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train · 44.6k rows

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998d45b2-5435-443a-b8ad-fdd01b50620f	community-detection-graph-convolutional	2306.14530	null	https://arxiv.org/abs/2306.14530v1	https://arxiv.org/pdf/2306.14530v1.pdf	Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization	The clustering algorithm plays a crucial role in speaker diarization systems. However, traditional clustering algorithms suffer from the complex distribution of speaker embeddings and lack of digging potential relationships between speakers in a session. We propose a novel graph-based clustering approach called Community Detection Graph Convolutional Network (CDGCN) to improve the performance of the speaker diarization system. The CDGCN-based clustering method consists of graph generation, sub-graph detection, and Graph-based Overlapped Speech Detection (Graph-OSD). Firstly, the graph generation refines the local linkages among speech segments. Secondly the sub-graph detection finds the optimal global partition of the speaker graph. Finally, we view speaker clustering for overlap-aware speaker diarization as an overlapped community detection task and design a Graph-OSD component to output overlap-aware labels. By capturing local and global information, the speaker diarization system with CDGCN clustering outperforms the traditional Clustering-based Speaker Diarization (CSD) systems on the DIHARD III corpus.	['Qingyang Hong', 'Lin Li', 'Haodong Zhou', 'Zhicong Chen', 'Jie Wang']	2023-06-26	null	null	null	null	['community-detection', 'clustering', 'speaker-diarization']	['graphs', 'methodology', 'speech']	[-3.90332401e-01 1.60120502e-01 3.18709344e-01 -5.19823492e-01 -4.39586282e-01 -5.44529498e-01 5.78489602e-01 4.36508030e-01 1.36677381e-02 -2.22510085e-01 5.08273900e-01 -3.79595101e-01 -2.35017806e-01 -7.00046897e-01 -1.01375595e-01 -7.82512069e-01 -5.34951687e-01 8.50443244e-01 2.65836388e-01 -1.49839729e-01 1.62762292e-02 5.97602129e-01 -1.55907309e+00 3.20547186e-02 8.29943776e-01 4.18542653e-01 7.22637847e-02 9.29746389e-01 -5.60939074e-01 1.99748844e-01 -8.95987153e-01 -2.46106107e-02 -3.84766199e-02 -6.49392068e-01 -5.52370906e-01 4.79887664e-01 3.28433424e-01 3.57182205e-01 -3.91897559e-01 1.11218131e+00 8.62337589e-01 3.19098234e-01 6.21639550e-01 -1.82363915e+00 -5.37733734e-01 1.35126162e+00 -5.47130525e-01 4.29618478e-01 2.33572707e-01 -3.93496752e-01 1.11335087e+00 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7021e183-95c1-4015-9330-92057f3a2765	exploring-transformer-s-potential-on	2204.03898	null	https://arxiv.org/abs/2204.03898v1	https://arxiv.org/pdf/2204.03898v1.pdf	Exploring Transformer's potential on automatic piano transcription	Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation. Based on a high-resolution piano transcription system, we explore the possibility of incorporating another powerful sequence transformation tool -- the Transformer -- to deal with the AMT problem. We argue that the properties of the Transformer make it more suitable for certain AMT subtasks. We confirm the Transformer's superiority on the velocity detection task by experiments on the MAESTRO dataset and a cross-dataset evaluation on the MAPS dataset. We observe a performance improvement on both frame-level and note-level metrics after introducing the Transformer network.	['Ye Wang', 'Jiqing Han', 'Emmanouil Benetos', 'Ziyi Guo', 'Longshen Ou']	2022-04-08	null	null	null	null	['music-transcription']	['music']	[ 4.39706057e-01 -2.22091720e-01 -1.90779492e-01 1.07133299e-01 -8.13886225e-01 -8.11804712e-01 6.57224119e-01 -3.50662649e-01 -4.01396215e-01 2.39927247e-01 4.74932224e-01 -2.38275647e-01 -2.80320197e-01 -4.78569210e-01 -5.22764921e-01 -2.91440159e-01 -1.31913021e-01 1.89716920e-01 5.11307940e-02 -3.61211389e-01 2.94176221e-01 4.10054535e-01 -1.36322367e+00 6.54585719e-01 2.61345744e-01 9.66136217e-01 -3.38721983e-02 9.33801651e-01 -8.35009888e-02 1.12665069e+00 -8.43120039e-01 -3.07548374e-01 5.57535648e-01 -6.98328197e-01 -8.71271849e-01 -4.23962921e-01 4.62289661e-01 4.30485718e-02 -4.07142520e-01 6.21353507e-01 7.49136865e-01 2.78194189e-01 5.71651340e-01 -1.15659642e+00 -4.39605504e-01 1.30753744e+00 -2.85641462e-01 3.34498316e-01 5.97005188e-01 -4.61749360e-02 1.33802283e+00 -7.23540068e-01 9.82114375e-01 9.31377709e-01 1.27042651e+00 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0d4febcf-abcf-479d-a0e8-da092196ae31	comixify-transform-video-into-a-comics	1812.03473	null	http://arxiv.org/abs/1812.03473v1	http://arxiv.org/pdf/1812.03473v1.pdf	Comixify: Transform video into a comics	In this paper, we propose a solution to transform a video into a comics. We approach this task using a neural style algorithm based on Generative Adversarial Networks (GANs). Several recent works in the field of Neural Style Transfer showed that producing an image in the style of another image is feasible. In this paper, we build up on these works and extend the existing set of style transfer use cases with a working application of video comixification. To that end, we train an end-to-end solution that transforms input video into a comics in two stages. In the first stage, we propose a state-of-the-art keyframes extraction algorithm that selects a subset of frames from the video to provide the most comprehensive video context and we filter those frames using image aesthetic estimation engine. In the second stage, the style of selected keyframes is transferred into a comics. To provide the most aesthetically compelling results, we selected the most state-of-the art style transfer solution and based on that implement our own ComixGAN framework. The final contribution of our work is a Web-based working application of video comixification available at http://comixify.ii.pw.edu.pl.	['Tomasz Trzciński', 'Przemysław Rokita', 'Paweł Andruszkiewicz', 'Maciej Pęśko', 'Adam Svystun']	2018-12-09	null	null	null	null	['transform-a-video-into-a-comics']	['computer-vision']	[ 4.86355007e-01 2.36212626e-01 2.63970613e-01 -1.69129208e-01 -3.09704930e-01 -6.93665087e-01 6.77160501e-01 -6.08185828e-01 -1.91442251e-01 8.38715434e-01 4.92038392e-02 -5.75789586e-02 1.59139812e-01 -1.11348736e+00 -1.06074846e+00 -4.21617955e-01 4.64829654e-01 2.55487412e-01 1.06137656e-01 -5.50610960e-01 3.81466359e-01 6.05713725e-01 -1.33594644e+00 5.94607830e-01 5.51504791e-01 9.11247373e-01 2.35231429e-01 7.98339069e-01 -1.84845045e-01 7.50826418e-01 -7.10929096e-01 -6.00115478e-01 6.11932933e-01 -1.08842742e+00 -7.22807825e-01 8.40606075e-03 5.25747597e-01 -3.70612830e-01 -3.91515374e-01 1.03747654e+00 4.08618331e-01 1.49671689e-01 6.25972807e-01 -1.37714517e+00 -7.26871490e-01 5.32735646e-01 -2.32431903e-01 -3.62432808e-01 3.81391585e-01 2.23309189e-01 6.28036082e-01 -7.73580372e-01 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8f228d31-8379-42d8-bef6-64f55fdc3a29	cross-modality-personalization-for-retrieval	null	null	http://openaccess.thecvf.com/content_CVPR_2019/html/Murrugarra-Llerena_Cross-Modality_Personalization_for_Retrieval_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Murrugarra-Llerena_Cross-Modality_Personalization_for_Retrieval_CVPR_2019_paper.pdf	Cross-Modality Personalization for Retrieval	Existing captioning and gaze prediction approaches do not consider the multiple facets of personality that affect how a viewer extracts meaning from an image. While there are methods that consider personalized captioning, they do not consider personalized perception across modalities, i.e. how a person's way of looking at an image (gaze) affects the way they describe it (captioning). In this work, we propose a model for modeling cross-modality personalized retrieval. In addition to modeling gaze and captions, we also explicitly model the personality of the users providing these samples. We incorporate constraints that encourage gaze and caption samples on the same image to be close in a learned space; we refer to this as content modeling. We also model style: we encourage samples provided by the same user to be close in a separate embedding space, regardless of the image on which they were provided. To leverage the complementary information that content and style constraints provide, we combine the embeddings from both networks. We show that our combined embeddings achieve better performance than existing approaches for cross-modal retrieval.	[' Adriana Kovashka', 'Nils Murrugarra-Llerena']	2019-06-01	null	null	null	cvpr-2019-6	['eye-tracking']	['computer-vision']	[ 7.33998418e-02 9.49802995e-02 -3.90155464e-01 -6.86147213e-01 -4.02854681e-01 -6.19230568e-01 8.05323720e-01 3.95700932e-02 -4.22905535e-01 1.63519621e-01 8.35444152e-01 2.55420178e-01 -2.34460663e-02 -2.01867402e-01 -7.39582777e-01 -4.21178579e-01 3.74745697e-01 1.21690668e-01 -2.30458245e-01 5.02801733e-03 4.28313285e-01 1.08150758e-01 -1.72017360e+00 4.25658673e-01 4.22812492e-01 9.95340288e-01 8.80987868e-02 6.77340865e-01 4.26305905e-02 6.06893957e-01 -3.56310308e-01 -6.06897175e-01 2.65374165e-02 -2.58074820e-01 -6.08323753e-01 2.11399451e-01 1.37332869e+00 -5.15052021e-01 -5.29665232e-01 8.92944872e-01 3.76343042e-01 1.66252121e-01 1.01904607e+00 -1.43358839e+00 -1.86886287e+00 4.22198892e-01 -6.94599748e-01 7.99000114e-02 4.75807071e-01 3.40979211e-02 1.26352060e+00 -7.95283735e-01 6.67712867e-01 1.23248577e+00 2.04160899e-01 1.08807778e+00 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1d9c0e3e-961f-4b03-bcf6-8783210bdf8d	matchnet-unifying-feature-and-metric-learning	null	null	http://openaccess.thecvf.com/content_cvpr_2015/html/Han_MatchNet_Unifying_Feature_2015_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2015/papers/Han_MatchNet_Unifying_Feature_2015_CVPR_paper.pdf	MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching	Motivated by recent successes on learning feature representations and on learning feature comparison functions, we propose a unified approach to combining both for training a patch matching system. Our system, dubbed MatchNet, consists of a deep convolutional network that extracts features from patches and a network of three fully connected layers that computes a similarity between the extracted features. To ensure experimental repeatability, we train MatchNet on standard datasets and employ an input sampler to augment the training set with synthetic exemplar pairs that reduce overfitting. Once trained, we achieve better computational efficiency during matching by disassembling MatchNet and separately applying the feature computation and similarity networks in two sequential stages. We perform a comprehensive set of experiments on standard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods. Our results confirm that our unified approach improves accuracy over previous state-of-the-art results on patch matching datasets, while reducing the storage requirement for descriptors. We make pre-trained MatchNet publicly available.	['Yangqing Jia', 'Thomas Leung', 'Xufeng Han', 'Rahul Sukthankar', 'Alexander C. Berg']	2015-06-01	null	null	null	cvpr-2015-6	['patch-matching']	['computer-vision']	[ 2.34479204e-01 -1.37122661e-01 -2.71594524e-01 -4.86434668e-01 -8.49236488e-01 -5.23356199e-01 6.42544389e-01 2.31725544e-01 -4.74841893e-01 2.50566691e-01 -4.10912260e-02 -7.00435191e-02 -1.69127032e-01 -1.05685604e+00 -9.85539794e-01 -2.56339908e-01 -1.99690863e-01 2.96555459e-01 3.91707629e-01 -2.44634002e-01 2.70912141e-01 8.85936677e-01 -1.89632452e+00 4.52502966e-01 5.87845981e-01 1.36603677e+00 -1.10620782e-01 4.48453009e-01 1.41830996e-01 4.23866302e-01 -3.61169189e-01 -5.01912355e-01 7.91398585e-01 -1.71797767e-01 -9.28773940e-01 -9.42840949e-02 1.06688106e+00 -3.97094399e-01 -6.45613253e-01 6.49703383e-01 6.07191026e-01 8.05098861e-02 3.81932557e-01 -1.29421246e+00 -6.83837891e-01 2.38062382e-01 -1.93622112e-01 9.69780385e-02 5.15406370e-01 2.00336903e-01 1.20750213e+00 -1.08161938e+00 6.35822117e-01 9.11406636e-01 1.40936267e+00 2.29417145e-01 -1.48585570e+00 -6.28389180e-01 -3.02937359e-01 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e580031a-aea2-43b4-8b8e-e7035d3cd350	understanding-influence-functions-and	2210.01072	null	https://arxiv.org/abs/2210.01072v1	https://arxiv.org/pdf/2210.01072v1.pdf	Understanding Influence Functions and Datamodels via Harmonic Analysis	Influence functions estimate effect of individual data points on predictions of the model on test data and were adapted to deep learning in Koh and Liang [2017]. They have been used for detecting data poisoning, detecting helpful and harmful examples, influence of groups of datapoints, etc. Recently, Ilyas et al. [2022] introduced a linear regression method they termed datamodels to predict the effect of training points on outputs on test data. The current paper seeks to provide a better theoretical understanding of such interesting empirical phenomena. The primary tool is harmonic analysis and the idea of noise stability. Contributions include: (a) Exact characterization of the learnt datamodel in terms of Fourier coefficients. (b) An efficient method to estimate the residual error and quality of the optimum linear datamodel without having to train the datamodel. (c) New insights into when influences of groups of datapoints may or may not add up linearly.	['Sanjeev Arora', 'Mark Braverman', 'Arushi Gupta', 'Nikunj Saunshi']	2022-10-03	null	null	null	null	['data-poisoning']	['adversarial']	[-2.05456018e-01 -2.79645890e-01 -1.74572796e-01 -1.70943961e-02 -6.55510604e-01 -5.93230426e-01 4.65137631e-01 3.90847802e-01 -7.58828521e-02 7.09043682e-01 3.09619009e-02 -2.21006721e-01 -5.85711360e-01 -7.91827559e-01 -1.09362638e+00 -1.08446813e+00 -6.18517935e-01 -1.95368901e-02 1.22949176e-01 -5.70049174e-02 7.21259236e-01 6.87237978e-01 -1.92580652e+00 3.14412653e-01 6.86407387e-01 9.31843698e-01 -2.82003194e-01 7.90751457e-01 1.97199315e-01 9.85022187e-01 -1.08299839e+00 -3.51856947e-02 2.39224806e-02 -5.36645114e-01 -4.85594600e-01 -1.14587493e-01 2.46244103e-01 -1.32331237e-01 -2.50105768e-01 9.96616781e-01 7.18061984e-01 3.88924293e-02 7.31344163e-01 -1.57006443e+00 -4.65772778e-01 9.04678166e-01 -5.15899360e-01 6.04442179e-01 1.37179300e-01 3.18021476e-01 5.24705708e-01 -7.86042392e-01 2.28603363e-01 8.68409336e-01 9.92331803e-01 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478d90ec-e4fa-4c35-8a5c-2eccd5456c8b	dlformer-discrete-latent-transformer-for	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Ren_DLFormer_Discrete_Latent_Transformer_for_Video_Inpainting_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Ren_DLFormer_Discrete_Latent_Transformer_for_Video_Inpainting_CVPR_2022_paper.pdf	DLFormer: Discrete Latent Transformer for Video Inpainting	Video inpainting remains a challenging problem to fill with plausible and coherent content in unknown areas in video frames despite the prevalence of data-driven methods. Although various transformer-based architectures yield promising result for this task, they still suffer from hallucinating blurry contents and long-term spatial-temporal inconsistency. While noticing the capability of discrete representation for complex reasoning and predictive learning, we propose a novel Discrete Latent Transformer (DLFormer) to reformulate video inpainting tasks into the discrete latent space rather the previous continuous feature space. Specifically, we first learn a unique compact discrete codebook and the corresponding autoencoder to represent the target video. Built upon these representative discrete codes obtained from the entire target video, the subsequent discrete latent transformer is capable to infer proper codes for unknown areas under a self-attention mechanism, and thus produces fine-grained content with long-term spatial-temporal consistency. Moreover, we further explicitly enforce the short-term consistency to relieve temporal visual jitters via a temporal aggregation block among adjacent frames. We conduct comprehensive quantitative and qualitative evaluations to demonstrate that our method significantly outperforms other state-of-the-art approaches in reconstructing visually-plausible and spatial-temporal coherent content with fine-grained details	['Chen Li', 'Xuemiao Xu', 'YuanYuan Zhao', 'Qingqing Zheng', 'Jingjing Ren']	2022-01-01	null	null	null	cvpr-2022-1	['video-inpainting']	['computer-vision']	[-2.62129325e-02 -1.47029087e-01 -2.02189118e-01 -2.09571168e-01 -7.62766480e-01 -2.32355833e-01 6.63130999e-01 -4.41544145e-01 1.95054814e-01 8.34776521e-01 5.82833350e-01 2.85407186e-01 -2.54199326e-01 -5.27380884e-01 -1.00653827e+00 -6.71340466e-01 8.31265748e-02 2.95564756e-02 8.49287584e-02 1.37063205e-01 3.91882807e-02 3.77082348e-01 -1.55757201e+00 6.31106555e-01 1.01887691e+00 1.15158200e+00 4.49013472e-01 4.11908448e-01 1.82027459e-01 1.39367163e+00 -3.79939198e-01 -3.25480253e-01 2.29794189e-01 -5.48237264e-01 -4.65043813e-01 6.10232890e-01 7.04869270e-01 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f0d7376e-7b92-4b06-88c4-267d8a26a14f	thompson-sampling-with-diffusion-generative	2301.05182	null	https://arxiv.org/abs/2301.05182v2	https://arxiv.org/pdf/2301.05182v2.pdf	Thompson Sampling with Diffusion Generative Prior	In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems. Our special focus is on the meta-learning for bandit framework, with the goal of learning a strategy that performs well across bandit tasks of a same class. To this end, we train a diffusion model that learns the underlying task distribution and combine Thompson sampling with the learned prior to deal with new tasks at test time. Our posterior sampling algorithm is designed to carefully balance between the learned prior and the noisy observations that come from the learner's interaction with the environment. To capture realistic bandit scenarios, we also propose a novel diffusion model training procedure that trains even from incomplete and/or noisy data, which could be of independent interest. Finally, our extensive experimental evaluations clearly demonstrate the potential of the proposed approach.	['Patrick Blöbaum', 'Branislav Kveton', 'Shiva Prasad Kasiviswanathan', 'Yu-Guan Hsieh']	2023-01-12	null	null	null	null	['thompson-sampling']	['methodology']	[ 4.89759333e-02 9.65685844e-02 -4.60504413e-01 -1.86533332e-01 -9.10340250e-01 -3.14514011e-01 8.48383009e-01 -5.78320622e-02 -4.43855226e-01 9.29349065e-01 2.52753705e-01 -2.61808157e-01 -4.00768846e-01 -7.57263362e-01 -9.53883350e-01 -9.32786942e-01 1.59970284e-01 7.78740048e-01 -2.76947711e-02 1.88134283e-01 2.59130895e-01 1.02530703e-01 -1.25145137e+00 3.68970394e-01 1.12404299e+00 1.01979530e+00 5.74394822e-01 6.74304545e-01 -1.21919326e-01 1.28798735e+00 -6.09068334e-01 -4.70043629e-01 2.97541916e-01 -4.43256170e-01 -7.55974889e-01 2.88364559e-01 -2.74265762e-02 -5.80794334e-01 -2.20305577e-01 1.02074218e+00 3.20903629e-01 4.65600699e-01 8.63593280e-01 -6.57256603e-01 -4.18025047e-01 7.11032152e-01 -5.36631525e-01 5.48806250e-01 -1.13515660e-01 1.89806476e-01 8.31800997e-01 -4.52842474e-01 4.04530376e-01 1.25970280e+00 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7f41bc90-a565-49ca-b974-ee9b4618bbf8	deep-learning-methods-for-fingerprint-based	2205.14935	null	https://arxiv.org/abs/2205.14935v1	https://arxiv.org/pdf/2205.14935v1.pdf	Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review	Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.	['Mohammad H. Mahoor', 'Fahad Alhomayani']	2022-05-30	null	null	null	null	['outdoor-positioning']	['miscellaneous']	[-1.33747503e-01 -4.36943293e-01 -5.57134926e-01 -7.29050577e-01 -5.91774046e-01 -5.92647314e-01 4.23305035e-01 -2.85627067e-01 -4.05870616e-01 9.67178702e-01 -4.90816049e-02 -6.64747059e-01 -4.37766135e-01 -8.88310909e-01 -6.50732994e-01 -7.43023574e-01 -1.94099084e-01 -3.66104953e-02 -3.68382663e-01 1.32047370e-01 6.01406872e-01 5.12603939e-01 -1.42117107e+00 -7.93187395e-02 7.71415353e-01 1.23743343e+00 1.09648675e-01 5.54590464e-01 -9.28789750e-02 6.76382482e-01 -8.15634251e-01 -5.58181763e-01 1.82802707e-01 1.66527197e-01 -5.99463701e-01 -9.77429032e-01 9.94067013e-01 -5.87081552e-01 -5.51435828e-01 4.69163001e-01 9.92701471e-01 1.63925156e-01 3.47191155e-01 -1.29261303e+00 -4.75017071e-01 4.50750291e-01 -2.01342002e-01 8.83752778e-02 5.93865693e-01 -8.91157389e-01 6.77684784e-01 -8.16398501e-01 -3.60469557e-02 6.54847085e-01 1.94216490e+00 3.92331511e-01 -7.67619014e-01 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830ba8a1-6c26-4e95-87e6-fc9f660cd000	towards-loosely-coupling-knowledge-graph	2202.03173	null	https://arxiv.org/abs/2202.03173v2	https://arxiv.org/pdf/2202.03173v2.pdf	Towards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based Reasoning	Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in many applications, such as product recommendation and question answering. The state-of-the-art approaches of knowledge graph embeddings and/or rule mining and reasoning are data-driven and, thus, solely based on the information the input knowledge graph contains. This leads to unsatisfactory prediction results which make such solutions inapplicable to crucial domains such as healthcare. To further enhance the accuracy of knowledge graph completion we propose to loosely-couple the data-driven power of knowledge graph embeddings with domain-specific reasoning stemming from experts or entailment regimes (e.g., OWL2). In this way, we not only enhance the prediction accuracy with domain knowledge that may not be included in the input knowledge graph but also allow users to plugin their own knowledge graph embedding and reasoning method. Our initial results show that we enhance the MRR accuracy of vanilla knowledge graph embeddings by up to 3x and outperform hybrid solutions that combine knowledge graph embeddings with rule mining and reasoning up to 3.5x MRR.	['Volker Markl', 'Abelardo Carlos Martinez Lorenzo', 'Zoi Kaoudi']	2022-02-07	null	null	null	null	['knowledge-graph-embeddings', 'knowledge-graph-embeddings', 'product-recommendation']	['graphs', 'methodology', 'miscellaneous']	[-4.84551340e-02 8.27393174e-01 -5.91694117e-01 -2.21232489e-01 -9.72500443e-02 -6.65769219e-01 2.66166747e-01 6.62435889e-01 -1.89445183e-01 5.85419774e-01 2.98629254e-01 -7.10959554e-01 -6.63658321e-01 -1.24399030e+00 -7.45809913e-01 -7.89762065e-02 1.10105485e-01 7.84519911e-01 2.78682053e-01 -4.60860729e-01 -3.12383860e-01 2.64286339e-01 -1.46323454e+00 3.86374533e-01 1.01904869e+00 7.91352868e-01 -3.47042739e-01 3.55267227e-01 -5.52922010e-01 1.00680947e+00 -7.95409903e-02 -9.40548301e-01 8.66323337e-02 2.45450854e-01 -1.06135416e+00 -1.72863260e-01 2.13439241e-01 -7.92422146e-02 -8.80488515e-01 1.03045571e+00 4.12716717e-02 1.63213998e-01 3.92370939e-01 -1.56136918e+00 -1.16297698e+00 9.23128486e-01 -2.62631923e-01 1.15357533e-01 6.71281397e-01 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6fbace31-73ed-443c-b702-7971e5cc4a9d	event-neural-networks	2112.00891	null	https://arxiv.org/abs/2112.00891v2	https://arxiv.org/pdf/2112.00891v2.pdf	Event Neural Networks	Video data is often repetitive; for example, the contents of adjacent frames are usually strongly correlated. Such redundancy occurs at multiple levels of complexity, from low-level pixel values to textures and high-level semantics. We propose Event Neural Networks (EvNets), which leverage this redundancy to achieve considerable computation savings during video inference. A defining characteristic of EvNets is that each neuron has state variables that provide it with long-term memory, which allows low-cost, high-accuracy inference even in the presence of significant camera motion. We show that it is possible to transform a wide range of neural networks into EvNets without re-training. We demonstrate our method on state-of-the-art architectures for both high- and low-level visual processing, including pose recognition, object detection, optical flow, and image enhancement. We observe roughly an order-of-magnitude reduction in computational costs compared to conventional networks, with minimal reductions in model accuracy.	['Yin Li', 'Mohit Gupta', 'Matthew Dutson']	2021-12-02	null	null	null	null	['2d-human-pose-estimation']	['computer-vision']	[ 3.27758282e-01 -4.19876814e-01 -2.96325833e-01 -2.55817860e-01 -3.69763315e-01 -3.90903890e-01 5.75140893e-01 9.35731605e-02 -5.33528566e-01 6.27988458e-01 6.26268461e-02 -1.54831693e-01 1.21359356e-01 -8.87129247e-01 -1.04349840e+00 -3.42008919e-01 -3.99571151e-01 -1.63865417e-01 4.73138988e-01 2.73653328e-01 7.52013102e-02 8.26851189e-01 -1.83253503e+00 5.82537949e-01 2.47820035e-01 1.44560957e+00 8.14325884e-02 1.10894930e+00 1.25455633e-01 1.43421292e+00 -3.60150516e-01 -1.70149162e-01 1.46047577e-01 -1.03956103e-01 -5.59994459e-01 2.70431101e-01 8.86175215e-01 -8.44766557e-01 -8.60864818e-01 1.00528765e+00 2.31944621e-01 4.08983707e-01 2.85460681e-01 -1.09623361e+00 -4.28465992e-01 1.73614144e-01 -3.06441069e-01 4.16241437e-01 1.74537495e-01 4.54283565e-01 9.55790460e-01 -9.10584033e-01 6.84182346e-01 1.21307087e+00 7.37291396e-01 3.30568641e-01 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faa76b00-2359-4f0b-abba-53945f941a90	privacy-utility-balanced-voice-de	2211.05446	null	https://arxiv.org/abs/2211.05446v1	https://arxiv.org/pdf/2211.05446v1.pdf	Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples	Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma when enjoying convenient voice services. Existing studies employ direct modification or text-based re-synthesis to de-identify users' voices, but resulting in inconsistent audibility in the presence of human participants. In this paper, we propose a voice de-identification system, which uses adversarial examples to balance the privacy and utility of voice services. Instead of typical additive examples inducing perceivable distortions, we design a novel convolutional adversarial example that modulates perturbations into real-world room impulse responses. Benefit from this, our system could preserve user identity from exposure by Automatic Speaker Identification (ASI) while remaining the voice perceptual quality for non-intrusive de-identification. Moreover, our system learns a compact speaker distribution through a conditional variational auto-encoder to sample diverse target embeddings on demand. Combining diverse target generation and input-specific perturbation construction, our system enables any-to-any identify transformation for adaptive de-identification. Experimental results show that our system could achieve 98% and 79% successful de-identification on mainstream ASIs and commercial systems with an objective Mel cepstral distortion of 4.31dB and a subjective mean opinion score of 4.48.	['Kui Ren', 'Feng Lin', 'Zhongjie Ba', 'Yingying Chen', 'Jiadi Yu', 'Li Lu', 'Meng Chen']	2022-11-10	null	null	null	null	['de-identification', 'speaker-identification']	['natural-language-processing', 'speech']	[ 1.23099819e-01 2.49915361e-01 1.75230488e-01 -7.88144842e-02 -1.17446756e+00 -1.07114291e+00 2.53495336e-01 -5.19997060e-01 -2.01214522e-01 5.16670942e-01 4.18900341e-01 -3.69507968e-01 2.57121772e-01 -3.46178174e-01 -5.44329286e-01 -7.04295933e-01 2.08154336e-01 -2.14323550e-01 -4.04117554e-01 5.85271195e-02 -2.40479827e-01 5.08901656e-01 -1.24240828e+00 2.02101156e-01 7.13102341e-01 9.69346225e-01 -2.69219160e-01 8.86546910e-01 2.71458983e-01 2.40349174e-01 -1.06318986e+00 -7.57907808e-01 4.70195830e-01 -4.05130774e-01 -3.43396962e-01 -1.71583787e-01 3.29682440e-01 -5.71151674e-01 -6.94684446e-01 1.14600229e+00 1.18940961e+00 1.59478150e-02 3.49712163e-01 -1.28222942e+00 -9.52612817e-01 4.92236227e-01 4.01169844e-02 5.90858236e-03 3.63397807e-01 4.61306840e-01 8.05252373e-01 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fae76cef-9c39-4ca9-9fcd-cdb5058ddc47	textfusenet-scene-text-detection-with-richer	null	null	https://doi.org/10.24963/ijcai.2020/72	https://www.ijcai.org/Proceedings/2020/0072.pdf	TextFuseNet: Scene Text Detection with Richer Fused Features	Arbitrary shape text detection in natural scenes is an extremely challenging task. Unlike existing text detection approaches that only perceive texts based on limited feature representations, we propose a novel framework, namely TextFuseNet, to exploit the use of richer features fused for text detection. More specifically, we propose to perceive texts from three levels of feature representations, i.e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection. The multi-level feature representation can adequately describe texts by dissecting them into individual characters while still maintaining their general semantics. TextFuseNet then collects and merges the texts’ features from different levels using a multi-path fusion architecture which can effectively align and fuse different representations. In practice, our proposed TextFuseNet can learn a more adequate description of arbitrary shapes texts, suppressing false positives and producing more accurate detection results. Our proposed framework can also be trained with weak supervision for those datasets that lack character-level annotations. Experiments on several datasets show that the proposed TextFuseNet achieves state-of-the-art performance. Specifically, we achieve an F-measure of 94.3% on ICDAR2013, 92.1% on ICDAR2015, 87.1% on Total-Text and 86.6% on CTW-1500, respectively.	['Zhe Chen', 'Jian Ye', 'Bo Du', 'Juhua Liu']	2020-05-17	null	null	null	null	['scene-text-detection']	['computer-vision']	[ 5.04295886e-01 -3.34803343e-01 1.22939438e-01 -1.87013388e-01 -9.98759389e-01 -4.76184845e-01 7.55363882e-01 4.84696269e-01 -3.39841634e-01 9.55659002e-02 2.37541586e-01 1.94924213e-02 2.49373540e-01 -7.58283734e-01 -4.30657595e-01 -6.87006116e-01 5.67123234e-01 2.99029648e-01 5.30055761e-01 -9.46357101e-02 2.93121994e-01 8.44598860e-02 -1.49370527e+00 6.52969003e-01 9.51307058e-01 9.71970439e-01 3.70972365e-01 7.60122418e-01 -4.43878949e-01 4.68531787e-01 -7.71564126e-01 -4.90319252e-01 2.01466173e-01 -7.41565228e-02 -2.70604551e-01 4.61061656e-01 5.93414366e-01 -4.77462590e-01 -4.49885398e-01 1.07095504e+00 4.72238123e-01 -3.07145882e-02 7.99732506e-01 -8.43240857e-01 -7.39509165e-01 6.79143846e-01 -1.06628716e+00 8.53555202e-02 4.58402365e-01 2.06884488e-01 1.24107730e+00 -1.24132204e+00 1.88020274e-01 1.56257546e+00 5.35199285e-01 3.77580553e-01 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97bc8eee-7ccd-4f33-8e6c-66575f4c47c6	enriching-unsupervised-user-embedding-via	2203.10627	null	https://arxiv.org/abs/2203.10627v2	https://arxiv.org/pdf/2203.10627v2.pdf	Enriching Unsupervised User Embedding via Medical Concepts	Clinical notes in Electronic Health Records (EHR) present rich documented information of patients to inference phenotype for disease diagnosis and study patient characteristics for cohort selection. Unsupervised user embedding aims to encode patients into fixed-length vectors without human supervisions. Medical concepts extracted from the clinical notes contain rich connections between patients and their clinical categories. However, existing unsupervised approaches of user embeddings from clinical notes do not explicitly incorporate medical concepts. In this study, we propose a concept-aware unsupervised user embedding that jointly leverages text documents and medical concepts from two clinical corpora, MIMIC-III and Diabetes. We evaluate user embeddings on both extrinsic and intrinsic tasks, including phenotype classification, in-hospital mortality prediction, patient retrieval, and patient relatedness. Experiments on the two clinical corpora show our approach exceeds unsupervised baselines, and incorporating medical concepts can significantly improve the baseline performance.	['Mark Dredze', 'Franck Dernoncourt', 'Xiaolei Huang']	2022-03-20	null	null	null	null	['phenotype-classification']	['medical']	[ 9.02050659e-02 1.88644290e-01 -4.83681887e-01 -4.47889417e-01 -8.77482951e-01 -3.48418564e-01 1.46177411e-01 1.18229437e+00 -6.48444533e-01 4.86840963e-01 9.62058544e-01 -1.60807461e-01 -2.07116500e-01 -6.55299246e-01 2.68284500e-01 -5.48301458e-01 -4.96476293e-01 9.84367609e-01 -6.56980872e-01 2.79787898e-01 -4.01750565e-01 1.61884919e-01 -7.67856240e-01 3.57178271e-01 4.96762127e-01 6.49322212e-01 -2.63131291e-01 8.79967690e-01 1.61207408e-01 7.12329328e-01 -2.87653893e-01 -4.02727336e-01 -1.42306283e-01 -3.47563863e-01 -5.59042692e-01 -6.62226230e-03 -5.98250562e-03 -1.82639763e-01 -6.11079574e-01 6.38935328e-01 1.02295935e+00 -2.53490418e-01 1.19082701e+00 -1.01583099e+00 -1.22291327e+00 5.29921234e-01 -3.42039652e-02 1.23695329e-01 3.64555359e-01 1.20520025e-01 1.58319664e+00 -7.66001880e-01 9.17137921e-01 7.68335640e-01 1.03993213e+00 9.77968097e-01 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556ffbe8-9bbe-46b3-bec3-b64e72a27bbb	co-clustering-vertices-and-hyperedges-via	2102.10169	null	https://arxiv.org/abs/2102.10169v1	https://arxiv.org/pdf/2102.10169v1.pdf	Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning	We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs). In this hypergraph model, the contribution of every vertex to each of its incident hyperedges is represented through an edge-dependent weight, conferring the model higher expressivity than the classical hypergraph. In our method, we leverage random walks with EDVWs to construct a hypergraph Laplacian and use its spectral properties to embed vertices and hyperedges in a common space. We then cluster these embeddings to obtain our proposed co-clustering method, of particular relevance in applications requiring the simultaneous clustering of data entities and features. Numerical experiments using real-world data demonstrate the effectiveness of our proposed approach in comparison with state-of-the-art alternatives.	['Santiago Segarra', 'Boning Li', 'Yu Zhu']	2021-02-19	null	null	null	null	['hypergraph-partitioning']	['graphs']	[-6.5719567e-02 3.1812897e-01 -2.6995358e-01 -7.3569663e-02 7.5152777e-02 -7.2100842e-01 5.0273257e-01 3.8887227e-01 -2.2268391e-01 3.7926331e-01 1.0379597e-03 -5.2910704e-02 -5.7792372e-01 -1.1737386e+00 -3.4816617e-01 -6.8320036e-01 -3.9461049e-01 4.6222615e-01 1.3317306e-01 7.7031374e-02 2.3467377e-02 4.5284486e-01 -1.2831842e+00 -1.3173379e-01 9.6747649e-01 3.9481282e-01 -1.4593399e-02 4.1908607e-01 -2.6206392e-01 7.9183802e-02 -5.6979887e-02 -6.3610709e-01 4.7145680e-01 -3.1387277e-02 -7.0346862e-01 4.6288416e-01 1.2218824e-01 4.7131413e-01 -6.1462510e-01 1.1887327e+00 7.5339384e-02 1.2315336e-01 8.4451836e-01 -1.4844693e+00 -9.4141787e-01 6.4672869e-01 -1.0848384e+00 -1.0942093e-02 2.6900399e-01 -3.5267046e-01 1.5576432e+00 -6.9489413e-01 6.4114392e-01 9.0361875e-01 4.1508558e-01 1.1423275e-01 -1.6192436e+00 -3.7674284e-01 8.7067671e-02 1.9622560e-01 -1.8252466e+00 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e54dc615-b0d5-41be-b2c7-de97583f997e	convolutional-neural-networks-on-non-uniform	1901.02070	null	http://arxiv.org/abs/1901.02070v1	http://arxiv.org/pdf/1901.02070v1.pdf	Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation	Convolutional Neural Networks (CNN) have been successful in processing data signals that are uniformly sampled in the spatial domain (e.g., images). However, most data signals do not natively exist on a grid, and in the process of being sampled onto a uniform physical grid suffer significant aliasing error and information loss. Moreover, signals can exist in different topological structures as, for example, points, lines, surfaces and volumes. It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh). To this end, we develop mathematical formulations for Non-Uniform Fourier Transforms (NUFT) to directly, and optimally, sample nonuniform data signals of different topologies defined on a simplex mesh into the spectral domain with no spatial sampling error. The spectral transform is performed in the Euclidean space, which removes the translation ambiguity from works on the graph spectrum. Our representation has four distinct advantages: (1) the process causes no spatial sampling error during the initial sampling, (2) the generality of this approach provides a unified framework for using CNNs to analyze signals of mixed topologies, (3) it allows us to leverage state-of-the-art backbone CNN architectures for effective learning without having to design a particular architecture for a particular data structure in an ad-hoc fashion, and (4) the representation allows weighted meshes where each element has a different weight (i.e., texture) indicating local properties. We achieve results on par with the state-of-the-art for the 3D shape retrieval task, and a new state-of-the-art for the point cloud to surface reconstruction task.	['Matthias Nießner', 'Chiyu "Max" Jiang', 'Jingwei Huang', 'Philip Marcus', 'Dequan Wang']	2019-01-07	convolutional-neural-networks-on-non-uniform-1	https://openreview.net/forum?id=B1G5ViAqFm	https://openreview.net/pdf?id=B1G5ViAqFm	iclr-2019-5	['3d-shape-retrieval']	['computer-vision']	[ 1.79403603e-01 -1.53781950e-01 2.33876422e-01 5.76012731e-02 -6.71511292e-01 -5.56777656e-01 3.85199606e-01 6.04307577e-02 -6.18458763e-02 4.98855948e-01 -2.30258852e-01 -7.69518539e-02 -3.17548305e-01 -1.36159599e+00 -1.00258422e+00 -6.25344872e-01 -3.19701374e-01 6.09050691e-01 1.41813695e-01 -2.07614094e-01 -1.29244626e-01 1.12802136e+00 -1.65385604e+00 1.90531150e-01 6.47135198e-01 1.45850420e+00 5.14365807e-02 3.06447744e-01 -4.33327079e-01 1.85168222e-01 -4.51208085e-01 -1.24863073e-01 3.57602507e-01 -6.83888271e-02 -6.57529235e-01 8.85558352e-02 9.13093090e-01 -9.70384628e-02 -2.31903806e-01 1.16656554e+00 3.14242065e-01 -1.52775720e-02 7.61361957e-01 -9.98982251e-01 -6.31222427e-01 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55eae385-03d5-4b57-b9bd-be54d33d532f	geometric-deep-learning-on-graphs-and	1611.08402	null	http://arxiv.org/abs/1611.08402v3	http://arxiv.org/pdf/1611.08402v3.pdf	Geometric deep learning on graphs and manifolds using mixture model CNNs	Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far focused on dealing with 1D, 2D, or 3D Euclidean-structured data such as acoustic signals, images, or videos. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics. In this paper, we propose a unified framework allowing to generalize CNN architectures to non-Euclidean domains (graphs and manifolds) and learn local, stationary, and compositional task-specific features. We show that various non-Euclidean CNN methods previously proposed in the literature can be considered as particular instances of our framework. We test the proposed method on standard tasks from the realms of image-, graph- and 3D shape analysis and show that it consistently outperforms previous approaches.	['Emanuele Rodolà', 'Federico Monti', 'Jan Svoboda', 'Michael M. Bronstein', 'Jonathan Masci', 'Davide Boscaini']	2016-11-25	geometric-deep-learning-on-graphs-and-1	http://openaccess.thecvf.com/content_cvpr_2017/html/Monti_Geometric_Deep_Learning_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Monti_Geometric_Deep_Learning_CVPR_2017_paper.pdf	cvpr-2017-7	['superpixel-image-classification', 'graph-regression']	['computer-vision', 'graphs']	[ 1.89628601e-01 1.19517129e-02 -2.79735550e-02 -3.30485672e-01 -3.21111977e-01 -3.46770853e-01 8.33738327e-01 4.16226149e-01 -3.98021281e-01 -4.04706644e-03 4.32707295e-02 -4.18733627e-01 -4.00311112e-01 -8.78952444e-01 -6.13749027e-01 -5.70571601e-01 -4.71965104e-01 2.80587673e-01 -4.38710637e-02 -1.44155368e-01 1.47665247e-01 1.05866241e+00 -1.16946852e+00 -7.91267231e-02 2.39135802e-01 1.05304611e+00 -3.99760395e-01 5.44935107e-01 1.99273415e-02 3.89130533e-01 -3.23121041e-01 -2.86403388e-01 2.52633423e-01 -1.77395809e-02 -8.40782046e-01 3.10912430e-01 6.30857289e-01 -1.73349702e-03 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c55b0c3e-ed57-48f9-afa4-2d0c43aef5a1	a-deep-ranking-model-for-spatio-temporal	1801.10312	null	http://arxiv.org/abs/1801.10312v1	http://arxiv.org/pdf/1801.10312v1.pdf	A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video	We address the problem of highlight detection from a 360 degree video by summarizing it both spatially and temporally. Given a long 360 degree video, we spatially select pleasantly-looking normal field-of-view (NFOV) segments from unlimited field of views (FOV) of the 360 degree video, and temporally summarize it into a concise and informative highlight as a selected subset of subshots. We propose a novel deep ranking model named as Composition View Score (CVS) model, which produces a spherical score map of composition per video segment, and determines which view is suitable for highlight via a sliding window kernel at inference. To evaluate the proposed framework, we perform experiments on the Pano2Vid benchmark dataset and our newly collected 360 degree video highlight dataset from YouTube and Vimeo. Through evaluation using both quantitative summarization metrics and user studies via Amazon Mechanical Turk, we demonstrate that our approach outperforms several state-of-the-art highlight detection methods. We also show that our model is 16 times faster at inference than AutoCam, which is one of the first summarization algorithms of 360 degree videos	['Sang-ho Lee', 'Joonil Na', 'Jaeyun Kang', 'Youngjae Yu', 'Gunhee Kim']	2018-01-31	null	null	null	null	['highlight-detection']	['computer-vision']	[-1.87319115e-01 -3.31731021e-01 -2.51052827e-01 -9.99686122e-02 -6.83665812e-01 -9.95280087e-01 5.54423749e-01 4.96852472e-02 -2.65101969e-01 1.42040059e-01 6.77795708e-01 1.65381268e-01 -7.33423978e-02 -3.41612786e-01 -8.90840352e-01 -2.79500991e-01 -3.92272413e-01 -3.88762027e-01 4.72871870e-01 2.42475539e-01 6.67758763e-01 4.15365040e-01 -1.65511298e+00 2.26212397e-01 6.77873492e-01 1.15953803e+00 3.37414265e-01 9.22142863e-01 2.75879651e-01 1.22869217e+00 -7.15944290e-01 -2.97809631e-01 4.54381049e-01 -1.82079673e-01 -4.37508523e-01 3.89977723e-01 1.33375919e+00 -9.64460254e-01 -7.32959747e-01 1.09330368e+00 7.40263879e-01 6.35324061e-01 2.95516819e-01 -1.15573978e+00 -5.72981000e-01 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51a2f038-0c18-49c1-9201-895d3c9217df	reservoir-computing-models-for-patient	1907.09504	null	https://arxiv.org/abs/1907.09504v1	https://arxiv.org/pdf/1907.09504v1.pdf	Reservoir Computing Models for Patient-Adaptable ECG Monitoring in Wearable Devices	The reservoir computing paradigm is employed to classify heartbeat anomalies online based on electrocardiogram signals. Inspired by the principles of information processing in the brain, reservoir computing provides a framework to design, train, and analyze recurrent neural networks (RNNs) for processing time-dependent information. Due to its computational efficiency and the fact that training amounts to a simple linear regression, this supervised learning algorithm has been variously considered as a strategy to implement useful computations not only on digital computers but also on emerging unconventional hardware platforms such as neuromorphic microchips. Here, this biological-inspired learning framework is exploited to devise an accurate patient-adaptive model that has the potential to be integrated into wearable cardiac events monitoring devices. The proposed patient-customized model was trained and tested on ECG recordings selected from the MIT-BIH arrhythmia database. Restrictive inclusion criteria were used to conduct the study only on ECGs including, at least, two classes of heartbeats with highly unequal number of instances. The results of extensive simulations showed this model not only provides accurate, cheap and fast patient-customized heartbeat classifier but also circumvents the problem of "imbalanced classes" when the readout weights are trained using weighted ridge-regression.	['Fatemeh Hadaeghi']	2019-07-22	null	null	null	null	['arrhythmia-detection', 'ecg-classification', 'electrocardiography-ecg']	['medical', 'medical', 'methodology']	[ 4.54204470e-01 -2.86626101e-01 2.23697320e-01 -2.71974742e-01 -1.56955376e-01 -2.65781164e-01 8.26830342e-02 2.77209193e-01 -4.42311645e-01 7.74149418e-01 -2.91787326e-01 -2.67016292e-01 -3.33617151e-01 -4.90574330e-01 -1.55619070e-01 -9.19182241e-01 -3.18354726e-01 2.05240056e-01 -1.76146552e-01 -3.57861607e-03 3.61433953e-01 6.34101987e-01 -1.69114125e+00 2.02835947e-01 8.38390350e-01 1.21605122e+00 2.42249165e-02 6.91792846e-01 2.34168634e-01 7.73603618e-01 -6.20260656e-01 3.70056033e-02 1.39350906e-01 -8.20894361e-01 4.32363637e-02 -4.72667158e-01 -3.28184336e-01 1.62761528e-02 -2.01283887e-01 7.22432494e-01 9.94185984e-01 -2.47779727e-01 4.54940915e-01 -7.46810615e-01 -3.49108055e-02 5.63313723e-01 8.54390860e-02 7.81819701e-01 -9.73237604e-02 1.35833681e-01 4.09964204e-01 -7.13857293e-01 4.01685238e-01 2.51584381e-01 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19d93b57-a542-4fac-a9f4-40052e1e95c6	pixmatch-unsupervised-domain-adaptation-via	2105.08128	null	https://arxiv.org/abs/2105.08128v1	https://arxiv.org/pdf/2105.08128v1.pdf	PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training	Unsupervised domain adaptation is a promising technique for semantic segmentation and other computer vision tasks for which large-scale data annotation is costly and time-consuming. In semantic segmentation, it is attractive to train models on annotated images from a simulated (source) domain and deploy them on real (target) domains. In this work, we present a novel framework for unsupervised domain adaptation based on the notion of target-domain consistency training. Intuitively, our work is based on the idea that in order to perform well on the target domain, a model's output should be consistent with respect to small perturbations of inputs in the target domain. Specifically, we introduce a new loss term to enforce pixelwise consistency between the model's predictions on a target image and a perturbed version of the same image. In comparison to popular adversarial adaptation methods, our approach is simpler, easier to implement, and more memory-efficient during training. Experiments and extensive ablation studies demonstrate that our simple approach achieves remarkably strong results on two challenging synthetic-to-real benchmarks, GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes. Code is available at: https://github.com/lukemelas/pixmatch	['Arjun K. Manrai', 'Luke Melas-Kyriazi']	2021-05-17	null	http://openaccess.thecvf.com//content/CVPR2021/html/Melas-Kyriazi_PixMatch_Unsupervised_Domain_Adaptation_via_Pixelwise_Consistency_Training_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Melas-Kyriazi_PixMatch_Unsupervised_Domain_Adaptation_via_Pixelwise_Consistency_Training_CVPR_2021_paper.pdf	cvpr-2021-1	['synthetic-to-real-translation']	['computer-vision']	[ 4.49201703e-01 3.96896988e-01 -3.01681273e-02 -5.65235019e-01 -9.57280397e-01 -6.59799814e-01 4.06861693e-01 -1.06283918e-01 -5.32498240e-01 7.96230197e-01 -3.84100527e-01 -1.00944489e-01 4.99304503e-01 -7.02552497e-01 -1.09681308e+00 -6.71865523e-01 3.90308946e-01 6.92772388e-01 6.85605168e-01 -1.40994504e-01 -1.37825191e-01 2.46681809e-01 -9.83495116e-01 6.14083111e-02 9.69959557e-01 8.67439210e-01 2.57967919e-01 5.97335577e-01 1.87659904e-01 5.89192033e-01 -6.15730107e-01 -5.24649858e-01 3.77133042e-01 -6.10698462e-01 -1.20917284e+00 3.27572614e-01 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8e5cfe81-a8d8-4bdb-b479-02b73e695756	on-the-use-of-benford-s-law-to-detect-gan	2004.07682	null	https://arxiv.org/abs/2004.07682v1	https://arxiv.org/pdf/2004.07682v1.pdf	On the use of Benford's law to detect GAN-generated images	The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery. The malicious diffusion of GAN-generated images may lead to serious social and political consequences (e.g., fake news spreading, opinion formation, etc.). It is therefore important to regulate the widespread distribution of synthetic imagery by developing solutions able to detect them. In this paper, we study the possibility of using Benford's law to discriminate GAN-generated images from natural photographs. Benford's law describes the distribution of the most significant digit for quantized Discrete Cosine Transform (DCT) coefficients. Extending and generalizing this property, we show that it is possible to extract a compact feature vector from an image. This feature vector can be fed to an extremely simple classifier for GAN-generated image detection purpose.	['Nicolò Bonettini', 'Simone Milani', 'Paolo Bestagini', 'Stefano Tubaro']	2020-04-16	null	null	null	null	['gan-image-forensics']	['computer-vision']	[ 7.42184758e-01 4.43833321e-01 8.46009031e-02 3.37453149e-02 -4.97536868e-01 -8.15282285e-01 9.72871840e-01 -3.39343190e-01 -1.99542090e-01 1.13502407e+00 -1.49666145e-01 -4.74353313e-01 4.74473149e-01 -1.15264833e+00 -6.57506943e-01 -9.52530324e-01 -7.21396040e-03 2.71324310e-02 6.45476505e-02 -2.79988557e-01 3.23612094e-01 7.29309142e-01 -1.24063730e+00 3.33803445e-01 8.03398788e-01 9.39769387e-01 -2.65009440e-02 9.02460933e-01 1.71367407e-01 1.16587090e+00 -1.23549652e+00 -6.22749090e-01 5.55258036e-01 -1.12129951e+00 -6.10691071e-01 1.49173945e-01 1.47684917e-01 -5.32400370e-01 -2.44415194e-01 1.61179638e+00 1.22648425e-01 -2.53005207e-01 9.16073620e-01 -1.48831928e+00 -8.52136612e-01 3.49060804e-01 -4.22073901e-01 -1.70800649e-02 3.35165024e-01 1.68719217e-01 3.87295693e-01 -3.45496029e-01 9.05169010e-01 1.00816679e+00 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a29bbcd3-c6ef-462b-b8a5-c6f47c7cb9c1	hsi-bert-hyperspectral-image-classification	null	null	https://doi.org/10.1109/TGRS.2019.2934760	https://doi.org/10.1109/TGRS.2019.2934760	HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers	Deep learning methods have been widely used in hyperspectral image classification and have achieved state-of-the-art performance. Nonetheless, the existing deep learning methods are restricted by a limited receptive field, inflexibility, and difficult generalization problems in hyperspectral image classification. To solve these problems, we propose HSI-BERT, where BERT stands for bidirectional encoder representations from transformers and HSI stands for hyperspectral imagery. The proposed HSI-BERT has a global receptive field that captures the global dependence among pixels regardless of their spatial distance. HSI-BERT is very flexible and enables the flexible and dynamic input regions. Furthermore, HSI-BERT has good generalization ability because the jointly trained HSI-BERT can be generalized from regions with different shapes without retraining. HSI-BERT is primarily built on a multihead self-attention (MHSA) mechanism in an MHSA layer. Moreover, several attentions are learned by different heads, and each head of the MHSA layer encodes the semantic context-aware representation to obtain discriminative features. Because all head-encoded features are merged, the resulting features exhibit spatial-spectral information that is essential for accurate pixel-level classification. Quantitative and qualitative results demonstrate that HSI-BERT outperforms any other CNN-based model in terms of both classification accuracy and computational time and achieves state-of-the-art performance on three widely used hyperspectral image data sets.	['Wei Li', 'Mengmeng Zhang', 'HongWei Yang', 'Lina Zhao', 'Ji He']	2019-09-04	null	null	null	ieee-transactions-on-geoscience-and-remote-16	['few-shot-image-classification']	['computer-vision']	[ 3.20715189e-01 -4.53460813e-01 -2.50710966e-03 -3.50282371e-01 -4.99953657e-01 -3.22406977e-01 2.35920057e-01 -2.31308013e-01 -2.13215485e-01 5.66313028e-01 4.27056067e-02 -8.93209130e-02 -6.23680651e-01 -1.09847713e+00 -6.53548300e-01 -1.22122550e+00 6.43191934e-02 -6.21841475e-02 9.48103145e-02 -2.23545551e-01 -9.83423293e-02 7.84579396e-01 -1.66623151e+00 3.23301941e-01 1.23604798e+00 1.45360732e+00 8.57764244e-01 2.06216186e-01 1.33451641e-01 3.87100071e-01 -3.03739697e-01 2.21958429e-01 3.89694095e-01 -1.72529891e-01 -5.99901497e-01 2.51132458e-01 3.35370451e-01 -2.96280950e-01 -5.01681924e-01 1.29214072e+00 4.91403013e-01 3.81270587e-01 5.77565968e-01 -1.08960497e+00 -1.26849127e+00 4.15904552e-01 -5.34992993e-01 1.75373942e-01 -3.34316492e-01 2.82334596e-01 9.71674800e-01 -8.18365216e-01 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2b286983-d169-49a0-a2b7-723c3eb1d177	gl2vec-learning-feature-representation-using	1812.05473	null	https://arxiv.org/abs/1812.05473v3	https://arxiv.org/pdf/1812.05473v3.pdf	Learning Features of Network Structures Using Graphlets	Networks are fundamental to the study of complex systems, ranging from social contacts, message transactions, to biological regulations and economical networks. In many realistic applications, these networks may vary over time. Modeling and analyzing such temporal properties is of additional interest as it can provide a richer characterization of relations between nodes in networks. In this paper, we explore the role of \emph{graphlets} in network classification for both static and temporal networks. Graphlets are small non-isomorphic induced subgraphs representing connected patterns in a network and their frequency can be used to assess network structures. We show that graphlet features, which are not captured by state-of-the-art methods, play a significant role in enhancing the performance of network classification. To that end, we propose two novel graphlet-based techniques, \emph{gl2vec} for network embedding, and \emph{gl-DCNN} for diffusion-convolutional neural networks. We demonstrate the efficacy and usability of \emph{gl2vec} and \emph{gl-DCNN} through extensive experiments using several real-world static and temporal networks. We find that features learned from graphlets can bring notable performance increases to state-of-the-art methods in network analysis.	['Liam Turner', 'Kun Tu', 'Jian Li', 'Dave Braines', 'Don Towsley']	2018-12-13	null	null	null	null	['learning-network-representations']	['methodology']	[-3.11788619e-02 -1.13885723e-01 -2.29830295e-01 -1.23054951e-01 4.24160212e-01 -7.61336327e-01 8.36573839e-01 5.43901443e-01 -1.69862688e-01 4.61843431e-01 1.29756242e-01 -5.42963505e-01 -5.66879094e-01 -1.22640085e+00 -3.81139874e-01 -5.95483780e-01 -8.41805816e-01 1.16332389e-01 4.13953483e-01 -5.73419571e-01 -2.34414354e-01 7.44430125e-01 -1.02040625e+00 -1.91974826e-02 3.40297133e-01 9.65503871e-01 -3.55683655e-01 5.42093396e-01 -9.62062702e-02 7.35691369e-01 -4.91989553e-01 -3.99378449e-01 8.56509134e-02 -3.21105242e-01 -6.69302046e-01 -2.06820607e-01 -8.66688117e-02 1.66316271e-01 -1.03455281e+00 9.57709074e-01 2.91311771e-01 1.13038965e-01 7.52132058e-01 -1.37245023e+00 -5.38127899e-01 6.99123085e-01 -5.17610490e-01 7.66097844e-01 2.71974891e-01 1.97145402e-01 1.32925892e+00 -4.61303174e-01 7.68293619e-01 1.34913790e+00 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f80fec59-469e-47eb-835a-5baf9d48756a	robust-point-cloud-segmentation-with-noisy	2212.03242	null	https://arxiv.org/abs/2212.03242v1	https://arxiv.org/pdf/2212.03242v1.pdf	Robust Point Cloud Segmentation with Noisy Annotations	Point cloud segmentation is a fundamental task in 3D. Despite recent progress on point cloud segmentation with the power of deep networks, current learning methods based on the clean label assumptions may fail with noisy labels. Yet, class labels are often mislabeled at both instance-level and boundary-level in real-world datasets. In this work, we take the lead in solving the instance-level label noise by proposing a Point Noise-Adaptive Learning (PNAL) framework. Compared to noise-robust methods on image tasks, our framework is noise-rate blind, to cope with the spatially variant noise rate specific to point clouds. Specifically, we propose a point-wise confidence selection to obtain reliable labels from the historical predictions of each point. A cluster-wise label correction is proposed with a voting strategy to generate the best possible label by considering the neighbor correlations. To handle boundary-level label noise, we also propose a variant ``PNAL-boundary " with a progressive boundary label cleaning strategy. Extensive experiments demonstrate its effectiveness on both synthetic and real-world noisy datasets. Even with $60\%$ symmetric noise and high-level boundary noise, our framework significantly outperforms its baselines, and is comparable to the upper bound trained on completely clean data. Moreover, we cleaned the popular real-world dataset ScanNetV2 for rigorous experiment. Our code and data is available at https://github.com/pleaseconnectwifi/PNAL.	['Jing Liao', 'Songfang Han', 'Dongdong Chen', 'Shuquan Ye']	2022-12-06	null	null	null	null	['point-cloud-segmentation']	['computer-vision']	[ 1.89264148e-01 -1.51648983e-01 -1.04355086e-02 -5.63483775e-01 -1.27551866e+00 -5.06418824e-01 4.23600942e-01 1.72163218e-01 -3.26099396e-01 4.76842970e-01 -4.71125215e-01 -2.11584345e-01 -2.67568622e-02 -8.31084669e-01 -9.49377120e-01 -8.28055501e-01 1.07850134e-01 7.34799027e-01 3.80932301e-01 2.74498820e-01 2.74257660e-01 7.33014405e-01 -1.52365434e+00 -9.36687365e-02 1.03074312e+00 1.08691716e+00 2.74713993e-01 2.43741244e-01 -6.18004382e-01 2.90943265e-01 -4.86764044e-01 -2.39305601e-01 6.89631462e-01 5.47799021e-02 -7.00346947e-01 2.96171695e-01 7.01095402e-01 8.91145989e-02 1.14414049e-02 1.43761063e+00 4.68494833e-01 2.20648944e-01 5.26861608e-01 -1.26939654e+00 -2.63124049e-01 2.82865644e-01 -1.01154888e+00 -9.21142325e-02 -2.05589399e-01 2.73839891e-01 8.30148876e-01 -1.05876768e+00 5.97052515e-01 1.12698030e+00 9.58584785e-01 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a8c6d9af-9902-4c40-a9ec-c1c5338eb9f5	score-based-diffusion-models-as-principled	2304.11751	null	https://arxiv.org/abs/2304.11751v1	https://arxiv.org/pdf/2304.11751v1.pdf	Score-Based Diffusion Models as Principled Priors for Inverse Imaging	It is important in computational imaging to understand the uncertainty of images reconstructed from imperfect measurements. We propose turning score-based diffusion models into principled priors (``score-based priors'') for analyzing a posterior of images given measurements. Previously, probabilistic priors were limited to handcrafted regularizers and simple distributions. In this work, we empirically validate the theoretically-proven probability function of a score-based diffusion model. We show how to sample from resulting posteriors by using this probability function for variational inference. Our results, including experiments on denoising, deblurring, and interferometric imaging, suggest that score-based priors enable principled inference with a sophisticated, data-driven image prior.	['William T. Freeman', 'Katherine L. Bouman', 'Huiwen Chang', 'Michael Rubinstein', 'Jamie Smith', 'Berthy T. Feng']	2023-04-23	null	null	null	null	['deblurring']	['computer-vision']	[ 6.57229960e-01 9.83017609e-02 3.98088932e-01 -5.03806114e-01 -1.01616871e+00 -3.44147056e-01 7.12797999e-01 -5.67270100e-01 -3.85403991e-01 7.81870723e-01 4.84588832e-01 -2.43024707e-01 -6.25842452e-01 -5.12029707e-01 -4.38860953e-01 -1.02878916e+00 -1.58692867e-01 2.28443652e-01 1.92661881e-01 3.57199013e-01 4.19945091e-01 4.18988883e-01 -9.72191632e-01 -1.35534912e-01 7.20306516e-01 7.53542483e-01 3.98072034e-01 8.16521168e-01 4.62727040e-01 6.80332541e-01 -6.55496955e-01 -2.45387524e-01 2.31388390e-01 -8.52649152e-01 -4.37247485e-01 2.52303451e-01 1.54772224e-02 -7.22825885e-01 -4.54786062e-01 1.48189676e+00 4.66619104e-01 -4.23372770e-03 1.05050814e+00 -7.37501204e-01 -7.38662839e-01 4.10279185e-01 -6.00047529e-01 3.47511172e-01 7.57258907e-02 3.40819985e-01 7.67550290e-01 -5.69528759e-01 7.22326398e-01 1.22164702e+00 6.32992804e-01 4.34006065e-01 -1.58419037e+00 -6.72919527e-02 -4.25534427e-01 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47658449-7384-4aa7-9582-572c7f9fb91c	deep-learning-and-medical-imaging-for-covid	2302.06611	null	https://arxiv.org/abs/2302.06611v1	https://arxiv.org/pdf/2302.06611v1.pdf	Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey	COVID-19 (Coronavirus disease 2019) has been quickly spreading since its outbreak, impacting financial markets and healthcare systems globally. Countries all around the world have adopted a number of extraordinary steps to restrict the spreading virus, where early COVID-19 diagnosis is essential. Medical images such as X-ray images and Computed Tomography scans are becoming one of the main diagnostic tools to combat COVID-19 with the aid of deep learning-based systems. In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis. We also provide a summary of the X-ray and CT image datasets used in various studies for COVID-19 detection. The key difficulties and potential applications of deep learning in fighting against COVID-19 are finally discussed. This work summarizes the latest methods of deep learning using medical images to diagnose COVID-19, highlighting the challenges and inspiring more studies to keep utilizing the advantages of deep learning to combat COVID-19.	['Philip S. Yu', 'Liqiang Nie', 'Jing He', 'Xiaorong Pu', 'Xinyue Chen', 'Aodi Yang', 'Yazhou Ren', 'Song Wu']	2023-02-13	null	null	null	null	['covid-19-detection']	['medical']	[-1.37804421e-02 -7.21814096e-01 -6.72282204e-02 -1.80286109e-01 -2.44001150e-01 -5.40188968e-01 1.86087951e-01 3.08197588e-01 -7.96303928e-01 4.96568054e-01 -1.11276209e-01 -4.47313637e-01 -2.72740930e-01 -6.03781104e-01 -2.22892076e-01 -9.33508337e-01 -4.54626322e-01 1.02736044e+00 -2.28850543e-01 -1.96002558e-01 -7.87602812e-02 9.66244996e-01 -7.46736169e-01 4.32692528e-01 7.06862032e-01 8.50505531e-01 5.51097691e-01 1.10198057e+00 1.88993722e-01 5.51496744e-01 -8.49077821e-01 -6.86777160e-02 3.35474499e-02 -3.92409712e-01 -6.92913473e-01 -3.07499319e-01 4.74228263e-02 -7.98871458e-01 -1.15355052e-01 6.53111577e-01 6.98557436e-01 -3.24444175e-01 1.06248963e+00 -9.40140665e-01 -4.55755144e-01 -2.55968362e-01 -4.88251597e-01 8.40567112e-01 -2.15861704e-02 1.74670041e-01 3.49100024e-01 -5.20504653e-01 7.78592765e-01 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-2.61594117e-01 -6.66023970e-01 -5.21884441e-01 1.51789084e-01 9.11056042e-01 -5.21273613e-01 -1.80997029e-01 -1.36408838e-03]	[15.565308570861816, -1.7011797428131104]
68cde664-5eb2-4419-9e80-858f3116b0d0	team-cogitat-at-neurips-2021-benchmarks-for	2202.03267	null	https://arxiv.org/abs/2202.03267v1	https://arxiv.org/pdf/2202.03267v1.pdf	Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition	Building subject-independent deep learning models for EEG decoding faces the challenge of strong covariate-shift across different datasets, subjects and recording sessions. Our approach to address this difficulty is to explicitly align feature distributions at various layers of the deep learning model, using both simple statistical techniques as well as trainable methods with more representational capacity. This follows in a similar vein as covariance-based alignment methods, often used in a Riemannian manifold context. The methodology proposed herein won first place in the 2021 Benchmarks in EEG Transfer Learning (BEETL) competition, hosted at the NeurIPS conference. The first task of the competition consisted of sleep stage classification, which required the transfer of models trained on younger subjects to perform inference on multiple subjects of older age groups without personalized calibration data, requiring subject-independent models. The second task required to transfer models trained on the subjects of one or more source motor imagery datasets to perform inference on two target datasets, providing a small set of personalized calibration data for multiple test subjects.	['Stefanos Zafeiriou', 'Dimitrios A. Adamos', 'Nikolaos Laskaris', 'Yannis Panagakis', 'Mehdi Bahri', 'Konstantinos Barmpas', 'Siegfried Ludwig', 'Stylianos Bakas']	2022-02-01	null	null	null	null	['eeg-decoding', 'eeg-decoding']	['medical', 'time-series']	[ 2.61433005e-01 1.81772277e-01 2.37666860e-01 -8.35310340e-01 -1.03791749e+00 -4.70768362e-01 5.51037729e-01 -1.47967800e-01 -8.70342553e-01 1.03321183e+00 8.80180746e-02 -5.68506718e-02 -2.96461105e-01 -2.02851415e-01 -8.55901539e-01 -8.17794859e-01 -3.51135612e-01 5.94331682e-01 -2.18262121e-01 5.86413257e-02 2.36859784e-01 2.33263776e-01 -1.28153133e+00 2.53287315e-01 8.63970816e-01 7.83535063e-01 3.38416815e-01 4.63629752e-01 5.58750451e-01 2.89431155e-01 -4.87492740e-01 -4.23734844e-01 1.43082887e-01 -4.67467755e-01 -6.37590051e-01 -3.23307723e-01 7.07013726e-01 1.42677695e-01 -1.74108908e-01 1.04746521e+00 7.53085375e-01 5.61923534e-02 1.06221950e+00 -1.35894740e+00 -5.53414524e-01 7.11366594e-01 -4.23186809e-01 4.23040479e-01 -1.40729714e-02 -1.97226375e-01 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4.14462179e-01 6.92721307e-01 4.61218178e-01 -1.36753345e+00 -5.18648982e-01 -1.25254834e+00 -7.93667853e-01 4.01951969e-01 1.74817264e-01 -7.58791804e-01 -2.69495726e-01 1.70618191e-01]	[12.929793357849121, 3.448474884033203]
505f3b21-7b0e-4809-aaa8-7bb0853eaec7	vice-variational-inference-for-concept-1	2205.00756	null	https://arxiv.org/abs/2205.00756v8	https://arxiv.org/pdf/2205.00756v8.pdf	VICE: Variational Interpretable Concept Embeddings	A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.	['Francisco Pereira', 'Martin N. Hebart', 'Robert A. Vandermeulen', 'Patrick McClure', 'Charles Y. Zheng', 'Lukas Muttenthaler']	2022-05-02	vice-variational-inference-for-concept	https://openreview.net/forum?id=-9ffJ9NQmal	https://openreview.net/pdf?id=-9ffJ9NQmal	null	['odd-one-out']	['reasoning']	[-1.39410526e-01 4.53622863e-02 -1.98444620e-01 -5.88353693e-01 -4.76459682e-01 -4.54338312e-01 6.09685421e-01 4.00982320e-01 -6.19801998e-01 6.61156952e-01 1.09077394e-01 -2.82314438e-02 -4.38524425e-01 -3.27986300e-01 -3.79679292e-01 -5.55458009e-01 -2.87160724e-01 8.22209418e-01 -2.13367581e-01 1.48655221e-01 4.33936298e-01 3.21880907e-01 -1.62026608e+00 7.62265772e-02 6.25418603e-01 8.90712917e-01 1.60798877e-01 5.93644261e-01 1.35690004e-01 1.51109412e-01 -4.23338503e-01 -4.10161138e-01 5.75863710e-03 -2.78302044e-01 -4.10894841e-01 -8.37022737e-02 2.91440934e-01 -3.61236781e-01 -4.34981212e-02 1.02569377e+00 7.98094720e-02 5.15929043e-01 1.75940108e+00 -1.68690193e+00 -1.20187712e+00 4.10512954e-01 -2.50162154e-01 2.77142406e-01 1.84345812e-01 -1.58429909e-02 1.38073802e+00 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907553f5-a97d-47e3-8f2c-aff45305afce	closed-form-sample-probing-for-learning	null	null	https://openreview.net/forum?id=ljxWpdBl4V	https://openreview.net/pdf?id=ljxWpdBl4V	Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning	Generative model based approaches have led to significant advances in zero-shot learning (ZSL) over the past few-years. These approaches typically aim to learn a conditional generator that synthesizes training samples of classes conditioned on class definitions. The final zero-shot learning model is then obtained by training a supervised classification model over the real and/or synthesized training samples of seen and unseen classes, combined. Therefore, naturally, the generative model needs to produce not only relevant samples, but also those that are sufficiently rich for classifier training purposes, which is handled by various heuristics in existing works. In this paper, we introduce a principled approach for training generative models directly for training data generation purposes. Our main observation is that the use of closed-form models opens doors to end-to-end training thanks to differentiability of the solvers. In our approach, at each generative model update step, we fit a task-specific closed-form ZSL model from generated samples, and measure its loss on novel samples all within the compute graph, a procedure that we refer to as sample probing. In this manner, the generator receives feedback directly based on the value of its samples for model training purposes. Our experimental results show that the proposed sample probing approach improves the ZSL results even when integrated into state-of-the-art generative models.	['Ramazan Gokberk Cinbis', 'Orhun Buğra Baran', 'Samet Cetin']	2021-09-29	null	null	null	iclr-2022-4	['sample-probing']	['computer-vision']	[ 5.39010108e-01 5.20138681e-01 -1.31307110e-01 -3.08173597e-01 -8.82119060e-01 -2.28113472e-01 7.95155048e-01 1.21077202e-01 -8.80976245e-02 7.75342405e-01 -2.95629889e-01 1.27584010e-01 -7.57442191e-02 -1.33370745e+00 -7.74228573e-01 -8.34996819e-01 1.30897701e-01 9.12631750e-01 2.65882522e-01 -6.55949786e-02 -5.58276195e-03 3.24700415e-01 -1.82015240e+00 1.38757512e-01 8.67764175e-01 8.22059453e-01 1.80740923e-01 6.87185049e-01 -1.80367902e-01 5.95975399e-01 -5.50787389e-01 -3.65424037e-01 6.78223372e-02 -9.23608482e-01 -4.76126671e-01 4.75982100e-01 1.42921031e-01 -4.40739281e-02 6.53224289e-02 9.12284255e-01 4.90167171e-01 4.56936985e-01 8.26756597e-01 -1.31779158e+00 -2.94961542e-01 7.37176299e-01 -1.64343432e-01 -2.32726261e-01 9.31734443e-02 3.85130614e-01 9.78916168e-01 -1.05537903e+00 8.27346444e-01 1.10428154e+00 3.59978795e-01 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e2f68915-91a9-46ff-aa36-f3fcc19ba314	evaluating-voice-conversion-based-privacy	1911.03934	null	https://arxiv.org/abs/1911.03934v2	https://arxiv.org/pdf/1911.03934v2.pdf	Evaluating Voice Conversion-based Privacy Protection against Informed Attackers	Speech data conveys sensitive speaker attributes like identity or accent. With a small amount of found data, such attributes can be inferred and exploited for malicious purposes: voice cloning, spoofing, etc. Anonymization aims to make the data unlinkable, i.e., ensure that no utterance can be linked to its original speaker. In this paper, we investigate anonymization methods based on voice conversion. In contrast to prior work, we argue that various linkage attacks can be designed depending on the attackers' knowledge about the anonymization scheme. We compare two frequency warping-based conversion methods and a deep learning based method in three attack scenarios. The utility of converted speech is measured via the word error rate achieved by automatic speech recognition, while privacy protection is assessed by the increase in equal error rate achieved by state-of-the-art i-vector or x-vector based speaker verification. Our results show that voice conversion schemes are unable to effectively protect against an attacker that has extensive knowledge of the type of conversion and how it has been applied, but may provide some protection against less knowledgeable attackers.	['Aurélien Bellet', 'Brij Mohan Lal Srivastava', 'Marc Tommasi', 'Nathalie Vauquier', 'Emmanuel Vincent', 'Md Sahidullah']	2019-11-10	null	null	null	null	['voice-cloning']	['speech']	[ 2.07278565e-01 4.78775352e-01 -1.10824838e-01 -4.25818861e-01 -9.21367586e-01 -1.13308287e+00 6.57996774e-01 3.04196119e-01 -4.73975420e-01 6.94457114e-01 4.70098317e-01 -4.74478394e-01 5.37841721e-03 -6.81664705e-01 -5.37036479e-01 -6.36539578e-01 5.86347319e-02 2.88701206e-01 -1.28502950e-01 -1.59959331e-01 -5.14439605e-02 8.20368111e-01 -1.09116757e+00 1.20801046e-01 4.22194421e-01 6.98510110e-01 -8.20608735e-01 6.69894695e-01 3.00546438e-02 3.25316846e-01 -1.09856129e+00 -9.93761599e-01 3.80276769e-01 -2.90540993e-01 -8.99158120e-01 -5.28018832e-01 2.48154297e-01 -2.99120605e-01 -5.52650809e-01 1.33153820e+00 7.58010209e-01 -2.53026724e-01 2.58697778e-01 -1.59160376e+00 -3.31815779e-01 9.00354326e-01 2.74925381e-02 -5.85744828e-02 6.33691907e-01 -5.22960648e-02 6.99391603e-01 -2.99672872e-01 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-1.88153297e-01 -6.84810817e-01 -4.22543883e-01 -7.83760726e-01 3.83092910e-01 6.38625562e-01 -2.36630753e-01 -5.95708668e-01 3.99273969e-02]	[13.964839935302734, 5.853367328643799]
eb73616c-20cb-4d19-af58-598539b0b827	cloud-removal-in-sentinel-2-imagery-using-a	null	null	https://www.sciencedirect.com/science/article/pii/S0924271620301398	https://www.sciencedirect.com/science/article/pii/S0924271620301398/pdfft?md5=c3ef69f32f9689b0417434f7c38eb4b2&pid=1-s2.0-S0924271620301398-main.pdf	Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion	Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessment, land-cover and land-use classification, and disaster assessment. However, one main problem severely affects the temporal and spatial availability of surface observations, namely cloud cover. The task of removing clouds from optical images has been subject of studies since decades. The advent of the Big Data era in satellite remote sensing opens new possibilities for tackling the problem using powerful data-driven deep learning methods. In this paper, a deep residual neural network architecture is designed to remove clouds from multispectral Sentinel-2 imagery. SAR-optical data fusion is used to exploit the synergistic properties of the two imaging systems to guide the image reconstruction. Additionally, a novel cloud-adaptive loss is proposed to maximize the retainment of original information. The network is trained and tested on a globally sampled dataset comprising real cloudy and cloud-free images. The proposed setup allows to remove even optically thick clouds by reconstructing an optical representation of the underlying land surface structure.	['Michael Schmitt', 'Xiao Xiang Zhu', 'Patrick Ebel', 'Andrea Meraner']	2020-07-02	null	null	null	null	['cloud-removal']	['computer-vision']	[ 4.71084327e-01 -4.73474801e-01 8.14419016e-02 -3.29089075e-01 -6.40374064e-01 -4.93070126e-01 3.23147863e-01 -2.52815634e-02 -4.32970673e-01 9.13962603e-01 -1.98687375e-01 -2.18128845e-01 -2.39703313e-01 -9.54024136e-01 -4.12903249e-01 -1.19491911e+00 -6.55591190e-02 7.97862411e-02 -2.19104260e-01 -3.41908455e-01 -7.89360255e-02 9.58531976e-01 -1.83900964e+00 1.08119704e-01 1.33337533e+00 1.10594571e+00 8.06372881e-01 2.24697456e-01 -1.43651530e-01 3.15118700e-01 1.44138038e-01 3.16555917e-01 6.03910685e-01 -9.78531316e-02 -3.13329905e-01 5.26647210e-01 6.14985943e-01 -2.94649005e-01 1.57425627e-02 1.44915938e+00 3.16516936e-01 7.31535852e-02 2.02724516e-01 -6.37759149e-01 -1.94633126e-01 -5.24732322e-02 -7.01936543e-01 5.62334573e-03 -5.72483182e-01 7.84462467e-02 7.63905168e-01 -8.97229373e-01 3.90346766e-01 7.79061377e-01 4.05292481e-01 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d3aa49dd-f2eb-4597-a78b-004bfc781a14	nscgcn-a-novel-deep-gcn-model-to-diagnosis	null	null	https://www.sciencedirect.com/science/article/pii/S0010482522008599	https://www.sciencedirect.com/sdfe/reader/pii/S0010482522008599/pdf	NSCGCN: A novel deep GCN model to diagnosis COVID-19	Aim Corona Virus Disease 2019 (COVID-19) was a lung disease with high mortality and was highly contagious. Early diagnosis of COVID-19 and distinguishing it from pneumonia was beneficial for subsequent treatment. Objectives Recently, Graph Convolutional Network (GCN) has driven a significant contribution to disease diagnosis. However, limited by the nature of the graph convolution algorithm, deep GCN has an over-smoothing problem. Most of the current GCN models are shallow neural networks, which do not exceed five layers. Furthermore, the objective of this study is to develop a novel deep GCN model based on the DenseGCN and the pre-trained model of deep Convolutional Neural Network (CNN) to complete the diagnosis of chest X-ray (CXR) images. Methods We apply the pre-trained model of deep CNN to perform feature extraction on the data to complete the extraction of pixel-level features in the image. And then, to extract the potential relationship between the obtained features, we propose Neighbourhood Feature Reconstruction Algorithm to reconstruct them into graph-structured data. Finally, we design a deep GCN model that exploits the graph-structured data to diagnose COVID-19 effectively. In the deep GCN model, we propose a Node-Self Convolution Algorithm (NSC) based on feature fusion to construct a deep GCN model called NSCGCN (Node-Self Convolution Graph Convolutional Network). Results Experiments were carried out on the Computed Tomography (CT) and CXR datasets. The results on the CT dataset confirmed that: compared with the six state-of-the-art (SOTA) shallow GCN models, the accuracy and sensitivity of the proposed NSCGCN had improve 8% as sensitivity (Sen.) = 87.50%, F1 score = 97.37%, precision (Pre.) = 89.10%, accuracy (Acc.) = 97.50%, area under the ROC curve (AUC) = 97.09%. Moreover, the results on the CXR dataset confirmed that: compared with the fourteen SOTA GCN models, sixteen SOTA CNN transfer learning models and eight SOTA COVID-19 diagnosis methods on the COVID-19 dataset. Our proposed method had best performances as Sen. = 96.45%, F1 score = 96.45%, Pre. = 96.61%, Acc. = 96.45%, AUC = 99.22%. Conclusion Our proposed NSCGCN model is effective and performed better than the thirty-eight SOTA methods. Thus, the proposed NSC could help build deep GCN models. Our proposed COVID-19 diagnosis method based on the NSCGCN model could help radiologists detect pneumonia from CXR images and distinguish COVID-19 from Ordinary Pneumonia (OPN).	['Yu-Dong Zhang', 'Shui-Hua Wang', 'Junding Sun', 'Chaochao Hu', 'Chaosheng Tang']	2022-10-13	null	null	null	computers-in-biology-and-medicine-2022-10	['covid-19-detection']	['medical']	[ 4.44844663e-02 -1.75419360e-01 9.27105993e-02 -9.44342185e-03 -9.11139175e-02 -1.93773843e-02 2.40272477e-01 -8.25850517e-02 -2.58322120e-01 7.09352612e-01 3.13483626e-02 -6.23280764e-01 -3.70404243e-01 -1.07980812e+00 -3.76954198e-01 -7.86573946e-01 -3.08760285e-01 5.02033532e-01 1.91280782e-01 2.61441857e-01 -2.46247560e-01 7.41301537e-01 -1.17802930e+00 4.16640103e-01 9.03868556e-01 9.51953053e-01 4.51267749e-01 1.03088641e+00 7.25392848e-02 8.57802629e-01 -2.34027088e-01 2.76751183e-02 -5.86640649e-02 -4.68659788e-01 -6.86578989e-01 -1.87966406e-01 4.53222096e-02 -4.83285218e-01 -4.23071861e-01 7.66120315e-01 6.75599217e-01 -2.65673786e-01 8.88855696e-01 -1.12832308e+00 -5.32251358e-01 1.61296040e-01 -3.20823193e-01 4.23852444e-01 -2.53157675e-01 7.30821788e-02 4.25637782e-01 -6.72589719e-01 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f8f297cd-85dc-428c-86e1-515b4808853d	sb-mtl-score-based-meta-transfer-learning-for	2012.01784	null	https://arxiv.org/abs/2012.01784v1	https://arxiv.org/pdf/2012.01784v1.pdf	SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning	While many deep learning methods have seen significant success in tackling the problem of domain adaptation and few-shot learning separately, far fewer methods are able to jointly tackle both problems in Cross-Domain Few-Shot Learning (CD-FSL). This problem is exacerbated under sharp domain shifts that typify common computer vision applications. In this paper, we present a novel, flexible and effective method to address the CD-FSL problem. Our method, called Score-based Meta Transfer-Learning (SB-MTL), combines transfer-learning and meta-learning by using a MAML-optimized feature encoder and a score-based Graph Neural Network. First, we have a feature encoder with specific layers designed to be fine-tuned. To do so, we apply a first-order MAML algorithm to find good initializations. Second, instead of directly taking the classification scores after fine-tuning, we interpret the scores as coordinates by mapping the pre-softmax classification scores onto a metric space. Subsequently, we apply a Graph Neural Network to propagate label information from the support set to the query set in our score-based metric space. We test our model on the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, which includes a range of target domains with highly varying dissimilarity to the miniImagenet source domain. We observe significant improvements in accuracy across 5, 20 and 50 shot, and on the four target domains. In terms of average accuracy, our model outperforms previous transfer-learning methods by 5.93% and previous meta-learning methods by 14.28%.	['Sheng Mei Shen', 'Bill Cai', 'John Cai']	2020-12-03	null	null	null	null	['cross-domain-few-shot', 'cross-domain-few-shot-learning']	['computer-vision', 'computer-vision']	[ 4.30496633e-01 -7.94816241e-02 -2.09935606e-01 -5.47486365e-01 -1.18146670e+00 -3.26259881e-01 6.96998000e-01 -4.55265790e-02 -5.43661296e-01 5.94955325e-01 1.06156081e-01 2.17205733e-01 -2.94118464e-01 -7.65180469e-01 -6.52949989e-01 -4.57099199e-01 -8.47224668e-02 5.64527869e-01 6.56164408e-01 -4.36132491e-01 1.49480090e-01 4.99136820e-02 -1.30307615e+00 3.98930669e-01 6.33727610e-01 8.86917174e-01 1.91772416e-01 5.69455385e-01 1.22327834e-01 7.41923153e-01 -5.64086080e-01 -3.41938585e-01 2.03190699e-01 -6.68928623e-01 -8.71454537e-01 3.33747342e-02 4.26190168e-01 -1.74268618e-01 -3.10339957e-01 1.11071193e+00 6.43197715e-01 5.67825198e-01 9.24329400e-01 -1.22252893e+00 -8.08138669e-01 2.82867998e-01 -6.72689140e-01 2.63182759e-01 2.24187493e-01 1.81834787e-01 1.06098676e+00 -9.87724960e-01 9.70226586e-01 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c8869d56-2791-45a7-a626-36963381c3c6	how-do-graph-networks-generalize-to-large-and	2204.02782	null	https://arxiv.org/abs/2204.02782v3	https://arxiv.org/pdf/2204.02782v3.pdf	GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets	Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse. These new datasets differ substantially in four aspects of complexity: 1. Chemical diversity (number of different elements), 2. system size (number of atoms per sample), 3. dataset size (number of data samples), and 4. domain shift (similarity of the training and test set). Despite these large differences, benchmarks on small and narrow datasets remain the predominant method of demonstrating progress in graph neural networks (GNNs) for molecular simulation, likely due to cheaper training compute requirements. This raises the question -- does GNN progress on small and narrow datasets translate to these more complex datasets? This work investigates this question by first developing the GemNet-OC model based on the large Open Catalyst 2020 (OC20) dataset. GemNet-OC outperforms the previous state-of-the-art on OC20 by 16% while reducing training time by a factor of 10. We then compare the impact of 18 model components and hyperparameter choices on performance in multiple datasets. We find that the resulting model would be drastically different depending on the dataset used for making model choices. To isolate the source of this discrepancy we study six subsets of the OC20 dataset that individually test each of the above-mentioned four dataset aspects. We find that results on the OC-2M subset correlate well with the full OC20 dataset while being substantially cheaper to train on. Our findings challenge the common practice of developing GNNs solely on small datasets, but highlight ways of achieving fast development cycles and generalizable results via moderately-sized, representative datasets such as OC-2M and efficient models such as GemNet-OC. Our code and pretrained model weights are open-sourced.	['Abhishek Das', 'C. Lawrence Zitnick', 'Zachary Ulissi', 'Stephan Günnemann', 'Anuroop Sriram', 'Muhammed Shuaibi', 'Johannes Gasteiger']	2022-04-06	null	null	null	null	['initial-structure-to-relaxed-energy-is2re']	['graphs']	[ 1.89137340e-01 -1.43220380e-01 -4.72120404e-01 8.79260153e-02 -4.76322025e-01 -8.46292615e-01 7.95574188e-01 3.07252675e-01 -5.90503037e-01 1.11368084e+00 -3.65660004e-02 -1.09222364e+00 -2.65860707e-01 -9.03665543e-01 -7.88822174e-01 -5.88977993e-01 -1.75423622e-01 6.64505482e-01 3.09870958e-01 -3.87834787e-01 2.73673624e-01 7.97991872e-01 -1.16905344e+00 4.60414141e-02 7.64485717e-01 3.86680633e-01 1.51578918e-01 5.35172045e-01 2.88062364e-01 4.80676055e-01 -4.19452727e-01 -3.65177765e-02 3.85061771e-01 -5.93488395e-01 -8.68713319e-01 -4.99768734e-01 4.75847214e-01 2.14976266e-01 -4.37495351e-01 7.47579932e-01 8.28093648e-01 2.39595532e-01 4.67231452e-01 -1.08598208e+00 -8.47887248e-02 6.44854724e-01 -1.81806043e-01 9.98586416e-02 1.06727704e-01 7.64149308e-01 7.42499352e-01 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6b306639-e948-4bb4-a7c2-6f37baf0ce3e	self-supervised-learning-of-depth-and-camera	1811.05304	null	http://arxiv.org/abs/1811.05304v1	http://arxiv.org/pdf/1811.05304v1.pdf	Self-Supervised Learning of Depth and Camera Motion from 360° Videos	As 360{\deg} cameras become prevalent in many autonomous systems (e.g., self-driving cars and drones), efficient 360{\deg} perception becomes more and more important. We propose a novel self-supervised learning approach for predicting the omnidirectional depth and camera motion from a 360{\deg} video. In particular, starting from the SfMLearner, which is designed for cameras with normal field-of-view, we introduce three key features to process 360{\deg} images efficiently. Firstly, we convert each image from equirectangular projection to cubic projection in order to avoid image distortion. In each network layer, we use Cube Padding (CP), which pads intermediate features from adjacent faces, to avoid image boundaries. Secondly, we propose a novel "spherical" photometric consistency constraint on the whole viewing sphere. In this way, no pixel will be projected outside the image boundary which typically happens in images with normal field-of-view. Finally, rather than naively estimating six independent camera motions (i.e., naively applying SfM-Learner to each face on a cube), we propose a novel camera pose consistency loss to ensure the estimated camera motions reaching consensus. To train and evaluate our approach, we collect a new PanoSUNCG dataset containing a large amount of 360{\deg} videos with groundtruth depth and camera motion. Our approach achieves state-of-the-art depth prediction and camera motion estimation on PanoSUNCG with faster inference speed comparing to equirectangular. In real-world indoor videos, our approach can also achieve qualitatively reasonable depth prediction by acquiring model pre-trained on PanoSUNCG.	['Hung-Kuo Chu', 'Meng-Li Shih', 'Shang-Ta Yang', 'Juan-Ting Lin', 'Hsien-Tzu Cheng', 'Hou-Ning Hu', 'Fu-En Wang', 'Min Sun']	2018-11-13	null	null	null	null	['depth-and-camera-motion']	['computer-vision']	[-7.32013062e-02 2.79886961e-01 -3.72601375e-02 -4.62089658e-01 -4.45598274e-01 -7.65017450e-01 4.28552926e-01 -7.21281767e-01 -5.17639160e-01 3.14445049e-01 -2.63833348e-02 -1.93732709e-01 2.01831654e-01 -7.22209752e-01 -1.35313952e+00 -6.91036105e-01 2.59474188e-01 3.09507221e-01 2.13905245e-01 4.75181490e-02 1.12303965e-01 4.33112472e-01 -1.54480052e+00 -7.53631368e-02 7.46180296e-01 1.06156754e+00 4.08253551e-01 7.58948386e-01 5.62249541e-01 6.62307799e-01 -1.21404938e-01 -4.65743989e-01 6.12027109e-01 2.82544736e-02 -4.61658895e-01 5.72493374e-01 1.16587400e+00 -7.57242918e-01 -6.52124822e-01 1.13082719e+00 4.18012440e-02 1.34788409e-01 4.96634334e-01 -1.15526819e+00 -6.01250082e-02 -3.64743657e-02 -7.97866940e-01 -1.65572971e-01 5.23896337e-01 2.81442076e-01 7.38193572e-01 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2237eaf8-87bd-43b8-91ce-d6a9e91584f4	beike-nlp-at-semeval-2022-task-4-prompt-based-1	2208.01312	null	https://arxiv.org/abs/2208.01312v1	https://arxiv.org/pdf/2208.01312v1.pdf	BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection	PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media.Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recognized, making the performance of common text-classification approaches disappointed. Targeting the PCL detection problem in SemEval-2022 Task 4, in this paper, we give an introduction to our team's solution, which exploits the power of prompt-based learning on paragraph classification. We reformulate the task as an appropriate cloze prompt and use pre-trained Masked Language Models to fill the cloze slot. For the two subtasks, binary classification and multi-label classification, DeBERTa model is adopted and fine-tuned to predict masked label words of task-specific prompts. On the evaluation dataset, for binary classification, our approach achieves an F1-score of 0.6406; for multi-label classification, our approach achieves an macro-F1-score of 0.4689 and ranks first in the leaderboard.	['Xiangang Li', 'Baochang Ma', 'Xianghui Sun', 'Deqiang Miao', 'Liangyu Chen', 'Chenxiao Dou', 'Yong Deng']	2022-08-02	beike-nlp-at-semeval-2022-task-4-prompt-based	https://aclanthology.org/2022.semeval-1.41	https://aclanthology.org/2022.semeval-1.41.pdf	semeval-naacl-2022-7	['semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-2-multi-label-pcl', 'semeval-2022-task-4-1-binary-pcl-detection']	['miscellaneous', 'music', 'natural-language-processing', 'natural-language-processing']	[ 2.20392123e-01 1.95721805e-01 -5.67728996e-01 -1.95701078e-01 -1.60550797e+00 -8.45207393e-01 8.12946439e-01 9.27541852e-01 -3.62809032e-01 4.50553358e-01 4.93322194e-01 -7.48919010e-01 2.04187125e-01 -1.67780414e-01 -8.81965980e-02 -5.28274655e-01 1.99488699e-01 2.20296681e-01 1.63867354e-01 1.34091422e-01 6.81802392e-01 1.08675264e-01 -1.09958661e+00 1.12127018e+00 6.63975298e-01 8.10931981e-01 -5.08069620e-02 8.15143228e-01 -3.62434924e-01 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e2c8fe37-0a00-4e53-bd53-ed9cd54b2292	controllable-neural-story-plot-generation-via	1809.10736	null	https://arxiv.org/abs/1809.10736v4	https://arxiv.org/pdf/1809.10736v4.pdf	Controllable Neural Story Plot Generation via Reward Shaping	Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.	['Pradyumna Tambwekar', 'Lara J. Martin', 'Brent Harrison', 'Mark O. Riedl', 'Murtaza Dhuliawala', 'Animesh Mehta']	2018-09-27	null	null	null	null	['story-completion']	['natural-language-processing']	[ 4.49991733e-01 5.68935096e-01 -4.24895883e-01 -3.91753465e-01 -9.93456244e-01 -5.82996726e-01 1.02644527e+00 4.28434461e-01 1.49818197e-01 9.58874583e-01 1.08989573e+00 -3.77880991e-01 3.08967203e-01 -8.91309261e-01 -5.63182473e-01 1.27103252e-04 7.85620809e-02 4.73795682e-01 2.19612837e-01 -3.41711164e-01 5.12439847e-01 -5.06764166e-02 -1.00735557e+00 8.82178843e-01 5.86520791e-01 -1.47494487e-02 4.40038174e-01 9.04423058e-01 -2.97202438e-01 1.61236775e+00 -9.03153837e-01 -2.45428070e-01 -3.27685118e-01 -1.29528749e+00 -8.99214208e-01 1.87261388e-01 -1.97912827e-01 -3.14440042e-01 -2.56924778e-02 5.06572306e-01 1.27047136e-01 1.99444011e-01 9.02245224e-01 -1.03657401e+00 -6.30948484e-01 1.41929519e+00 -5.11349738e-01 2.19531432e-02 8.07810366e-01 1.02699414e-01 1.08606660e+00 -5.50169468e-01 1.21933675e+00 1.20418489e+00 4.34717298e-01 6.09862745e-01 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1.80684760e-01 -4.52101454e-02 -3.01371980e-02 2.61702001e-01 4.27635550e-01]	[11.658209800720215, 8.84457015991211]
605de7f6-8657-4d0d-96ed-e83595a3e923	zeroq-a-novel-zero-shot-quantization	2001.00281	null	https://arxiv.org/abs/2001.00281v1	https://arxiv.org/pdf/2001.00281v1.pdf	ZeroQ: A Novel Zero Shot Quantization Framework	Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the original training dataset for retraining during quantization. This is often not possible for applications with sensitive or proprietary data, e.g., due to privacy and security concerns. Existing zero-shot quantization methods use different heuristics to address this, but they result in poor performance, especially when quantizing to ultra-low precision. Here, we propose ZeroQ , a novel zero-shot quantization framework to address this. ZeroQ enables mixed-precision quantization without any access to the training or validation data. This is achieved by optimizing for a Distilled Dataset, which is engineered to match the statistics of batch normalization across different layers of the network. ZeroQ supports both uniform and mixed-precision quantization. For the latter, we introduce a novel Pareto frontier based method to automatically determine the mixed-precision bit setting for all layers, with no manual search involved. We extensively test our proposed method on a diverse set of models, including ResNet18/50/152, MobileNetV2, ShuffleNet, SqueezeNext, and InceptionV3 on ImageNet, as well as RetinaNet-ResNet50 on the Microsoft COCO dataset. In particular, we show that ZeroQ can achieve 1.71\% higher accuracy on MobileNetV2, as compared to the recently proposed DFQ method. Importantly, ZeroQ has a very low computational overhead, and it can finish the entire quantization process in less than 30s (0.5\% of one epoch training time of ResNet50 on ImageNet). We have open-sourced the ZeroQ framework\footnote{https://github.com/amirgholami/ZeroQ}.	['Zhen Dong', 'Kurt Keutzer', 'Yaohui Cai', 'Michael W. Mahoney', 'Zhewei Yao', 'Amir Gholami']	2020-01-01	zeroq-a-novel-zero-shot-quantization-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Cai_ZeroQ_A_Novel_Zero_Shot_Quantization_Framework_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Cai_ZeroQ_A_Novel_Zero_Shot_Quantization_Framework_CVPR_2020_paper.pdf	cvpr-2020-6	['data-free-quantization', 'data-free-quantization']	['computer-vision', 'methodology']	[ 8.95623565e-02 -2.98955739e-01 -1.67647272e-01 -3.26716870e-01 -8.19751322e-01 -5.82074761e-01 2.27028430e-01 1.63934812e-01 -1.00290644e+00 7.21517384e-01 -2.32782468e-01 -5.15233576e-01 -1.52716860e-01 -8.07204604e-01 -8.29740107e-01 -5.98783672e-01 2.16367930e-01 -8.50741789e-02 3.72517616e-01 -8.70910287e-02 3.53103548e-01 2.46203274e-01 -1.70642889e+00 -1.87367052e-02 8.08388889e-01 1.40694499e+00 2.32685104e-01 7.18296051e-01 4.49982882e-02 4.67832267e-01 -7.45515406e-01 -7.33475626e-01 4.74868655e-01 8.39292929e-02 -5.62851191e-01 -3.50608736e-01 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203c4de6-e628-4915-a695-967b67435b97	x2teeth-3d-teeth-reconstruction-from-a-single	2108.13004	null	https://arxiv.org/abs/2108.13004v1	https://arxiv.org/pdf/2108.13004v1.pdf	X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph	3D teeth reconstruction from X-ray is important for dental diagnosis and many clinical operations. However, no existing work has explored the reconstruction of teeth for a whole cavity from a single panoramic radiograph. Different from single object reconstruction from photos, this task has the unique challenge of constructing multiple objects at high resolutions. To conquer this task, we develop a novel ConvNet X2Teeth that decomposes the task into teeth localization and single-shape estimation. We also introduce a patch-based training strategy, such that X2Teeth can be end-to-end trained for optimal performance. Extensive experiments show that our method can successfully estimate the 3D structure of the cavity and reflect the details for each tooth. Moreover, X2Teeth achieves a reconstruction IoU of 0.681, which significantly outperforms the encoder-decoder method by $1.71X and the retrieval-based method by $1.52X. Our method can also be promising for other multi-anatomy 3D reconstruction tasks.	['Lei He', 'Kun Wang', 'Liang Qiu', 'Jiawei Yang', 'Weinan Song', 'Yuan Liang']	2021-08-30	null	null	null	null	['object-reconstruction']	['computer-vision']	[ 1.13084584e-01 5.71997464e-01 -1.20871753e-01 -5.83279550e-01 -1.63082337e+00 3.08271617e-01 6.45340979e-02 -1.09435022e-01 -1.24460116e-01 3.28014940e-01 2.89055645e-01 -1.38899654e-01 1.77697062e-01 -7.66346216e-01 -1.01064491e+00 -5.94333053e-01 2.27223486e-01 5.15194058e-01 1.87919557e-01 2.20095500e-01 5.16006090e-02 4.42332357e-01 -1.54176760e+00 1.98292941e-01 4.52935398e-01 9.91991639e-01 8.10503662e-01 3.91948014e-01 -1.32638589e-01 6.16490364e-01 -3.58314633e-01 -3.05435717e-01 -1.35787860e-01 -9.56025049e-02 -6.96448803e-01 3.85224938e-01 9.17393208e-01 -9.75584447e-01 -3.63090247e-01 9.03460801e-01 8.88639510e-01 -4.91766632e-01 4.38314825e-01 -3.18075508e-01 -7.54015267e-01 3.76542062e-01 -7.87049890e-01 1.66288409e-02 3.39015543e-01 -6.42901957e-02 7.63497531e-01 -9.71928835e-01 6.49415314e-01 1.59532213e+00 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c773dbda-e279-42cf-be4c-3c8494e626e2	deep-manifold-learning-reveals-hidden	2012.12854	null	https://arxiv.org/abs/2012.12854v2	https://arxiv.org/pdf/2012.12854v2.pdf	Deep manifold learning reveals hidden dynamics of proteasome autoregulation	The 2.5-MDa 26S proteasome maintains proteostasis and regulates myriad cellular processes. How polyubiquitylated substrate interactions regulate proteasome activity is not understood. Here we introduce a deep manifold learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy (cryo-EM) reconstructions of nonequilibrium conformational continuum and reconstitutes hidden dynamics of proteasome autoregulation in the act of substrate degradation. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of free-energy landscapes, which directs 3D clustering via an energy-based particle-voting algorithm. In blind assessments using simulated heterogeneous cryo-EM datasets, AlphaCryo4D achieved 3D classification accuracy three times that of conventional method and reconstructed continuous conformational changes of a 130-kDa protein at sub-3-angstrom resolution. By using AlphaCryo4D to analyze a single experimental cryo-EM dataset, we identified 64 conformers of the substrate-bound human 26S proteasome, revealing conformational entanglement of two regulatory particles in the doubly capped holoenzymes and their energetic differences with singly capped ones. Novel ubiquitin-binding sites are discovered on the RPN2, RPN10 and Alpha5 subunits to remodel polyubiquitin chains for deubiquitylation and recycle. Importantly, AlphaCryo4D choreographs single-nucleotide-exchange dynamics of proteasomal AAA-ATPase motor during translocation initiation, which upregulates proteolytic activity by allosterically promoting nucleophilic attack. Our systemic analysis illuminates a grand hierarchical allostery for proteasome autoregulation.	['Youdong Mao', 'Yuanchen Dong', 'Yinping Ma', 'Wei Li Wang', 'Shuwen Zhang', 'Zhaolong Wu']	2020-12-23	null	null	null	null	['3d-classification', 'cryogenic-electron-microscopy-cryo-em']	['computer-vision', 'computer-vision']	[-1.91319004e-01 -2.98360020e-01 -4.45569068e-01 2.51511514e-01 -6.25943124e-01 -1.11226869e+00 4.51734662e-01 -9.31214839e-02 -5.44286907e-01 1.20356309e+00 4.78162497e-01 -6.32251501e-01 -4.82978821e-02 -2.50013262e-01 -9.06937778e-01 -1.25985968e+00 -1.77233405e-02 8.62818480e-01 -1.94933951e-01 2.28857435e-02 3.97136696e-02 7.93862939e-01 -9.72415507e-01 4.89502579e-01 4.84199286e-01 1.64246947e-01 2.51340806e-01 9.07421052e-01 8.10906813e-02 8.36944208e-02 -3.34956460e-02 -3.60506661e-02 1.00203305e-01 -4.26336646e-01 -4.29653853e-01 -1.54375359e-01 1.22625731e-01 2.99448217e-03 -1.46441013e-01 3.82104605e-01 6.55937552e-01 -2.66604513e-01 1.01790082e+00 -4.35153931e-01 -6.99067473e-01 -2.51767665e-01 -3.25204670e-01 5.29123187e-01 2.74347633e-01 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702e46ab-b7d4-4af4-94bd-81faf40529e0	stepsize-learning-for-policy-gradient-methods	2306.07741	null	https://arxiv.org/abs/2306.07741v1	https://arxiv.org/pdf/2306.07741v1.pdf	Stepsize Learning for Policy Gradient Methods in Contextual Markov Decision Processes	Policy-based algorithms are among the most widely adopted techniques in model-free RL, thanks to their strong theoretical groundings and good properties in continuous action spaces. Unfortunately, these methods require precise and problem-specific hyperparameter tuning to achieve good performance, and tend to struggle when asked to accomplish a series of heterogeneous tasks. In particular, the selection of the step size has a crucial impact on their ability to learn a highly performing policy, affecting the speed and the stability of the training process, and often being the main culprit for poor results. In this paper, we tackle these issues with a Meta Reinforcement Learning approach, by introducing a new formulation, known as meta-MDP, that can be used to solve any hyperparameter selection problem in RL with contextual processes. After providing a theoretical Lipschitz bound to the difference of performance in different tasks, we adopt the proposed framework to train a batch RL algorithm to dynamically recommend the most adequate step size for different policies and tasks. In conclusion, we present an experimental campaign to show the advantages of selecting an adaptive learning rate in heterogeneous environments.	['Marcello Restelli', 'Francesco Corda', 'Luca Sabbioni']	2023-06-13	null	null	null	null	['policy-gradient-methods']	['methodology']	[ 2.03753784e-01 -2.17967644e-01 -3.80307555e-01 4.61950898e-02 -7.86406338e-01 -3.88230234e-01 6.95686817e-01 1.81421533e-01 -9.24738646e-01 1.07631481e+00 -2.14161113e-01 -6.49674162e-02 -5.21457195e-01 -4.44978267e-01 -4.50683177e-01 -1.12523639e+00 1.96381271e-01 5.67655802e-01 2.28741854e-01 -2.68838227e-01 5.61543643e-01 3.47303838e-01 -1.75146580e+00 -3.23862225e-01 9.42542851e-01 9.09096301e-01 6.38532162e-01 5.04785180e-01 -1.06023803e-01 7.42171168e-01 -7.43379891e-01 -1.29160443e-02 1.50430754e-01 -6.01114333e-01 -5.04602432e-01 -8.84711370e-03 -2.23209783e-01 5.37687503e-02 4.68003303e-01 8.70064855e-01 8.64381135e-01 6.49473846e-01 7.76410401e-01 -9.61965322e-01 3.65374625e-01 5.75186491e-01 -3.91998172e-01 1.88951120e-01 1.79538399e-01 1.26206309e-01 9.19677734e-01 -2.00433493e-01 4.92123038e-01 1.17017460e+00 6.93432614e-02 6.99027538e-01 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-4.80268151e-01 2.70250827e-01 1.15924798e-01 -1.02544582e+00 -3.29833210e-01 -6.05856419e-01]	[4.338506698608398, 2.3747875690460205]
3eb5d70b-c78d-403f-a781-acc73ca604bb	revisiting-model-self-interpretability-in-a	2303.06876	null	https://arxiv.org/abs/2303.06876v2	https://arxiv.org/pdf/2303.06876v2.pdf	Revisiting model self-interpretability in a decision-theoretic way for binary medical image classification	Interpretability is highly desired for deep neural network-based classifiers, especially when addressing high-stake decisions in medical imaging. Commonly used post-hoc interpretability methods have the limitation that they can produce plausible but different interpretations of a given model, leading to ambiguity about which one to choose. To address this problem, a novel decision-theory-motivated approach is investigated to establish a self-interpretable model, given a pretrained deep binary black-box medical image classifier. This approach involves utilizing a self-interpretable encoder-decoder model in conjunction with a single-layer fully connected network with unity weights. The model is trained to estimate the test statistic of the given trained black-box deep binary classifier to maintain a similar accuracy. The decoder output image, referred to as an equivalency map, is an image that represents a transformed version of the to-be-classified image that, when processed by the fixed fully connected layer, produces the same test statistic value as the original classifier. The equivalency map provides a visualization of the transformed image features that directly contribute to the test statistic value and, moreover, permits quantification of their relative contributions. Unlike the traditional post-hoc interpretability methods, the proposed method is self-interpretable, quantitative, and fundamentally based on decision theory. Detailed quantitative and qualitative analysis have been performed with three different medical image binary classification tasks.	['Mark A. Anastasio', 'Sourya Sengupta']	2023-03-13	null	null	null	null	['unity']	['computer-vision']	[ 6.62089705e-01 8.67893398e-01 -3.87887955e-01 -8.70555997e-01 -4.35595602e-01 -2.82625735e-01 3.30780804e-01 5.39721370e-01 -4.30425823e-01 6.56329930e-01 7.85509050e-02 -6.13893211e-01 -4.29070026e-01 -6.13042235e-01 -5.43097675e-01 -7.64744341e-01 1.52936175e-01 4.92384136e-01 -4.55960870e-01 2.80857086e-01 1.18575357e-01 3.94491285e-01 -1.35570586e+00 6.45086706e-01 9.47224140e-01 1.40949082e+00 -1.43671036e-01 5.87858558e-01 2.32141197e-01 9.68347073e-01 -6.87986970e-01 -5.85514784e-01 7.32971057e-02 -6.52768314e-01 -1.07542813e+00 7.58935288e-02 3.27993989e-01 -3.88334066e-01 2.22288482e-02 1.35262287e+00 9.70690101e-02 -3.34528357e-01 1.10205519e+00 -1.28520095e+00 -1.00453830e+00 6.66073024e-01 -2.00973868e-01 3.57844621e-01 2.84779407e-02 2.29842842e-01 1.06289625e+00 -5.45181692e-01 4.30823296e-01 1.42540789e+00 4.47886884e-01 3.88672829e-01 -1.37735975e+00 -2.78714120e-01 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5.46839178e-01 -5.78251481e-01 -4.45312232e-01 6.07699640e-02]	[8.842531204223633, 5.458734512329102]
2b36ce19-745f-435a-af69-69960bae6c5d	ji-yu-shuang-bian-ma-qi-de-yi-xue-wen-ben	null	null	https://aclanthology.org/2021.ccl-1.8	https://aclanthology.org/2021.ccl-1.8.pdf	基于双编码器的医学文本中文分词(Chinese word segmentation of medical text based on dual-encoder)	“中文分词是自然语言处理领域的基础工作,然而前人的医学文本分词工作都只是直接套用通用分词的方法,而医学文本多专用术语的特点让分词系统需要对医学专用术语和医学文本中的非医学术语文本提供不同的分词粒度。本文提出了双编码器医学文本中文分词模型,利用辅助编码器为医学专有术语提供粗粒度表示。模型将需要粗粒度分词的医学专用术语和需要通用分词粒度的文本分开,在提升医学专用术语的分词能力的同时最大限度地避免了其粗粒度对于医学文本中通用文本分词的干扰。”	['Baobao Chang', 'Yuan Zong']	null	null	null	null	ccl-2021-8	['chinese-word-segmentation']	['natural-language-processing']	[-6.57347143e-01 -7.17176020e-01 6.60555601e-01 3.30682337e-01 2.03810364e-01 -1.33680212e+00 5.37603684e-02 6.89713895e-01 2.41473407e-01 1.48743248e+00 1.34709001e-01 -1.42886460e-01 -4.89055008e-01 -1.19244766e+00 -1.99777126e-01 -1.16379762e+00 -6.80088252e-02 1.59540975e+00 3.91643345e-01 -2.80248463e-01 7.41792262e-01 8.15490842e-01 -8.94793630e-01 1.87169060e-01 1.00041723e+00 1.08936846e+00 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0ba6846e-0d83-480c-919e-c87ea4f54dd7	attention-based-part-assembly-for-3d	2304.10986	null	https://arxiv.org/abs/2304.10986v1	https://arxiv.org/pdf/2304.10986v1.pdf	Attention-based Part Assembly for 3D Volumetric Shape Modeling	Modeling a 3D volumetric shape as an assembly of decomposed shape parts is much more challenging, but semantically more valuable than direct reconstruction from a full shape representation. The neural network needs to implicitly learn part relations coherently, which is typically performed by dedicated network layers that can generate transformation matrices for each part. In this paper, we propose a VoxAttention network architecture for attention-based part assembly. We further propose a variant of using channel-wise part attention and show the advantages of this approach. Experimental results show that our method outperforms most state-of-the-art methods for the part relation-aware 3D shape modeling task.	['Jürgen Beyerer', 'Julius Pfrommer', 'Junwei Zheng', 'Chengzhi Wu']	2023-04-17	null	null	null	null	['3d-shape-modeling']	['computer-vision']	[-4.72210087e-02 5.30711353e-01 1.52818367e-01 -5.54301739e-01 -4.81656402e-01 -3.79427165e-01 6.59357846e-01 -2.08559304e-01 3.07330132e-01 2.64064401e-01 4.60989952e-01 -1.05662306e-03 8.61642361e-02 -8.78876209e-01 -1.22504282e+00 -2.39014670e-01 3.15815359e-01 1.07327294e+00 8.72892365e-02 -2.56913304e-01 -1.72574624e-01 1.21832049e+00 -1.09241974e+00 3.92918140e-01 4.48730439e-01 1.10534072e+00 7.66209662e-02 2.39259139e-01 -6.44659936e-01 6.93331897e-01 -1.60657540e-01 -5.22483051e-01 4.37360942e-01 -5.25983535e-02 -9.72518325e-01 5.83301127e-01 5.91899872e-01 -5.11405647e-01 -3.29666108e-01 6.69875026e-01 3.49073142e-01 -4.19964902e-02 8.87786806e-01 -9.70149338e-01 -9.38625097e-01 7.44043410e-01 -3.44860524e-01 -3.83631736e-01 -1.29270693e-03 -8.32342729e-02 1.00950515e+00 -1.20329928e+00 7.55109787e-01 1.57130718e+00 7.64149189e-01 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-3.77009697e-02 -9.59018528e-01 -4.07191366e-01 6.61931276e-01 1.38897210e-01 -9.35597360e-01 1.84465691e-01 -1.45730600e-01]	[8.7123384475708, -3.615499496459961]
87657afa-a909-4f2a-a4b3-661f9f3f66e6	toward-a-geometric-theory-of-manifold	2303.04203	null	https://arxiv.org/abs/2303.04203v1	https://arxiv.org/pdf/2303.04203v1.pdf	Toward a Geometric Theory of Manifold Untangling	It has been hypothesized that the ventral stream processing for object recognition is based on a mechanism called cortically local subspace untangling. A mathematical abstraction of object recognition by the visual cortex is how to untangle the manifolds associated with different object category. Such a manifold untangling problem is closely related to the celebrated kernel trick in metric space. In this paper, we conjecture that there is a more general solution to manifold untangling in the topological space without artificially defining any distance metric. Geometrically, we can either $embed$ a manifold in a higher dimensional space to promote selectivity or $flatten$ a manifold to promote tolerance. General strategies of both global manifold embedding and local manifold flattening are presented and connected with existing work on the untangling of image, audio, and language data. We also discuss the implications of untangling the manifold into motor control and internal representations.	['Shuo Wang', 'Xin Li']	2023-03-07	null	null	null	null	['object-recognition']	['computer-vision']	[ 1.81771979e-01 2.61790931e-01 1.46739498e-01 -4.58660126e-01 -3.20134044e-01 -8.10519397e-01 6.25534475e-01 -1.88802123e-01 -5.48637211e-01 1.08366109e-01 2.38955632e-01 -2.20263287e-01 -2.56993949e-01 -4.94471282e-01 -6.72304392e-01 -5.96623838e-01 -2.62217969e-01 -2.28271946e-01 9.57718939e-02 -1.88358855e-02 7.53456056e-01 5.68096995e-01 -1.38637984e+00 3.36489797e-01 4.85662341e-01 6.41512752e-01 1.45180091e-01 8.01728964e-01 -2.57493947e-02 2.13545084e-01 -2.22679377e-01 4.39976417e-02 5.09578526e-01 -7.78399467e-01 -1.01215434e+00 1.52181178e-01 7.69844890e-01 4.72222008e-02 -4.45269257e-01 1.28072369e+00 -2.34224107e-02 1.22069363e-02 9.80206311e-01 -1.22372353e+00 -8.28159750e-01 3.79934013e-01 -3.52595061e-01 4.91630048e-01 7.16763288e-02 -2.97357768e-01 1.03922248e+00 -1.07429135e+00 7.06391692e-01 1.37564039e+00 1.98066652e-01 6.23703539e-01 -1.74077654e+00 -5.18463910e-01 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5e12c3fd-09a6-444b-8c86-c68f48fd4b43	asgard-a-single-cell-guided-pipeline-to-aid	2109.06377	null	https://arxiv.org/abs/2109.06377v4	https://arxiv.org/pdf/2109.06377v4.pdf	ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs	Intercellular heterogeneity is a major obstacle to successful precision medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose a new drug recommendation system called: A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD). ASGARD defines a novel drug score predicting drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. We tested ASGARD on multiple diseases, including breast cancer, acute lymphoblastic leukemia, and coronavirus disease 2019 (COVID-19). On single-drug therapy, ASGARD shows significantly better average accuracy (AUC of 0.92) compared to two other bulk-cell-based drug repurposing methods (AUC of 0.80 and 0.76). It is also considerably better (AUC of 0.82) than other cell cluster level predicting methods (AUC of 0.67 and 0.55). In addition, ASGARD is also validated by the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. Many top-ranked drugs are either approved by FDA or in clinical trials treating corresponding diseases. In silico cell-type specific drop-out experiments using triple-negative breast cancers show the importance of T cells in the tumor microenvironment in affecting drug predictions. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD.	['David Garmire', 'Qianhui Huang', 'Haodong Liang', 'Lana X. Garmire', 'Duxin Sun', 'Yijun Li', 'Yuheng Du', 'Yao Xiao', 'Bing He']	2021-09-14	null	null	null	null	['drug-response-prediction']	['medical']	[ 1.46216944e-01 -3.79444927e-01 -8.35349679e-01 2.40046591e-01 -1.00137126e+00 -8.75129402e-01 4.86804813e-01 7.17797816e-01 2.26562008e-01 1.11480534e+00 4.34412271e-01 -6.90093875e-01 -2.15104491e-01 -7.36204088e-01 -3.06759387e-01 -9.35521185e-01 2.93200731e-01 9.55609620e-01 -1.95989504e-01 -5.18626608e-02 9.02243108e-02 6.32699311e-01 -6.75709009e-01 6.83306098e-01 9.93682563e-01 3.37461203e-01 -2.12968975e-01 7.19137132e-01 2.73662084e-03 2.18559563e-01 -1.57084450e-01 4.69227806e-02 1.20121213e-02 -5.43641090e-01 -3.36780876e-01 -5.28114498e-01 7.88137019e-02 -5.24428599e-02 -4.91000824e-02 5.79230428e-01 9.51136172e-01 -7.02043712e-01 6.98300242e-01 -9.71580803e-01 -2.53700197e-01 8.05184394e-02 -3.35320532e-01 -5.09504378e-02 4.37975615e-01 3.51970017e-01 7.51854122e-01 -7.58533895e-01 1.15313148e+00 8.24707210e-01 6.41785741e-01 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591a1d4a-bffc-449d-b99e-66a4e6f1dd73	a-recurrent-cnn-for-online-object-detection	2212.11172	null	https://arxiv.org/abs/2212.11172v2	https://arxiv.org/pdf/2212.11172v2.pdf	A recurrent CNN for online object detection on raw radar frames	Automotive radar sensors provide valuable information for advanced driving assistance systems (ADAS). Radars can reliably estimate the distance to an object and the relative velocity, regardless of weather and light conditions. However, radar sensors suffer from low resolution and huge intra-class variations in the shape of objects. Exploiting the time information (e.g., multiple frames) has been shown to help to capture better the dynamics of objects and, therefore, the variation in the shape of objects. Most temporal radar object detectors use 3D convolutions to learn spatial and temporal information. However, these methods are often non-causal and unsuitable for real-time applications. This work presents RECORD, a new recurrent CNN architecture for online radar object detection. We propose an end-to-end trainable architecture mixing convolutions and ConvLSTMs to learn spatio-temporal dependencies between successive frames. Our model is causal and requires only the past information encoded in the memory of the ConvLSTMs to detect objects. Our experiments show such a method's relevance for detecting objects in different radar representations (range-Doppler, range-angle) and outperform state-of-the-art models on the ROD2021 and CARRADA datasets while being less computationally expensive.	['Thomas Oberlin', 'Didier Salle', 'Rufin VanRullen', 'Colin Decourt']	2022-12-21	null	null	null	null	['radar-object-detection']	['robots']	[ 2.98504792e-02 -7.18650281e-01 4.73183915e-02 -7.43384421e-01 -4.38886851e-01 -4.61994082e-01 9.72353399e-01 -2.07335308e-01 -5.05723357e-01 5.01614988e-01 -2.15761080e-01 -3.03007543e-01 -2.80215919e-01 -7.90217102e-01 -6.41313255e-01 -7.97277808e-01 -3.79536927e-01 2.88103014e-01 5.85357904e-01 -2.68039554e-01 1.40337572e-01 1.20557296e+00 -1.76096606e+00 4.99862850e-01 2.07210153e-01 1.31973493e+00 1.93876609e-01 9.29763556e-01 2.65300758e-02 8.81703615e-01 -7.30388999e-01 1.83035731e-01 2.11138830e-01 -1.02228120e-01 1.79415166e-01 -2.62308985e-01 8.62384021e-01 -4.14227784e-01 -1.06295574e+00 6.67309523e-01 2.85976291e-01 2.67663926e-01 5.23337603e-01 -1.04310179e+00 -4.57524717e-01 1.47011712e-01 -3.86118174e-01 9.17105794e-01 -2.39590734e-01 1.87471390e-01 6.02495551e-01 -5.48641980e-01 3.06809336e-01 1.44331205e+00 5.16277492e-01 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e0296a03-46b0-4e64-bd50-b77948fbc46e	voxel-based-network-for-shape-completion-by	2108.09936	null	https://arxiv.org/abs/2108.09936v1	https://arxiv.org/pdf/2108.09936v1.pdf	Voxel-based Network for Shape Completion by Leveraging Edge Generation	Deep learning technique has yielded significant improvements in point cloud completion with the aim of completing missing object shapes from partial inputs. However, most existing methods fail to recover realistic structures due to over-smoothing of fine-grained details. In this paper, we develop a voxel-based network for point cloud completion by leveraging edge generation (VE-PCN). We first embed point clouds into regular voxel grids, and then generate complete objects with the help of the hallucinated shape edges. This decoupled architecture together with a multi-scale grid feature learning is able to generate more realistic on-surface details. We evaluate our model on the publicly available completion datasets and show that it outperforms existing state-of-the-art approaches quantitatively and qualitatively. Our source code is available at https://github.com/xiaogangw/VE-PCN.	['Gim Hee Lee', 'Marcelo H Ang Jr', 'Xiaogang Wang']	2021-08-23	null	http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Voxel-Based_Network_for_Shape_Completion_by_Leveraging_Edge_Generation_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Voxel-Based_Network_for_Shape_Completion_by_Leveraging_Edge_Generation_ICCV_2021_paper.pdf	iccv-2021-1	['point-cloud-completion']	['computer-vision']	[-2.37945601e-01 1.50564998e-01 5.40363312e-01 -1.17044352e-01 -1.01382267e+00 -5.43816388e-01 6.60187542e-01 -1.09673366e-01 9.03522670e-02 6.92237616e-01 2.11755291e-01 6.34444728e-02 1.49532616e-01 -1.12036538e+00 -1.10212171e+00 -2.05326796e-01 6.44297600e-02 7.83928931e-01 1.75066337e-01 -9.18836892e-02 1.38932914e-01 9.93311942e-01 -1.32024586e+00 3.57823670e-01 9.72150028e-01 6.02565467e-01 3.32714438e-01 4.84340221e-01 -2.04414651e-01 1.73159152e-01 1.42668094e-02 -3.29228222e-01 4.82908338e-01 2.46197298e-01 -5.38150012e-01 1.38000637e-01 6.02715671e-01 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50697555-e434-431a-8dfb-2f4ec73ee674	hida-towards-holistic-indoor-understanding	2107.03180	null	https://arxiv.org/abs/2107.03180v1	https://arxiv.org/pdf/2107.03180v1.pdf	HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor	Independently exploring unknown spaces or finding objects in an indoor environment is a daily but challenging task for visually impaired people. However, common 2D assistive systems lack depth relationships between various objects, resulting in difficulty to obtain accurate spatial layout and relative positions of objects. To tackle these issues, we propose HIDA, a lightweight assistive system based on 3D point cloud instance segmentation with a solid-state LiDAR sensor, for holistic indoor detection and avoidance. Our entire system consists of three hardware components, two interactive functions~(obstacle avoidance and object finding) and a voice user interface. Based on voice guidance, the point cloud from the most recent state of the changing indoor environment is captured through an on-site scanning performed by the user. In addition, we design a point cloud segmentation model with dual lightweight decoders for semantic and offset predictions, which satisfies the efficiency of the whole system. After the 3D instance segmentation, we post-process the segmented point cloud by removing outliers and projecting all points onto a top-view 2D map representation. The system integrates the information above and interacts with users intuitively by acoustic feedback. The proposed 3D instance segmentation model has achieved state-of-the-art performance on ScanNet v2 dataset. Comprehensive field tests with various tasks in a user study verify the usability and effectiveness of our system for assisting visually impaired people in holistic indoor understanding, obstacle avoidance and object search.	['Rainer Stiefelhagen', 'Kunyu Peng', 'Jiaming Zhang', 'Kailun Yang', 'Ruiping Liu', 'Huayao Liu']	2021-07-07	null	null	null	null	['3d-instance-segmentation-1']	['computer-vision']	[-8.18707049e-03 -3.45811695e-01 4.71447676e-01 -3.62246811e-01 -5.65366864e-01 -3.37631911e-01 -1.06455222e-01 7.94302821e-02 -5.70887506e-01 1.64510772e-01 -8.18252861e-02 -5.72871149e-01 -3.01847368e-01 -7.25884974e-01 -3.85901541e-01 -2.58018255e-01 1.07863605e-01 7.69649804e-01 7.27254093e-01 -2.87609518e-01 4.54726428e-01 8.89787257e-01 -2.02549767e+00 5.57608306e-02 1.26701486e+00 9.92969453e-01 1.21563935e+00 6.93828225e-01 -3.92772764e-01 -2.65234768e-01 -4.69554901e-01 2.04692930e-01 4.43129152e-01 5.13569057e-01 -2.62318313e-01 1.70946389e-01 4.19136286e-01 -8.69211853e-01 -4.25126642e-01 9.68154907e-01 1.07623982e+00 -1.34665577e-03 3.62873077e-01 -1.19906902e+00 -1.37798235e-01 -4.62625951e-01 -2.79127806e-01 8.98973942e-02 7.81879783e-01 4.85860914e-01 4.15153652e-01 -1.17705262e+00 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67233434-09b2-44de-a2c9-4e8c4434107e	uniformerv2-spatiotemporal-learning-by-arming	null	null	https://openreview.net/forum?id=d77RVuVg-Mf	https://openreview.net/pdf?id=d77RVuVg-Mf	UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer	Learning discriminative spatiotemporal representation is the key problem of video understanding. Recently, Vision Transformers (ViTs) have shown their power in learning long-term video dependency with self-attention. Unfortunately, they exhibit limitations in tackling local video redundancy, due to the blind global comparison among tokens. UniFormer has successfully alleviated this issue, by unifying convolution and self-attention as a relation aggregator in the transformer format. However, this model has to require a tiresome and complicated image-pretraining phrase, before being finetuned on videos. This blocks its wide usage in practice. On the contrary, open-sourced ViTs are readily available and well-pretrained with rich image supervision. Based on these observations, we propose a generic paradigm to build a powerful family of video networks, by arming the pretrained ViTs with efficient UniFormer designs. We call this family UniFormerV2, since it inherits the concise style of the UniFormer block. But it contains brand-new local and global relation aggregators, which allow for preferable accuracy-computation balance by seamlessly integrating advantages from both ViTs and UniFormer. Without any bells and whistles, our UniFormerV2 gets the state-of-the-art recognition performance on 8 popular video benchmarks, including scene-related Kinetics-400/600/700 and Moments in Time, temporal-related Something-Something V1/V2, untrimmed ActivityNet and HACS. In particular, it is the first model to achieve 90% top-1 accuracy on Kinetics-400, to our best knowledge. The models will be released afterward.	['Anonymous']	2022-09-22	null	null	null	iclr2023-submitted-2022-9	['action-classification']	['computer-vision']	[-9.60788503e-02 -3.87860745e-01 -5.34239173e-01 -8.05836171e-02 -4.92241681e-01 -4.98986393e-01 6.38638496e-01 -3.91709924e-01 -4.68342811e-01 5.32672465e-01 2.28337973e-01 -2.74262697e-01 -6.95140213e-02 -4.13217157e-01 -9.61381674e-01 -9.33779597e-01 -1.08954221e-01 9.44918673e-03 4.02956396e-01 -3.14708143e-01 -3.29470672e-02 2.24516824e-01 -1.68993664e+00 4.20783848e-01 7.22972810e-01 1.34885073e+00 3.88826609e-01 5.51790297e-01 9.69691947e-02 1.37457180e+00 -1.82419926e-01 -5.44407606e-01 1.46198198e-01 -2.77383745e-01 -6.95740998e-01 -1.58775076e-02 6.58670545e-01 -5.21392226e-01 -9.26415086e-01 8.08739126e-01 4.71382052e-01 5.80472648e-02 5.61127543e-01 -1.34393334e+00 -8.33004057e-01 6.21672153e-01 -5.83965123e-01 5.34274995e-01 2.79734135e-01 4.08565968e-01 1.14438939e+00 -8.74241650e-01 5.35295844e-01 9.37181830e-01 6.50048912e-01 4.88871366e-01 -9.87501383e-01 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e03fab18-7ba4-45f7-bfb9-d2fbea379146	ssn-armm-lt-edi-acl2022-hope-speech-detection	null	null	https://aclanthology.org/2022.ltedi-1.22	https://aclanthology.org/2022.ltedi-1.22.pdf	SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model	In recent years social media has become one of the major forums for expressing human views and emotions. With the help of smartphones and high-speed internet, anyone can express their views on Social media. However, this can also lead to the spread of hatred and violence in society. Therefore it is necessary to build a method to find and support helpful social media content. In this paper, we studied Natural Language Processing approach for detecting Hope speech in a given sentence. The task was to classify the sentences into ‘Hope speech’ and ‘Non-hope speech’. The dataset was provided by LT-EDI organizers with text from Youtube comments. Based on the task description, we developed a system using the pre-trained language model BERT to complete this task. Our model achieved 1st rank in the Kannada language with a weighted average F1 score of 0.750, 2nd rank in the Malayalam language with a weighted average F1 score of 0.740, 3rd rank in the Tamil language with a weighted average F1 score of 0.390 and 6th rank in the English language with a weighted average F1 score of 0.880.	['Mirnalinee T T', 'Sakaya Milton Rajendram', 'Rajalakshmi Sivanaiah', 'Angel S', 'Aravind P', 'Prathyush S', 'Praveenkumar Vijayakumar']	null	null	null	null	ltedi-acl-2022-5	['hope-speech-detection']	['natural-language-processing']	[-4.91105676e-01 3.31545234e-01 8.49789456e-02 -2.84997493e-01 -4.78423595e-01 -4.18091387e-01 6.85950518e-01 4.63329732e-01 -4.27364916e-01 6.42382562e-01 4.89453822e-01 -8.73167664e-02 2.41086379e-01 -6.11662388e-01 -7.49566406e-02 -2.61413962e-01 9.15412307e-02 3.56340455e-03 1.18431389e-01 -5.23269713e-01 6.54958546e-01 7.98487440e-02 -1.10358346e+00 4.84558791e-01 9.09650445e-01 6.72904313e-01 2.76394546e-01 6.62923634e-01 -4.96172965e-01 1.45024347e+00 -8.22191834e-01 -6.37007117e-01 -2.95093387e-01 -4.11786884e-01 -9.66948628e-01 -9.08057950e-03 -1.15432784e-01 6.56182924e-03 -2.13937581e-01 1.15323079e+00 3.41758668e-01 1.34282678e-01 5.20566821e-01 -1.11632013e+00 -6.19714558e-01 7.79159188e-01 -4.90594596e-01 1.12583414e-01 6.89599872e-01 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5f2f33f2-a288-4939-b786-87b3a00a8efe	fosi-hybrid-first-and-second-order	2302.08484	null	https://arxiv.org/abs/2302.08484v3	https://arxiv.org/pdf/2302.08484v3.pdf	FOSI: Hybrid First and Second Order Optimization	Though second-order optimization methods are highly effective, popular approaches in machine learning such as SGD and Adam use only first-order information due to the difficulty of computing curvature in high dimensions. We present FOSI, a novel meta-algorithm that improves the performance of any first-order optimizer by efficiently incorporating second-order information during the optimization process. In each iteration, FOSI implicitly splits the function into two quadratic functions defined on orthogonal subspaces, then uses a second-order method to minimize the first, and the base optimizer to minimize the other. We prove FOSI converges and further show it improves the condition number for a large family of optimizers. Our empirical evaluation demonstrates that FOSI improves the convergence rate and optimization time of GD, Heavy-Ball, and Adam when applied to several deep neural networks training tasks such as audio classification, transfer learning, and object classification, as well as when applied to convex functions. Furthermore, our results show that FOSI outperforms other second-order methods such as K-FAC and L-BFGS.	['Assaf Schuster', 'Moshe Gabel', 'Hadar Sivan']	2023-02-16	null	null	null	null	['audio-classification']	['audio']	[-5.64669967e-01 1.55415624e-01 -7.98690096e-02 -2.24044532e-01 -7.82720864e-01 -4.89729255e-01 -1.31561846e-01 7.05098063e-02 -3.86141807e-01 7.20624626e-01 -4.80317837e-03 -4.43384945e-01 -2.58889019e-01 -3.28356028e-01 -9.12702560e-01 -7.10197508e-01 -2.90334702e-01 6.07234180e-01 -1.37076914e-01 -8.81736726e-02 2.89059162e-01 4.54706252e-01 -8.40944827e-01 -2.18477368e-01 1.04020798e+00 1.19446564e+00 -1.65483102e-01 6.07254744e-01 2.72158206e-01 3.15628797e-01 -1.42997682e-01 -3.99641722e-01 5.88495076e-01 -1.94448486e-01 -7.74649918e-01 -2.21030954e-02 5.28970301e-01 -2.75203496e-01 -1.99682921e-01 9.66839135e-01 5.66389799e-01 4.59427565e-01 5.75234413e-01 -1.31504130e+00 -4.21413511e-01 4.13109303e-01 -4.86687392e-01 -1.15112364e-02 -1.46520764e-01 -5.96817546e-02 1.31670737e+00 -1.27182853e+00 1.89873785e-01 1.34787321e+00 1.10294080e+00 4.78070617e-01 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1de67b55-ee5f-4ad6-8ff9-51bddb8ee59c	towards-coreference-resolution-for-early	null	null	https://aclanthology.org/2022.cltw-1.12	https://aclanthology.org/2022.cltw-1.12.pdf	Towards Coreference Resolution for Early Irish	In this article, we present an outline of some of the issues involved in developing a semi-supervised procedure for coreference resolution for early Irish as part of a wider enterprise to create a parsed corpus of historical Irish with enriched annotation for information structure and anaphoric coreference. We outline the ways in which existing resources, notably the POMIC historical Irish corpus and the Cesax annotation algorithm, have had to be adapted, the first to provide suitable input for coreference resolution, the second to cope with specific aspects of early Irish grammar. We also outline features of a part-of-speech tagger that we have developed for early Irish as part of the first task and with a view to expanding the size of the future corpus.	['David Willis', 'Marieke Meelen', 'Mark Darling']	null	null	null	null	cltw-lrec-2022-6	['coreference-resolution']	['natural-language-processing']	[ 4.86384511e-01 7.17015862e-01 -1.47651941e-01 -6.70657694e-01 -1.08223987e+00 -7.62164652e-01 6.25244737e-01 2.91924894e-01 -7.50693917e-01 9.29507792e-01 1.21274126e+00 -1.83575064e-01 -6.77244782e-01 -3.96744370e-01 4.41381987e-03 -3.74807268e-01 1.02622189e-01 1.31141579e+00 5.39092541e-01 -6.87532902e-01 5.58924675e-01 4.89076763e-01 -1.34346712e+00 6.62421167e-01 2.74967611e-01 6.30737990e-02 3.47620279e-01 5.58703959e-01 -8.52993354e-02 8.54241192e-01 -4.95738685e-01 -6.32106364e-01 -2.20364705e-01 -4.73331958e-01 -1.78769875e+00 -3.07707667e-01 -5.56094199e-02 2.27123886e-01 -1.66575402e-01 8.55609119e-01 6.72644556e-01 3.15575093e-01 1.95869938e-01 -8.02526116e-01 4.39905107e-01 1.18066204e+00 1.37967259e-01 6.23961449e-01 9.00104821e-01 -5.68617404e-01 1.08895242e+00 -3.93716902e-01 1.33713865e+00 1.35320818e+00 5.72011173e-01 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d6cbed16-d3b8-427d-b9bd-1bb7288352aa	a-quadratic-0-1-programming-approach-for-word	2201.04877	null	https://arxiv.org/abs/2201.04877v1	https://arxiv.org/pdf/2201.04877v1.pdf	A Quadratic 0-1 Programming Approach for Word Sense Disambiguation	Word Sense Disambiguation (WSD) is the task to determine the sense of an ambiguous word in a given context. Previous approaches for WSD have focused on supervised and knowledge-based methods, but inter-sense interactions patterns or regularities for disambiguation remain to be found. We argue the following cause as one of the major difficulties behind finding the right patterns: for a particular context, the intended senses of a sequence of ambiguous words are dependent on each other, i.e. the choice of one word's sense is associated with the choice of another word's sense, making WSD a combinatorial optimization problem.In this work, we approach the interactions between senses of different target words by a Quadratic 0-1 Integer Programming model (QIP) that maximizes the objective function consisting of (1) the similarity between candidate senses of a target word and the word in a context (the sense-word similarity), and (2) the semantic interactions (relatedness) between senses of all words in the context (the sense-sense relatedness).	['Boliang Lin']	2022-01-13	null	null	null	null	['word-sense-disambiguation', 'word-similarity']	['natural-language-processing', 'natural-language-processing']	[ 3.01017225e-01 -1.47749424e-01 -1.87017545e-01 -2.87091017e-01 -1.89801037e-01 -8.57336044e-01 4.19921726e-01 8.21756899e-01 -6.94632113e-01 6.16501212e-01 3.06686401e-01 -1.58606052e-01 -3.95287097e-01 -7.96968937e-01 7.07900338e-03 -6.15677357e-01 4.95959632e-02 2.98379838e-01 3.74625474e-01 -7.10073829e-01 6.26616657e-01 2.19948992e-01 -1.47853601e+00 -1.38302222e-01 1.01868486e+00 7.52458572e-01 9.42872465e-01 1.77309439e-01 -7.56643116e-01 1.42908067e-01 -7.67618120e-01 1.48993097e-02 7.47494027e-02 -4.98421729e-01 -1.21093750e+00 5.80360331e-02 -1.29937932e-01 8.18831623e-01 3.54331136e-01 1.45082581e+00 3.96698207e-01 5.09064138e-01 4.46462423e-01 -1.14248431e+00 -5.99986374e-01 4.01090235e-01 -4.37600374e-01 3.23587775e-01 6.54319823e-01 -2.89638579e-01 1.50657690e+00 -6.31654382e-01 5.83886206e-01 1.29884326e+00 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81e424bd-47be-4ae3-aded-242933d10c77	what-s-in-your-hands-3d-reconstruction-of	2204.07153	null	https://arxiv.org/abs/2204.07153v1	https://arxiv.org/pdf/2204.07153v1.pdf	What's in your hands? 3D Reconstruction of Generic Objects in Hands	Our work aims to reconstruct hand-held objects given a single RGB image. In contrast to prior works that typically assume known 3D templates and reduce the problem to 3D pose estimation, our work reconstructs generic hand-held object without knowing their 3D templates. Our key insight is that hand articulation is highly predictive of the object shape, and we propose an approach that conditionally reconstructs the object based on the articulation and the visual input. Given an image depicting a hand-held object, we first use off-the-shelf systems to estimate the underlying hand pose and then infer the object shape in a normalized hand-centric coordinate frame. We parameterized the object by signed distance which are inferred by an implicit network which leverages the information from both visual feature and articulation-aware coordinates to process a query point. We perform experiments across three datasets and show that our method consistently outperforms baselines and is able to reconstruct a diverse set of objects. We analyze the benefits and robustness of explicit articulation conditioning and also show that this allows the hand pose estimation to further improve in test-time optimization.	['Shubham Tulsiani', 'Abhinav Gupta', 'Yufei Ye']	2022-04-14	null	http://openaccess.thecvf.com//content/CVPR2022/html/Ye_Whats_in_Your_Hands_3D_Reconstruction_of_Generic_Objects_in_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Ye_Whats_in_Your_Hands_3D_Reconstruction_of_Generic_Objects_in_CVPR_2022_paper.pdf	cvpr-2022-1	['3d-pose-estimation']	['computer-vision']	[-1.54960051e-01 -5.35581708e-02 -2.27796510e-01 -1.80970177e-01 -9.68438566e-01 -1.00850534e+00 5.90939105e-01 -4.32606131e-01 -2.42997333e-01 2.60493964e-01 2.22895220e-01 1.18634865e-01 -1.07888207e-01 -2.99591273e-01 -1.00403893e+00 -4.98365134e-01 4.10527438e-01 1.28474045e+00 2.43349507e-01 1.21812046e-01 2.60407805e-01 1.05074179e+00 -1.55881011e+00 2.08982140e-01 1.16700962e-01 1.11776423e+00 4.13920760e-01 8.81057799e-01 2.83066303e-01 4.99021560e-01 -4.45060521e-01 -2.39213258e-01 6.17383361e-01 4.28633206e-03 -9.06237006e-01 4.56922323e-01 8.72731090e-01 -6.34667814e-01 -4.28219914e-01 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d5bffee1-cd2e-4e7a-a64b-71408dc746ee	factorvae-a-probabilistic-dynamic-factor	null	null	https://ojs.aaai.org/index.php/AAAI/article/view/20369	https://ojs.aaai.org/index.php/AAAI/article/view/20369/20128	FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns	As an asset pricing model in economics and finance, factor model has been widely used in quantitative investment. Towards building more effective factor models, recent years have witnessed the paradigm shift from linear models to more flexible nonlinear data-driven machine learning models. However, due to low signal-to-noise ratio of the financial data, it is quite challenging to learn effective factor models. In this paper, we propose a novel factor model, FactorVAE, as a probabilistic model with inherent randomness for noise modeling. Essentially, our model integrates the dynamic factor model (DFM) with the variational autoencoder (VAE) in machine learning, and we propose a prior-posterior learning method based on VAE, which can effectively guide the learning of model by approximating an optimal posterior factor model with future information. Particularly, considering that risk modeling is important for the noisy stock data, FactorVAE can estimate the variances from the distribution over the latent space of VAE, in addition to predicting returns. The experiments on the real stock market data demonstrate the effectiveness of FactorVAE, which outperforms various baseline methods.	['Jian Li', 'Qizhong Zhang', 'Lei Wang', 'Yitong Duan']	2022-06-28	null	null	null	aaai-conference-on-artificial-intelligence-6	['stock-price-prediction']	['time-series']	[-7.56512702e-01 -3.03792804e-01 1.61453709e-02 -2.10226700e-01 -6.25269771e-01 -4.34874505e-01 6.87267542e-01 -6.20907307e-01 -2.26508811e-01 4.85238820e-01 4.78655219e-01 -2.93766677e-01 -1.23165332e-01 -1.08646894e+00 -5.79509914e-01 -8.14533412e-01 1.74808025e-01 3.72515827e-01 8.35584179e-02 -3.28842141e-02 1.73938021e-01 6.97465241e-02 -9.86579299e-01 -1.39238387e-01 5.35276949e-01 1.14236784e+00 2.79459417e-01 2.82502651e-01 -3.86863470e-01 1.01670396e+00 -5.78674018e-01 -8.60699654e-01 4.18128371e-01 -2.32933536e-01 2.41330355e-01 -2.24152967e-01 -7.25964785e-01 -7.99196422e-01 -5.93024969e-01 1.36657321e+00 2.22439766e-01 1.83180034e-01 9.57526684e-01 -1.12358487e+00 -7.87014782e-01 1.04037786e+00 -7.13110447e-01 3.35971177e-01 -4.75720048e-01 -8.45020264e-02 1.07901239e+00 -9.37667906e-01 4.25684899e-02 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24395ae7-581c-4689-8cce-0b8a6c5ed50e	can-predictive-models-be-used-for-causal	2306.10551	null	https://arxiv.org/abs/2306.10551v1	https://arxiv.org/pdf/2306.10551v1.pdf	Can predictive models be used for causal inference?	Supervised machine learning (ML) and deep learning (DL) algorithms excel at predictive tasks, but it is commonly assumed that they often do so by exploiting non-causal correlations, which may limit both interpretability and generalizability. Here, we show that this trade-off between explanation and prediction is not as deep and fundamental as expected. Whereas ML and DL algorithms will indeed tend to use non-causal features for prediction when fed indiscriminately with all data, it is possible to constrain the learning process of any ML and DL algorithm by selecting features according to Pearl's backdoor adjustment criterion. In such a situation, some algorithms, in particular deep neural networks, can provide near unbiased effect estimates under feature collinearity. Remaining biases are explained by the specific algorithmic structures as well as hyperparameter choice. Consequently, optimal hyperparameter settings are different when tuned for prediction or inference, confirming the general expectation of a trade-off between prediction and explanation. However, the effect of this trade-off is small compared to the effect of a causally constrained feature selection. Thus, once the causal relationship between the features is accounted for, the difference between prediction and explanation may be much smaller than commonly assumed. We also show that such causally constrained models generalize better to new data with altered collinearity structures, suggesting generalization failure may often be due to a lack of causal learning. Our results not only provide a perspective for using ML for inference of (causal) effects but also help to improve the generalizability of fitted ML and DL models to new data.	['Florian Hartig', 'Maximilian Pichler']	2023-06-18	null	null	null	null	['causal-inference', 'causal-inference']	['knowledge-base', 'miscellaneous']	[ 3.06339681e-01 4.04852450e-01 -6.31074965e-01 -4.98431355e-01 -1.89841419e-01 -6.62949681e-01 7.39154994e-01 1.95411518e-01 -2.41905108e-01 8.48721445e-01 5.20224333e-01 -5.91231763e-01 -5.23267329e-01 -8.41443121e-01 -9.48318958e-01 -6.99475825e-01 -2.50433475e-01 4.89603907e-01 -3.01790357e-01 6.92057610e-02 3.06978613e-01 4.57544416e-01 -1.46686924e+00 2.08397642e-01 8.52331460e-01 4.69514340e-01 -7.17563108e-02 4.11346823e-01 9.37038362e-02 6.13763869e-01 -2.24850699e-01 -5.26472449e-01 1.25731871e-01 -5.65457582e-01 -7.41062999e-01 -1.64675817e-01 4.00808603e-01 -3.15476030e-01 -2.42763519e-01 7.82679439e-01 1.84956729e-01 -8.00590664e-02 9.55080152e-01 -1.35353982e+00 -7.22569644e-01 1.05056214e+00 -4.10653323e-01 1.51601270e-01 4.30124700e-02 4.51628625e-01 1.55648458e+00 -6.84125304e-01 4.21401292e-01 1.40820229e+00 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b7f34daa-7c61-4c9a-aef4-a2e37ef632b2	unified-generative-adversarial-networks-for	1912.06112	null	https://arxiv.org/abs/1912.06112v2	https://arxiv.org/pdf/1912.06112v2.pdf	Unified Generative Adversarial Networks for Controllable Image-to-Image Translation	We propose a unified Generative Adversarial Network (GAN) for controllable image-to-image translation, i.e., transferring an image from a source to a target domain guided by controllable structures. In addition to conditioning on a reference image, we show how the model can generate images conditioned on controllable structures, e.g., class labels, object keypoints, human skeletons, and scene semantic maps. The proposed model consists of a single generator and a discriminator taking a conditional image and the target controllable structure as input. In this way, the conditional image can provide appearance information and the controllable structure can provide the structure information for generating the target result. Moreover, our model learns the image-to-image mapping through three novel losses, i.e., color loss, controllable structure guided cycle-consistency loss, and controllable structure guided self-content preserving loss. Also, we present the Fr\'echet ResNet Distance (FRD) to evaluate the quality of the generated images. Experiments on two challenging image translation tasks, i.e., hand gesture-to-gesture translation and cross-view image translation, show that our model generates convincing results, and significantly outperforms other state-of-the-art methods on both tasks. Meanwhile, the proposed framework is a unified solution, thus it can be applied to solving other controllable structure guided image translation tasks such as landmark guided facial expression translation and keypoint guided person image generation. To the best of our knowledge, we are the first to make one GAN framework work on all such controllable structure guided image translation tasks. Code is available at https://github.com/Ha0Tang/GestureGAN.	['Hong Liu', 'Hao Tang', 'Nicu Sebe']	2019-12-12	null	null	null	null	['gesture-to-gesture-translation']	['computer-vision']	[ 7.27036417e-01 3.18623453e-01 -1.51382983e-01 -4.38741535e-01 -9.44081545e-01 -6.69291735e-01 5.75553894e-01 -7.96878040e-01 -1.00719005e-01 8.13804328e-01 4.86656167e-02 9.21930149e-02 2.75770754e-01 -9.37505722e-01 -1.08735168e+00 -1.03386474e+00 7.57017970e-01 4.75665182e-01 -2.57607251e-01 -4.72042635e-02 -6.09758571e-02 3.41625005e-01 -1.12422860e+00 -2.71169432e-02 8.15655410e-01 8.60669792e-01 2.28844360e-01 4.10253495e-01 1.32761270e-01 6.80379868e-01 -4.88030493e-01 -5.19595504e-01 2.13827640e-01 -9.92452383e-01 -6.83483839e-01 2.86778927e-01 5.18455863e-01 -4.34054852e-01 -3.63293082e-01 1.11762691e+00 5.78098416e-01 6.57529570e-04 6.22894168e-01 -1.66749644e+00 -1.16027510e+00 3.41523051e-01 -6.43031836e-01 -4.06103283e-01 4.29309070e-01 3.93564284e-01 6.31993592e-01 -8.35716307e-01 8.78272593e-01 1.33537877e+00 1.36536285e-01 1.05416822e+00 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-4.48292017e-01 7.94646800e-01 2.48788372e-01 -5.47748089e-01 -1.81293815e-01 4.90202248e-01]	[11.847354888916016, -0.4252040684223175]
f82e6a6e-bda7-45e5-8547-fe679d62dba3	modeling-the-temporal-nature-of-human	1511.06660	null	http://arxiv.org/abs/1511.06660v5	http://arxiv.org/pdf/1511.06660v5.pdf	Modeling the Temporal Nature of Human Behavior for Demographics Prediction	Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of the datasets. For these reasons, there has been a growing interest in predicting demographic information from mobile phone metadata. Previous work focused on creating increasingly advanced features to be modeled with standard machine learning algorithms. We here instead model the raw mobile phone metadata directly using deep learning, exploiting the temporal nature of the patterns in the data. From high-level assumptions we design a data representation and convolutional network architecture for modeling patterns within a week. We then examine three strategies for aggregating patterns across weeks and show that our method reaches state-of-the-art accuracy on both age and gender prediction using only the temporal modality in mobile metadata. We finally validate our method on low activity users and evaluate the modeling assumptions.	['Yves-Alexandre de Montjoye', "Alex 'Sandy' Pentland", 'Pål Sundsøy', 'Bjarke Felbo', 'Sune Lehmann']	2015-11-20	null	null	null	null	['gender-prediction']	['computer-vision']	[-2.21592247e-01 -1.77505910e-02 -5.85222185e-01 -5.54585040e-01 -2.82499135e-01 -2.52411991e-01 1.06775773e+00 4.72732127e-01 -8.78264189e-01 8.90851319e-01 9.96678829e-01 -5.56761205e-01 -1.14821441e-01 -8.01274061e-01 -5.37052572e-01 -3.06651980e-01 -2.85916537e-01 5.58555365e-01 -4.92759883e-01 -1.79869190e-01 -1.42384559e-01 2.38022402e-01 -1.64216733e+00 3.31188709e-01 6.97317660e-01 9.13780391e-01 -2.76203930e-01 6.73355043e-01 -1.94695845e-01 7.10733056e-01 -2.91021407e-01 -4.09441233e-01 2.90765390e-02 -1.92395411e-02 -9.32603657e-01 -4.25604224e-01 3.99078101e-01 -6.16275430e-01 -6.31983995e-01 1.90761432e-01 7.09798515e-01 -1.25087485e-01 6.51517391e-01 -1.22387004e+00 -3.12552392e-01 8.08693647e-01 -4.84833539e-01 4.39078867e-01 5.80277622e-01 -1.32185578e-01 6.08802557e-01 -5.35809994e-01 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5.19699931e-01 -9.73652244e-01 1.95284128e-01 -1.18671632e+00 -3.51497084e-01 4.29806858e-01 4.47213173e-01 -6.86138451e-01 2.89150560e-03 -3.47861797e-02]	[6.58803653717041, 2.1000099182128906]
4e68433e-0bf4-4101-a59a-c0c10400d99a	segment-anything-model-for-medical-images	2304.14660	null	https://arxiv.org/abs/2304.14660v4	https://arxiv.org/pdf/2304.14660v4.pdf	Segment Anything Model for Medical Images?	The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It designed a novel promotable segmentation task, ensuring zero-shot image segmentation using the pre-trained model via two main modes including automatic everything and manual prompt. SAM has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging due to the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. Meanwhile, zero-shot and efficient MIS can well reduce the annotation time and boost the development of medical image analysis. Hence, SAM seems to be a potential tool and its performance on large medical datasets should be further validated. We collected and sorted 52 open-source datasets, and built a large medical segmentation dataset with 16 modalities, 68 objects, and 553K slices. We conducted a comprehensive analysis of different SAM testing strategies on the so-called COSMOS 553K dataset. Extensive experiments validate that SAM performs better with manual hints like points and boxes for object perception in medical images, leading to better performance in prompt mode compared to everything mode. Additionally, SAM shows remarkable performance in some specific objects and modalities, but is imperfect or even totally fails in other situations. Finally, we analyze the influence of different factors (e.g., the Fourier-based boundary complexity and size of the segmented objects) on SAM's segmentation performance. Extensive experiments validate that SAM's zero-shot segmentation capability is not sufficient to ensure its direct application to the MIS.	['Dong Ni', 'Fajin Dong', 'Deng-Ping Fan', 'Xindi Hu', 'Haozhe Chi', 'Chaoyu Chen', 'Jiongquan Chen', 'Junxuan Yu', 'Rusi Chen', 'Xinrui Zhou', 'Ao Chang', 'Han Zhou', 'Lian Liu', 'Xin Yang', 'Yuhao Huang']	2023-04-28	null	null	null	null	['zero-shot-segmentation']	['computer-vision']	[ 2.23952129e-01 1.56323984e-01 -3.09676528e-01 -2.27340847e-01 -8.03827524e-01 -3.93325061e-01 3.07004362e-01 -3.23184915e-02 -5.65088332e-01 3.58509094e-01 -1.96231082e-01 -3.05018753e-01 -2.02020884e-01 -5.16099870e-01 -3.88496608e-01 -8.40469599e-01 2.50013441e-01 5.44142842e-01 8.28286946e-01 -1.58515826e-01 2.32208550e-01 1.93291545e-01 -1.41967177e+00 1.85646802e-01 1.06129920e+00 8.89646471e-01 5.15718460e-01 7.36590207e-01 -2.03277394e-01 4.72731888e-01 -5.00091076e-01 -4.07975674e-01 1.58109277e-01 -4.92446363e-01 -1.04229939e+00 2.33327180e-01 1.69882640e-01 -2.72886127e-01 7.55029842e-02 1.16146243e+00 7.13191867e-01 -1.36870779e-02 5.24978101e-01 -1.03073847e+00 -4.81734693e-01 5.48770666e-01 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f7d9221a-18c1-42d9-96b8-1b64c1cbc8f8	weakly-supervised-learning-of-mid-level	1611.05603	null	http://arxiv.org/abs/1611.05603v1	http://arxiv.org/pdf/1611.05603v1.pdf	Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization	State-of-the-art methods treat pedestrian attribute recognition as a multi-label image classification problem. The location information of person attributes is usually eliminated or simply encoded in the rigid splitting of whole body in previous work. In this paper, we formulate the task in a weakly-supervised attribute localization framework. Based on GoogLeNet, firstly, a set of mid-level attribute features are discovered by novelly designed detection layers, where a max-pooling based weakly-supervised object detection technique is used to train these layers with only image-level labels without the need of bounding box annotations of pedestrian attributes. Secondly, attribute labels are predicted by regression of the detection response magnitudes. Finally, the locations and rough shapes of pedestrian attributes can be inferred by performing clustering on a fusion of activation maps of the detection layers, where the fusion weights are estimated as the correlation strengths between each attribute and its relevant mid-level features. Extensive experiments are performed on the two currently largest pedestrian attribute datasets, i.e. the PETA dataset and the RAP dataset. Results show that the proposed method has achieved competitive performance on attribute recognition, compared to other state-of-the-art methods. Moreover, the results of attribute localization are visualized to understand the characteristics of the proposed method.	['Zhang Zhang', 'Kaiqi Huang', 'Biao Leng', 'Kai Yu', 'Dangwei Li']	2016-11-17	null	null	null	null	['pedestrian-attribute-recognition', 'multi-label-image-classification']	['computer-vision', 'computer-vision']	[ 8.54196623e-02 -2.89152097e-02 -9.18834507e-02 -8.59734118e-01 -5.56935012e-01 -3.32617104e-01 6.73625171e-01 5.39397001e-01 -6.78936124e-01 6.54180646e-01 7.08256215e-02 4.22299296e-01 7.62185082e-02 -7.97995508e-01 -7.08150625e-01 -9.77462530e-01 8.92953947e-03 6.36452079e-01 1.72359377e-01 -4.02816630e-04 4.37179916e-02 2.73348331e-01 -1.82374835e+00 5.03546834e-01 6.97674513e-01 1.51998782e+00 -1.60098672e-01 2.23995611e-01 2.06124038e-01 4.57762539e-01 -3.32751781e-01 -6.64021969e-01 1.29337341e-01 3.90270464e-02 -6.35309517e-01 3.19644719e-01 7.91625440e-01 -1.17825389e-01 -1.10325992e-01 1.08716536e+00 4.71590668e-01 1.56179950e-01 9.01885748e-01 -1.25331366e+00 -6.08277738e-01 9.53921527e-02 -6.64470375e-01 7.16476142e-02 4.94551927e-01 1.48706302e-01 1.06597340e+00 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fe7d64db-7849-4672-8d18-e5e29670f495	alexa-conversations-an-extensible-data-driven	2104.09088	null	https://arxiv.org/abs/2104.09088v1	https://arxiv.org/pdf/2104.09088v1.pdf	Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems	Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Training each component requires annotations which are hard to obtain for every new domain, limiting scalability of such systems. Similarly, rule-based dialogue systems require extensive writing and maintenance of rules and do not scale either. End-to-End dialogue systems, on the other hand, do not require module-specific annotations but need a large amount of data for training. To overcome these problems, in this demo, we present Alexa Conversations, a new approach for building goal-oriented dialogue systems that is scalable, extensible as well as data efficient. The components of this system are trained in a data-driven manner, but instead of collecting annotated conversations for training, we generate them using a novel dialogue simulator based on a few seed dialogues and specifications of APIs and entities provided by the developer. Our approach provides out-of-the-box support for natural conversational phenomena like entity sharing across turns or users changing their mind during conversation without requiring developers to provide any such dialogue flows. We exemplify our approach using a simple pizza ordering task and showcase its value in reducing the developer burden for creating a robust experience. Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over $50\%$ improvement in turn-level action signature prediction accuracy.	['Eddie Wang', 'Nikko Strom', 'Minmin Shen', 'Abhishek Sethi', 'Vittorio Perera', 'Shachi Paul', 'Yi Pan', 'Vishal Naik', 'Angeliki Metallinou', 'Arindam Mandal', 'Qing Liu', 'Chien-Wei Lin', 'Anuj Goyal', 'Peter Ku', 'Prakash Krishnan', 'Jiun-Yu Kao', 'Abhay Jha', 'Jan Jezabek', 'Dilek Hakkani-Tur', 'Rahul Goel', 'Shuyang Gao', 'Raefer Gabriel', 'Maryam Fazel-Zarandi', 'Tagyoung Chung', 'Shubhra Chandra', 'Arijit Biswas', 'Nehal Belgamwar', 'Vincent Auvray', 'Sanchit Agarwal', 'Suranjit Adhikari', 'Anish Acharya']	2021-04-19	null	https://aclanthology.org/2021.naacl-demos.15	https://aclanthology.org/2021.naacl-demos.15.pdf	naacl-2021-4	['goal-oriented-dialogue-systems']	['natural-language-processing']	[-9.89563018e-02 7.19474792e-01 8.36464539e-02 -6.00709736e-01 -5.38598001e-01 -1.04105234e+00 8.36051166e-01 2.28781048e-02 -1.80378526e-01 8.64568710e-01 2.83375859e-01 -5.64588606e-01 1.43768445e-01 -6.10443950e-01 -2.94852406e-01 1.16440035e-01 -5.86101972e-02 9.08356547e-01 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7aa5a8d3-dd40-4a36-a272-a69a8ded36d2	ensemble-of-heterogeneous-flexible-neural	1705.05592	null	http://arxiv.org/abs/1705.05592v1	http://arxiv.org/pdf/1705.05592v1.pdf	Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming	Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population.	['Václav Snášel', 'Ajith Abraham', 'Varun Kumar Ojha']	2017-05-16	null	null	null	null	['time-series-regression']	['time-series']	[ 2.02780128e-01 -2.85615951e-01 2.78845578e-01 -1.87328771e-01 2.27270387e-02 -4.15972054e-01 3.19652003e-03 7.28452876e-02 -3.14377636e-01 1.15895212e+00 -2.61697024e-01 1.78498149e-01 -9.93553460e-01 -9.99676168e-01 -4.39247847e-01 -1.24920595e+00 3.70942019e-02 5.28136909e-01 -1.21267557e-01 -4.26563323e-01 5.92115998e-01 5.30001938e-01 -2.01653743e+00 -6.59777671e-02 1.74841666e+00 1.05946684e+00 4.60266858e-01 1.34943143e-01 -1.60557702e-01 2.72028625e-01 -6.06305838e-01 -2.96647847e-01 2.69026786e-01 -4.13552016e-01 -2.39593282e-01 -1.44283339e-01 -4.02184874e-01 3.92366886e-01 3.98281842e-01 7.55546510e-01 8.47478807e-01 4.84302938e-01 8.28346133e-01 -1.22775030e+00 -6.08709693e-01 7.91289032e-01 -8.92449170e-02 -9.03180838e-02 -3.23877543e-01 -2.51696017e-02 5.80604672e-01 -6.93326235e-01 4.20297205e-01 1.08182216e+00 6.99581087e-01 2.97580391e-01 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2cbcde0e-c1ce-4174-b64b-4ba664cf800e	defeenet-consecutive-3d-human-motion	2304.04496	null	https://arxiv.org/abs/2304.04496v2	https://arxiv.org/pdf/2304.04496v2.pdf	DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback	Let us rethink the real-world scenarios that require human motion prediction techniques, such as human-robot collaboration. Current works simplify the task of predicting human motions into a one-off process of forecasting a short future sequence (usually no longer than 1 second) based on a historical observed one. However, such simplification may fail to meet practical needs due to the neglect of the fact that motion prediction in real applications is not an isolated ``observe then predict'' unit, but a consecutive process composed of many rounds of such unit, semi-overlapped along the entire sequence. As time goes on, the predicted part of previous round has its corresponding ground truth observable in the new round, but their deviation in-between is neither exploited nor able to be captured by existing isolated learning fashion. In this paper, we propose DeFeeNet, a simple yet effective network that can be added on existing one-off prediction models to realize deviation perception and feedback when applied to consecutive motion prediction task. At each prediction round, the deviation generated by previous unit is first encoded by our DeFeeNet, and then incorporated into the existing predictor to enable a deviation-aware prediction manner, which, for the first time, allows for information transmit across adjacent prediction units. We design two versions of DeFeeNet as MLP-based and GRU-based, respectively. On Human3.6M and more complicated BABEL, experimental results indicate that our proposed network improves consecutive human motion prediction performance regardless of the basic model.	['Jianfeng Lu', 'Weiqing Li', 'Dong Wei', 'Bin Li', 'Huaijiang Sun', 'Xiaoning Sun']	2023-04-10	null	http://openaccess.thecvf.com//content/CVPR2023/html/Sun_DeFeeNet_Consecutive_3D_Human_Motion_Prediction_With_Deviation_Feedback_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_DeFeeNet_Consecutive_3D_Human_Motion_Prediction_With_Deviation_Feedback_CVPR_2023_paper.pdf	cvpr-2023-1	['motion-prediction', 'human-motion-prediction']	['computer-vision', 'time-series']	[ 2.84230351e-01 5.28729022e-01 -2.09238082e-01 -1.49586335e-01 -2.98985839e-01 -9.83860046e-02 3.76354575e-01 -1.55707970e-01 -3.88269186e-01 7.30032563e-01 2.49152154e-01 -1.36408985e-01 1.85064331e-01 -6.46684527e-01 -7.79008031e-01 -7.52886534e-01 -2.43079856e-01 3.69865328e-01 6.64428592e-01 -3.68457705e-01 -1.11409150e-01 2.34263882e-01 -1.36453855e+00 5.00272453e-01 4.57514852e-01 9.10526514e-01 6.70949519e-01 7.72181749e-01 2.22764879e-01 1.05242872e+00 -4.89524513e-01 8.25298652e-02 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eb2d0c1a-f6e5-47ac-940c-26bebb4797db	otiea-ontology-enhanced-triple-intrinsic	2305.01561	null	https://arxiv.org/abs/2305.01561v1	https://arxiv.org/pdf/2305.01561v1.pdf	OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment	Cross-lingual and cross-domain knowledge alignment without sufficient external resources is a fundamental and crucial task for fusing irregular data. As the element-wise fusion process aiming to discover equivalent objects from different knowledge graphs (KGs), entity alignment (EA) has been attracting great interest from industry and academic research recent years. Most of existing EA methods usually explore the correlation between entities and relations through neighbor nodes, structural information and external resources. However, the complex intrinsic interactions among triple elements and role information are rarely modeled in these methods, which may lead to the inadequate illustration for triple. In addition, external resources are usually unavailable in some scenarios especially cross-lingual and cross-domain applications, which reflects the little scalability of these methods. To tackle the above insufficiency, a novel universal EA framework (OTIEA) based on ontology pair and role enhancement mechanism via triple-aware attention is proposed in this paper without introducing external resources. Specifically, an ontology-enhanced triple encoder is designed via mining intrinsic correlations and ontology pair information instead of independent elements. In addition, the EA-oriented representations can be obtained in triple-aware entity decoder by fusing role diversity. Finally, a bidirectional iterative alignment strategy is deployed to expand seed entity pairs. The experimental results on three real-world datasets show that our framework achieves a competitive performance compared with baselines.	['Chaoqun Jiang', 'Min Yang', 'Xueyan Zhao', 'Chengxiang Tan', 'Zhishuo Zhang']	2023-05-02	null	null	null	null	['entity-alignment', 'entity-alignment']	['knowledge-base', 'natural-language-processing']	[ 7.05797076e-02 8.31696391e-02 -4.62563753e-01 -3.76319766e-01 -4.39495087e-01 -2.31788367e-01 3.36043417e-01 3.46848398e-01 -4.12706316e-01 8.12659740e-01 4.65366602e-01 1.21743746e-01 -5.60425282e-01 -9.99109149e-01 -6.87792063e-01 -5.26724517e-01 2.62491047e-01 3.48323762e-01 3.44596535e-01 -5.70520163e-01 -2.08935603e-01 -1.52246475e-01 -1.54640484e+00 1.32151157e-01 1.39637828e+00 9.65258896e-01 2.77636796e-01 -3.48477960e-01 -5.49084306e-01 6.04684055e-01 -1.98819771e-01 -8.82578373e-01 1.06277257e-01 -3.64701658e-01 -6.00914061e-01 -9.29083023e-03 -1.32441863e-01 1.24758512e-01 -3.75007272e-01 1.23203182e+00 6.69511259e-01 -2.05757022e-01 1.26385912e-01 -1.30939412e+00 -7.17820764e-01 1.08014810e+00 -6.60727024e-01 1.22510210e-01 2.89636612e-01 -7.98903778e-02 1.20234966e+00 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f78e87ca-33e2-431b-8d13-8cfd4ea1a96d	semantic-segmentation-of-trajectories-with-1	1912.05727	null	https://arxiv.org/abs/1912.05727v1	https://arxiv.org/pdf/1912.05727v1.pdf	Semantic segmentation of trajectories with improved agent models for pedestrian behavior analysis	In this paper, we propose a method for semantic segmentation of pedestrian trajectories based on pedestrian behavior models, or agents. The agents model the dynamics of pedestrian movements in two-dimensional space using a linear dynamics model and common start and goal locations of trajectories. First, agent models are estimated from the trajectories obtained from image sequences. Our method is built on top of the Mixture model of Dynamic pedestrian Agents (MDA); however, the MDA's trajectory modeling and estimation are improved. Then, the trajectories are divided into semantically meaningful segments. The subsegments of a trajectory are modeled by applying a hidden Markov model using the estimated agent models. Experimental results with a real trajectory dataset show the effectiveness of the proposed method as compared to the well-known classical Ramer-Douglas-Peucker algorithm and also to the original MDA model.	['Kazufumi Kaneda', 'Daisuke Ogawa', 'Toru Tamaki', 'Bisser Raytchev']	2019-12-12	null	null	null	null	['trajectory-modeling']	['time-series']	[-6.01920664e-01 -2.36394957e-01 -3.11264634e-01 -1.21787511e-01 -1.17129169e-01 -5.43629885e-01 8.36618185e-01 8.47367123e-02 -6.27371669e-01 5.79391181e-01 1.07356966e-01 -2.58922040e-01 1.80466592e-01 -9.46518064e-01 -5.47761321e-01 -7.09922194e-01 -1.57590449e-01 7.09628940e-01 7.87709653e-01 5.82320504e-02 -5.95946275e-02 6.60990834e-01 -1.31555092e+00 -1.00103758e-01 1.07903636e+00 3.42403740e-01 2.49521583e-01 1.06547594e+00 -3.72389793e-01 9.87860978e-01 -3.36611986e-01 -5.47086596e-01 5.37714250e-02 -3.64398330e-01 -7.41739213e-01 5.12814820e-01 -2.30003357e-01 -5.42228580e-01 -5.79241097e-01 1.02047908e+00 -1.85419574e-01 6.09040558e-01 9.58631933e-01 -1.78662360e+00 -3.88493508e-01 4.71639812e-01 -4.96217102e-01 1.12214230e-01 1.72964737e-01 1.87711269e-01 3.41102898e-01 -6.18319571e-01 6.09128475e-01 1.52385461e+00 5.97901225e-01 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-5.75516939e-01 5.18659830e-01 1.57946438e-01 -7.22172320e-01 9.61988047e-02 5.89144051e-01]	[6.164719104766846, 0.9434864521026611]
87cb1579-fb9f-466b-8103-c9cae2ad0411	self-supervised-graph-structure-refinement	2211.06545	null	https://arxiv.org/abs/2211.06545v4	https://arxiv.org/pdf/2211.06545v4.pdf	Self-Supervised Graph Structure Refinement for Graph Neural Networks	Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. However, as GSL is essentially a link prediction task, whose goal may largely differ from the goal of the downstream task. The inconsistency of these two goals limits the GSL methods to learn the potential optimal graph structure. Moreover, the joint learning framework suffers from scalability issues in terms of time and space during the process of estimation and optimization of the adjacency matrix. To mitigate these issues, we propose a graph structure refinement (GSR) framework with a pretrain-finetune pipeline. Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks. Then, the graph structure is refined by adding and removing edges according to the edge probabilities estimated by the pre-trained model. Finally, the fine-tuning GNN is initialized by the pre-trained model and optimized toward downstream tasks. With the refined graph structure remaining static in the fine-tuning space, GSR avoids estimating and optimizing graph structure in the fine-tuning phase which enjoys great scalability and efficiency. Moreover, the fine-tuning GNN is boosted by both migrating knowledge and refining graphs. Extensive experiments are conducted to evaluate the effectiveness (best performance on six benchmark datasets), efficiency, and scalability (13.8x faster using 32.8% GPU memory compared to the best GSL baseline on Cora) of the proposed model.	['Yanfang Ye', 'Chuxu Zhang', 'Mingxuan Ju', 'Qianlong Wen', 'Jianan Zhao']	2022-11-12	null	null	null	null	['graph-structure-learning']	['graphs']	[ 9.88794938e-02 2.94015557e-01 -2.21295819e-01 -1.81859583e-01 -4.97292131e-01 -5.89673400e-01 4.81816679e-01 1.30783021e-01 -4.13553238e-01 4.23114538e-01 4.08650227e-02 -4.74773645e-01 -2.50345230e-01 -1.01542199e+00 -8.12614858e-01 -6.02600992e-01 -1.30319655e-01 4.08643007e-01 4.36227679e-01 -1.64744362e-01 4.83577065e-02 3.93720001e-01 -1.11252856e+00 -9.52523574e-02 8.46515417e-01 7.62139618e-01 3.88908505e-01 6.93041801e-01 -1.13948420e-01 6.58381522e-01 -1.88527346e-01 -5.96148491e-01 3.23777169e-01 -1.38712615e-01 -7.52686739e-01 -5.01015736e-03 5.82568228e-01 -7.93160349e-02 -5.06059825e-01 1.10713530e+00 4.60773319e-01 6.22981898e-02 2.55810410e-01 -1.30730486e+00 -4.05619949e-01 8.21580112e-01 -7.34154046e-01 9.69929844e-02 -2.22802937e-01 1.25168085e-01 1.44795501e+00 -6.50144160e-01 6.16509914e-01 1.04686880e+00 8.15914214e-01 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-2.87052184e-01 -6.87806368e-01 -4.23639357e-01 2.25603506e-01 -4.01691273e-02 -4.39085096e-01 -2.49024332e-02 -2.14309961e-01]	[7.182196140289307, 6.245276927947998]
83752878-297f-4720-8940-4f26ffde0f44	a-big-data-approach-towards-sarcasm-detection	2306.00445	null	https://arxiv.org/abs/2306.00445v1	https://arxiv.org/pdf/2306.00445v1.pdf	A big data approach towards sarcasm detection in Russian	We present a set of deterministic algorithms for Russian inflection and automated text synthesis. These algorithms are implemented in a publicly available web-service www.passare.ru. This service provides functions for inflection of single words, word matching and synthesis of grammatically correct Russian text. Selected code and datasets are available at https://github.com/passare-ru/PassareFunctions/ Performance of the inflectional functions has been tested against the annotated corpus of Russian language OpenCorpora, compared with that of other solutions, and used for estimating the morphological variability and complexity of different parts of speech in Russian.	['T. A. Zhukov', 'T. M. Sadykov', 'A. A. Gurin']	2023-06-01	null	null	null	null	['sarcasm-detection']	['natural-language-processing']	[-2.93555439e-01 -7.84707442e-02 -6.77989870e-02 -2.76086509e-01 -8.67017388e-01 -1.11983645e+00 4.82317865e-01 4.69232559e-01 -2.76882082e-01 8.29567730e-01 5.42080522e-01 -8.06648076e-01 7.60056973e-02 -8.70525599e-01 -2.01100960e-01 -3.83948594e-01 7.22009599e-01 7.66419530e-01 -1.37922361e-01 -7.46686101e-01 5.16642630e-01 5.23173749e-01 -1.44952536e+00 3.39186281e-01 1.09251499e+00 1.21539406e-01 3.63053739e-01 9.68359590e-01 -7.35226572e-01 3.66856128e-01 -7.58350551e-01 -6.30431950e-01 2.81801913e-02 -4.80580479e-01 -1.20847023e+00 -1.86872646e-01 1.65504009e-01 1.66053295e-01 -2.48431593e-01 1.28827095e+00 4.95332241e-01 2.07767606e-01 6.14660919e-01 -4.35218632e-01 -1.02538288e+00 1.77808166e+00 3.28304619e-02 4.75475222e-01 6.87839091e-01 1.58659637e-01 1.12160122e+00 -1.01801527e+00 7.86812484e-01 1.27761436e+00 3.77950013e-01 7.32959628e-01 -1.31250536e+00 -5.81649005e-01 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2.87393600e-01 -2.29232028e-01 1.71081364e-01 -5.16350210e-01 6.64361790e-02]	[10.779770851135254, 10.404287338256836]
a8635b98-ad34-492a-b708-58772de0c829	bioelectra-pretrained-biomedical-text-encoder	null	null	https://aclanthology.org/2021.bionlp-1.16	https://aclanthology.org/2021.bionlp-1.16.pdf	BioELECTRA:Pretrained Biomedical text Encoder using Discriminators	Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. We apply ‘replaced token detection’ pretraining technique proposed by ELECTRA and pretrain a biomedical language model from scratch using biomedical text and vocabulary. We introduce BioELECTRA, a biomedical domain-specific language encoder model that adapts ELECTRA for the Biomedical domain. WE evaluate our model on the BLURB and BLUE biomedical NLP benchmarks. BioELECTRA outperforms the previous models and achieves state of the art (SOTA) on all the 13 datasets in BLURB benchmark and on all the 4 Clinical datasets from BLUE Benchmark across 7 different NLP tasks. BioELECTRA pretrained on PubMed and PMC full text articles performs very well on Clinical datasets as well. BioELECTRA achieves new SOTA 86.34%(1.39% accuracy improvement) on MedNLI and 64% (2.98% accuracy improvement) on PubMedQA dataset.	['Malaikannan Sankarasubbu', 'Bhuvana Kundumani', 'Kamal raj Kanakarajan']	2021-06-11	null	null	null	acl-anthology-2021-6	['medical-named-entity-recognition']	['natural-language-processing']	[ 1.72168389e-01 3.20263118e-01 -5.91185749e-01 -5.78950226e-01 -1.22527659e+00 -1.23175934e-01 2.20916763e-01 6.49438381e-01 -1.09298539e+00 1.42037213e+00 3.65153760e-01 -4.05264020e-01 -1.26803026e-01 -4.17104453e-01 -1.09201467e+00 -3.19768965e-01 -1.56433657e-01 9.35567737e-01 -3.72340947e-01 5.52833639e-02 -3.11066061e-01 1.82260215e-01 -5.86244345e-01 8.68685305e-01 6.82103753e-01 8.41753542e-01 7.64785334e-02 6.36465609e-01 -1.19228609e-01 7.62348711e-01 -5.10821879e-01 -4.27153081e-01 -1.58694357e-01 -9.38300863e-02 -1.06778920e+00 -8.54211807e-01 5.06316960e-01 1.95090488e-01 -3.22533071e-01 7.69589305e-01 9.40983295e-01 -4.28464890e-01 7.36079872e-01 -6.15689218e-01 -6.78500295e-01 1.12499571e+00 -5.45356214e-01 3.09048086e-01 8.49416852e-02 -8.52706060e-02 8.66823673e-01 -1.00807500e+00 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760e85f1-c048-449f-a9c2-78f1af86947e	auebtwittersentiment-at-semeval-2016-task-4-a	null	null	https://aclanthology.org/S16-1012	https://aclanthology.org/S16-1012.pdf	aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis	null	['Stavros Giorgis', 'John Pavlopoulos', 'Prodromos Malakasiotis', 'Ion Androutsopoulos', 'Apostolos Rousas']	2016-06-01	null	null	null	semeval-2016-6	['twitter-sentiment-analysis']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-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 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ffb5e504-82f0-4413-a10c-9e1e57f0319e	algorithmic-improvements-for-deep	1911.12511	null	https://arxiv.org/abs/1911.12511v1	https://arxiv.org/pdf/1911.12511v1.pdf	Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction	Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.	['Marc G. Bellemare', 'Hugo Larochelle', 'William Fedus', 'Doina Precup', 'Vishal Jain']	2019-11-28	null	null	null	null	['text-based-games']	['playing-games']	[ 2.11632811e-02 3.92746538e-01 -2.77007729e-01 2.47780591e-01 -6.22023821e-01 -7.45027721e-01 7.99767852e-01 -1.57333557e-02 -8.34715366e-01 1.11153686e+00 3.71936142e-01 -5.75454354e-01 -4.54141766e-01 -8.46853137e-01 -5.88921010e-01 -5.75573385e-01 -2.77955472e-01 6.01051390e-01 3.81799012e-01 -8.87389839e-01 2.93958396e-01 6.14119209e-02 -1.37366498e+00 5.76782450e-02 6.60360634e-01 6.44893587e-01 2.83433914e-01 1.01010764e+00 2.86147028e-01 1.54016924e+00 -7.35967338e-01 -2.25939423e-01 5.81165195e-01 -5.62611401e-01 -9.92873549e-01 3.10788184e-01 -1.40308440e-01 -7.83726811e-01 -6.78669333e-01 8.57357979e-01 4.16228950e-01 3.00181806e-01 5.10216177e-01 -1.40276217e+00 -2.10563108e-01 7.19188213e-01 -2.75131375e-01 1.63746387e-01 4.23590630e-01 5.59438407e-01 1.46164513e+00 -1.48622870e-01 6.84263408e-01 1.10243845e+00 2.49505967e-01 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-7.30391026e-01 1.90583467e-01 5.38143575e-01 -7.04151809e-01 -5.00396863e-02 -4.84099925e-01]	[3.8457467555999756, 1.4820863008499146]
953d7aef-02f7-4bdc-8245-4fa9b7edfd52	optical-flow-reuse-based-bidirectional	2110.06786	null	https://arxiv.org/abs/2110.06786v3	https://arxiv.org/pdf/2110.06786v3.pdf	Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution	In this paper, we consider the task of space-time video super-resolution (ST-VSR), which can increase the spatial resolution and frame rate for a given video simultaneously. Despite the remarkable progress of recent methods, most of them still suffer from high computational costs and inefficient long-range information usage. To alleviate these problems, we propose a Bidirectional Recurrence Network (BRN) with the optical-flow-reuse strategy to better use temporal knowledge from long-range neighboring frames for high-efficiency reconstruction. Specifically, an efficient and memory-saving multi-frame motion utilization strategy is proposed by reusing the intermediate flow of adjacent frames, which considerably reduces the computation burden of frame alignment compared with traditional LSTM-based designs. In addition, the proposed hidden state in BRN is updated by the reused optical flow and refined by the Feature Refinement Module (FRM) for further optimization. Moreover, by utilizing intermediate flow estimation, the proposed method can inference non-linear motion and restore details better. Extensive experiments demonstrate that our optical-flow-reuse-based bidirectional recurrent network (OFR-BRN) is superior to state-of-the-art methods in accuracy and efficiency.	['Zhenzhong Chen', 'Han Zhu', 'Huairui Wang', 'Yuantong Zhang']	2021-10-13	null	null	null	null	['space-time-video-super-resolution', 'video-super-resolution']	['computer-vision', 'computer-vision']	[ 2.52382159e-01 -5.70568442e-01 -3.81433904e-01 -2.84961332e-02 -4.39204633e-01 7.96499103e-02 1.75600633e-01 -6.81582451e-01 -3.65611494e-01 9.60597277e-01 5.18182576e-01 -6.68726563e-02 -1.79208964e-01 -6.64554477e-01 -4.70704079e-01 -7.23587990e-01 2.17347741e-01 -4.13042247e-01 6.68907404e-01 -1.31572679e-01 4.02680248e-01 5.31463861e-01 -1.53846323e+00 2.73473531e-01 7.20358610e-01 1.01777804e+00 8.02728057e-01 5.43706119e-01 -8.64439905e-02 1.37053990e+00 -1.66449323e-01 1.36843622e-01 1.51088789e-01 -6.06360316e-01 -7.95202971e-01 9.60630029e-02 4.86228019e-01 -8.74713719e-01 -9.54751313e-01 8.49817693e-01 4.99248713e-01 6.67515039e-01 -1.41159266e-01 -5.53239524e-01 -7.67294526e-01 2.45217875e-01 -8.25648308e-01 7.48339713e-01 2.85190314e-01 9.28591788e-02 6.48181558e-01 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8eb4a4b6-be15-4bd6-9d84-3be366d0ef14	rwen-tts-relation-aware-word-encoding-network	2212.07939	null	https://arxiv.org/abs/2212.07939v1	https://arxiv.org/pdf/2212.07939v1.pdf	RWEN-TTS: Relation-aware Word Encoding Network for Natural Text-to-Speech Synthesis	With the advent of deep learning, a huge number of text-to-speech (TTS) models which produce human-like speech have emerged. Recently, by introducing syntactic and semantic information w.r.t the input text, various approaches have been proposed to enrich the naturalness and expressiveness of TTS models. Although these strategies showed impressive results, they still have some limitations in utilizing language information. First, most approaches only use graph networks to utilize syntactic and semantic information without considering linguistic features. Second, most previous works do not explicitly consider adjacent words when encoding syntactic and semantic information, even though it is obvious that adjacent words are usually meaningful when encoding the current word. To address these issues, we propose Relation-aware Word Encoding Network (RWEN), which effectively allows syntactic and semantic information based on two modules (i.e., Semantic-level Relation Encoding and Adjacent Word Relation Encoding). Experimental results show substantial improvements compared to previous works.	['Insoo Oh', 'Yoonseok Hong', 'HyeongRae Noh', 'Shinhyeok Oh']	2022-12-15	null	null	null	null	['text-to-speech-synthesis']	['speech']	[ 1.89043716e-01 4.06260490e-01 -4.33130383e-01 -5.28664291e-01 -2.11553618e-01 -2.03408971e-01 5.49465537e-01 2.54950225e-01 -2.87285596e-01 5.07904291e-01 5.85872650e-01 -5.72717071e-01 7.70180598e-02 -9.91853535e-01 -3.40738505e-01 -2.13819981e-01 2.87622482e-01 2.09509045e-01 4.75807041e-01 -5.35415411e-01 -7.12478310e-02 6.71889558e-02 -1.34759116e+00 4.61387515e-01 1.05453539e+00 8.83139372e-01 4.35779750e-01 2.17331544e-01 -8.72287214e-01 9.73960221e-01 -7.81461120e-01 -4.74937797e-01 -2.30063409e-01 -5.14613867e-01 -1.02431023e+00 -1.24602113e-02 -2.38211438e-01 -3.95042039e-02 -4.39458758e-01 9.45635140e-01 2.27109641e-01 3.47409695e-01 3.39797199e-01 -1.17836332e+00 -1.11825275e+00 1.17251754e+00 -1.26817778e-01 1.32342905e-01 5.92842758e-01 -7.39132017e-02 1.35958707e+00 -7.42122293e-01 4.18736607e-01 1.61090970e+00 3.77783060e-01 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26556a8b-bf4d-4397-a53e-22577d46e313	module-wise-network-quantization-for-6d	2303.06753	null	https://arxiv.org/abs/2303.06753v1	https://arxiv.org/pdf/2303.06753v1.pdf	Module-Wise Network Quantization for 6D Object Pose Estimation	Many edge applications, such as collaborative robotics and spacecraft rendezvous, can benefit from 6D object pose estimation, but must do so on embedded platforms. Unfortunately, existing 6D pose estimation networks are typically too large for deployment in such situations and must therefore be compressed, while maintaining reliable performance. In this work, we present an approach to doing so by quantizing such networks. More precisely, we introduce a module-wise quantization strategy that, in contrast to uniform and mixed-precision quantization, accounts for the modular structure of typical 6D pose estimation frameworks. We demonstrate that uniquely compressing these modules outperforms uniform and mixed-precision quantization techniques. Moreover, our experiments evidence that module-wise quantization can lead to a significant accuracy boost. We showcase the generality of our approach using different datasets, quantization methodologies, and network architectures, including the recent ZebraPose.	['Mathieu Salzmann', 'Yinlin Hu', 'Andrew Price', 'Saqib Javed']	2023-03-12	null	null	null	null	['6d-pose-estimation-1', '6d-pose-estimation']	['computer-vision', 'computer-vision']	[-1.78519621e-01 1.16312191e-01 -3.12851220e-01 -1.32687539e-01 -4.84906793e-01 -8.69894922e-01 6.49223149e-01 1.22160502e-01 -2.98913896e-01 6.16629541e-01 1.00219898e-01 -4.44636852e-01 -4.27482486e-01 -9.24342752e-01 -8.39524329e-01 -1.96339756e-01 -5.35505235e-01 7.64206290e-01 1.89665407e-01 -3.65631312e-01 3.01951408e-01 6.67853892e-01 -1.56934202e+00 -4.26935792e-01 5.55475891e-01 1.26498294e+00 6.14665588e-03 7.05978811e-01 1.78837016e-01 4.69832450e-01 -5.93735456e-01 -2.21603721e-01 5.84164023e-01 1.36391789e-01 -6.85700655e-01 1.27844438e-01 6.05426490e-01 -6.62999094e-01 -3.86184394e-01 9.94683504e-01 4.09056664e-01 5.17992377e-02 4.97964025e-01 -1.33466601e+00 3.41123305e-02 4.63232547e-01 -1.32481441e-01 -9.88704637e-02 4.08125401e-01 1.23518206e-01 1.01973832e+00 -6.87168300e-01 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ae22372e-dfca-49ba-b6e3-ccff2b423378	taxonomic-class-incremental-learning	2304.05547	null	https://arxiv.org/abs/2304.05547v1	https://arxiv.org/pdf/2304.05547v1.pdf	Taxonomic Class Incremental Learning	The problem of continual learning has attracted rising attention in recent years. However, few works have questioned the commonly used learning setup, based on a task curriculum of random class. This differs significantly from human continual learning, which is guided by taxonomic curricula. In this work, we propose the Taxonomic Class Incremental Learning (TCIL) problem. In TCIL, the task sequence is organized based on a taxonomic class tree. We unify existing approaches to CIL and taxonomic learning as parameter inheritance schemes and introduce a new such scheme for the TCIL learning. This enables the incremental transfer of knowledge from ancestor to descendant class of a class taxonomy through parameter inheritance. Experiments on CIFAR-100 and ImageNet-100 show the effectiveness of the proposed TCIL method, which outperforms existing SOTA methods by 2% in terms of final accuracy on CIFAR-100 and 3% on ImageNet-100.	['Nuno Vasconcelos', 'Zhiyuan Hu', 'Zonghuan Li', 'Yuzhao Chen']	2023-04-12	null	null	null	null	['class-incremental-learning']	['computer-vision']	[ 2.32874095e-01 -7.75770396e-02 -2.35294864e-01 -5.51329672e-01 -3.07732791e-01 -5.81354499e-01 4.73354250e-01 2.90568501e-01 -7.51025736e-01 1.07609951e+00 -1.08126909e-01 -3.70881051e-01 -4.68996733e-01 -8.31656694e-01 -7.35692441e-01 -5.56587517e-01 -1.61094919e-01 4.74022537e-01 7.29036570e-01 -2.18389072e-02 2.63205320e-01 1.15474343e-01 -1.88271832e+00 5.94497502e-01 1.21291435e+00 9.76889253e-01 1.72276959e-01 3.49110514e-01 -2.88131952e-01 9.61721122e-01 -7.59035110e-01 -3.64333719e-01 1.19026676e-01 -4.46285307e-01 -1.24467874e+00 1.34596854e-01 1.00314665e+00 -5.48699163e-02 3.02571952e-01 1.02754605e+00 2.98254013e-01 2.82964945e-01 7.80423284e-01 -1.38948953e+00 -5.57238102e-01 9.14075851e-01 -5.42827487e-01 3.36088866e-01 8.63288343e-02 -2.89651334e-01 1.04578876e+00 -7.69602299e-01 5.04512668e-01 1.25533712e+00 7.42826760e-01 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-4.54311222e-01 3.26922983e-01 1.42464176e-01 -9.84281361e-01 -2.64507383e-01 4.36780661e-01]	[9.537623405456543, 3.350789785385132]
c1cae501-1b22-49a5-84d0-4846b17d1dcc	solving-audio-inverse-problems-with-a	2210.15228	null	https://arxiv.org/abs/2210.15228v3	https://arxiv.org/pdf/2210.15228v3.pdf	Solving Audio Inverse Problems with a Diffusion Model	This paper presents CQT-Diff, a data-driven generative audio model that can, once trained, be used for solving various different audio inverse problems in a problem-agnostic setting. CQT-Diff is a neural diffusion model with an architecture that is carefully constructed to exploit pitch-equivariant symmetries in music. This is achieved by preconditioning the model with an invertible Constant-Q Transform (CQT), whose logarithmically-spaced frequency axis represents pitch equivariance as translation equivariance. The proposed method is evaluated with objective and subjective metrics in three different and varied tasks: audio bandwidth extension, inpainting, and declipping. The results show that CQT-Diff outperforms the compared baselines and ablations in audio bandwidth extension and, without retraining, delivers competitive performance against modern baselines in audio inpainting and declipping. This work represents the first diffusion-based general framework for solving inverse problems in audio processing.	['Vesa Välimäki', 'Jaakko Lehtinen', 'Eloi Moliner']	2022-10-27	null	null	null	null	['bandwidth-extension', 'audio-inpainting', 'bandwidth-extension']	['audio', 'audio', 'speech']	[ 4.02788013e-01 -1.15059324e-01 2.27392271e-01 -2.80459896e-02 -1.36261082e+00 -6.99577332e-01 4.72405344e-01 -4.93624598e-01 -2.26552650e-01 3.58427376e-01 7.09520280e-01 6.28655404e-02 -3.46978515e-01 -2.34359071e-01 -7.10991561e-01 -8.04428995e-01 -1.92291379e-01 3.55008632e-01 -2.71637172e-01 -4.46547478e-01 3.20128351e-01 2.43240014e-01 -1.27254713e+00 4.79595631e-01 6.01236820e-01 8.11944067e-01 4.20587398e-02 1.15142512e+00 5.78576088e-01 6.15091026e-01 -7.07233608e-01 -3.08633238e-01 4.72160727e-01 -7.89936721e-01 -6.18468463e-01 -1.97792888e-01 6.13985837e-01 -2.14387193e-01 -2.94258326e-01 7.82549679e-01 1.05563879e+00 1.60327181e-01 7.20957279e-01 -1.05228257e+00 -8.71571362e-01 9.56093371e-01 -6.61600471e-01 4.07877356e-01 3.92074496e-01 2.39825156e-02 1.20238125e+00 -1.32163370e+00 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2ec6edca-0907-40bc-91f3-4883dbe30955	a-unified-mammogram-analysis-method-via	1808.10646	null	http://arxiv.org/abs/1808.10646v1	http://arxiv.org/pdf/1808.10646v1.pdf	A Unified Mammogram Analysis Method via Hybrid Deep Supervision	Automatic mammogram classification and mass segmentation play a critical role in a computer-aided mammogram screening system. In this work, we present a unified mammogram analysis framework for both whole-mammogram classification and segmentation. Our model is designed based on a deep U-Net with residual connections, and equipped with the novel hybrid deep supervision (HDS) scheme for end-to-end multi-task learning. As an extension of deep supervision (DS), HDS not only can force the model to learn more discriminative features like DS, but also seamlessly integrates segmentation and classification tasks into one model, thus the model can benefit from both pixel-wise and image-wise supervisions. We extensively validate the proposed method on the widely-used INbreast dataset. Ablation study corroborates that pixel-wise and image-wise supervisions are mutually beneficial, evidencing the efficacy of HDS. The results of 5-fold cross validation indicate that our unified model matches state-of-the-art performance on both mammogram segmentation and classification tasks, which achieves an average segmentation Dice similarity coefficient (DSC) of 0.85 and a classification accuracy of 0.89. The code is available at https://github.com/angrypudding/hybrid-ds.	['Albert C. S. Chung', 'Han Zhang', 'Rongzhao Zhang']	2018-08-31	null	null	null	null	['whole-mammogram-classification']	['medical']	[ 4.09347206e-01 3.72060984e-01 -5.06869256e-01 -7.81796694e-01 -8.65011632e-01 6.66478202e-02 3.12349021e-01 3.09314519e-01 -5.07265508e-01 3.97770375e-01 -4.66116332e-02 -5.52905262e-01 -1.13461182e-01 -8.81642461e-01 -7.78116405e-01 -7.39540875e-01 -6.94879591e-02 3.48289847e-01 4.76489365e-01 -8.31475407e-02 -2.93873847e-01 2.82362282e-01 -1.11650240e+00 3.32087517e-01 9.67114091e-01 1.29381347e+00 2.48015195e-01 7.34870017e-01 2.96573013e-01 7.06129491e-01 -6.69026095e-03 -7.21895039e-01 2.39809334e-01 -4.98407841e-01 -7.73886204e-01 5.17546907e-02 2.96685070e-01 -5.52782714e-01 -3.00423026e-01 9.38642025e-01 6.14944220e-01 -3.40776116e-01 5.75470090e-01 -6.22229934e-01 -5.49877584e-01 6.84496701e-01 -9.34403718e-01 4.06885982e-01 -2.60233939e-01 -2.86992006e-02 8.86094213e-01 -4.30944443e-01 4.68662977e-01 8.53036046e-01 7.67474830e-01 5.01736224e-01 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3.25752169e-01 3.91031802e-01 -3.69076699e-01 3.61590609e-02 8.75991806e-02]	[14.957585334777832, -2.4450595378875732]
0f8cf2b4-9599-438e-bf28-48b3334b2cd6	feature-independent-action-spotting-without	null	null	http://openaccess.thecvf.com/content_cvpr_2014/html/Sun_Feature-Independent_Action_Spotting_2014_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2014/papers/Sun_Feature-Independent_Action_Spotting_2014_CVPR_paper.pdf	Feature-Independent Action Spotting Without Human Localization, Segmentation or Frame-wise Tracking	In this paper, we propose an unsupervised framework for action spotting in videos that does not depend on any specific feature (e.g. HOG/HOF, STIP, silhouette, bag-of-words, etc.). Furthermore, our solution requires no human localization, segmentation, or framewise tracking. This is achieved by treating the problem holistically as that of extracting the internal dynamics of video cuboids by modeling them in their natural form as multilinear tensors. To extract their internal dynamics, we devised a novel Two-Phase Decomposition (TP-Decomp) of a tensor that generates very compact and discriminative representations that are robust to even heavily perturbed data. Technically, a Rank-based Tensor Core Pyramid (Rank-TCP) descriptor is generated by combining multiple tensor cores under multiple ranks, allowing to represent video cuboids in a hierarchical tensor pyramid. The problem then reduces to a template matching problem, which is solved efficiently by using two boosting strategies: (1) to reduce search space, we filter the dense trajectory cloud extracted from the target video; (2) to boost the matching speed, we perform matching in an iterative coarse-to-fine manner. Experiments on 5 benchmarks show that our method outperforms current state-of-the-art under various challenging conditions. We also created a challenging dataset called Heavily Perturbed Video Array (HPVA) to validate the robustness of our framework under heavily perturbed situations.	['Hassan Foroosh', 'Chuan Sun', 'Marshall Tappen']	2014-06-01	null	null	null	cvpr-2014-6	['action-spotting']	['computer-vision']	[ 1.42704383e-01 -5.92099726e-01 -1.02877393e-01 1.87198192e-01 -7.36142993e-01 -6.86430931e-01 5.01381695e-01 -2.35786676e-01 -2.55797982e-01 2.18659267e-01 3.65268946e-01 1.63890168e-01 -2.36987248e-01 -4.85629529e-01 -7.25211382e-01 -9.55444455e-01 -1.75502539e-01 3.03856492e-01 4.61405993e-01 -2.70760320e-02 3.30043554e-01 5.04512668e-01 -1.71429229e+00 5.04346430e-01 7.59441733e-01 1.30786920e+00 -3.50649469e-02 4.40345824e-01 1.86515003e-01 8.99681449e-01 -2.63217092e-01 -2.20792785e-01 5.51990449e-01 -1.68697670e-01 -8.03498149e-01 7.83604741e-01 7.35351145e-01 -2.94301033e-01 -3.36097836e-01 9.43302274e-01 -3.45439054e-02 2.34710455e-01 5.45712113e-01 -1.23278594e+00 -8.59020948e-02 5.13367765e-02 -8.09815526e-01 5.55060469e-02 5.91348827e-01 2.26342469e-01 1.05035782e+00 -1.23899794e+00 8.06105793e-01 1.25452423e+00 5.31058133e-01 -1.57235023e-02 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7.94568241e-01 2.04067454e-01 -1.12175357e+00 -2.96508282e-01 2.81731427e-01]	[8.19711971282959, 0.39359423518180847]
5a24b3c3-4628-4a1c-a9e3-1bc12c72f6a8	loss-function-entropy-regularization-for	2205.00224	null	https://arxiv.org/abs/2205.00224v2	https://arxiv.org/pdf/2205.00224v2.pdf	Loss Function Entropy Regularization for Diverse Decision Boundaries	Is it possible to train several classifiers to perform meaningful crowd-sourcing to produce a better prediction label set without ground-truth annotation? This paper will modify the contrastive learning objectives to automatically train a self-complementing ensemble to produce a state-of-the-art prediction on the CIFAR10 and CIFAR100-20 tasks. This paper will present a straightforward method to modify a single unsupervised classification pipeline to automatically generate an ensemble of neural networks with varied decision boundaries to learn a more extensive feature set of classes. Loss Function Entropy Regularization (LFER) are regularization terms to be added to the pre-training and contrastive learning loss functions. LFER is a gear to modify the entropy state of the output space of unsupervised learning, thereby diversifying the latent representation of decision boundaries of neural networks. Ensemble trained with LFER has higher successful prediction accuracy for samples near decision boundaries. LFER is an adequate gear to perturb decision boundaries and has produced classifiers that beat state-of-the-art at the contrastive learning stage. Experiments show that LFER can produce an ensemble with accuracy comparable to the state-of-the-art yet have varied latent decision boundaries. It allows us to perform meaningful verification for samples near decision boundaries, encouraging the correct classification of near-boundary samples. By compounding the probability of correct prediction of a single sample amongst an ensemble of neural network trained, our method can improve upon a single classifier by denoising and affirming correct feature mappings.	['Sue Sin Chong']	2022-04-30	null	null	null	null	['unsupervised-image-classification']	['computer-vision']	[ 3.60287219e-01 4.94324863e-01 2.87244111e-01 -5.72274685e-01 -5.84453106e-01 -4.54840034e-01 4.94247019e-01 -1.62157223e-01 -5.55444717e-01 9.60514545e-01 5.25734276e-02 -4.71085496e-02 6.99511394e-02 -7.22791493e-01 -6.68715954e-01 -8.57774079e-01 1.16635174e-01 5.46220779e-01 3.73703897e-01 -2.97147810e-01 -2.57289018e-02 4.02412266e-01 -1.90866709e+00 8.59144986e-01 9.56264615e-01 1.13357222e+00 -8.55744928e-02 4.42781270e-01 1.79594029e-02 9.89529490e-01 -1.01515961e+00 -4.21678394e-01 5.25758028e-01 -5.52515209e-01 -7.55452514e-01 -1.55330881e-01 8.87862563e-01 1.40514627e-01 2.95271069e-01 7.78148174e-01 6.28153443e-01 2.93826699e-01 1.04846776e+00 -1.22726977e+00 -7.43127048e-01 6.32043123e-01 -3.80863808e-02 1.75134823e-01 4.93751951e-02 4.11455393e-01 6.77981257e-01 -9.54460621e-01 5.42080641e-01 1.02700734e+00 9.80803609e-01 8.55527461e-01 -1.46693468e+00 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e2718c89-731f-4a10-95cf-f17333f57ac4	hypothesis-transfer-learning-with-surrogate	2305.19694	null	https://arxiv.org/abs/2305.19694v1	https://arxiv.org/pdf/2305.19694v1.pdf	Hypothesis Transfer Learning with Surrogate Classification Losses	Hypothesis transfer learning (HTL) contrasts domain adaptation by allowing for a previous task leverage, named the source, into a new one, the target, without requiring access to the source data. Indeed, HTL relies only on a hypothesis learnt from such source data, relieving the hurdle of expansive data storage and providing great practical benefits. Hence, HTL is highly beneficial for real-world applications relying on big data. The analysis of such a method from a theoretical perspective faces multiple challenges, particularly in classification tasks. This paper deals with this problem by studying the learning theory of HTL through algorithmic stability, an attractive theoretical framework for machine learning algorithms analysis. In particular, we are interested in the statistical behaviour of the regularized empirical risk minimizers in the case of binary classification. Our stability analysis provides learning guarantees under mild assumptions. Consequently, we derive several complexity-free generalization bounds for essential statistical quantities like the training error, the excess risk and cross-validation estimates. These refined bounds allow understanding the benefits of transfer learning and comparing the behaviour of standard losses in different scenarios, leading to valuable insights for practitioners.	['Guillaume Staerman', 'Anass Aghbalou']	2023-05-31	null	null	null	null	['generalization-bounds']	['methodology']	[ 1.20213591e-01 4.49138165e-01 -2.98310459e-01 -2.06777558e-01 -1.02131808e+00 -4.32774454e-01 1.24563694e-01 5.19902408e-01 -5.33718824e-01 9.88099873e-01 -3.31871003e-01 -4.13539201e-01 -3.20102513e-01 -8.49675894e-01 -1.03065979e+00 -1.13565660e+00 -2.06729636e-01 3.40670079e-01 4.89897653e-02 -3.70100252e-02 -1.04447985e-02 3.47201318e-01 -1.33793783e+00 -1.21794410e-01 1.02831995e+00 1.39234746e+00 -8.48454908e-02 1.99062899e-01 1.52119219e-01 3.48141372e-01 -3.95107478e-01 -6.74244642e-01 5.33621490e-01 -3.55822563e-01 -5.35819829e-01 -4.98112589e-02 3.13785464e-01 2.29240283e-01 3.80154133e-01 1.22894728e+00 5.60555696e-01 1.97440431e-01 7.03706980e-01 -1.35581565e+00 -2.96494871e-01 4.92027104e-01 -3.38574708e-01 -1.79284811e-02 -7.25526065e-02 -7.83220604e-02 1.09079814e+00 -6.85369790e-01 3.48268032e-01 7.92753935e-01 8.35178912e-01 4.04960155e-01 -1.28076887e+00 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3.39444011e-01 5.29034019e-01 -6.12698317e-01 -6.74729943e-01 1.06978510e-02]	[8.292923927307129, 4.121738910675049]
5dbdbe9c-2ab5-4d06-86af-92dfe6ec0907	bosphorussign22k-sign-language-recognition	2004.01283	null	https://arxiv.org/abs/2004.01283v2	https://arxiv.org/pdf/2004.01283v2.pdf	BosphorusSign22k Sign Language Recognition Dataset	Sign Language Recognition is a challenging research domain. It has recently seen several advancements with the increased availability of data. In this paper, we introduce the BosphorusSign22k, a publicly available large scale sign language dataset aimed at computer vision, video recognition and deep learning research communities. The primary objective of this dataset is to serve as a new benchmark in Turkish Sign Language Recognition for its vast lexicon, the high number of repetitions by native signers, high recording quality, and the unique syntactic properties of the signs it encompasses. We also provide state-of-the-art human pose estimates to encourage other tasks such as Sign Language Production. We survey other publicly available datasets and expand on how BosphorusSign22k can contribute to future research that is being made possible through the widespread availability of similar Sign Language resources. We have conducted extensive experiments and present baseline results to underpin future research on our dataset.	['Lale Akarun', 'Necati Cihan Camgöz', 'Ahmet Alp Kındıroğlu', 'Oğulcan Özdemir']	2020-04-02	bosphorussign22k-sign-language-recognition-1	https://aclanthology.org/2020.signlang-1.30	https://aclanthology.org/2020.signlang-1.30.pdf	lrec-2020-5	['sign-language-production']	['natural-language-processing']	[-4.89213467e-02 -6.05168462e-01 -2.68093228e-01 -5.44186413e-01 -7.85934091e-01 -6.33600414e-01 6.96336508e-01 -1.18745518e+00 -4.84988332e-01 4.74472702e-01 8.29010665e-01 -1.47744268e-03 1.79763526e-01 6.14455789e-02 -4.71377403e-01 -5.94805658e-01 -4.78240885e-02 3.24082971e-01 2.93654710e-01 -2.93512970e-01 1.89200819e-01 6.04189336e-01 -1.78781283e+00 3.11360240e-01 4.72990543e-01 6.67115152e-01 -2.14393467e-01 6.88094854e-01 3.97948295e-01 9.64500248e-01 -3.45962703e-01 -3.64167392e-01 5.13499737e-01 -5.96348703e-01 -5.33919036e-01 -3.64526361e-02 1.38630939e+00 -7.24094152e-01 -7.75969684e-01 6.44260526e-01 9.58366394e-01 -7.53413066e-02 5.52821457e-01 -1.31825614e+00 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3640ffaf-2960-486b-b21f-5cd498d92806	overview-of-the-fifth-social-media-mining-for	null	null	https://aclanthology.org/2020.smm4h-1.4	https://aclanthology.org/2020.smm4h-1.4.pdf	Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020	The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics. The fifth iteration of the Social Media Mining for Health Applications (#SMM4H) shared tasks sought to advance the use of Twitter data (tweets) for pharmacovigilance, toxicovigilance, and epidemiology of birth defects. In addition to re-runs of three tasks, #SMM4H 2020 included new tasks for detecting adverse effects of medications in French and Russian tweets, characterizing chatter related to prescription medication abuse, and detecting self reports of birth defect pregnancy outcomes. The five tasks required methods for binary classification, multi-class classification, and named entity recognition (NER). With 29 teams and a total of 130 system submissions, participation in the #SMM4H shared tasks continues to grow.	['Graciela Gonzalez-Hernandez', 'Davy Weissenbacher', 'Elena Tutubalina', 'Abeed Sarker', 'Karen O’Connor', 'Anne-Lyse Minard', 'Zulfat Miftahutdinov', 'Arjun Magge', 'Ivan Flores', 'Ilseyar Alimova', 'Ari Klein']	null	null	null	null	smm4h-coling-2020-12	['epidemiology']	['medical']	[-4.66012117e-03 3.63189787e-01 -4.13516253e-01 -3.23210746e-01 -9.32677448e-01 -3.87546897e-01 5.45874000e-01 1.23433530e+00 -5.21665156e-01 7.26050258e-01 4.64258373e-01 -5.82063973e-01 -5.04257418e-02 -7.64169216e-01 -5.80075264e-01 -1.56471990e-02 -2.84293413e-01 5.05002916e-01 -3.48011851e-01 1.04907922e-01 1.76091507e-01 3.94418448e-01 -1.05517352e+00 7.85914421e-01 6.75576210e-01 3.69845003e-01 -2.77748495e-01 6.13010347e-01 -1.32766262e-01 1.11657214e+00 -6.26304030e-01 -4.14442271e-01 -3.65546733e-01 -2.35572860e-01 -9.88971949e-01 -6.42674744e-01 3.09207886e-02 9.79271457e-02 -1.26083242e-02 7.75505245e-01 9.24108744e-01 -1.31651565e-01 4.65798497e-01 -9.42977726e-01 -5.36162376e-01 8.51252913e-01 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1497990b-5699-4d31-9143-2c47426f8612	housex-a-fine-grained-house-music-dataset-and	2207.11690	null	https://arxiv.org/abs/2207.11690v2	https://arxiv.org/pdf/2207.11690v2.pdf	HouseX: A Fine-grained House Music Dataset and its Potential in the Music Industry	Machine sound classification has been one of the fundamental tasks of music technology. A major branch of sound classification is the classification of music genres. However, though covering most genres of music, existing music genre datasets often do not contain fine-grained labels that indicate the detailed sub-genres of music. In consideration of the consistency of genres of songs in a mixtape or in a DJ (live) set, we have collected and annotated a dataset of house music that provide 4 sub-genre labels, namely future house, bass house, progressive house and melodic house. Experiments show that our annotations well exhibit the characteristics of different categories. Also, we have built baseline models that classify the sub-genre based on the mel-spectrograms of a track, achieving strongly competitive results. Besides, we have put forward a few application scenarios of our dataset and baseline model, with a simulated sci-fi tunnel as a short demo built and rendered in a 3D modeling software, with the colors of the lights automated by the output of our model.	['Xinyu Li']	2022-07-24	null	null	null	null	['sound-classification']	['audio']	[-9.60620418e-02 -5.87154210e-01 2.37079605e-01 -3.81312184e-02 -8.92081797e-01 -1.05341959e+00 3.56251091e-01 -8.13909844e-02 7.10534230e-02 3.92433524e-01 4.41273451e-01 4.69489545e-02 -4.77933645e-01 -5.97735822e-01 -2.34859601e-01 -5.32141447e-01 -2.61077464e-01 5.15102088e-01 3.51067901e-01 -2.18509004e-01 4.01088387e-01 3.20366979e-01 -2.00297618e+00 5.88243246e-01 1.57675162e-01 1.15778804e+00 1.32261813e-01 8.23754907e-01 -8.75400230e-02 6.09727919e-01 -9.85902131e-01 -1.16758153e-01 3.06205302e-01 -7.11845696e-01 -8.48661065e-01 -3.93419787e-02 5.37598670e-01 1.64133295e-01 -8.96695331e-02 9.00502264e-01 7.06633627e-01 8.72708187e-02 6.29799783e-01 -1.23298335e+00 8.69986713e-02 9.92695689e-01 -3.08827072e-01 -2.97708251e-02 6.10952020e-01 -1.98836938e-01 1.41518831e+00 -4.20571864e-01 5.86336136e-01 8.85556877e-01 8.77128601e-01 2.82892019e-01 -1.14174986e+00 -9.63444769e-01 -2.82941252e-01 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1d217d0a-c989-4c8f-b87a-2bc19792e31a	a-polynomial-time-iterative-algorithm-for-1	2306.00266	null	https://arxiv.org/abs/2306.00266v1	https://arxiv.org/pdf/2306.00266v1.pdf	A polynomial-time iterative algorithm for random graph matching with non-vanishing correlation	We propose an efficient algorithm for matching two correlated Erd\H{o}s--R\'enyi graphs with $n$ vertices whose edges are correlated through a latent vertex correspondence. When the edge density $q= n^{- \alpha+o(1)}$ for a constant $\alpha \in [0,1)$, we show that our algorithm has polynomial running time and succeeds to recover the latent matching as long as the edge correlation is non-vanishing. This is closely related to our previous work on a polynomial-time algorithm that matches two Gaussian Wigner matrices with non-vanishing correlation, and provides the first polynomial-time random graph matching algorithm (regardless of the regime of $q$) when the edge correlation is below the square root of the Otter's constant (which is $\approx 0.338$).	['Zhangsong Li', 'Jian Ding']	2023-06-01	null	null	null	null	['graph-matching']	['graphs']	[ 3.36819410e-01 4.13218141e-01 -1.25663310e-01 1.61272794e-01 -8.27628672e-01 -4.56480592e-01 7.02681988e-02 1.26929045e-01 -4.27942216e-01 2.98397690e-01 -4.74982619e-01 -6.83462262e-01 -5.24709463e-01 -1.21475470e+00 -6.28733695e-01 -8.05448830e-01 -8.38041723e-01 8.13046813e-01 2.41982237e-01 -2.84530699e-01 1.55386016e-01 1.85283750e-01 -1.36499751e+00 -5.09275496e-01 7.48856962e-01 8.55826855e-01 -2.85259157e-01 1.25579393e+00 1.00091986e-01 4.42356706e-01 -2.06687614e-01 -8.90872836e-01 7.43043363e-01 -6.23622119e-01 -7.92011499e-01 -1.18096426e-01 2.43564144e-01 4.00274754e-01 -9.38528895e-01 1.70739245e+00 2.56169498e-01 3.97539884e-02 7.26026595e-01 -1.33622301e+00 -4.56075400e-01 8.72755289e-01 -8.24020028e-01 1.79817677e-01 3.76744539e-01 -2.90026486e-01 1.23503184e+00 -1.74281299e-01 7.28409052e-01 1.00040042e+00 9.75404859e-01 1.97628573e-01 -1.32583773e+00 -1.16732943e+00 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e0854907-f7f8-4871-860e-09d85b4cc075	pscnn-a-885-86-tops-w-programmable-sram-based	2205.01569	null	https://arxiv.org/abs/2205.01569v1	https://arxiv.org/pdf/2205.01569v1.pdf	PSCNN: A 885.86 TOPS/W Programmable SRAM-based Computing-In-Memory Processor for Keyword Spotting	Computing-in-memory (CIM) has attracted significant attentions in recent years due to its massive parallelism and low power consumption. However, current CIM designs suffer from large area overhead of small CIM macros and bad programmablity for model execution. This paper proposes a programmable CIM processor with a single large sized CIM macro instead of multiple smaller ones for power efficient computation and a flexible instruction set to support various binary 1-D convolution Neural Network (CNN) models in an easy way. Furthermore, the proposed architecture adopts the pooling write-back method to support fused or independent convolution/pooling operations to reduce 35.9\% of latency, and the flexible ping-pong feature SRAM to fit different feature map sizes during layer-by-layer execution.The design fabricated in TSMC 28nm technology achieves 150.8 GOPS throughput and 885.86 TOPS/W power efficiency at 10 MHz when executing our binary keyword spotting model, which has higher power efficiency and flexibility than previous designs.	['Tian-Sheuan Chang', 'Shu-Hung Kuo']	2022-05-02	null	null	null	null	['keyword-spotting']	['speech']	[ 1.10342994e-01 -2.98712879e-01 -4.34010923e-01 -3.85333210e-01 1.31435081e-01 -7.60969371e-02 1.93352610e-01 -8.85565951e-03 -7.91072011e-01 4.50885177e-01 -2.23586991e-01 -7.88602054e-01 6.11895807e-02 -1.12124300e+00 -4.82371897e-01 -5.99156439e-01 3.63944560e-01 -1.91374421e-01 5.88446856e-01 4.12389264e-02 3.57247293e-01 6.73540890e-01 -1.51273143e+00 2.76210606e-01 5.90609670e-01 1.42453182e+00 6.31908417e-01 4.16297197e-01 -2.83704370e-01 6.54405415e-01 -7.13238716e-01 -4.44629937e-01 3.02465200e-01 2.58730590e-01 -2.51966536e-01 -5.02685010e-01 3.85470599e-01 -4.60642427e-01 -7.00977862e-01 1.02928936e+00 3.85042310e-01 -5.34284294e-01 1.97652802e-01 -9.67932940e-01 -2.95730889e-01 9.10143316e-01 -6.61681950e-01 4.12606895e-01 -4.47246641e-01 1.39968216e-01 4.05759096e-01 -5.97119808e-01 3.88100818e-02 1.11362219e+00 4.29770797e-01 4.34078425e-01 -8.50722015e-01 -1.21772063e+00 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7a41e8f1-e298-47bf-a3c5-2f5458ff0671	an-enhanced-span-based-decomposition-method	2109.13023	null	https://arxiv.org/abs/2109.13023v3	https://arxiv.org/pdf/2109.13023v3.pdf	An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling	Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain. Recently, the metric-based meta-learning framework has been recognized as a promising approach for FSSL. However, most prior works assign a label to each token based on the token-level similarities, which ignores the integrality of named entities or slots. To this end, in this paper, we propose ESD, an Enhanced Span-based Decomposition method for FSSL. ESD formulates FSSL as a span-level matching problem between test query and supporting instances. Specifically, ESD decomposes the span matching problem into a series of span-level procedures, mainly including enhanced span representation, class prototype aggregation and span conflicts resolution. Extensive experiments show that ESD achieves the new state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is proven to be more robust in the nested and noisy tagging scenarios. Our code is available at https://github.com/Wangpeiyi9979/ESD.	['Zhifang Sui', 'Baobao Chang', 'Yunbo Cao', 'Qingyu Zhou', 'Tianyu Liu', 'Runxin Xu', 'Peiyi Wang']	2021-09-27	null	https://aclanthology.org/2022.naacl-main.369	https://aclanthology.org/2022.naacl-main.369.pdf	naacl-2022-7	['few-shot-ner']	['natural-language-processing']	[ 1.09367035e-01 -1.88543141e-01 -5.26945293e-01 -4.10524219e-01 -1.09714258e+00 -5.33617854e-01 1.87343016e-01 2.74787456e-01 -3.73068005e-01 8.19023371e-01 8.34978744e-02 -2.33504474e-01 -1.90256596e-01 -6.18946493e-01 -2.60281831e-01 -6.04043901e-01 -1.68453064e-02 4.52158689e-01 5.04950404e-01 -3.45566696e-05 1.48339853e-01 2.92185068e-01 -1.56264710e+00 3.27478349e-01 9.10529315e-01 1.04967451e+00 2.14696407e-01 8.06339905e-02 -8.72616529e-01 6.52744591e-01 -5.24218440e-01 -4.64273989e-01 -1.99439675e-02 -3.41763467e-01 -1.11231089e+00 1.88010752e-01 7.67850131e-02 3.29238564e-01 -2.80400574e-01 1.11253238e+00 6.19607389e-01 4.57768440e-01 2.42041230e-01 -1.52516389e+00 -4.47292149e-01 8.39177728e-01 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1ab3c206-c154-4a74-81b8-6cc5afe30577	lake-ice-detection-from-sentinel-1-sar-with	2002.07040	null	https://arxiv.org/abs/2002.07040v2	https://arxiv.org/pdf/2002.07040v2.pdf	Lake Ice Detection from Sentinel-1 SAR with Deep Learning	Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN). We report results on two winters ( 2016 - 17 and 2017 - 18 ) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.	['Pascal Imhof', 'Roberto Aguilar', 'Manu Tom', 'Konrad Schindler', 'Emmanuel Baltsavias', 'Silvan Leinss']	2020-02-17	null	null	null	null	['sentinel-1-sar-processing', 'lake-ice-detection', 'change-detection-for-remote-sensing-images', 'lake-ice-detection', 'segmentation-of-remote-sensing-imagery', 'remote-sensing-image-classification', 'the-semantic-segmentation-of-remote-sensing']	['computer-code', 'computer-vision', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous']	[-2.02962197e-02 -2.70141780e-01 1.89954206e-01 -5.17747939e-01 -5.25444150e-01 -8.38673472e-01 2.61482388e-01 1.93868876e-01 -7.44769931e-01 9.01518822e-01 -1.44769177e-01 -5.12272239e-01 2.52065837e-01 -9.81768310e-01 -6.29271448e-01 -8.21776330e-01 -6.64230704e-01 4.76156712e-01 -8.78855959e-02 -5.87381303e-01 -3.79260331e-01 6.15262747e-01 -1.49206614e+00 -1.45792618e-01 1.05296004e+00 6.87435031e-01 3.17786604e-01 7.43919671e-01 1.17288776e-01 -6.83335885e-02 -2.65299827e-01 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b22e6379-2a2e-4663-9427-e35b1196d2f3	hyper-gan-transferring-unconditional-to	2112.02219	null	https://arxiv.org/abs/2112.02219v2	https://arxiv.org/pdf/2112.02219v2.pdf	Transferring Unconditional to Conditional GANs with Hyper-Modulation	GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. Many of the available high-quality pretrained GANs are unconditional (like StyleGAN). For many applications, however, conditional GANs are preferable, because they provide more control over the generation process, despite often suffering more training difficulties. Therefore, in this paper, we focus on transferring from high-quality pretrained unconditional GANs to conditional GANs. This requires architectural adaptation of the pretrained GAN to perform the conditioning. To this end, we propose hyper-modulated generative networks that allow for shared and complementary supervision. To prevent the additional weights of the hypernetwork to overfit, with subsequent mode collapse on small target domains, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the transfer, we apply contrastive learning in the discriminator, which effectively works on very limited batch sizes. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on standard benchmarks.	['Bogdan Raducanu', 'Joost Van de Weijer', 'Yaxing Wang', 'Héctor Laria']	2021-12-04	null	null	null	null	['conditional-image-generation']	['computer-vision']	[ 3.71281147e-01 8.25234577e-02 -3.64199257e-03 -3.23003411e-01 -6.96564496e-01 -3.77547532e-01 7.42574632e-01 -5.59647202e-01 -4.03724074e-01 1.14091468e+00 -1.51548162e-01 -3.42538431e-02 1.63996220e-01 -1.18261325e+00 -8.23168874e-01 -1.11841035e+00 2.92579979e-01 4.63745266e-01 2.25311205e-01 -1.61514536e-01 -2.65541106e-01 3.91204268e-01 -1.47324133e+00 -4.82185325e-03 1.31904149e+00 1.06872618e+00 4.53228325e-01 4.86858755e-01 -7.38929510e-02 6.47156894e-01 -7.25499332e-01 -5.79990387e-01 2.02544406e-01 -9.90954041e-01 -5.31247556e-01 -1.36480378e-02 2.01363489e-01 -2.52683043e-01 7.83700421e-02 9.63922083e-01 5.83979070e-01 2.16332272e-01 8.08158636e-01 -1.15803349e+00 -5.42068243e-01 4.24487442e-01 -4.70917791e-01 -1.10143080e-01 -4.42119576e-02 4.64194715e-01 8.13100755e-01 -6.56019330e-01 4.36088920e-01 9.26700115e-01 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-9.07265186e-01 -4.83832061e-01 -1.13070738e+00 -4.94972080e-01 4.96970177e-01 -2.47458834e-02 -7.05363214e-01 -4.99680310e-01 1.82558820e-01]	[11.594554901123047, -0.2638377547264099]
69b33484-c2f0-4b51-9906-1bc1a001e78b	temporal-subsampling-diminishes-small-spatial	2305.00100	null	https://arxiv.org/abs/2305.00100v1	https://arxiv.org/pdf/2305.00100v1.pdf	Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence	The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use datasets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this often overlooked processing step affects the quality of an emulator's predictions. We implement two ML architectures from a class of methods called reservoir computing: (1) a form of Nonlinear Vector Autoregression (NVAR), and (2) an Echo State Network (ESN). Despite their simplicity, it is well documented that these architectures excel at predicting low dimensional chaotic dynamics. We are therefore motivated to test these architectures in an idealized setting of predicting high dimensional geophysical turbulence as represented by Surface Quasi-Geostrophic dynamics. In all cases, subsampling the training data consistently leads to an increased bias at small spatial scales that resembles numerical diffusion. Interestingly, the NVAR architecture becomes unstable when the temporal resolution is increased, indicating that the polynomial based interactions are insufficient at capturing the detailed nonlinearities of the turbulent flow. The ESN architecture is found to be more robust, suggesting a benefit to the more expensive but more general structure. Spectral errors are reduced by including a penalty on the kinetic energy density spectrum during training, although the subsampling related errors persist. Future work is warranted to understand how the temporal resolution of training data affects other ML architectures.	['Tse-Chun Chen', 'Jason A. Platt', 'Stephen G. Penny', 'Timothy A. Smith']	2023-04-28	null	null	null	null	['numerical-integration']	['miscellaneous']	[-1.41859099e-01 -4.55969244e-01 4.31442410e-01 4.67085168e-02 -1.53580502e-01 -6.23655319e-01 1.21697080e+00 -7.18743801e-02 -4.26833302e-01 8.03944528e-01 8.09147283e-02 -6.65498555e-01 -7.84983486e-02 -7.25593090e-01 -3.46322060e-01 -9.53990817e-01 -5.70855260e-01 8.97016749e-02 -3.55066583e-02 -4.72109497e-01 2.77661949e-01 8.33547711e-01 -1.64981937e+00 -2.13154167e-01 9.66100514e-01 9.26342666e-01 -8.01906437e-02 8.37250471e-01 -1.91421479e-01 7.78973341e-01 -4.12685722e-01 2.00554505e-01 3.49214017e-01 -5.74918628e-01 -3.36047232e-01 -2.39321530e-01 1.96407869e-01 -1.83729336e-01 -4.83612791e-02 8.55330110e-01 3.44261676e-01 6.18349314e-01 7.51640558e-01 -5.91735303e-01 -2.36204743e-01 2.17734933e-01 -1.14001922e-01 4.61393952e-01 -1.76174313e-01 6.60179913e-01 5.31820059e-01 -9.85704422e-01 4.70204026e-01 1.12627959e+00 1.06598866e+00 1.02705091e-01 -1.70173872e+00 -3.29715550e-01 -9.69978124e-02 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3c5b95cc-73fd-4853-8013-a7998a28c874	natural-language-processing-on-customer-note	2305.02029	null	https://arxiv.org/abs/2305.02029v1	https://arxiv.org/pdf/2305.02029v1.pdf	Natural language processing on customer note data	Automatic analysis of customer data for businesses is an area that is of interest to companies. Business to business data is studied rarely in academia due to the sensitive nature of such information. Applying natural language processing can speed up the analysis of prohibitively large sets of data. This paper addresses this subject and applies sentiment analysis, topic modelling and keyword extraction to a B2B data set. We show that accurate sentiment can be extracted from the notes automatically and the notes can be sorted by relevance into different topics. We see that without clear separation topics can lack relevance to a business context.	['Peter Appleby', 'Matthew Shardlow', 'Tom Armitage', 'Jozef Baca', 'David Webb', 'Andrew Hilditch']	2023-05-03	null	null	null	null	['keyword-extraction']	['natural-language-processing']	[ 6.83985502e-02 1.87408745e-01 -4.76215482e-01 -8.68243098e-01 -8.17318082e-01 -7.57571399e-01 6.82504714e-01 1.07689190e+00 -5.81616640e-01 7.10976183e-01 4.60935652e-01 -5.40478885e-01 -1.73628077e-01 -9.37225759e-01 -2.95523018e-01 -5.25407553e-01 3.34208280e-01 7.67253101e-01 3.78202707e-01 -4.25455749e-01 9.44831908e-01 3.89136046e-01 -1.52360690e+00 6.98365092e-01 2.89616972e-01 1.22118592e+00 1.99898750e-01 5.35953045e-01 -1.05969620e+00 6.47117615e-01 -7.60837972e-01 -4.81270492e-01 2.67096609e-01 2.18916554e-02 -9.37481999e-01 8.02402422e-02 2.32928872e-01 1.44437686e-01 6.24428868e-01 7.44815171e-01 9.08442140e-02 2.03490302e-01 6.43266618e-01 -1.13697803e+00 -4.24171165e-02 8.09322119e-01 -4.75611776e-01 4.42496300e-01 1.78583637e-01 -7.98084974e-01 1.33482099e+00 -8.46385419e-01 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7af25c74-7f09-4738-a6e8-b92690420358	distractor-aware-neuron-intrinsic-learning	2007.09979	null	https://arxiv.org/abs/2007.09979v2	https://arxiv.org/pdf/2007.09979v2.pdf	Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications	Medical image analysis benefits Computer Aided Diagnosis (CADx). A fundamental analyzing approach is the classification of medical images, which serves for skin lesion diagnosis, diabetic retinopathy grading, and cancer classification on histological images. When learning these discriminative classifiers, we observe that the convolutional neural networks (CNNs) are vulnerable to distractor interference. This is due to the similar sample appearances from different categories (i.e., small inter-class distance). Existing attempts select distractors from input images by empirically estimating their potential effects to the classifier. The essences of how these distractors affect CNN classification are not known. In this paper, we explore distractors from the CNN feature space via proposing a neuron intrinsic learning method. We formulate a novel distractor-aware loss that encourages large distance between the original image and its distractor in the feature space. The novel loss is combined with the original classification loss to update network parameters by back-propagation. Neuron intrinsic learning first explores distractors crucial to the deep classifier and then uses them to robustify CNN inherently. Extensive experiments on medical image benchmark datasets indicate that the proposed method performs favorably against the state-of-the-art approaches.	['Lijun Gong', 'Yefeng Zheng', 'Kai Ma']	2020-07-20	null	null	null	null	['diabetic-retinopathy-grading']	['medical']	[ 2.20831975e-01 1.41323477e-01 -2.51202941e-01 -3.20131689e-01 -4.36687052e-01 -8.15658644e-02 3.92628103e-01 5.11400960e-02 -5.31146467e-01 7.07171321e-01 -1.96910545e-01 -2.64307372e-02 -3.38830501e-01 -4.64843750e-01 -6.83277488e-01 -1.34893537e+00 1.28340855e-01 6.42489493e-02 8.35298672e-02 -3.51159014e-02 3.84741426e-01 9.16092634e-01 -1.51304662e+00 6.60256028e-01 9.63629246e-01 1.44666088e+00 8.70969743e-02 3.09033304e-01 -3.80999148e-02 1.09441960e+00 -6.65849268e-01 -8.03617239e-02 2.18244612e-01 -3.23477775e-01 -8.11334074e-01 7.41884485e-02 5.24463594e-01 -1.20581537e-01 -3.11538577e-01 1.40932047e+00 5.28209567e-01 -8.70830715e-02 1.03012979e+00 -1.11684287e+00 -8.67709935e-01 3.29007268e-01 -5.63473880e-01 6.37332320e-01 -4.54384655e-01 4.40547913e-01 6.02585435e-01 -9.63126421e-01 3.98555547e-01 1.00685740e+00 6.62499845e-01 8.40534210e-01 -1.00990701e+00 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4.16834503e-01 7.31004119e-01 -8.67633283e-01 -3.99588108e-01 3.95125523e-02]	[14.926706314086914, -2.5589828491210938]
fcc829c2-942e-4b22-8f85-204c1b57b653	competing-for-shareable-arms-in-multi-player	2305.19158	null	https://arxiv.org/abs/2305.19158v1	https://arxiv.org/pdf/2305.19158v1.pdf	Competing for Shareable Arms in Multi-Player Multi-Armed Bandits	Competitions for shareable and limited resources have long been studied with strategic agents. In reality, agents often have to learn and maximize the rewards of the resources at the same time. To design an individualized competing policy, we model the competition between agents in a novel multi-player multi-armed bandit (MPMAB) setting where players are selfish and aim to maximize their own rewards. In addition, when several players pull the same arm, we assume that these players averagely share the arms' rewards by expectation. Under this setting, we first analyze the Nash equilibrium when arms' rewards are known. Subsequently, we propose a novel SelfishMPMAB with Averaging Allocation (SMAA) approach based on the equilibrium. We theoretically demonstrate that SMAA could achieve a good regret guarantee for each player when all players follow the algorithm. Additionally, we establish that no single selfish player can significantly increase their rewards through deviation, nor can they detrimentally affect other players' rewards without incurring substantial losses for themselves. We finally validate the effectiveness of the method in extensive synthetic experiments.	['Peng Cui', 'Bo Li', 'Xingxuan Zhang', 'Haotian Wang', 'Renzhe Xu']	2023-05-30	null	null	null	null	['multi-armed-bandits']	['miscellaneous']	[-1.33125231e-01 2.63748735e-01 -4.16324973e-01 1.35031030e-01 -6.92558408e-01 -8.36321294e-01 8.87685940e-02 -1.33355215e-01 -8.11842680e-01 1.36244380e+00 -5.53375147e-02 -5.94813786e-02 -6.48827493e-01 -7.24305451e-01 -6.30069792e-01 -1.01383209e+00 -1.00724496e-01 7.93266892e-01 -1.06399275e-01 -1.28673598e-01 1.31823689e-01 3.49456578e-01 -1.00639677e+00 -1.32611468e-01 9.27462757e-01 1.07665682e+00 6.98813647e-02 5.95606148e-01 1.41556725e-01 1.19442487e+00 -8.72118056e-01 -4.62210774e-01 8.86645019e-01 -4.50772285e-01 -6.26830518e-01 1.78596258e-01 -4.84671146e-01 -6.77459002e-01 -1.87872723e-01 1.17735493e+00 4.44300920e-01 3.86268020e-01 3.50500852e-01 -1.48125005e+00 -7.58180991e-02 1.25665474e+00 -1.19476748e+00 1.21445820e-01 -1.44952964e-02 1.28697067e-01 1.27043140e+00 1.99152499e-01 3.86918902e-01 9.61678028e-01 9.59343761e-02 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72fa801f-35e1-4984-be84-5e77cf6ac62d	fine-grained-is-too-coarse-a-novel-data	2305.18668	null	https://arxiv.org/abs/2305.18668v1	https://arxiv.org/pdf/2305.18668v1.pdf	Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph Generation	Learning to compose visual relationships from raw images in the form of scene graphs is a highly challenging task due to contextual dependencies, but it is essential in computer vision applications that depend on scene understanding. However, no current approaches in Scene Graph Generation (SGG) aim at providing useful graphs for downstream tasks. Instead, the main focus has primarily been on the task of unbiasing the data distribution for predicting more fine-grained relations. That being said, all fine-grained relations are not equally relevant and at least a part of them are of no use for real-world applications. In this work, we introduce the task of Efficient SGG that prioritizes the generation of relevant relations, facilitating the use of Scene Graphs in downstream tasks such as Image Generation. To support further approaches in this task, we present a new dataset, VG150-curated, based on the annotations of the popular Visual Genome dataset. We show through a set of experiments that this dataset contains more high-quality and diverse annotations than the one usually adopted by approaches in SGG. Finally, we show the efficiency of this dataset in the task of Image Generation from Scene Graphs. Our approach can be easily replicated to improve the quality of other Scene Graph Generation datasets.	['Cédric Buche', 'Anne-Gwenn Bosser', 'Paulo Santos', 'Neau Maëlic']	2023-05-30	null	null	null	null	['scene-graph-generation', 'image-generation-from-scene-graphs', 'scene-understanding']	['computer-vision', 'computer-vision', 'computer-vision']	[ 6.53837025e-01 4.16773021e-01 2.75501490e-01 -3.78265262e-01 -4.33432162e-01 -6.36412978e-01 8.39862049e-01 5.13190508e-01 -2.20263138e-01 8.47667992e-01 1.96909785e-01 -3.59331906e-01 -2.51350939e-01 -1.08154905e+00 -9.08121645e-01 -5.70294380e-01 9.10805240e-02 5.86326420e-01 6.35638952e-01 -5.21487772e-01 7.46963546e-02 5.12785256e-01 -1.83165634e+00 6.04845762e-01 6.58782482e-01 6.38077199e-01 3.50994766e-01 6.43148720e-01 -1.26235217e-01 8.09175611e-01 -6.27492130e-01 -6.24737918e-01 1.27288595e-01 -6.92750216e-01 -9.90494668e-01 1.27052486e-01 4.47046757e-01 1.55398846e-01 8.03449079e-02 9.06683087e-01 3.91040891e-01 1.57376304e-01 7.40253866e-01 -1.42089725e+00 -3.10308397e-01 6.91093862e-01 -2.81697869e-01 -4.51403074e-02 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46c5c70d-d5cf-4de8-879f-bbf12d173785	motion-encoded-particle-swarm-optimization	2010.02039	null	https://arxiv.org/abs/2010.02039v1	https://arxiv.org/pdf/2010.02039v1.pdf	Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs	This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24\% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.	['Quang Phuc Ha', 'Manh Duong Phung']	2020-10-05	null	null	null	null	['metaheuristic-optimization']	['methodology']	[ 2.66547054e-01 -3.71430606e-01 2.56743431e-01 5.09816051e-01 3.85077953e-01 -5.07209897e-01 4.69724298e-01 9.21554193e-02 -4.13753569e-01 1.05346429e+00 -4.74464208e-01 -8.65315944e-02 -7.87459791e-01 -9.27564085e-01 -3.16277556e-02 -1.27492106e+00 -5.52520752e-01 2.64370233e-01 6.77780449e-01 -3.51512194e-01 4.29336160e-01 6.67321265e-01 -1.75202286e+00 -8.75923872e-01 1.18901885e+00 9.44554150e-01 5.63079178e-01 8.35879564e-01 4.70406026e-01 2.95417160e-01 -1.02967095e+00 4.10525724e-02 3.90163571e-01 -4.19894040e-01 -2.03005254e-01 1.00700222e-01 -6.76322103e-01 4.46357578e-01 6.20670170e-02 1.35757744e+00 4.88121599e-01 6.85948610e-01 4.62010205e-01 -1.48644698e+00 -4.18228835e-01 9.56000462e-02 -8.04866374e-01 6.01215184e-01 1.63948715e-01 1.18571140e-01 3.35706294e-01 -2.96015084e-01 4.69842315e-01 1.22347510e+00 5.96509635e-01 1.57879606e-01 -5.19936562e-01 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-5.38444340e-01 -6.72615409e-01 -1.10385165e-01 -1.35416830e+00 -6.84166670e-01 -4.28425372e-01]	[5.581517696380615, 3.350738048553467]
e72d4a0e-c00e-4710-b64e-5dd6a9c268bf	partial-video-domain-adaptation-with-partial	2107.04941	null	https://arxiv.org/abs/2107.04941v1	https://arxiv.org/pdf/2107.04941v1.pdf	Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network	Partial Domain Adaptation (PDA) is a practical and general domain adaptation scenario, which relaxes the fully shared label space assumption such that the source label space subsumes the target one. The key challenge of PDA is the issue of negative transfer caused by source-only classes. For videos, such negative transfer could be triggered by both spatial and temporal features, which leads to a more challenging Partial Video Domain Adaptation (PVDA) problem. In this paper, we propose a novel Partial Adversarial Temporal Attentive Network (PATAN) to address the PVDA problem by utilizing both spatial and temporal features for filtering source-only classes. Besides, PATAN constructs effective overall temporal features by attending to local temporal features that contribute more toward the class filtration process. We further introduce new benchmarks to facilitate research on PVDA problems, covering a wide range of PVDA scenarios. Empirical results demonstrate the state-of-the-art performance of our proposed PATAN across the multiple PVDA benchmarks.	['Zhenghua Chen', 'Kezhi Mao', 'Qi Li', 'Haozhi Cao', 'Jianfei Yang', 'Yuecong Xu']	2021-07-11	null	http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Partial_Video_Domain_Adaptation_With_Partial_Adversarial_Temporal_Attentive_Network_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Partial_Video_Domain_Adaptation_With_Partial_Adversarial_Temporal_Attentive_Network_ICCV_2021_paper.pdf	iccv-2021-1	['partial-domain-adaptation']	['methodology']	[ 2.82507420e-01 -2.66870379e-01 -4.99292195e-01 -8.83545280e-02 -6.80249989e-01 -6.73798144e-01 6.76326990e-01 -3.32169354e-01 -2.57610947e-01 8.24963093e-01 1.40619352e-01 1.01574231e-02 3.54081951e-02 -7.47097015e-01 -6.89138174e-01 -9.82879460e-01 -1.40792444e-01 1.71674155e-02 5.85025549e-01 -3.30050409e-01 -2.62873769e-01 3.70438486e-01 -1.26738727e+00 3.87259871e-01 6.58131778e-01 1.22481418e+00 -6.09040521e-02 5.18000722e-01 1.41116427e-02 8.49925935e-01 -7.25774765e-01 -2.75172442e-01 3.88014585e-01 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9ac6398a-faaa-44c3-a303-7375bbdb6861	learning-dynamic-facial-radiance-fields-for	2207.11770	null	https://arxiv.org/abs/2207.11770v1	https://arxiv.org/pdf/2207.11770v1.pdf	Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis	Talking head synthesis is an emerging technology with wide applications in film dubbing, virtual avatars and online education. Recent NeRF-based methods generate more natural talking videos, as they better capture the 3D structural information of faces. However, a specific model needs to be trained for each identity with a large dataset. In this paper, we propose Dynamic Facial Radiance Fields (DFRF) for few-shot talking head synthesis, which can rapidly generalize to an unseen identity with few training data. Different from the existing NeRF-based methods which directly encode the 3D geometry and appearance of a specific person into the network, our DFRF conditions face radiance field on 2D appearance images to learn the face prior. Thus the facial radiance field can be flexibly adjusted to the new identity with few reference images. Additionally, for better modeling of the facial deformations, we propose a differentiable face warping module conditioned on audio signals to deform all reference images to the query space. Extensive experiments show that with only tens of seconds of training clip available, our proposed DFRF can synthesize natural and high-quality audio-driven talking head videos for novel identities with only 40k iterations. We highly recommend readers view our supplementary video for intuitive comparisons. Code is available in https://sstzal.github.io/DFRF/.	['Jiwen Lu', 'Jie zhou', 'Yueqi Duan', 'Zheng Zhu', 'Wanhua Li', 'Shuai Shen']	2022-07-24	null	null	null	null	['talking-head-generation', 'talking-face-generation']	['computer-vision', 'computer-vision']	[ 5.84670156e-02 1.58396080e-01 7.83038288e-02 -7.61801422e-01 -6.34885371e-01 -5.21523297e-01 5.40640533e-01 -1.11640513e+00 5.19391559e-02 5.15000165e-01 4.31931406e-01 3.94421309e-01 2.50874996e-01 -4.98249352e-01 -9.24771070e-01 -7.81841278e-01 3.46510410e-01 1.53310791e-01 -1.58685476e-01 -3.25506657e-01 -4.34113257e-02 7.25411057e-01 -1.80957472e+00 1.42113436e-02 5.68257809e-01 1.01071393e+00 1.83513716e-01 6.56551957e-01 9.88239422e-02 5.00708461e-01 -4.81762171e-01 -5.82985878e-01 3.01674068e-01 -3.91187757e-01 -5.66360056e-01 3.46831560e-01 9.55979168e-01 -5.70074975e-01 -7.24878490e-01 1.02166879e+00 7.86143839e-01 5.38611650e-01 4.23098087e-01 -1.21781123e+00 -7.76098967e-01 2.48312593e-01 -5.09740770e-01 -1.21360347e-01 8.10640395e-01 3.24653745e-01 4.46860284e-01 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4e7b912c-1d45-42b3-b5c9-e8bdbb538d2a	learning-co-segmentation-by-segment-swapping	2110.15904	null	https://arxiv.org/abs/2110.15904v2	https://arxiv.org/pdf/2110.15904v2.pdf	Learning Co-segmentation by Segment Swapping for Retrieval and Discovery	The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts of a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation task. We present a simple yet surprisingly effective approach to overcome this difficulty: we generate synthetic training pairs by selecting segments in an image and copy-pasting them into another image. We then learn to predict the repeated region masks. We find that it is crucial to predict the correspondences as an auxiliary task and to use Poisson blending and style transfer on the training pairs to generalize on real data. We analyse results with two deep architectures relevant to our joint image analysis task: a transformer-based architecture and Sparse Nc-Net, a recent network designed to predict coarse correspondences using 4D convolutions. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset and achieves competitive performance on two place recognition benchmarks, Tokyo247 and Pitts30K. We also demonstrate the potential of our approach for unsupervised image collection analysis by introducing a spectral graph clustering approach to object discovery and demonstrating it on the object discovery dataset of \cite{rubinstein2013unsupervised} and the Brueghel dataset. Our code and data are available at http://imagine.enpc.fr/~shenx/SegSwap/.	['Mathieu Aubry', 'Armand Joulin', 'Alexei A. Efros', 'Xi Shen']	2021-10-29	null	null	null	null	['graph-clustering', 'spectral-graph-clustering']	['graphs', 'graphs']	[ 5.19550025e-01 -1.97161958e-01 3.82870346e-01 -2.83319533e-01 -8.43762457e-01 -8.11125040e-01 9.08950448e-01 -2.42508814e-01 -3.30939412e-01 4.13024038e-01 -1.72966704e-01 -1.87007412e-01 -1.99916407e-01 -8.20059359e-01 -1.10162210e+00 -6.32884204e-01 4.18083258e-02 7.70751774e-01 3.59318525e-01 -1.09683640e-01 4.35865194e-01 8.88585985e-01 -1.64183891e+00 5.24714947e-01 5.45147955e-01 1.06907332e+00 6.46687984e-01 8.02172601e-01 -3.35959077e-01 5.76197445e-01 -4.54897583e-01 -3.79114747e-01 7.70220697e-01 -4.35985267e-01 -1.05749142e+00 6.01832330e-01 1.00974452e+00 1.20122068e-01 -3.96408588e-01 9.81610954e-01 3.17881674e-01 2.63112634e-01 9.08760071e-01 -1.10623908e+00 -6.31721854e-01 2.91524470e-01 -6.13698661e-01 3.78953278e-01 7.78351054e-02 2.64050215e-01 9.73333061e-01 -9.82540667e-01 7.28763282e-01 9.57517862e-01 8.30524147e-01 2.00393930e-01 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91566d65-1094-41af-86d4-48a973989989	shape-is-almost-all-persistent-homology	2304.07554	null	https://arxiv.org/abs/2304.07554v1	https://arxiv.org/pdf/2304.07554v1.pdf	Shape is (almost) all!: Persistent homology features (PHFs) are an information rich input for efficient molecular machine learning	3-D shape is important to chemistry, but how important? Machine learning works best when the inputs are simple and match the problem well. Chemistry datasets tend to be very small compared to those generally used in machine learning so we need to get the most from each datapoint. Persistent homology measures the topological shape properties of point clouds at different scales and is used in topological data analysis. Here we investigate what persistent homology captures about molecular structure and create persistent homology features (PHFs) that encode a molecule's shape whilst losing most of the symbolic detail like atom labels, valence, charge, bonds etc. We demonstrate the usefulness of PHFs on a series of chemical datasets: QM7, lipophilicity, Delaney and Tox21. PHFs work as well as the best benchmarks. PHFs are very information dense and much smaller than other encoding methods yet found, meaning ML algorithms are much more energy efficient. PHFs success despite losing a large amount of chemical detail highlights how much of chemistry can be simplified to topological shape.	['Ella Gale']	2023-04-15	null	null	null	null	['topological-data-analysis']	['graphs']	[-8.86871945e-03 1.59740075e-01 -3.05839807e-01 -9.54999253e-02 -6.19400561e-01 -9.71438646e-01 6.98328078e-01 1.08714604e+00 -2.05503684e-02 1.12025297e+00 7.33688250e-02 -3.55559230e-01 -2.39567265e-01 -1.14606154e+00 -9.24382031e-01 -9.88875628e-01 -6.44847751e-01 8.17447662e-01 2.89996773e-01 -3.97162944e-01 8.35820913e-01 1.00885594e+00 -1.31828701e+00 4.38891709e-01 6.24284923e-01 7.90427983e-01 -1.34859802e-02 5.33024788e-01 -3.08911353e-01 3.90273303e-01 -2.07075477e-01 -3.98995072e-01 1.45646244e-01 -2.58555561e-01 -1.02997863e+00 -7.82125592e-01 7.08221138e-01 5.70208430e-01 4.16510105e-02 9.03207958e-01 5.47959387e-01 5.88349663e-02 1.18346214e+00 -8.90624762e-01 -4.85165805e-01 3.48360687e-02 -2.30950326e-01 -7.12639466e-03 7.23393679e-01 6.86843768e-02 1.09842741e+00 -9.86310065e-01 1.34575069e+00 1.36983931e+00 9.81806457e-01 1.57177314e-01 -1.40295601e+00 -2.71027714e-01 -3.48745853e-01 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b0db0022-3465-4d4a-925a-968f09a0ce44	contrastive-learning-enhanced-nearest	null	null	https://aclanthology.org/2022.acl-short.75	https://aclanthology.org/2022.acl-short.75.pdf	Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification	Multi-Label Text Classification (MLTC) is a fundamental and challenging task in natural language processing. Previous studies mainly focus on learning text representation and modeling label correlation but neglect the rich knowledge from the existing similar instances when predicting labels of a specific text. To make up for this oversight, we propose a k nearest neighbor (kNN) mechanism which retrieves several neighbor instances and interpolates the model output with their labels. Moreover, we design a multi-label contrastive learning objective that makes the model aware of the kNN classification process and improves the quality of the retrieved neighbors while inference. Extensive experiments show that our method can bring consistent and significant performance improvement to multiple MLTC models including the state-of-the-art pretrained and non-pretrained ones.	['Xinyu Dai', 'Ran Wang', 'Xi’ao Su']	null	null	null	null	acl-2022-5	['multi-label-text-classification', 'multi-label-text-classification']	['methodology', 'natural-language-processing']	[ 2.17759445e-01 -2.37906188e-01 -7.10197270e-01 -8.37105632e-01 -1.01924670e+00 -5.22487879e-01 6.64996445e-01 5.91529489e-01 -5.22038043e-01 5.73264718e-01 3.76554608e-01 -8.46204832e-02 -2.48823389e-01 -5.46050549e-01 -4.96168107e-01 -5.33510149e-01 4.77931231e-01 9.03132856e-01 1.26536548e-01 1.60854384e-01 3.95661563e-01 8.86060819e-02 -1.42289782e+00 9.36886549e-01 8.72475982e-01 1.13724244e+00 -5.58518898e-03 3.76210064e-01 -5.15783072e-01 1.18506527e+00 -2.82696217e-01 -5.51914871e-01 -1.65539041e-01 -1.79482728e-01 -1.05436850e+00 -1.47233084e-01 7.11632013e-01 -7.01016039e-02 -9.19139236e-02 9.49637711e-01 2.69233108e-01 2.61801988e-01 1.26420343e+00 -1.20375228e+00 -1.05031419e+00 1.11465192e+00 -6.61529899e-01 -9.34327673e-03 1.41587183e-01 -6.18079185e-01 1.32622111e+00 -1.13573694e+00 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a4625f33-258f-46e9-b40c-fb972ded54e3	brain2pix-fully-convolutional-naturalistic	null	null	https://openreview.net/forum?id=15TmaAcmHus	https://openreview.net/pdf?id=15TmaAcmHus	Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity	Reconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance data that leverages the recent success of image-to-image transformation networks. This is achieved by exploiting spatial information obtained from retinotopic mappings across the visual system. More specifically, we first determine what position each voxel in a particular region of interest would represent on the visual field based on its corresponding receptive field location. Then, the 2D image representation of the brain activity on the visual field is passed to a fully convolutional image-to-image network trained to recover the original stimuli using VGG feature loss with an adversarial regularizer. In our experiments we show that our method offers a significant improvement over existing techniques.	['Anonymous']	2021-01-01	null	null	null	null	['video-reconstruction']	['computer-vision']	[ 5.65815926e-01 2.07131729e-01 3.03713512e-02 -2.90761918e-01 -5.84762931e-01 -6.88068271e-01 6.45833015e-01 -4.71730769e-01 -6.84293509e-01 7.22207665e-01 4.67959404e-01 6.12328984e-02 6.22154362e-02 -4.09122884e-01 -1.31251109e+00 -6.86864316e-01 2.26300079e-02 2.51893491e-01 1.21800117e-02 2.90759176e-01 3.69924217e-01 6.47785187e-01 -1.13686633e+00 4.89132643e-01 3.04481655e-01 1.00875008e+00 5.07656455e-01 4.73341376e-01 4.83304381e-01 9.09525633e-01 -2.09631175e-01 2.40883142e-01 4.68426526e-01 -6.40349329e-01 -9.19478118e-01 -9.02304128e-02 7.49373734e-01 -5.44266939e-01 -7.00376093e-01 1.11952424e+00 4.07286078e-01 3.02413613e-01 7.96037734e-01 -7.23472476e-01 -6.96083069e-01 2.43052959e-01 -6.11899197e-01 7.53876388e-01 2.22640023e-01 1.24783985e-01 5.85180163e-01 -8.66003871e-01 1.12450898e+00 1.12232220e+00 2.67489582e-01 5.54667115e-01 -1.71828830e+00 -5.56499481e-01 5.92370220e-02 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5.30872643e-01 4.06375080e-01 -7.81356812e-01 -3.08461022e-02 3.17199200e-01]	[10.7228422164917, 2.5072832107543945]

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