816529e-01 -1.93707824e-01
-6.03926003e-01 -8.89621794e-01 5.64691901e-01 -2.74178535e-01
-2.94295430e-01 6.92911983e-01 -2.78921485e-01 -4.38677967e-01
2.86538988e-01 -1.25381804e+00 -9.97235060e-01 -7.91600108e-01
-6.93273991e-02 5.59601426e-01 9.11690831e-01 -2.52201229... | [9.943495750427246, 0.18685507774353027] |
961f1663-7159-4cd7-a557-72f745a108ed | improving-low-resource-rrg-parsing-with-cross | null | null | https://aclanthology.org/2022.coling-1.384 | https://aclanthology.org/2022.coling-1.384.pdf | Improving Low-resource RRG Parsing with Cross-lingual Self-training | This paper considers the task of parsing low-resource languages in a scenario where parallel English data and also a limited seed of annotated sentences in the target language are available, as for example in bootstrapping parallel treebanks. We focus on constituency parsing using Role and Reference Grammar (RRG), a th... | ['Simon Petitjean', 'Tatiana Bladier', 'Kilu von Prince', 'Jakub Waszczuk', 'Laura Kallmeyer', 'Kilian Evang'] | null | null | null | null | coling-2022-10 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.46526599e-02 4.12955880e-01 6.20646775e-02 -3.38777691e-01
-9.07036006e-01 -8.56312990e-01 2.87008613e-01 2.45922640e-01
-9.64474261e-01 9.14860666e-01 4.70085084e-01 -6.64104998e-01
1.21861257e-01 -8.81936729e-01 -5.72954297e-01 -5.03774881e-01
3.70291173e-02 7.19291031e-01 3.11691105e-01 -4.80781674... | [10.447131156921387, 9.96452808380127] |
136d230b-4c0e-4b0e-820e-6c1f68f557d8 | risk-stratification-of-lung-nodules-using-3d | 1704.08797 | null | http://arxiv.org/abs/1704.08797v1 | http://arxiv.org/pdf/1704.08797v1.pdf | Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning | Risk stratification of lung nodules is a task of primary importance in lung
cancer diagnosis. Any improvement in robust and accurate nodule
characterization can assist in identifying cancer stage, prognosis, and
improving treatment planning. In this study, we propose a 3D Convolutional
Neural Network (CNN) based nodule... | ['Qi Song', 'Sarfaraz Hussein', 'Ulas Bagci', 'Kunlin Cao'] | 2017-04-28 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 3.16178471e-01 2.55651355e-01 -3.89903367e-01 -3.78354579e-01
-1.08106947e+00 -2.27506250e-01 3.70104581e-01 3.12215894e-01
-3.97721976e-01 5.46521723e-01 3.30876410e-01 -4.47687298e-01
-3.84375364e-01 -8.04083824e-01 -4.71324861e-01 -7.01698244e-01
8.71689022e-02 8.38636100e-01 4.34442550e-01 1.29230559... | [15.387168884277344, -2.1347172260284424] |
aa92c38c-91ad-4097-9598-c6b22474d69b | neural-network-compression-by-joint-sparsity | 2210.07451 | null | https://arxiv.org/abs/2210.07451v1 | https://arxiv.org/pdf/2210.07451v1.pdf | Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction | Compression of convolutional neural network models has recently been dominated by pruning approaches. A class of previous works focuses solely on pruning the unimportant filters to achieve network compression. Another important direction is the design of sparsity-inducing constraints which has also been explored in iso... | ['Erik Meijering', 'Antonio Robles-Kelly', 'Syed S. Naqvi', 'Tariq M. Khan'] | 2022-10-14 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 5.03090084e-01 1.87335253e-01 -5.52691817e-02 -5.67332983e-01
2.09882751e-01 -2.21831813e-01 1.98973611e-01 1.01489089e-01
-9.08563197e-01 6.16308689e-01 -6.16867580e-02 -3.91679645e-01
-2.22950056e-01 -8.44999015e-01 -6.95046902e-01 -3.78372192e-01
-1.26664117e-01 6.96629956e-02 5.88629246e-01 1.30806863... | [8.599573135375977, 3.1038525104522705] |
76844ac4-df6f-4b3e-9d8b-279dc29ebfbf | modeling-dynamic-environments-with-scene | 2305.17537 | null | https://arxiv.org/abs/2305.17537v4 | https://arxiv.org/pdf/2305.17537v4.pdf | Modeling Dynamic Environments with Scene Graph Memory | Embodied AI agents that search for objects in large environments such as households often need to make efficient decisions by predicting object locations based on partial information. We pose this as a new type of link prediction problem: link prediction on partially observable dynamic graphs. Our graph is a representa... | ['Li Fei-Fei', 'Roberto Martín-Martín', 'Silvio Savarese', 'Jiajun Wu', 'Ruohan Zhang', 'Emily Jin', 'Chengshu Li', 'Tanmay Agarwal', 'Michael Lingelbach', 'Andrey Kurenkov'] | 2023-05-27 | null | null | null | null | ['link-prediction'] | ['graphs'] | [ 4.57137916e-03 2.51102328e-01 -1.04179382e-01 -2.74542183e-01
7.87028596e-02 -5.45819581e-01 5.58384478e-01 4.63357687e-01
-2.67360151e-01 6.25552297e-01 2.95970887e-01 7.56533816e-02
-2.47823179e-01 -1.21321070e+00 -1.06355727e+00 -3.21354449e-01
-8.60190153e-01 1.17826724e+00 6.41308308e-01 -2.73106903... | [4.841824531555176, 0.5528905987739563] |
4c163752-36a7-4c89-8809-f56656d90cf3 | dcase-2021-task-3-spectrotemporally-aligned | 2106.15190 | null | https://arxiv.org/abs/2106.15190v1 | https://arxiv.org/pdf/2106.15190v1.pdf | DCASE 2021 Task 3: Spectrotemporally-aligned Features for Polyphonic Sound Event Localization and Detection | Sound event localization and detection consists of two subtasks which are sound event detection and direction-of-arrival estimation. While sound event detection mainly relies on time-frequency patterns to distinguish different sound classes, direction-of-arrival estimation uses magnitude or phase differences between mi... | ['Woon Seng Gan', 'Douglas L. Jones', 'Ngoc Khanh Nguyen', 'Karn Watcharasupat', 'Thi Ngoc Tho Nguyen'] | 2021-06-29 | null | null | null | null | ['direction-of-arrival-estimation', 'sound-event-localization-and-detection'] | ['audio', 'audio'] | [ 1.19503532e-02 -8.30161095e-01 6.24643028e-01 -2.31730133e-01
-1.45311666e+00 -7.34983146e-01 2.56504953e-01 3.57197016e-01
-4.19741362e-01 2.65680432e-01 5.30884385e-01 -2.66119093e-01
-2.59024620e-01 -4.43810940e-01 -5.47849655e-01 -7.20629930e-01
-5.05986989e-01 -4.90411907e-01 4.46719944e-01 3.34985495... | [15.156414985656738, 5.27189826965332] |
7d5b1d9e-1ca2-42f0-84ce-54fb703dafda | resolving-language-and-vision-ambiguities | 1604.02125 | null | http://arxiv.org/abs/1604.02125v4 | http://arxiv.org/pdf/1604.02125v4.pdf | Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes | We present an approach to simultaneously perform semantic segmentation and
prepositional phrase attachment resolution for captioned images. Some
ambiguities in language cannot be resolved without simultaneously reasoning
about an associated image. If we consider the sentence "I shot an elephant in
my pajamas", looking ... | ['Kevin Kochersberger', 'Aishwarya Agrawal', 'Yash Goyal', 'Stanislaw Antol', 'Dhruv Batra', 'Ankit Laddha', 'Gordon Christie'] | 2016-04-07 | null | null | null | emnlp-2016-11 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 3.45667809e-01 6.11220419e-01 -5.64938448e-02 -5.10778248e-01
-1.22617054e+00 -7.99292028e-01 6.08595967e-01 -3.39352409e-04
-4.95020896e-01 5.62604487e-01 2.87132025e-01 -2.68526882e-01
1.69709086e-01 -5.30302465e-01 -8.83426368e-01 -2.90904999e-01
4.39422727e-01 9.25611198e-01 4.61390853e-01 -2.20971286... | [10.63011360168457, 1.518006682395935] |
fb60389d-faa8-46c6-a191-298ada5e1f54 | lightweight-uncertainty-aware-conformalized | 2303.02207 | null | https://arxiv.org/abs/2303.02207v1 | https://arxiv.org/pdf/2303.02207v1.pdf | Lightweight, Uncertainty-Aware Conformalized Visual Odometry | Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics, and recent progress in the field has produced highly accurate point predictions in complex environments. However, emerging autonomous edge robotics devices like insect-scale drones and surgical robots lack a computationally efficien... | ['Amit Ranjan Trivedi', 'Theja Tulabandhula', 'Danilo Erricolo', 'Alex C. Stutts'] | 2023-03-03 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-1.35662019e-01 4.08080548e-01 -3.06749314e-01 -3.52041185e-01
-1.14347196e+00 -3.16362321e-01 3.50450575e-01 7.66492262e-02
-5.62768698e-01 1.09739661e+00 6.59498498e-02 -9.57732722e-02
-5.09350419e-01 -5.60927153e-01 -1.10846090e+00 -7.09860146e-01
-2.68130660e-01 7.17879653e-01 3.94201986e-02 1.68774307... | [7.535497665405273, -1.4412871599197388] |
8a01a481-0472-45e1-8ee3-c361e8a6292d | imagined-speech-classification-using-eeg | null | null | https://www.researchgate.net/publication/309967859_Imagined_Speech_Classification_using_EEG | https://www.researchgate.net/publication/309967859_Imagined_Speech_Classification_using_EEG | Imagined speech classification using EEG | The objective of this work is to assess the possibility of using (Electroencephalogram) EEG for communication between different subjects. Here EEG signals are recorded from 13 subjects by inducing the subjects to imagine the English vowels ‘a’, ‘e’, ‘i’, ‘o’ and ‘u’ through visual stimulus. These recorded signals are t... | ['Shenbaga Devi. S.', 'Madan Raj. M.', 'Rajkumar R.', 'Kamalakkannan Ravi'] | 2014-12-01 | null | null | null | advances-in-biomedical-science-and | ['eeg', 'classification-1', 'eeg'] | ['methodology', 'methodology', 'time-series'] | [ 3.99705589e-01 -2.70612448e-01 5.74952960e-01 -4.12674725e-01
-2.90047657e-02 -6.44356370e-01 3.97169113e-01 6.40848577e-02
-4.51525360e-01 1.43385684e+00 -3.13889161e-02 -7.36224204e-02
-3.35480869e-01 -1.78593531e-01 -2.32211724e-01 -5.99088550e-01
-4.68393713e-01 -1.89502105e-01 -2.77314931e-01 5.59528396... | [13.27430248260498, 3.3155486583709717] |
b33186ab-25c1-4349-9fbb-589460eb81df | samplernn-an-unconditional-end-to-end-neural | 1612.07837 | null | http://arxiv.org/abs/1612.07837v2 | http://arxiv.org/pdf/1612.07837v2.pdf | SampleRNN: An Unconditional End-to-End Neural Audio Generation Model | In this paper we propose a novel model for unconditional audio generation
based on generating one audio sample at a time. We show that our model, which
profits from combining memory-less modules, namely autoregressive multilayer
perceptrons, and stateful recurrent neural networks in a hierarchical structure
is able to ... | ['Aaron Courville', 'Rithesh Kumar', 'Jose Sotelo', 'Ishaan Gulrajani', 'Soroush Mehri', 'Shubham Jain', 'Yoshua Bengio', 'Kundan Kumar'] | 2016-12-22 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.55818784e-01 1.87561139e-01 1.78711027e-01 -3.59546125e-01
-8.27646375e-01 -2.59084225e-01 7.01324403e-01 -2.88867146e-01
4.43206728e-02 1.04853725e+00 7.44499147e-01 -7.37694651e-02
-2.32389793e-01 -7.10101902e-01 -7.47523844e-01 -5.33743024e-01
-4.07110006e-01 1.56445783e-02 3.36149424e-01 -3.63737315... | [15.618600845336914, 5.748224258422852] |
8f3c40d0-29ae-4c89-a9e0-8c669c62fd75 | algorithmic-optimisations-for-iterative | 1304.7211 | null | http://arxiv.org/abs/1304.7211v1 | http://arxiv.org/pdf/1304.7211v1.pdf | Algorithmic Optimisations for Iterative Deconvolution Methods | We investigate possibilities to speed up iterative algorithms for non-blind
image deconvolution. We focus on algorithms in which convolution with the
point-spread function to be deconvolved is used in each iteration, and aim at
accelerating these convolution operations as they are typically the most
expensive part of t... | ['Martin Erler', 'Martin Welk'] | 2013-04-26 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 2.19637722e-01 -1.88564450e-01 5.91594875e-01 -1.10108331e-02
-2.36520439e-01 -5.87210357e-01 6.12489581e-01 -1.01856947e-01
-9.78965402e-01 9.53559399e-01 2.06936151e-03 -3.78078938e-01
-3.54320824e-01 -5.60829520e-01 -3.55567694e-01 -8.50662231e-01
-3.52433980e-01 3.65626872e-01 4.92214024e-01 -1.17941007... | [11.64954662322998, -2.601712703704834] |
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