Split (2)

train · 44.6k rows

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14b2435a-74af-478e-8018-ef4ff6334acb	face-recognition-under-varying-blur	1902.10885	null	http://arxiv.org/abs/1902.10885v1	http://arxiv.org/pdf/1902.10885v1.pdf	Face Recognition Under Varying Blur, Illumination and Expression in an Unconstrained Environment	Face recognition system is one of the esteemed research areas in pattern recognition and computer vision as long as its major challenges. A few challenges in recognizing faces are blur, illumination, and varied expressions. Blur is natural while taking photographs using cameras, mobile phones, etc. Blur can be uniform and non-uniform. Usually non-uniform blur happens in images taken using handheld image devices. Distinguishing or handling a blurred image in a face recognition system is generally tough. Under varying lighting conditions, it is challenging to identify the person correctly. Diversified facial expressions such as happiness, sad, surprise, fear, anger changes or deforms the faces from normal images. Identifying faces with facial expressions is also a challenging task, due to the deformation caused by the facial expressions. To solve these issues, a pre-processing step was carried out after which Blur and Illumination-Robust Face recognition (BIRFR) algorithm was performed. The test image and training images with facial expression are transformed to neutral face using Facial expression removal (FER) peration. Every training image is transformed based on the optimal Transformation Spread Function (TSF), and illumination coefficients. Local Binary Pattern (LBP) features extracted from test image and transformed training image is used for classification.	['Anubha Pearline. S', 'Hemalatha. M']	2019-02-28	null	null	null	null	['robust-face-recognition']	['computer-vision']	[ 3.54108095e-01 -6.38659120e-01 1.93154648e-01 -6.08575642e-01 2.50976324e-01 -6.31944358e-01 2.11138546e-01 -1.08448219e+00 -1.00468457e-01 1.04418492e+00 1.04157135e-01 3.00468296e-01 -1.41798705e-01 -2.13226870e-01 -3.19785893e-01 -1.00437915e+00 2.73652643e-01 -1.64942577e-01 -1.23520523e-01 7.32354969e-02 3.33116084e-01 1.07674313e+00 -1.81794679e+00 2.58393914e-01 6.79823160e-01 1.13948989e+00 8.01082626e-02 6.60297990e-01 -1.18675113e-01 8.50279152e-01 -9.46645379e-01 -4.24383968e-01 1.84285730e-01 -4.60157812e-01 -7.06157625e-01 6.94848895e-01 4.37462330e-01 -4.40800965e-01 -9.62154344e-02 1.55961728e+00 3.49485427e-01 1.53305903e-01 7.01370835e-01 -1.31229699e+00 -1.23934770e+00 -4.09646899e-01 -9.16789949e-01 3.92075151e-01 4.17539656e-01 1.24859527e-01 -3.37501645e-01 -1.23869681e+00 2.59005994e-01 1.64046705e+00 4.46178436e-01 8.56416523e-01 -1.00496757e+00 -6.90560400e-01 -3.45336765e-01 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8e4e3037-1e79-46d9-a723-85ff1a826dec	community-detection-in-complex-networks-via	2303.12212	null	https://arxiv.org/abs/2303.12212v2	https://arxiv.org/pdf/2303.12212v2.pdf	Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering	Community detection is a critical challenge in analysing real graphs, including social, transportation, citation, cybersecurity, and many other networks. This article proposes three new, general, hierarchical frameworks to deal with this task. The introduced approach supports various linkage-based clustering algorithms, vertex proximity matrices, and graph representation learning models. We compare over a hundred module combinations on the Stochastic Block Model graphs and real-life datasets. We observe that our best pipelines (Wasserman-Faust and the mutual information-based PPMI proximity, as well as the deep learning-based DNGR representations) perform competitively to the state-of-the-art Leiden and Louvain algorithms. At the same time, unlike the latter, they remain hierarchical. Thus, they output a series of nested partitions of all possible cardinalities which are compatible with each other. This feature is crucial when the number of correct partitions is unknown in advance.	['Marek Gagolewski', 'Grzegorz Siudem', 'Łukasz Brzozowski']	2023-03-21	null	null	null	null	['stochastic-block-model', 'community-detection']	['graphs', 'graphs']	[-2.17120126e-01 3.28874076e-03 -1.17748298e-01 1.30408019e-01 -2.04134926e-01 -9.52664554e-01 9.49087083e-01 7.20084310e-01 -2.01936718e-02 7.91388750e-01 5.24860732e-02 -6.63230002e-01 -9.23906267e-01 -1.26435268e+00 -4.67357755e-01 -6.16944551e-01 -7.67789662e-01 1.11704898e+00 5.36563277e-01 -1.47455424e-01 2.45820269e-01 5.84868431e-01 -1.30579805e+00 2.73082286e-01 7.92189419e-01 5.04090667e-01 -1.86621681e-01 7.49747574e-01 -3.17480564e-01 8.49403977e-01 -1.75134882e-01 -5.97704291e-01 -1.38425855e-02 5.11957444e-02 -7.78024614e-01 -1.61356151e-01 1.98977858e-01 1.81833386e-01 -7.59514034e-01 9.28769648e-01 3.16541523e-01 -2.19280899e-01 1.06191385e+00 -1.63709855e+00 -3.86330247e-01 1.06424701e+00 -9.35624897e-01 2.04892948e-01 4.74923790e-01 -3.85489315e-02 1.41451037e+00 -5.57173967e-01 9.38086808e-01 1.36219883e+00 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27d5253a-9833-46eb-8934-17a611ff361f	multitalent-a-multi-dataset-approach-to	2303.14444	null	https://arxiv.org/abs/2303.14444v1	https://arxiv.org/pdf/2303.14444v1.pdf	MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation	The medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmentation. Current practices continue to limit model training and supervised pre-training to one or a few similar datasets, neglecting the synergistic potential of other available annotated data. We propose MultiTalent, a method that leverages multiple CT datasets with diverse and conflicting class definitions to train a single model for a comprehensive structure segmentation. Our results demonstrate improved segmentation performance compared to previous related approaches, systematically, also compared to single dataset training using state-of-the-art methods, especially for lesion segmentation and other challenging structures. We show that MultiTalent also represents a powerful foundation model that offers a superior pre-training for various segmentation tasks compared to commonly used supervised or unsupervised pre-training baselines. Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance. The code and model weights will be published here: [tba]	['Klaus H. Maier-Hein', 'Michael Baumgartner', 'Maximilian Zenk', 'Tassilo Wald', 'Fabian Isensee', 'Constantin Ulrich']	2023-03-25	null	null	null	null	['lesion-segmentation', 'unsupervised-pre-training']	['medical', 'methodology']	[ 5.5809599e-01 4.6428034e-01 -5.7189369e-01 -5.0673866e-01 -1.4483876e+00 -6.7713988e-01 2.4730283e-01 3.3269146e-01 -3.5278809e-01 4.9538502e-01 3.1361461e-01 -5.3176516e-01 -7.8179941e-02 -4.1567308e-01 -3.2168040e-01 -7.6246601e-01 -7.4377949e-03 8.8888156e-01 5.1184797e-01 1.4231068e-01 -1.9395144e-01 5.0556725e-01 -8.0281335e-01 5.5929863e-01 1.0383437e+00 8.4701192e-01 3.4519580e-01 5.8511829e-01 -1.3375404e-01 7.4331021e-01 -7.3641159e-02 -3.7299040e-01 2.4322155e-01 -2.8501639e-01 -1.2825416e+00 1.8658228e-01 5.6555301e-01 -3.3482820e-01 -9.8214783e-02 7.4402481e-01 7.9056501e-01 -2.9844403e-01 7.5483578e-01 -6.9172448e-01 -2.2730814e-01 8.4954453e-01 -5.5625749e-01 5.9117788e-01 -1.4094472e-01 4.1472656e-01 7.3741800e-01 -4.6506774e-01 8.0907625e-01 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c3b6d485-c01a-44a6-bf10-1b996c9f83a1	extractive-email-thread-summarization-can-we	null	null	https://aclanthology.org/W12-1513	https://aclanthology.org/W12-1513.pdf	Extractive email thread summarization: Can we do better than He Said She Said?	null	['Pablo Duboue']	2012-05-01	null	null	null	ws-2012-5	['email-thread-summarization']	['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 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 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1342bae8-6670-42ac-afcb-5f35ce8d5656	jbnu-cclab-at-semeval-2022-task-12-machine	null	null	https://aclanthology.org/2022.semeval-1.231	https://aclanthology.org/2022.semeval-1.231.pdf	JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their Descriptions	This paper describes our system in the SemEval-2022 Task 12: ‘linking mathematical symbols to their descriptions’, achieving first on the leaderboard for all the subtasks comprising named entity extraction (NER) and relation extraction (RE). Our system is a two-stage pipeline model based on SciBERT that detects symbols, descriptions, and their relationships in scientific documents. The system consists of 1) machine reading comprehension(MRC)-based NER model, where each entity type is represented as a question and its entity mention span is extracted as an answer using an MRC model, and 2) span pair classification for RE, where two entity mentions and their type markers are encoded into span representations that are then fed to a Softmax classifier. In addition, we deploy a rule-based symbol tokenizer to improve the detection of the exact boundary of symbol entities. Regularization and ensemble methods are further explored to improve the RE model.	['Seung-Hoon Na', 'Sung-Min Lee']	null	null	null	null	semeval-naacl-2022-7	['joint-entity-and-relation-extraction', 'machine-reading-comprehension']	['natural-language-processing', 'natural-language-processing']	[ 2.93335855e-01 5.35712779e-01 -2.10137174e-01 -4.59716707e-01 -8.05849016e-01 -6.34578407e-01 5.62593699e-01 8.01050603e-01 -5.24892926e-01 7.15947688e-01 1.81774080e-01 -7.44071364e-01 4.87508327e-02 -8.46360803e-01 -9.56240952e-01 5.88191636e-02 -1.61737368e-01 3.24071139e-01 1.67024717e-01 1.22218635e-02 5.40199578e-01 2.64354229e-01 -1.17732751e+00 4.37543362e-01 1.32254612e+00 1.09376872e+00 1.00854240e-01 7.45614350e-01 -7.65841305e-01 1.34018159e+00 -8.86795878e-01 -4.50055331e-01 -3.18922341e-01 -3.82640846e-02 -1.24319994e+00 -6.12240195e-01 5.14586091e-01 2.65008628e-01 -1.93311036e-01 8.31866980e-01 2.86050975e-01 2.01283470e-01 7.67233729e-01 -8.38278949e-01 -9.07826185e-01 1.13187683e+00 -3.10154170e-01 9.88641977e-02 5.54004788e-01 -1.87088430e-01 1.08953118e+00 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28cb5982-74e7-4d5e-a3ea-19dcf1636582	modeling-tag-prediction-based-on-question	2307.01420	null	https://arxiv.org/abs/2307.01420v1	https://arxiv.org/pdf/2307.01420v1.pdf	Modeling Tag Prediction based on Question Tagging Behavior Analysis of CommunityQA Platform Users	In community question-answering platforms, tags play essential roles in effective information organization and retrieval, better question routing, faster response to questions, and assessment of topic popularity. Hence, automatic assistance for predicting and suggesting tags for posts is of high utility to users of such platforms. To develop better tag prediction across diverse communities and domains, we performed a thorough analysis of users' tagging behavior in 17 StackExchange communities. We found various common inherent properties of this behavior in those diverse domains. We used the findings to develop a flexible neural tag prediction architecture, which predicts both popular tags and more granular tags for each question. Our extensive experiments and obtained performance show the effectiveness of our model	['Silviu Cucerzan', 'Nirupama Chandrasekaran', 'Michael Gamon', 'Kuntal Kumar Pal']	2023-07-04	null	null	null	null	['retrieval', 'community-question-answering', 'question-answering', 'community-question-answering']	['methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing']	[-4.96060193e-01 3.96264046e-02 -4.54850197e-01 -4.35572654e-01 -9.71422911e-01 -6.08115375e-01 5.13120353e-01 4.66026038e-01 -2.50711232e-01 5.37717938e-01 7.56197214e-01 -3.68436456e-01 -4.15767938e-01 -7.03206241e-01 -1.67570919e-01 -2.12550700e-01 -2.97107249e-01 6.57936037e-01 9.66081202e-01 -1.49014533e-01 6.53408885e-01 -3.13623160e-01 -1.13656616e+00 6.32946372e-01 1.11567938e+00 8.72490764e-01 3.37786674e-01 4.87605333e-01 -6.29401088e-01 1.25646532e+00 -2.13201880e-01 -5.44607759e-01 -2.80703515e-01 2.22381711e-01 -1.02836597e+00 -1.90488487e-01 3.74493182e-01 -3.07709008e-01 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75b01158-d02d-4f9a-af92-8f2d672c5196	change-detection-methods-for-remote-sensing	2305.05813	null	https://arxiv.org/abs/2305.05813v1	https://arxiv.org/pdf/2305.05813v1.pdf	Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review	Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and learning frameworks in the methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper will shed some light on the community and inspire further research efforts in the change detection task.	['Shiming Xiang', 'Qi Zhao', 'Zhaoyang Xu', 'Shuchang Lyu', 'Xiangtai Li', 'Yunmeng Huang', 'Guangliang Cheng']	2023-05-09	null	null	null	null	['change-detection', 'change-detection-for-remote-sensing-images']	['computer-vision', 'miscellaneous']	[ 5.96354783e-01 -7.47641206e-01 -1.59599125e-01 -4.98568147e-01 -3.67450923e-01 -5.97720981e-01 5.82851470e-01 1.94133222e-01 -4.03437912e-01 5.71478128e-01 1.50346593e-03 -3.25393677e-01 -2.85395712e-01 -1.15891349e+00 -1.53113648e-01 -9.79051769e-01 -5.54129183e-01 -2.80724138e-01 -5.59403636e-02 -5.63884795e-01 -2.95463647e-03 9.29553151e-01 -1.80372250e+00 -1.22769706e-01 9.24139321e-01 6.57393992e-01 2.71201849e-01 5.19707501e-01 1.46336526e-01 1.57928094e-01 -2.50032663e-01 7.16768280e-02 2.66584128e-01 -2.29611218e-01 -6.99020982e-01 2.47320712e-01 5.36616147e-01 -4.13750172e-01 -2.30465084e-01 1.35125649e+00 7.57653892e-01 1.05055466e-01 4.74409997e-01 -9.54282522e-01 -5.09419143e-01 4.16648388e-01 -9.42326725e-01 6.72444403e-01 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23a23dc8-1b31-4d3b-8712-e2880d342d6c	consistent-polynomial-time-unseeded-graph	1807.11027	null	http://arxiv.org/abs/1807.11027v1	http://arxiv.org/pdf/1807.11027v1.pdf	Consistent polynomial-time unseeded graph matching for Lipschitz graphons	We propose a consistent polynomial-time method for the unseeded node matching problem for networks with smooth underlying structures. Despite widely conjectured by the research community that the structured graph matching problem to be significantly easier than its worst case counterpart, well-known to be NP-hard, the statistical version of the problem has stood a challenge that resisted any solution both provable and polynomial-time. The closest existing work requires quasi-polynomial time. Our method is based on the latest advances in graphon estimation techniques and analysis on the concentration of empirical Wasserstein distances. Its core is a simple yet unconventional sampling-and-matching scheme that reduces the problem from unseeded to seeded. Our method allows flexible efficiencies, is convenient to analyze and potentially can be extended to more general settings. Our work enables a rich variety of subsequent estimations and inferences.	['Yuan Zhang']	2018-07-29	null	null	null	null	['graphon-estimation']	['graphs']	[ 4.52887952e-01 5.95542133e-01 -2.50665009e-01 -2.64710605e-01 -7.92266250e-01 -8.70704830e-01 3.32174689e-01 1.21472985e-01 -1.66439321e-02 6.81595922e-01 -2.09643289e-01 -7.86217332e-01 -7.73719192e-01 -9.22776043e-01 -6.66840672e-01 -8.02786887e-01 -6.05784297e-01 8.83821487e-01 5.99905014e-01 -2.01005936e-01 1.43235698e-01 4.81123924e-01 -1.05631781e+00 -4.13924903e-01 5.81768811e-01 6.99446201e-01 -8.41833875e-02 9.68843997e-01 1.48683280e-01 4.64770883e-01 4.00482826e-02 -8.19304347e-01 6.49260998e-01 -4.00650740e-01 -1.02675712e+00 -7.59678409e-02 9.00517344e-01 2.28737406e-02 -7.12230623e-01 1.31051254e+00 4.88618642e-01 -2.50587538e-02 4.85114694e-01 -1.84166598e+00 -6.01696849e-01 7.12158144e-01 -5.92033207e-01 2.98673332e-01 4.81378585e-01 -3.67331207e-01 1.50129473e+00 -3.16269040e-01 6.64790809e-01 1.45870531e+00 1.07377172e+00 3.29045415e-01 -1.65120530e+00 -5.84654510e-01 -2.07160950e-01 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645248ed-d876-4f7b-9adb-5f4d932a5b95	what-do-we-expect-from-multiple-choice-qa	2011.10647	null	https://arxiv.org/abs/2011.10647v1	https://arxiv.org/pdf/2011.10647v1.pdf	What do we expect from Multiple-choice QA Systems?	The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities. However, using various perturbations, multiple recent works have shown that good performance on a dataset might not indicate performance that correlates well with human's expectations from models that "understand" language. In this work we consider a top performing model on several Multiple Choice Question Answering (MCQA) datasets, and evaluate it against a set of expectations one might have from such a model, using a series of zero-information perturbations of the model's inputs. Our results show that the model clearly falls short of our expectations, and motivates a modified training approach that forces the model to better attend to the inputs. We show that the new training paradigm leads to a model that performs on par with the original model while better satisfying our expectations.	['Dan Roth', 'Nitish Gupta', 'Krunal Shah']	2020-11-20	null	https://aclanthology.org/2020.findings-emnlp.317	https://aclanthology.org/2020.findings-emnlp.317.pdf	findings-of-the-association-for-computational	['multiple-choice-qa']	['natural-language-processing']	[ 1.98432788e-01 6.06507659e-01 2.27988616e-01 -7.24954009e-01 -1.21940041e+00 -7.92782903e-01 7.75115788e-01 2.12301314e-01 -5.02778172e-01 8.30185533e-01 3.99472088e-01 -9.66886818e-01 -1.57954887e-01 -7.68821716e-01 -8.66614699e-01 -2.10184902e-01 6.02815390e-01 1.02887225e+00 5.14611423e-01 -8.80346537e-01 4.23207939e-01 3.08085769e-01 -1.20917368e+00 7.15209007e-01 9.55727935e-01 4.15322274e-01 -1.36875257e-01 8.71997416e-01 -1.53680146e-01 1.23861265e+00 -8.13648462e-01 -6.94606006e-01 5.26062287e-02 -4.47577894e-01 -1.51856768e+00 -3.79123956e-01 6.62147939e-01 4.45984975e-02 -6.86964914e-02 8.62790465e-01 2.47012079e-01 5.70894629e-02 5.38704395e-01 -1.00109494e+00 -7.84907043e-01 6.61776841e-01 1.48419231e-01 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f442b3de-eb41-4afb-9ce3-a0e619e89380	mvp-multi-view-prompting-improves-aspect	2305.12627	null	https://arxiv.org/abs/2305.12627v1	https://arxiv.org/pdf/2305.12627v1.pdf	MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction	Generative methods greatly promote aspect-based sentiment analysis via generating a sequence of sentiment elements in a specified format. However, existing studies usually predict sentiment elements in a fixed order, which ignores the effect of the interdependence of the elements in a sentiment tuple and the diversity of language expression on the results. In this work, we propose Multi-view Prompting (MvP) that aggregates sentiment elements generated in different orders, leveraging the intuition of human-like problem-solving processes from different views. Specifically, MvP introduces element order prompts to guide the language model to generate multiple sentiment tuples, each with a different element order, and then selects the most reasonable tuples by voting. MvP can naturally model multi-view and multi-task as permutations and combinations of elements, respectively, outperforming previous task-specific designed methods on multiple ABSA tasks with a single model. Extensive experiments show that MvP significantly advances the state-of-the-art performance on 10 datasets of 4 benchmark tasks, and performs quite effectively in low-resource settings. Detailed evaluation verified the effectiveness, flexibility, and cross-task transferability of MvP.	['Yujiu Yang', 'Qingyan Guo', 'Zhibin Gou']	2023-05-22	null	null	null	null	['term-extraction', 'aspect-sentiment-opinion-triplet-extraction', 'aspect-based-sentiment-analysis', 'aspect-category-polarity', 'aspect-category-opinion-sentiment-quadruple', 'extract-aspect', 'extract-aspect-polarity-tuple', 'hidden-aspect-detection', 'aspect-sentiment-triplet-extraction', 'aspect-category-detection']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 1.89080939e-01 -1.27559662e-01 -1.81346029e-01 -8.95688951e-01 -9.35503662e-01 -7.00724840e-01 6.89359069e-01 -1.85969561e-01 6.52052835e-02 5.28774738e-01 6.73404038e-01 -4.53743413e-02 1.54065505e-01 -7.56267667e-01 -6.48544908e-01 -5.69835603e-01 5.77361643e-01 8.47347677e-01 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83bf675a-3919-4da8-a2c5-475de3ea5c19	on-the-power-of-saturated-transformers-a-view	2106.16213	null	https://arxiv.org/abs/2106.16213v3	https://arxiv.org/pdf/2106.16213v3.pdf	Saturated Transformers are Constant-Depth Threshold Circuits	Transformers have become a standard neural network architecture for many NLP problems, motivating theoretical analysis of their power in terms of formal languages. Recent work has shown that transformers with hard attention are quite limited in power (Hahn, 2020), as they can be simulated by constant-depth AND/OR circuits (Hao et al. 2021). However, hard attention is a strong assumption, which may complicate the relevance of these results in practice. In this work, we analyze the circuit complexity of transformers with saturated attention: a generalization of hard attention that more closely captures the attention patterns learnable in practical transformers. We first show that saturated transformers transcend the known limitations of hard-attention transformers. We then prove saturated transformers with floating-point values can be simulated by constant-depth threshold circuits, giving the class $\mathsf{TC}^0$ as an upper bound on the formal languages they recognize.	['Ashish Sabharwal', 'Noah A. Smith', 'William Merrill']	2021-06-30	null	null	null	null	['hard-attention']	['methodology']	[ 3.96454066e-01 5.85075200e-01 -2.25533117e-02 -1.43789276e-01 -6.20481312e-01 -9.05923009e-01 3.13941687e-01 -3.43353301e-02 -2.72135586e-01 5.44043839e-01 -2.92205103e-02 -9.03962255e-01 -1.31000027e-01 -1.18560255e+00 -1.12458897e+00 -4.02881056e-01 -1.48701265e-01 6.07505977e-01 3.15476060e-01 -3.40260625e-01 -4.90377098e-03 5.50085723e-01 -1.68229806e+00 3.67011249e-01 6.19520545e-01 1.10016394e+00 9.96867046e-02 8.14866900e-01 -2.17596516e-01 1.04936743e+00 -5.31962097e-01 -5.37715197e-01 2.97213286e-01 -2.03157574e-01 -1.16078329e+00 -6.53945088e-01 7.52385914e-01 -1.65724084e-01 -3.79837245e-01 1.35663319e+00 1.80233687e-01 -1.82808042e-01 3.90800118e-01 -1.41603971e+00 -1.03334129e+00 1.17698181e+00 -3.83627415e-02 5.23479998e-01 1.65456370e-01 1.86068475e-01 1.63361728e+00 -5.03291190e-01 4.98203523e-02 1.18882668e+00 7.85330892e-01 6.98005617e-01 -1.23591208e+00 -8.18740845e-01 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0a99a989-526f-445a-b31d-1ae69db9e7c7	clcc-contrastive-learning-for-color-constancy	2106.04989	null	https://arxiv.org/abs/2106.04989v1	https://arxiv.org/pdf/2106.04989v1.pdf	CLCC: Contrastive Learning for Color Constancy	In this paper, we present CLCC, a novel contrastive learning framework for color constancy. Contrastive learning has been applied for learning high-quality visual representations for image classification. One key aspect to yield useful representations for image classification is to design illuminant invariant augmentations. However, the illuminant invariant assumption conflicts with the nature of the color constancy task, which aims to estimate the illuminant given a raw image. Therefore, we construct effective contrastive pairs for learning better illuminant-dependent features via a novel raw-domain color augmentation. On the NUS-8 dataset, our method provides $17.5\%$ relative improvements over a strong baseline, reaching state-of-the-art performance without increasing model complexity. Furthermore, our method achieves competitive performance on the Gehler dataset with $3\times$ fewer parameters compared to top-ranking deep learning methods. More importantly, we show that our model is more robust to different scenes under close proximity of illuminants, significantly reducing $28.7\%$ worst-case error in data-sparse regions.	['Kevin Jou', 'Yu-Lin Chang', 'Chia-Ping Chen', 'Yu-Hao Huang', 'Hsuan-Chao Chiu', 'Chia-Che Chang', 'Yi-Chen Lo']	2021-06-09	null	http://openaccess.thecvf.com//content/CVPR2021/html/Lo_CLCC_Contrastive_Learning_for_Color_Constancy_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Lo_CLCC_Contrastive_Learning_for_Color_Constancy_CVPR_2021_paper.pdf	cvpr-2021-1	['color-constancy']	['computer-vision']	[ 1.74510762e-01 -5.19803643e-01 -1.80556774e-01 -3.60364199e-01 -7.87575841e-01 -4.90353137e-01 3.98123354e-01 -1.68233991e-01 -3.67092282e-01 5.37398756e-01 4.18418534e-02 -1.79505631e-01 2.11155951e-01 -5.88154912e-01 -9.29725170e-01 -9.08368945e-01 -5.55541255e-02 -2.55121976e-01 -2.13902220e-01 -2.45199516e-01 2.91509181e-01 5.63024402e-01 -1.65361226e+00 2.74292380e-01 8.20598066e-01 1.39202428e+00 1.86627269e-01 6.67274415e-01 -1.62304208e-01 5.80700934e-01 -2.94373959e-01 -1.12428196e-01 6.16041601e-01 -4.68850881e-01 -4.53002036e-01 1.75780416e-01 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91242a31-b111-4b48-bfd1-1b568cd0391f	storytelling-of-photo-stream-with	1606.00625	null	http://arxiv.org/abs/1606.00625v1	http://arxiv.org/pdf/1606.00625v1.pdf	Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network	Visual storytelling aims to generate human-level narrative language (i.e., a natural paragraph with multiple sentences) from a photo streams. A typical photo story consists of a global timeline with multi-thread local storylines, where each storyline occurs in one different scene. Such complex structure leads to large content gaps at scene transitions between consecutive photos. Most existing image/video captioning methods can only achieve limited performance, because the units in traditional recurrent neural networks (RNN) tend to "forget" the previous state when the visual sequence is inconsistent. In this paper, we propose a novel visual storytelling approach with Bidirectional Multi-thread Recurrent Neural Network (BMRNN). First, based on the mined local storylines, a skip gated recurrent unit (sGRU) with delay control is proposed to maintain longer range visual information. Second, by using sGRU as basic units, the BMRNN is trained to align the local storylines into the global sequential timeline. Third, a new training scheme with a storyline-constrained objective function is proposed by jointly considering both global and local matches. Experiments on three standard storytelling datasets show that the BMRNN model outperforms the state-of-the-art methods.	['Yu Liu', 'Jianlong Fu', 'Chang Wen Chen', 'Tao Mei']	2016-06-02	null	null	null	null	['visual-storytelling']	['natural-language-processing']	[ 3.68363947e-01 -5.38919959e-03 -2.90369809e-01 -2.46268719e-01 -6.98596597e-01 -2.78006941e-01 8.48587930e-01 -3.34421396e-01 1.04993045e-01 7.54867435e-01 7.85481274e-01 -1.33842990e-01 4.61707622e-01 -6.60018146e-01 -9.67989206e-01 -6.84924722e-01 4.75301385e-01 7.92645141e-02 3.17851424e-01 -1.90553874e-01 2.45674580e-01 -1.09193861e-01 -1.32767200e+00 8.38065743e-01 4.27403688e-01 5.80546558e-01 8.81962419e-01 7.28738666e-01 -4.62987483e-01 1.66359437e+00 -7.00870633e-01 -2.42810994e-01 -1.78114653e-01 -1.08965087e+00 -7.14175820e-01 4.52504307e-01 4.43041205e-01 -5.73975980e-01 -6.82475805e-01 7.20359921e-01 3.90944928e-01 3.79944444e-01 1.96989849e-01 -1.22009158e+00 -7.11695671e-01 9.56415415e-01 -7.98443556e-01 7.63382902e-03 5.61093569e-01 2.70357341e-01 9.48539257e-01 -9.13049340e-01 1.10790634e+00 1.10097289e+00 4.02630568e-01 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9ed876e0-6bfe-4e16-9a02-6730d1b2195d	rpbert-a-text-image-relation-propagation	2102.02967	null	https://arxiv.org/abs/2102.02967v1	https://arxiv.org/pdf/2102.02967v1.pdf	RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER	Recently multimodal named entity recognition (MNER) has utilized images to improve the accuracy of NER in tweets. However, most of the multimodal methods use attention mechanisms to extract visual clues regardless of whether the text and image are relevant. Practically, the irrelevant text-image pairs account for a large proportion in tweets. The visual clues that are unrelated to the texts will exert uncertain or even negative effects on multimodal model learning. In this paper, we introduce a method of text-image relation propagation into the multimodal BERT model. We integrate soft or hard gates to select visual clues and propose a multitask algorithm to train on the MNER datasets. In the experiments, we deeply analyze the changes in visual attention before and after the use of text-image relation propagation. Our model achieves state-of-the-art performance on the MNER datasets.	['Fangsheng Weng', 'Yindu Su', 'Kai Zhang', 'Jiquan Wang', 'Lin Sun']	2021-02-05	null	null	null	null	['multi-modal-named-entity-recognition']	['natural-language-processing']	[-8.64545554e-02 -2.45445654e-01 9.19870436e-02 -4.46926415e-01 -8.79988790e-01 -5.04792333e-01 6.99680269e-01 1.04453757e-01 -1.13035083e+00 5.36637902e-01 2.21110150e-01 -8.98049176e-02 3.93852293e-01 -5.35771549e-01 -8.25194657e-01 -6.91658080e-01 3.98482740e-01 2.54469365e-01 3.95756960e-01 -3.16409498e-01 1.79263294e-01 2.12956622e-01 -1.10616195e+00 9.24069941e-01 5.01469791e-01 8.47014308e-01 2.78891921e-01 6.91198707e-01 -6.05914950e-01 9.95482683e-01 -6.26376629e-01 -9.76841152e-01 -1.97414234e-01 -4.14138794e-01 -6.35981798e-01 -1.23492643e-01 4.47456717e-01 -2.37810820e-01 -6.61175072e-01 1.05174422e+00 8.05474341e-01 1.57634810e-01 7.67339945e-01 -1.34908009e+00 -9.72190738e-01 8.41815472e-01 -9.69568551e-01 4.33032691e-01 2.75368035e-01 1.74806103e-01 8.28522086e-01 -1.41776264e+00 5.22212267e-01 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26d2dd45-ffa1-4bdb-8675-70ad6aa7f1b2	real-time-instance-segmentation-of-surgical	2111.04911	null	https://arxiv.org/abs/2111.04911v2	https://arxiv.org/pdf/2111.04911v2.pdf	Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion	Precise instrument segmentation aid surgeons to navigate the body more easily and increase patient safety. While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to 1) complex surgical environment, and 2) model design with both optimal accuracy and speed. Deep learning gives us the opportunity to learn complex environment from large surgery scene environments and placements of these instruments in real world scenarios. The Robust Medical Instrument Segmentation 2019 challenge (ROBUST-MIS) provides more than 10,000 frames with surgical tools in different clinical settings. In this paper, we use a light-weight single stage instance segmentation model complemented with a convolutional block attention module for achieving both faster and accurate inference. We further improve accuracy through data augmentation and optimal anchor localisation strategies. To our knowledge, this is the first work that explicitly focuses on both real-time performance and improved accuracy. Our approach out-performed top team performances in the ROBUST-MIS challenge with over 44% improvement on both area-based metric MI_DSC and distance-based metric MI_NSD. We also demonstrate real-time performance (> 60 frames-per-second) with different but competitive variants of our final approach.	['Sharib Ali', 'Leonardo Chang', 'Gilberto Ochoa-Ruiz', 'Juan Carlos Angeles-Ceron']	2021-11-09	null	null	null	null	['real-time-instance-segmentation']	['computer-vision']	[ 1.80493712e-01 2.44616523e-01 -2.60525733e-01 -6.32190555e-02 -1.24708319e+00 -6.79473639e-01 1.41535506e-01 3.08737814e-01 -8.22952390e-01 4.29866850e-01 1.74360096e-01 -6.28001988e-01 -2.29462415e-01 -8.09707493e-02 -7.71849871e-01 -3.40486974e-01 -2.26977512e-01 6.59522533e-01 2.28410393e-01 -7.26556331e-02 1.32321522e-01 6.90754116e-01 -9.93766069e-01 3.54918977e-03 6.45566165e-01 8.93704534e-01 2.56388336e-01 1.07851398e+00 3.06168139e-01 6.66956306e-01 -2.01971635e-01 -2.32159406e-01 7.00821996e-01 -1.73836857e-01 -7.51720428e-01 -2.43712470e-01 4.95481402e-01 -3.64836127e-01 -1.75612494e-01 7.80569136e-01 7.70362973e-01 -9.74993035e-02 1.98155314e-01 -4.71454144e-01 2.36103937e-01 4.30820584e-01 -5.48726082e-01 1.62495777e-01 1.90527618e-01 3.89424950e-01 4.53112453e-01 -4.22910929e-01 8.31212878e-01 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a553065e-03e7-4054-858c-3f21ddd299aa	mt3-multi-task-multitrack-music-transcription-1	2111.03017	null	https://arxiv.org/abs/2111.03017v4	https://arxiv.org/pdf/2111.03017v4.pdf	MT3: Multi-Task Multitrack Music Transcription	Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding. Unlike Automatic Speech Recognition (ASR), which typically focuses on the words of a single speaker, AMT often requires transcribing multiple instruments simultaneously, all while preserving fine-scale pitch and timing information. Further, many AMT datasets are "low-resource", as even expert musicians find music transcription difficult and time-consuming. Thus, prior work has focused on task-specific architectures, tailored to the individual instruments of each task. In this work, motivated by the promising results of sequence-to-sequence transfer learning for low-resource Natural Language Processing (NLP), we demonstrate that a general-purpose Transformer model can perform multi-task AMT, jointly transcribing arbitrary combinations of musical instruments across several transcription datasets. We show this unified training framework achieves high-quality transcription results across a range of datasets, dramatically improving performance for low-resource instruments (such as guitar), while preserving strong performance for abundant instruments (such as piano). Finally, by expanding the scope of AMT, we expose the need for more consistent evaluation metrics and better dataset alignment, and provide a strong baseline for this new direction of multi-task AMT.	['Jesse Engel', 'Curtis Hawthorne', 'Ethan Manilow', 'Ian Simon', 'Josh Gardner']	2021-11-04	mt3-multi-task-multitrack-music-transcription	https://openreview.net/forum?id=iMSjopcOn0p	https://openreview.net/pdf?id=iMSjopcOn0p	iclr-2022-4	['music-transcription']	['music']	[ 5.01475036e-01 -3.38839740e-01 -6.49813637e-02 -3.46319121e-03 -1.59776950e+00 -1.17980778e+00 1.40049592e-01 -2.92443246e-01 -8.43404084e-02 3.26704890e-01 4.96835679e-01 -1.67953730e-01 -1.58309758e-01 -2.85565376e-01 -7.44458258e-01 -5.25627851e-01 6.18755445e-02 5.67839086e-01 -4.03429389e-01 -3.06060851e-01 8.06265846e-02 1.46130726e-01 -1.27926469e+00 5.18855929e-01 4.22760695e-01 8.17408562e-01 2.41830230e-01 8.80399466e-01 -1.81836709e-02 5.40328801e-01 -8.27740908e-01 -5.71914077e-01 3.00505489e-01 -6.73227847e-01 -7.67875850e-01 -9.36662406e-03 7.89313138e-01 5.75070120e-02 -1.92727163e-01 7.64478445e-01 8.01468313e-01 2.62145191e-01 4.94220853e-01 -8.49114478e-01 -4.97615695e-01 1.16336167e+00 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7136499c-ebbf-4bf2-91e7-19ebc96858b7	factkg-fact-verification-via-reasoning-on	2305.06590	null	https://arxiv.org/abs/2305.06590v2	https://arxiv.org/pdf/2305.06590v2.pdf	FactKG: Fact Verification via Reasoning on Knowledge Graphs	In real world applications, knowledge graphs (KG) are widely used in various domains (e.g. medical applications and dialogue agents). However, for fact verification, KGs have not been adequately utilized as a knowledge source. KGs can be a valuable knowledge source in fact verification due to their reliability and broad applicability. A KG consists of nodes and edges which makes it clear how concepts are linked together, allowing machines to reason over chains of topics. However, there are many challenges in understanding how these machine-readable concepts map to information in text. To enable the community to better use KGs, we introduce a new dataset, FactKG: Fact Verification via Reasoning on Knowledge Graphs. It consists of 108k natural language claims with five types of reasoning: One-hop, Conjunction, Existence, Multi-hop, and Negation. Furthermore, FactKG contains various linguistic patterns, including colloquial style claims as well as written style claims to increase practicality. Lastly, we develop a baseline approach and analyze FactKG over these reasoning types. We believe FactKG can advance both reliability and practicality in KG-based fact verification.	['Edward Choi', 'James Thorne', 'Yohan Jo', 'Yeonsu Kwon', 'Sungjin Park', 'Jiho Kim']	2023-05-11	null	null	null	null	['fact-verification']	['natural-language-processing']	[-3.29330921e-01 6.46866500e-01 -8.60746145e-01 -3.07140440e-01 -5.21609604e-01 -8.46513808e-01 7.59879112e-01 7.95045793e-01 1.33683592e-01 1.01147437e+00 5.99889219e-01 -7.21594095e-01 -2.57952571e-01 -1.23088753e+00 -7.62653589e-01 -4.12748493e-02 1.78468022e-02 3.62968177e-01 6.26275778e-01 -5.43250620e-01 3.04210335e-01 1.03659347e-01 -9.97277915e-01 6.49743497e-01 9.21505094e-01 8.58106613e-01 -4.70857233e-01 -2.74082143e-02 -4.01875317e-01 1.61286533e+00 -6.64864719e-01 -1.52978623e+00 -3.01582161e-02 -3.02669019e-01 -1.24627638e+00 -2.38718629e-01 2.76562482e-01 2.24004760e-02 -2.25009933e-01 1.26008809e+00 1.16840392e-01 -2.36544490e-01 5.57123840e-01 -1.55137074e+00 -9.47787583e-01 1.50057399e+00 -5.84845126e-01 1.22096516e-01 7.89556086e-01 -3.20942134e-01 1.44826686e+00 -5.17723680e-01 9.80301738e-01 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32ade26c-7f3b-4fd5-bd21-777531d18dc1	intrinsic-image-transformation-via-scale	1805.10253	null	http://arxiv.org/abs/1805.10253v1	http://arxiv.org/pdf/1805.10253v1.pdf	Intrinsic Image Transformation via Scale Space Decomposition	We introduce a new network structure for decomposing an image into its intrinsic albedo and shading. We treat this as an image-to-image transformation problem and explore the scale space of the input and output. By expanding the output images (albedo and shading) into their Laplacian pyramid components, we develop a multi-channel network structure that learns the image-to-image transformation function in successive frequency bands in parallel, within each channel is a fully convolutional neural network with skip connections. This network structure is general and extensible, and has demonstrated excellent performance on the intrinsic image decomposition problem. We evaluate the network on two benchmark datasets: the MPI-Sintel dataset and the MIT Intrinsic Images dataset. Both quantitative and qualitative results show our model delivers a clear progression over state-of-the-art.	['Zicheng Liao', 'Chengyi Zhang', 'Lechao Cheng']	2018-05-25	intrinsic-image-transformation-via-scale-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Cheng_Intrinsic_Image_Transformation_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Cheng_Intrinsic_Image_Transformation_CVPR_2018_paper.pdf	cvpr-2018-6	['intrinsic-image-decomposition']	['computer-vision']	[ 5.84057331e-01 -1.37951136e-01 3.05238396e-01 -4.67235267e-01 -4.44069386e-01 -5.63310564e-01 4.81553555e-01 -8.22543979e-01 -2.38844201e-01 1.52902976e-01 1.66223288e-01 -1.84845522e-01 3.06694180e-01 -7.20366120e-01 -9.58496630e-01 -8.98460269e-01 -1.44552827e-01 -2.60329153e-02 1.35059385e-02 -2.58102268e-01 -1.34034112e-01 4.91762280e-01 -1.28587449e+00 5.68948805e-01 6.86413646e-01 9.37592864e-01 -8.32876936e-02 1.16522753e+00 1.45715967e-01 1.00044918e+00 -2.82864660e-01 7.37062320e-02 7.75309980e-01 -3.29786271e-01 -8.03627133e-01 4.60012764e-01 1.09041262e+00 -6.32868052e-01 -4.93423104e-01 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974f979d-d54a-472d-b1f3-8d436604f602	learning-robust-self-attention-features-for	2305.06273	null	https://arxiv.org/abs/2305.06273v1	https://arxiv.org/pdf/2305.06273v1.pdf	Learning Robust Self-attention Features for Speech Emotion Recognition with Label-adaptive Mixup	Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose a self-attention based method with combined use of label-adaptive mixup and center loss. By adapting label probabilities in mixup and fitting center loss to the mixup training scheme, our proposed method achieves a superior performance to the state-of-the-art methods.	['Dazhi Jiang', 'Lichao Zhang', 'Lei Kang']	2023-05-07	null	null	null	null	['speech-emotion-recognition']	['speech']	[ 1.69904694e-01 -9.23310816e-02 3.21551450e-02 -6.66109979e-01 -6.71113074e-01 2.78203301e-02 5.03579319e-01 -1.42713383e-01 -6.66331351e-01 6.13204420e-01 2.35023931e-01 1.44023925e-01 4.62787360e-01 -3.28576453e-02 -2.90061265e-01 -4.98617291e-01 1.56146303e-01 4.66911107e-01 -4.00430858e-02 -2.62547165e-01 -5.66581190e-02 4.05279726e-01 -1.71007156e+00 3.52515906e-01 9.18633878e-01 1.37440586e+00 -9.54094753e-02 8.78914535e-01 -4.60604280e-01 1.06547463e+00 -7.15101421e-01 -6.34349108e-01 -2.63747871e-01 -6.31120205e-01 -9.02835727e-01 1.25568956e-01 -8.62501413e-02 1.86329946e-01 -2.10989133e-01 1.10511899e+00 7.79111266e-01 7.56401837e-01 7.68374622e-01 -1.63290751e+00 -6.71918809e-01 2.70893425e-01 -3.24004531e-01 1.43904269e-01 3.64191204e-01 -2.45455772e-01 9.01641846e-01 -9.24762905e-01 2.94287235e-01 1.49204874e+00 5.10552168e-01 8.79717827e-01 -7.19123602e-01 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b0f4daa6-96d9-4a7a-9b32-1b4a3a25c581	divergence-aware-federated-self-supervised-1	2204.04385	null	https://arxiv.org/abs/2204.04385v1	https://arxiv.org/pdf/2204.04385v1.pdf	Divergence-aware Federated Self-Supervised Learning	Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras and phones), often resulted from privacy constraints. Extensive attention has been paid to SSL approaches based on Siamese networks. However, such an effort has not yet revealed deep insights into various fundamental building blocks for the federated self-supervised learning (FedSSL) architecture. We aim to fill in this gap via in-depth empirical study and propose a new method to tackle the non-independently and identically distributed (non-IID) data problem of decentralized data. Firstly, we introduce a generalized FedSSL framework that embraces existing SSL methods based on Siamese networks and presents flexibility catering to future methods. In this framework, a server coordinates multiple clients to conduct SSL training and periodically updates local models of clients with the aggregated global model. Using the framework, our study uncovers unique insights of FedSSL: 1) stop-gradient operation, previously reported to be essential, is not always necessary in FedSSL; 2) retaining local knowledge of clients in FedSSL is particularly beneficial for non-IID data. Inspired by the insights, we then propose a new approach for model update, Federated Divergence-aware Exponential Moving Average update (FedEMA). FedEMA updates local models of clients adaptively using EMA of the global model, where the decay rate is dynamically measured by model divergence. Extensive experiments demonstrate that FedEMA outperforms existing methods by 3-4% on linear evaluation. We hope that this work will provide useful insights for future research.	['Shuai Zhang', 'Yonggang Wen', 'Weiming Zhuang']	2022-04-09	divergence-aware-federated-self-supervised	https://openreview.net/forum?id=oVE1z8NlNe	https://openreview.net/pdf?id=oVE1z8NlNe	iclr-2022-4	['federated-unsupervised-learning']	['methodology']	[-1.10625632e-01 3.01482771e-02 -4.80492532e-01 -5.32177091e-01 -7.73934424e-01 -6.42843008e-01 6.06678784e-01 -1.82771370e-01 -4.18423772e-01 9.05306518e-01 9.10716951e-02 -2.35702321e-01 -2.81891793e-01 -3.42387736e-01 -9.37084079e-01 -9.04885292e-01 -2.34070376e-01 4.26882982e-01 -7.31571242e-02 2.02299610e-01 -4.70235087e-02 6.37797058e-01 -1.36729634e+00 5.44902980e-02 7.21253514e-01 1.19935465e+00 -8.66741538e-02 3.03737372e-01 -1.53308302e-01 1.09052312e+00 -2.68856257e-01 -6.24175668e-01 7.28741825e-01 -5.45393884e-01 -8.00190449e-01 1.55055106e-01 4.25767094e-01 -5.60371578e-01 -2.82530069e-01 1.02281225e+00 2.80233622e-01 4.54748124e-02 5.52224398e-01 -1.63347363e+00 -4.78066713e-01 5.82457244e-01 -4.06445086e-01 -3.02234758e-03 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fa6d9654-7c08-4082-af8e-c6c6a7e67f75	semi-blind-and-l1-robust-system	2008.08758	null	http://arxiv.org/abs/2008.08758v1	http://arxiv.org/pdf/2008.08758v1.pdf	Semi-Blind and l1 Robust System Identification for Anemia Management	Chronic diseases such as cancer, diabetes, heart diseases, chronic kidney disease (CKD) require a drug management system that ensures a stable and robust output of the patient's condition in response to drug dosage. In the case of CKD, the patients suffer from the deficiency of red blood cell count and external human recombinant erythropoietin (EPO) is required to maintain healthy levels of hemoglobin (Hb). Anemia is a common comorbidity in patients with CKD. For an efficient and robust anemia management system for CKD patients instead of traditional population-based approaches, individualized patient-specific approaches are needed. Hence, individualized system (patient) models for patient-specific drug-dose responses are required. In this research, system identification for CKD is performed for individual patients. For control-oriented system identification, two robust identification techniques are applied: (1) l1 robust identification considering zero initial conditions and (2) semi-blind robust system identification considering non-zero initial conditions. The EPO data of patients are used as the input and Hb data is used as the output of the system. For this study, individualized patient models are developed by using patient-specific data. The ARX one-step-ahead prediction technique is used for model validation at real patient data. The performance of these two techniques is compared by calculating minimum means square error (MMSE). By comparison, we show that the semi-blind robust identification technique gives better results as compared to l1 robust identification.	[]	2020-08-20	null	null	null	null	['blood-cell-count']	['computer-vision']	[-6.07102662e-02 -1.09323114e-01 -1.91712335e-01 -3.08476359e-01 -1.44629702e-01 2.14666245e-03 1.70461535e-02 5.56453586e-01 -2.07925245e-01 9.58277166e-01 5.74971922e-02 -2.04200611e-01 -2.89202899e-01 -6.89327955e-01 -4.27247211e-02 -7.43410587e-01 4.59307581e-02 5.67960382e-01 -4.39912587e-01 -1.01384506e-01 2.68509269e-01 4.25207078e-01 -1.45114315e+00 -1.38624772e-01 1.34022570e+00 7.05566525e-01 -1.38785467e-01 9.74010766e-01 -3.08492430e-03 7.86339045e-01 -3.46902728e-01 5.51352382e-01 2.57422060e-01 -8.82091522e-01 -1.09252982e-01 2.89857179e-01 -2.16955349e-01 -5.33166766e-01 -5.01594841e-02 8.17679703e-01 9.82101262e-01 1.07896477e-02 8.89748216e-01 -9.71122801e-01 -3.62728447e-01 1.05947472e-01 -2.87812799e-01 -7.54287764e-02 3.23287278e-01 5.36816537e-01 2.49168813e-01 -7.26370692e-01 -1.02497600e-01 1.09750760e+00 7.05617249e-01 5.66637576e-01 -1.54379106e+00 -2.14414909e-01 -5.20773828e-01 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f0e67321-72a1-4b39-aad2-918b977b076b	anomaly-clustering-grouping-images-into	2112.11573	null	https://arxiv.org/abs/2112.11573v2	https://arxiv.org/pdf/2112.11573v2.pdf	Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types	We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data. Unlike object-centered image clustering, anomaly clustering is particularly challenging as anomalous patterns are subtle and local. We present a simple yet effective clustering framework using a patch-based pretrained deep embeddings and off-the-shelf clustering methods. We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings. The weight defines the importance of instances (i.e., patch embeddings) in the bag, which may highlight defective regions. We compute weights in an unsupervised way or in a semi-supervised way when labeled normal data is available. Extensive experimental studies show the effectiveness of the proposed clustering framework along with a novel distance function upon exist-ing multiple instance or deep clustering frameworks. Over-all, our framework achieves 0.451 and 0.674 normalized mutual information scores on MVTec object and texture categories and further improve with a few labeled normal data (0.577, 0.669), far exceeding the baselines (0.244, 0.273) or state-of-the-art deep clustering methods (0.176, 0.277).	['Tomas Pfister', 'Chen-Yu Lee', 'Chun-Liang Li', 'Jinsung Yoon', 'Kihyuk Sohn']	2021-12-21	null	null	null	null	['image-clustering']	['computer-vision']	[-1.80843160e-01 -8.47209990e-02 3.37533653e-01 -5.07960439e-01 -7.29729354e-01 -4.75773811e-01 5.32973170e-01 6.35239363e-01 -1.29676551e-01 -1.20720722e-01 1.16141170e-01 1.79429606e-01 -3.54983300e-01 -6.40634358e-01 -4.88154888e-01 -1.17044699e+00 -6.64061427e-01 4.47598189e-01 1.55295506e-01 3.13715972e-02 3.77987802e-01 4.98938739e-01 -1.77833378e+00 2.78316885e-01 8.81770670e-01 1.31377244e+00 -4.11177903e-01 5.53605795e-01 -1.95813134e-01 5.10283232e-01 -5.86091220e-01 -9.28153992e-02 1.38726056e-01 -2.84918159e-01 -3.94441724e-01 5.74436247e-01 6.20069504e-01 -1.87417164e-01 -3.18199009e-01 1.10531640e+00 4.10063833e-01 3.08768690e-01 9.30020332e-01 -1.49704337e+00 -9.43505585e-01 1.68160617e-01 -1.03608263e+00 4.84301031e-01 1.50206089e-02 2.82079484e-02 1.00564098e+00 -1.07565379e+00 2.34868899e-01 9.34535027e-01 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db4adfde-b998-44ba-ac62-c083bd819854	transformer-based-image-generation-from-scene	2303.04634	null	https://arxiv.org/abs/2303.04634v1	https://arxiv.org/pdf/2303.04634v1.pdf	Transformer-based Image Generation from Scene Graphs	Graph-structured scene descriptions can be efficiently used in generative models to control the composition of the generated image. Previous approaches are based on the combination of graph convolutional networks and adversarial methods for layout prediction and image generation, respectively. In this work, we show how employing multi-head attention to encode the graph information, as well as using a transformer-based model in the latent space for image generation can improve the quality of the sampled data, without the need to employ adversarial models with the subsequent advantage in terms of training stability. The proposed approach, specifically, is entirely based on transformer architectures both for encoding scene graphs into intermediate object layouts and for decoding these layouts into images, passing through a lower dimensional space learned by a vector-quantized variational autoencoder. Our approach shows an improved image quality with respect to state-of-the-art methods as well as a higher degree of diversity among multiple generations from the same scene graph. We evaluate our approach on three public datasets: Visual Genome, COCO, and CLEVR. We achieve an Inception Score of 13.7 and 12.8, and an FID of 52.3 and 60.3, on COCO and Visual Genome, respectively. We perform ablation studies on our contributions to assess the impact of each component. Code is available at https://github.com/perceivelab/trf-sg2im	['Concetto Spampinato', 'Simone Palazzo', 'Renato Sortino']	2023-03-08	null	null	null	null	['image-generation-from-scene-graphs']	['computer-vision']	[ 3.19213212e-01 3.29568118e-01 4.14787889e-01 8.62262095e-04 -6.02977574e-01 -7.88534582e-01 7.51800656e-01 -8.49040300e-02 1.32265732e-01 5.44806004e-01 8.91153067e-02 -2.07853451e-01 1.38052121e-01 -1.13995349e+00 -1.28634799e+00 -7.42834330e-01 2.06254765e-01 3.86022091e-01 1.38181020e-02 -1.32116675e-01 6.87025934e-02 4.74242091e-01 -1.47669053e+00 3.51454198e-01 9.54905272e-01 8.11034322e-01 3.37842613e-01 9.66396213e-01 -4.02532928e-02 9.02996063e-01 -8.19414139e-01 -7.63197601e-01 1.78872332e-01 -4.78314638e-01 -4.92087930e-01 3.41328442e-01 5.11493325e-01 -1.92295119e-01 -5.41332483e-01 7.99342811e-01 5.80884397e-01 -1.12549067e-01 7.27796614e-01 -1.35549462e+00 -1.17294812e+00 6.01898432e-01 -3.13731998e-01 -4.95027512e-01 2.71371722e-01 5.31191587e-01 8.76116514e-01 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99a38446-5646-4e42-b68e-f67245967262	you-only-look-once-unified-real-time-object	1506.02640	null	http://arxiv.org/abs/1506.02640v5	http://arxiv.org/pdf/1506.02640v5.pdf	You Only Look Once: Unified, Real-Time Object Detection	We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset.	['Ali Farhadi', 'Santosh Divvala', 'Ross Girshick', 'Joseph Redmon']	2015-06-08	you-only-look-once-unified-real-time-object-1	http://openaccess.thecvf.com/content_cvpr_2016/html/Redmon_You_Only_Look_CVPR_2016_paper.html	http://openaccess.thecvf.com/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf	cvpr-2016-6	['object-counting']	['computer-vision']	[-1.57727152e-01 -1.18209809e-01 -5.24630100e-02 -2.43758395e-01 -7.53507257e-01 -5.66957474e-01 5.66647291e-01 -1.02383969e-02 -7.36976027e-01 1.11807562e-01 -2.72960365e-01 -1.82702407e-01 6.28031969e-01 -8.26200783e-01 -7.89382398e-01 -4.07381684e-01 -2.73522884e-01 4.26081181e-01 1.00476813e+00 5.04831830e-03 7.04993308e-02 5.62124908e-01 -1.55192053e+00 5.93305111e-01 6.83436403e-03 9.48126733e-01 5.48162684e-03 1.47038126e+00 1.86575145e-01 1.05746377e+00 -8.12334836e-01 -2.92457968e-01 4.38044012e-01 -5.30261397e-02 -5.37418723e-01 -1.62348405e-01 1.21061659e+00 -7.06312895e-01 -6.68330193e-01 9.19095039e-01 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4f6343d2-e708-4343-a076-cc22563f8f35	evolutionary-multiparty-distance-minimization	2207.13390	null	https://arxiv.org/abs/2207.13390v1	https://arxiv.org/pdf/2207.13390v1.pdf	Evolutionary Multiparty Distance Minimization	In the field of evolutionary multiobjective optimization, the decision maker (DM) concerns conflicting objectives. In the real-world applications, there usually exist more than one DM and each DM concerns parts of these objectives. Multiparty multiobjective optimization problems (MPMOPs) are proposed to depict the MOP with multiple decision makers involved, where each party concerns about certain some objectives of all. However, in the evolutionary computation field, there is not much attention paid on MPMOPs. This paper constructs a series of MPMOPs based on distance minimization problems (DMPs), whose Pareto optimal solutions can be vividly visualized. To address MPMOPs, the new proposed algorithm OptMPNDS3 uses the multiparty initializing method to initialize the population and takes JADE2 operator to generate the offsprings. OptMPNDS3 is compared with OptAll, OptMPNDS and OptMPNDS2 on the problem suite. The result shows that OptMPNDS3 is strongly comparable to other algorithms	['Yuhui Shi', 'Yatong Chang', 'Xin Lin', 'Wenjian Luo', 'Zeneng She']	2022-07-27	null	null	null	null	['multiobjective-optimization']	['methodology']	[-1.06964936e-03 -2.19355926e-01 2.83437520e-01 -3.16064298e-01 -2.13280976e-01 -4.29461211e-01 5.02368249e-02 2.50283808e-01 -2.83236235e-01 1.06056046e+00 -1.79424152e-01 2.50929780e-02 -8.86597633e-01 -9.20649230e-01 -2.79888123e-01 -9.33403790e-01 1.62298717e-02 9.14060712e-01 -2.12184727e-01 -5.77717364e-01 6.77880704e-01 1.47859782e-01 -2.02875280e+00 3.93800676e-01 1.52287281e+00 9.78500128e-01 2.13956639e-01 5.24107039e-01 -3.45904320e-01 -2.63527364e-01 -9.18419123e-01 -8.37810278e-01 4.58824724e-01 -3.40966463e-01 -5.83378017e-01 8.28898326e-02 -9.42352787e-02 4.65013802e-01 7.14787126e-01 1.19287038e+00 9.29550469e-01 3.59412163e-01 6.94194317e-01 -2.15682960e+00 -6.62977755e-01 7.44995236e-01 -1.05858207e+00 8.73355046e-02 1.94178596e-01 3.30602415e-02 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9c362038-ec0d-4fca-9364-3d1856c4cadc	on-the-relation-between-color-image-denoising	1704.01372	null	http://arxiv.org/abs/1704.01372v1	http://arxiv.org/pdf/1704.01372v1.pdf	On the Relation between Color Image Denoising and Classification	Large amount of image denoising literature focuses on single channel images and often experimentally validates the proposed methods on tens of images at most. In this paper, we investigate the interaction between denoising and classification on large scale dataset. Inspired by classification models, we propose a novel deep learning architecture for color (multichannel) image denoising and report on thousands of images from ImageNet dataset as well as commonly used imagery. We study the importance of (sufficient) training data, how semantic class information can be traded for improved denoising results. As a result, our method greatly improves PSNR performance by 0.34 - 0.51 dB on average over state-of-the art methods on large scale dataset. We conclude that it is beneficial to incorporate in classification models. On the other hand, we also study how noise affect classification performance. In the end, we come to a number of interesting conclusions, some being counter-intuitive.	['Luc van Gool', 'Radu Timofte', 'Jiqing Wu', 'Zhiwu Huang']	2017-04-05	null	null	null	null	['color-image-denoising']	['computer-vision']	[ 2.64165998e-01 -3.81730437e-01 3.16194922e-01 -4.47843313e-01 -8.01579773e-01 -5.24076581e-01 4.46876526e-01 8.26807413e-03 -6.62965000e-01 7.00005710e-01 1.98692411e-01 -4.41727377e-02 -1.19232886e-01 -9.32162285e-01 -8.97371411e-01 -9.50171351e-01 -1.64377257e-01 -3.59870404e-01 -3.57986763e-02 -3.78636688e-01 3.23328078e-01 2.97557354e-01 -1.40283775e+00 4.18466151e-01 7.03444183e-01 1.27430081e+00 1.43802583e-01 7.15343833e-01 1.76398247e-01 7.89416432e-01 -5.24700522e-01 -5.51007688e-01 6.75980687e-01 -3.46468151e-01 -5.02924681e-01 2.29942366e-01 7.38138318e-01 -4.94441330e-01 -3.64727467e-01 1.44140351e+00 7.85056233e-01 -6.98456019e-02 4.06028092e-01 -1.05450392e+00 -7.52415001e-01 5.03381312e-01 -6.68203473e-01 3.36246610e-01 -3.43991697e-01 1.51321203e-01 7.53341675e-01 -7.06252694e-01 6.02143466e-01 1.24367714e+00 9.12013710e-01 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-8.44502270e-01 6.73909009e-01 5.52908301e-01 -3.05251390e-01 -4.23157960e-01 3.77380878e-01]	[11.444655418395996, -2.310603141784668]
60803432-652d-4727-9f82-b3b1c0161fbe	deliberate-self-attention-network-with	2009.09112	null	https://arxiv.org/abs/2009.09112v2	https://arxiv.org/pdf/2009.09112v2.pdf	An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis	In recent years, several online platforms have seen a rapid increase in the number of review systems that request users to provide aspect-level feedback. Document-level Multi-aspect Sentiment Classification (DMSC), where the goal is to predict the ratings/sentiment from a review at an individual aspect level, has become a challenging and imminent problem. To tackle this challenge, we propose a deliberate self-attention-based deep neural network model, namely FEDAR, for the DMSC problem, which can achieve competitive performance while also being able to interpret the predictions made. FEDAR is equipped with a highway word embedding layer to transfer knowledge from pre-trained word embeddings, an RNN encoder layer with output features enriched by pooling and factorization techniques, and a deliberate self-attention layer. In addition, we also propose an Attention-driven Keywords Ranking (AKR) method, which can automatically discover aspect keywords and aspect-level opinion keywords from the review corpus based on the attention weights. These keywords are significant for rating predictions by FEDAR. Since crowdsourcing annotation can be an alternate way to recover missing ratings of reviews, we propose a LEcture-AuDience (LEAD) strategy to estimate model uncertainty in the context of multi-task learning, so that valuable human resources can focus on the most uncertain predictions. Our extensive set of experiments on five different open-domain DMSC datasets demonstrate the superiority of the proposed FEDAR and LEAD models. We further introduce two new DMSC datasets in the healthcare domain and benchmark different baseline models and our models on them. Attention weights visualization results and visualization of aspect and opinion keywords demonstrate the interpretability of our model and the effectiveness of our AKR method.	['Chandan K. Reddy', 'Tian Shi', 'Ping Wang']	2020-09-18	null	null	null	null	['extract-aspect']	['natural-language-processing']	[-2.50262003e-02 2.91186690e-01 -1.21871367e-01 -7.33454108e-01 -1.24666119e+00 -3.69118690e-01 5.93451977e-01 3.71328205e-01 -3.30229908e-01 5.37690580e-01 6.96873188e-01 -3.68487358e-01 -2.35741287e-02 -5.32968402e-01 -8.16485226e-01 -3.01846802e-01 3.65701646e-01 5.34878075e-01 -1.41613513e-01 -4.46789324e-01 3.69593263e-01 -1.32427514e-01 -1.43343294e+00 8.31150711e-01 9.85385954e-01 1.41746044e+00 5.45024648e-02 6.64656997e-01 -4.06101167e-01 8.63255739e-01 -7.75393665e-01 -1.02119577e+00 -1.06714386e-02 5.82500137e-02 -7.95926154e-01 -3.51200737e-02 2.30086269e-03 -2.01265857e-01 2.60950029e-01 7.66545534e-01 6.15019321e-01 6.97088316e-02 8.87996376e-01 -1.06158292e+00 -1.28895462e+00 8.44986856e-01 -5.60961127e-01 2.69511971e-03 4.19963747e-01 -1.85798332e-01 1.39988434e+00 -1.35740983e+00 1.46118388e-01 1.29365814e+00 5.08791387e-01 5.11528075e-01 -5.39277315e-01 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-4.55461711e-01 5.17516017e-01 3.26627493e-01 -3.40492189e-01 -4.96109813e-01 -2.17357188e-01]	[11.390952110290527, 6.660897731781006]
7235a44a-ded2-4cc2-abc5-81501e220f65	skeleton-image-representation-for-3d-action	1909.05704	null	https://arxiv.org/abs/1909.05704v1	https://arxiv.org/pdf/1909.05704v1.pdf	Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints	In the last years, the computer vision research community has studied on how to model temporal dynamics in videos to employ 3D human action recognition. To that end, two main baseline approaches have been researched: (i) Recurrent Neural Networks (RNNs) with Long-Short Term Memory (LSTM); and (ii) skeleton image representations used as input to a Convolutional Neural Network (CNN). Although RNN approaches present excellent results, such methods lack the ability to efficiently learn the spatial relations between the skeleton joints. On the other hand, the representations used to feed CNN approaches present the advantage of having the natural ability of learning structural information from 2D arrays (i.e., they learn spatial relations from the skeleton joints). To further improve such representations, we introduce the Tree Structure Reference Joints Image (TSRJI), a novel skeleton image representation to be used as input to CNNs. The proposed representation has the advantage of combining the use of reference joints and a tree structure skeleton. While the former incorporates different spatial relationships between the joints, the latter preserves important spatial relations by traversing a skeleton tree with a depth-first order algorithm. Experimental results demonstrate the effectiveness of the proposed representation for 3D action recognition on two datasets achieving state-of-the-art results on the recent NTU RGB+D~120 dataset.	['William Robson Schwartz', 'François Brémond', 'Carlos Caetano']	2019-09-11	null	null	null	null	['3d-human-action-recognition']	['computer-vision']	[ 2.75835484e-01 -2.58332584e-03 -5.04421473e-01 -2.03326762e-01 -1.41745701e-01 8.47394019e-02 8.62765729e-01 -4.79869843e-01 -4.92527246e-01 3.32977831e-01 3.88224214e-01 -2.72469260e-02 -2.26359777e-02 -6.24825418e-01 -7.42438257e-01 -8.33679020e-01 -1.18581839e-01 1.39252424e-01 4.96813953e-01 -2.12245375e-01 2.10748196e-01 8.32006812e-01 -1.57628477e+00 3.14086199e-01 2.99086511e-01 1.33491087e+00 -4.68866676e-02 4.45383072e-01 -2.86830634e-01 1.33171082e+00 -3.76174390e-01 2.35551149e-02 3.58745456e-01 -5.03815413e-01 -7.91991115e-01 7.25725219e-02 2.95820057e-01 -5.53612053e-01 -8.95900965e-01 7.22595096e-01 2.86490440e-01 2.96570063e-01 5.99544287e-01 -9.84690845e-01 -4.32564914e-01 2.73892343e-01 -7.32540727e-01 3.86479199e-01 4.06708449e-01 1.34207517e-01 8.28912675e-01 -7.23040640e-01 6.73027575e-01 1.38041389e+00 4.07103658e-01 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ee13c83a-dd7d-4430-b386-8ea60f15907e	a-brief-overview-of-physics-inspired	2201.12810	null	https://arxiv.org/abs/2201.12810v1	https://arxiv.org/pdf/2201.12810v1.pdf	A Brief Overview of Physics-inspired Metaheuristic Optimization Techniques	Metaheuristic algorithms are methods devised to efficiently solve computationally challenging optimization problems. Researchers have taken inspiration from various natural and physical processes alike to formulate meta-heuristics that have successfully provided near-optimal or optimal solutions to several engineering tasks. This chapter focuses on meta-heuristic algorithms modelled upon non-linear physical phenomena having a concrete optimization paradigm, having shown formidable exploration and exploitation abilities for such optimization problems. Specifically, this chapter focuses on several popular physics-based metaheuristics as well as describing the underlying unique physical processes associated with each algorithm.	['Rishav Pramanik', 'Aritra Marik', 'Soumitri Chattopadhyay']	2022-01-30	null	null	null	null	['metaheuristic-optimization']	['methodology']	[ 3.51082124e-02 -1.00350522e-01 -1.36249781e-01 3.13901097e-01 -2.71175474e-01 -2.26628914e-01 5.17550051e-01 3.62482518e-01 9.30595491e-03 1.19385457e+00 -4.28811103e-01 -1.01550095e-01 -1.01981199e+00 -8.48706961e-01 -2.58999556e-01 -1.16512012e+00 -4.40311342e-01 6.31707191e-01 -1.58468455e-01 -6.01605773e-01 1.00162733e+00 7.76491940e-01 -2.01431417e+00 -3.30695271e-01 1.17839408e+00 8.92854035e-01 3.77369553e-01 7.69023418e-01 -3.65569592e-01 5.19741923e-02 -5.95632493e-01 -1.15093440e-01 1.26176909e-01 -3.70077074e-01 -8.34031105e-01 -5.13158850e-02 -6.15297794e-01 8.09547603e-01 -3.78213704e-01 8.93471897e-01 3.93122941e-01 8.58021557e-01 8.65350187e-01 -1.45268595e+00 -6.02439404e-01 -6.61738515e-02 -5.54040968e-01 5.05540669e-01 4.75351363e-01 5.02434611e-01 8.69755268e-01 -4.78084207e-01 4.45939779e-01 1.25194895e+00 3.95466417e-01 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6b7937bc-a331-4744-b482-26defef4641c	symbolic-discovery-of-optimization-algorithms	2302.06675	null	https://arxiv.org/abs/2302.06675v4	https://arxiv.org/pdf/2302.06675v4.pdf	Symbolic Discovery of Optimization Algorithms	We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we also introduce program selection and simplification strategies. Our method discovers a simple and effective optimization algorithm, $\textbf{Lion}$ ($\textit{Evo$\textbf{L}$ved S$\textbf{i}$gn M$\textbf{o}$me$\textbf{n}$tum}$). It is more memory-efficient than Adam as it only keeps track of the momentum. Different from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation. We compare Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on different tasks. On image classification, Lion boosts the accuracy of ViT by up to 2% on ImageNet and saves up to 5x the pre-training compute on JFT. On vision-language contrastive learning, we achieve 88.3% $\textit{zero-shot}$ and 91.1% $\textit{fine-tuning}$ accuracy on ImageNet, surpassing the previous best results by 2% and 0.1%, respectively. On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2.3x. For autoregressive, masked language modeling, and fine-tuning, Lion exhibits a similar or better performance compared to Adam. Our analysis of Lion reveals that its performance gain grows with the training batch size. It also requires a smaller learning rate than Adam due to the larger norm of the update produced by the sign function. Additionally, we examine the limitations of Lion and identify scenarios where its improvements are small or not statistically significant. Lion is also successfully deployed in production systems such as Google search ads CTR model.	['Quoc V. Le', 'Yifeng Lu', 'Cho-Jui Hsieh', 'Thang Luong', 'Xuanyi Dong', 'Hieu Pham', 'Yao Liu', 'Kaiyuan Wang', 'Esteban Real', 'Da Huang', 'Chen Liang', 'Xiangning Chen']	2023-02-13	null	null	null	null	['zero-shot-transfer-image-classification']	['computer-vision']	[-1.33393914e-01 -2.48150215e-01 -3.93311113e-01 -3.85463148e-01 -7.89254665e-01 -6.43462300e-01 3.92434627e-01 -4.31189872e-02 -9.18434143e-01 3.29738230e-01 -5.20001531e-01 -7.01285899e-01 -1.25954032e-01 -5.15913188e-01 -1.07710552e+00 -4.96960253e-01 -1.32575601e-01 4.10497367e-01 1.05163880e-01 -1.75218493e-01 6.74148053e-02 1.96539193e-01 -1.39207721e+00 7.28508383e-02 6.99903905e-01 1.35861087e+00 3.62412721e-01 7.29557097e-01 -1.91870138e-01 1.01077282e+00 -5.64718723e-01 -5.51632404e-01 3.75467151e-01 -1.06868863e-01 -7.23944843e-01 -4.16320831e-01 8.55693996e-01 -4.13652331e-01 -4.68385637e-01 1.18548679e+00 4.59369540e-01 1.21327490e-01 3.34183842e-01 -1.07606483e+00 -6.50940359e-01 8.07793140e-01 -8.08569014e-01 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81267d09-02f2-4452-98b0-d068b3299c22	regularized-graph-structure-learning-with	2210.06126	null	https://arxiv.org/abs/2210.06126v1	https://arxiv.org/pdf/2210.06126v1.pdf	Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting	Multivariate time-series forecasting is a critical task for many applications, and graph time-series network is widely studied due to its capability to capture the spatial-temporal correlation simultaneously. However, most existing works focus more on learning with the explicit prior graph structure, while ignoring potential information from the implicit graph structure, yielding incomplete structure modeling. Some recent works attempt to learn the intrinsic or implicit graph structure directly while lacking a way to combine explicit prior structure with implicit structure together. In this paper, we propose Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure. RGSL consists of two innovative modules. First, we derive an implicit dense similarity matrix through node embedding, and learn the sparse graph structure using the Regularized Graph Generation (RGG) based on the Gumbel Softmax trick. Second, we propose a Laplacian Matrix Mixed-up Module (LM3) to fuse the explicit graph and implicit graph together. We conduct experiments on three real-word datasets. Results show that the proposed RGSL model outperforms existing graph forecasting algorithms with a notable margin, while learning meaningful graph structure simultaneously. Our code and models are made publicly available at https://github.com/alipay/RGSL.git.	['Alex Liu', 'Liang Wang', 'Yan Huang', 'Jianguo Li', 'Weichen Yu', 'Ting Li', 'Hongyuan Yu']	2022-10-12	null	null	null	null	['graph-structure-learning']	['graphs']	[-5.67010641e-02 1.83081239e-01 -2.46505708e-01 -4.61782992e-01 -2.13105172e-01 -3.30667704e-01 5.67192852e-01 9.92103145e-02 2.01248020e-01 3.90786916e-01 4.78025407e-01 -3.08622152e-01 -2.19463512e-01 -8.59112620e-01 -7.20400214e-01 -8.49446177e-01 -2.89439678e-01 7.21604526e-02 -1.94297820e-01 -1.18091978e-01 2.12069340e-02 3.90067324e-02 -8.55525315e-01 -7.86253512e-02 1.06713045e+00 7.95789540e-01 2.42511630e-01 2.61756033e-01 -2.60999352e-01 1.06533468e+00 -2.38708407e-02 -2.64652580e-01 1.24772243e-01 -3.84338021e-01 -3.59553069e-01 2.92701662e-01 9.23385769e-02 -1.19972602e-01 -7.62369156e-01 1.10981250e+00 2.63296783e-01 9.22475830e-02 4.82545495e-01 -1.42144573e+00 -9.55035329e-01 7.13621020e-01 -7.35819757e-01 -6.31041154e-02 1.23040557e-01 2.20130980e-01 1.24675882e+00 -7.35056639e-01 4.17214066e-01 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4b3e4eb8-a012-485c-bac5-d120e9fa700b	alert-calm-down-there-is-nothing-to-worry	null	null	https://aclanthology.org/L14-1566	https://aclanthology.org/L14-1566.pdf	Alert!... Calm Down, There is Nothing to Worry About. Warning and Soothing Speech Synthesis.	Presence of appropriate acoustic cues of affective features in the synthesized speech can be a prerequisite for the proper evaluation of the semantic content by the message recipient. In the recent work the authors have focused on the research of expressive speech synthesis capable of generating naturally sounding synthetic speech at various levels of arousal. The synthesizer should be able to produce speech in Slovak in different styles from extremely urgent warnings, insisting messages, alerts, through comments, and neutral style speech to soothing messages and very calm speech. A three-step method was used for recording both - the high-activation and low-activation expressive speech databases. The acoustic properties of the obtained databases are discussed. Several synthesizers with different levels of arousal were designed using these databases and their outputs are compared to the original voice of the voice talent. A possible ambiguity of acoustic cues is pointed out and the relevance of the semantic meaning of the sentences both in the sentence set for the speech database recording and in the set for subjective synthesizer testing is discussed.	["R{\\'o}bert Sabo", "Mari{\\'a}n Ritomsk{\\'y}", "Mari{\\'a}n Trnka", 'Sakhia Darjaa', 'Milan Rusko']	2014-05-01	null	null	null	lrec-2014-5	['expressive-speech-synthesis']	['speech']	[ 1.88630596e-01 3.84564131e-01 4.26812500e-01 -6.88461959e-01 -4.89769608e-01 -4.71179783e-01 7.41643250e-01 1.59625858e-01 -2.17824668e-01 6.60846591e-01 5.15971005e-01 -2.00881227e-03 -1.87962770e-01 -5.08149803e-01 -2.78196726e-02 -6.65395796e-01 4.03406382e-01 3.38851333e-01 -1.25977144e-01 -6.69829011e-01 2.18891129e-01 7.28975892e-01 -1.97328150e+00 4.89501745e-01 4.38182414e-01 7.59298801e-01 4.23806250e-01 1.02556217e+00 -2.07760528e-01 7.22847104e-01 -1.25585830e+00 -1.45298108e-01 -1.70700654e-01 -7.36439168e-01 -4.53702122e-01 5.33528745e-01 2.90724970e-02 1.55767635e-01 2.08462670e-01 1.14807785e+00 8.02447677e-01 4.01245832e-01 6.86423421e-01 -9.27263737e-01 -2.84080803e-01 6.41354144e-01 5.58639944e-01 1.55889928e-01 7.64842331e-01 1.56681314e-01 7.08046079e-01 -1.03948951e+00 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933ba739-ed24-4b92-8b5a-b386f051b017	learning-subpocket-prototypes-for	2305.13997	null	https://arxiv.org/abs/2305.13997v1	https://arxiv.org/pdf/2305.13997v1.pdf	Learning Subpocket Prototypes for Generalizable Structure-based Drug Design	Generating molecules with high binding affinities to target proteins (a.k.a. structure-based drug design) is a fundamental and challenging task in drug discovery. Recently, deep generative models have achieved remarkable success in generating 3D molecules conditioned on the protein pocket. However, most existing methods consider molecular generation for protein pockets independently while neglecting the underlying connections such as subpocket-level similarities. Subpockets are the local protein environments of ligand fragments and pockets with similar subpockets may bind the same molecular fragment (motif) even though their overall structures are different. Therefore, the trained models can hardly generalize to unseen protein pockets in real-world applications. In this paper, we propose a novel method DrugGPS for generalizable structure-based drug design. With the biochemical priors, we propose to learn subpocket prototypes and construct a global interaction graph to model the interactions between subpocket prototypes and molecular motifs. Moreover, a hierarchical graph transformer encoder and motif-based 3D molecule generation scheme are used to improve the model's performance. The experimental results show that our model consistently outperforms baselines in generating realistic drug candidates with high affinities in challenging out-of-distribution settings.	['Qi Liu', 'Zaixi Zhang']	2023-05-22	null	null	null	null	['drug-discovery', '3d-molecule-generation']	['medical', 'medical']	[ 2.99753517e-01 1.24020956e-01 -3.72918278e-01 -1.74554661e-01 -7.36693323e-01 -5.59875965e-01 4.72334087e-01 1.24060676e-01 9.95231271e-02 1.42109561e+00 1.92520082e-01 -3.99683088e-01 4.96651828e-02 -8.75739872e-01 -1.31554341e+00 -1.02842963e+00 -2.90082637e-02 9.09711480e-01 2.07707658e-01 -2.69982755e-01 1.97569549e-01 5.38491905e-01 -9.49544489e-01 5.70415556e-01 1.11181641e+00 2.59439647e-01 4.17432129e-01 3.08901876e-01 -6.64949976e-03 2.07125559e-01 -5.37299573e-01 -2.91138977e-01 9.32728574e-02 -7.45358884e-01 -6.27207458e-01 -1.31617516e-01 2.82740176e-01 -1.34387478e-01 -2.73775220e-01 7.80305326e-01 7.59105980e-01 6.81293756e-02 1.12105381e+00 -6.06143594e-01 -9.02195215e-01 2.08888784e-01 -5.04436135e-01 -2.85660028e-01 5.59765458e-01 3.26842964e-01 1.03260911e+00 -1.02043092e+00 8.08358073e-01 1.36965466e+00 4.97933120e-01 7.72618711e-01 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fd0ae046-6d51-4be8-9d86-8c07b747e5ff	generalizable-wireless-navigation-through	2306.06766	null	https://arxiv.org/abs/2306.06766v1	https://arxiv.org/pdf/2306.06766v1.pdf	Generalizable Wireless Navigation through Physics-Informed Reinforcement Learning in Wireless Digital Twin	The growing focus on indoor robot navigation utilizing wireless signals has stemmed from the capability of these signals to capture high-resolution angular and temporal measurements. However, employing end-to-end generic reinforcement learning (RL) for wireless indoor navigation (WIN) in initially unknown environments remains a significant challenge, due to its limited generalization ability and poor sample efficiency. At the same time, purely model-based solutions, based on radio frequency propagation, are simple and generalizable, but unable to find optimal decisions in complex environments. This work proposes a novel physics-informed RL (PIRL) were a standard distance-to-target-based cost along with physics-informed terms on the optimal trajectory. The proposed PIRL is evaluated using a wireless digital twin (WDT) built upon simulations of a large class of indoor environments from the AI Habitat dataset augmented with electromagnetic radiation (EM) simulation for wireless signals. It is shown that the PIRL significantly outperforms both standard RL and purely physics-based solutions in terms of generalizability and performance. Furthermore, the resulting PIRL policy is explainable in that it is empirically consistent with the physics heuristic.	['Quanyan Zhu', 'Sundeep Rangan', 'Yaqi Hu', 'Haozhe Lei', 'Tao Li', 'Mingsheng Yin']	2023-06-11	null	null	null	null	['robot-navigation']	['robots']	[ 1.50692746e-01 6.87147230e-02 1.62211969e-01 -2.77982831e-01 -1.04541838e+00 -4.51954395e-01 4.28885520e-01 5.03330976e-02 -5.38996875e-01 1.23372543e+00 -1.39889479e-01 -4.75537360e-01 -1.07221389e+00 -1.05766511e+00 -1.01976883e+00 -8.07613075e-01 -8.90558720e-01 3.44193608e-01 -9.57396068e-03 -3.78386021e-01 -3.44334580e-02 3.84696931e-01 -1.43441498e+00 -6.62099004e-01 1.02478051e+00 1.06678796e+00 6.86162829e-01 8.05669785e-01 3.17186058e-01 5.37296474e-01 -3.36229950e-01 3.24870408e-01 3.18371028e-01 -2.20674187e-01 -2.67391175e-01 -7.11535037e-01 4.70684990e-02 -2.41158396e-01 -4.80985045e-01 5.90908110e-01 9.13868129e-01 5.73659420e-01 5.09467542e-01 -1.36797512e+00 -8.53665546e-02 2.90573061e-01 -4.29995023e-02 -1.66673213e-01 6.71817243e-01 -5.31030707e-02 6.91916227e-01 -3.81277829e-01 4.64178890e-01 1.21727848e+00 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17b8572c-308e-4bd6-b2a5-e254ad471d4e	root-aligned-smiles-for-molecular	2203.11444	null	https://arxiv.org/abs/2203.11444v5	https://arxiv.org/pdf/2203.11444v5.pdf	Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction	Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule representations. However, the general-purpose SMILES neglects the characteristics of chemical reactions, where the molecular graph topology is largely unaltered from reactants to products, resulting in the suboptimal performance of SMILES if straightforwardly applied. In this article, we propose the root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES for more efficient synthesis prediction. Due to the strict one-to-one mapping and reduced edit distance, the computational model is largely relieved from learning the complex syntax and dedicated to learning the chemical knowledge for reactions. We compare the proposed R-SMILES with various state-of-the-art baselines and show that it significantly outperforms them all, demonstrating the superiority of the proposed method.	['Shaolun Yao', 'Mingli Song', 'Tingjun Hou', 'Min Wu', 'Lingxiang Jia', 'Tiantao Liu', 'Zunlei Feng', 'Jie Song', 'Zipeng Zhong']	2022-03-22	null	null	null	null	['chemical-reaction-prediction', 'retrosynthesis']	['medical', 'medical']	[ 6.62169755e-01 -2.53644288e-02 -4.89856392e-01 -1.11744821e-01 -3.36583704e-01 -8.82416487e-01 7.59529769e-01 4.04224068e-01 -7.61973066e-03 9.90518093e-01 2.06374303e-01 -6.33593619e-01 2.08479583e-01 -9.04447913e-01 -1.01044297e+00 -9.68666852e-01 2.80149102e-01 1.26969054e-01 8.24416578e-02 -2.90554285e-01 4.46307659e-01 5.99183977e-01 -1.09178853e+00 2.63976455e-01 1.03398216e+00 5.71187556e-01 4.56920862e-01 4.21504915e-01 -4.55023944e-01 8.07475984e-01 -3.55848074e-01 -6.03507638e-01 1.16813168e-01 -7.65028715e-01 -6.17820859e-01 -5.06782770e-01 9.27353650e-02 1.92592531e-01 -3.83265734e-01 1.04225326e+00 4.20849323e-01 1.96721554e-01 1.02532697e+00 -8.55333567e-01 -5.75906932e-01 6.66433692e-01 -3.01687509e-01 -1.90085888e-01 5.42441130e-01 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31163c99-424b-428f-a21e-3fa3d51309ef	robust-kalman-filters-with-unknown-covariance	2110.08740	null	https://arxiv.org/abs/2110.08740v3	https://arxiv.org/pdf/2110.08740v3.pdf	Robust Kalman filters with unknown covariance of multiplicative noise	In this paper, state and noise covariance estimation problems for linear system with unknown multiplicative noise are considered. The measurement likelihood is modelled as a mixture of two Gaussian distributions and a Student's t distribution, respectively. The unknown covariance of multiplicative noise is modelled as an inverse Gamma/Wishart distribution and the initial condition is formulated as the nominal covariance. By using robust design and choosing hierarchical priors, two variational Bayesian based robust Kalman filters are proposed. Stability and covergence of the proposed filters, the covariance parameters, the VB inference, and the estimation error dynamics are analyzed. The lower and upper bounds are also provided to guarantee the performance of the proposed filters. A target tracking simulation is provided to validate the effectiveness of the proposed filters.	['Ziyang Meng', 'Xingkai Yu']	2021-10-17	null	null	null	null	['robust-design']	['miscellaneous']	[-1.04953477e-03 2.52502412e-02 -1.51121125e-01 5.21309078e-02 -4.83502239e-01 -4.02030945e-01 4.98695016e-01 -2.95228094e-01 -3.27640206e-01 9.12506282e-01 -2.21070275e-01 -2.71821767e-01 -7.60707736e-01 -5.34509003e-01 -4.57485229e-01 -1.14958549e+00 5.62313087e-02 -4.52517681e-02 2.17580184e-01 2.66767800e-01 8.46388265e-02 2.33499482e-01 -1.18426716e+00 -9.29394782e-01 8.49694192e-01 1.23795903e+00 2.10570976e-01 8.37497771e-01 4.04531449e-01 4.66196805e-01 -7.18803406e-01 6.58447221e-02 4.03644741e-01 -8.27656016e-02 4.28029060e-01 4.20894355e-01 -1.90929547e-01 -4.56115425e-01 -4.10937279e-01 1.69363356e+00 3.83470029e-01 5.54740429e-01 8.20403874e-01 -1.18921506e+00 -2.46513322e-01 2.49822140e-01 -5.36004364e-01 2.35919803e-01 -4.33860481e-01 1.06926121e-01 1.66120261e-01 -5.75783849e-01 -6.62599951e-02 1.43504393e+00 4.22960848e-01 3.04805666e-01 -9.21548009e-01 -8.05988133e-01 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79e23f55-06a4-424c-9237-cde3153071c7	crossing-roads-of-federated-learning-and	2304.08602	null	https://arxiv.org/abs/2304.08602v1	https://arxiv.org/pdf/2304.08602v1.pdf	Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives	Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented.	['Wilfried Elmenreich', 'Wathiq Mansoor', 'Fodil Fadli', 'Faycal Bensaali', 'Abbes Amira', 'Yassine Himeur', 'Roumaysa Bousselidj', 'Hafsa Bousbiat']	2023-04-17	null	null	null	null	['load-forecasting']	['miscellaneous']	[-1.96226507e-01 3.69135067e-02 -1.92491740e-01 -6.25892758e-01 -6.22508347e-01 -6.58162653e-01 2.81221300e-01 3.02512020e-01 2.07757562e-01 1.03089917e+00 -1.05883433e-02 -4.06119376e-01 -1.83535948e-01 -8.71676445e-01 -2.19095454e-01 -1.25051749e+00 -4.40432817e-01 2.28978276e-01 -4.77478206e-01 1.11840144e-01 -1.99322820e-01 7.19980478e-01 -1.19373178e+00 3.94149944e-02 1.12415075e+00 1.51249981e+00 -1.95684046e-01 4.12680842e-02 2.21260488e-01 5.37411928e-01 -8.99075389e-01 -4.55941737e-01 4.11869168e-01 -3.02608192e-01 -7.76066482e-01 -1.49806261e-01 -4.40756410e-01 -3.78527731e-01 -1.11629248e-01 1.41345894e+00 6.37122333e-01 -1.14584111e-01 3.56228799e-01 -2.10518432e+00 -7.72378920e-03 1.01438868e+00 -2.90568054e-01 -1.26213163e-01 1.28792822e-01 4.42398898e-02 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80a984b3-44c5-4001-aa30-6be2e25d5890	mvstylizer-an-efficient-edge-assisted-video	2005.11630	null	https://arxiv.org/abs/2005.11630v2	https://arxiv.org/pdf/2005.11630v2.pdf	MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones	Recent research has made great progress in realizing neural style transfer of images, which denotes transforming an image to a desired style. Many users start to use their mobile phones to record their daily life, and then edit and share the captured images and videos with other users. However, directly applying existing style transfer approaches on videos, i.e., transferring the style of a video frame by frame, requires an extremely large amount of computation resources. It is still technically unaffordable to perform style transfer of videos on mobile phones. To address this challenge, we propose MVStylizer, an efficient edge-assisted photorealistic video style transfer system for mobile phones. Instead of performing stylization frame by frame, only key frames in the original video are processed by a pre-trained deep neural network (DNN) on edge servers, while the rest of stylized intermediate frames are generated by our designed optical-flow-based frame interpolation algorithm on mobile phones. A meta-smoothing module is also proposed to simultaneously upscale a stylized frame to arbitrary resolution and remove style transfer related distortions in these upscaled frames. In addition, for the sake of continuously enhancing the performance of the DNN model on the edge server, we adopt a federated learning scheme to keep retraining each DNN model on the edge server with collected data from mobile clients and syncing with a global DNN model on the cloud server. Such a scheme effectively leverages the diversity of collected data from various mobile clients and efficiently improves the system performance. Our experiments demonstrate that MVStylizer can generate stylized videos with an even better visual quality compared to the state-of-the-art method while achieving 75.5$\times$ speedup for 1920$\times$1080 videos.	['Yiran Chen', 'Chunpeng Wu', 'Ang Li', 'Bin Ni']	2020-05-24	null	null	null	null	['video-style-transfer']	['computer-vision']	[ 2.69796759e-01 -3.96901160e-01 -3.45001183e-02 -1.44072786e-01 -3.23422074e-01 -6.18896902e-01 1.87861070e-01 -7.88383782e-01 -4.28189844e-01 6.49895132e-01 1.65341217e-02 -4.30952668e-01 5.40286660e-01 -8.78165841e-01 -8.98994982e-01 -5.82348526e-01 3.55818093e-01 -1.64696574e-02 2.43802845e-01 -4.63660024e-02 7.57526308e-02 5.25641084e-01 -1.38871229e+00 4.25332248e-01 7.22007871e-01 1.14821565e+00 4.72913474e-01 8.75676155e-01 -2.82450527e-01 9.41462278e-01 -3.92039508e-01 -5.91232538e-01 4.40397471e-01 -5.00221252e-01 -5.42392552e-01 3.34216416e-01 6.68031991e-01 -1.00271821e+00 -8.76051545e-01 1.05947030e+00 3.91367882e-01 1.08182222e-01 1.60075009e-01 -1.30111611e+00 -8.26358557e-01 1.13479458e-01 -5.03809571e-01 1.16720788e-01 1.77140534e-01 3.58604699e-01 6.10991478e-01 -8.48904490e-01 7.97099590e-01 1.18103337e+00 5.01733780e-01 7.77623415e-01 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5.63342392e-01 1.61052674e-01 -2.66839147e-01 -1.39632896e-01 7.26550996e-01]	[10.840279579162598, -1.0301084518432617]
5cc530c3-1643-4fb6-9063-13778c613b57	look-closer-to-see-better-recurrent-attention	null	null	http://openaccess.thecvf.com/content_cvpr_2017/html/Fu_Look_Closer_to_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Fu_Look_Closer_to_CVPR_2017_paper.pdf	Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition	Recognizing fine-grained categories (e.g., bird species) is difficult due to the challenges of discriminative region localization and fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that region detection and fine-grained feature learning are mutually correlated and thus can reinforce each other. In this paper, we propose a novel recurrent attention convolutional neural network (RA-CNN) which recursively learns discriminative region attention and region-based feature representation at multiple scales in a mutual reinforced way. The learning at each scale consists of a classification sub-network and an attention proposal sub-network (APN). The APN starts from full images, and iteratively generates region attention from coarse to fine by taking previous prediction as a reference, while the finer scale network takes as input an amplified attended region from previous scale in a recurrent way. The proposed RA-CNN is optimized by an intra-scale classification loss and an inter-scale ranking loss, to mutually learn accurate region attention and fine-grained representation. RA-CNN does not need bounding box/part annotations and can be trained end-to-end. We conduct comprehensive experiments and show that RA-CNN achieves the best performance in three fine-grained tasks, with relative accuracy gains of 3.3%, 3.7%, 3.8%, on CUB Birds, Stanford Dogs and Stanford Cars, respectively.	['Jianlong Fu', 'Heliang Zheng', 'Tao Mei']	2017-07-01	null	null	null	cvpr-2017-7	['fine-grained-image-recognition']	['computer-vision']	[-1.53067559e-01 -1.51457772e-01 -1.47956446e-01 -4.57137585e-01 -8.84486258e-01 -4.72406864e-01 4.06425655e-01 1.65280044e-01 -5.82356751e-01 6.11551523e-01 1.75415382e-01 1.25349417e-01 5.04582450e-02 -7.84558475e-01 -8.48231256e-01 -5.48733056e-01 -6.76854700e-02 2.32049674e-01 5.67887485e-01 5.90016805e-02 5.87981455e-02 7.28016317e-01 -1.37456644e+00 3.50191295e-01 7.76194513e-01 1.50341499e+00 2.31032521e-01 5.27018785e-01 6.27737492e-02 9.05792117e-01 -4.40062821e-01 -1.79572448e-01 2.49452159e-01 -4.66997828e-03 -7.52735138e-01 5.34813479e-02 6.55813634e-01 -4.61679667e-01 -2.99451381e-01 8.30435038e-01 3.56287867e-01 3.92314970e-01 6.42022431e-01 -1.06384039e+00 -9.93466020e-01 4.98128563e-01 -1.01879609e+00 5.02329886e-01 -2.21318156e-01 4.53477874e-02 1.07423186e+00 -1.07696986e+00 1.04665920e-01 1.33540058e+00 5.76660395e-01 3.65021914e-01 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7c682290-605f-49d3-b596-333d708930cb	on-the-fairness-of-swarm-learning-in-skin	2109.12176	null	https://arxiv.org/abs/2109.12176v1	https://arxiv.org/pdf/2109.12176v1.pdf	On the Fairness of Swarm Learning in Skin Lesion Classification	in healthcare. However, the existing AI model may be biased in its decision marking. The bias induced by data itself, such as collecting data in subgroups only, can be mitigated by including more diversified data. Distributed and collaborative learning is an approach to involve training models in massive, heterogeneous, and distributed data sources, also known as nodes. In this work, we target on examining the fairness issue in Swarm Learning (SL), a recent edge-computing based decentralized machine learning approach, which is designed for heterogeneous illnesses detection in precision medicine. SL has achieved high performance in clinical applications, but no attempt has been made to evaluate if SL can improve fairness. To address the problem, we present an empirical study by comparing the fairness among single (node) training, SL, centralized training. Specifically, we evaluate on large public available skin lesion dataset, which contains samples from various subgroups. The experiments demonstrate that SL does not exacerbate the fairness problem compared to centralized training and improves both performance and fairness compared to single training. However, there still exists biases in SL model and the implementation of SL is more complex than the alternative two strategies.	['Xiaoxiao Li', 'Yifan Wu', 'Di Fan']	2021-09-24	null	null	null	null	['skin-lesion-classification']	['medical']	[-1.68457150e-01 7.64954761e-02 -4.65741754e-01 -1.82748884e-01 -3.68824989e-01 3.91572453e-02 3.75446267e-02 5.00179768e-01 -5.36073983e-01 9.89795864e-01 3.08088481e-01 -1.57032147e-01 -4.98181343e-01 -7.29461014e-01 -2.43642777e-01 -9.09301221e-01 -2.52136320e-01 5.05649805e-01 -1.14518091e-01 4.51240800e-02 -2.96573564e-02 1.26972362e-01 -1.09044230e+00 4.00314391e-01 1.23781967e+00 8.61511469e-01 -2.13902503e-01 4.31762338e-01 -3.51351537e-02 1.14621520e+00 -8.89798999e-01 -5.63952088e-01 4.19280767e-01 -4.92604762e-01 -5.89341819e-01 -4.15202767e-01 -2.48731017e-01 -1.57930851e-01 2.82197297e-01 9.89955604e-01 1.20100796e+00 -2.71707863e-01 5.22578001e-01 -1.71203840e+00 -5.36154091e-01 9.71710622e-01 -7.62562871e-01 2.00728968e-01 -8.12863782e-02 2.50692498e-02 6.36691809e-01 -2.19268829e-01 5.46170652e-01 1.17221272e+00 1.06432414e+00 5.43532670e-01 -7.43687809e-01 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-5.93469143e-01 -5.94262064e-01 4.34193343e-01 -2.67056245e-02 -4.05738086e-01 -5.31311631e-01 -5.40848434e-01]	[6.154947757720947, 6.435591220855713]
9618edca-2edb-4515-a1a6-978c98d9e5d7	image-quality-assessment-for-machine-learning	2203.14258	null	https://arxiv.org/abs/2203.14258v1	https://arxiv.org/pdf/2203.14258v1.pdf	Image quality assessment for machine learning tasks using meta-reinforcement learning	In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor provides an objective estimate of task amenability. In this work, we use an IQA controller to predict the task amenability which, itself being parameterised by neural networks, can be trained simultaneously with the task predictor. We further develop a meta-reinforcement learning framework to improve the adaptability for both IQA controllers and task predictors, such that they can be fine-tuned efficiently on new datasets or meta-tasks. We demonstrate the efficacy of the proposed task-specific, adaptable IQA approach, using two clinical applications for ultrasound-guided prostate intervention and pneumonia detection on X-ray images.	['Yipeng Hu', 'Dean C. Barratt', 'J. Alison Noble', 'Geoffrey A. Sonn', 'Richard E. Fan', 'Mirabela Rusu', 'Qianye Yang', 'Zachary M. C. Baum', 'Vasilis Stavrinides', 'Yunguan Fu', 'Shaheer U. Saeed']	2022-03-27	null	null	null	null	['pneumonia-detection']	['medical']	[ 8.63997042e-01 3.82266462e-01 -1.03512034e-01 -5.08686602e-01 -1.01106322e+00 -3.85650098e-01 3.24945867e-01 3.38790387e-01 -5.30692279e-01 4.42961127e-01 -2.12753758e-01 -3.53490233e-01 -5.54373980e-01 -6.71503425e-01 -6.83341026e-01 -6.56728506e-01 -7.94293061e-02 5.28844297e-01 1.08974455e-02 2.10333616e-01 3.81133884e-01 2.94298142e-01 -1.42040789e+00 2.73093164e-01 1.27044928e+00 1.16884840e+00 6.38722479e-01 1.02524436e+00 4.54717368e-01 6.37026727e-01 -4.45413560e-01 -6.78357854e-02 1.46120995e-01 -2.72599339e-01 -1.08652627e+00 6.25418797e-02 1.97301403e-01 7.06991479e-02 2.14956850e-01 9.01475251e-01 6.27135038e-01 8.94377828e-02 9.06379402e-01 -1.07404149e+00 -5.69495894e-02 2.32811898e-01 1.74997821e-02 3.56761515e-01 -1.10219769e-01 4.40718740e-01 7.23697126e-01 -3.78313124e-01 4.57205623e-01 1.04328609e+00 4.56100911e-01 5.46163261e-01 -1.29395545e+00 -4.09519225e-01 -7.15048462e-02 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608f503b-593c-4a70-8ab5-4ae609082eea	a-robustly-optimized-bmrc-for-aspect	null	null	https://aclanthology.org/2022.naacl-main.20	https://aclanthology.org/2022.naacl-main.20.pdf	A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction	Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis. It aims to explore the triplets of aspects, opinions and sentiments with complex correspondence from the context. The bidirectional machine reading comprehension (BMRC), can effectively deal with ASTE task, but several problems remains, such as query conflict and probability unilateral decrease. Therefore, this paper presents a robustly optimized BMRC method by incorporating four improvements. The word segmentation is applied to facilitate the semantic learning. Exclusive classifiers are designed to avoid the interference between different queries. A span matching rule is proposed to select the aspects and opinions that better represent the expectations of the model. The probability generation strategy is also introduced to obtain the predicted probability for aspects, opinions and aspect-opinion pairs. We have conducted extensive experiments on multiple benchmark datasets, where our model achieves the state-of-the-art performance.	['Zuhe Li', 'Kaiwen Li', 'Shu Liu']	null	null	null	null	naacl-2022-7	['aspect-based-sentiment-analysis', 'machine-reading-comprehension', 'aspect-sentiment-triplet-extraction']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 3.28992248e-01 -1.15218610e-02 -2.84906507e-01 -6.81948721e-01 -1.05957890e+00 -4.12424266e-01 6.27860069e-01 3.16145629e-01 -4.02496368e-01 4.82625902e-01 5.07449806e-01 -4.15874898e-01 -2.17145905e-02 -6.84035778e-01 -4.63223308e-01 -5.31026244e-01 6.10279799e-01 5.45280039e-01 4.06533092e-01 -4.18839186e-01 7.73650467e-01 -3.60773981e-01 -1.63340139e+00 7.08082259e-01 1.01180959e+00 1.22274733e+00 1.10545851e-01 4.31159765e-01 -8.99311304e-01 7.00554311e-01 -8.20949912e-01 -8.58114362e-01 -1.71042144e-01 -4.60656732e-01 -8.17444324e-01 3.28920595e-02 -3.01476233e-02 1.66659102e-01 4.92226154e-01 1.13529599e+00 4.97324258e-01 -1.69361889e-01 8.57235312e-01 -1.36269784e+00 -6.04817092e-01 7.78982222e-01 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f3ed7856-0528-478f-8a61-3a1f78f7fc07	mutual-wasserstein-discrepancy-minimization	2301.12197	null	https://arxiv.org/abs/2301.12197v2	https://arxiv.org/pdf/2301.12197v2.pdf	Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation	Self-supervised sequential recommendation significantly improves recommendation performance by maximizing mutual information with well-designed data augmentations. However, the mutual information estimation is based on the calculation of Kullback Leibler divergence with several limitations, including asymmetrical estimation, the exponential need of the sample size, and training instability. Also, existing data augmentations are mostly stochastic and can potentially break sequential correlations with random modifications. These two issues motivate us to investigate an alternative robust mutual information measurement capable of modeling uncertainty and alleviating KL divergence limitations. To this end, we propose a novel self-supervised learning framework based on Mutual WasserStein discrepancy minimization MStein for the sequential recommendation. We propose the Wasserstein Discrepancy Measurement to measure the mutual information between augmented sequences. Wasserstein Discrepancy Measurement builds upon the 2-Wasserstein distance, which is more robust, more efficient in small batch sizes, and able to model the uncertainty of stochastic augmentation processes. We also propose a novel contrastive learning loss based on Wasserstein Discrepancy Measurement. Extensive experiments on four benchmark datasets demonstrate the effectiveness of MStein over baselines. More quantitative analyses show the robustness against perturbations and training efficiency in batch size. Finally, improvements analysis indicates better representations of popular users or items with significant uncertainty. The source code is at https://github.com/zfan20/MStein.	['Philip S Yu', 'Hao Peng', 'Zhiwei Liu', 'Ziwei Fan']	2023-01-28	null	null	null	null	['mutual-information-estimation']	['methodology']	[ 1.67372841e-02 -1.65544674e-01 -2.61884093e-01 -4.82555538e-01 -6.48028076e-01 -3.57745916e-01 4.21244651e-01 1.31713584e-01 -4.61404324e-01 7.14085698e-01 3.20939928e-01 -2.50039935e-01 -4.12295729e-01 -7.92055309e-01 -6.63783014e-01 -7.03450084e-01 -1.84326693e-01 1.98947191e-01 -1.09120227e-01 -2.01683447e-01 2.76680440e-01 8.71836469e-02 -1.56705499e+00 -1.85721219e-02 1.15575063e+00 1.14077210e+00 2.31325850e-01 3.49187791e-01 -1.32198200e-01 3.95399809e-01 -2.41914213e-01 -2.89227039e-01 5.35277426e-01 -5.84421933e-01 -4.10291910e-01 -2.82109249e-02 1.85109392e-01 -3.22910845e-01 -2.58402348e-01 1.00473082e+00 4.86563623e-01 5.90555012e-01 7.80538261e-01 -1.19310045e+00 -7.98307061e-01 1.03790975e+00 -4.93403047e-01 1.68906674e-01 1.71972468e-01 -1.29922658e-01 1.26688659e+00 -9.97510135e-01 -2.07733996e-02 8.96283686e-01 9.61075127e-01 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-1.26757503e+00 -9.00606573e-01 6.30618453e-01 -1.76169872e-02 -5.20079553e-01 -6.34502769e-01 -2.84510434e-01]	[10.02226448059082, 5.548157691955566]
5139907f-9023-4be0-b7a9-3060b98cc907	a-machine-learning-model-for-stock-market	1402.7351	null	http://arxiv.org/abs/1402.7351v1	http://arxiv.org/pdf/1402.7351v1.pdf	A Machine Learning Model for Stock Market Prediction	Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange.	['Mustafa Abdul Salam', 'Omar S. Soliman', 'Osman Hegazy']	2014-02-28	null	null	null	null	['stock-market-prediction']	['time-series']	[-6.33701742e-01 1.26430020e-01 -3.66918266e-01 -1.36451527e-01 2.74660707e-01 -7.08443820e-01 8.91899109e-01 -3.33094060e-01 -2.74445087e-01 1.19035959e+00 2.66677529e-01 -1.02282214e+00 4.39347148e-01 -1.12784135e+00 -1.38205532e-02 -4.76825982e-01 -7.26412460e-02 3.99725169e-01 4.59557801e-01 -3.20186079e-01 1.02077854e+00 1.01677763e+00 -7.70705163e-01 -1.33924752e-01 -3.65083486e-01 1.70275450e+00 -7.58706257e-02 6.38342977e-01 -6.59871340e-01 1.65058720e+00 -6.49200439e-01 -7.05985010e-01 7.05943227e-01 -7.15378404e-01 -4.51142848e-01 -9.28356349e-02 -7.39907920e-01 -4.92305428e-01 -1.15750633e-01 1.39875865e+00 -5.26039183e-01 -3.88950050e-01 2.99435705e-01 -1.04132974e+00 -1.84056655e-01 5.42202353e-01 -1.50396749e-01 7.07825303e-01 1.32169306e-01 1.55602053e-01 1.25879180e+00 -8.55328262e-01 4.57738042e-01 5.43670058e-01 4.75006670e-01 -4.00283299e-02 -8.67376685e-01 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-1.66901839e+00 1.08749866e-02 5.65125883e-01 -5.41888624e-02 -5.60456097e-01 1.83804676e-01]	[4.529783248901367, 4.189852714538574]
0be63c24-85f5-4e76-855c-72d87f49c41b	understanding-mcmc-dynamics-as-flows-on-the	1902.00282	null	https://arxiv.org/abs/1902.00282v3	https://arxiv.org/pdf/1902.00282v3.pdf	Understanding MCMC Dynamics as Flows on the Wasserstein Space	It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs). But no more MCMC dynamics is understood in this way. In this work, by developing novel concepts, we propose a theoretical framework that recognizes a general MCMC dynamics as the fiber-gradient Hamiltonian flow on the Wasserstein space of a fiber-Riemannian Poisson manifold. The "conservation + convergence" structure of the flow gives a clear picture on the behavior of general MCMC dynamics. The framework also enables ParVI simulation of MCMC dynamics, which enriches the ParVI family with more efficient dynamics, and also adapts ParVI advantages to MCMCs. We develop two ParVI methods for a particular MCMC dynamics and demonstrate the benefits in experiments.	['Jun Zhu', 'Chang Liu', 'Jingwei Zhuo']	2019-02-01	null	null	null	null	['novel-concepts']	['reasoning']	[-3.55018407e-01 -3.18809241e-01 2.87872583e-01 -8.81447569e-02 1.10334292e-01 -3.62168640e-01 7.84803152e-01 -3.30391198e-01 -6.05475366e-01 7.94267237e-01 -2.09496412e-02 -2.41386786e-01 -2.87221491e-01 -8.40338409e-01 -6.30198240e-01 -1.00347912e+00 -2.48247713e-01 5.57146668e-01 2.77986079e-01 -2.83339530e-01 4.64013487e-01 4.94332612e-01 -1.17224562e+00 -5.04192829e-01 7.75629103e-01 9.73162428e-02 3.58733088e-01 8.98866653e-01 -1.41425207e-01 3.65695685e-01 1.10333182e-01 -2.71296859e-01 -2.21450776e-02 -5.74104488e-01 -8.38004053e-01 -2.43929982e-01 1.81944594e-01 -1.50573239e-01 -1.07945025e-01 1.20417845e+00 1.26721501e-01 5.18911779e-01 1.02192140e+00 -1.34908080e+00 -6.04932129e-01 1.44395724e-01 -4.06817853e-01 3.52500767e-01 9.48006660e-02 7.04646334e-02 9.55785871e-01 -9.61488008e-01 1.00709546e+00 1.64016104e+00 8.04599941e-01 7.53928304e-01 -1.40881860e+00 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4.41293538e-01 3.13219070e-01 -8.65725398e-01 -7.69428134e-01 -3.89962196e-02]	[7.0061492919921875, 3.9679691791534424]
ddc0021e-d8ad-4847-8a54-eabfcdc48c80	wdc-products-a-multi-dimensional-entity	2301.09521	null	https://arxiv.org/abs/2301.09521v2	https://arxiv.org/pdf/2301.09521v2.pdf	WDC Products: A Multi-Dimensional Entity Matching Benchmark	The difficulty of an entity matching task depends on a combination of multiple factors such as the amount of corner-case pairs, the fraction of entities in the test set that have not been seen during training, and the size of the development set. Current entity matching benchmarks usually represent single points in the space along such dimensions or they provide for the evaluation of matching methods along a single dimension, for instance the amount of training data. This paper presents WDC Products, an entity matching benchmark which provides for the systematic evaluation of matching systems along combinations of three dimensions while relying on real-world data. The three dimensions are (i) amount of corner-cases (ii) generalization to unseen entities, and (iii) development set size (training set plus validation set). Generalization to unseen entities is a dimension not covered by any of the existing English-language benchmarks yet but is crucial for evaluating the robustness of entity matching systems. Instead of learning how to match entity pairs, entity matching can also be formulated as a multi-class classification task that requires the matcher to recognize individual entities. WDC Products is the first benchmark that provides a pair-wise and a multi-class formulation of the same tasks. We evaluate WDC Products using several state-of-the-art matching systems, including Ditto, HierGAT, and R-SupCon. The evaluation shows that all matching systems struggle with unseen entities to varying degrees. It also shows that for entity matching contrastive learning is more training data efficient compared to cross-encoders.	['Christian Bizer', 'Reng Chiz Der', 'Ralph Peeters']	2023-01-23	null	null	null	null	['data-integration', 'entity-resolution']	['knowledge-base', 'natural-language-processing']	[-5.78201748e-02 -1.03958227e-01 -1.98578268e-01 -4.17418808e-01 -8.48559976e-01 -9.07155335e-01 8.82578373e-01 5.21576643e-01 -8.82462800e-01 5.18785775e-01 -5.50035387e-02 -3.19575697e-01 -3.37014705e-01 -9.15708065e-01 -7.85530925e-01 -1.31056339e-01 -1.12208083e-01 9.04800773e-01 2.91312844e-01 -5.69640160e-01 -9.00951996e-02 5.21453261e-01 -1.72136426e+00 4.12953019e-01 7.02753484e-01 8.65411222e-01 -1.41502127e-01 4.26785827e-01 -5.62455840e-02 1.65560588e-01 -6.88930750e-01 -8.92807543e-01 4.38365668e-01 -5.11610284e-02 -1.00399089e+00 -5.18808722e-01 1.04967797e+00 6.40453324e-02 -1.16383322e-01 9.31933761e-01 7.24324048e-01 -1.03747576e-01 7.67300367e-01 -1.55265570e+00 -7.46299326e-01 6.59009516e-01 -9.43006054e-02 3.40532243e-01 6.44484758e-01 -1.21995047e-01 1.23260772e+00 -1.08360958e+00 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9a387290-739b-481a-9dd7-9af1a2ba7754	texttt-tasksource-structured-dataset	2301.05948	null	https://arxiv.org/abs/2301.05948v3	https://arxiv.org/pdf/2301.05948v3.pdf	tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation	The HuggingFace Datasets Hub hosts thousands of datasets, offering exciting opportunities for language model training and evaluation. However, datasets for a specific task type often have different schemas, making harmonization challenging. Multi-task training or evaluation necessitates manual work to fit data into task templates. Several initiatives independently tackle this issue by releasing harmonized datasets or providing harmonization codes to preprocess datasets into a consistent format. We identify patterns across previous preprocessing efforts, such as column name mapping and extracting specific sub-fields from structured data in a column. We then propose a structured annotation framework that ensures our annotations are fully exposed and not hidden within unstructured code. We release a dataset annotation framework and dataset annotations for more than 500 English tasks\footnote{\url{https://github.com/sileod/tasksource}}. These annotations include metadata, such as the names of columns to be used as input or labels for all datasets, which can save time for future dataset preprocessing, regardless of whether our framework is utilized. We fine-tune a multi-task text encoder on all tasksource tasks, outperforming every publicly available text encoder of comparable size in an external evaluation.	['Damien Sileo']	2023-01-14	null	null	null	null	['linguistic-acceptability']	['natural-language-processing']	[ 6.28242791e-02 -7.69835263e-02 -3.61561835e-01 -7.85841823e-01 -1.14673769e+00 -9.31574404e-01 4.55361247e-01 3.66570026e-01 -7.56165266e-01 7.73037016e-01 5.23653388e-01 -3.80213678e-01 8.80307704e-02 -4.31822181e-01 -9.40519094e-01 -1.29585579e-01 4.94551361e-01 7.42526352e-01 -1.83646947e-01 -1.24297768e-01 -7.73514509e-02 -2.97406733e-01 -1.20269978e+00 8.70310366e-01 6.20915592e-01 8.52282047e-01 3.92021358e-01 3.84635627e-01 -4.11508054e-01 5.78833580e-01 -7.09201217e-01 -1.03415823e+00 3.31394136e-01 1.19128585e-01 -1.12819171e+00 -3.22417378e-01 6.59364045e-01 5.77575564e-02 -5.57945184e-02 8.02796781e-01 6.48655653e-01 -1.94320381e-01 4.33604628e-01 -1.46089673e+00 -6.90280199e-01 1.28639412e+00 -3.37184548e-01 4.66278940e-03 6.48645917e-03 -4.82762838e-03 1.11843717e+00 -8.88553560e-01 9.75259781e-01 7.23266780e-01 9.43609953e-01 5.55253088e-01 -1.29157627e+00 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fe024b64-4f7b-479c-bfa1-2b3ecc8d748b	tcube-domain-agnostic-neural-time-series	2110.05633	null	https://arxiv.org/abs/2110.05633v1	https://arxiv.org/pdf/2110.05633v1.pdf	TCube: Domain-Agnostic Neural Time-series Narration	The task of generating rich and fluent narratives that aptly describe the characteristics, trends, and anomalies of time-series data is invaluable to the sciences (geology, meteorology, epidemiology) or finance (trades, stocks, or sales and inventory). The efforts for time-series narration hitherto are domain-specific and use predefined templates that offer consistency but lead to mechanical narratives. We present TCube (Time-series-to-text), a domain-agnostic neural framework for time-series narration, that couples the representation of essential time-series elements in the form of a dense knowledge graph and the translation of said knowledge graph into rich and fluent narratives through the transfer-learning capabilities of PLMs (Pre-trained Language Models). TCube's design primarily addresses the challenge that lies in building a neural framework in the complete paucity of annotated training data for time-series. The design incorporates knowledge graphs as an intermediary for the representation of essential time-series elements which can be linearized for textual translation. To the best of our knowledge, TCube is the first investigation of the use of neural strategies for time-series narration. Through extensive evaluations, we show that TCube can improve the lexical diversity of the generated narratives by up to 65.38% while still maintaining grammatical integrity. The practicality and deployability of TCube is further validated through an expert review (n=21) where 76.2% of participating experts wary of auto-generated narratives favored TCube as a deployable system for time-series narration due to its richer narratives. Our code-base, models, and datasets, with detailed instructions for reproducibility is publicly hosted at https://github.com/Mandar-Sharma/TCube.	['Naren Ramakrishnan', 'John S. Brownstein', 'Mandar Sharma']	2021-10-11	null	null	null	null	['epidemiology']	['medical']	[-4.45641205e-02 2.64195681e-01 3.79418060e-02 -7.96132535e-02 -7.77294517e-01 -9.17207241e-01 9.00023103e-01 2.20203511e-02 -2.43219715e-02 6.61811054e-01 7.52704799e-01 -7.63851643e-01 -1.56064808e-01 -9.07175303e-01 -6.36858642e-01 -2.67518073e-01 -4.62080628e-01 2.04102531e-01 -3.91074568e-01 -7.29771793e-01 1.28990129e-01 4.98388082e-01 -1.17024946e+00 4.99938965e-01 5.65436602e-01 7.54870415e-01 -1.44385220e-02 6.23885632e-01 -1.88780904e-01 1.05005121e+00 -8.13274205e-01 -4.76819426e-01 -5.36274426e-02 -5.86555779e-01 -6.91828370e-01 -3.93170238e-01 -1.41780488e-02 -2.97595203e-01 -5.84193707e-01 2.61429012e-01 4.76876378e-01 1.13443494e-01 6.92076623e-01 -1.19475067e+00 -1.08163679e+00 1.10116637e+00 -1.96613483e-02 4.26445752e-01 4.37007695e-01 3.31263185e-01 8.93782258e-01 -6.53212428e-01 9.60526824e-01 8.85525823e-01 1.08704567e+00 3.48055393e-01 -9.85968590e-01 -6.65929019e-01 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4.12323117e-01 2.64705032e-01 -3.62317801e-01 -8.86496231e-02 4.24063653e-01]	[11.13805866241455, 8.827312469482422]
b07fdbb3-3148-4d3c-8767-ccb378b374f3	on-universal-black-box-domain-adaptation	2104.04665	null	https://arxiv.org/abs/2104.04665v1	https://arxiv.org/pdf/2104.04665v1.pdf	On Universal Black-Box Domain Adaptation	In this paper, we study an arguably least restrictive setting of domain adaptation in a sense of practical deployment, where only the interface of source model is available to the target domain, and where the label-space relations between the two domains are allowed to be different and unknown. We term such a setting as Universal Black-Box Domain Adaptation (UB$^2$DA). The great promise that UB$^2$DA makes, however, brings significant learning challenges, since domain adaptation can only rely on the predictions of unlabeled target data in a partially overlapped label space, by accessing the interface of source model. To tackle the challenges, we first note that the learning task can be converted as two subtasks of in-class\footnote{In this paper we use in-class (out-class) to describe the classes observed (not observed) in the source black-box model.} discrimination and out-class detection, which can be respectively learned by model distillation and entropy separation. We propose to unify them into a self-training framework, regularized by consistency of predictions in local neighborhoods of target samples. Our framework is simple, robust, and easy to be optimized. Experiments on domain adaptation benchmarks show its efficacy. Notably, by accessing the interface of source model only, our framework outperforms existing methods of universal domain adaptation that make use of source data and/or source models, with a newly proposed (and arguably more reasonable) metric of H-score, and performs on par with them with the metric of averaged class accuracy.	['Kui Jia', 'Changxing Ding', 'Hui Tang', 'Yabin Zhang', 'Bin Deng']	2021-04-10	null	null	null	null	['universal-domain-adaptation']	['computer-vision']	[ 5.38189650e-01 3.21408570e-01 -6.97353005e-01 -4.68848765e-01 -9.43624556e-01 -9.07387257e-01 7.14998841e-01 1.64900303e-01 -4.43625897e-01 1.02407372e+00 -2.91127801e-01 -2.99085528e-01 -1.52288377e-01 -6.07723951e-01 -7.21438468e-01 -8.38217616e-01 1.26051530e-01 6.30834341e-01 2.85035878e-01 -1.32067040e-01 -3.24986689e-02 2.63126075e-01 -1.35041904e+00 4.72209379e-02 8.34546745e-01 1.30196416e+00 -6.39565066e-02 3.94022167e-01 -3.04739885e-02 5.04178762e-01 -4.64032024e-01 -4.35390472e-01 4.50031281e-01 -5.05301893e-01 -9.24521089e-01 -3.84967290e-02 3.33399624e-01 -1.67773262e-01 5.04041417e-03 1.07792079e+00 3.16009164e-01 2.43798479e-01 1.08579481e+00 -1.24500132e+00 -6.60674274e-01 2.99710751e-01 -2.77243465e-01 2.16427594e-01 2.18082070e-01 -6.46044314e-02 1.11418271e+00 -7.15921998e-01 6.31019592e-01 8.27902496e-01 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0f6735b6-bf48-4e12-ba73-2801bd21f620	towards-identifying-alternative	null	null	https://aclanthology.org/2022.coling-1.70	https://aclanthology.org/2022.coling-1.70.pdf	Towards Identifying Alternative-Lexicalization Signals of Discourse Relations	The task of shallow discourse parsing in the Penn Discourse Treebank (PDTB) framework has traditionally been restricted to identifying those relations that are signaled by a discourse connective (“explicit”) and those that have no signal at all (“implicit”). The third type, the more flexible group of “AltLex” realizations has been neglected because of its small amount of occurrences in the PDTB2 corpus. Their number has grown significantly in the recent PDTB3, and in this paper, we present the first approaches for recognizing these “alternative lexicalizations”. We compare the performance of a pattern-based approach and a sequence labeling model, add an experiment on the pre-classification of candidate sentences, and provide an initial qualitative analysis of the error cases made by both models.	['Manfred Stede', 'René Knaebel']	null	null	null	null	coling-2022-10	['discourse-parsing']	['natural-language-processing']	[ 2.66197801e-01 9.52833831e-01 -2.39852950e-01 -4.93056476e-01 -6.77204132e-01 -7.33764291e-01 9.10581291e-01 6.93250537e-01 -3.86043221e-01 1.15258026e+00 8.60903978e-01 -8.33032072e-01 -1.34915113e-01 -7.45131016e-01 -2.60891646e-01 -4.34268892e-01 1.06829159e-01 4.81084585e-01 6.81517065e-01 -3.77231181e-01 3.24832261e-01 1.40738890e-01 -1.26996958e+00 7.11418867e-01 4.49552149e-01 3.97033125e-01 2.86580354e-01 3.04785132e-01 -7.59376287e-01 1.05320120e+00 -9.25065339e-01 -4.18335140e-01 -2.70558804e-01 -6.14223063e-01 -1.53476310e+00 1.64093807e-01 1.38947085e-01 -6.12152275e-03 -1.82628445e-02 9.56396520e-01 1.81311339e-01 6.21211249e-03 3.86174679e-01 -6.23393118e-01 -1.30956829e-01 1.21121311e+00 -2.25734875e-01 3.93401474e-01 8.67621839e-01 -1.63397044e-01 1.28312457e+00 -5.40388405e-01 1.28922665e+00 1.49811482e+00 4.33932513e-01 6.52583838e-01 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-1.13691604e+00 -3.54667544e-01 2.58618474e-01 -9.14119110e-02 -5.36049485e-01 -1.14123143e-01 -1.22830402e-02]	[10.687607765197754, 9.395936012268066]
3b9e3fd0-3d97-4c33-91fc-e8c2a032fea1	xlent-mining-a-large-cross-lingual-entity	2104.08597	null	https://arxiv.org/abs/2104.08597v2	https://arxiv.org/pdf/2104.08597v2.pdf	XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment	Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification. While knowledge bases contain a large number of entities in high-resource languages such as English and French, corresponding entities for lower-resource languages are often missing. To address this, we propose Lexical-Semantic-Phonetic Align (LSP-Align), a technique to automatically mine cross-lingual entity lexica from mined web data. We demonstrate LSP-Align outperforms baselines at extracting cross-lingual entity pairs and mine 164 million entity pairs from 120 different languages aligned with English. We release these cross-lingual entity pairs along with the massively multilingual tagged named entity corpus as a resource to the NLP community.	['Adithya Renduchintala', 'Philipp Koehn', 'Francisco Guzmán', 'James Cross', 'Ahmed El-Kishky']	2021-04-17	null	https://aclanthology.org/2021.emnlp-main.814	https://aclanthology.org/2021.emnlp-main.814.pdf	emnlp-2021-11	['multilingual-nlp']	['natural-language-processing']	[-4.31115299e-01 -6.46054223e-02 -7.41752803e-01 -3.50925922e-01 -1.58252180e+00 -1.22832108e+00 3.41229051e-01 2.56968111e-01 -8.31501544e-01 1.38996375e+00 6.45426214e-01 -3.20839673e-01 2.05546528e-01 -6.09531760e-01 -9.15083230e-01 7.01723471e-02 1.96544200e-01 7.94579566e-01 1.13493325e-02 -1.34014562e-01 5.90555668e-02 5.62126786e-02 -8.28313768e-01 5.31673670e-01 1.38451731e+00 2.03355491e-01 2.52533376e-01 -1.17108814e-01 -9.24419045e-01 2.74400592e-01 -1.49922803e-01 -1.13269949e+00 1.31723911e-01 -3.00128043e-01 -1.05048490e+00 -4.91238326e-01 4.92614686e-01 5.67573905e-01 4.37680274e-01 1.28430164e+00 4.40954506e-01 -2.49496669e-01 5.60000300e-01 -9.70444977e-01 -5.70552468e-01 1.23957288e+00 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97a23e1f-d215-4a68-9404-11213c45ea89	fusion-of-global-and-local-knowledge-for	2302.11051	null	https://arxiv.org/abs/2302.11051v1	https://arxiv.org/pdf/2302.11051v1.pdf	Fusion of Global and Local Knowledge for Personalized Federated Learning	Personalized federated learning, as a variant of federated learning, trains customized models for clients using their heterogeneously distributed data. However, it is still inconclusive about how to design personalized models with better representation of shared global knowledge and personalized pattern. To bridge the gap, we in this paper explore personalized models with low-rank and sparse decomposition. Specifically, we employ proper regularization to extract a low-rank global knowledge representation (GKR), so as to distill global knowledge into a compact representation. Subsequently, we employ a sparse component over the obtained GKR to fuse the personalized pattern into the global knowledge. As a solution, we propose a two-stage proximal-based algorithm named \textbf{Fed}erated learning with mixed \textbf{S}parse and \textbf{L}ow-\textbf{R}ank representation (FedSLR) to efficiently search for the mixed models. Theoretically, under proper assumptions, we show that the GKR trained by FedSLR can at least sub-linearly converge to a stationary point of the regularized problem, and that the sparse component being fused can converge to its stationary point under proper settings. Extensive experiments also demonstrate the superior empirical performance of FedSLR. Moreover, FedSLR reduces the number of parameters, and lowers the down-link communication complexity, which are all desirable for federated learning algorithms. Source code is available in \url{https://github.com/huangtiansheng/fedslr}.	['DaCheng Tao', 'Weiwei Lin', 'Yan Sun', 'Li Shen', 'Tiansheng Huang']	2023-02-21	null	null	null	null	['personalized-federated-learning']	['methodology']	[-3.41342360e-01 -5.30481189e-02 -6.26188219e-01 -4.60832119e-01 -1.17824554e+00 -4.63400841e-01 -8.11477676e-02 -4.34232086e-01 2.74381310e-01 5.97746253e-01 5.27199149e-01 -1.50194108e-01 -5.94681501e-01 -6.06220543e-01 -8.99303734e-01 -8.98179114e-01 1.53852552e-01 4.68205184e-01 -2.80845106e-01 1.53125197e-01 -2.02953249e-01 2.97949344e-01 -1.30204022e+00 5.03077209e-01 9.31469977e-01 9.68143821e-01 4.46578979e-01 3.34960401e-01 -4.09130752e-01 1.01555622e+00 -1.06705325e-02 -6.21931195e-01 3.08034360e-01 -3.04435432e-01 -8.85908604e-01 5.99195436e-02 1.78801328e-01 -4.20667738e-01 -7.33194888e-01 1.11879539e+00 4.20669496e-01 2.24609703e-01 2.37196788e-01 -1.05933285e+00 -7.41604507e-01 1.03010082e+00 -5.50660372e-01 6.65953308e-02 2.42946297e-01 5.88189662e-02 1.15459430e+00 -1.07749224e+00 5.50304949e-01 1.03155041e+00 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2ee10a66-189f-4df8-9844-b4942f7c8fcd	constituency-lattice-encoding-for-aspect-term	null	null	https://aclanthology.org/2020.coling-main.73	https://aclanthology.org/2020.coling-main.73.pdf	Constituency Lattice Encoding for Aspect Term Extraction	One of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms. In this paper, we aim to address this issue by incorporating the span annotations of constituents of a sentence to leverage the syntactic information in neural network models. To this end, we first construct a constituency lattice structure based on the constituents of a constituency tree. Then, we present two approaches to encoding the constituency lattice using BiLSTM-CRF and BERT as the base models, respectively. We experimented on two benchmark datasets to evaluate the two models, and the results confirm their superiority with respective 3.17 and 1.35 points gained in F1-Measure over the current state of the art. The improvements justify the effectiveness of the constituency lattice for aspect term extraction.	['Qinliang Su', 'Weizhou Shen', 'Xiaojun Quan', 'Kun Li', 'Yunyi Yang']	2020-12-01	null	null	null	coling-2020-8	['aspect-term-extraction-and-sentiment']	['natural-language-processing']	[ 2.27245241e-01 4.07526314e-01 -3.45945388e-01 -3.65380973e-01 -7.99667180e-01 -5.06399155e-01 6.49405837e-01 2.41784289e-01 -4.88969028e-01 6.93365753e-01 4.62414443e-01 -4.07888651e-01 4.36659098e-01 -8.04891825e-01 -4.48430389e-01 -4.86267626e-01 1.84396937e-01 -2.27261279e-02 3.55489701e-02 -2.85141200e-01 2.67596155e-01 -5.05499020e-02 -1.30571413e+00 4.77580994e-01 7.29846001e-01 1.16886425e+00 -8.86840522e-02 3.18847686e-01 -6.43896163e-01 4.57102507e-01 -6.38604403e-01 -5.54257691e-01 -4.65099402e-02 -1.69816166e-01 -7.39021242e-01 -2.85556936e-03 1.81645602e-02 2.62946915e-02 1.79354578e-01 9.52338219e-01 1.32038757e-01 -1.78032026e-01 6.37006283e-01 -7.40447819e-01 -4.33594495e-01 8.25015187e-01 -8.02604258e-01 2.22658142e-01 1.55520618e-01 -3.31411064e-01 1.65156257e+00 -6.99315667e-01 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fb0b1bce-da60-4f48-acb7-59451237df42	image-based-navigation-in-real-world	2202.01069	null	https://arxiv.org/abs/2202.01069v1	https://arxiv.org/pdf/2202.01069v1.pdf	Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation	Navigating complex indoor environments requires a deep understanding of the space the robotic agent is acting into to correctly inform the navigation process of the agent towards the goal location. In recent learning-based navigation approaches, the scene understanding and navigation abilities of the agent are achieved simultaneously by collecting the required experience in simulation. Unfortunately, even if simulators represent an efficient tool to train navigation policies, the resulting models often fail when transferred into the real world. One possible solution is to provide the navigation model with mid-level visual representations containing important domain-invariant properties of the scene. But, what are the best representations that facilitate the transfer of a model to the real-world? How can they be combined? In this work we address these issues by proposing a benchmark of Deep Learning architectures to combine a range of mid-level visual representations, to perform a PointGoal navigation task following a Reinforcement Learning setup. All the proposed navigation models have been trained with the Habitat simulator on a synthetic office environment and have been tested on the same real-world environment using a real robotic platform. To efficiently assess their performance in a real context, a validation tool has been proposed to generate realistic navigation episodes inside the simulator. Our experiments showed that navigation models can benefit from the multi-modal input and that our validation tool can provide good estimation of the expected navigation performance in the real world, while saving time and resources. The acquired synthetic and real 3D models of the environment, together with the code of our validation tool built on top of Habitat, are publicly available at the following link: https://iplab.dmi.unict.it/EmbodiedVN/	['Giovanni Maria Farinella', 'Corrado Santoro', 'Luigi Gulino', 'Antonino Furnari', 'Marco Rosano']	2022-02-02	null	null	null	null	['pointgoal-navigation']	['robots']	[-1.59755334e-01 6.32678047e-02 4.67513859e-01 -2.57990152e-01 -2.12441653e-01 -8.07819664e-01 7.81308413e-01 -3.49928886e-02 -7.41941273e-01 8.11358392e-01 -2.18445897e-01 -4.98315483e-01 -2.26793036e-01 -1.02055800e+00 -9.90373194e-01 -4.50734168e-01 -5.15974343e-01 6.81588054e-01 2.67720073e-01 -6.88589036e-01 1.72094017e-01 5.83576024e-01 -2.00070047e+00 -4.93526459e-02 1.00830913e+00 6.76312149e-01 8.93908978e-01 7.08786964e-01 1.26854524e-01 7.19738901e-01 -5.35511017e-01 3.74182224e-01 5.35746634e-01 -3.10787767e-01 -7.15619504e-01 -1.41621768e-01 -5.70826344e-02 -3.89132112e-01 -2.40061924e-01 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bd044cec-c786-4a24-a238-831b826b33e3	implicit-pdf-non-parametric-representation-of	2106.05965	null	https://arxiv.org/abs/2106.05965v2	https://arxiv.org/pdf/2106.05965v2.pdf	Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold	Single image pose estimation is a fundamental problem in many vision and robotics tasks, and existing deep learning approaches suffer by not completely modeling and handling: i) uncertainty about the predictions, and ii) symmetric objects with multiple (sometimes infinite) correct poses. To this end, we introduce a method to estimate arbitrary, non-parametric distributions on SO(3). Our key idea is to represent the distributions implicitly, with a neural network that estimates the probability given the input image and a candidate pose. Grid sampling or gradient ascent can be used to find the most likely pose, but it is also possible to evaluate the probability at any pose, enabling reasoning about symmetries and uncertainty. This is the most general way of representing distributions on manifolds, and to showcase the rich expressive power, we introduce a dataset of challenging symmetric and nearly-symmetric objects. We require no supervision on pose uncertainty -- the model trains only with a single pose per example. Nonetheless, our implicit model is highly expressive to handle complex distributions over 3D poses, while still obtaining accurate pose estimation on standard non-ambiguous environments, achieving state-of-the-art performance on Pascal3D+ and ModelNet10-SO(3) benchmarks.	['Ameesh Makadia', 'Srikumar Ramalingam', 'Varun Jampani', 'Carlos Esteves', 'Kieran Murphy']	2021-06-10	implicit-pdf-non-parametric-representation-of-1	https://implicit-pdf.github.io/	https://arxiv.org/pdf/2106.05965.pdf	null	['3d-pose-estimation', '3d-rotation-estimation']	['computer-vision', 'computer-vision']	[-1.42987281e-01 2.71954417e-01 -7.34273763e-03 -3.50556433e-01 -9.89897072e-01 -8.12547982e-01 7.15132535e-01 -9.61548164e-02 -4.59277064e-01 7.41756320e-01 8.89935996e-03 7.28890002e-02 -9.84940082e-02 -4.93651867e-01 -1.10760498e+00 -6.37450099e-01 -1.31698549e-01 1.42643034e+00 1.52174726e-01 9.49174687e-02 1.55519403e-03 7.57501125e-01 -1.56044316e+00 -4.72771972e-02 5.54446995e-01 1.16983330e+00 2.38662958e-01 7.21617997e-01 1.61311522e-01 4.35977608e-01 -5.30776501e-01 -6.00988686e-01 2.56759018e-01 2.75750369e-01 -8.57397139e-01 2.65740126e-01 1.00459290e+00 -5.83094656e-01 -1.62239850e-01 1.18331861e+00 2.69956112e-01 -5.54969534e-02 1.03858781e+00 -1.33408153e+00 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b9594021-fbaf-4784-8ffb-5cbfba0cbca6	natural-language-inspired-approach-for	null	null	https://aclanthology.org/W12-1015	https://aclanthology.org/W12-1015.pdf	Natural Language Inspired Approach for Handwritten Text Line Detection in Legacy Documents	null	["ro H{\\'e}ctor", 'Alej Toselli', 'Vicente Bosch', 'Enrique Vidal']	2012-04-01	null	null	null	ws-2012-4	['document-layout-analysis', 'line-detection']	['computer-vision', 'computer-vision']	[-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 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 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ab17c088-c456-4401-8cd3-1b947cc09a3d	interactive-feature-fusion-for-end-to-end	2110.05267	null	https://arxiv.org/abs/2110.05267v2	https://arxiv.org/pdf/2110.05267v2.pdf	Interactive Feature Fusion for End-to-End Noise-Robust Speech Recognition	Speech enhancement (SE) aims to suppress the additive noise from a noisy speech signal to improve the speech's perceptual quality and intelligibility. However, the over-suppression phenomenon in the enhanced speech might degrade the performance of downstream automatic speech recognition (ASR) task due to the missing latent information. To alleviate such problem, we propose an interactive feature fusion network (IFF-Net) for noise-robust speech recognition to learn complementary information from the enhanced feature and original noisy feature. Experimental results show that the proposed method achieves absolute word error rate (WER) reduction of 4.1% over the best baseline on RATS Channel-A corpus. Our further analysis indicates that the proposed IFF-Net can complement some missing information in the over-suppressed enhanced feature.	['Eng Siong Chng', 'Chen Chen', 'Nana Hou', 'Yuchen Hu']	2021-10-11	null	null	null	null	['robust-speech-recognition']	['speech']	[ 5.51796913e-01 5.95909208e-02 2.49505341e-01 -3.92745972e-01 -1.13326645e+00 -6.25755638e-02 3.71086031e-01 -3.93184930e-01 -5.32280743e-01 5.12388647e-01 9.93696928e-01 -4.48854923e-01 -5.33950864e-04 -1.93580743e-02 -2.97788739e-01 -9.30104256e-01 2.26397589e-01 -7.67954707e-01 -1.07153736e-01 -4.71815974e-01 -1.83544055e-01 1.85984328e-01 -1.65052831e+00 4.50064152e-01 1.13485515e+00 9.74014461e-01 9.14037168e-01 4.75182265e-01 -4.78945263e-02 4.69061017e-01 -1.05626559e+00 8.72707739e-02 2.59070694e-01 -3.63583297e-01 -2.66333312e-01 8.53344128e-02 -1.44314334e-01 -2.40995064e-01 -8.72180402e-01 1.57556510e+00 9.49161291e-01 3.68768543e-01 8.59678686e-02 -7.56020665e-01 -5.38243294e-01 1.01688576e+00 -1.34814098e-01 5.24244487e-01 1.30689695e-01 9.75142121e-02 7.43738890e-01 -1.21523833e+00 6.47096708e-02 1.38608146e+00 2.13614821e-01 7.69913495e-01 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0ceb8140-b86d-45a0-b006-6ea0f42da1da	on-the-predictive-accuracy-of-neural-temporal	2306.17066	null	https://arxiv.org/abs/2306.17066v2	https://arxiv.org/pdf/2306.17066v2.pdf	On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data	Temporal Point Processes (TPPs) serve as the standard mathematical framework for modeling asynchronous event sequences in continuous time. However, classical TPP models are often constrained by strong assumptions, limiting their ability to capture complex real-world event dynamics. To overcome this limitation, researchers have proposed Neural TPPs, which leverage neural network parametrizations to offer more flexible and efficient modeling. While recent studies demonstrate the effectiveness of Neural TPPs, they often lack a unified setup, relying on different baselines, datasets, and experimental configurations. This makes it challenging to identify the key factors driving improvements in predictive accuracy, hindering research progress. To bridge this gap, we present a comprehensive large-scale experimental study that systematically evaluates the predictive accuracy of state-of-the-art neural TPP models. Our study encompasses multiple real-world and synthetic event sequence datasets, following a carefully designed unified setup. We thoroughly investigate the influence of major architectural components such as event encoding, history encoder, and decoder parametrization on both time and mark prediction tasks. Additionally, we delve into the less explored area of probabilistic calibration for neural TPP models. By analyzing our results, we draw insightful conclusions regarding the significance of history size and the impact of architectural components on predictive accuracy. Furthermore, we shed light on the miscalibration of mark distributions in neural TPP models. Our study aims to provide valuable insights into the performance and characteristics of neural TPP models, contributing to a better understanding of their strengths and limitations.	['Souhaib Ben Taieb', 'Tanguy Bosser']	2023-06-29	null	null	null	null	['point-processes']	['methodology']	[ 2.52793223e-01 -2.91266829e-01 -4.99271214e-01 -2.19764963e-01 -7.15582311e-01 -5.74996114e-01 6.56242132e-01 3.94443244e-01 -3.14536184e-01 5.41488051e-01 2.95130193e-01 -6.14952624e-01 -2.05808684e-01 -7.26899266e-01 -9.08509731e-01 -4.57016051e-01 -2.94876873e-01 3.99046689e-01 4.73934948e-01 1.07651353e-01 1.84145078e-01 5.22059560e-01 -1.55583227e+00 3.07355314e-01 3.90454382e-01 8.88844967e-01 -1.32792637e-01 5.43941081e-01 1.11289006e-02 1.00874221e+00 -7.74596155e-01 -4.04414386e-01 1.05355404e-01 -2.03278318e-01 -3.06563288e-01 -4.26667839e-01 -7.50454441e-02 -4.01524872e-01 -7.12939799e-01 4.88430411e-01 6.13777101e-01 5.02307415e-02 5.74263871e-01 -1.50899267e+00 -3.48682642e-01 1.13970065e+00 -3.05480540e-01 5.65503359e-01 4.21173833e-02 4.38279599e-01 1.05459619e+00 -4.20783162e-01 3.09911728e-01 9.43469703e-01 1.07861543e+00 2.30204746e-01 -1.27417755e+00 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1.18109278e-01 3.54927815e-02 -6.08861983e-01 -8.24642181e-02 9.26885083e-02]	[7.084506511688232, 3.402465581893921]
28be6ae6-c147-423f-89b1-0f9705352500	systematic-generalization-on-gscan-with	2009.05552	null	https://arxiv.org/abs/2009.05552v2	https://arxiv.org/pdf/2009.05552v2.pdf	Systematic Generalization on gSCAN with Language Conditioned Embedding	Systematic Generalization refers to a learning algorithm's ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data. As shown in recent work, state-of-the-art deep learning models fail dramatically even on tasks for which they are designed when the test set is systematically different from the training data. We hypothesize that explicitly modeling the relations between objects in their contexts while learning their representations will help achieve systematic generalization. Therefore, we propose a novel method that learns objects' contextualized embeddings with dynamic message passing conditioned on the input natural language and end-to-end trainable with other downstream deep learning modules. To our knowledge, this model is the first one that significantly outperforms the provided baseline and reaches state-of-the-art performance on grounded-SCAN (gSCAN), a grounded natural language navigation dataset designed to require systematic generalization in its test splits.	['Raymond J. Mooney', 'Tong Gao', 'Qi Huang']	2020-09-11	null	https://aclanthology.org/2020.aacl-main.49	https://aclanthology.org/2020.aacl-main.49.pdf	asian-chapter-of-the-association-for	['systematic-generalization']	['reasoning']	[ 2.09647477e-01 2.97815830e-01 -1.58580050e-01 -8.03640604e-01 -5.39598405e-01 -5.26085138e-01 7.70211101e-01 3.95192027e-01 -8.55895400e-01 4.95892107e-01 2.43723944e-01 -5.66752493e-01 -8.54476020e-02 -9.68043208e-01 -1.24843323e+00 -2.56312877e-01 -4.54343349e-01 8.14525902e-01 5.66403210e-01 -5.15569270e-01 3.07498544e-01 2.81644046e-01 -1.83342135e+00 6.07569158e-01 8.13253224e-01 7.94483542e-01 3.57131839e-01 6.28661156e-01 -6.36289492e-02 7.89631784e-01 -2.91409373e-01 1.66766141e-02 3.37109149e-01 -3.76077503e-01 -1.09121692e+00 -4.25711334e-01 7.80646205e-01 -4.63630080e-01 -4.45547432e-01 8.07708740e-01 2.60845304e-01 4.74871069e-01 6.82692826e-01 -1.13498962e+00 -1.24994922e+00 7.58441150e-01 1.96422115e-01 2.31555939e-01 2.81310886e-01 4.98159349e-01 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5.57629287e-01 6.21471703e-01 9.28702019e-03 -8.91000748e-01 -7.82195032e-01 -8.30038726e-01 -2.57034332e-01 7.92162538e-01 7.26874053e-01 -6.73950970e-01 -3.81150454e-01 -8.44771415e-02]	[9.643350601196289, 6.992115020751953]
76128ac4-1c70-4206-9f6f-44d46e4d2685	clarinet-a-music-retrieval-system	2210.12648	null	https://arxiv.org/abs/2210.12648v2	https://arxiv.org/pdf/2210.12648v2.pdf	Clarinet: A Music Retrieval System	A MIDI based approach for music recognition is proposed and implemented in this paper. Our Clarinet music retrieval system is designed to search piano MIDI files with high recall and speed. We design a novel melody extraction algorithm that improves recall results by more than 10%. We also implement 3 algorithms for retrieval-two self designed (RSA Note and RSA Time), and a modified version of the Mongeau Sankoff Algorithm. Algorithms to achieve tempo and scale invariance are also discussed in this paper. The paper also contains detailed experimentation and benchmarks with four different metrics. Clarinet achieves recall scores of more than 94%.	['Siddhant Sharma', 'Rohan Sharma', 'Kshitij Alwadhi']	2022-10-23	null	null	null	null	['melody-extraction']	['music']	[ 1.68029591e-03 -5.93123913e-01 -3.70670080e-01 1.07304394e-01 -1.29990375e+00 -1.00997591e+00 3.90869796e-01 1.52387917e-01 -3.10968012e-01 4.92219627e-01 4.77517962e-01 2.46335804e-01 -7.90362597e-01 -3.90085101e-01 2.27968216e-01 -3.09741169e-01 -1.67011887e-01 2.69974768e-01 2.75135696e-01 -5.05913854e-01 8.97010207e-01 4.31782603e-01 -1.62348783e+00 5.01415610e-01 8.35270137e-02 9.11172867e-01 -9.32193846e-02 1.50321984e+00 2.77188241e-01 7.88868785e-01 -1.11382389e+00 -1.54698640e-03 3.98179322e-01 -7.09791064e-01 -8.37381959e-01 -7.25504875e-01 3.51105511e-01 -4.65665385e-02 -3.06699425e-01 6.18568897e-01 9.64753389e-01 4.82718289e-01 6.63536787e-01 -8.95776510e-01 -8.79582539e-02 1.05423760e+00 -5.43647408e-01 4.35660601e-01 8.53448629e-01 -4.17795479e-01 1.20177460e+00 -5.03521442e-01 4.39811170e-01 7.06301749e-01 9.28713560e-01 1.45567343e-01 -8.84098887e-01 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3c5912a3-9d09-49e4-a3a2-213dc86e0925	dreampaint-few-shot-inpainting-of-e-commerce	2305.01257	null	https://arxiv.org/abs/2305.01257v1	https://arxiv.org/pdf/2305.01257v1.pdf	DreamPaint: Few-Shot Inpainting of E-Commerce Items for Virtual Try-On without 3D Modeling	We introduce DreamPaint, a framework to intelligently inpaint any e-commerce product on any user-provided context image. The context image can be, for example, the user's own image for virtual try-on of clothes from the e-commerce catalog on themselves, the user's room image for virtual try-on of a piece of furniture from the e-commerce catalog in their room, etc. As opposed to previous augmented-reality (AR)-based virtual try-on methods, DreamPaint does not use, nor does it require, 3D modeling of neither the e-commerce product nor the user context. Instead, it directly uses 2D images of the product as available in product catalog database, and a 2D picture of the context, for example taken from the user's phone camera. The method relies on few-shot fine tuning a pre-trained diffusion model with the masked latents (e.g., Masked DreamBooth) of the catalog images per item, whose weights are then loaded on a pre-trained inpainting module that is capable of preserving the characteristics of the context image. DreamPaint allows to preserve both the product image and the context (environment/user) image without requiring text guidance to describe the missing part (product/context). DreamPaint also allows to intelligently infer the best 3D angle of the product to place at the desired location on the user context, even if that angle was previously unseen in the product's reference 2D images. We compare our results against both text-guided and image-guided inpainting modules and show that DreamPaint yields superior performance in both subjective human study and quantitative metrics.	['Ismail B. Tutar', 'Amir Tavanaei', 'Suren Kumar', 'Karim Bouyarmane', 'Mehmet Saygin Seyfioglu']	2023-05-02	null	null	null	null	['virtual-try-on']	['computer-vision']	[ 2.94654995e-01 9.96260494e-02 1.13453396e-01 -1.66698486e-01 -5.83590686e-01 -8.06338906e-01 3.05967480e-01 -1.62077501e-01 -8.99160653e-02 1.11203440e-01 1.71359386e-02 -2.51726985e-01 1.50932163e-01 -6.88203812e-01 -8.00129890e-01 -6.77262247e-01 4.61410999e-01 3.90097260e-01 6.90783411e-02 -3.38048398e-01 3.63113195e-01 6.81683958e-01 -1.28344238e+00 4.83022809e-01 1.66000277e-01 1.04027677e+00 9.13229108e-01 7.05053508e-01 -6.59097955e-02 3.35991263e-01 -5.69916725e-01 -5.38353622e-01 6.35431886e-01 -4.70778376e-01 -3.01753193e-01 8.35752606e-01 7.35309958e-01 -5.16956747e-01 -4.26202357e-01 1.20450854e+00 7.24757165e-02 1.63430139e-01 2.89055020e-01 -7.04344928e-01 -1.07163823e+00 -1.13247618e-01 -7.27520108e-01 -3.47805582e-02 8.00893962e-01 2.83037007e-01 6.99385703e-01 -1.09897935e+00 1.29513240e+00 1.04193878e+00 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e69f9bd1-201a-4c38-82da-f8156e1812c0	detecting-kissing-scenes-in-a-database-of	1906.01843	null	https://arxiv.org/abs/1906.01843v1	https://arxiv.org/pdf/1906.01843v1.pdf	Detecting Kissing Scenes in a Database of Hollywood Films	Detecting scene types in a movie can be very useful for application such as video editing, ratings assignment, and personalization. We propose a system for detecting kissing scenes in a movie. This system consists of two components. The first component is a binary classifier that predicts a binary label (i.e. kissing or not) given a features exctracted from both the still frames and audio waves of a one-second segment. The second component aggregates the binary labels for contiguous non-overlapping segments into a set of kissing scenes. We experimented with a variety of 2D and 3D convolutional architectures such as ResNet, DesnseNet, and VGGish and developed a highly accurate kissing detector that achieves a validation F1 score of 0.95 on a diverse database of Hollywood films ranging many genres and spanning multiple decades. The code for this project is available at http://github.com/amirziai/kissing-detector.	['Amir Ziai']	2019-06-05	null	null	null	null	['kiss-detection']	['computer-vision']	[-6.91317543e-02 -3.60120803e-01 -2.19254807e-01 -6.79451287e-01 -6.71350062e-01 -8.50539923e-01 4.45451587e-01 2.37835288e-01 -4.05856520e-02 1.16729818e-01 1.21492818e-01 1.30604550e-01 5.11134751e-02 -6.64528072e-01 -8.09969485e-01 -3.46515507e-01 -3.73794436e-01 1.14434483e-02 5.42248666e-01 -2.98671991e-01 3.44965160e-01 2.07896590e-01 -1.71200716e+00 7.09556282e-01 2.43416324e-01 1.62934935e+00 -2.30828431e-02 7.48737097e-01 6.29568920e-02 7.50200391e-01 -6.91976488e-01 -3.61214668e-01 3.49655837e-01 -2.96436876e-01 -6.10195637e-01 2.35227421e-01 1.02403069e+00 -2.45973781e-01 -3.88770819e-01 9.42603230e-01 2.83314943e-01 2.02045105e-02 4.93764907e-01 -1.18764210e+00 -1.67955443e-01 5.35073519e-01 -5.82364440e-01 5.39991617e-01 3.88047099e-01 -2.48245984e-01 1.14994037e+00 -1.01755452e+00 8.33417356e-01 9.19774175e-01 6.11023903e-01 1.18664645e-01 -1.02557600e+00 -8.12232494e-01 -7.14349523e-02 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38bc8c1c-cdc0-4b01-aa38-032bc9f043a8	perceptual-learned-source-channel-coding-for	2205.13120	null	https://arxiv.org/abs/2205.13120v1	https://arxiv.org/pdf/2205.13120v1.pdf	Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission	As one novel approach to realize end-to-end wireless image semantic transmission, deep learning-based joint source-channel coding (deep JSCC) method is emerging in both deep learning and communication communities. However, current deep JSCC image transmission systems are typically optimized for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or multi-scale structural similarity (MS-SSIM). But for low transmission rates, due to the imperfect wireless channel, these distortion metrics lose significance as they favor pixel-wise preservation. To account for human visual perception in semantic communications, it is of great importance to develop new deep JSCC systems optimized beyond traditional PSNR and MS-SSIM metrics. In this paper, we introduce adversarial losses to optimize deep JSCC, which tends to preserve global semantic information and local texture. Our new deep JSCC architecture combines encoder, wireless channel, decoder/generator, and discriminator, which are jointly learned under both perceptual and adversarial losses. Our method yields human visually much more pleasing results than state-of-the-art engineered image coded transmission systems and traditional deep JSCC systems. A user study confirms that achieving the perceptually similar end-to-end image transmission quality, the proposed method can save about 50\% wireless channel bandwidth cost.	['Kai Niu', 'Dekun Zhou', 'Zhongwei Si', 'Jincheng Dai', 'Sixian Wang', 'Jun Wang']	2022-05-26	null	null	null	null	['ms-ssim']	['computer-vision']	[ 3.09669465e-01 7.38132149e-02 -9.12147388e-02 -2.39525452e-01 -7.22041070e-01 -8.72663707e-02 2.24557117e-01 -1.40957683e-01 -4.34954196e-01 7.57224083e-01 1.87565684e-01 -1.93735376e-01 6.70553818e-02 -7.41321087e-01 -7.32090473e-01 -7.06112742e-01 -4.66130853e-01 -5.82876146e-01 3.26939791e-01 -1.50581196e-01 7.41066709e-02 1.56988174e-01 -1.06343842e+00 2.37911999e-01 8.64350319e-01 1.64718246e+00 5.86249650e-01 1.27755070e+00 3.53151821e-02 1.14599371e+00 -8.92694950e-01 -8.44604611e-01 2.07000121e-01 -5.72653651e-01 -7.09948540e-01 -2.52506196e-01 2.90222049e-01 -6.42716110e-01 -9.45357859e-01 1.25755358e+00 1.02599299e+00 -5.18903613e-01 3.72069836e-01 -1.25834537e+00 -1.10726178e+00 4.81866777e-01 -4.14517999e-01 2.47688219e-02 1.52887702e-01 5.75543195e-02 9.61948991e-01 -2.98062176e-01 2.95392901e-01 1.23318732e+00 8.40425253e-01 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e4b5d05b-cdf2-41cc-bdc5-8435ff507751	group-r-cnn-for-weakly-semi-supervised-object	2205.05920	null	https://arxiv.org/abs/2205.05920v1	https://arxiv.org/pdf/2205.05920v1.pdf	Group R-CNN for Weakly Semi-supervised Object Detection with Points	We study the problem of weakly semi-supervised object detection with points (WSSOD-P), where the training data is combined by a small set of fully annotated images with bounding boxes and a large set of weakly-labeled images with only a single point annotated for each instance. The core of this task is to train a point-to-box regressor on well-labeled images that can be used to predict credible bounding boxes for each point annotation. We challenge the prior belief that existing CNN-based detectors are not compatible with this task. Based on the classic R-CNN architecture, we propose an effective point-to-box regressor: Group R-CNN. Group R-CNN first uses instance-level proposal grouping to generate a group of proposals for each point annotation and thus can obtain a high recall rate. To better distinguish different instances and improve precision, we propose instance-level proposal assignment to replace the vanilla assignment strategy adopted in the original R-CNN methods. As naive instance-level assignment brings converging difficulty, we propose instance-aware representation learning which consists of instance-aware feature enhancement and instance-aware parameter generation to overcome this issue. Comprehensive experiments on the MS-COCO benchmark demonstrate the effectiveness of our method. Specifically, Group R-CNN significantly outperforms the prior method Point DETR by 3.9 mAP with 5% well-labeled images, which is the most challenging scenario. The source code can be found at https://github.com/jshilong/GroupRCNN	['Kai Chen', 'Aojun Zhou', 'Xinjiang Wang', 'Liyang Liu', 'Zhuoran Yu', 'Shilong Zhang']	2022-05-12	null	http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Group_R-CNN_for_Weakly_Semi-Supervised_Object_Detection_With_Points_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Group_R-CNN_for_Weakly_Semi-Supervised_Object_Detection_With_Points_CVPR_2022_paper.pdf	cvpr-2022-1	['semi-supervised-object-detection']	['computer-vision']	[-6.76035956e-02 3.45416129e-01 -4.06802833e-01 -5.21086693e-01 -1.42117071e+00 -5.36662102e-01 5.68371594e-01 2.39595518e-01 -5.74760616e-01 3.54525924e-01 -1.56639174e-01 -2.42793206e-02 2.86624044e-01 -7.18752921e-01 -1.13313508e+00 -6.29739046e-01 6.28609583e-02 6.54139817e-01 6.29790664e-01 -1.28929615e-01 1.82080254e-01 4.44301248e-01 -1.47143376e+00 4.16198373e-01 6.42583549e-01 1.27826619e+00 3.20594847e-01 4.37815070e-01 4.82101738e-02 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d4c8b6c6-9e36-480f-bb33-9e19d7a22fe5	very-low-resource-sentence-alignment-luhya	null	null	https://aclanthology.org/2022.loresmt-1.1	https://aclanthology.org/2022.loresmt-1.1.pdf	Very Low Resource Sentence Alignment: Luhya and Swahili	Language-agnostic sentence embeddings generated by pre-trained models such as LASER and LaBSE are attractive options for mining large datasets to produce parallel corpora for low-resource machine translation. We test LASER and LaBSE in extracting bitext for two related low-resource African languages: Luhya and Swahili. For this work, we created a new parallel set of nearly 8000 Luhya-English sentences which allows a new zero-shot test of LASER and LaBSE. We find that LaBSE significantly outperforms LASER on both languages. Both LASER and LaBSE however perform poorly at zero-shot alignment on Luhya, achieving just 1.5% and 22.0% successful alignments respectively (P@1 score). We fine-tune the embeddings on a small set of parallel Luhya sentences and show significant gains, improving the LaBSE alignment accuracy to 53.3%. Further, restricting the dataset to sentence embedding pairs with cosine similarity above 0.7 yielded alignments with over 85% accuracy.	['Bruce Bassett', 'Everlyn Chimoto']	null	null	null	null	loresmt-coling-2022-10	['sentence-embeddings', 'sentence-embeddings']	['methodology', 'natural-language-processing']	[-9.29199014e-05 1.46636199e-02 -2.66291678e-01 -5.31758010e-01 -1.48914576e+00 -6.81490421e-01 6.78826213e-01 2.53067285e-01 -8.63434672e-01 9.30686295e-01 6.73764408e-01 -7.79019833e-01 2.28125229e-01 -6.62029684e-01 -6.96766675e-01 -3.28263134e-01 2.41508111e-02 8.34941506e-01 -3.89990479e-01 -4.91785765e-01 2.00023428e-01 2.61614993e-02 -6.92714751e-01 4.04378653e-01 1.05801940e+00 -1.81719899e-01 2.19440386e-01 9.51718926e-01 -1.74762949e-01 -1.28578335e-01 -4.69481528e-01 -7.77631581e-01 4.40605819e-01 -4.25529897e-01 -9.29682493e-01 -4.94732916e-01 7.35616028e-01 -1.40120059e-01 -6.92464188e-02 6.88593090e-01 8.08513105e-01 -3.19780141e-01 5.82826018e-01 -9.00100052e-01 -1.12009645e+00 9.01816130e-01 -7.77233005e-01 7.10963011e-01 4.72314775e-01 1.61911562e-01 1.50647712e+00 -1.38322628e+00 7.89701879e-01 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352bc5cf-dd0d-4ad8-86b5-23825f7cb20e	object-guided-instance-segmentation-with	2106.07159	null	https://arxiv.org/abs/2106.07159v1	https://arxiv.org/pdf/2106.07159v1.pdf	Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images	Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. In this paper, we propose a novel box-based instance segmentation method. Box-based instance segmentation methods capture objects via bounding boxes and then perform individual segmentation within each bounding box region. However, existing methods can hardly differentiate the target from its neighboring objects within the same bounding box region due to their similar textures and low-contrast boundaries. To deal with this problem, in this paper, we propose an object-guided instance segmentation method. Our method first detects the center points of the objects, from which the bounding box parameters are then predicted. To perform segmentation, an object-guided coarse-to-fine segmentation branch is built along with the detection branch. The segmentation branch reuses the object features as guidance to separate target object from the neighboring ones within the same bounding box region. To further improve the segmentation quality, we design an auxiliary feature refinement module that densely samples and refines point-wise features in the boundary regions. Experimental results on three biological image datasets demonstrate the advantages of our method. The code will be available at https://github.com/yijingru/ObjGuided-Instance-Segmentation.	['Dimitris N. Metaxas', 'Daniel J. Hoeppner', 'Wei Fan', 'Lianyi Han', 'Hui Qu', 'Qiaoying Huang', 'Bo Liu', 'Hui Tang', 'Pengxiang Wu', 'Jingru Yi']	2021-06-14	null	null	null	null	['plant-phenotyping']	['computer-vision']	[ 1.97265416e-01 -3.03152855e-02 -2.20926270e-01 -3.48665863e-01 -4.64791119e-01 -3.92219931e-01 2.27688834e-01 4.87391055e-01 -1.21154338e-01 5.01734436e-01 -3.58273327e-01 1.04935028e-01 2.04417948e-02 -1.03323376e+00 -6.31783664e-01 -8.91213953e-01 1.63633212e-01 6.39241815e-01 8.88825059e-01 1.99576512e-01 5.34939647e-01 6.64805532e-01 -1.11794198e+00 1.29828215e-01 1.10534155e+00 1.00028980e+00 5.12365460e-01 2.80842900e-01 -4.38762099e-01 -7.81850815e-02 -4.49666500e-01 3.46339613e-01 1.43528104e-01 -4.50166762e-01 -6.33860290e-01 4.67018425e-01 -3.10178511e-02 -2.23944247e-01 2.81140268e-01 1.08779895e+00 1.69448122e-01 5.39442189e-02 7.15989947e-01 -1.00962603e+00 -2.37395629e-01 1.81131735e-01 -8.92030418e-01 1.08901232e-01 -2.40251780e-01 1.91710405e-02 7.23349988e-01 -9.14797485e-01 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6.54677510e-01 -7.91146457e-01 5.28307594e-02 -6.97784245e-01 -7.34169185e-01 2.04856470e-01 1.38421386e-01 -6.83811307e-01 -1.08438686e-01 2.17210695e-01]	[9.651213645935059, 0.19423963129520416]
80893968-863c-4582-980c-066cab3285e3	dynamic-community-detection-via-adversarial	2207.03580	null	https://arxiv.org/abs/2207.03580v1	https://arxiv.org/pdf/2207.03580v1.pdf	Dynamic Community Detection via Adversarial Temporal Graph Representation Learning	Dynamic community detection has been prospered as a powerful tool for quantifying changes in dynamic brain network connectivity patterns by identifying strongly connected sets of nodes. However, as the network science problems and network data to be processed become gradually more sophisticated, it awaits a better method to efficiently learn low dimensional representation from dynamic network data and reveal its latent function that changes over time in the brain network. In this work, an adversarial temporal graph representation learning (ATGRL) framework is proposed to detect dynamic communities from a small sample of brain network data. It adopts a novel temporal graph attention network as an encoder to capture more efficient spatio-temporal features by attention mechanism in both spatial and temporal dimensions. In addition, the framework employs adversarial training to guide the learning of temporal graph representation and optimize the measurable modularity loss to maximize the modularity of community. Experiments on the real-world brain networks datasets are demonstrated to show the effectiveness of this new method.	['Shuqiang Wang', 'Yanyan Shen', 'Changhong Jing', 'Changwei Gong']	2022-06-29	null	null	null	null	['dynamic-community-detection']	['graphs']	[ 1.60726324e-01 1.91632174e-02 7.62426704e-02 -4.29768749e-02 2.04151526e-01 -4.90657061e-01 3.86875480e-01 -1.21822096e-01 -1.57200202e-01 3.46301138e-01 8.80224779e-02 -1.83790103e-01 -5.94169855e-01 -8.04240942e-01 -3.25636506e-01 -7.69033849e-01 -8.63276601e-01 2.48510584e-01 3.34982365e-01 -1.01792865e-01 2.83342728e-04 4.37160283e-01 -7.50073850e-01 -2.35973950e-02 8.45754743e-01 5.09029984e-01 2.66475827e-01 5.95006526e-01 1.38157219e-01 9.07336771e-01 -2.35354319e-01 -3.39825094e-01 2.54910171e-01 -3.69685471e-01 -8.06569755e-01 1.00581065e-01 -1.86914224e-02 1.79447219e-01 -1.02341855e+00 1.37401879e+00 4.99033332e-01 6.54927120e-02 6.67271852e-01 -1.54225922e+00 -9.00633454e-01 6.66158617e-01 -8.86016548e-01 9.27652538e-01 1.10970102e-01 3.43507320e-01 1.01179326e+00 -4.18245107e-01 8.14420283e-01 1.28806698e+00 4.41917092e-01 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c0df1007-68af-4426-8639-d3dd69dd6dad	a-comprehensive-survey-on-video-frame	null	null	https://linkspringer.53yu.com/article/10.1007/s00371-020-02016-y	https://linkspringer.53yu.com/article/10.1007/s00371-020-02016-y	A comprehensive survey on video frame interpolation techniques	Video frame interpolation is an important area in the computer vision research activities for video post-processing, surveillance, and video restoration tasks. It aims toward increasing the frame rate of a video sequence by calculating intermittent frames between consecutive input frames. This ensures extra smooth, clear motion in order to make animation fluid enough and reduce display motion blur. Advanced deep learning algorithms have the potential to discover knowledge from large-scale diverse video data. These algorithms gain insights about intermediate motion and provide new opportunities to further improve video interpolation technologies. This survey demonstrates a comprehensive overview of about a good number of contributions over past decade pertinent to the latest developments in this domain. The survey paper highlights common challenges in the area of video frame interpolation based on three key aspects: high visual quality, low complexity, and high efficiency of interpolated output from regular videos with the standard frame rate. We scrutinize the architectures, workflows, performance, advantages, and disadvantages and generate a broad categorization along with an overview of experimental results of various state-of-the-art methods executed on benchmark datasets. This survey discusses applications of diverse interpolation frameworks. It provides a backbone reference that inspires future researchers to optimize current techniques on academic and industrial grounds.	['Kshitija Pandya & Ashray Aggarwal', 'Disha Varshney', 'Anil Singh Parihar']	2021-01-04	null	null	null	the-visual-computer-2021-1	['video-restoration']	['computer-vision']	[ 2.95513719e-01 -6.83894873e-01 -3.85581821e-01 -8.88849795e-02 -3.78715664e-01 -1.13153338e-01 4.16410923e-01 -4.38148916e-01 -3.06076944e-01 8.56992126e-01 1.50103912e-01 -2.67084479e-01 2.43373811e-01 -3.84071141e-01 -8.59194756e-01 -9.40700889e-01 -3.44296455e-01 -3.25077474e-01 4.02891934e-01 -1.12957984e-01 2.94561327e-01 5.94674885e-01 -1.81601036e+00 7.38750994e-01 6.02067411e-01 9.75350797e-01 3.55402619e-01 1.01584363e+00 2.19167825e-02 1.24350190e+00 -6.52775049e-01 -2.25841299e-01 3.58388811e-01 -5.18221855e-01 -6.29422009e-01 2.66470402e-01 9.21865284e-01 -9.19614673e-01 -6.69170976e-01 6.83878899e-01 4.07411635e-01 1.83223665e-01 2.04759970e-01 -1.34614456e+00 -6.19326890e-01 3.76934931e-02 -6.19183004e-01 7.18144536e-01 6.13436997e-01 4.59472656e-01 4.10504490e-01 -9.02687490e-01 7.54993975e-01 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fd2f7861-e19f-4132-8a56-091d51f3adf8	contrastmotion-self-supervised-scene-motion	2304.12589	null	https://arxiv.org/abs/2304.12589v1	https://arxiv.org/pdf/2304.12589v1.pdf	ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds	In this paper, we propose a novel self-supervised motion estimator for LiDAR-based autonomous driving via BEV representation. Different from usually adopted self-supervised strategies for data-level structure consistency, we predict scene motion via feature-level consistency between pillars in consecutive frames, which can eliminate the effect caused by noise points and view-changing point clouds in dynamic scenes. Specifically, we propose \textit{Soft Discriminative Loss} that provides the network with more pseudo-supervised signals to learn discriminative and robust features in a contrastive learning manner. We also propose \textit{Gated Multi-frame Fusion} block that learns valid compensation between point cloud frames automatically to enhance feature extraction. Finally, \textit{pillar association} is proposed to predict pillar correspondence probabilities based on feature distance, and whereby further predicts scene motion. Extensive experiments show the effectiveness and superiority of our \textbf{ContrastMotion} on both scene flow and motion prediction tasks. The code is available soon.	['Yuexin Ma', 'Ji Zhang', 'Yandong Guo', 'Xinge Zhu', 'Hui Zhou', 'Xiangze Jia']	2023-04-25	null	null	null	null	['motion-prediction']	['computer-vision']	[ 4.04355638e-02 -3.68640721e-01 -4.53364879e-01 -8.58154476e-01 -6.45399809e-01 -2.56848156e-01 4.95287359e-01 -3.20359796e-01 -4.81056362e-01 8.01975250e-01 -1.11626036e-01 8.03346038e-02 -2.13283554e-01 -7.82650590e-01 -8.80010426e-01 -7.44136870e-01 1.99885026e-01 2.68645257e-01 7.16875017e-01 -5.60114011e-02 2.97919869e-01 6.66175067e-01 -1.90094423e+00 -4.24113236e-02 1.00627196e+00 8.77735138e-01 7.61727452e-01 5.64743757e-01 -1.25081703e-01 9.50901866e-01 4.75504585e-02 -6.84321001e-02 3.09219629e-01 -1.08251303e-01 -3.12734842e-01 1.81213766e-01 1.01551664e+00 -6.75652683e-01 -7.86210954e-01 9.48052883e-01 2.87474304e-01 3.81044030e-01 6.99189782e-01 -1.38571322e+00 9.29492265e-02 2.81213522e-02 -8.14429641e-01 5.79357445e-01 1.90316126e-01 4.73529220e-01 8.38073730e-01 -1.28416479e+00 7.05581129e-01 1.38373411e+00 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-6.95115030e-01 -2.16287792e-01 2.87642360e-01 5.95123470e-01 -1.01810288e+00 -4.85407263e-01 2.55011052e-01]	[8.351264953613281, -2.1813957691192627]
68879b09-1df3-4d13-8285-7bea4a4861a9	onegan-simultaneous-unsupervised-learning-of	1912.13471	null	https://arxiv.org/abs/1912.13471v2	https://arxiv.org/pdf/1912.13471v2.pdf	OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering	We present a method for simultaneously learning, in an unsupervised manner, (i) a conditional image generator, (ii) foreground extraction and segmentation, (iii) clustering into a two-level class hierarchy, and (iv) object removal and background completion, all done without any use of annotation. The method combines a Generative Adversarial Network and a Variational Auto-Encoder, with multiple encoders, generators and discriminators, and benefits from solving all tasks at once. The input to the training scheme is a varied collection of unlabeled images from the same domain, as well as a set of background images without a foreground object. In addition, the image generator can mix the background from one image, with a foreground that is conditioned either on that of a second image or on the index of a desired cluster. The method obtains state of the art results in comparison to the literature methods, when compared to the current state of the art in each of the tasks.	['Yaniv Benny', 'Lior Wolf']	2019-12-31	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5448_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710511.pdf	eccv-2020-8	['foreground-segmentation']	['computer-vision']	[ 9.35970008e-01 4.09865528e-01 1.55106723e-01 2.99102310e-02 -8.97553742e-01 -6.92630351e-01 8.26151371e-01 -1.17004335e-01 -4.14291620e-01 8.76291990e-01 -3.37796658e-01 -1.33622333e-01 3.47219080e-01 -7.70244420e-01 -8.62859249e-01 -1.38420475e+00 2.82723814e-01 9.36675251e-01 6.36581242e-01 5.27562141e-01 -1.39466986e-01 5.07539570e-01 -1.54577065e+00 2.11160347e-01 7.99157679e-01 1.00007069e+00 2.98745036e-01 9.72624004e-01 -2.92478758e-03 1.21431518e+00 -7.86355436e-01 -4.00423527e-01 2.92588115e-01 -7.04748452e-01 -7.32881248e-01 9.40793514e-01 3.60827535e-01 -1.24352284e-01 -2.22864762e-01 1.00463355e+00 1.16545402e-01 2.63273101e-02 9.89459753e-01 -1.26735282e+00 -4.24381644e-01 3.01369399e-01 -6.12652123e-01 -1.74066678e-01 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b3f33ba7-144f-48e3-b57b-f148efb937cf	person-retrieval-in-surveillance-using	2105.02414	null	https://arxiv.org/abs/2105.02414v1	https://arxiv.org/pdf/2105.02414v1.pdf	Person Retrieval in Surveillance Using Textual Query: A Review	Recent advancement of research in biometrics, computer vision, and natural language processing has discovered opportunities for person retrieval from surveillance videos using textual query. The prime objective of a surveillance system is to locate a person using a description, e.g., a short woman with a pink t-shirt and white skirt carrying a black purse. She has brown hair. Such a description contains attributes like gender, height, type of clothing, colour of clothing, hair colour, and accessories. Such attributes are formally known as soft biometrics. They help bridge the semantic gap between a human description and a machine as a textual query contains the person's soft biometric attributes. It is also not feasible to manually search through huge volumes of surveillance footage to retrieve a specific person. Hence, automatic person retrieval using vision and language-based algorithms is becoming popular. In comparison to other state-of-the-art reviews, the contribution of the paper is as follows: 1. Recommends most discriminative soft biometrics for specifiic challenging conditions. 2. Integrates benchmark datasets and retrieval methods for objective performance evaluation. 3. A complete snapshot of techniques based on features, classifiers, number of soft biometric attributes, type of the deep neural networks, and performance measures. 4. The comprehensive coverage of person retrieval from handcrafted features based methods to end-to-end approaches based on natural language description.	['Mehul S Raval', 'Hiren Galiyawala']	2021-05-06	null	null	null	null	['person-retrieval']	['computer-vision']	[ 1.64316788e-01 -5.41109264e-01 -1.57555014e-01 -5.74701488e-01 -5.62780440e-01 -8.90495181e-01 7.93430626e-01 2.02860415e-01 -3.71496856e-01 6.23431683e-01 5.30211963e-02 3.48575890e-01 -2.02359006e-01 -5.19533992e-01 -1.73070356e-01 -7.37127125e-01 2.37686545e-01 3.80696148e-01 -3.26977342e-01 -1.85515061e-01 -3.99613269e-02 5.22867501e-01 -1.46831191e+00 2.14802697e-02 9.26833227e-02 1.51206601e+00 -3.78285408e-01 6.28957093e-01 3.35605562e-01 1.71095774e-01 -6.81376517e-01 -1.12129569e+00 4.23727334e-01 1.78111345e-02 -5.20451069e-01 5.43222278e-02 1.04676521e+00 -6.38535082e-01 -5.17503500e-01 9.51492131e-01 8.55138123e-01 -2.00496361e-01 9.66847479e-01 -1.05550218e+00 -1.00620365e+00 -4.59708571e-02 -6.15255773e-01 -1.41565397e-03 1.13686645e+00 1.04489841e-01 7.25223243e-01 -7.12878108e-01 4.70756680e-01 1.13573039e+00 1.01817310e+00 8.14139366e-01 -7.14771450e-01 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5.68296731e-01 6.72483206e-01 -5.56985140e-01 2.05823481e-01 4.90075827e-01]	[14.563446044921875, 0.9080914855003357]
60466315-50b7-4ffe-ab91-b0ee6d434c79	unified-multimodal-pre-training-and-prompt	2112.05587	null	https://arxiv.org/abs/2112.05587v2	https://arxiv.org/pdf/2112.05587v2.pdf	Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation	Most existing vision-language pre-training methods focus on understanding tasks and use BERT-like objectives (masked language modeling and image-text matching) during pretraining. Although they perform well in many understanding downstream tasks, e.g., visual question answering, image-text retrieval and visual entailment, they do not possess the ability to generate. To tackle this problem, we propose Unified multimodal pre-training for both Vision-Language understanding and generation (UniVL). The proposed UniVL is capable of handling both understanding tasks and generative tasks. We augment existing pretraining paradigms that only use random masks with causal masks, i.e., triangular masks that mask out future tokens, such that the pre-trained models can have autoregressive generation abilities by design. We formulate several previous understanding tasks as a text generation task and propose to use prompt-based method for fine-tuning on different downstream tasks. Our experiments show that there is a trade-off between understanding tasks and generation tasks while using the same model, and a feasible way to improve both tasks is to use more data. Our UniVL framework attains comparable performance to recent vision-language pre-training methods on both understanding tasks and generation tasks. Moreover, we demostrate that prompt-based finetuning is more data-efficient - it outperforms discriminative methods in few-shot scenarios.	['Yu-Gang Jiang', 'Jingjing Chen', 'Wenhan Xiong', 'Zuxuan Wu', 'Tianyi Liu']	2021-12-10	null	null	null	null	['visual-entailment']	['reasoning']	[ 5.46705842e-01 2.68290490e-01 -3.36639993e-02 -4.70331341e-01 -6.80348992e-01 -5.28536260e-01 1.18133593e+00 -5.06261736e-02 -5.17738938e-01 5.06243527e-01 1.99119449e-01 -4.41421807e-01 9.04017091e-02 -7.57027268e-01 -9.49092090e-01 -4.44743872e-01 5.78234494e-01 6.62608922e-01 2.10823312e-01 -2.57345051e-01 3.35354924e-01 1.31202593e-01 -1.76866984e+00 6.59307301e-01 1.14620960e+00 7.21328080e-01 7.23323762e-01 8.98005724e-01 -2.82816142e-01 8.47369969e-01 -4.15667415e-01 -4.54511106e-01 6.40675705e-03 -5.42122066e-01 -9.45232749e-01 2.11206347e-01 6.14845335e-01 -4.50473547e-01 -2.47334450e-01 7.59792924e-01 3.49585027e-01 3.00095320e-01 8.97953212e-01 -1.25816619e+00 -1.10457265e+00 6.47803009e-01 -4.73065346e-01 1.31470338e-01 3.81502658e-01 4.54240769e-01 8.49165916e-01 -1.21717203e+00 4.69488502e-01 1.57137036e+00 3.31597924e-01 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-9.05018076e-02 4.57030684e-01 1.99067757e-01 -6.73229158e-01 -6.04736842e-02 5.40191948e-01]	[10.766263008117676, 1.5862948894500732]
465441b7-6e46-40b4-b3b1-e4deb0ed3a20	improving-accuracy-without-losing	2212.06620	null	https://arxiv.org/abs/2212.06620v1	https://arxiv.org/pdf/2212.06620v1.pdf	Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting	In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpretability. Recent algorithms often combine machine learning (hereafter ML) methodology with decomposition to improve prediction accuracy. However, incorporating ML is generally considered to sacrifice interpretability inevitably. In addition, existing hybrid algorithms usually rely on theoretical models with statistical assumptions and focus only on the accuracy of aggregate predictions, and thus suffer from accuracy problems, especially in component estimates. In response to the above issues, this research explores the possibility of improving accuracy without losing interpretability in time series forecasting. We first quantitatively define interpretability for data-driven forecasts and systematically review the existing forecasting algorithms from the perspective of interpretability. Accordingly, we propose the W-R algorithm, a hybrid algorithm that combines decomposition and ML from a novel perspective. Specifically, the W-R algorithm replaces the standard additive combination function with a weighted variant and uses ML to modify the estimates of all components simultaneously. We mathematically analyze the theoretical basis of the algorithm and validate its performance through extensive numerical experiments. In general, the W-R algorithm outperforms all decomposition-based and ML benchmarks. Based on P50_QL, the algorithm relatively improves by 8.76% in accuracy on the practical sales forecasts of JD.com and 77.99% on a public dataset of electricity loads. This research offers an innovative perspective to combine the statistical and ML algorithms, and JD.com has implemented the W-R algorithm to make accurate sales predictions and guide its marketing activities.	['ZuoJun Max Shen', 'Hao Hu', 'Yongzhi Qi', 'Jianshen Zhang', 'Zhengxin Shi', 'Yiqi Sun']	2022-12-13	null	null	null	null	['marketing']	['miscellaneous']	[-1.76006276e-02 -1.72830656e-01 -4.06086683e-01 -3.18931848e-01 -4.60484087e-01 -5.55238664e-01 3.76725733e-01 9.55818743e-02 3.23814839e-01 5.48540652e-01 1.47469327e-01 -7.34095037e-01 -4.41743672e-01 -8.50125730e-01 -3.17446649e-01 -9.17414188e-01 -1.15934849e-01 3.16654146e-01 -7.62000740e-01 -3.53554696e-01 2.21186250e-01 4.33300912e-01 -1.50565338e+00 2.06956714e-01 1.52868533e+00 1.48075402e+00 -6.27943128e-02 1.65057346e-01 -2.71928668e-01 5.56838214e-01 -6.60067141e-01 -4.75722134e-01 5.10286748e-01 -3.19298267e-01 -1.50357991e-01 2.58351535e-01 -3.10579360e-01 -3.77672106e-01 2.93660074e-01 6.91472650e-01 2.46995941e-01 2.84648817e-02 5.31811059e-01 -1.67714775e+00 -6.31597161e-01 7.81464577e-01 -8.95296872e-01 5.77913933e-02 3.14499229e-01 -6.96742162e-02 9.80740726e-01 -8.96041751e-01 -8.23651999e-02 9.73703444e-01 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-1.03463399e+00 -8.31296444e-01 -7.49580622e-01 1.50334671e-01 1.41933590e-01 -6.63599670e-01 -6.25790000e-01 6.88355342e-02]	[6.559615135192871, 2.965211868286133]
30fd6066-bc4f-4276-85dd-4ede15570a13	temporal-viewpoint-transportation-plan-for	2210.16820	null	https://arxiv.org/abs/2210.16820v1	https://arxiv.org/pdf/2210.16820v1.pdf	Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition	We propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To factor out misalignment between query and support sequences of 3D body joints, we propose an advanced variant of Dynamic Time Warping which jointly models each smooth path between the query and support frames to achieve simultaneously the best alignment in the temporal and simulated camera viewpoint spaces for end-to-end learning under the limited few-shot training data. Sequences are encoded with a temporal block encoder based on Simple Spectral Graph Convolution, a lightweight linear Graph Neural Network backbone. We also include a setting with a transformer. Finally, we propose a similarity-based loss which encourages the alignment of sequences of the same class while preventing the alignment of unrelated sequences. We show state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D Multiview Activity II.	['Piotr Koniusz', 'Lei Wang']	2022-10-30	null	null	null	null	['few-shot-action-recognition']	['computer-vision']	[ 2.55327344e-01 -3.04261800e-02 -4.16809082e-01 -2.70568162e-01 -7.32158422e-01 -1.39943376e-01 5.85184574e-01 -5.39886057e-01 -3.82149696e-01 1.96966827e-01 5.61231375e-01 3.68704200e-01 1.08847551e-01 -6.58356026e-02 -8.47316742e-01 -5.35877526e-01 -4.22815561e-01 4.02784258e-01 5.07135689e-01 -1.25404805e-01 1.63345505e-02 3.86719108e-01 -1.17732799e+00 3.22860420e-01 7.02887177e-02 8.42790425e-01 9.79038849e-02 1.00379419e+00 4.32449371e-01 1.26913369e+00 -2.51164347e-01 -3.16678196e-01 5.41242063e-01 -6.11009121e-01 -6.82779551e-01 8.03220570e-01 8.92714262e-01 -6.94987833e-01 -8.75475824e-01 5.61953604e-01 8.14537704e-01 5.48000336e-01 4.98929739e-01 -1.40262341e+00 -1.21641330e-01 1.44109160e-01 -8.34561169e-01 2.70904094e-01 7.71548450e-01 4.33336258e-01 1.05456150e+00 -7.95429349e-01 1.23992717e+00 1.24234533e+00 6.29322290e-01 5.97064555e-01 -1.06151068e+00 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-6.97935879e-01 5.95272303e-01 3.88353288e-01 -8.86479855e-01 -1.19253226e-01 2.73963332e-01]	[7.663495063781738, 0.027440015226602554]
8d17edcd-ad4c-43a9-8326-96254b7326b6	multi-object-representation-learning-with	1903.00450	null	https://arxiv.org/abs/1903.00450v3	https://arxiv.org/pdf/1903.00450v3.pdf	Multi-Object Representation Learning with Iterative Variational Inference	Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representation learning focuses on feature learning without even considering multiple objects, or treats segmentation as an (often supervised) preprocessing step. Instead, we argue for the importance of learning to segment and represent objects jointly. We demonstrate that, starting from the simple assumption that a scene is composed of multiple entities, it is possible to learn to segment images into interpretable objects with disentangled representations. Our method learns -- without supervision -- to inpaint occluded parts, and extrapolates to scenes with more objects and to unseen objects with novel feature combinations. We also show that, due to the use of iterative variational inference, our system is able to learn multi-modal posteriors for ambiguous inputs and extends naturally to sequences.	['Matthew Botvinick', 'Daniel Zoran', 'Raphaël Lopez Kaufman', 'Klaus Greff', 'Chris Burgess', 'Alexander Lerchner', 'Nick Watters', 'Rishabh Kabra', 'Loic Matthey']	2019-03-01	null	null	null	null	['unsupervised-object-segmentation', 'systematic-generalization']	['computer-vision', 'reasoning']	[ 6.73767030e-01 4.40441310e-01 -1.62745744e-01 -4.80681121e-01 -8.38799775e-01 -7.38267601e-01 7.66597927e-01 9.45449322e-02 -2.79773355e-01 6.24508083e-01 3.04494590e-01 -1.80729747e-01 -7.89829493e-02 -7.89245963e-01 -9.37021136e-01 -7.38644540e-01 5.18838577e-02 7.98692465e-01 -1.51090762e-02 8.98671895e-02 1.42154053e-01 5.60907125e-01 -1.48662889e+00 3.73115093e-01 6.71183765e-01 4.82580066e-01 2.96895325e-01 8.25797498e-01 -6.82534501e-02 6.25411212e-01 -4.38803285e-01 -4.07304794e-01 2.55876273e-01 -4.11476880e-01 -1.18445265e+00 7.75975168e-01 5.15139639e-01 -3.00368220e-01 -2.10602432e-01 9.16375458e-01 -2.28069395e-01 3.81480008e-01 8.70357573e-01 -9.40555990e-01 -9.88983214e-01 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e5a7723e-3a5f-4788-aa16-0d9ab48cd0c7	skin-lesion-segmentation-u-nets-versus	1710.01248	null	http://arxiv.org/abs/1710.01248v1	http://arxiv.org/pdf/1710.01248v1.pdf	Skin Lesion Segmentation: U-Nets versus Clustering	Many automatic skin lesion diagnosis systems use segmentation as a preprocessing step to diagnose skin conditions because skin lesion shape, border irregularity, and size can influence the likelihood of malignancy. This paper presents, examines and compares two different approaches to skin lesion segmentation. The first approach uses U-Nets and introduces a histogram equalization based preprocessing step. The second approach is a C-Means clustering based approach that is much simpler to implement and faster to execute. The Jaccard Index between the algorithm output and hand segmented images by dermatologists is used to evaluate the proposed algorithms. While many recently proposed deep neural networks to segment skin lesions require a significant amount of computational power for training (i.e., computer with GPUs), the main objective of this paper is to present methods that can be used with only a CPU. This severely limits, for example, the number of training instances that can be presented to the U-Net. Comparing the two proposed algorithms, U-Nets achieved a significantly higher Jaccard Index compared to the clustering approach. Moreover, using the histogram equalization for preprocessing step significantly improved the U-Net segmentation results.	['Shivam Kalra', 'Kevin Michael', 'H. R. Tizhoosh', 'Bill S. Lin']	2017-09-27	null	null	null	null	['skin-lesion-segmentation']	['medical']	[ 5.63345671e-01 -1.07094022e-02 -5.17180860e-02 -2.83706129e-01 -2.93539196e-01 -5.21628201e-01 2.16174141e-01 8.36583018e-01 -7.90451586e-01 5.79003513e-01 -5.42945445e-01 -4.46073592e-01 -1.55193120e-01 -1.17118919e+00 -1.36156172e-01 -9.49458659e-01 2.68596172e-01 4.75345910e-01 4.42592174e-01 1.75510272e-01 4.45489168e-01 7.94837475e-01 -1.51152885e+00 2.45373994e-01 8.74681413e-01 9.76890743e-01 1.22299433e-01 8.87037635e-01 -4.57012951e-01 2.10489869e-01 -5.48632085e-01 -4.63236496e-02 2.11821854e-01 -4.55475926e-01 -6.40003264e-01 3.29298347e-01 3.37129503e-01 -2.01371402e-01 2.01722473e-01 1.04270434e+00 6.20380163e-01 1.86069787e-01 9.12273169e-01 -7.96109200e-01 -3.65552641e-02 3.13722521e-01 -7.29705393e-01 1.62726685e-01 3.50215845e-02 -1.18627027e-01 3.74137610e-01 -9.70135331e-02 6.75664961e-01 7.50054359e-01 9.02731180e-01 4.05887604e-01 -8.46923470e-01 -2.34446719e-01 -4.78322119e-01 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2.87061006e-01 -2.42464110e-01 -2.49236569e-01 -8.88439938e-02 5.60626015e-02]	[15.560223579406738, -3.014772891998291]
5dc8ba6d-8d82-4bed-82a9-a914d8fac98a	a-classification-of-g-invariant-shallow	2205.09219	null	https://arxiv.org/abs/2205.09219v5	https://arxiv.org/pdf/2205.09219v5.pdf	A Classification of $G$-invariant Shallow Neural Networks	When trying to fit a deep neural network (DNN) to a $G$-invariant target function with $G$ a group, it only makes sense to constrain the DNN to be $G$-invariant as well. However, there can be many different ways to do this, thus raising the problem of ``$G$-invariant neural architecture design'': What is the optimal $G$-invariant architecture for a given problem? Before we can consider the optimization problem itself, we must understand the search space, the architectures in it, and how they relate to one another. In this paper, we take a first step towards this goal; we prove a theorem that gives a classification of all $G$-invariant single-hidden-layer or ``shallow'' neural network ($G$-SNN) architectures with ReLU activation for any finite orthogonal group $G$, and we prove a second theorem that characterizes the inclusion maps or ``network morphisms'' between the architectures that can be leveraged during neural architecture search (NAS). The proof is based on a correspondence of every $G$-SNN to a signed permutation representation of $G$ acting on the hidden neurons; the classification is equivalently given in terms of the first cohomology classes of $G$, thus admitting a topological interpretation. The $G$-SNN architectures corresponding to nontrivial cohomology classes have, to our knowledge, never been explicitly identified in the literature previously. Using a code implementation, we enumerate the $G$-SNN architectures for some example groups $G$ and visualize their structure. Finally, we prove that architectures corresponding to inequivalent cohomology classes coincide in function space only when their weight matrices are zero, and we discuss the implications of this for NAS.	['James Ostrowski', 'Devanshu Agrawal']	2022-05-18	null	null	null	null	['classification']	['methodology']	[ 1.33830577e-01 5.90900362e-01 7.69826397e-02 -8.47294256e-02 4.67981584e-03 -6.94013774e-01 3.16763729e-01 -3.37470949e-01 -1.75348088e-01 2.72223383e-01 2.68194694e-02 -7.03380287e-01 -4.58957046e-01 -1.04159319e+00 -8.81923378e-01 -1.05657244e+00 -5.87849200e-01 3.37994963e-01 5.60689755e-02 -6.20097101e-01 2.19837412e-01 4.54413474e-01 -1.71076298e+00 1.13449365e-01 3.22648078e-01 1.10486019e+00 1.22371912e-01 6.82002127e-01 -1.95198834e-01 3.62114221e-01 -2.27455780e-01 -3.32330853e-01 8.71932030e-01 -6.03645980e-01 -1.08410800e+00 -4.95096624e-01 8.42489481e-01 3.28566968e-01 -3.00069720e-01 1.46926618e+00 1.54178157e-01 8.59390348e-02 7.64571905e-01 -1.25033879e+00 -7.47043908e-01 7.29825616e-01 -6.51493743e-02 2.33429566e-01 -2.56829947e-01 -6.29649833e-02 1.57654583e+00 -7.99086750e-01 6.55814826e-01 1.23085034e+00 7.13572204e-01 7.34587848e-01 -1.63879561e+00 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2.74680555e-01 4.09009695e-01 -5.25099576e-01 -6.11733198e-01 -7.93706104e-02]	[8.245593070983887, 3.347116231918335]
fc82b7d5-0b26-48bc-a0cb-4d9965ea9941	on-testing-and-comparing-fair-classifiers	2302.05906	null	https://arxiv.org/abs/2302.05906v1	https://arxiv.org/pdf/2302.05906v1.pdf	On Testing and Comparing Fair classifiers under Data Bias	In this paper, we consider a theoretical model for injecting data bias, namely, under-representation and label bias (Blum & Stangl, 2019). We theoretically and empirically study its effect on the accuracy and fairness of fair classifiers. Theoretically, we prove that the Bayes optimal group-aware fair classifier on the original data distribution can be recovered by simply minimizing a carefully chosen reweighed loss on the bias-injected distribution. Through extensive experiments on both synthetic and real-world datasets (e.g., Adult, German Credit, Bank Marketing, COMPAS), we empirically audit pre-, in-, and post-processing fair classifiers from standard fairness toolkits for their fairness and accuracy by injecting varying amounts of under-representation and label bias in their training data (but not the test data). Our main observations are: (1) The fairness and accuracy of many standard fair classifiers degrade severely as the bias injected in their training data increases, (2) A simple logistic regression model trained on the right data can often outperform, in both accuracy and fairness, most fair classifiers trained on biased training data, and (3) A few, simple fairness techniques (e.g., reweighing, exponentiated gradients) seem to offer stable accuracy and fairness guarantees even when their training data is injected with under-representation and label bias. Our experiments also show how to integrate a measure of data bias risk in the existing fairness dashboards for real-world deployments	['Rajiv Ratn Shah', 'Amit Deshpande', 'Mohit Sharma']	2023-02-12	null	null	null	null	['marketing']	['miscellaneous']	[-2.09641993e-01 1.92275465e-01 -5.84090114e-01 -8.98594260e-01 -3.35070044e-01 -7.02745378e-01 6.40344083e-01 2.84447610e-01 -8.06751132e-01 1.08033609e+00 -3.03074624e-02 -4.79026407e-01 2.92116180e-02 -6.59984112e-01 -4.82904375e-01 -5.02031386e-01 -1.68247029e-01 3.71533722e-01 -2.54890770e-01 6.47458155e-03 4.38807487e-01 5.04488230e-01 -1.38822079e+00 -1.11126050e-01 1.13802993e+00 1.13218451e+00 -8.31993818e-01 5.82348406e-01 2.35249966e-01 1.21716464e+00 -8.34187448e-01 -1.30818248e+00 6.98468506e-01 -4.87260997e-01 -8.19356084e-01 -4.68175232e-01 8.01089644e-01 -7.69804299e-01 2.28273999e-02 1.14448929e+00 6.03493154e-01 -9.78289992e-02 9.91655111e-01 -1.86817336e+00 -6.10573530e-01 8.58128011e-01 -6.58561468e-01 1.59209162e-01 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2845a672-ac0d-45d1-bd6c-eaeebcec52f4	ur-funny-a-multimodal-language-dataset-for	1904.06618	null	http://arxiv.org/abs/1904.06618v1	http://arxiv.org/pdf/1904.06618v1.pdf	UR-FUNNY: A Multimodal Language Dataset for Understanding Humor	Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research.	['Md. Kamrul Hasan', 'Md. Iftekhar Tanveer', 'Louis-Philippe Morency', 'Jianyuan Zhong', 'Wasifur Rahman', 'Mohammed', 'Hoque', 'Amir Zadeh']	2019-04-14	ur-funny-a-multimodal-language-dataset-for-1	https://aclanthology.org/D19-1211	https://aclanthology.org/D19-1211.pdf	ijcnlp-2019-11	['humor-detection']	['natural-language-processing']	[-1.29850432e-01 -8.09029490e-02 -9.65358466e-02 6.93910336e-03 -9.75890085e-02 -6.99606538e-01 1.12534344e+00 6.93274662e-02 3.77756134e-02 5.83780944e-01 1.17742884e+00 1.92524791e-01 3.67475748e-01 -3.02880198e-01 1.82252601e-01 -4.58887279e-01 1.72847390e-01 1.96416199e-01 -2.33534664e-01 -7.14688599e-01 6.36259139e-01 4.31151241e-01 -1.44484639e+00 8.16388488e-01 2.92743314e-02 2.86949992e-01 -1.80385217e-01 1.06992042e+00 -6.03094041e-01 1.76523137e+00 -5.71051776e-01 -5.36135137e-01 -3.59276533e-01 -8.79668355e-01 -9.42202091e-01 1.14415668e-01 2.87683934e-01 -2.14374155e-01 -3.94594908e-01 1.03477991e+00 5.58835328e-01 1.75630465e-01 5.77074230e-01 -1.21090627e+00 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d80f83ad-95fb-4c31-9e40-1126a39ca380	neural-machine-translation-with-synchronous	null	null	https://aclanthology.org/2021.acl-srw.33	https://aclanthology.org/2021.acl-srw.33.pdf	Neural Machine Translation with Synchronous Latent Phrase Structure	It is reported that grammatical information is useful for machine translation (MT) task. However, the annotation of grammatical information requires the highly human resources. Furthermore, it is not trivial to adapt grammatical information to MT since grammatical annotation usually adapts tokenization standards which might not be suitable to capture the relation of two languages, and the use of sub-word tokenization, e.g., Byte-Pair-Encoding, to alleviate out-of-vocabulary problem might not be compatible with those annotations. In this work, we propose two methods to explicitly incorporate grammatical information without supervising annotation; first, latent phrase structure is induced in an unsupervised fashion from a multi-head attention mechanism; second, the induced phrase structures in encoder and decoder are synchronized so that they are compatible with each other using constraints during training. We demonstrate that our approach produces better performance and explainability in two tasks, translation and alignment tasks without extra resources. Although we could not obtain the high quality phrase structure in constituency parsing when evaluated monolingually, we find that the induced phrase structures enhance the explainability of translation through the synchronization constraint.	['Taro Watanabe', 'Shintaro Harada']	2021-08-01	null	null	null	acl-2021-5	['constituency-parsing']	['natural-language-processing']	[ 2.14871287e-01 3.64335597e-01 -3.27587456e-01 -6.84150279e-01 -9.18941438e-01 -5.54832101e-01 2.73192346e-01 1.45823225e-01 -3.96688104e-01 9.92710769e-01 2.49319866e-01 -6.31159484e-01 4.11426812e-01 -5.58216333e-01 -9.81257737e-01 -4.07261580e-01 3.39379817e-01 4.98302549e-01 -5.89056648e-02 -4.01704788e-01 1.72374010e-01 -1.69717401e-01 -9.73130107e-01 4.88849819e-01 1.51326251e+00 2.88788885e-01 7.82534719e-01 1.61755100e-01 -5.81019044e-01 2.29262561e-01 -5.88064075e-01 -6.33000195e-01 2.70769477e-01 -6.17039502e-01 -1.00264108e+00 1.38627626e-02 1.83269992e-01 -1.48766890e-01 2.63734043e-01 1.13165987e+00 2.97570795e-01 -3.52551043e-01 3.20820332e-01 -7.54694402e-01 -8.41052771e-01 1.02586091e+00 -3.19971889e-01 2.97438502e-01 2.34955743e-01 -3.88560332e-02 1.20698106e+00 -8.51600528e-01 6.68654382e-01 1.19308805e+00 3.98040116e-01 6.06870651e-01 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5225cd68-0d24-4d9f-9b4f-34a89a6d216c	self-supervised-learning-for-modeling-gamma	2302.07700	null	https://arxiv.org/abs/2302.07700v1	https://arxiv.org/pdf/2302.07700v1.pdf	Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars	Blazars are active galactic nuclei with relativistic jets pointed almost directly at Earth. Blazars are characterized by strong, apparently stochastic flux variability at virtually all observed wavelengths and timescales, from minutes to years, the physical origin of which is still poorly understood. In the high-energy gamma-ray band, the Large Area Telescope aboard the Fermi space telescope (Fermi-LAT) has conducted regular monitoring of thousands of blazars since 2008. Deep learning can help uncover structure in gamma-ray blazars' complex variability patterns that traditional methods based on parametric statistical modeling or manual feature engineering may miss. In this work, we propose using a self-supervised Transformer encoder architecture to construct an effective representation of blazar gamma-ray variability. Measurement errors, upper limits, and missing data are accommodated using learned encodings. The model predicts a set of quantiles for the flux probability distribution at each time step, an architecture naturally suited for describing data generated by a stochastic process. As a proof of concept for how the model output can be analyzed to extract scientifically relevant information, a preliminary search for weekly-timescale time-reversal asymmetry in gamma-ray blazar light curves was conducted, finding no significant evidence for asymmetry.	['Aryeh Brill']	2023-02-15	null	null	null	null	['feature-engineering']	['methodology']	[-3.00454378e-01 -3.77783418e-01 -8.75058174e-02 -4.35825855e-01 -6.86398327e-01 -7.24397600e-01 1.05877900e+00 -2.24410743e-01 -1.48560643e-01 8.40156376e-01 1.09098546e-01 -6.46157324e-01 -5.18975377e-01 -8.12498271e-01 -6.28548861e-01 -1.22895133e+00 -1.71693444e-01 9.94049788e-01 -1.00831434e-01 -1.81629479e-01 2.66221672e-01 6.82205737e-01 -1.12161553e+00 2.57932544e-01 6.64405748e-02 9.68056262e-01 2.41196841e-01 9.17234898e-01 7.03540891e-02 8.85643363e-01 -7.18358159e-01 -1.97555590e-02 3.29345793e-01 -6.49236441e-01 -3.07096392e-01 -1.73215821e-01 -1.44825801e-01 -7.18984082e-02 -4.40137535e-01 8.78305495e-01 1.98027924e-01 5.92516065e-02 8.16328943e-01 -9.25431430e-01 -4.94357079e-01 4.34703559e-01 -4.92826551e-01 7.29831517e-01 -3.05136800e-01 3.86690706e-01 9.85584974e-01 -3.93907785e-01 4.57822770e-01 9.60330725e-01 5.42649806e-01 9.38676149e-02 -1.25678027e+00 -4.64871407e-01 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c73fcb17-2f7e-4efb-8203-ced5efcbf61e	optimality-of-graph-scanning-statistic-for	2102.05821	null	https://arxiv.org/abs/2102.05821v1	https://arxiv.org/pdf/2102.05821v1.pdf	Optimality of Graph Scanning Statistic for Online Community Detection	Sequential change-point detection for graphs is a fundamental problem for streaming network data types and has wide applications in social networks and power systems. Given fixed vertices and a sequence of random graphs, the objective is to detect the change-point where the underlying distribution of the random graph changes. In particular, we focus on the local change that only affects a subgraph. We adopt the classical Erdos-Renyi model and revisit the generalized likelihood ratio (GLR) detection procedure. The scan statistic is computed by sequentially estimating the most-likely subgraph where the change happens. We provide theoretical analysis for the asymptotic optimality of the proposed procedure based on the GLR framework. We demonstrate the efficiency of our detection algorithm using simulations.	['Yao Xie', 'Liyan Xie']	2021-02-11	null	null	null	null	['online-community-detection']	['graphs']	[ 4.25352424e-01 7.31722638e-02 -3.49956095e-01 1.18666761e-01 -1.55922949e-01 -7.44237959e-01 3.31547797e-01 6.00427270e-01 6.00386970e-02 6.37563646e-01 -4.78256643e-01 -3.25049609e-01 -4.44576353e-01 -1.21729553e+00 -6.68363154e-01 -8.06939483e-01 -8.13735306e-01 4.31132942e-01 5.03145814e-01 2.40614533e-01 6.23134933e-02 6.14748299e-01 -5.17367482e-01 -8.36453855e-01 5.93336523e-01 6.14994943e-01 1.83063261e-02 8.56948316e-01 3.59958947e-01 3.33548874e-01 -6.85246944e-01 -6.86253086e-02 -2.58965902e-02 -6.35213733e-01 -3.40316296e-01 3.50812465e-01 -4.28692847e-01 -2.93094933e-01 -6.97410047e-01 1.47965813e+00 2.96671838e-01 -2.22940326e-01 7.06719577e-01 -1.71889985e+00 -9.87247229e-02 1.01771545e+00 -1.42131627e+00 9.69556272e-01 5.58017850e-01 -3.36661398e-01 1.04172599e+00 -4.25147861e-01 5.80860198e-01 1.13612783e+00 5.03124714e-01 1.23054292e-02 -1.22921264e+00 -6.25992894e-01 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-2.05449015e-01 4.43520814e-01 -4.05858099e-01 -1.00630260e+00 -4.50442702e-01]	[6.8934454917907715, 5.115460395812988]
0f3ab87e-30fd-44dd-8eab-d2a15b8b6286	explaining-neural-matrix-factorization-with	2010.05516	null	https://arxiv.org/abs/2010.05516v4	https://arxiv.org/pdf/2010.05516v4.pdf	Explaining Neural Matrix Factorization with Gradient Rollback	Explaining the predictions of neural black-box models is an important problem, especially when such models are used in applications where user trust is crucial. Estimating the influence of training examples on a learned neural model's behavior allows us to identify training examples most responsible for a given prediction and, therefore, to faithfully explain the output of a black-box model. The most generally applicable existing method is based on influence functions, which scale poorly for larger sample sizes and models. We propose gradient rollback, a general approach for influence estimation, applicable to neural models where each parameter update step during gradient descent touches a smaller number of parameters, even if the overall number of parameters is large. Neural matrix factorization models trained with gradient descent are part of this model class. These models are popular and have found a wide range of applications in industry. Especially knowledge graph embedding methods, which belong to this class, are used extensively. We show that gradient rollback is highly efficient at both training and test time. Moreover, we show theoretically that the difference between gradient rollback's influence approximation and the true influence on a model's behavior is smaller than known bounds on the stability of stochastic gradient descent. This establishes that gradient rollback is robustly estimating example influence. We also conduct experiments which show that gradient rollback provides faithful explanations for knowledge base completion and recommender datasets.	['Mathias Niepert', 'Timo Sztyler', 'Carolin Lawrence']	2020-10-12	null	null	null	null	['knowledge-base-completion', 'knowledge-base-completion', 'influence-approximation']	['graphs', 'knowledge-base', 'methodology']	[-8.20986629e-02 3.38588119e-01 -5.53887069e-01 -3.76939476e-01 -1.01425223e-01 -3.81037235e-01 3.23652655e-01 2.30258405e-01 -3.10729086e-01 8.08886111e-01 -1.14299327e-01 -3.91902238e-01 -3.05697590e-01 -8.83286178e-01 -1.29911721e+00 -6.14012539e-01 -2.22916037e-01 4.18951482e-01 1.47538453e-01 -2.73120046e-01 2.00033814e-01 3.46127838e-01 -1.43424702e+00 -6.10307977e-02 8.28316689e-01 8.11756432e-01 4.87650372e-02 6.87364817e-01 -1.00748889e-01 7.13741124e-01 -4.79804367e-01 -7.17550397e-01 -5.50588779e-02 -4.30241555e-01 -5.40466011e-01 -2.77673006e-01 2.54626602e-01 -9.99972001e-02 -2.19298244e-01 9.90278125e-01 9.71600413e-02 2.15114757e-01 5.46991110e-01 -1.26766527e+00 -5.96397817e-01 1.09530556e+00 -3.15965652e-01 2.27473453e-01 -4.10691928e-03 -4.62304980e-01 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3.56493562e-01 3.45537573e-01 9.18314695e-01 -5.15426397e-01 -9.22939122e-01 -9.25488591e-01 -7.32238710e-01 1.54091239e-01 6.88201562e-02 -6.38521373e-01 -6.06359243e-01 -1.92700684e-01]	[8.343616485595703, 4.613187313079834]
ae196abd-8a79-40f3-abf6-7bae93a2d1cb	an-in-depth-exploration-of-person-re	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.pdf	An In-Depth Exploration of Person Re-Identification and Gait Recognition in Cloth-Changing Conditions	The target of person re-identification (ReID) and gait recognition is consistent, that is to match the target pedestrian under surveillance cameras. For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched under controlled conditions. To tackle this problem, we propose a Cloth-Changing benchmark for Person re-identification and Gait recognition (CCPG). It is a cloth-changing dataset, and there are several highlights in CCPG, (1) it provides 200 identities and over 16K sequences are captured indoors and outdoors, (2) each identity has seven different cloth-changing statuses, which is hardly seen in previous datasets, (3) RGB and silhouettes version data are both available for research purposes. Moreover, aiming to investigate the cloth-changing problem systematically, comprehensive experiments are conducted on video-based ReID and gait recognition methods. The experimental results demonstrate the superiority of ReID and gait recognition separately in different cloth-changing conditions and suggest that gait recognition is a potential solution for addressing the cloth-changing problem. Our dataset will be available at https://github.com/BNU-IVC/CCPG.	['Yao Zhao', 'Yongzhen Huang', 'Xu Liu', 'Chunshui Cao', 'Chunjie Zhang', 'Saihui Hou', 'Weijia Li']	2023-01-01	null	null	null	cvpr-2023-1	['person-re-identification', 'gait-recognition']	['computer-vision', 'computer-vision']	[-1.28466681e-01 -9.12740409e-01 -5.16381152e-02 -1.78318933e-01 -1.43140823e-01 -3.90890032e-01 4.26123649e-01 -3.41941684e-01 -3.50725263e-01 7.81319141e-01 8.31887051e-02 1.84156969e-01 1.44521266e-01 -5.89749157e-01 -5.11464357e-01 -1.00531375e+00 7.89847632e-04 5.33454567e-02 2.04697743e-01 -2.86118448e-01 3.54040824e-02 5.18078506e-01 -1.80246842e+00 -2.15373710e-01 5.83245277e-01 8.17245245e-01 1.78291395e-01 7.10799575e-01 3.51677418e-01 2.09495351e-01 -5.50268531e-01 -8.43688488e-01 5.15304923e-01 -4.41496760e-01 -3.66416007e-01 2.96811759e-01 7.67703891e-01 -5.53174198e-01 -8.78695786e-01 1.09548974e+00 7.57851422e-01 9.99614298e-02 3.94861370e-01 -1.48729420e+00 -7.77076662e-01 -1.39293388e-01 -5.35033941e-01 3.66385520e-01 8.26784492e-01 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27b738c9-78b0-45c3-bc1e-b1b51e2dc8ca	unsupervised-neural-machine-translation	1710.11041	null	http://arxiv.org/abs/1710.11041v2	http://arxiv.org/pdf/1710.11041v2.pdf	Unsupervised Neural Machine Translation	In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal. In this work, we completely remove the need of parallel data and propose a novel method to train an NMT system in a completely unsupervised manner, relying on nothing but monolingual corpora. Our model builds upon the recent work on unsupervised embedding mappings, and consists of a slightly modified attentional encoder-decoder model that can be trained on monolingual corpora alone using a combination of denoising and backtranslation. Despite the simplicity of the approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014 French-to-English and German-to-English translation. The model can also profit from small parallel corpora, and attains 21.81 and 15.24 points when combined with 100,000 parallel sentences, respectively. Our implementation is released as an open source project.	['Mikel Artetxe', 'Kyunghyun Cho', 'Gorka Labaka', 'Eneko Agirre']	2017-10-30	unsupervised-neural-machine-translation-2	https://openreview.net/forum?id=Sy2ogebAW	https://openreview.net/pdf?id=Sy2ogebAW	iclr-2018-1	['unsupervised-machine-translation']	['natural-language-processing']	[ 5.17367758e-02 7.00683072e-02 -1.16667956e-01 -3.85751784e-01 -1.37442148e+00 -5.68270504e-01 8.70216548e-01 -1.09123550e-01 -8.41016352e-01 9.35803175e-01 1.67624772e-01 -5.56698084e-01 3.30130696e-01 -5.91512918e-01 -9.28491652e-01 -5.11697888e-01 4.11251545e-01 8.01754475e-01 -1.40973508e-01 -5.99469900e-01 -5.98077029e-02 4.61969748e-02 -9.60755706e-01 2.40851223e-01 1.09578753e+00 4.98863310e-01 4.10017908e-01 4.19536203e-01 -2.80453295e-01 3.03554982e-01 -4.18252647e-01 -9.84376192e-01 5.02196133e-01 -5.73996902e-01 -7.03291893e-01 -1.81604415e-01 5.57158828e-01 -1.28185764e-01 -1.74515441e-01 1.15599751e+00 8.60755205e-01 -3.09359193e-01 5.09022772e-01 -7.87105501e-01 -8.92005801e-01 9.41227317e-01 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2fc9e92b-8c99-43c2-b8cb-f398c4407d68	efficient-sequence-transduction-by-jointly	2304.06795	null	https://arxiv.org/abs/2304.06795v2	https://arxiv.org/pdf/2304.06795v2.pdf	Efficient Sequence Transduction by Jointly Predicting Tokens and Durations	This paper introduces a novel Token-and-Duration Transducer (TDT) architecture for sequence-to-sequence tasks. TDT extends conventional RNN-Transducer architectures by jointly predicting both a token and its duration, i.e. the number of input frames covered by the emitted token. This is achieved by using a joint network with two outputs which are independently normalized to generate distributions over tokens and durations. During inference, TDT models can skip input frames guided by the predicted duration output, which makes them significantly faster than conventional Transducers which process the encoder output frame by frame. TDT models achieve both better accuracy and significantly faster inference than conventional Transducers on different sequence transduction tasks. TDT models for Speech Recognition achieve better accuracy and up to 2.82X faster inference than conventional Transducers. TDT models for Speech Translation achieve an absolute gain of over 1 BLEU on the MUST-C test compared with conventional Transducers, and its inference is 2.27X faster. In Speech Intent Classification and Slot Filling tasks, TDT models improve the intent accuracy by up to over 1% (absolute) over conventional Transducers, while running up to 1.28X faster. Our implementation of the TDT model will be open-sourced with the NeMo (https://github.com/NVIDIA/NeMo) toolkit.	['Boris Ginsburg', 'Shinji Watanabe', 'He Huang', 'Somshubra Majumdar', 'Fei Jia', 'Hainan Xu']	2023-04-13	null	null	null	null	['intent-classification', 'slot-filling']	['natural-language-processing', 'natural-language-processing']	[ 5.71268022e-01 3.57099563e-01 -2.54594147e-01 -5.02306938e-01 -1.19200075e+00 -6.32215381e-01 6.14815831e-01 -1.56044886e-01 -3.85977834e-01 4.37568665e-01 3.54607999e-01 -8.53199720e-01 7.17595041e-01 -4.89336818e-01 -7.32643425e-01 -4.23856765e-01 4.35498767e-02 7.19840169e-01 2.25159153e-01 1.34798467e-01 -2.51855310e-02 -9.85614583e-02 -1.36808228e+00 5.08043289e-01 4.85321939e-01 1.03193378e+00 4.82085168e-01 1.34356344e+00 -1.53344393e-01 1.13178241e+00 -8.07174265e-01 -1.07949503e-01 -1.31594822e-01 -6.12535179e-01 -8.47133100e-01 -2.41237104e-01 1.39601409e-01 -4.40699041e-01 -4.59106058e-01 5.91005921e-01 6.43511653e-01 2.58226395e-01 4.52243835e-01 -1.11684775e+00 -7.24707186e-01 1.01126540e+00 -1.55898094e-01 4.45822269e-01 5.61626613e-01 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801e8ad2-8a72-4c8b-93b1-1e739c951df8	noise-tolerant-learning-for-audio-visual	2205.07611	null	https://arxiv.org/abs/2205.07611v2	https://arxiv.org/pdf/2205.07611v2.pdf	Noise-Tolerant Learning for Audio-Visual Action Recognition	Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating multiple modalities to improve the performance or robustness of a model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely on high-quality manual annotations and assume that modalities among multi-modal data provide relevant semantic information. Unfortunately, most widely used video datasets are collected from the Internet and inevitably contain noisy labels and noisy correspondence. To solve this problem, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters to both noisy labels and noisy correspondence. Our method consists of two phases and aims to rectify noise by the inherent correlation between modalities. A noise-tolerant contrastive training phase is performed first to learn robust model parameters unaffected by the noisy labels. To reduce the influence of noisy correspondence, we propose a cross-modal noise estimation component to adjust the consistency between different modalities. Since the noisy correspondence existed at the instance level, a category-level contrastive loss is proposed to further alleviate the interference of noisy correspondence. Then in the hybrid supervised training phase, we calculate the distance metric among features to obtain corrected labels, which are used as complementary supervision. In addition, we investigate the noisy correspondence in real-world datasets and conduct comprehensive experiments with synthetic and real noise data. The results verify the advantageous performance of our method compared to state-of-the-art methods.	['Yan Chen', 'Feng Tian', 'Kaiyao Miao', 'Minnan Luo', 'Qinghua Zheng', 'Haochen Han']	2022-05-16	null	null	null	null	['noise-estimation']	['medical']	[ 4.29601222e-01 -6.09742284e-01 4.18103809e-06 -1.55160442e-01 -1.03788042e+00 -2.15928212e-01 5.88347793e-01 -2.75293946e-01 -4.39923853e-01 4.70950603e-01 3.92590851e-01 4.30014372e-01 -4.12144721e-01 -5.50768375e-01 -5.87792456e-01 -1.00377834e+00 4.43081886e-01 -7.87315518e-02 3.58455300e-01 1.32926106e-02 1.46260083e-01 -4.82773632e-02 -1.83383417e+00 4.28365916e-01 9.24386978e-01 1.16436803e+00 1.69476330e-01 7.26468563e-02 -2.14831248e-01 8.06234598e-01 -5.43408871e-01 -5.03971204e-02 3.47659469e-01 -5.26178837e-01 -4.69814420e-01 4.61620539e-01 2.38156289e-01 4.23295163e-02 -3.97281498e-01 1.45606053e+00 7.30801105e-01 2.78982908e-01 3.13142985e-01 -1.29244637e+00 -3.11880261e-01 3.07790101e-01 -6.37267113e-01 1.27045333e-01 4.51829165e-01 1.24441229e-01 7.50734150e-01 -8.83622289e-01 3.24792087e-01 1.29109895e+00 5.86558878e-01 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f7d1f3c0-25da-4225-85e7-79b0453a772c	acoustic-scene-clustering-using-joint	2306.05621	null	https://arxiv.org/abs/2306.05621v1	https://arxiv.org/pdf/2306.05621v1.pdf	Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration	Recent efforts have been made on acoustic scene classification in the audio signal processing community. In contrast, few studies have been conducted on acoustic scene clustering, which is a newly emerging problem. Acoustic scene clustering aims at merging the audio recordings of the same class of acoustic scene into a single cluster without using prior information and training classifiers. In this study, we propose a method for acoustic scene clustering that jointly optimizes the procedures of feature learning and clustering iteration. In the proposed method, the learned feature is a deep embedding that is extracted from a deep convolutional neural network (CNN), while the clustering algorithm is the agglomerative hierarchical clustering (AHC). We formulate a unified loss function for integrating and optimizing these two procedures. Various features and methods are compared. The experimental results demonstrate that the proposed method outperforms other unsupervised methods in terms of the normalized mutual information and the clustering accuracy. In addition, the deep embedding outperforms many state-of-the-art features.	['Qianhua He', 'Yuhan Zhang', 'Wucheng Wang', 'Mingle Liu', 'Yanxiong Li']	2023-06-09	null	null	null	null	['audio-signal-processing', 'acoustic-scene-classification', 'scene-classification']	['audio', 'audio', 'computer-vision']	[ 9.58290473e-02 -3.96001130e-01 5.52929938e-01 -6.95176601e-01 -8.83936048e-01 -1.17560454e-01 2.48326510e-01 3.02489370e-01 -6.17043614e-01 -1.33778483e-01 1.51444584e-01 3.40630889e-01 -3.36822599e-01 -5.01360178e-01 -3.86409312e-01 -1.10423124e+00 -1.61892235e-01 1.43233955e-01 6.22373186e-02 4.14951921e-01 1.79233864e-01 1.61428481e-01 -1.72858012e+00 1.66849270e-01 5.79832971e-01 1.24202108e+00 5.31255662e-01 5.11513293e-01 1.93847716e-02 7.04789162e-01 -5.88691533e-01 1.09384716e-01 -6.64126053e-02 -4.78203446e-01 -5.93716085e-01 3.85930866e-01 7.94703215e-02 2.27300137e-01 -2.77409226e-01 1.15597367e+00 7.39854634e-01 4.58472431e-01 6.17325842e-01 -1.34317076e+00 -1.61310211e-01 7.37688780e-01 -4.68345612e-01 -6.10982962e-02 -9.55136716e-02 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1b94db3d-29ec-44c1-89cf-da29827e1e8a	a-conditional-generative-chatbot-using	2306.02074	null	https://arxiv.org/abs/2306.02074v1	https://arxiv.org/pdf/2306.02074v1.pdf	A Conditional Generative Chatbot using Transformer Model	A Chatbot serves as a communication tool between a human user and a machine to achieve an appropriate answer based on the human input. In more recent approaches, a combination of Natural Language Processing and sequential models are used to build a generative Chatbot. The main challenge of these models is their sequential nature, which leads to less accurate results. To tackle this challenge, in this paper, a novel end-to-end architecture is proposed using conditional Wasserstein Generative Adversarial Networks and a transformer model for answer generation in Chatbots. While the generator of the proposed model consists of a full transformer model to generate an answer, the discriminator includes only the encoder part of a transformer model followed by a classifier. To the best of our knowledge, this is the first time that a generative Chatbot is proposed using the embedded transformer in both generator and discriminator models. Relying on the parallel computing of the transformer model, the results of the proposed model on the Cornell Movie-Dialog corpus and the Chit-Chat datasets confirm the superiority of the proposed model compared to state-of-the-art alternatives using different evaluation metrics.	['Razieh Rastgoo', 'Kourosh Kiani', 'Nura Esfandiari']	2023-06-03	null	null	null	null	['chatbot', 'chatbot', 'answer-generation']	['methodology', 'natural-language-processing', 'natural-language-processing']	[ 2.13682711e-01 4.58048195e-01 6.77801907e-01 -3.20342451e-01 -7.85646737e-01 -6.28922701e-01 8.32612157e-01 -3.73159684e-02 -3.95302773e-01 7.30585039e-01 -8.40728953e-02 -1.48166999e-01 3.08259487e-01 -8.42253506e-01 -2.98107386e-01 -5.36467612e-01 6.23362660e-01 7.74894536e-01 5.25704622e-01 -5.37221789e-01 2.58319676e-01 -6.29051775e-02 -1.24291241e+00 5.02662301e-01 1.03420591e+00 8.41438472e-01 2.20825911e-01 8.94833684e-01 -2.46360257e-01 1.38878572e+00 -9.48042750e-01 -9.77640569e-01 2.50229120e-01 -1.21302938e+00 -9.63746548e-01 -1.69161171e-01 -1.00222118e-01 -3.90023947e-01 -1.36660144e-01 7.57172167e-01 7.04582930e-01 3.13559055e-01 7.47640610e-01 -1.29900491e+00 -7.17683911e-01 9.23570871e-01 3.15601856e-01 -2.98849195e-01 5.39867640e-01 2.80314207e-01 1.08177936e+00 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9bf7ebd8-2464-4923-aba4-8370b6de2caa	dual-adaptive-transformations-for-weakly	2207.09084	null	https://arxiv.org/abs/2207.09084v1	https://arxiv.org/pdf/2207.09084v1.pdf	Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation	Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training. However, existing methods remain challenging to accurately segment 3D point clouds since limited annotated data may lead to insufficient guidance for label propagation to unlabeled data. Considering the smoothness-based methods have achieved promising progress, in this paper, we advocate applying the consistency constraint under various perturbations to effectively regularize unlabeled 3D points. Specifically, we propose a novel DAT (\textbf{D}ual \textbf{A}daptive \textbf{T}ransformations) model for weakly supervised point cloud segmentation, where the dual adaptive transformations are performed via an adversarial strategy at both point-level and region-level, aiming at enforcing the local and structural smoothness constraints on 3D point clouds. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets. Extensive experiments demonstrate that our model can effectively leverage the unlabeled 3D points and achieve significant performance gains on both datasets, setting new state-of-the-art performance for weakly supervised point cloud segmentation.	['Chen Qian', 'Jianfei Cai', 'Guosheng Lin', 'Yicheng Wu', 'Zhonghua Wu']	2022-07-19	null	null	null	null	['point-cloud-segmentation']	['computer-vision']	[ 1.60705179e-01 3.67326438e-01 -3.54586124e-01 -4.37186927e-01 -1.06595623e+00 -9.17647839e-01 4.22784835e-01 8.91655460e-02 -1.02666251e-01 2.29458570e-01 -4.84622389e-01 -4.37844664e-01 2.68727273e-01 -6.32377446e-01 -1.21445382e+00 -6.05051696e-01 2.34579921e-01 8.87864649e-01 5.09538054e-01 -5.30767255e-02 8.62261280e-02 9.88738835e-01 -1.28853214e+00 -2.81990260e-01 1.04440570e+00 9.45624232e-01 8.49047378e-02 1.34740263e-01 -4.83492821e-01 3.58754486e-01 -1.79400072e-01 -3.19708347e-01 8.67066801e-01 2.65216768e-01 -8.75713587e-01 4.69355077e-01 7.22903907e-01 -2.96312332e-01 -2.79679373e-02 1.19877660e+00 1.44322738e-01 -8.00630730e-03 5.80250919e-01 -1.29634237e+00 -4.63391095e-01 2.03863472e-01 -9.79232967e-01 -1.46981508e-01 3.19260396e-02 2.61400878e-01 8.08846176e-01 -1.03008151e+00 6.11678660e-01 1.03189242e+00 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4f586d01-1e65-45bf-8fd2-0ea716b28a47	geolayoutlm-geometric-pre-training-for-visual	2304.10759	null	https://arxiv.org/abs/2304.10759v1	https://arxiv.org/pdf/2304.10759v1.pdf	GeoLayoutLM: Geometric Pre-training for Visual Information Extraction	Visual information extraction (VIE) plays an important role in Document Intelligence. Generally, it is divided into two tasks: semantic entity recognition (SER) and relation extraction (RE). Recently, pre-trained models for documents have achieved substantial progress in VIE, particularly in SER. However, most of the existing models learn the geometric representation in an implicit way, which has been found insufficient for the RE task since geometric information is especially crucial for RE. Moreover, we reveal another factor that limits the performance of RE lies in the objective gap between the pre-training phase and the fine-tuning phase for RE. To tackle these issues, we propose in this paper a multi-modal framework, named GeoLayoutLM, for VIE. GeoLayoutLM explicitly models the geometric relations in pre-training, which we call geometric pre-training. Geometric pre-training is achieved by three specially designed geometry-related pre-training tasks. Additionally, novel relation heads, which are pre-trained by the geometric pre-training tasks and fine-tuned for RE, are elaborately designed to enrich and enhance the feature representation. According to extensive experiments on standard VIE benchmarks, GeoLayoutLM achieves highly competitive scores in the SER task and significantly outperforms the previous state-of-the-arts for RE (\eg, the F1 score of RE on FUNSD is boosted from 80.35\% to 89.45\%). The code and models are publicly available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/GeoLayoutLM	['Cong Yao', 'Qi Zheng', 'Changxu Cheng', 'Chuwei Luo']	2023-04-21	null	http://openaccess.thecvf.com//content/CVPR2023/html/Luo_GeoLayoutLM_Geometric_Pre-Training_for_Visual_Information_Extraction_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Luo_GeoLayoutLM_Geometric_Pre-Training_for_Visual_Information_Extraction_CVPR_2023_paper.pdf	cvpr-2023-1	['document-ai', 'semantic-entity-labeling', 'key-information-extraction']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-1.87390327e-01 9.45202932e-02 -2.25963295e-01 -2.18373224e-01 -7.37916946e-01 -6.93306983e-01 9.06526685e-01 4.16836441e-01 -4.31863993e-01 5.08984089e-01 2.52928734e-01 -2.83033341e-01 -7.21517131e-02 -1.02373123e+00 -7.47511685e-01 -5.40235162e-01 1.12357482e-01 5.37263215e-01 1.43188804e-01 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cb0e5f33-6454-4b06-87e7-ec2ddfd87a76	target-speaker-voice-activity-detection-via	2210.16127	null	https://arxiv.org/abs/2210.16127v3	https://arxiv.org/pdf/2210.16127v3.pdf	Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction	Target-speaker voice activity detection is currently a promising approach for speaker diarization in complex acoustic environments. This paper presents a novel Sequence-to-Sequence Target-Speaker Voice Activity Detection (Seq2Seq-TSVAD) method that can efficiently address the joint modeling of large-scale speakers and predict high-resolution voice activities. Experimental results show that larger speaker capacity and higher output resolution can significantly reduce the diarization error rate (DER), which achieves the new state-of-the-art performance of 4.55% on the VoxConverse test set and 10.77% on Track 1 of the DIHARD-III evaluation set under the widely-used evaluation metrics.	['Ming Li', 'Xiaoyi Qin', 'Yucong Zhang', 'Weiqing Wang', 'Ming Cheng']	2022-10-28	null	null	null	null	['activity-detection']	['computer-vision']	[ 1.78946182e-01 -2.65132934e-01 -1.80331901e-01 -2.59667963e-01 -1.89470589e+00 -5.57042956e-01 5.22015512e-01 -5.92233002e-01 -3.16227406e-01 4.45899576e-01 4.57280517e-01 -1.02254026e-01 2.82734066e-01 2.20472917e-01 -5.34070916e-02 -7.84952044e-01 -2.69452751e-01 3.64942312e-01 1.96987525e-01 9.23219472e-02 -1.09312534e-01 4.18523759e-01 -1.78938413e+00 3.18645626e-01 7.48191237e-01 7.92010903e-01 2.65169084e-01 1.35842419e+00 8.02965611e-02 3.10290337e-01 -1.29916155e+00 1.78962350e-01 -1.98948100e-01 -8.99392664e-01 -3.70951593e-01 -4.89614010e-02 6.79960847e-01 -4.88366233e-03 -1.80185765e-01 9.20843005e-01 1.34820449e+00 2.64973402e-01 5.31843305e-01 -8.33124995e-01 2.67348349e-01 8.03413570e-01 -2.33745649e-01 9.76664960e-01 4.78531957e-01 1.51221231e-01 9.75449383e-01 -1.16837716e+00 2.01955646e-01 1.51191163e+00 3.34253281e-01 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44a1814e-60da-4c64-9162-3e5e883641e2	goal-representations-for-instruction	2307.00117	null	https://arxiv.org/abs/2307.00117v1	https://arxiv.org/pdf/2307.00117v1.pdf	Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control	Our goal is for robots to follow natural language instructions like "put the towel next to the microwave." But getting large amounts of labeled data, i.e. data that contains demonstrations of tasks labeled with the language instruction, is prohibitive. In contrast, obtaining policies that respond to image goals is much easier, because any autonomous trial or demonstration can be labeled in hindsight with its final state as the goal. In this work, we contribute a method that taps into joint image- and goal- conditioned policies with language using only a small amount of language data. Prior work has made progress on this using vision-language models or by jointly training language-goal-conditioned policies, but so far neither method has scaled effectively to real-world robot tasks without significant human annotation. Our method achieves robust performance in the real world by learning an embedding from the labeled data that aligns language not to the goal image, but rather to the desired change between the start and goal images that the instruction corresponds to. We then train a policy on this embedding: the policy benefits from all the unlabeled data, but the aligned embedding provides an interface for language to steer the policy. We show instruction following across a variety of manipulation tasks in different scenes, with generalization to language instructions outside of the labeled data. Videos and code for our approach can be found on our website: http://tiny.cc/grif .	['Sergey Levine', 'Anca Dragan', 'Andrey Kolobov', 'Mihai Jalobeanu', 'Ching-An Cheng', 'Philippe Hansen-Estruch', 'Homer Walke', 'Kuan Fang', 'Andre He', 'Vivek Myers']	2023-06-30	null	null	null	null	['instruction-following']	['natural-language-processing']	[ 1.52317882e-01 2.33361349e-01 -7.56105706e-02 -6.92327797e-01 -6.29497170e-01 -8.26024413e-01 7.81949461e-01 -1.48338184e-01 -7.91039348e-01 5.51008880e-01 2.44778410e-01 -5.43428838e-01 3.37278903e-01 -4.40481156e-01 -9.86535907e-01 -5.36467135e-01 9.73317213e-03 6.03093684e-01 6.45165369e-02 -3.56390774e-01 3.07786822e-01 3.33492696e-01 -1.35078382e+00 8.95663425e-02 3.97169381e-01 4.24070060e-01 6.69202447e-01 9.42871928e-01 1.02063462e-01 1.08641958e+00 -2.20969766e-01 3.03250104e-01 4.61655885e-01 -3.75265777e-01 -9.16490614e-01 2.46891990e-01 5.34068823e-01 -6.35810852e-01 -4.54338044e-01 1.03936112e+00 -1.49342569e-03 4.44028854e-01 6.22958958e-01 -1.43004155e+00 -8.93783212e-01 3.60919476e-01 -2.71577388e-01 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cc96e4a4-4bff-4646-aa96-9a392726a457	low-dose-ct-image-denoising-using-parallel	2005.06724	null	https://arxiv.org/abs/2005.06724v1	https://arxiv.org/pdf/2005.06724v1.pdf	Low-Dose CT Image Denoising Using Parallel-Clone Networks	Deep neural networks have a great potential to improve image denoising in low-dose computed tomography (LDCT). Popular ways to increase the network capacity include adding more layers or repeating a modularized clone model in a sequence. In such sequential architectures, the noisy input image and end output image are commonly used only once in the training model, which however limits the overall learning performance. In this paper, we propose a parallel-clone neural network method that utilizes a modularized network model and exploits the benefit of parallel input, parallel-output loss, and clone-toclone feature transfer. The proposed model keeps a similar or less number of unknown network weights as compared to conventional models but can accelerate the learning process significantly. The method was evaluated using the Mayo LDCT dataset and compared with existing deep learning models. The results show that the use of parallel input, parallel-output loss, and clone-to-clone feature transfer all can contribute to an accelerated convergence of deep learning and lead to improved image quality in testing. The parallel-clone network has been demonstrated promising for LDCT image denoising.	['Guobao Wang', 'Siqi Li']	2020-05-14	null	null	null	null	['medical-image-denoising']	['computer-vision']	[ 7.22203404e-02 -1.78293243e-01 1.58034459e-01 -4.43329841e-01 -6.86410785e-01 1.94750074e-02 1.56146660e-01 2.21402496e-02 -1.01956379e+00 6.58793867e-01 -9.32315551e-03 1.34885516e-02 8.98067355e-02 -6.45714223e-01 -6.70710921e-01 -1.05308092e+00 6.58386350e-02 1.51476741e-01 3.69787604e-01 -9.12069306e-02 -5.81485927e-02 4.33024287e-01 -1.13481939e+00 6.83360994e-01 7.94511139e-01 1.01861393e+00 5.81485212e-01 6.41332030e-01 -1.86252922e-01 1.15002847e+00 -4.70747173e-01 -1.96833044e-01 2.65661061e-01 -4.13957149e-01 -5.97062171e-01 -3.73313069e-01 3.18123221e-01 -5.42556167e-01 -4.44309145e-01 8.89698505e-01 1.12944984e+00 -6.70596063e-02 4.30310816e-01 -6.51162982e-01 -1.36004597e-01 3.60955119e-01 -4.34602886e-01 4.73494083e-01 -4.35230523e-01 8.54086801e-02 4.07902032e-01 -7.35803425e-01 4.34244961e-01 9.63521600e-01 1.19506979e+00 6.92742288e-01 -1.27715266e+00 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88f390a4-2dba-41e3-a24e-c2f22f72f90a	event-driven-query-expansion	2012.12065	null	https://arxiv.org/abs/2012.12065v1	https://arxiv.org/pdf/2012.12065v1.pdf	Event-Driven Query Expansion	A significant number of event-related queries are issued in Web search. In this paper, we seek to improve retrieval performance by leveraging events and specifically target the classic task of query expansion. We propose a method to expand an event-related query by first detecting the events related to it. Then, we derive the candidates for expansion as terms semantically related to both the query and the events. To identify the candidates, we utilize a novel mechanism to simultaneously embed words and events in the same vector space. We show that our proposed method of leveraging events improves query expansion performance significantly compared with state-of-the-art methods on various newswire TREC datasets.	['Kira Radinsky', 'Ido Guy', 'Guy D. Rosin']	2020-12-22	null	null	null	null	['ad-hoc-information-retrieval']	['natural-language-processing']	[ 8.20663720e-02 -3.97689313e-01 -5.43658257e-01 -2.47143924e-01 -1.38328946e+00 -6.60700023e-01 9.54472721e-01 7.16198683e-01 -9.51967597e-01 3.70748103e-01 8.27129245e-01 -4.83473726e-02 -3.51980209e-01 -9.18857336e-01 -5.42806089e-01 -1.14500649e-01 -2.19753549e-01 4.88468528e-01 7.34105229e-01 -4.40762818e-01 4.08035487e-01 2.97008753e-01 -1.21497786e+00 4.45847481e-01 5.11924326e-01 9.83484805e-01 6.65303096e-02 3.26957434e-01 -2.45098233e-01 4.58793938e-01 -5.51209748e-01 -1.37792006e-01 -3.73338163e-02 -3.03043276e-01 -9.68495905e-01 -4.35949743e-01 1.75556932e-02 -1.05940029e-01 -9.24741864e-01 7.97212303e-01 5.42905748e-01 6.57209694e-01 5.50195217e-01 -7.03896642e-01 -6.67704523e-01 8.08542907e-01 -5.09450495e-01 9.33984578e-01 6.97840154e-01 -7.81682491e-01 1.50222826e+00 -9.82894540e-01 8.38956296e-01 1.07556915e+00 -4.26388206e-03 2.14908391e-01 -7.17435122e-01 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6.45531893e-01 3.18712443e-02 -4.25947428e-01 -4.05039877e-01 -6.44353852e-02]	[11.525793075561523, 7.600564479827881]
26be0717-5af3-464a-b84e-82acb1f9d22c	conical-classification-for-computationally	2111.00375	null	https://arxiv.org/abs/2111.00375v1	https://arxiv.org/pdf/2111.00375v1.pdf	Conical Classification For Computationally Efficient One-Class Topic Determination	As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute.	['Sameer Khanna']	2021-10-31	null	null	null	null	['one-class-classification']	['miscellaneous']	[ 3.20975155e-01 -2.85080254e-01 -3.71154457e-01 -1.56644896e-01 -7.43795097e-01 -9.72519457e-01 8.54076743e-01 6.82830095e-01 -3.96462679e-01 4.81411487e-01 2.38197863e-01 -9.17668939e-01 -5.00481963e-01 -8.41960371e-01 -4.33857329e-02 -4.45099503e-01 -4.23801169e-02 7.63645887e-01 3.38195622e-01 -9.00344402e-02 4.46236402e-01 6.10745013e-01 -1.66960192e+00 2.72844553e-01 7.52104640e-01 8.38199556e-01 -3.87824699e-02 9.03168678e-01 -4.84246999e-01 5.91279209e-01 -6.37881815e-01 -2.93152422e-01 3.05313647e-01 -3.30407172e-01 -1.01168537e+00 2.68032908e-01 3.38803351e-01 -8.54912177e-02 -6.65474758e-02 8.25539410e-01 1.20805874e-02 1.61561683e-01 9.79164898e-01 -1.30750656e+00 2.39524972e-02 4.19971675e-01 -7.32232809e-01 4.72762525e-01 5.87819993e-01 -4.85009253e-01 1.36555362e+00 -5.71740389e-01 4.96004790e-01 1.08394730e+00 5.78020215e-01 -8.67032707e-02 -1.42658615e+00 -5.24928689e-01 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6c01d413-315f-41dd-841b-503dc9b4c03d	toward-estimating-personal-well-being-using	1910.10082	null	https://arxiv.org/abs/1910.10082v1	https://arxiv.org/pdf/1910.10082v1.pdf	Toward estimating personal well-being using voice	Estimating personal well-being draws increasing attention particularly from healthcare and pharmaceutical industries. We propose an approach to estimate personal well-being in terms of various measurements such as anxiety, sleep quality and mood using voice. With clinically validated questionnaires to score those measurements in a self-assessed way, we extract salient features from voice and train regression models with deep neural networks. Experiments with the collected database of 219 subjects show promising results in predicting the well-being related measurements; concordance correlation coefficients (CCC) between self-assessed scores and predicted scores are 0.41 for anxiety, 0.44 for sleep quality and 0.38 for mood.	["Henry O'Connell", 'Samuel Kim', 'Namhee Kwon']	2019-10-22	null	null	null	null	['sleep-quality-prediction']	['medical']	[-3.28541249e-01 4.04508054e-01 -6.61019921e-01 -8.29709411e-01 -1.00662029e+00 3.69041152e-02 -1.05380818e-01 5.70489824e-01 -3.42788458e-01 1.02588928e+00 7.89643168e-01 3.27727556e-01 -2.83205420e-01 -7.23510087e-01 2.14444071e-01 -3.46928447e-01 -9.74807590e-02 4.97008115e-02 -8.70043278e-01 -5.04572988e-02 -7.45241195e-02 2.28904516e-01 -1.02732360e+00 -3.26620549e-01 9.41073537e-01 1.45741260e+00 -6.72984064e-01 7.05963135e-01 1.07167117e-01 5.45319915e-01 -7.23975182e-01 -5.29590189e-01 -3.67485881e-01 -3.95856053e-01 -6.40766621e-01 2.73248136e-01 3.94205637e-02 -8.17300230e-02 2.10666463e-01 8.01621497e-01 7.45602012e-01 1.03986159e-01 2.99641818e-01 -1.15173769e+00 -8.66406441e-01 4.46524441e-01 -6.48139268e-02 1.23510167e-01 9.28595841e-01 -2.96650305e-02 7.80329525e-01 -4.52259451e-01 -2.04951644e-01 9.51854467e-01 1.06605279e+00 7.73068011e-01 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245bca76-ad12-4cfe-a54e-5ca16131900e	rethinking-image-cropping-exploring-diverse	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Jia_Rethinking_Image_Cropping_Exploring_Diverse_Compositions_From_Global_Views_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Jia_Rethinking_Image_Cropping_Exploring_Diverse_Compositions_From_Global_Views_CVPR_2022_paper.pdf	Rethinking Image Cropping: Exploring Diverse Compositions From Global Views	Existing image cropping works mainly use anchor evaluation methods or coordinate regression methods. However, it is difficult for pre-defined anchors to cover good crops globally, and the regression methods ignore the cropping diversity. In this paper, we regard image cropping as a set prediction problem. A set of crops regressed from multiple learnable anchors is matched with the labeled good crops, and a classifier is trained using the matching results to select a valid subset from all the predictions. This new perspective equips our model with globality and diversity, mitigating the shortcomings but inherit the strengthens of previous methods. Despite the advantages, the set prediction method causes inconsistency between the validity labels and the crops. To deal with this problem, we propose to smooth the validity labels with two different methods. The first method that uses crop qualities as direct guidance is designed for the datasets with nearly dense quality labels. The second method based on the self distillation can be used in sparsely labeled datasets. Experimental results on the public datasets show the merits of our approach over state-of-the-art counterparts.	['Ran He', 'Chaoyou Fu', 'Huaibo Huang', 'Gengyun Jia']	2022-01-01	null	null	null	cvpr-2022-1	['image-cropping']	['computer-vision']	[ 3.23094606e-01 3.97061259e-02 -7.47557342e-01 -3.89114559e-01 -6.63593173e-01 -4.65569645e-01 2.54239023e-01 1.96738139e-01 1.47634208e-01 6.12795830e-01 1.05297931e-01 1.04859009e-01 -1.19554155e-01 -1.20008349e+00 -8.43811333e-01 -9.78115201e-01 1.83975011e-01 2.39894614e-02 2.88127124e-01 -4.37992722e-01 2.74672002e-01 -1.29550740e-01 -1.94432390e+00 2.62499779e-01 1.40946651e+00 1.29144633e+00 6.56103551e-01 -1.01205327e-01 -4.42749083e-01 5.69222391e-01 -3.69070351e-01 -2.55020797e-01 5.93168139e-01 -2.98020661e-01 -8.93335566e-02 1.38230860e-01 3.89832705e-01 -3.02173167e-01 3.43167216e-01 1.31126833e+00 1.85060441e-01 -4.22102779e-01 6.74610853e-01 -1.51446319e+00 -1.16833854e+00 8.35883379e-01 -1.01055622e+00 -6.14534795e-01 1.64308488e-01 -2.23514289e-01 1.04449093e+00 -9.23169672e-01 5.12674034e-01 1.12421465e+00 8.78719449e-01 1.48793206e-01 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4.07438517e-01 1.93194151e-01 -7.87567437e-01 -2.71866560e-01 2.49185994e-01]	[11.295357704162598, -1.1273329257965088]
ab957e90-402e-4c4e-9a0c-868587ef56be	visualbackprop-for-learning-using-privileged	1805.09474	null	http://arxiv.org/abs/1805.09474v1	http://arxiv.org/pdf/1805.09474v1.pdf	VisualBackProp for learning using privileged information with CNNs	In many machine learning applications, from medical diagnostics to autonomous driving, the availability of prior knowledge can be used to improve the predictive performance of learning algorithms and incorporate `physical,' `domain knowledge,' or `common sense' concepts into training of machine learning systems as well as verify constraints/properties of the systems. We explore the learning using privileged information paradigm and show how to incorporate the privileged information, such as segmentation mask available along with the classification label of each example, into the training stage of convolutional neural networks. This is done by augmenting the CNN model with an architectural component that effectively focuses model's attention on the desired region of the input image during the training process and that is transparent to the network's label prediction mechanism at testing. This component effectively corresponds to the visualization strategy for identifying the parts of the input, often referred to as visualization mask, that most contribute to the prediction, yet uses this strategy in reverse to the classical setting in order to enforce the desired visualization mask instead. We verify our proposed algorithms through exhaustive experiments on benchmark ImageNet and PASCAL VOC data sets and achieve improvements in the performance of $2.4\%$ and $2.7\%$ over standard single-supervision model training. Finally, we confirm the effectiveness of our approach on skin lesion classification problem.	['Devansh Bisla', 'Anna Choromanska']	2018-05-24	null	null	null	null	['skin-lesion-classification']	['medical']	[ 4.92612571e-01 6.07730925e-01 -2.48420402e-01 -4.15085346e-01 -1.44245557e-03 -3.58103186e-01 4.11659837e-01 4.37887833e-02 -5.05646110e-01 7.55545318e-01 -5.31744540e-01 -5.58291316e-01 -3.39477897e-01 -8.54571402e-01 -9.63124335e-01 -9.73775685e-01 1.34159345e-02 2.76708812e-01 4.03527588e-01 -3.78124639e-02 1.15464672e-01 6.87314749e-01 -1.70993960e+00 3.25862527e-01 7.50240624e-01 1.31364572e+00 2.08859760e-02 4.09583539e-01 -1.06026456e-02 9.03239846e-01 -5.96713364e-01 -1.17045969e-01 2.11325735e-01 4.21375073e-02 -8.04109693e-01 1.20780103e-01 4.01632279e-01 -1.30776931e-02 4.44899462e-02 1.01084161e+00 9.42836404e-02 1.02289423e-01 4.97352391e-01 -1.23247349e+00 -2.94334322e-01 3.71043563e-01 -3.28652084e-01 5.09901643e-02 -1.86976045e-01 4.31009293e-01 7.65141726e-01 -3.99773121e-01 8.36127102e-01 7.22008884e-01 1.69397742e-01 7.69080758e-01 -1.14923918e+00 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d383a79e-1e6e-4cb3-b635-1821da1e7ac8	learning-the-spectrogram-temporal-resolution	2210.01719	null	https://arxiv.org/abs/2210.01719v2	https://arxiv.org/pdf/2210.01719v2.pdf	Learning the Spectrogram Temporal Resolution for Audio Classification	The audio spectrogram is a time-frequency representation that has been widely used for audio classification. The temporal resolution of a spectrogram depends on hop size. Previous works generally assume the hop size should be a constant value such as ten milliseconds. However, a fixed hop size or resolution is not always optimal for different types of sound. This paper proposes a novel method, DiffRes, that enables differentiable temporal resolution learning to improve the performance of audio classification models. Given a spectrogram calculated with a fixed hop size, DiffRes merges non-essential time frames while preserving important frames. DiffRes acts as a "drop-in" module between an audio spectrogram and a classifier, and can be end-to-end optimized. We evaluate DiffRes on the mel-spectrogram, followed by state-of-the-art classifier backbones, and apply it to five different subtasks. Compared with using the fixed-resolution mel-spectrogram, the DiffRes-based method can achieve the same or better classification accuracy with at least 25% fewer temporal dimensions on the feature level, which alleviates the computational cost at the same time. Starting from a high-temporal-resolution spectrogram such as one-millisecond hop size, we show that DiffRes can improve classification accuracy with the same computational complexity.	['Mark D. Plumbley', 'Wenwu Wang', 'Qiuqiang Kong', 'Xubo Liu', 'Haohe Liu']	2022-10-04	null	null	null	null	['classification']	['methodology']	[ 7.76570588e-02 -5.29296756e-01 -5.40340953e-02 -1.06070831e-01 -1.12694311e+00 -4.43372160e-01 6.20661452e-02 2.54885167e-01 -4.27256793e-01 4.69249964e-01 1.56205773e-01 -6.97195381e-02 -2.79628724e-01 -5.61304152e-01 -2.30230302e-01 -4.66913074e-01 -4.10349309e-01 -2.02464774e-01 6.04554474e-01 -2.50507798e-02 2.47657284e-01 1.11712061e-01 -1.95888209e+00 8.72315705e-01 4.92019594e-01 1.30885339e+00 2.41882935e-01 1.02803934e+00 -7.11187124e-02 6.15333974e-01 -8.86089146e-01 9.88182575e-02 1.61585331e-01 -4.99693036e-01 -8.24165344e-01 -2.47392565e-01 5.25440693e-01 -8.00586399e-03 -5.20091020e-02 6.83986187e-01 5.99871993e-01 3.81899923e-01 1.60418123e-01 -1.20929551e+00 -8.70783776e-02 8.88096929e-01 -3.24571580e-01 6.17037773e-01 5.57131648e-01 -2.37488374e-01 1.17185879e+00 -7.50418127e-01 1.50573447e-01 1.28414428e+00 9.43230689e-01 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