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train · 44.6k rows

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efb58071-ce4c-45d2-866e-5c172ef938c6	sit-at-mixmt-2022-fluent-translation-built-on	2210.11670	null	https://arxiv.org/abs/2210.11670v2	https://arxiv.org/pdf/2210.11670v2.pdf	SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models	This paper describes the Stevens Institute of Technology's submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask $1$ Hindi/English to Hinglish and subtask $2$ Hinglish to English translation. Our findings lie in the improvements made through the use of large pre-trained multilingual NMT models and in-domain datasets, as well as back-translation and ensemble techniques. The translation output is automatically evaluated against the reference translations using ROUGE-L and WER. Our system achieves the $1^{st}$ position on subtask $2$ according to ROUGE-L, WER, and human evaluation, $1^{st}$ position on subtask $1$ according to WER and human evaluation, and $3^{rd}$ position on subtask $1$ with respect to ROUGE-L metric.	['Jia Xu', 'Preet Jhanglani', 'Girish Amar Budhrani', 'Hrishikesh Kanade', 'Abdul Rafae Khan']	2022-10-21	null	null	null	null	['nmt']	['computer-code']	[ 1.94738016e-01 1.42955169e-01 -1.16767153e-01 -3.62586826e-01 -1.77844846e+00 -6.76775336e-01 7.66997576e-01 -2.11605668e-01 -6.32830679e-01 1.23191381e+00 1.02265269e-01 -7.39989519e-01 5.98662384e-02 -2.82840401e-01 -8.68465126e-01 -6.33499324e-02 1.03422187e-01 6.55131757e-01 -2.06026569e-01 -5.78683138e-01 1.28614098e-01 -4.55049090e-02 -8.84345114e-01 4.73159820e-01 1.02840018e+00 7.63191164e-01 1.86080173e-01 7.95965791e-01 1.22081049e-01 4.44298029e-01 -3.50362092e-01 -9.32179093e-01 4.20229971e-01 -8.11506391e-01 -1.00563800e+00 -5.45381725e-01 3.66962075e-01 -7.55578727e-02 2.50698049e-02 1.07829344e+00 6.81825280e-01 8.28897581e-03 7.10772932e-01 -8.44124377e-01 -6.20837092e-01 7.98888862e-01 -4.20284241e-01 1.38485640e-01 6.54217958e-01 2.96674132e-01 1.36081684e+00 -1.29918599e+00 8.32178354e-01 1.03244209e+00 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a6bedbf8-c87f-4268-9c0a-bd10a9f086cb	automatic-product-copywriting-for-e-commerce	2112.11915	null	https://arxiv.org/abs/2112.11915v1	https://arxiv.org/pdf/2112.11915v1.pdf	Automatic Product Copywriting for E-Commerce	Product copywriting is a critical component of e-commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. In this paper, we report our experience deploying the proposed Automatic Product Copywriting Generation (APCG) system into the JD.com e-commerce product recommendation platform. It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening. For selected domains, the models are trained and updated daily with the updated training data. In addition, the model is also used as a real-time writing assistant tool on our live broadcast platform. The APCG system has been deployed in JD.com since Feb 2021. By Sep 2021, it has generated 2.53 million product descriptions, and improved the overall averaged click-through rate (CTR) and the Conversion Rate (CVR) by 4.22% and 3.61%, compared to baselines, respectively on a year-on-year basis. The accumulated Gross Merchandise Volume (GMV) made by our system is improved by 213.42%, compared to the number in Feb 2021.	['Lingfei Wu', 'Han Yu', 'Bo Long', 'Yun Xiao', 'Xueqi He', 'Zhen He', 'Zhuoye Ding', 'Jiajia Chen', 'Shiliang Diao', 'Jing Zhou', 'Hainan Zhang', 'Yanyan Zou', 'Xueying Zhang']	2021-12-15	null	null	null	null	['product-recommendation']	['miscellaneous']	[ 0.17907602 -0.03413224 -0.30544865 -0.28754577 -0.8618309 -0.8810105 0.62109065 0.20097083 -0.14135066 0.39667 0.24566415 -0.33367515 0.2680692 -0.92018527 -0.74785674 -0.2371259 0.1924357 0.63738483 0.09522975 -0.7427198 0.44455522 0.18702216 -1.2371986 0.5566819 0.9173652 1.3249756 0.32927772 0.56509733 -0.12329567 0.5216566 -0.5376391 -0.88256603 0.3844629 -0.2344435 -0.3154396 0.26562825 0.1720442 -0.49593246 -0.07479676 1.0505061 0.39566112 -0.09471878 0.30923858 -0.9505704 -1.06656 0.82788044 -0.6678172 -0.1548219 0.3571469 0.2546812 1.3003235 -0.8715396 0.82282084 0.794162 0.39990416 0.3016309 -1.0526738 -0.6782484 0.01700655 -0.13461883 -1.0680219 -0.247831 0.5409074 -0.49138045 0.98360014 0.17629616 0.5551197 0.87376314 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047e4f4a-46ac-41ac-8d30-77e3113d06df	classification-of-tumor-histology-via	null	null	http://openaccess.thecvf.com/content_cvpr_2013/html/Chang_Classification_of_Tumor_2013_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2013/papers/Chang_Classification_of_Tumor_2013_CVPR_paper.pdf	Classification of Tumor Histology via Morphometric Context	Image-based classification of tissue histology, in terms of different components (e.g., normal signature, categories of aberrant signatures), provides a series of indices for tumor composition. Subsequently, aggregation of these indices in each whole slide image (WSI) from a large cohort can provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical and biological variations that are always present in a large cohort. In this paper, we propose two algorithms for classification of tissue histology based on robust representations of morphometric context, which are built upon nuclear level morphometric features at various locations and scales within the spatial pyramid matching (SPM) framework. These methods have been evaluated on two distinct datasets of different tumor types collected from The Cancer Genome Atlas (TCGA), and the experimental results indicate that our methods are (i) extensible to different tumor types; (ii) robust in the presence of wide technical and biological variations; (iii) invariant to different nuclear segmentation strategies; and (iv) scalable with varying training sample size. In addition, our experiments suggest that enforcing sparsity, during the construction of morphometric context, further improves the performance of the system.	['Bahram Parvin', 'Paul Spellman', 'Alexander Borowsky', 'Hang Chang']	2013-06-01	null	null	null	cvpr-2013-6	['nuclear-segmentation']	['medical']	[ 4.95425522e-01 -2.99748629e-01 -2.25728095e-01 -2.67229497e-01 -1.05799651e+00 -5.22920549e-01 5.98240197e-01 7.41359949e-01 -3.24413329e-01 4.06189471e-01 2.84420520e-01 -2.28235498e-01 -4.54012275e-01 -7.53074467e-01 -2.05447108e-01 -1.19283056e+00 -1.98365927e-01 4.13291276e-01 3.86876255e-01 1.27309319e-02 4.15392041e-01 8.24131846e-01 -1.23798764e+00 2.86228538e-01 6.26419961e-01 1.07122231e+00 2.86687583e-01 6.90934598e-01 -1.58837378e-01 3.54896486e-01 -2.42937267e-01 5.69110960e-02 1.83485538e-01 -2.51061112e-01 -7.19513178e-01 2.06998751e-01 4.19206530e-01 1.82762682e-01 -9.07442719e-02 1.22616959e+00 2.89575875e-01 -3.84299755e-01 8.05716276e-01 -7.90614486e-01 -1.80234328e-01 2.29885042e-01 -7.44731188e-01 3.58929604e-01 -8.29682127e-02 2.55804688e-01 9.48979378e-01 -7.57736087e-01 8.87517512e-01 6.66064560e-01 1.01362896e+00 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23718a62-6923-490d-9d2d-1a2a8fa037ed	perceptual-optimization-of-a-biologically	2206.09146	null	https://arxiv.org/abs/2206.09146v2	https://arxiv.org/pdf/2206.09146v2.pdf	A Perceptually Optimized and Self-Calibrated Tone Mapping Operator	With the increasing popularity and accessibility of high dynamic range (HDR) photography, tone mapping operators (TMOs) for dynamic range compression are practically demanding. In this paper, we develop a two-stage neural network-based TMO that is self-calibrated and perceptually optimized. In Stage one, motivated by the physiology of the early stages of the human visual system, we first decompose an HDR image into a normalized Laplacian pyramid. We then use two lightweight deep neural networks (DNNs), taking the normalized representation as input and estimating the Laplacian pyramid of the corresponding LDR image. We optimize the tone mapping network by minimizing the normalized Laplacian pyramid distance (NLPD), a perceptual metric aligning with human judgments of tone-mapped image quality. In Stage two, the input HDR image is self-calibrated to compute the final LDR image. We feed the same HDR image but rescaled with different maximum luminances to the learned tone mapping network, and generate a pseudo-multi-exposure image stack with different detail visibility and color saturation. We then train another lightweight DNN to fuse the LDR image stack into a desired LDR image by maximizing a variant of the structural similarity index for multi-exposure image fusion (MEF-SSIM), which has been proven perceptually relevant to fused image quality. The proposed self-calibration mechanism through MEF enables our TMO to accept uncalibrated HDR images, while being physiology-driven. Extensive experiments show that our method produces images with consistently better visual quality. Additionally, since our method builds upon three lightweight DNNs, it is among the fastest local TMOs.	['Kede Ma', 'Yuming Fang', 'Chenyang Le', 'Peibei Cao']	2022-06-18	null	null	null	null	['multi-exposure-image-fusion', 'tone-mapping']	['computer-vision', 'computer-vision']	[ 6.36950374e-01 -2.21486732e-01 -3.91037315e-02 -3.05852830e-01 -5.59643328e-01 -3.19177747e-01 1.76806986e-01 -3.78716171e-01 -5.19599378e-01 5.72904825e-01 7.93852285e-02 -1.94044515e-01 9.10148025e-02 -8.08131695e-01 -9.69449401e-01 -5.97054064e-01 1.79682076e-01 -2.14640439e-01 1.70473933e-01 -3.14069062e-01 1.21720664e-01 6.38237417e-01 -1.83746171e+00 1.38083681e-01 1.16085482e+00 1.36368525e+00 5.40259302e-01 1.14161217e+00 3.74047220e-01 8.74937892e-01 -5.99506915e-01 -4.90104645e-01 7.11359859e-01 -6.12958670e-01 -5.97976208e-01 3.32817361e-02 8.28403771e-01 -6.26985192e-01 -6.23500705e-01 1.27410960e+00 7.50302672e-01 1.85740903e-01 2.40141764e-01 -1.04167259e+00 -1.18048358e+00 3.05910856e-01 -7.05253601e-01 2.90191412e-01 1.90562874e-01 4.37877387e-01 7.31434047e-01 -8.12191129e-01 6.35377526e-01 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9234b803-07c9-4767-8d3e-2ba5ee525867	time-domain-speech-super-resolution-with-gan	2209.01702	null	https://arxiv.org/abs/2209.01702v1	https://arxiv.org/pdf/2209.01702v1.pdf	Time-domain speech super-resolution with GAN based modeling for telephony speaker verification	Automatic Speaker Verification (ASV) technology has become commonplace in virtual assistants. However, its performance suffers when there is a mismatch between the train and test domains. Mixed bandwidth training, i.e., pooling training data from both domains, is a preferred choice for developing a universal model that works for both narrowband and wideband domains. We propose complementing this technique by performing neural upsampling of narrowband signals, also known as bandwidth extension. Our main goal is to discover and analyze high-performing time-domain Generative Adversarial Network (GAN) based models to improve our downstream state-of-the-art ASV system. We choose GANs since they (1) are powerful for learning conditional distribution and (2) allow flexible plug-in usage as a pre-processor during the training of downstream task (ASV) with data augmentation. Prior works mainly focus on feature-domain bandwidth extension and limited experimental setups. We address these limitations by 1) using time-domain extension models, 2) reporting results on three real test sets, 2) extending training data, and 3) devising new test-time schemes. We compare supervised (conditional GAN) and unsupervised GANs (CycleGAN) and demonstrate average relative improvement in Equal Error Rate of 8.6% and 7.7%, respectively. For further analysis, we study changes in spectrogram visual quality, audio perceptual quality, t-SNE embeddings, and ASV score distributions. We show that our bandwidth extension leads to phenomena such as a shift of telephone (test) embeddings towards wideband (train) signals, a negative correlation of perceptual quality with downstream performance, and condition-independent score calibration.	['Najim Dehak', 'Piotr Żelasko', 'Laureano Moro-Velázquez', 'Jesús Villalba', 'Saurabh Kataria']	2022-09-04	null	null	null	null	['bandwidth-extension', 'bandwidth-extension']	['audio', 'speech']	[ 3.36143941e-01 -5.46403900e-02 1.12255275e-01 -4.85097587e-01 -1.43903506e+00 -7.86712646e-01 5.11707187e-01 -3.41395795e-01 -2.52243996e-01 6.06784761e-01 3.39107245e-01 -7.35117435e-01 3.87765616e-02 -4.41174507e-01 -5.66667855e-01 -7.49296546e-01 1.29166394e-01 2.12895095e-01 -1.90365175e-03 -2.13634968e-01 -3.50216955e-01 3.84330809e-01 -1.40542328e+00 1.37280390e-01 9.20464754e-01 9.58909273e-01 -2.87508760e-02 9.38843668e-01 1.36422679e-01 3.17276895e-01 -1.07329977e+00 -5.43050408e-01 3.35255265e-01 -6.33120835e-01 -5.96627057e-01 -1.52717009e-01 3.71219784e-01 -3.06911796e-01 -2.09947616e-01 8.02869260e-01 1.09942794e+00 6.38417676e-02 4.93418068e-01 -1.34583521e+00 -5.31629443e-01 7.95382082e-01 -3.17764461e-01 2.75277384e-02 1.48168474e-01 4.12843406e-01 7.54146874e-01 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6818c40b-4d06-4326-9668-8977fae37b2f	craft-shared-tasks-2019-overview-integrated	null	null	https://aclanthology.org/D19-5725	https://aclanthology.org/D19-5725.pdf	CRAFT Shared Tasks 2019 Overview --- Integrated Structure, Semantics, and Coreference	As part of the BioNLP Open Shared Tasks 2019, the CRAFT Shared Tasks 2019 provides a platform to gauge the state of the art for three fundamental language processing tasks {---} dependency parse construction, coreference resolution, and ontology concept identification {---} over full-text biomedical articles. The structural annotation task requires the automatic generation of dependency parses for each sentence of an article given only the article text. The coreference resolution task focuses on linking coreferring base noun phrase mentions into chains using the symmetrical and transitive identity relation. The ontology concept annotation task involves the identification of concept mentions within text using the classes of ten distinct ontologies in the biomedical domain, both unmodified and augmented with extension classes. This paper provides an overview of each task, including descriptions of the data provided to participants and the evaluation metrics used, and discusses participant results relative to baseline performances for each of the three tasks.	['Harrison Pielke-Lombardo', 'Negacy Hailu', 'Manuel R. Ciosici', 'Michael Regan', 'Michael Bada', 'William Baumgartner', 'Sampo Pyysalo', 'Lawrence Hunter']	2019-11-01	null	null	null	ws-2019-11	['medical-named-entity-recognition']	['natural-language-processing']	[ 5.29787958e-01 8.08984041e-01 -2.74099737e-01 -7.38934696e-01 -1.16443539e+00 -6.06767237e-01 5.27166486e-01 1.03383577e+00 -9.33509111e-01 1.06043231e+00 6.35760009e-01 -9.81212705e-02 -5.31525731e-01 -3.49834532e-01 -4.33637679e-01 -4.56234157e-01 -1.59113705e-01 1.19922316e+00 9.04621035e-02 -2.27971837e-01 1.88362285e-01 1.47135019e-01 -1.18491089e+00 8.40513468e-01 5.44254005e-01 5.47815800e-01 1.63060814e-01 5.67888141e-01 -3.50900143e-01 3.85501206e-01 -3.96958202e-01 -9.49053407e-01 -4.30175573e-01 -7.21483752e-02 -1.44709027e+00 -8.99974823e-01 3.76336873e-01 4.68327165e-01 7.23455995e-02 1.10087371e+00 7.93918967e-01 1.19381949e-01 3.59497309e-01 -1.07583129e+00 -2.18129501e-01 1.14099109e+00 -1.56928182e-01 6.44638598e-01 9.34210956e-01 -4.75374609e-01 1.58623576e+00 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b0824db0-2c07-4931-89a2-b383180b8438	empirical-interpretation-of-the-relationship	2306.17500	null	https://arxiv.org/abs/2306.17500v1	https://arxiv.org/pdf/2306.17500v1.pdf	Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition	Speech emotion recognition (SER) is vital for obtaining emotional intelligence and understanding the contextual meaning of speech. Variations of consonant-vowel (CV) phonemic boundaries can enrich acoustic context with linguistic cues, which impacts SER. In practice, speech emotions are treated as single labels over an acoustic segment for a given time duration. However, phone boundaries within speech are not discrete events, therefore the perceived emotion state should also be distributed over potentially continuous time-windows. This research explores the implication of acoustic context and phone boundaries on local markers for SER using an attention-based approach. The benefits of using a distributed approach to speech emotion understanding are supported by the results of cross-corpora analysis experiments. Experiments where phones and words are mapped to the attention vectors along with the fundamental frequency to observe the overlapping distributions and thereby the relationship between acoustic context and emotion. This work aims to bridge psycholinguistic theory research with computational modelling for SER.	['Thomas Hain', 'Rosanna Milner', 'Md Asif Jalal', 'Anna Ollerenshaw']	2023-06-30	null	null	null	null	['emotion-recognition', 'emotional-intelligence', 'speech-emotion-recognition']	['computer-vision', 'natural-language-processing', 'speech']	[ 1.55981183e-01 -7.24131092e-02 1.92827135e-01 -5.50983965e-01 -4.67710823e-01 -3.82782519e-01 2.32679084e-01 3.20832253e-01 -4.86445665e-01 3.19758147e-01 5.83699644e-01 -1.14598662e-01 -1.41223073e-02 -4.38314974e-01 -4.93399084e-01 -5.46655715e-01 -1.46149442e-01 -3.65094066e-01 -1.91612035e-01 -2.30270013e-01 3.61791253e-01 4.12970871e-01 -2.01381540e+00 4.21309590e-01 7.34912336e-01 7.76106894e-01 5.83377421e-01 8.26491714e-01 -4.54377383e-01 3.79774928e-01 -1.12253630e+00 -2.10292667e-01 -4.46840346e-01 -7.06407964e-01 -4.36102122e-01 -3.64235863e-02 2.29301769e-02 2.97775447e-01 1.90063208e-01 1.14455187e+00 5.83205163e-01 6.62320375e-01 4.64920282e-01 -9.60107684e-01 -8.09983015e-01 8.35169435e-01 -2.54895594e-02 5.46537578e-01 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bdd133c1-4c24-45cc-99b2-ef230d50b42a	price-does-matter-modeling-price-and-interest	2205.04181	null	https://arxiv.org/abs/2205.04181v1	https://arxiv.org/pdf/2205.04181v1.pdf	Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation	Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices. To address the above challenges, we propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation. Towards the first challenge, we devise a heterogeneous hypergraph to represent heterogeneous information and rich relations among them. A dual-channel aggregating mechanism is then designed to aggregate various information in the heterogeneous hypergraph. After that, we extract users' price preferences and interest preferences via attention layers. As to the second challenge, a co-guided learning scheme is designed to model the relations between price and interest preferences and enhance the learning of each other. Finally, we predict user actions based on item features and users' price and interest preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoHHN. Further analysis reveals the significance of price for session-based recommendation.	['Hongfei Lin', 'Haifeng Liu', 'Fenglong Ma', 'Chenliang Li', 'Liang Yang', 'Bo Xu', 'Xiaokun Zhang']	2022-05-09	null	null	null	null	['session-based-recommendations']	['miscellaneous']	[-1.71402127e-01 -1.34410381e-01 -8.71224046e-01 -5.81039667e-01 -3.48086774e-01 -3.39681298e-01 7.78635666e-02 5.90423606e-02 -1.28961101e-01 5.21507621e-01 6.67667687e-01 -8.58235061e-02 -4.43339288e-01 -1.15775681e+00 -5.69264710e-01 -5.85376441e-01 -3.01084220e-01 3.56198877e-01 -5.00668027e-02 -4.50075597e-01 2.71502584e-01 -1.31907955e-01 -1.41676533e+00 3.91443938e-01 1.19867706e+00 1.11959386e+00 2.26601526e-01 3.03118020e-01 -2.53205627e-01 3.90362889e-01 -5.14360331e-02 -6.35432780e-01 4.95154738e-01 -4.89061594e-01 -5.27157247e-01 2.25655675e-01 -4.09051925e-02 -4.12705928e-01 -4.16353226e-01 9.43237722e-01 4.62555140e-01 3.20282668e-01 5.83171785e-01 -1.29086649e+00 -1.17264473e+00 1.42663538e+00 -4.89273697e-01 1.25528220e-03 4.59554225e-01 -2.79582351e-01 1.71149313e+00 -4.74385679e-01 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b7eeb427-333f-4ff3-a3ad-152c39488e1c	causal-coupled-mechanisms-a-control-method	2209.07368	null	https://arxiv.org/abs/2209.07368v1	https://arxiv.org/pdf/2209.07368v1.pdf	Causal Coupled Mechanisms: A Control Method with Cooperation and Competition for Complex System	Complex systems are ubiquitous in the real world and tend to have complicated and poorly understood dynamics. For their control issues, the challenge is to guarantee accuracy, robustness, and generalization in such bloated and troubled environments. Fortunately, a complex system can be divided into multiple modular structures that human cognition appears to exploit. Inspired by this cognition, a novel control method, Causal Coupled Mechanisms (CCMs), is proposed that explores the cooperation in division and competition in combination. Our method employs the theory of hierarchical reinforcement learning (HRL), in which 1) the high-level policy with competitive awareness divides the whole complex system into multiple functional mechanisms, and 2) the low-level policy finishes the control task of each mechanism. Specifically for cooperation, a cascade control module helps the series operation of CCMs, and a forward coupled reasoning module is used to recover the coupling information lost in the division process. On both synthetic systems and a real-world biological regulatory system, the CCM method achieves robust and state-of-the-art control results even with unpredictable random noise. Moreover, generalization results show that reusing prepared specialized CCMs helps to perform well in environments with different confounders and dynamics.	['Xue Li', 'Yi Guan', 'Xinmiao Yu', 'Jingchi Jiang', 'Xuehui Yu']	2022-09-15	null	null	null	null	['hierarchical-reinforcement-learning']	['methodology']	[-1.36940509e-01 1.20211877e-01 6.25043660e-02 4.24783140e-01 3.31791222e-01 -6.11235678e-01 6.60383165e-01 1.34518445e-01 -5.34978919e-02 1.04001510e+00 -1.74468234e-01 -6.54278249e-02 -3.66203904e-01 -8.42875361e-01 -7.17211306e-01 -1.08316731e+00 -2.08592296e-01 2.98062116e-01 5.80957353e-01 -8.67843032e-01 2.88833171e-01 2.33098522e-01 -1.42727041e+00 -9.32374299e-02 1.11839151e+00 5.46174943e-01 4.74989295e-01 5.69310606e-01 9.95373204e-02 1.10532367e+00 -4.76682603e-01 1.72599033e-01 2.07738787e-01 -6.40506327e-01 -5.92622638e-01 -5.06879278e-02 -7.01119721e-01 1.31303981e-01 -2.94117063e-01 9.45783496e-01 5.51308632e-01 -7.77285397e-02 4.45671022e-01 -1.23421669e+00 -4.37111437e-01 4.85234886e-01 -4.10097122e-01 1.64027717e-02 1.59777656e-01 6.57862067e-01 6.30303264e-01 -3.63875836e-01 4.66457307e-01 1.43336916e+00 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be94d7f7-79a2-467d-a6e9-d5cbf4489f81	lad-language-models-as-data-for-zero-shot	2207.14393	null	https://arxiv.org/abs/2207.14393v1	https://arxiv.org/pdf/2207.14393v1.pdf	LAD: Language Models as Data for Zero-Shot Dialog	To facilitate zero-shot generalization in taskoriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce linguistic diversity. LAD achieves significant performance gains in zero-shot settings on intent prediction (+15%), slot filling (+31.4 F-1) and next action prediction (+11 F1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs. LAD is open-sourced, with the code and data available at https://github.com/Shikib/lad.	['Maxine Eskenazi', 'Yasemin Altun', 'Shikib Mehri']	2022-07-28	null	https://aclanthology.org/2022.sigdial-1.55	https://aclanthology.org/2022.sigdial-1.55.pdf	sigdial-acl-2022-9	['slot-filling']	['natural-language-processing']	[-2.48497739e-01 6.33986831e-01 -4.88221437e-01 -6.55834258e-01 -8.35795522e-01 -5.49808085e-01 7.93903589e-01 -1.95993781e-01 -4.41132307e-01 1.00654447e+00 7.39824533e-01 -4.42487776e-01 4.71569091e-01 -3.95487010e-01 -1.72009468e-01 1.07160583e-02 2.89540496e-02 1.03556561e+00 2.31563061e-01 -9.32223499e-01 1.30086213e-01 -7.01395422e-02 -9.65317369e-01 6.29378200e-01 8.56623769e-01 6.53940201e-01 3.21106613e-01 8.70134592e-01 -4.29922700e-01 1.03116357e+00 -7.09302366e-01 -5.08854389e-01 1.21552616e-01 -5.22293687e-01 -1.13559663e+00 -5.75328991e-02 -1.72351580e-02 -6.55609787e-01 -3.68675947e-01 5.51103652e-01 6.39917076e-01 6.53713167e-01 4.41174537e-01 -1.22316146e+00 -7.37222373e-01 7.97414720e-01 -3.62149030e-02 7.03417882e-02 6.55398607e-01 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95e48b71-f233-4b6f-873a-3360a8b924f4	a-continuous-occlusion-model-for-road-scene	null	null	http://openaccess.thecvf.com/content_cvpr_2016/html/Dhiman_A_Continuous_Occlusion_CVPR_2016_paper.html	http://openaccess.thecvf.com/content_cvpr_2016/papers/Dhiman_A_Continuous_Occlusion_CVPR_2016_paper.pdf	A Continuous Occlusion Model for Road Scene Understanding	We present a physically interpretable, continuous 3D model for handling occlusions with applications to road scene understanding. We probabilistically assign each point in space to an object with a theoretical modeling of the reflection and transmission probabilities for the corresponding camera ray. Our modeling is unified in handling occlusions across a variety of scenarios, such as associating structure from motion point tracks with potentially occluded objects or modeling object detection scores in applications such as 3D localization. For point track association, our model uniformly handles static and dynamic objects, which is an advantage over motion segmentation approaches traditionally used in multibody SFM. Detailed experiments on the KITTI dataset show the superiority of the proposed method over both state-of-the-art motion segmentation and a baseline that heuristically uses detection bounding boxes for resolving occlusions. We also demonstrate how our continuous occlusion model may be applied to the task of 3D localization in road scenes.	['Jason J. Corso', 'Quoc-Huy Tran', 'Manmohan Chandraker', 'Vikas Dhiman']	2016-06-01	null	null	null	cvpr-2016-6	['road-scene-understanding']	['computer-vision']	[ 6.16112016e-02 1.05624259e-01 -4.41401720e-01 -2.24053264e-01 -8.44134212e-01 -6.76308453e-01 7.39102244e-01 5.52912429e-02 -2.19890535e-01 3.30079496e-01 -2.88769267e-02 -5.09509921e-01 -1.30139604e-01 -7.60749519e-01 -9.47401941e-01 -5.97713411e-01 -3.99049893e-02 1.26150799e+00 1.04665792e+00 2.51968712e-01 5.41265346e-02 1.08580613e+00 -1.59779334e+00 -1.06539495e-01 6.38829648e-01 5.99596143e-01 4.03763741e-01 6.55623496e-01 -4.45811212e-01 2.09797591e-01 -3.13428581e-01 -2.47555628e-01 2.57610232e-01 3.40401322e-01 -6.97802365e-01 4.65604365e-01 8.81148398e-01 -3.65779161e-01 -3.82718116e-01 5.37276208e-01 -6.00802973e-02 4.54468936e-01 1.01119423e+00 -1.31300604e+00 -1.48699293e-02 1.73484370e-01 -1.14570880e+00 1.93930998e-01 3.46255511e-01 -1.14415631e-01 8.24782014e-01 -8.03891718e-01 7.14274347e-01 1.66184151e+00 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1.49000138e-01 -4.18887228e-01 -3.68793339e-01 2.04143479e-01 4.97478038e-01 -1.28089249e+00 -3.25292379e-01 1.46789253e-01]	[7.81953763961792, -2.363063335418701]
513d1f00-df62-4559-9e90-0e3725c6d8f2	m-vaal-multimodal-variational-adversarial	2306.12376	null	https://arxiv.org/abs/2306.12376v1	https://arxiv.org/pdf/2306.12376v1.pdf	M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks	Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation. Active learning attempts to minimize the need for large annotated samples by actively sampling the most informative examples for annotation. These examples contribute significantly to improving the performance of supervised machine learning models, and thus, active learning can play an essential role in selecting the most appropriate information in deep learning-based diagnosis, clinical assessments, and treatment planning. Although some existing works have proposed methods for sampling the best examples for annotation in medical image analysis, they are not task-agnostic and do not use multimodal auxiliary information in the sampler, which has the potential to increase robustness. Therefore, in this work, we propose a Multimodal Variational Adversarial Active Learning (M-VAAL) method that uses auxiliary information from additional modalities to enhance the active sampling. We applied our method to two datasets: i) brain tumor segmentation and multi-label classification using the BraTS2018 dataset, and ii) chest X-ray image classification using the COVID-QU-Ex dataset. Our results show a promising direction toward data-efficient learning under limited annotations.	['Cristian A. Linte', 'Danail Stoyanov', 'Bishesh Khanal', 'Binod Bhattarai', 'Bidur Khanal']	2023-06-21	null	null	null	null	['tumor-segmentation', 'brain-tumor-segmentation', 'active-learning', 'active-learning']	['computer-vision', 'medical', 'methodology', 'natural-language-processing']	[ 6.32721782e-01 4.91757393e-01 -6.36985362e-01 -4.55557495e-01 -1.56225598e+00 -3.01517576e-01 4.20257509e-01 5.64888895e-01 -7.81724393e-01 8.23636651e-01 9.25097615e-02 -5.49206771e-02 -7.75533989e-02 -6.51555777e-01 -4.57826614e-01 -1.19732928e+00 3.06989312e-01 8.51150155e-01 2.16925144e-01 1.66257799e-01 -1.54319763e-01 2.42915899e-01 -9.49939549e-01 3.59090596e-01 1.16051054e+00 9.93224323e-01 2.32851595e-01 2.90286124e-01 -2.69372880e-01 1.03037918e+00 -3.42420250e-01 -4.86220121e-01 -1.58452287e-01 -5.34117997e-01 -9.30368721e-01 3.23240966e-01 -1.12455254e-02 -1.45737186e-01 1.56884819e-01 1.00990045e+00 6.02945149e-01 5.64530529e-02 9.27572131e-01 -1.12790120e+00 -1.39843866e-01 7.35062659e-01 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0de10265-1214-4b7d-8634-870e520b205c	invisible-to-visible-privacy-aware-human	2204.07280	null	https://arxiv.org/abs/2204.07280v1	https://arxiv.org/pdf/2204.07280v1.pdf	Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder	In action understanding in indoor, we have to recognize human pose and action considering privacy. Although camera images can be used for highly accurate human action recognition, camera images do not preserve privacy. Therefore, we propose a new task for human instance segmentation from invisible information, especially airborne ultrasound, for action recognition. To perform instance segmentation from invisible information, we first convert sound waves to reflected sound directional images (sound images). Although the sound images can roughly identify the location of a person, the detailed shape is ambiguous. To address this problem, we propose a collaborative learning variational autoencoder (CL-VAE) that simultaneously uses sound and RGB images during training. In inference, it is possible to obtain instance segmentation results only from sound images. As a result of performance verification, CL-VAE could estimate human instance segmentations more accurately than conventional variational autoencoder and some other models. Since this method can obtain human segmentations individually, it could be applied to human action recognition tasks with privacy protection.	['Takayoshi Yamashita', 'Kazuki Kozuka', 'Yasunori Ishii', 'Risako Tanigawa']	2022-04-15	null	null	null	null	['action-understanding', 'human-instance-segmentation']	['computer-vision', 'computer-vision']	[ 4.11778718e-01 9.98794660e-02 1.47518963e-01 -4.07843560e-01 -6.77299738e-01 -5.21303654e-01 2.27262482e-01 -4.42154050e-01 -5.94474554e-01 6.18142128e-01 -7.91473389e-02 -8.11061449e-03 2.45734692e-01 -1.03558660e+00 -8.42148662e-01 -8.69172812e-01 7.20146656e-01 4.15000498e-01 1.74418762e-01 4.95452911e-01 5.94440103e-02 4.64505225e-01 -1.64658916e+00 4.20470357e-01 6.55525625e-01 1.07563519e+00 4.65874411e-02 9.27151620e-01 -1.02293946e-01 6.99968159e-01 -7.95152664e-01 -4.91546541e-01 3.48460764e-01 -4.66451257e-01 -7.88496733e-01 4.08801824e-01 5.31846642e-01 -1.00963759e+00 -1.95787013e-01 1.23595786e+00 3.43108982e-01 5.59746444e-01 6.23226464e-01 -1.42745936e+00 -6.25959158e-01 3.59616101e-01 -3.55597436e-01 -2.78883159e-01 7.08151221e-01 -1.14014745e-01 7.73180127e-01 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9f55315c-00b9-4928-a8c1-4a0d9427bb5d	optimized-hybrid-focal-margin-loss-for-crack	2302.04395	null	https://arxiv.org/abs/2302.04395v1	https://arxiv.org/pdf/2302.04395v1.pdf	Optimized Hybrid Focal Margin Loss for Crack Segmentation	Many loss functions have been derived from cross-entropy loss functions such as large-margin softmax loss and focal loss. The large-margin softmax loss makes the classification more rigorous and prevents overfitting. The focal loss alleviates class imbalance in object detection by down-weighting the loss of well-classified examples. Recent research has shown that these two loss functions derived from cross entropy have valuable applications in the field of image segmentation. However, to the best of our knowledge, there is no unified formulation that combines these two loss functions so that they can not only be transformed mutually, but can also be used to simultaneously address class imbalance and overfitting. To this end, we subdivide the entropy-based loss into the regularizer-based entropy loss and the focal-based entropy loss, and propose a novel optimized hybrid focal loss to handle extreme class imbalance and prevent overfitting for crack segmentation. We have evaluated our proposal in comparison with three crack segmentation datasets (DeepCrack-DB, CRACK500 and our private PanelCrack dataset). Our experiments demonstrate that the focal margin component can significantly increase the IoU of cracks by 0.43 on DeepCrack-DB and 0.44 on our PanelCrack dataset, respectively.	['Jiajie Chen']	2023-02-09	null	null	null	null	['crack-segmentation']	['computer-vision']	[ 9.49512646e-02 1.04687095e-01 -4.85975295e-01 -4.58581984e-01 -1.02549767e+00 -2.08438128e-01 -2.52944291e-01 2.70559460e-01 -5.39627016e-01 6.84653401e-01 -3.61449718e-01 -6.13297075e-02 -4.56305109e-02 -8.75954092e-01 -5.54206371e-01 -8.04042220e-01 3.47521573e-01 8.29131342e-03 6.87607348e-01 1.22059353e-01 3.86692911e-01 2.13872030e-01 -1.36798024e+00 3.72919530e-01 1.33887887e+00 1.35106111e+00 -9.64114384e-04 3.33821893e-01 1.46166861e-01 6.58286750e-01 -4.99864548e-01 -5.15590668e-01 1.40817657e-01 -5.31958938e-02 -7.56620586e-01 -2.31583044e-01 4.17635977e-01 -1.19458221e-01 -2.45162800e-01 8.78762305e-01 7.30420589e-01 -1.83587790e-01 7.53779173e-01 -1.12997234e+00 -4.12043184e-01 7.98278332e-01 -9.40358460e-01 1.77887380e-01 7.60459006e-02 1.41615182e-01 1.30269217e+00 -6.58464909e-01 2.29518831e-01 1.07178390e+00 1.05010080e+00 2.72498876e-01 -9.53371942e-01 -7.31264889e-01 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d51ad70f-3b37-4262-83eb-8a615b15c655	t-ukapo-at-semeval-2020-task-6-def-n-tly-not	null	null	https://aclanthology.org/2020.semeval-1.95	https://aclanthology.org/2020.semeval-1.95.pdf	T\"uKaPo at SemEval-2020 Task 6: Def(n)tly Not BERT: Definition Extraction Using pre-BERT Methods in a post-BERT World	We describe our system (T{\"u}KaPo) submitted for Task 6: DeftEval, at SemEval 2020. We developed a hybrid approach that combined existing CNN and RNN methods and investigated the impact of purely-syntactic and semantic features on the task of definition extraction. Our final model achieved a F1-score of 0.6851 in subtask 1, i.e, sentence classification.	['Haemanth Santhi Ponnusamy', 'Madeeswaran Kannan']	2020-12-01	null	null	null	semeval-2020	['definition-extraction']	['natural-language-processing']	[ 1.41435757e-01 4.52725112e-01 -1.33227870e-01 -5.76222777e-01 -6.52790844e-01 -7.93428063e-01 6.69228792e-01 8.25426206e-02 -9.83209431e-01 1.06759644e+00 3.52836251e-01 -6.71622396e-01 1.18328825e-01 -7.34603107e-01 -6.53633952e-01 1.73834637e-01 3.07069957e-01 2.32586294e-01 1.20391697e-01 -4.95751977e-01 3.23859556e-03 -1.31949931e-01 -1.06588221e+00 9.09771323e-01 4.46959078e-01 1.40907884e+00 -1.30032031e-02 9.61131155e-01 -1.56436101e-01 1.16207612e+00 -1.13908660e+00 -8.95748973e-01 -2.17488796e-01 -2.73694664e-01 -1.33117986e+00 -5.82582355e-01 6.31726027e-01 8.28501880e-02 -3.83224130e-01 1.06676221e+00 5.96586227e-01 2.91521996e-01 4.29121643e-01 -5.89997232e-01 -9.20881629e-01 1.44190383e+00 2.19115671e-02 5.95626473e-01 4.26356792e-01 9.10406113e-02 1.36774600e+00 -1.04631805e+00 1.00537646e+00 1.01400924e+00 9.71944571e-01 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ecb8ef62-5a0c-45ea-a48a-7d73fcd6172d	iterative-constrained-clustering-for	null	null	https://aclanthology.org/E14-1029	https://aclanthology.org/E14-1029.pdf	Iterative Constrained Clustering for Subjectivity Word Sense Disambiguation	null	['Rada Mihalcea', 'Janyce Wiebe', 'Cem Akkaya']	2014-04-01	null	null	null	eacl-2014-4	['subjectivity-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 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 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a2458fee-8b2d-4835-8d32-457587ceed9a	automating-horizon-scanning-in-future-studies	null	null	https://aclanthology.org/2022.lrec-1.34	https://aclanthology.org/2022.lrec-1.34.pdf	Automating Horizon Scanning in Future Studies	We introduce document retrieval and comment generation tasks for automating horizon scanning. This is an important task in the field of futurology that collects sufficient information for predicting drastic societal changes in the mid- or long-term future. The steps used are: 1) retrieving news articles that imply drastic changes, and 2) writing subjective comments on each article for others’ ease of understanding. As a first step in automating these tasks, we create a dataset that contains 2,266 manually collected news articles with comments written by experts. We analyze the collected documents and comments regarding characteristic words, the distance to general articles, and contents in the comments. Furthermore, we compare several methods for automating horizon scanning. Our experiments show that 1) manually collected articles are different from general articles regarding the words used and semantic distances, 2) the contents in the comment can be classified into several categories, and 3) a supervised model trained on our dataset achieves a better performance. The contributions are: 1) we propose document retrieval and comment generation tasks for horizon scanning, 2) create and analyze a new dataset, and 3) report the performance of several models and show that comment generation tasks are challenging.	['Akihiko Murai', 'Yuichi Washida', 'Yukari Nagai', 'Hiroki Igarashi', 'Sohei Washino', 'Suzuko Nishino', 'Tatsuya Ishigaki']	null	null	null	null	lrec-2022-6	['comment-generation']	['natural-language-processing']	[ 9.68635920e-03 2.75672257e-01 -5.10581613e-01 -2.49643877e-01 -1.06707144e+00 -7.57221580e-01 9.66754794e-01 7.61951268e-01 -2.97539502e-01 9.04872119e-01 1.08825731e+00 -4.07090604e-01 7.55831972e-03 -7.81631708e-01 -5.11934340e-01 -3.18681329e-01 1.10471867e-01 5.09823442e-01 2.72125721e-01 -4.71448511e-01 1.15297544e+00 -1.15278840e-01 -1.32390463e+00 4.89107519e-01 1.30005038e+00 9.72738504e-01 1.06680505e-01 6.99549615e-01 -3.35470110e-01 8.46646845e-01 -9.19059098e-01 -5.67211568e-01 1.88075155e-02 -6.22703075e-01 -9.12516832e-01 -5.91367576e-03 1.97300643e-01 -3.72946709e-01 2.43731573e-01 1.07098019e+00 5.19899786e-01 1.21896274e-01 1.25123823e+00 -7.20639169e-01 -1.09293699e+00 1.17633998e+00 -4.24163342e-01 2.88815230e-01 6.37738943e-01 -3.20958793e-01 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a8fcf1cf-102d-40d3-be25-aa609e6b17aa	structural-information-preserving-for-graph-1	2102.06749	null	https://arxiv.org/abs/2102.06749v1	https://arxiv.org/pdf/2102.06749v1.pdf	Structural Information Preserving for Graph-to-Text Generation	The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs. We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information. In particular, we introduce two types of autoencoding losses, each individually focusing on different aspects (a.k.a. views) of input graphs. The losses are then back-propagated to better calibrate our model via multi-task training. Experiments on two benchmarks for graph-to-text generation show the effectiveness of our approach over a state-of-the-art baseline. Our code is available at \url{http://github.com/Soistesimmer/AMR-multiview}.	['Dong Yu', 'Yubin Ge', 'Kun Xu', 'Yue Zhang', 'Jinsong Su', 'Ante Wang', 'Linfeng Song']	2021-02-12	structural-information-preserving-for-graph	https://aclanthology.org/2020.acl-main.712	https://aclanthology.org/2020.acl-main.712.pdf	acl-2020-6	['data-to-text-generation']	['natural-language-processing']	[ 5.30860007e-01 6.70074344e-01 -7.17135742e-02 -2.80637830e-01 -9.38457906e-01 -7.22616136e-01 9.11545455e-01 1.37768283e-01 2.76680142e-01 8.99533093e-01 6.63381457e-01 -3.03352445e-01 3.34998101e-01 -1.12737858e+00 -1.08938253e+00 -5.36598265e-01 4.14903224e-01 5.20434201e-01 -6.94452878e-03 -5.52880287e-01 8.84566754e-02 1.35188028e-01 -1.14898312e+00 7.34629691e-01 8.94520342e-01 5.40866733e-01 1.86710000e-01 7.48061955e-01 -2.23460093e-01 8.60429585e-01 -6.64514303e-01 -9.30084884e-01 1.14946932e-01 -7.83564568e-01 -8.34965706e-01 1.58165902e-01 4.47362334e-01 -1.31279171e-01 -4.55011159e-01 1.09064639e+00 5.76099813e-01 1.16753578e-02 7.43861496e-01 -1.26220083e+00 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-3.55765402e-01 -8.15497711e-02 3.96343209e-02 4.51939613e-01 3.98674369e-01 5.24116457e-01 8.44760656e-01 6.58259392e-01 -8.29473555e-01 -9.07250047e-01 -9.54239488e-01 -3.66572946e-01 6.34950578e-01 2.92794645e-01 -1.42991036e-01 -4.27491277e-01 1.78902894e-01]	[10.281126022338867, 8.288256645202637]
ba8e4ddd-cf90-455d-948e-434eed69cb23	pgd-a-large-scale-professional-go-dataset-for	2205.00254	null	https://arxiv.org/abs/2205.00254v1	https://arxiv.org/pdf/2205.00254v1.pdf	PGD: A Large-scale Professional Go Dataset for Data-driven Analytics	Lee Sedol is on a winning streak--does this legend rise again after the competition with AlphaGo? Ke Jie is invincible in the world championship--can he still win the title this time? Go is one of the most popular board games in East Asia, with a stable professional sports system that has lasted for decades in China, Japan, and Korea. There are mature data-driven analysis technologies for many sports, such as soccer, basketball, and esports. However, developing such technology for Go remains nontrivial and challenging due to the lack of datasets, meta-information, and in-game statistics. This paper creates the Professional Go Dataset (PGD), containing 98,043 games played by 2,148 professional players from 1950 to 2021. After manual cleaning and labeling, we provide detailed meta-information for each player, game, and tournament. Moreover, the dataset includes analysis results for each move in the match evaluated by advanced AlphaZero-based AI. To establish a benchmark for PGD, we further analyze the data and extract meaningful in-game features based on prior knowledge related to Go that can indicate the game status. With the help of complete meta-information and constructed in-game features, our results prediction system achieves an accuracy of 75.30%, much higher than several state-of-the-art approaches (64%-65%). As far as we know, PGD is the first dataset for data-driven analytics in Go and even in board games. Beyond this promising result, we provide more examples of tasks that benefit from our dataset. The ultimate goal of this paper is to bridge this ancient game and the modern data science community. It will advance research on Go-related analytics to enhance the fan experience, help players improve their ability, and facilitate other promising aspects. The dataset will be made publicly available.	['Yifan Gao']	2022-04-30	null	null	null	null	['board-games']	['playing-games']	[-3.75131369e-01 -3.70636225e-01 -3.63441199e-01 -6.43102601e-02 -8.32013845e-01 -4.07905132e-01 -2.20027268e-02 1.89025924e-01 -6.08891308e-01 5.72141230e-01 2.95648545e-01 -4.87536639e-02 -5.38548291e-01 -1.14554465e+00 -4.79778945e-01 -3.62728894e-01 -2.11638242e-01 5.37165046e-01 3.25644314e-01 -1.16472661e+00 5.00193477e-01 3.71291884e-03 -1.90152025e+00 4.47269827e-01 6.84525907e-01 9.94574368e-01 1.31332710e-01 5.29800057e-01 2.99588919e-01 9.61272895e-01 -6.89085662e-01 -9.06536937e-01 5.92087865e-01 -4.99553055e-01 -7.65360117e-01 -5.11723757e-01 3.21314447e-02 2.81142499e-02 -4.73673761e-01 7.35100925e-01 4.98747170e-01 3.42989445e-01 1.10638082e-01 -1.26156211e+00 -1.88236371e-01 1.00292718e+00 -5.00052810e-01 3.92413944e-01 4.50483531e-01 3.56994510e-01 1.36803424e+00 -4.95431215e-01 6.50594115e-01 4.53184217e-01 8.41610312e-01 3.23568970e-01 -6.06338561e-01 -8.74510288e-01 -9.69770178e-02 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dc5b7501-a625-4a51-8581-0a0ec867bcaa	knowledge-diffusion-process-common-islamic	2002.04067	null	https://arxiv.org/abs/2002.04067v1	https://arxiv.org/pdf/2002.04067v1.pdf	Knowledge Diffusion Process & Common Islamic Banking Governance Principles: Integrative Perspective (s) of Managers and Shariah Scholars	Islamic banks being commercial entities strive to earn profit within shariah ambit. Therefore, they seem to be basing themselves upon two knowledge streams namely i) Islamic jurisprudence principles, and ii) banking principles. Islamic jurisprudence principles primarily aim at bringing shariah compliance while banking principles focus profitability. These principles, making two schools of thought in the discipline, however, have their unique philosophies, principles, and practices, which are now gradually diffusing into an emergent set of governance principles basing the contemporary Islamic banking theory and practice. Governance systems of Islamic banks have elements of both conventional as well as Shariah, and need to have principles having components of banking and shariah sufficiently diffused for their successful operations in a longer term. Aim of this research is to review the literature about the knowledge diffusion process of islamic banking principles which guides the governance of Islamic banks. This study review the literature using a method in which focus remain on bridging different areas which in this case are knowledge diffusion and islamic banking governance principles.	['Shakir Ullah', 'Karim Ullah', 'Adnan Malik']	2020-01-23	null	null	null	null	['jurisprudence']	['miscellaneous']	[-6.55275822e-01 1.95429370e-01 -4.64056969e-01 -7.53401145e-02 2.37228468e-01 -3.64006072e-01 5.32121360e-01 1.73457165e-03 1.84867401e-02 6.48441732e-01 6.94236994e-01 -8.24076653e-01 -3.20115209e-01 -1.11580861e+00 1.17080703e-01 -8.14351201e-01 4.10825789e-01 3.60686481e-01 -2.41148323e-01 -9.56079364e-01 1.15403283e+00 6.47752702e-01 -8.21064532e-01 7.83615094e-03 7.05152333e-01 3.90896112e-01 -7.40494847e-01 2.63838291e-01 -1.72106400e-01 1.11452234e+00 -4.98162955e-01 -8.75158310e-01 3.08027476e-01 -8.56654942e-01 -1.05145204e+00 1.67057127e-01 -2.03799263e-01 -6.31453812e-01 1.94529340e-01 5.39004266e-01 2.96785980e-01 -2.97585487e-01 7.79343545e-01 -6.10580862e-01 -1.72116780e+00 7.24719584e-01 -5.73089004e-01 4.52170342e-01 1.14667192e-01 2.35606983e-01 1.15035307e+00 -8.67210567e-01 1.19679019e-01 1.01607525e+00 3.39722663e-01 2.86380142e-01 -7.20135331e-01 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8dd70e53-149a-43f4-91f1-b7ca0795c275	blind-face-restoration-via-integrating-face	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Zhu_Blind_Face_Restoration_via_Integrating_Face_Shape_and_Generative_Priors_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Zhu_Blind_Face_Restoration_via_Integrating_Face_Shape_and_Generative_Priors_CVPR_2022_paper.pdf	Blind Face Restoration via Integrating Face Shape and Generative Priors	Blind face restoration, which aims to reconstruct high-quality images from low-quality inputs, can benefit many applications. Although existing generative-based methods achieve significant progress in producing high-quality images, they often fail to restore natural face shapes and high-fidelity facial details from severely-degraded inputs. In this work, we propose to integrate shape and generative priors to guide the challenging blind face restoration. Firstly, we set up a shape restoration module to recover reasonable facial geometry with 3D reconstruction. Secondly, a pretrained facial generator is adopted as decoder to generate photo-realistic high-resolution images. To ensure high-fidelity, hierarchical spatial features extracted from the low-quality inputs and rendered 3D images are inserted into the decoder with our proposed Adaptive Feature Fusion Block (AFFB). Moreover, we introduce hybrid-level losses to jointly train the shape and generative priors together with other network parts such that these two priors better adapt to our blind face restoration task. The proposed Shape and Generative Prior integrated Network (SGPN) can restore high-quality images with clear face shapes and realistic facial details. Experimental results on synthetic and real-world datasets demonstrate SGPN performs favorably against state-of-the-art blind face restoration methods.	['Ying Tai', 'Chengjie Wang', 'Xiaozhong Ji', 'Xinyi Zhang', 'Wenqing Chu', 'Junwei Zhu', 'Feida Zhu']	2022-01-01	null	null	null	cvpr-2022-1	['blind-face-restoration']	['computer-vision']	[ 3.43054175e-01 6.08786717e-02 4.98787642e-01 -5.70070863e-01 -9.03801501e-01 -8.82606879e-02 6.26771033e-01 -1.02306426e+00 1.79794773e-01 7.35014021e-01 4.72694308e-01 1.35974377e-01 -2.36507580e-02 -8.70516241e-01 -8.82395446e-01 -8.88329446e-01 4.49212432e-01 2.73688614e-01 -2.10781813e-01 -2.04469115e-01 6.45303205e-02 8.54652226e-01 -1.93940258e+00 3.45976561e-01 1.03040552e+00 9.30677772e-01 2.29777232e-01 4.37804937e-01 3.53586040e-02 4.39103842e-01 -2.58042544e-01 -6.11574113e-01 4.90672946e-01 -4.93423760e-01 -3.92513454e-01 5.68984449e-01 6.36636913e-01 -8.26879919e-01 -5.25536001e-01 1.12682152e+00 8.00637007e-01 1.00085281e-01 8.09109628e-01 -8.95632386e-01 -1.18935263e+00 -8.93507153e-02 -7.12059259e-01 -1.57632396e-01 6.27889216e-01 4.89495873e-01 3.08957875e-01 -1.36511588e+00 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c13e4781-e223-4c51-81e6-744d096d4a4d	survcaus-representation-balancing-for	2203.15672	null	https://arxiv.org/abs/2203.15672v1	https://arxiv.org/pdf/2203.15672v1.pdf	SurvCaus : Representation Balancing for Survival Causal Inference	Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years. Most of the time, individual effects are better presented as Conditional Average Treatment Effects (CATE). Recently, representation balancing techniques have gained considerable momentum in causal inference from observational data, still limited to continuous (and binary) outcomes. However, in numerous pathologies, the outcome of interest is a (possibly censored) survival time. Our paper proposes theoretical guarantees for a representation balancing framework applied to counterfactual inference in a survival setting using a neural network capable of predicting the factual and counterfactual survival functions (and then the CATE), in the presence of censorship, at the individual level. We also present extensive experiments on synthetic and semisynthetic datasets that show that the proposed extensions outperform baseline methods.	['Blaise Hanczar', 'Agathe Guilloux', 'Ayoub Abraich']	2022-03-29	null	null	null	null	['counterfactual-inference']	['miscellaneous']	[ 4.71884757e-01 2.28748828e-01 -9.91564214e-01 -4.48319137e-01 -7.30257511e-01 -1.67678460e-01 6.41695142e-01 4.71672982e-01 -3.32156301e-01 1.46165514e+00 7.27071285e-01 -5.59826612e-01 -6.02717578e-01 -8.40444148e-01 -8.12957942e-01 -8.04843426e-01 -6.74811661e-01 3.99459094e-01 -6.11917436e-01 2.59312928e-01 3.11306506e-01 3.88052344e-01 -1.49493384e+00 -6.65715039e-02 8.51760864e-01 6.13732278e-01 -6.44492209e-01 3.16679657e-01 3.14129621e-01 7.43878007e-01 -4.05408233e-01 -5.32743633e-01 -8.65337551e-02 -2.10247546e-01 -6.23969197e-01 -3.40462238e-01 5.69055140e-01 -3.02256912e-01 -5.16103148e-01 5.66306591e-01 6.70545101e-01 4.23053727e-02 1.02025068e+00 -1.58511531e+00 -7.78558671e-01 9.85756636e-01 -5.92814922e-01 -1.05530331e-02 9.87500101e-02 -1.09864086e-01 8.86056364e-01 -3.22686106e-01 3.93074781e-01 1.43754244e+00 7.46851563e-01 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5965322a-dad9-4cb2-8368-2cdba117ab86	self-supervised-video-representation-using	2010.15464	null	https://arxiv.org/abs/2010.15464v2	https://arxiv.org/pdf/2010.15464v2.pdf	Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation Leaning	Recently, pretext-task based methods are proposed one after another in self-supervised video feature learning. Meanwhile, contrastive learning methods also yield good performance. Usually, new methods can beat previous ones as claimed that they could capture "better" temporal information. However, there exist setting differences among them and it is hard to conclude which is better. It would be much more convincing in comparison if these methods have reached as closer to their performance limits as possible. In this paper, we start from one pretext-task baseline, exploring how far it can go by combining it with contrastive learning, data pre-processing, and data augmentation. A proper setting has been found from extensive experiments, with which huge improvements over the baselines can be achieved, indicating a joint optimization framework can boost both pretext task and contrastive learning. We denote the joint optimization framework as Pretext-Contrastive Learning (PCL). The other two pretext task baselines are used to validate the effectiveness of PCL. And we can easily outperform current state-of-the-art methods in the same training manner, showing the effectiveness and the generality of our proposal. It is convenient to treat PCL as a standard training strategy and apply it to many other works in self-supervised video feature learning.	['Toshihiko Yamasaki', 'Xueting Wang', 'Li Tao']	2020-10-29	null	null	null	null	['self-supervised-action-recognition']	['computer-vision']	[ 8.43693614e-02 -3.25631678e-01 -5.76571584e-01 -2.84769267e-01 -4.88387346e-01 -2.49166250e-01 9.78620172e-01 -2.68422157e-01 -6.64807320e-01 6.79804564e-01 2.18936995e-01 6.02008700e-02 -2.27108687e-01 -4.12391454e-01 -6.90299749e-01 -9.17717457e-01 -3.17390561e-01 6.14849590e-02 4.88773882e-01 -4.15973932e-01 1.74444482e-01 1.05708972e-01 -1.83075416e+00 4.15117264e-01 6.46178782e-01 9.08196867e-01 2.09387869e-01 3.13432336e-01 8.81345756e-03 8.25989842e-01 -4.33115304e-01 -3.12844187e-01 3.07905346e-01 -4.05751169e-01 -9.95727777e-01 2.12955415e-01 4.92696226e-01 -1.73368990e-01 -5.23174107e-01 7.63696611e-01 4.65467066e-01 3.42124194e-01 5.67830086e-01 -1.48635828e+00 -4.25150365e-01 3.94571006e-01 -6.40814841e-01 3.52163315e-01 4.79195774e-01 9.96160209e-02 1.04609144e+00 -8.45756471e-01 7.89178550e-01 1.06059265e+00 6.80060923e-01 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f91b3aa2-3356-45d1-858e-31922a51ec64	improving-machine-translation-with-phrase	2301.08008	null	https://arxiv.org/abs/2301.08008v1	https://arxiv.org/pdf/2301.08008v1.pdf	Improving Machine Translation with Phrase Pair Injection and Corpus Filtering	In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation (NMT) systems. We extract parallel phrases and sentences from the pseudo-parallel corpus and augment it with the parallel corpus to train the NMT models. With the proposed approach, we observe an improvement in the Machine Translation (MT) system for 3 low-resource language pairs, Hindi-Marathi, English-Marathi, and English-Pashto, and 6 translation directions by up to 2.7 BLEU points, on the FLORES test data. These BLEU score improvements are over the models trained using the whole pseudo-parallel corpus augmented with the parallel corpus.	['Pushpak Bhattacharyya', 'Akshay Batheja']	2023-01-19	null	null	null	null	['nmt']	['computer-code']	[ 3.49932253e-01 3.24778371e-02 -3.70218992e-01 -2.21626386e-01 -1.52308857e+00 -7.94745386e-01 8.60759735e-01 -2.45236501e-01 -7.55995154e-01 1.11985731e+00 1.83260664e-01 -7.48018086e-01 4.19980437e-01 -3.13452154e-01 -1.07085419e+00 -4.62855697e-01 2.21926346e-01 1.10726798e+00 -2.19657436e-01 -7.95172274e-01 5.37808686e-02 -3.13272476e-02 -4.43740934e-01 5.89836359e-01 1.08062088e+00 3.03573102e-01 3.43968600e-01 5.84938407e-01 -1.28484100e-01 6.85502365e-02 -5.12301505e-01 -8.72975051e-01 5.66647172e-01 -6.20583892e-01 -9.19485807e-01 -4.03283656e-01 4.22794253e-01 -2.80103497e-02 -3.49923879e-01 7.67550707e-01 5.69656551e-01 -1.91652745e-01 5.16318262e-01 -6.60154998e-01 -7.99088120e-01 1.17773473e+00 -8.75059187e-01 4.90096629e-01 8.85995850e-02 5.65085113e-02 1.12041175e+00 -1.34907007e+00 8.85716021e-01 1.23258448e+00 4.46374893e-01 6.90020144e-01 -1.28069866e+00 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8946e361-e017-4be3-876a-71d98b01ac59	building-3d-object-models-during-manipulation	1905.03907	null	https://arxiv.org/abs/1905.03907v1	https://arxiv.org/pdf/1905.03907v1.pdf	Building 3D Object Models during Manipulation by Reconstruction-Aware Trajectory Optimization	Object shape provides important information for robotic manipulation; for instance, selecting an effective grasp depends on both the global and local shape of the object of interest, while reaching into clutter requires accurate surface geometry to avoid unintended contact with the environment. Model-based 3D object manipulation is a widely studied problem; however, obtaining the accurate 3D object models for multiple objects often requires tedious work. In this letter, we exploit Gaussian process implicit surfaces (GPIS) extracted from RGB-D sensor data to grasp an unknown object. We propose a reconstruction-aware trajectory optimization that makes use of the extracted GPIS model plan a motion to improve the ability to estimate the object's 3D geometry, while performing a pick-and-place action. We present a probabilistic approach for a robot to autonomously learn and track the object, while achieve the manipulation task. We use a sampling-based trajectory generation method to explore the unseen parts of the object using the estimated conditional entropy of the GPIS model. We validate our method with physical robot experiments across eleven different objects of varying shape from the YCB object dataset. Our experiments show that our reconstruction-aware trajectory optimization provides higher-quality 3D object reconstruction when compared with directly solving the manipulation task or using a heuristic to view unseen portions of the object.	['Kanrun Huang', 'Tucker Hermans']	2019-05-10	null	null	null	null	['3d-object-reconstruction']	['computer-vision']	[ 1.12466358e-01 2.22220886e-02 -5.90986982e-02 -2.53125757e-01 -8.30070257e-01 -6.20440185e-01 1.26048759e-01 7.13496804e-02 -1.75514013e-01 3.80364925e-01 -3.25542539e-01 4.07954827e-02 -3.99941951e-01 -7.10688412e-01 -1.01621222e+00 -8.90762150e-01 -3.59655768e-02 1.22720182e+00 3.09085011e-01 1.93406969e-01 4.43759084e-01 1.02158475e+00 -1.34488201e+00 -7.73452893e-02 8.63968551e-01 1.09916627e+00 9.96380508e-01 4.26840097e-01 -4.59187590e-02 3.44245993e-02 -2.66951531e-01 1.44091398e-01 4.47335243e-01 8.72100294e-02 -4.89739418e-01 3.48156452e-01 -1.67553544e-01 -4.76922840e-01 -1.06096126e-01 1.02377212e+00 1.04123056e-01 3.64122093e-01 9.86763418e-01 -1.11958814e+00 -2.15445384e-01 1.94663107e-01 -4.58460510e-01 -5.66872776e-01 2.75361419e-01 4.53886211e-01 5.73336482e-01 -9.00963962e-01 7.29400635e-01 1.54829168e+00 1.41354248e-01 4.56268877e-01 -1.17439330e+00 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1cbc4059-d335-416e-924d-1ed11037b8f2	visual-fault-detection-of-multi-scale-key	2211.14522	null	https://arxiv.org/abs/2211.14522v1	https://arxiv.org/pdf/2211.14522v1.pdf	Visual Fault Detection of Multi-scale Key Components in Freight Trains	Fault detection for key components in the braking system of freight trains is critical for ensuring railway transportation safety. Despite the frequently employed methods based on deep learning, these fault detectors are highly reliant on hardware resources and are complex to implement. In addition, no train fault detectors consider the drop in accuracy induced by scale variation of fault parts. This paper proposes a lightweight anchor-free framework to solve the above problems. Specifically, to reduce the amount of computation and model size, we introduce a lightweight backbone and adopt an anchor-free method for localization and regression. To improve detection accuracy for multi-scale parts, we design a feature pyramid network to generate rectangular layers of different sizes to map parts with similar aspect ratios. Experiments on four fault datasets show that our framework achieves 98.44% accuracy while the model size is only 22.5 MB, outperforming state-of-the-art detectors.	['Guodong Sun', 'Bo Wu', 'Huilin Pan', 'Yang Zhou', 'Yang Zhang']	2022-11-26	null	null	null	null	['fault-detection']	['miscellaneous']	[-2.29980558e-01 -3.50566983e-01 -4.96136285e-02 -2.34617010e-01 -9.17059064e-01 -1.72518134e-01 1.51131311e-02 -8.60504434e-02 -1.91362545e-01 3.62460852e-01 -4.11944389e-01 -4.15032268e-01 -1.07363038e-01 -8.14378500e-01 -8.04608107e-01 -4.64252174e-01 8.83254260e-02 2.89635807e-01 1.02889144e+00 -1.67694837e-01 4.79463696e-01 8.37357759e-01 -1.42174911e+00 3.68958950e-01 6.61974132e-01 1.22717059e+00 1.60258278e-01 3.76870751e-01 7.72676915e-02 6.87576532e-01 -1.10226369e+00 -1.23587139e-01 2.00394958e-01 -9.16938707e-02 -4.48884070e-01 1.24606259e-01 3.94197404e-01 -4.72200930e-01 -1.00036383e+00 8.56172860e-01 5.97896934e-01 -4.51496005e-01 2.87933081e-01 -1.52505040e+00 -1.24595977e-01 5.14747679e-01 -8.46841514e-01 4.26007986e-01 7.04746344e-04 -2.48347253e-01 4.49306786e-01 -1.07857943e+00 5.81539469e-03 8.24340165e-01 1.09338212e+00 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a2d07b7a-a7dd-47b4-b172-67e03dc948d2	misa-modality-invariant-and-specific	2005.03545	null	https://arxiv.org/abs/2005.03545v3	https://arxiv.org/pdf/2005.03545v3.pdf	MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis	Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality-invariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework.	['Soujanya Poria', 'Roger Zimmermann', 'Devamanyu Hazarika']	2020-05-07	null	null	null	null	['humor-detection']	['natural-language-processing']	[ 1.36711359e-01 -3.37144673e-01 -2.53344983e-01 -2.22263768e-01 -9.42603111e-01 -6.02098107e-01 7.26140141e-01 6.91158324e-02 -9.45277512e-02 3.24079245e-01 1.03602159e+00 2.42687166e-01 2.92047918e-01 -2.41979107e-01 -4.61953759e-01 -8.04802418e-01 4.73208994e-01 -1.97018147e-01 -4.81445700e-01 -6.47862911e-01 1.25017643e-01 -9.55709368e-02 -1.54348373e+00 8.38828981e-01 5.74222207e-01 1.19970036e+00 -3.60856622e-01 4.46084052e-01 -2.20227182e-01 1.13751543e+00 -2.13672802e-01 -7.40991354e-01 -1.87794253e-01 -4.85244185e-01 -6.78673387e-01 1.48634305e-02 3.02685767e-01 1.86225865e-02 -4.87706721e-01 9.00803685e-01 6.47808015e-01 2.69091934e-01 8.23566556e-01 -1.47105622e+00 -4.95629847e-01 7.56700456e-01 -7.20063567e-01 -2.39694342e-01 7.49876976e-01 -1.04239196e-01 1.37131107e+00 -1.32892907e+00 5.73402226e-01 1.33677578e+00 5.33250988e-01 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-1.18014669e+00 -4.74560320e-01 -7.39240587e-01 -4.48175550e-01 9.85120773e-01 3.25990230e-01 -7.58448720e-01 -3.27695221e-01 1.76810279e-01]	[13.154638290405273, 5.0840744972229]
95dcf7dd-7cdc-42d5-8027-172726fd3ff3	resatom-system-protein-and-ligand-affinity	2105.05125	null	https://arxiv.org/abs/2105.05125v1	https://arxiv.org/pdf/2105.05125v1.pdf	ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning	Motivation: Protein-ligand affinity prediction is an important part of structure-based drug design. It includes molecular docking and affinity prediction. Although molecular dynamics can predict affinity with high accuracy at present, it is not suitable for large-scale virtual screening. The existing affinity prediction and evaluation functions based on deep learning mostly rely on experimentally-determined conformations. Results: We build a predictive model of protein-ligand affinity through the ResNet neural network with added attention mechanism. The resulting ResAtom-Score model achieves Pearson's correlation coefficient R = 0.833 on the CASF-2016 benchmark test set. At the same time, we evaluated the performance of a variety of existing scoring functions in combination with ResAtom-Score in the absence of experimentally-determined conformations. The results show that the use of {\Delta}VinaRF20 in combination with ResAtom-Score can achieve affinity prediction close to scoring functions in the presence of experimentally-determined conformations. These results suggest that ResAtom system may be used for in silico screening of small molecule ligands with target proteins in the future. Availability: https://github.com/wyji001/ResAtom	['Yong Huang', 'Yanwen Duan', 'Shuo Wu', 'Yeji Wang']	2021-04-17	null	null	null	null	['molecular-docking']	['medical']	[-2.12681651e-01 -4.72341985e-01 -4.09421861e-01 -3.48776042e-01 -7.66107202e-01 -6.11803412e-01 2.42092699e-01 5.83584130e-01 -6.41836703e-01 1.53020406e+00 -4.43652347e-02 -5.98413289e-01 -6.88410178e-02 -6.42762423e-01 -8.87983024e-01 -9.44031179e-01 -5.53284027e-02 8.46672297e-01 3.11890751e-01 -3.91618192e-01 1.67196751e-01 8.61916602e-01 -9.34895635e-01 3.00154895e-01 8.91003847e-01 6.74478889e-01 4.62843888e-02 3.61754537e-01 1.30846530e-01 4.75285023e-01 -4.06445950e-01 -7.35673085e-02 3.30646560e-02 -2.03647405e-01 -7.91289628e-01 -1.04384649e+00 2.65025407e-01 -1.69108391e-01 -1.04369134e-01 7.08179414e-01 1.04107594e+00 4.14238498e-02 6.72366500e-01 -4.66608256e-01 -5.34332156e-01 1.35583669e-01 -1.12245075e-01 4.99678582e-01 5.52374244e-01 3.61835629e-01 1.03715134e+00 -1.19029164e+00 6.20809376e-01 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e7fc2672-30f6-4d64-a5b7-e02c1f40db1a	single-image-haze-removal-using-a-generative	1810.09479	null	https://arxiv.org/abs/1810.09479v2	https://arxiv.org/pdf/1810.09479v2.pdf	Single Image Haze Removal using a Generative Adversarial Network	Traditional methods to remove haze from images rely on estimating a transmission map. When dealing with single images, this becomes an ill-posed problem due to the lack of depth information. In this paper, we propose an end-to-end learning based approach which uses a modified conditional Generative Adversarial Network to directly remove haze from an image. We employ the usage of the Tiramisu model in place of the classic U-Net model as the generator owing to its higher parameter efficiency and performance. Moreover, a patch based discriminator was used to reduce artefacts in the output. To further improve the perceptual quality of the output, a hybrid weighted loss function was designed and used to train the model. Experiments on synthetic and real world hazy images demonstrates that our model performs competitively with the state of the art methods.	['Bharath Raj N.', 'Venkateswaran N']	2018-10-22	null	null	null	null	['single-image-haze-removal']	['computer-vision']	[ 4.36008036e-01 2.21684705e-02 7.32795835e-01 -1.25500709e-01 -6.43467367e-01 -2.27556750e-01 5.08720398e-01 -2.94798523e-01 -3.64558309e-01 9.16465878e-01 -5.03525548e-02 -1.63469493e-01 1.73830874e-02 -1.02878153e+00 -6.95230663e-01 -1.15629947e+00 2.88257778e-01 -1.27107471e-01 5.36924779e-01 -2.66129106e-01 2.16942802e-01 2.89545506e-01 -1.47879577e+00 1.29949436e-01 1.24989355e+00 8.69117618e-01 3.27630937e-01 6.20163500e-01 1.87836841e-01 8.83013785e-01 -8.61603141e-01 -3.14050585e-01 6.00978971e-01 -7.59734809e-01 -1.01639383e-01 1.29532158e-01 6.08771741e-01 -4.34987813e-01 -3.29097807e-01 1.10221899e+00 6.17047250e-01 2.97352701e-01 7.61034429e-01 -1.04118252e+00 -5.03629208e-01 9.70409531e-03 -4.74201977e-01 3.62669490e-02 -1.02242026e-02 6.00612834e-02 6.06776714e-01 -8.25550973e-01 3.63472432e-01 8.65068376e-01 6.07040048e-01 4.14320558e-01 -1.17186201e+00 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f39ede64-8036-4310-a1b5-fc374ab481f4	deep-hybrid-real-and-synthetic-training-for	1807.11226	null	http://arxiv.org/abs/1807.11226v1	http://arxiv.org/pdf/1807.11226v1.pdf	Deep Hybrid Real and Synthetic Training for Intrinsic Decomposition	Intrinsic image decomposition is the process of separating the reflectance and shading layers of an image, which is a challenging and underdetermined problem. In this paper, we propose to systematically address this problem using a deep convolutional neural network (CNN). Although deep learning (DL) has been recently used to handle this application, the current DL methods train the network only on synthetic images as obtaining ground truth reflectance and shading for real images is difficult. Therefore, these methods fail to produce reasonable results on real images and often perform worse than the non-DL techniques. We overcome this limitation by proposing a novel hybrid approach to train our network on both synthetic and real images. Specifically, in addition to directly supervising the network using synthetic images, we train the network by enforcing it to produce the same reflectance for a pair of images of the same real-world scene with different illuminations. Furthermore, we improve the results by incorporating a bilateral solver layer into our system during both training and test stages. Experimental results show that our approach produces better results than the state-of-the-art DL and non-DL methods on various synthetic and real datasets both visually and numerically.	['Ravi Ramamoorthi', 'Sai Bi', 'Nima Khademi Kalantari']	2018-07-30	null	null	null	null	['intrinsic-image-decomposition']	['computer-vision']	[ 4.48174506e-01 -1.09233586e-02 6.72178745e-01 -3.24884355e-01 -4.15695250e-01 -3.47066015e-01 3.94155085e-01 -5.67048967e-01 -3.21756661e-01 7.61708498e-01 -3.63587022e-01 -1.79547027e-01 3.02325398e-01 -7.68555105e-01 -8.38350892e-01 -8.92455280e-01 5.11898041e-01 2.08816752e-01 2.56987989e-01 -5.59279881e-02 2.58435048e-02 5.70728242e-01 -1.62098169e+00 2.89631009e-01 1.08221412e+00 9.75360632e-01 2.31636971e-01 5.50313532e-01 1.91516243e-02 7.54113972e-01 -4.21032816e-01 -1.77704617e-01 7.74041414e-01 -6.03934050e-01 -6.04189694e-01 5.09492457e-01 7.02267110e-01 -5.15820384e-01 -6.32216260e-02 1.06651092e+00 4.57943588e-01 3.17896828e-02 4.00243789e-01 -9.94725883e-01 -3.96252275e-01 -1.12200826e-01 -9.66929376e-01 -4.44563389e-01 2.35860765e-01 1.83279499e-01 6.67169213e-01 -9.98766840e-01 2.89216638e-01 9.82333660e-01 6.55139685e-01 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86a6f954-2d9c-424d-a65e-cbfe866dc49b	multiscale-multimodal-transformer-for	null	null	https://openreview.net/pdf?id=aqP3WFwMPbe	https://openreview.net/pdf?id=aqP3WFwMPbe	Multiscale Multimodal Transformer for Multimodal Action Recognition	While action recognition has been an active research area for several years, most existing approaches merely leverage the video modality as opposed to humans that efficiently process video and audio cues simultaneously. This limits the usage of recent models to applications where the actions are visually well-defined. On the other hand, audio and video can be perceived in a hierarchical structure, e.g., from audio signal per sampling time point to audio activities and the whole category in the audio classification. In this work, we develop a multiscale multimodal Transformer (MMT) that employs hierarchical representation learning. Particularly, MMT is composed of a novel multiscale audio Transformer (MAT) and a multiscale video Transformer. Furthermore, we propose a set of multimodal supervised contrastive objectives called audio-video contrastive loss (AVC) and intra-modal contrastive loss (IMC) that specifically align the two modalities for robust multimodal representation fusion. MMT surpasses previous state-of-the-art approaches by 7.3%, 1.6% and 2.1% on Kinetics-Sounds, Epic-Kitchens-100 and VGGSound in terms of the top-1 accuracy without external training data. Moreover, our MAT significantly outperforms AST by 22.2%, 4.4% and 4.7% on the three public benchmark datasets and is 3x more efficient based on the number of FLOPs. Through extensive ablation studies and visualizations, we demonstrate that the proposed MMT can effectively capture semantically more separable feature representations from a combination of video and audio signals.	['Mohamed Omar', 'Linda Liu', 'Xiang Hao', 'Xiaohang Sun', 'Jingru Yi', 'Wentao Zhu']	2022-09-22	null	null	null	submitted-to-iclr-2022-9	['audio-classification', 'multi-modal-classification']	['audio', 'miscellaneous']	[ 4.91477072e-01 -5.93165398e-01 -9.58618894e-02 -4.89901304e-02 -1.27347183e+00 -4.63254094e-01 6.18169904e-01 3.04078400e-01 -2.44519562e-01 3.23270202e-01 4.36978757e-01 1.37012959e-01 -9.49746522e-04 -4.90284592e-01 -6.55001760e-01 -6.43560469e-01 -5.15861996e-02 -1.99783787e-01 3.08575124e-01 -1.85299013e-02 1.69242948e-01 -2.15744060e-02 -1.94650030e+00 7.44475901e-01 6.84358776e-01 1.45624399e+00 -1.71793178e-01 5.66364646e-01 1.59103237e-02 1.04256213e+00 -4.24743265e-01 -2.22034290e-01 1.75298005e-01 -4.63584036e-01 -5.43922246e-01 1.45686477e-01 6.43687069e-01 -3.10513765e-01 -3.50773245e-01 8.93664062e-01 6.53070331e-01 1.63195252e-01 6.16724789e-01 -1.51657951e+00 -2.98093617e-01 5.29754937e-01 -1.01608753e+00 7.36744329e-02 7.15411007e-01 1.16714403e-01 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98a425d9-3850-4869-847e-608271ab6664	on-the-sensitivity-of-reward-inference-to	2212.04717	null	https://arxiv.org/abs/2212.04717v1	https://arxiv.org/pdf/2212.04717v1.pdf	On the Sensitivity of Reward Inference to Misspecified Human Models	Inferring reward functions from human behavior is at the center of value alignment - aligning AI objectives with what we, humans, actually want. But doing so relies on models of how humans behave given their objectives. After decades of research in cognitive science, neuroscience, and behavioral economics, obtaining accurate human models remains an open research topic. This begs the question: how accurate do these models need to be in order for the reward inference to be accurate? On the one hand, if small errors in the model can lead to catastrophic error in inference, the entire framework of reward learning seems ill-fated, as we will never have perfect models of human behavior. On the other hand, if as our models improve, we can have a guarantee that reward accuracy also improves, this would show the benefit of more work on the modeling side. We study this question both theoretically and empirically. We do show that it is unfortunately possible to construct small adversarial biases in behavior that lead to arbitrarily large errors in the inferred reward. However, and arguably more importantly, we are also able to identify reasonable assumptions under which the reward inference error can be bounded linearly in the error in the human model. Finally, we verify our theoretical insights in discrete and continuous control tasks with simulated and human data.	['Anca Dragan', 'Kush Bhatia', 'Joey Hong']	2022-12-09	null	null	null	null	['continuous-control']	['playing-games']	[ 1.77875727e-01 5.42907894e-01 -2.17505485e-01 -2.41267264e-01 -3.40953052e-01 -6.17274761e-01 1.90395936e-01 2.09729135e-01 -7.71278739e-01 1.04320657e+00 -1.43616557e-01 -4.00513500e-01 -1.61320522e-01 -7.15326130e-01 -8.67306530e-01 -5.44895113e-01 -2.07863063e-01 4.06231046e-01 -9.08491611e-02 -2.69285262e-01 2.78312445e-01 1.94069520e-01 -1.11743188e+00 -2.50315547e-01 1.01078939e+00 9.14752483e-01 -1.53474748e-01 7.42412686e-01 5.65057218e-01 1.19402027e+00 -4.39243972e-01 -7.85497785e-01 4.17989820e-01 -5.33527970e-01 -8.72317791e-01 -1.51520491e-01 -1.11720234e-01 -7.08243966e-01 -9.13296826e-03 1.39062846e+00 1.92733496e-01 5.68442494e-02 6.52929783e-01 -1.36139393e+00 -8.19216549e-01 6.80171371e-01 -2.34254271e-01 -6.17842227e-02 1.11868575e-01 5.10855556e-01 1.15561318e+00 2.90599968e-02 4.41857755e-01 1.12939131e+00 2.85884559e-01 6.70659661e-01 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-5.63674688e-01 3.64932358e-01 5.03365338e-01 -6.11307919e-01 -5.54786980e-01 -1.91826314e-01]	[4.146288871765137, 2.272106409072876]
bfe02e06-1a46-4392-8e21-529129a87c1e	generative-pretraining-from-pixels	null	null	https://openai.com/blog/image-gpt/	https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf	Generative Pretraining from Pixels	Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full finetuning, matching the top supervised pre-trained models. An even larger model trained on a mixture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features.	['Mark Chen', 'Jeff Wu', 'Rewon Child', 'Ilya Sutskever', 'David Luan', 'Alec Radford', 'Heewoo Jun', 'Prafulla Dhariwal']	2020-07-17	null	https://proceedings.icml.cc/static/paper_files/icml/2020/6022-Paper.pdf	https://proceedings.icml.cc/static/paper_files/icml/2020/6022-Paper.pdf	icml-2020-1	['self-supervised-image-classification']	['computer-vision']	[ 4.62087631e-01 3.15384269e-01 -4.33302850e-01 -5.11262357e-01 -1.04421139e+00 -5.12498736e-01 8.34825754e-01 -2.02108324e-01 -5.03245533e-01 5.56411386e-01 3.91023725e-01 -1.33824319e-01 1.11766025e-01 -8.55513752e-01 -9.20572281e-01 -5.62728524e-01 -1.91821352e-01 3.78319770e-01 1.27737179e-01 -1.44994453e-01 2.36618042e-01 3.74652833e-01 -1.31682181e+00 6.43842936e-01 2.19728053e-01 1.26073873e+00 1.63824067e-01 8.31290960e-01 -4.49648425e-02 1.27177382e+00 -2.77377248e-01 5.55099510e-02 4.54130203e-01 -2.95454878e-02 -9.74320292e-01 1.07754737e-01 1.08593225e+00 -3.91322255e-01 -7.54687965e-01 8.12641323e-01 2.85474718e-01 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60f0ddfb-00f9-48b4-bce7-85b967789d38	online-refinement-of-a-scene-recognition	2208.06636	null	https://arxiv.org/abs/2208.06636v1	https://arxiv.org/pdf/2208.06636v1.pdf	Online Refinement of a Scene Recognition Model for Mobile Robots by Observing Human's Interaction with Environments	This paper describes a method of online refinement of a scene recognition model for robot navigation considering traversable plants, flexible plant parts which a robot can push aside while moving. In scene recognition systems that consider traversable plants growing out to the paths, misclassification may lead the robot to getting stuck due to the traversable plants recognized as obstacles. Yet, misclassification is inevitable in any estimation methods. In this work, we propose a framework that allows for refining a semantic segmentation model on the fly during the robot's operation. We introduce a few-shot segmentation based on weight imprinting for online model refinement without fine-tuning. Training data are collected via observation of a human's interaction with the plant parts. We propose novel robust weight imprinting to mitigate the effect of noise included in the masks generated by the interaction. The proposed method was evaluated through experiments using real-world data and shown to outperform an ordinary weight imprinting and provide competitive results to fine-tuning with model distillation while requiring less computational cost.	['Jun Miura', 'Hiroaki Masuzawa', 'Shigemichi Matsuzaki']	2022-08-13	null	null	null	null	['scene-recognition']	['computer-vision']	[ 1.05469406e+00 5.71526110e-01 1.35709763e-01 -3.53129119e-01 -7.21931383e-02 -5.52089453e-01 1.28929734e-01 1.57020673e-01 -2.16235057e-01 5.08812116e-03 -6.96871698e-01 -3.83996457e-01 -4.76129591e-01 -8.13144445e-01 -7.17921734e-01 -4.14389044e-01 1.38101056e-02 9.01227117e-01 6.89657092e-01 -2.62241215e-01 5.44821978e-01 6.94588542e-01 -1.49485970e+00 -3.62475552e-02 1.06709576e+00 7.98117280e-01 1.32323039e+00 7.78242588e-01 -2.39344329e-01 2.58598506e-01 -4.08789515e-01 2.17515513e-01 7.77793229e-01 4.99849133e-02 -1.15570104e+00 8.45617771e-01 2.54099846e-01 -1.20710485e-01 9.69684944e-02 1.29556394e+00 -2.30564296e-01 4.03686941e-01 6.02784634e-01 -1.17454278e+00 -3.07121009e-01 5.08648992e-01 -5.55440366e-01 -3.19681495e-01 1.03387147e-01 1.05477601e-01 4.86739844e-01 -8.43114913e-01 6.72152340e-01 1.29765642e+00 7.09319651e-01 4.15820360e-01 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6dc7d511-1f8c-477a-ab3e-ed1588d0b181	internal-external-boundary-attention-fusion	2307.00212	null	https://arxiv.org/abs/2307.00212v1	https://arxiv.org/pdf/2307.00212v1.pdf	Internal-External Boundary Attention Fusion for Glass Surface Segmentation	Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly characterized by their visual appearances because they contain the visual appearance of other reflected or transmitted surfaces as well. Detecting glass regions from a single-color image is a challenging task. Recent deep-learning approaches have paid attention to the description of glass surface boundary where the transition of visual appearances between glass and non-glass surfaces are observed. In this work, we analytically investigate how glass surface boundary helps to characterize glass objects. Inspired by prior semantic segmentation approaches with challenging image types such as X-ray or CT scans, we propose separated internal-external boundary attention modules that individually learn and selectively integrate visual characteristics of the inside and outside region of glass surface from a single color image. Our proposed method is evaluated on six public benchmarks comparing with state-of-the-art methods showing promising results.	['Seungkyu Lee', 'Dongshen Han']	2023-07-01	null	null	null	null	['transparent-objects']	['computer-vision']	[ 6.96281612e-01 3.64477456e-01 9.98429880e-02 -4.82327431e-01 -6.17674112e-01 -6.19782329e-01 2.34948114e-01 4.93244007e-02 1.30107924e-01 -3.03834099e-02 -2.89647907e-01 1.83997765e-01 5.35348833e-01 -7.58778512e-01 -8.16549182e-01 -9.24134612e-01 3.00415069e-01 5.35005391e-01 6.86305106e-01 1.34429708e-01 1.67057246e-01 3.76811862e-01 -1.57157326e+00 8.03406119e-01 6.20550692e-01 1.36357546e+00 2.95555711e-01 5.01866043e-01 -5.08150578e-01 3.15428019e-01 -2.30475903e-01 -2.15989441e-01 3.87811244e-01 -3.81377250e-01 -8.17505598e-01 5.92063010e-01 1.14593291e+00 -4.07861322e-01 1.24962136e-01 1.23265016e+00 -1.48707837e-01 -1.01418301e-01 1.01579535e+00 -9.51361895e-01 -8.31424713e-01 1.61992073e-01 -5.48198283e-01 -3.30699801e-01 4.40070719e-01 2.79949576e-01 1.15360177e+00 -6.58212662e-01 5.23653626e-01 1.16718602e+00 5.23630679e-01 6.48942769e-01 -1.11860681e+00 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8.85446370e-01 6.53743923e-01 -7.87017643e-01 -5.43317378e-01 9.15628821e-02]	[9.548358917236328, -1.0202367305755615]
6559034c-920d-4bea-a988-106e5ecfe6fc	codebert-a-pre-trained-model-for-programming	2002.08155	null	https://arxiv.org/abs/2002.08155v4	https://arxiv.org/pdf/2002.08155v4.pdf	CodeBERT: A Pre-Trained Model for Programming and Natural Languages	We present CodeBERT, a bimodal pre-trained model for programming language (PL) and nat-ural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language codesearch, code documentation generation, etc. We develop CodeBERT with Transformer-based neural architecture, and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators. This enables us to utilize both bimodal data of NL-PL pairs and unimodal data, where the former provides input tokens for model training while the latter helps to learn better generators. We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters. Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks. Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and evaluate in a zero-shot setting where parameters of pre-trained models are fixed. Results show that CodeBERT performs better than previous pre-trained models on NL-PL probing.	['Nan Duan', 'Daya Guo', 'Zhangyin Feng', 'Xiaocheng Feng', 'Ting Liu', 'Ming Zhou', 'Ming Gong', 'Duyu Tang', 'Daxin Jiang', 'Bing Qin', 'Linjun Shou']	2020-02-19	null	https://aclanthology.org/2020.findings-emnlp.139	https://aclanthology.org/2020.findings-emnlp.139.pdf	findings-of-the-association-for-computational	['type-prediction', 'code-documentation-generation', 'code-search', 'code-search', 'code-documentation-generation']	['computer-code', 'computer-code', 'computer-code', 'computer-vision', 'natural-language-processing']	[-1.97810397e-01 2.92619795e-01 -5.44469714e-01 -6.29283190e-02 -1.36909735e+00 -6.43959284e-01 6.28520072e-01 1.78276002e-02 9.46937278e-02 5.07487416e-01 4.55678165e-01 -7.64521480e-01 3.50167871e-01 -8.07206690e-01 -7.34350801e-01 -3.33265871e-01 7.67957121e-02 6.29833102e-01 1.00803971e-01 -2.16459244e-01 3.82924944e-01 -1.06136322e-01 -1.65719879e+00 7.64517605e-01 1.07288921e+00 5.93850195e-01 6.59407854e-01 8.96285415e-01 -5.27888536e-01 1.10100138e+00 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a054a7f7-943e-4abf-a03c-53df8b328d7c	compositional-prompt-tuning-with-motion-cues	2302.00268	null	https://arxiv.org/abs/2302.00268v1	https://arxiv.org/pdf/2302.00268v1.pdf	Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection	Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary predictions trained on limited base categories, e.g., object classification and detection. In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of video data. In particular, we present Relation Prompt (RePro) for Open-vocabulary Video Visual Relation Detection (Open-VidVRD), where conventional prompt tuning is easily biased to certain subject-object combinations and motion patterns. To this end, RePro addresses the two technical challenges of Open-VidVRD: 1) the prompt tokens should respect the two different semantic roles of subject and object, and 2) the tuning should account for the diverse spatio-temporal motion patterns of the subject-object compositions. Without bells and whistles, our RePro achieves a new state-of-the-art performance on two VidVRD benchmarks of not only the base training object and predicate categories, but also the unseen ones. Extensive ablations also demonstrate the effectiveness of the proposed compositional and multi-mode design of prompts. Code is available at https://github.com/Dawn-LX/OpenVoc-VidVRD.	['Qianru Sun', 'Jun Xiao', 'Hanwang Zhang', 'Long Chen', 'Kaifeng Gao']	2023-02-01	null	null	null	null	['video-visual-relation-detection']	['computer-vision']	[-1.08155780e-01 -2.66068220e-01 -4.16416883e-01 -1.42339751e-01 -5.67403913e-01 -7.67490566e-01 7.93699384e-01 -1.60522908e-01 -2.16100365e-01 2.54692137e-01 3.18360150e-01 -3.16944093e-01 1.88433751e-01 -2.70039380e-01 -7.26617992e-01 -5.66078901e-01 1.48951381e-01 3.14889640e-01 5.63521206e-01 -2.75563985e-01 -1.29113480e-01 2.76348501e-01 -1.80128193e+00 6.57967031e-01 3.05606008e-01 1.18673193e+00 5.89473128e-01 8.59418809e-01 -3.32689136e-02 1.00452018e+00 -1.94260538e-01 -2.67053723e-01 1.61444336e-01 -8.58887210e-02 -7.42765665e-01 -3.26820500e-02 1.05078876e+00 -5.46679020e-01 -7.91027844e-01 8.67598534e-01 2.54763335e-01 2.57910162e-01 8.35731864e-01 -1.57964802e+00 -1.17097282e+00 3.52707654e-01 -4.39580619e-01 6.19982660e-01 3.68111998e-01 6.03010833e-01 1.43538558e+00 -1.30868089e+00 8.13661456e-01 1.28324115e+00 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7f2f1f32-b167-4f28-8de4-a5d60d913fc1	unsupervised-sentiment-analysis-of-plastic	2307.02640	null	https://arxiv.org/abs/2307.02640v1	https://arxiv.org/pdf/2307.02640v1.pdf	Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts	The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases based on the sheer volume and velocity of textual data. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. Using a word ranking method, term frequency-inverse document frequency (TF-IDF), to create features across documents, it is possible to perform unsupervised analytics, machine learning (ML) that can group the documents without a human manually labeling the data. For large datasets with thousands of features, t-distributed stochastic neighbor embedding (t-SNE), k-means clustering and Latent Dirichlet allocation (LDA) are employed to learn top words and generate topics for a Reddit and Twitter combined corpus. Using extremely simple deep learning models, this study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery based on a tweet or subreddit post with almost 90% accuracy. Furthermore, the model is capable of achieving higher accuracy on the unsupervised sentiment task than on a rudimentary supervised document classification task. Therefore, unsupervised learning may be considered a viable option in labeling social media documents for NLP tasks.	['Alexandrea K. Ramnarine']	2023-07-05	null	null	null	null	['sentiment-analysis', 'document-classification']	['natural-language-processing', 'natural-language-processing']	[ 2.00293407e-01 5.10212243e-01 -5.88269889e-01 -4.20857221e-01 -6.37426555e-01 -3.08908135e-01 9.01531041e-01 1.03988659e+00 -6.97432339e-01 5.48975170e-01 7.14297712e-01 -3.26158941e-01 -1.37479022e-01 -8.27247262e-01 -1.08640783e-01 -7.15242684e-01 -2.65586466e-01 6.94903314e-01 -4.58166540e-01 -3.80097181e-01 5.11907816e-01 3.69206935e-01 -1.71159518e+00 6.77522123e-01 6.53969109e-01 9.65697229e-01 1.13011986e-01 4.60950643e-01 -7.47866929e-01 9.30326164e-01 -4.60165858e-01 -3.50449473e-01 5.29001169e-02 5.48699237e-02 -7.07959175e-01 -1.24583341e-01 -1.65101200e-01 -1.77124277e-01 2.18823642e-01 7.85035789e-01 3.97988021e-01 -4.68238890e-02 1.04668021e+00 -1.02215886e+00 -3.33270758e-01 7.13543534e-01 -4.61975545e-01 -6.62361458e-02 4.90405709e-01 -3.28664005e-01 1.18434608e+00 -1.07855296e+00 7.80991852e-01 1.12664449e+00 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9.08138037e-01 -8.56658876e-01 -1.87675774e-01 -9.95336533e-01 -7.15718150e-01 5.78671515e-01 1.08136445e-01 -3.51867229e-01 -6.08994544e-01 1.80394202e-01]	[10.551353454589844, 8.266257286071777]
fecdf339-a47f-46af-ab0d-3e9b8ecec09a	automated-vulnerability-detection-in-source	1807.04320	null	http://arxiv.org/abs/1807.04320v2	http://arxiv.org/pdf/1807.04320v2.pdf	Automated Vulnerability Detection in Source Code Using Deep Representation Learning	Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ open-source code available to develop a large-scale function-level vulnerability detection system using machine learning. To supplement existing labeled vulnerability datasets, we compiled a vast dataset of millions of open-source functions and labeled it with carefully-selected findings from three different static analyzers that indicate potential exploits. The labeled dataset is available at: https://osf.io/d45bw/. Using these datasets, we developed a fast and scalable vulnerability detection tool based on deep feature representation learning that directly interprets lexed source code. We evaluated our tool on code from both real software packages and the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature representation learning on source code is a promising approach for automated software vulnerability detection.	['Paul M. Ellingwood', 'Rebecca L. Russell', 'Marc W. McConley', 'Louis Kim', 'Tomo Lazovich', 'Onur Ozdemir', 'Jacob A. Harer', 'Lei H. Hamilton']	2018-07-11	null	null	null	null	['vulnerability-detection']	['miscellaneous']	[-3.50710183e-01 -1.88493863e-01 -4.46120769e-01 -4.49931443e-01 -1.18590558e+00 -1.43986380e+00 3.21856700e-02 4.93168503e-01 2.72245049e-01 1.52030930e-01 2.08848283e-01 -1.22681952e+00 2.56327242e-01 -9.45916772e-01 -8.14122081e-01 2.46068940e-01 -3.58239859e-01 -4.72983658e-01 2.77053952e-01 -2.20607966e-01 8.19299042e-01 3.28685254e-01 -1.20721567e+00 6.49520278e-01 5.33759356e-01 5.82584798e-01 -4.59900260e-01 6.92186952e-01 -2.95100808e-01 8.71416986e-01 -1.02193320e+00 -7.40771115e-01 4.81716752e-01 3.09159666e-01 -1.07914948e+00 -6.95899367e-01 3.04196239e-01 -4.25399780e-01 -1.04757890e-01 1.46259558e+00 3.43689680e-01 -7.24515438e-01 5.02726249e-02 -1.33775210e+00 -6.20285273e-01 1.13756585e+00 -8.64293635e-01 6.86655283e-01 6.02302611e-01 3.97393674e-01 9.61897194e-01 -6.44022226e-01 4.82606322e-01 9.11051214e-01 8.35016370e-01 2.70483702e-01 -1.03012276e+00 -7.52652586e-01 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fd5bf2b5-baf6-462e-8cac-4e8b2a67acd9	map-induction-compositional-spatial-submap-1	2110.12301	null	https://arxiv.org/abs/2110.12301v2	https://arxiv.org/pdf/2110.12301v2.pdf	Map Induction: Compositional spatial submap learning for efficient exploration in novel environments	Humans are expert explorers. Understanding the computational cognitive mechanisms that support this efficiency can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that humans explore new environments efficiently by inferring the structure of unobserved spaces using spatial information collected from previously explored spaces. This cognitive process can be modeled computationally using program induction in a Hierarchical Bayesian framework that explicitly reasons about uncertainty with strong spatial priors. Using a new behavioral Map Induction Task, we demonstrate that this computational framework explains human exploration behavior better than non-inductive models and outperforms state-of-the-art planning algorithms when applied to a realistic spatial navigation domain.	['Ila Fiete', 'Josh Tenenbaum', 'Marta Kryven', 'Aidan Curtis', 'Sugandha Sharma']	2021-10-23	map-induction-compositional-spatial-submap	https://openreview.net/forum?id=1NUsBU-7HAL	https://openreview.net/pdf?id=1NUsBU-7HAL	iclr-2022-4	['program-induction']	['computer-code']	[-1.73817091e-02 5.20815790e-01 -1.19054966e-01 -5.33727050e-01 -2.46573657e-01 -4.72853035e-01 6.02409840e-01 5.07553756e-01 -6.71952009e-01 8.25187266e-01 4.78349060e-01 -7.44665980e-01 -4.33667600e-01 -1.14599395e+00 -9.08758342e-01 -7.60836303e-02 -6.51641130e-01 1.15199077e+00 3.68077815e-01 2.00572852e-02 7.49614418e-01 3.14609557e-01 -1.47736764e+00 1.02347516e-01 1.23001540e+00 1.39124721e-01 6.66928113e-01 5.55697799e-01 -3.58959809e-02 1.12310743e+00 2.39033803e-01 1.70554653e-01 9.07629058e-02 -1.97224855e-01 -1.29175329e+00 -3.69376361e-01 -4.34567094e-01 -5.16233206e-01 -3.89510721e-01 8.52353990e-01 -2.40838200e-01 9.08101380e-01 5.63144505e-01 -7.19223738e-01 -5.62009633e-01 8.70070219e-01 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29e59af2-5bcb-4c26-9d33-adf1b374fcbd	neural-network-state-estimation-for-full	1811.06654	null	http://arxiv.org/abs/1811.06654v2	http://arxiv.org/pdf/1811.06654v2.pdf	Neural network state estimation for full quantum state tomography	An efficient state estimation model, neural network estimation (NNE), empowered by machine learning techniques, is presented for full quantum state tomography (FQST). A parameterized function based on neural network is applied to map the measurement outcomes to the estimated quantum states. Parameters are updated with supervised learning procedures. From the computational complexity perspective our algorithm is the most efficient one among existing state estimation algorithms for full quantum state tomography. We perform numerical tests to prove both the accuracy and scalability of our model.	['Shuqi Xu', 'Qian Xu']	2018-11-16	null	null	null	null	['quantum-state-tomography']	['medical']	[ 5.34875154e-01 1.19146936e-01 -4.17127639e-01 -3.82495463e-01 -7.43577659e-01 -2.87219018e-01 5.61442435e-01 -2.44823962e-01 -5.62170625e-01 1.19543529e+00 -2.10243136e-01 -7.24933982e-01 -3.81971270e-01 -7.45509684e-01 -5.79810381e-01 -8.28348696e-01 -3.86200510e-02 6.19683504e-01 -1.76748797e-01 -2.24645197e-01 5.61434746e-01 6.81971312e-01 -8.01970541e-01 -2.74285644e-01 8.39886725e-01 9.66301560e-01 -2.55387455e-01 9.29184496e-01 1.75774187e-01 9.06723082e-01 -2.63266981e-01 -3.27224463e-01 3.55438918e-01 -5.59719622e-01 -9.97525632e-01 -6.74302459e-01 -4.44277264e-02 -5.67870438e-01 -1.18003833e+00 1.63393867e+00 4.03417975e-01 1.22628734e-01 6.79681003e-01 -8.67504537e-01 -1.72525242e-01 9.52061057e-01 5.17628789e-01 1.03763074e-01 3.09525102e-01 1.69455498e-01 7.95636535e-01 -5.16691208e-01 8.07232082e-01 7.83547759e-01 5.01284778e-01 6.27404988e-01 -1.48987842e+00 -8.34481716e-01 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1906e928-a09a-42da-9769-0e38baae555a	implicit-neural-representations-for	2112.08539	null	https://arxiv.org/abs/2112.08539v1	https://arxiv.org/pdf/2112.08539v1.pdf	Implicit Neural Representations for Deconvolving SAS Images	Synthetic aperture sonar (SAS) image resolution is constrained by waveform bandwidth and array geometry. Specifically, the waveform bandwidth determines a point spread function (PSF) that blurs the locations of point scatterers in the scene. In theory, deconvolving the reconstructed SAS image with the scene PSF restores the original distribution of scatterers and yields sharper reconstructions. However, deconvolution is an ill-posed operation that is highly sensitive to noise. In this work, we leverage implicit neural representations (INRs), shown to be strong priors for the natural image space, to deconvolve SAS images. Importantly, our method does not require training data, as we perform our deconvolution through an analysis-bysynthesis optimization in a self-supervised fashion. We validate our method on simulated SAS data created with a point scattering model and real data captured with an in-air circular SAS. This work is an important first step towards applying neural networks for SAS image deconvolution.	['Suren Jayasuriya', 'Daniel C. Brown', 'Thomas Blanford', 'Albert Reed']	2021-12-16	null	null	null	null	['image-deconvolution']	['computer-vision']	[ 6.59269929e-01 -5.36537655e-02 9.35863614e-01 -2.30942190e-01 -8.07191014e-01 -7.02867091e-01 4.67731178e-01 -4.59896386e-01 -3.50091696e-01 5.87989688e-01 4.81121600e-01 -1.39666768e-02 -2.75294572e-01 -5.89038968e-01 -9.26773608e-01 -1.08690596e+00 -6.31621107e-02 3.32671106e-01 -3.00014298e-02 -1.67938292e-01 6.62484542e-02 5.92175066e-01 -1.41876280e+00 2.81197369e-01 7.46006668e-01 6.19549334e-01 4.80186194e-01 9.33277309e-01 3.77579451e-01 8.23609173e-01 -6.38344228e-01 -8.34184811e-02 2.89735764e-01 -4.94606227e-01 -1.17742173e-01 7.36127198e-02 5.19967973e-01 -3.62655282e-01 -5.73772967e-01 1.14435077e+00 2.45280668e-01 3.11642677e-01 8.20451558e-01 -4.13991839e-01 -6.78385317e-01 3.69706333e-01 -4.91355181e-01 2.19303504e-01 -8.23119432e-02 1.05278723e-01 6.32099569e-01 -1.06083751e+00 2.23111391e-01 7.82429397e-01 1.13797724e+00 1.65045679e-01 -1.32529104e+00 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f7bb8ee3-6504-4ebe-ab40-c5518e18739e	a-unified-framework-for-information-theoretic	2305.11042	null	https://arxiv.org/abs/2305.11042v1	https://arxiv.org/pdf/2305.11042v1.pdf	A unified framework for information-theoretic generalization bounds	This paper presents a general methodology for deriving information-theoretic generalization bounds for learning algorithms. The main technical tool is a probabilistic decorrelation lemma based on a change of measure and a relaxation of Young's inequality in $L_{\psi_p}$ Orlicz spaces. Using the decorrelation lemma in combination with other techniques, such as symmetrization, couplings, and chaining in the space of probability measures, we obtain new upper bounds on the generalization error, both in expectation and in high probability, and recover as special cases many of the existing generalization bounds, including the ones based on mutual information, conditional mutual information, stochastic chaining, and PAC-Bayes inequalities. In addition, the Fernique-Talagrand upper bound on the expected supremum of a subgaussian process emerges as a special case.	['Maxim Raginsky', 'Yifeng Chu']	2023-05-18	null	null	null	null	['generalization-bounds']	['methodology']	[ 3.32831472e-01 7.46246725e-02 5.06467093e-03 -1.88108414e-01 -4.56304789e-01 -6.58779800e-01 2.62876838e-01 3.44808936e-01 -5.63239932e-01 1.04119253e+00 -2.40164921e-01 -3.50213498e-01 -8.48369777e-01 -7.52739429e-01 -4.54283983e-01 -1.21587408e+00 -2.80112505e-01 2.63784200e-01 1.28160626e-01 5.94345853e-02 2.45894611e-01 7.90898621e-01 -1.54925036e+00 -4.39852685e-01 9.10913944e-01 1.23683429e+00 -2.56773293e-01 6.03159070e-01 -2.39176713e-02 3.01613033e-01 -4.03889805e-01 -6.86242759e-01 2.45589212e-01 -5.57568312e-01 -8.13793361e-01 -2.22934201e-01 1.34047344e-02 3.47525567e-01 -1.68259904e-01 1.41177726e+00 3.40137601e-01 2.42618591e-01 1.13374519e+00 -1.34917808e+00 -4.77299333e-01 7.52332270e-01 -6.48257136e-01 4.68514085e-01 2.63637125e-01 -7.43627131e-01 1.07316291e+00 -4.78589356e-01 1.64483160e-01 8.84579062e-01 6.62654519e-01 2.38514259e-01 -1.31078291e+00 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a4ef17e6-16c0-4a3d-80a9-74034f4d8df3	yolop-you-only-look-once-for-panoptic-driving	2108.11250	null	https://arxiv.org/abs/2108.11250v7	https://arxiv.org/pdf/2108.11250v7.pdf	YOLOP: You Only Look Once for Panoptic Driving Perception	A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making the reasonable decision while driving. We present a panoptic driving perception network (YOLOP) to perform traffic object detection, drivable area segmentation and lane detection simultaneously. It is composed of one encoder for feature extraction and three decoders to handle the specific tasks. Our model performs extremely well on the challenging BDD100K dataset, achieving state-of-the-art on all three tasks in terms of accuracy and speed. Besides, we verify the effectiveness of our multi-task learning model for joint training via ablative studies. To our best knowledge, this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS) and maintain excellent accuracy. To facilitate further research, the source codes and pre-trained models are released at https://github.com/hustvl/YOLOP.	['Wenyu Liu', 'Wenqing Cheng', 'Xiang Bai', 'Xinggang Wang', 'Weitian Zhang', 'Manwen Liao', 'Dong Wu']	2021-08-25	null	null	null	null	['drivable-area-detection']	['computer-vision']	[-1.19816504e-01 -2.92841464e-01 -2.19327405e-01 -6.15691721e-01 -6.49610639e-01 -3.28240275e-01 5.19370496e-01 -2.15970576e-01 -3.20440531e-01 1.76274940e-01 -4.51875001e-01 -8.09621155e-01 3.70662034e-01 -5.96459210e-01 -7.63250411e-01 -5.04238665e-01 2.44667962e-01 1.70048133e-01 8.59497726e-01 -2.97792256e-01 1.78627580e-01 3.80935013e-01 -2.09764457e+00 3.67604673e-01 8.10516000e-01 1.44286120e+00 6.37786508e-01 9.80882645e-01 5.16458333e-01 7.32789516e-01 -1.68726012e-01 -4.20763105e-01 3.95442873e-01 2.76817113e-01 -2.20779896e-01 -1.64182279e-02 7.58617520e-01 -5.92361748e-01 -6.55025184e-01 1.02264941e+00 5.28767705e-01 -6.40703291e-02 4.16712224e-01 -1.48833108e+00 -4.61642772e-01 -1.93559960e-01 -5.24365604e-01 4.83884096e-01 -9.13529024e-02 7.25950241e-01 7.35108852e-01 -9.21193302e-01 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7fa2192e-5e9f-40a6-905f-037aa1f12c39	blackvip-black-box-visual-prompting-for	2303.14773	null	https://arxiv.org/abs/2303.14773v2	https://arxiv.org/pdf/2303.14773v2.pdf	BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning	With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention. While recent PETL methods showcase impressive performance, they rely on optimistic assumptions: 1) the entire parameter set of a PTM is available, and 2) a sufficiently large memory capacity for the fine-tuning is equipped. However, in most real-world applications, PTMs are served as a black-box API or proprietary software without explicit parameter accessibility. Besides, it is hard to meet a large memory requirement for modern PTMs. In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with gradient correction (SPSA-GC). The Coordinator designs input-dependent image-shaped visual prompts, which improves few-shot adaptation and robustness on distribution/location shift. SPSA-GC efficiently estimates the gradient of a target model to update Coordinator. Extensive experiments on 16 datasets demonstrate that BlackVIP enables robust adaptation to diverse domains without accessing PTMs' parameters, with minimal memory requirements. Code: \url{https://github.com/changdaeoh/BlackVIP}	['Kyungwoo Song', 'Hosik Choi', 'Jiyoung Jung', 'Geunyoung Jung', 'Yongtaek Lim', 'Hee-young Lee', 'Hyeji Hwang', 'Changdae Oh']	2023-03-26	null	http://openaccess.thecvf.com//content/CVPR2023/html/Oh_BlackVIP_Black-Box_Visual_Prompting_for_Robust_Transfer_Learning_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Oh_BlackVIP_Black-Box_Visual_Prompting_for_Robust_Transfer_Learning_CVPR_2023_paper.pdf	cvpr-2023-1	['visual-prompting']	['computer-vision']	[-1.54003024e-01 -2.35605612e-01 -2.75384456e-01 -1.43238485e-01 -1.00666130e+00 -5.90754271e-01 5.51783442e-01 -2.31557578e-01 -4.30805951e-01 6.78896308e-01 -5.50987460e-02 -3.10491562e-01 1.59075722e-01 -3.43490481e-01 -9.83536303e-01 -8.03939760e-01 3.26338202e-01 5.96866846e-01 6.69514894e-01 -1.05940722e-01 9.41087082e-02 2.71419644e-01 -1.30202484e+00 7.49881472e-03 8.94162357e-01 9.94956076e-01 6.74898803e-01 6.54316843e-01 -1.82929859e-01 3.33275825e-01 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91a4b629-03b4-48c2-aa53-5b513484fc07	human-language-modeling-1	2205.05128	null	https://arxiv.org/abs/2205.05128v1	https://arxiv.org/pdf/2205.05128v1.pdf	Human Language Modeling	Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently. Here, we propose human language modeling (HuLM), a hierarchical extension to the language modeling problem whereby a human-level exists to connect sequences of documents (e.g. social media messages) and capture the notion that human language is moderated by changing human states. We introduce, HaRT, a large-scale transformer model for the HuLM task, pre-trained on approximately 100,000 social media users, and demonstrate its effectiveness in terms of both language modeling (perplexity) for social media and fine-tuning for 4 downstream tasks spanning document- and user-levels: stance detection, sentiment classification, age estimation, and personality assessment. Results on all tasks meet or surpass the current state-of-the-art.	['H. Andrew Schwartz', 'Niranjan Balasubramanian', 'Matthew Matero', 'Nikita Soni']	2022-05-10	null	https://aclanthology.org/2022.findings-acl.52	https://aclanthology.org/2022.findings-acl.52.pdf	findings-acl-2022-5	['age-estimation', 'age-estimation']	['computer-vision', 'miscellaneous']	[-1.20040290e-01 4.76332366e-01 -4.46951210e-01 -4.42440450e-01 -3.76543939e-01 -4.85898376e-01 1.29238319e+00 4.08284277e-01 -5.19256592e-01 5.77568829e-01 6.22242808e-01 -4.58346009e-01 6.40259981e-01 -6.40783250e-01 -3.90506327e-01 2.85972189e-02 -7.73628205e-02 8.26713264e-01 -1.16568953e-01 -4.01649266e-01 -5.79469129e-02 -3.49301398e-02 -1.05973732e+00 3.93606544e-01 6.35313451e-01 4.72974330e-01 -2.53999293e-01 8.65512490e-01 -2.89302886e-01 1.09906185e+00 -3.24765205e-01 -9.35149908e-01 -3.99144530e-01 -2.17556104e-01 -9.30547059e-01 1.59421578e-01 2.27358967e-01 -1.65632769e-01 -3.46939296e-01 7.82831907e-01 3.81385893e-01 -1.64877415e-01 7.23114729e-01 -1.31803238e+00 -1.08495271e+00 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a215efc2-7f25-4a09-b512-db1321bc6af7	flicu-a-federated-learning-workflow-for	2205.15104	null	https://arxiv.org/abs/2205.15104v1	https://arxiv.org/pdf/2205.15104v1.pdf	FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction	Although Machine Learning (ML) can be seen as a promising tool to improve clinical decision-making for supporting the improvement of medication plans, clinical procedures, diagnoses, or medication prescriptions, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silos, making traditional machine learning challenging. Federated learning can counteract those limitations by training machine learning models over data silos while keeping the sensitive data localized. This study proposes a federated learning workflow for ICU mortality prediction. Hereby, the applicability of federated learning as an alternative to centralized machine learning and local machine learning is investigated by introducing federated learning to the binary classification problem of predicting ICU mortality. We extract multivariate time series data from the MIMIC-III database (lab values and vital signs), and benchmark the predictive performance of four deep sequential classifiers (FRNN, LSTM, GRU, and 1DCNN) varying the patient history window lengths (8h, 16h, 24h, 48h) and the number of FL clients (2, 4, 8). The experiments demonstrate that both centralized machine learning and federated learning are comparable in terms of AUPRC and F1-score. Furthermore, the federated approach shows superior performance over local machine learning. Thus, the federated approach can be seen as a valid and privacy-preserving alternative to centralized machine learning for classifying ICU mortality when sharing sensitive patient data between hospitals is not possible.	['Panagiotis Papapetrou', 'Jaakko Hollmén', 'David Pitts', 'Annaclaudia Montanino', 'Ioanna Miliou', 'Lena Mondrejevski']	2022-05-30	null	null	null	null	['icu-mortality']	['medical']	[-3.74174751e-02 -4.04273793e-02 -6.29094958e-01 -4.05888170e-01 -6.72927260e-01 -5.55021644e-01 9.67486668e-03 7.67303824e-01 -5.06750524e-01 9.40801620e-01 6.49905205e-02 -6.52452290e-01 -5.48495889e-01 -7.54790187e-01 -4.23074573e-01 -9.22157526e-01 -3.55265796e-01 4.63818550e-01 -4.19893473e-01 4.51729387e-01 -2.42187649e-01 7.38683522e-01 -1.14459682e+00 7.45614409e-01 5.84084451e-01 1.35844696e+00 -7.22081721e-01 4.32350159e-01 4.78756567e-03 1.16094339e+00 -5.85180163e-01 -3.35181087e-01 5.02530277e-01 -4.41873282e-01 -7.48261809e-01 -5.00492513e-01 -1.04460776e-01 -4.76171494e-01 -8.28582272e-02 7.05641448e-01 7.18304396e-01 -1.24480307e-01 5.91944121e-02 -1.23828244e+00 -2.38428593e-01 6.76656246e-01 2.27223903e-01 -6.39104694e-02 8.46221894e-02 1.38247237e-01 5.74531674e-01 -1.71673596e-01 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c7d35d3f-3a93-4378-8c3a-af4edb0e0d16	leveraging-class-abstraction-for-commonsense	2201.12126	null	https://arxiv.org/abs/2201.12126v2	https://arxiv.org/pdf/2201.12126v2.pdf	Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods	Enabling reinforcement learning (RL) agents to leverage a knowledge base while learning from experience promises to advance RL in knowledge intensive domains. However, it has proven difficult to leverage knowledge that is not manually tailored to the environment. We propose to use the subclass relationships present in open-source knowledge graphs to abstract away from specific objects. We develop a residual policy gradient method that is able to integrate knowledge across different abstraction levels in the class hierarchy. Our method results in improved sample efficiency and generalisation to unseen objects in commonsense games, but we also investigate failure modes, such as excessive noise in the extracted class knowledge or environments with little class structure.	['Herke van Hoof', 'Ilaria Tiddi', 'Niklas Höpner']	2022-01-28	null	null	null	null	['policy-gradient-methods']	['methodology']	[ 1.33856654e-01 4.85874653e-01 -1.98115278e-02 -1.14095071e-02 -5.93952835e-01 -9.16483581e-01 5.11743009e-01 1.21119745e-01 -7.71493673e-01 1.34595954e+00 1.82054415e-01 -1.07648894e-01 -6.23049676e-01 -1.02281451e+00 -7.07123816e-01 -4.97506708e-01 -3.97171795e-01 6.72686398e-01 5.43174148e-01 -3.95174950e-01 1.55865759e-01 6.17825568e-01 -1.67793345e+00 1.59867480e-01 9.10293579e-01 6.44928455e-01 3.62369537e-01 7.62928545e-01 -1.09660685e-01 1.28418434e+00 -6.14610434e-01 -3.95296276e-01 3.54128391e-01 -4.24910426e-01 -1.04406881e+00 -1.48741573e-01 2.72310823e-01 -3.55912358e-01 -4.93473257e-04 9.00780737e-01 3.73534262e-01 5.37290573e-01 5.65621257e-01 -1.02246106e+00 -5.66959560e-01 8.63051951e-01 -7.49367401e-02 3.06067467e-01 1.82967633e-01 5.28654635e-01 1.07904315e+00 -2.43930966e-01 9.46079314e-01 1.18638873e+00 6.24763489e-01 8.97262156e-01 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7474daf2-cf43-491f-b8b2-15bc06ba8448	domain-adaptive-semantic-segmentation-with	2104.13613	null	https://arxiv.org/abs/2104.13613v2	https://arxiv.org/pdf/2104.13613v2.pdf	Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation	Domain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervision from auxiliary tasks~(such as depth estimation) has the potential to heal this shift because many visual tasks are closely related to each other. However, such a supervision is not always available. In this work, we leverage the guidance from self-supervised depth estimation, which is available on both domains, to bridge the domain gap. On the one hand, we propose to explicitly learn the task feature correlation to strengthen the target semantic predictions with the help of target depth estimation. On the other hand, we use the depth prediction discrepancy from source and target depth decoders to approximate the pixel-wise adaptation difficulty. The adaptation difficulty, inferred from depth, is then used to refine the target semantic segmentation pseudo-labels. The proposed method can be easily implemented into existing segmentation frameworks. We demonstrate the effectiveness of our approach on the benchmark tasks SYNTHIA-to-Cityscapes and GTA-to-Cityscapes, on which we achieve the new state-of-the-art performance of $55.0\%$ and $56.6\%$, respectively. Our code is available at \url{https://qin.ee/corda}.	['Luc van Gool', 'Olga Fink', 'Lukas Hoyer', 'Dengxin Dai', 'Qin Wang']	2021-04-28	null	http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Domain_Adaptive_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Domain_Adaptive_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_ICCV_2021_paper.pdf	iccv-2021-1	['synthetic-to-real-translation']	['computer-vision']	[ 2.84882098e-01 4.74493802e-01 -3.03020567e-01 -5.93988061e-01 -9.94952679e-01 -3.99854273e-01 2.31821835e-01 -4.70597334e-02 -4.59289759e-01 5.66876531e-01 -4.19314802e-02 -2.71691121e-02 2.96892256e-01 -7.06513882e-01 -8.30692291e-01 -8.03207695e-01 5.13305306e-01 3.79498124e-01 5.57908595e-01 -1.80133820e-01 1.74710557e-01 -4.28612810e-03 -1.36443496e+00 2.25898981e-01 1.32107198e+00 1.25111473e+00 6.83340073e-01 3.15220535e-01 -1.93126485e-01 5.12602448e-01 -1.75734073e-01 -4.04408574e-01 3.87589902e-01 -3.21585596e-01 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d367514c-56b4-4c44-992b-2fe67d040341	rethink-diversity-in-deep-learning-testing	2305.15698	null	https://arxiv.org/abs/2305.15698v1	https://arxiv.org/pdf/2305.15698v1.pdf	Rethink Diversity in Deep Learning Testing	Deep neural networks (DNNs) have demonstrated extraordinary capabilities and are an integral part of modern software systems. However, they also suffer from various vulnerabilities such as adversarial attacks and unfairness. Testing deep learning (DL) systems is therefore an important task, to detect and mitigate those vulnerabilities. Motivated by the success of traditional software testing, which often employs diversity heuristics, various diversity measures on DNNs have been proposed to help efficiently expose the buggy behavior of DNNs. In this work, we argue that many DNN testing tasks should be treated as directed testing problems rather than general-purpose testing tasks, because these tasks are specific and well-defined. Hence, the diversity-based approach is less effective. Following our argument based on the semantics of DNNs and the testing goal, we derive $6$ metrics that can be used for DNN testing and carefully analyze their application scopes. We empirically show their efficacy in exposing bugs in DNNs compared to recent diversity-based metrics. Moreover, we also notice discrepancies between the practices of the software engineering (SE) community and the DL community. We point out some of these gaps, and hopefully, this can lead to bridging the SE practice and DL findings.	['Somesh Jha', 'Jihye Choi', 'Zi Wang']	2023-05-25	null	null	null	null	['dnn-testing']	['adversarial']	[-1.21918239e-01 6.65616095e-02 -3.49971764e-02 -3.79386574e-01 -3.04613173e-01 -6.90488875e-01 1.72470644e-01 -8.45763385e-02 -1.08748466e-01 7.06274867e-01 -2.29381442e-01 -9.13484752e-01 -2.68193096e-01 -9.56482947e-01 -8.91398311e-01 -3.98845345e-01 -1.37435049e-01 -1.04509987e-01 3.86843920e-01 -4.51215953e-01 3.57919186e-01 4.33480084e-01 -1.72321510e+00 7.27384537e-02 1.05113804e+00 6.95115328e-01 -3.70618731e-01 5.09064376e-01 -5.27144372e-02 9.33733106e-01 -1.00008774e+00 -8.35364401e-01 2.09493965e-01 -5.38089931e-01 -7.95117199e-01 -4.20882434e-01 5.31163633e-01 -3.93945307e-01 -1.59351304e-01 1.71712649e+00 4.65171635e-01 -3.46166134e-01 7.76695134e-03 -1.90199375e+00 -5.04140079e-01 1.00319767e+00 -2.68662602e-01 2.01823622e-01 -7.88763538e-02 1.91559970e-01 1.16268492e+00 -1.58509210e-01 4.79897022e-01 1.04661500e+00 7.46295750e-01 9.49060261e-01 -9.89320159e-01 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48b44b8c-330f-4c73-9869-75cbd3b93b05	nuts-network-for-unsupervised-telegraphic	null	null	https://openreview.net/forum?id=rkGcYi09Km	https://openreview.net/pdf?id=rkGcYi09Km	NUTS: Network for Unsupervised Telegraphic Summarization	Extractive summarization methods operate by ranking and selecting the sentences which best encapsulate the theme of a given document. They do not fare well in domains like fictional narratives where there is no central theme and core information is not encapsulated by a small set of sentences. For the purpose of reducing the size of the document while conveying the idea expressed by each sentence, we need more sentence specific methods. Telegraphic summarization, which selects short segments across several sentences, is better suited for such domains. Telegraphic summarization captures the plot better by retaining shorter versions of each sentence while not really concerning itself with grammatically linking these segments. In this paper, we propose an unsupervised deep learning network (NUTS) to generate telegraphic summaries. We use multiple encoder-decoder networks and learn to drop portions of the text that are inferable from the chosen segments. The model is agnostic to both sentence length and style. We demonstrate that the summaries produced by our model show significant quantitative and qualitative improvement over those produced by existing methods and baselines.	['Manish Shrivastava', 'Sajal Maheshwari', 'Tirth Maniar', 'Chanakya Malireddy']	2018-09-27	null	null	null	null	['extractive-summarization']	['natural-language-processing']	[ 5.07299483e-01 5.51090717e-01 -7.10699633e-02 -4.93509322e-01 -1.17985690e+00 -7.09431946e-01 7.52372861e-01 5.12895644e-01 -2.20767826e-01 8.95864725e-01 1.25607467e+00 5.86792873e-03 2.68109068e-02 -6.64032698e-01 -7.50114143e-01 -3.02520037e-01 1.06946819e-01 4.23303634e-01 -1.68381423e-01 -5.32558322e-01 7.73122489e-01 1.34782732e-01 -1.04680812e+00 9.49855566e-01 1.08569431e+00 4.25683111e-01 3.24604243e-01 8.80084932e-01 -3.42210948e-01 1.04619694e+00 -1.32358682e+00 -4.62022156e-01 -2.86793053e-01 -1.07389915e+00 -9.47426260e-01 2.14797691e-01 8.60752583e-01 -4.94630665e-01 -2.73848414e-01 7.48311520e-01 5.79463482e-01 1.16403373e-02 7.49797583e-01 -6.40013814e-01 -5.91159701e-01 1.53233016e+00 -5.35413861e-01 -9.95017588e-03 5.78797460e-01 -2.31527835e-01 1.34085572e+00 -6.17563903e-01 9.05363202e-01 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fbe2f902-d929-4c8f-91db-67dcd43be030	self-appearance-aided-differential-evolution	2110.04658	null	https://arxiv.org/abs/2110.04658v1	https://arxiv.org/pdf/2110.04658v1.pdf	Self-appearance-aided Differential Evolution for Motion Transfer	Image animation transfers the motion of a driving video to a static object in a source image, while keeping the source identity unchanged. Great progress has been made in unsupervised motion transfer recently, where no labelled data or ground truth domain priors are needed. However, current unsupervised approaches still struggle when there are large motion or viewpoint discrepancies between the source and driving images. In this paper, we introduce three measures that we found to be effective for overcoming such large viewpoint changes. Firstly, to achieve more fine-grained motion deformation fields, we propose to apply Neural-ODEs for parametrizing the evolution dynamics of the motion transfer from source to driving. Secondly, to handle occlusions caused by large viewpoint and motion changes, we take advantage of the appearance flow obtained from the source image itself ("self-appearance"), which essentially "borrows" similar structures from other regions of an image to inpaint missing regions. Finally, our framework is also able to leverage the information from additional reference views which help to drive the source identity in spite of varying motion state. Extensive experiments demonstrate that our approach outperforms the state-of-the-arts by a significant margin (~40%), across six benchmarks varying from human faces, human bodies to robots and cartoon characters. Model generality analysis indicates that our approach generalises the best across different object categories as well.	['Ser-Nam Lim', 'Camille Couprie', 'Maxime Oquab', 'Ashish Shah', 'Yipin Zhou', 'Xuefei Cao', 'Rui Wang', 'Peirong Liu']	2021-10-09	null	null	null	null	['image-animation']	['computer-vision']	[ 2.80457616e-01 1.40363485e-01 -1.33920446e-01 -2.17286527e-01 -4.28783625e-01 -6.03919506e-01 7.48131216e-01 -5.74885488e-01 -1.80259332e-01 7.56380320e-01 1.41706258e-01 3.32502872e-01 4.05911982e-01 -5.05352676e-01 -8.94944370e-01 -8.83865416e-01 1.63890451e-01 2.84165114e-01 5.08531690e-01 -3.64017904e-01 2.05674335e-01 4.61438388e-01 -1.50676715e+00 1.57877922e-01 6.22912467e-01 5.74123979e-01 2.89987266e-01 5.15217781e-01 7.86355287e-02 9.74030435e-01 -3.64100337e-01 -2.64244586e-01 4.93725508e-01 -5.78182280e-01 -9.84951377e-01 4.74808753e-01 7.30653286e-01 -6.07698381e-01 -5.57404816e-01 1.05438673e+00 2.36087769e-01 3.40206772e-01 5.70744455e-01 -1.28753245e+00 -4.68299806e-01 1.57916516e-01 -7.66791880e-01 5.77083491e-02 4.63180155e-01 5.03840029e-01 5.08498371e-01 -7.57305861e-01 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d5a962ca-b9bc-41be-a80c-2eb6b78c0efe	towards-reliable-neural-machine-translation	2303.10966	null	https://arxiv.org/abs/2303.10966v1	https://arxiv.org/pdf/2303.10966v1.pdf	Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning	Neural machine translation (NMT) has achieved remarkable success in producing high-quality translations. However, current NMT systems suffer from a lack of reliability, as their outputs that are often affected by lexical or syntactic changes in inputs, resulting in large variations in quality. This limitation hinders the practicality and trustworthiness of NMT. A contributing factor to this problem is that NMT models trained with the one-to-one paradigm struggle to handle the source diversity phenomenon, where inputs with the same meaning can be expressed differently. In this work, we treat this problem as a bilevel optimization problem and present a consistency-aware meta-learning (CAML) framework derived from the model-agnostic meta-learning (MAML) algorithm to address it. Specifically, the NMT model with CAML (named CoNMT) first learns a consistent meta representation of semantically equivalent sentences in the outer loop. Subsequently, a mapping from the meta representation to the output sentence is learned in the inner loop, allowing the NMT model to translate semantically equivalent sentences to the same target sentence. We conduct experiments on the NIST Chinese to English task, three WMT translation tasks, and the TED M2O task. The results demonstrate that CoNMT effectively improves overall translation quality and reliably handles diverse inputs.	['Min Zhang', 'Changfeng Zhu', 'Wensen Cheng', 'Qiang Wang', 'Rongxiang Weng']	2023-03-20	null	null	null	null	['nmt', 'bilevel-optimization']	['computer-code', 'methodology']	[ 4.85272139e-01 -2.90288329e-01 -3.91680241e-01 -5.87192893e-01 -1.32479668e+00 -5.06906569e-01 6.43479824e-01 -2.30976939e-01 -1.41889900e-01 8.29520404e-01 2.86403269e-01 -4.24574167e-01 3.07798177e-01 -5.56736171e-01 -9.92073476e-01 -5.41623890e-01 6.42496526e-01 6.40084803e-01 -4.42818224e-01 -4.02768493e-01 9.60933715e-02 -2.00492695e-01 -1.09266758e+00 8.84729803e-01 1.18581021e+00 6.27847910e-01 4.10855472e-01 2.97051877e-01 -5.17503202e-01 4.57268715e-01 -8.60077202e-01 -7.64038861e-01 3.52273822e-01 -9.37752306e-01 -8.87643158e-01 -1.10109560e-01 4.92886722e-01 2.89798304e-02 9.40162688e-02 1.19212461e+00 4.89729464e-01 -1.92705616e-01 6.91396713e-01 -1.24088502e+00 -1.08259511e+00 9.03645158e-01 -4.20376062e-01 -5.66077791e-02 7.34889433e-02 4.47393619e-02 1.05675817e+00 -1.31778264e+00 5.74072599e-01 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9.45982814e-01 -4.32785720e-01 -1.02962804e+00 -1.21097660e+00 7.74082392e-02 5.52326322e-01 2.22420901e-01 -3.13863695e-01 -1.44355744e-01 2.91235605e-03]	[11.644682884216309, 10.104180335998535]
687f2da3-0ea4-40b2-ba21-0fc926bf8f8d	intel-labs-at-ego4d-challenge-2022-a-better	2210.07764	null	https://arxiv.org/abs/2210.07764v1	https://arxiv.org/pdf/2210.07764v1.pdf	Intel Labs at Ego4D Challenge 2022: A Better Baseline for Audio-Visual Diarization	This report describes our approach for the Audio-Visual Diarization (AVD) task of the Ego4D Challenge 2022. Specifically, we present multiple technical improvements over the official baselines. First, we improve the detection performance of the camera wearer's voice activity by modifying the training scheme of its model. Second, we discover that an off-the-shelf voice activity detection model can effectively remove false positives when it is applied solely to the camera wearer's voice activities. Lastly, we show that better active speaker detection leads to a better AVD outcome. Our final method obtains 65.9% DER on the test set of Ego4D, which significantly outperforms all the baselines. Our submission achieved 1st place in the Ego4D Challenge 2022.	['Kyle Min']	2022-10-14	null	null	null	null	['activity-detection']	['computer-vision']	[-1.35372162e-01 7.94854090e-02 -2.87871301e-01 -1.08383045e-01 -1.09120834e+00 -6.56986117e-01 7.44577885e-01 -5.27156651e-01 -1.78850561e-01 1.60234049e-01 7.16460586e-01 1.10441104e-01 4.20635045e-01 1.78049766e-02 -3.64441812e-01 -4.15506661e-01 6.44062310e-02 -4.58505787e-02 2.80653656e-01 2.68629611e-01 -1.08975753e-01 4.07308668e-01 -1.71967196e+00 3.39766026e-01 5.17214954e-01 1.03047109e+00 -1.63548142e-01 9.34887767e-01 5.54458797e-01 3.97060335e-01 -8.81462753e-01 -1.15025543e-01 2.73422331e-01 -1.68594435e-01 -6.00866020e-01 9.24209356e-02 1.05386734e+00 -7.37046540e-01 -7.43616402e-01 9.38912034e-01 8.94683957e-01 -6.64652437e-02 6.38576269e-01 -1.57138669e+00 -2.41718531e-01 2.54583750e-02 -5.32925963e-01 4.21587944e-01 5.78219056e-01 2.79417515e-01 8.09900641e-01 -1.14242780e+00 4.63970244e-01 1.26668382e+00 6.21960819e-01 9.80328739e-01 -1.27610040e+00 -8.96064103e-01 -3.47531512e-02 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cd850887-62ae-47b1-9faf-ba35d933beab	deep-feature-compression-for-collaborative	1802.03931	null	http://arxiv.org/abs/1802.03931v1	http://arxiv.org/pdf/1802.03931v1.pdf	Deep feature compression for collaborative object detection	Recent studies have shown that the efficiency of deep neural networks in mobile applications can be significantly improved by distributing the computational workload between the mobile device and the cloud. This paradigm, termed collaborative intelligence, involves communicating feature data between the mobile and the cloud. The efficiency of such approach can be further improved by lossy compression of feature data, which has not been examined to date. In this work we focus on collaborative object detection and study the impact of both near-lossless and lossy compression of feature data on its accuracy. We also propose a strategy for improving the accuracy under lossy feature compression. Experiments indicate that using this strategy, the communication overhead can be reduced by up to 70% without sacrificing accuracy.	['Ivan V. Bajic', 'Hyomin Choi']	2018-02-12	null	null	null	null	['feature-compression']	['computer-vision']	[ 9.40337107e-02 -2.60379046e-01 1.14759497e-01 -4.12256986e-01 -4.53064799e-01 -1.76474392e-01 3.02542806e-01 1.96803525e-01 -8.36113513e-01 6.53801918e-01 -2.22352877e-01 -2.13872254e-01 -2.98582047e-01 -1.00911188e+00 -7.45318651e-01 -7.28406191e-01 -1.43854797e-01 6.56487048e-02 3.22893530e-01 2.26577312e-01 2.45766476e-01 5.75695753e-01 -2.05558300e+00 3.61798406e-01 7.30443358e-01 1.42035699e+00 5.09287536e-01 7.77429879e-01 -1.29454851e-01 8.49377334e-01 -7.90491343e-01 -5.72062731e-01 3.02155823e-01 -8.30667540e-02 -6.38922691e-01 -1.38778746e-01 6.51910752e-02 -6.97662473e-01 -3.57736528e-01 1.23421323e+00 7.53841281e-01 -7.88081363e-02 2.40589112e-01 -1.41879880e+00 -3.23268563e-01 6.04921579e-01 -4.78563726e-01 4.23981667e-01 -1.80000588e-02 -5.74473500e-01 6.69927537e-01 -6.87296450e-01 4.07214254e-01 7.63391256e-01 8.50263894e-01 2.84691006e-01 -5.16844809e-01 -6.90499663e-01 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cc7d4f07-d8d4-43c8-9331-ddf656336282	deep-learning-for-iris-recognition-a-review	2303.08514	null	https://arxiv.org/abs/2303.08514v1	https://arxiv.org/pdf/2303.08514v1.pdf	Deep Learning for Iris Recognition: A Review	Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.	['Jinghua Zhang', 'Xu Han', 'Hongli Chang', 'Renye Zhang', 'Siliang He', 'Yimin Yin']	2023-03-15	null	null	null	null	['iris-recognition', 'feature-engineering']	['computer-vision', 'methodology']	[-7.93592166e-03 -5.02777040e-01 -5.09465098e-01 -4.39335376e-01 3.84800173e-02 -3.58530819e-01 1.96960211e-01 -1.46556392e-01 -2.83700824e-01 3.51345986e-01 -2.11497545e-02 -3.47095847e-01 -1.87680840e-01 -6.19468391e-01 -1.11773260e-01 -1.02867627e+00 1.33959725e-01 4.92034182e-02 -7.10245073e-01 2.10208625e-01 5.54048896e-01 9.03162658e-01 -1.53959715e+00 5.15485853e-02 8.17274153e-01 8.84651363e-01 -9.72667456e-01 7.20137954e-01 4.89750728e-02 5.49087107e-01 -8.47835422e-01 -5.79223990e-01 3.43971342e-01 -4.59821731e-01 -9.64404106e-01 2.33134463e-01 8.11972082e-01 -5.88477015e-01 -4.91901010e-01 9.90831673e-01 1.01254153e+00 -5.82464449e-02 5.34202635e-01 -6.52661920e-01 -8.48547876e-01 4.86139506e-02 -7.61543989e-01 2.70139575e-01 4.70406830e-01 3.45205963e-01 2.58046657e-01 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dffda9f8-f174-4ec9-a193-3374d482402b	unsupervised-domain-adaptation-for-sparse	2211.03988	null	https://arxiv.org/abs/2211.03988v1	https://arxiv.org/pdf/2211.03988v1.pdf	Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gaps	IR models using a pretrained language model significantly outperform lexical approaches like BM25. In particular, SPLADE, which encodes texts to sparse vectors, is an effective model for practical use because it shows robustness to out-of-domain datasets. However, SPLADE still struggles with exact matching of low-frequency words in training data. In addition, domain shifts in vocabulary and word frequencies deteriorate the IR performance of SPLADE. Because supervision data are scarce in the target domain, addressing the domain shifts without supervision data is necessary. This paper proposes an unsupervised domain adaptation method by filling vocabulary and word-frequency gaps. First, we expand a vocabulary and execute continual pretraining with a masked language model on a corpus of the target domain. Then, we multiply SPLADE-encoded sparse vectors by inverse document frequency weights to consider the importance of documents with lowfrequency words. We conducted experiments using our method on datasets with a large vocabulary gap from a source domain. We show that our method outperforms the present stateof-the-art domain adaptation method. In addition, our method achieves state-of-the-art results, combined with BM25.	['Naoaki Okazaki', 'Hiroki Iida']	2022-11-08	null	null	null	null	['continual-pretraining']	['methodology']	[ 1.34449229e-01 -2.20005661e-01 -8.09376538e-01 -2.86381036e-01 -1.13488817e+00 -6.35431647e-01 6.35733664e-01 1.16167054e-01 -7.80758798e-01 8.89358699e-01 6.23289049e-01 -9.91496742e-02 1.18745573e-01 -7.82556593e-01 -7.07232058e-01 -3.74704003e-01 4.44983900e-01 8.72548223e-01 4.23627138e-01 -6.67926550e-01 1.84306890e-01 -2.33236086e-02 -1.19083190e+00 4.83030766e-01 8.87247622e-01 6.55118823e-01 5.16350865e-01 -1.27138349e-03 -8.10997128e-01 3.59479457e-01 -7.04306364e-01 -2.96831101e-01 1.89600512e-01 -3.73874396e-01 -8.18126202e-01 -7.29098022e-02 4.75728214e-01 -1.34527057e-01 -3.62829268e-01 9.57108140e-01 5.31631410e-01 3.95179868e-01 7.68532872e-01 -5.77944994e-01 -1.00623524e+00 9.84479487e-01 -6.96479380e-01 3.03875238e-01 2.58295417e-01 -5.28552592e-01 1.08130729e+00 -1.22451127e+00 7.94172287e-01 1.34512150e+00 5.11162221e-01 6.07296228e-01 -1.34860480e+00 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6.42869711e-01 1.77478135e-01 -7.43601918e-01 -3.67938608e-01 7.71093145e-02]	[11.020556449890137, 8.041537284851074]
457f5898-bb96-4817-9484-cdec7edc85cc	simple-and-controllable-music-generation	2306.05284	null	https://arxiv.org/abs/2306.05284v1	https://arxiv.org/pdf/2306.05284v1.pdf	Simple and Controllable Music Generation	We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for cascading several models, e.g., hierarchically or upsampling. Following this approach, we demonstrate how MusicGen can generate high-quality samples, while being conditioned on textual description or melodic features, allowing better controls over the generated output. We conduct extensive empirical evaluation, considering both automatic and human studies, showing the proposed approach is superior to the evaluated baselines on a standard text-to-music benchmark. Through ablation studies, we shed light over the importance of each of the components comprising MusicGen. Music samples, code, and models are available at https://github.com/facebookresearch/audiocraft.	['Alexandre Défossez', 'Yossi Adi', 'Gabriel Synnaeve', 'David Kant', 'Tal Remez', 'Itai Gat', 'Felix Kreuk', 'Jade Copet']	2023-06-08	null	null	null	null	['text-to-music-generation', 'music-generation', 'music-generation', 'text-to-music-generation']	['audio', 'audio', 'music', 'music']	[ 3.59364271e-01 -3.51284817e-02 -2.49212563e-01 6.77124038e-02 -1.35595918e+00 -7.48199284e-01 8.78164411e-01 6.72383308e-02 -2.86902636e-01 5.00571012e-01 6.61544561e-01 -9.74898636e-02 2.52332211e-01 -4.25507367e-01 -7.27881372e-01 -5.23539543e-01 -1.86797064e-02 2.27664754e-01 -2.18734428e-01 -2.14166865e-02 2.40749851e-01 -2.06522956e-01 -1.51552558e+00 5.33360302e-01 7.50232637e-01 7.15943635e-01 2.68085927e-01 8.01437914e-01 8.07663426e-02 7.58622885e-01 -7.69977212e-01 -5.85979939e-01 1.17472261e-01 -7.92600989e-01 -5.69848955e-01 -1.05643377e-01 2.43913546e-01 -3.50450635e-01 -6.03573285e-02 7.60264933e-01 6.61774755e-01 1.23403460e-01 6.03405237e-01 -1.05228078e+00 -4.26148087e-01 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f7e03592-bae8-4201-a12c-fb50c2b8f39e	clear-generative-counterfactual-explanations	2210.08443	null	https://arxiv.org/abs/2210.08443v2	https://arxiv.org/pdf/2210.08443v2.pdf	CLEAR: Generative Counterfactual Explanations on Graphs	Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted label?". The comparison of this instance before and after perturbation can enhance human interpretation. Most existing studies on counterfactual explanations are limited in tabular data or image data. In this work, we study the problem of counterfactual explanation generation on graphs. A few studies have explored counterfactual explanations on graphs, but many challenges of this problem are still not well-addressed: 1) optimizing in the discrete and disorganized space of graphs; 2) generalizing on unseen graphs; and 3) maintaining the causality in the generated counterfactuals without prior knowledge of the causal model. To tackle these challenges, we propose a novel framework CLEAR which aims to generate counterfactual explanations on graphs for graph-level prediction models. Specifically, CLEAR leverages a graph variational autoencoder based mechanism to facilitate its optimization and generalization, and promotes causality by leveraging an auxiliary variable to better identify the underlying causal model. Extensive experiments on both synthetic and real-world graphs validate the superiority of CLEAR over the state-of-the-art methods in different aspects.	['Jundong Li', 'Aidong Zhang', 'Saumitra Mishra', 'Ruocheng Guo', 'Jing Ma']	2022-10-16	null	null	null	null	['counterfactual-explanation', 'explanation-generation']	['miscellaneous', 'natural-language-processing']	[ 4.97893244e-01 8.95940423e-01 -5.10510683e-01 -2.26018578e-01 -1.27357528e-01 -2.93374628e-01 7.97124207e-01 6.28906488e-02 4.86937791e-01 9.42412198e-01 6.45316482e-01 -6.03032172e-01 -2.15725601e-01 -9.74418283e-01 -1.09804475e+00 -4.80271816e-01 -2.73480028e-01 3.86113852e-01 -2.32945666e-01 -8.18641782e-02 2.02806249e-01 1.66725721e-02 -1.35115051e+00 2.57223248e-01 1.20746541e+00 5.21591187e-01 -5.43958135e-03 3.98457438e-01 3.35021317e-02 9.23968911e-01 -5.83387017e-01 -7.91435421e-01 8.54267851e-02 -6.97134316e-01 -9.25979853e-01 3.86940271e-01 4.31693286e-01 -7.63252974e-02 -5.96511245e-01 1.15780807e+00 1.22827686e-01 2.73305982e-01 8.78182054e-01 -1.76229846e+00 -1.61971891e+00 1.23100901e+00 -6.02389574e-01 1.66437194e-01 2.54834145e-01 2.57447124e-01 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68b736db-4548-45ed-9fad-3d204ed18e9d	recognizability-embedding-enhancement-for	2304.10066	null	https://arxiv.org/abs/2304.10066v1	https://arxiv.org/pdf/2304.10066v1.pdf	Recognizability Embedding Enhancement for Very Low-Resolution Face Recognition and Quality Estimation	Very low-resolution face recognition (VLRFR) poses unique challenges, such as tiny regions of interest and poor resolution due to extreme standoff distance or wide viewing angle of the acquisition devices. In this paper, we study principled approaches to elevate the recognizability of a face in the embedding space instead of the visual quality. We first formulate a robust learning-based face recognizability measure, namely recognizability index (RI), based on two criteria: (i) proximity of each face embedding against the unrecognizable faces cluster center and (ii) closeness of each face embedding against its positive and negative class prototypes. We then devise an index diversion loss to push the hard-to-recognize face embedding with low RI away from unrecognizable faces cluster to boost the RI, which reflects better recognizability. Additionally, a perceptibility attention mechanism is introduced to attend to the most recognizable face regions, which offers better explanatory and discriminative traits for embedding learning. Our proposed model is trained end-to-end and simultaneously serves recognizability-aware embedding learning and face quality estimation. To address VLRFR, our extensive evaluations on three challenging low-resolution datasets and face quality assessment demonstrate the superiority of the proposed model over the state-of-the-art methods.	['Andrew Beng Jin Teoh', 'Jaewoo Park', 'Cheng-Yaw Low', 'Tiong-Sik Ng', 'Jacky Chen Long Chai']	2023-04-20	null	http://openaccess.thecvf.com//content/CVPR2023/html/Chai_Recognizability_Embedding_Enhancement_for_Very_Low-Resolution_Face_Recognition_and_Quality_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Chai_Recognizability_Embedding_Enhancement_for_Very_Low-Resolution_Face_Recognition_and_Quality_CVPR_2023_paper.pdf	cvpr-2023-1	['face-recognition']	['computer-vision']	[ 3.66508216e-02 -4.74610068e-02 -2.57773958e-02 -5.39411247e-01 -6.49708569e-01 -2.61844039e-01 4.14247066e-01 -3.74897093e-01 -6.73227981e-02 2.38147557e-01 2.73198187e-01 2.83683985e-01 -4.55216229e-01 -7.45201766e-01 -6.73609376e-01 -7.26282179e-01 -5.64998612e-02 1.79255366e-01 -3.46718997e-01 4.18054834e-02 6.77378401e-02 1.13100803e+00 -1.79111135e+00 2.50546426e-01 7.03582823e-01 1.35104287e+00 6.21446222e-02 3.59751284e-01 4.86344039e-01 3.74451607e-01 -4.45756763e-01 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801e5575-3f86-4f46-9372-b4c3e26d2538	improved-and-interpretable-defense-to	2207.13036	null	https://arxiv.org/abs/2207.13036v4	https://arxiv.org/pdf/2207.13036v4.pdf	Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense	Deep neural networks (DNNs) are known to be vulnerable to adversarial examples that are crafted with imperceptible perturbations, i.e., a small change in an input image can induce a mis-classification, and thus threatens the reliability of deep learning based deployment systems. Adversarial training (AT) is often adopted to improve robustness through training a mixture of corrupted and clean data. However, most of AT based methods are ineffective in dealing with transferred adversarial examples which are generated to fool a wide spectrum of defense models, and thus cannot satisfy the generalization requirement raised in real-world scenarios. Moreover, adversarially training a defense model in general cannot produce interpretable predictions towards the inputs with perturbations, whilst a highly interpretable robust model is required by different domain experts to understand the behaviour of a DNN. In this work, we propose a novel approach based on Jacobian norm and Selective Input Gradient Regularization (J-SIGR), which suggests the linearized robustness through Jacobian normalization and also regularizes the perturbation-based saliency maps to imitate the model's interpretable predictions. As such, we achieve both the improved defense and high interpretability of DNNs. Finally, we evaluate our method across different architectures against powerful adversarial attacks. Experiments demonstrate that the proposed J-SIGR confers improved robustness against transferred adversarial attacks, and we also show that the predictions from the neural network are easy to interpret.	['Xianghua Xie', 'Mohammed Bennamoun', 'Farid Boussaid', 'Haifeng Zhao', 'Lin Wu', 'Deyin Liu']	2022-07-09	null	null	null	null	['adversarial-defense']	['adversarial']	[ 4.80140984e-01 3.80244106e-01 4.10832793e-01 -2.70606190e-01 -3.09454471e-01 -9.38429594e-01 6.45340323e-01 -3.46253932e-01 -1.41941354e-01 6.05156183e-01 -9.13895220e-02 -4.15258348e-01 2.83035915e-02 -6.95467710e-01 -1.20822096e+00 -8.20951283e-01 1.45953402e-01 -2.89595276e-02 2.08934024e-01 -4.87103194e-01 -3.78691452e-03 8.51293802e-01 -1.13625360e+00 1.91224247e-01 1.08009923e+00 1.10173810e+00 -8.89276937e-02 6.16769433e-01 3.55412453e-01 9.85433936e-01 -1.16482866e+00 -7.91012168e-01 6.12600565e-01 -3.60178053e-01 -5.76422334e-01 -2.43143842e-01 4.51332122e-01 -4.48270857e-01 -6.81387246e-01 1.55752611e+00 3.22230607e-01 1.85713992e-02 5.77905118e-01 -1.52113616e+00 -1.32287490e+00 3.88952702e-01 -2.05213800e-01 2.80930728e-01 -1.23275304e-02 5.83144963e-01 5.27102590e-01 -6.35341644e-01 1.42680645e-01 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-8.99031579e-01 -8.98516476e-01 -7.41729915e-01 -3.20443332e-01 7.26002038e-01 5.61142147e-01 -6.23676598e-01 -9.33948159e-01 7.45048374e-02]	[5.619331359863281, 8.003409385681152]
15c9d153-ba6c-4c5f-8ccd-6f8a27d62fc0	multi-expert-adversarial-attack-detection-in	2108.09891	null	https://arxiv.org/abs/2108.09891v2	https://arxiv.org/pdf/2108.09891v2.pdf	Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency	The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually inconspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a fundamental requirement for robust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach to achieve this goal by checking context inconsistency, which is suitable for any DNN-based ReID systems. Specifically, three kinds of context inconsistencies caused by adversarial attacks are employed to learn a detector for distinguishing the perturbed examples, i.e., a) the embedding distances between a perturbed query person image and its top-K retrievals are generally larger than those between a benign query image and its top-K retrievals, b) the embedding distances among the top-K retrievals of a perturbed query image are larger than those of a benign query image, c) the top-K retrievals of a benign query image obtained with multiple expert ReID models tend to be consistent, which is not preserved when attacks are present. Extensive experiments on the Market1501 and DukeMTMC-ReID datasets show that, as the first adversarial attack detection approach for ReID, MEAAD effectively detects various adversarial attacks and achieves high ROC-AUC (over 97.5%).	['Amit K. Roy-Chowdhury', 'Yaonan Wang', 'Min Liu', 'Shasha Li', 'Xueping Wang']	2021-08-23	null	http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Multi-Expert_Adversarial_Attack_Detection_in_Person_Re-Identification_Using_Context_Inconsistency_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Multi-Expert_Adversarial_Attack_Detection_in_Person_Re-Identification_Using_Context_Inconsistency_ICCV_2021_paper.pdf	iccv-2021-1	['adversarial-attack-detection', 'adversarial-attack-detection']	['computer-vision', 'knowledge-base']	[-1.46123096e-01 -3.96558762e-01 4.50900137e-01 -1.01910137e-01 -5.40706635e-01 -9.11384881e-01 7.39370883e-01 8.73821080e-02 -4.63898420e-01 6.81723416e-01 -1.64718255e-02 1.77442771e-03 -7.61167854e-02 -8.00273716e-01 -8.30901563e-01 -9.43392277e-01 -1.86256707e-01 3.74253511e-01 2.31834084e-01 -3.24689955e-01 -1.51385695e-01 6.77048504e-01 -1.45065808e+00 -2.40101162e-02 6.56899333e-01 8.08349431e-01 -5.34375429e-01 6.10696137e-01 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e49f166a-c9ea-40cc-abee-31f013d03b09	generalized-low-rank-update-model-parameter	2306.12670	null	https://arxiv.org/abs/2306.12670v1	https://arxiv.org/pdf/2306.12670v1.pdf	Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications	In this study, we have developed an incremental machine learning (ML) method that efficiently obtains the optimal model when a small number of instances or features are added or removed. This problem holds practical importance in model selection, such as cross-validation (CV) and feature selection. Among the class of ML methods known as linear estimators, there exists an efficient model update framework called the low-rank update that can effectively handle changes in a small number of rows and columns within the data matrix. However, for ML methods beyond linear estimators, there is currently no comprehensive framework available to obtain knowledge about the updated solution within a specific computational complexity. In light of this, our study introduces a method called the Generalized Low-Rank Update (GLRU) which extends the low-rank update framework of linear estimators to ML methods formulated as a certain class of regularized empirical risk minimization, including commonly used methods such as SVM and logistic regression. The proposed GLRU method not only expands the range of its applicability but also provides information about the updated solutions with a computational complexity proportional to the amount of dataset changes. To demonstrate the effectiveness of the GLRU method, we conduct experiments showcasing its efficiency in performing cross-validation and feature selection compared to other baseline methods.	['Ichiro Takeuchi', 'Kouichi Taji', 'Noriaki Hashimoto', 'Hiroyuki Hanada']	2023-06-22	null	null	null	null	['model-selection']	['methodology']	[ 3.75999570e-01 -2.72146761e-01 -5.53731978e-01 -3.32308412e-01 -8.87090683e-01 -3.56825501e-01 1.99504182e-01 4.02396411e-01 -3.38186085e-01 1.02369940e+00 -4.92518902e-01 -2.75121003e-01 -6.04199111e-01 -7.22322583e-01 -7.53277242e-01 -6.22505844e-01 -2.15165347e-01 2.94765592e-01 -1.57894325e-02 6.27762452e-02 2.68538922e-01 6.28263533e-01 -1.58760715e+00 -1.22907482e-01 1.25125420e+00 1.23576272e+00 -5.10329530e-02 2.02904657e-01 2.26378366e-01 2.62462795e-01 -1.88553303e-01 -3.67903382e-01 4.11911160e-01 -3.85653704e-01 -5.15751481e-01 1.11462027e-01 4.00389463e-01 -2.61270583e-01 1.18910626e-01 8.94091427e-01 3.25491518e-01 3.51077616e-01 7.16203690e-01 -1.32152236e+00 -1.13709606e-01 5.83135605e-01 -6.51864231e-01 8.27756152e-02 2.88164556e-01 -2.63196737e-01 1.19644237e+00 -1.28431070e+00 5.22394955e-01 1.15003216e+00 9.52544153e-01 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bd9e74f3-ec77-446e-b1c0-4e47558c58bd	zero-resource-cross-lingual-named-entity	1911.09812	null	https://arxiv.org/abs/1911.09812v1	https://arxiv.org/pdf/1911.09812v1.pdf	Zero-Resource Cross-Lingual Named Entity Recognition	Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features. However, these models still require manually annotated training data, which is not available for many languages. In this paper, we propose an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsupervised way without relying on any bilingual dictionary or parallel data. Our model achieves this through word-level adversarial learning and augmented fine-tuning with parameter sharing and feature augmentation. Experiments on five different languages demonstrate the effectiveness of our approach, outperforming existing models by a good margin and setting a new SOTA for each language pair.	['Prathyusha Jwalapuram', 'Shafiq Joty', 'M Saiful Bari']	2019-11-22	null	null	null	null	['low-resource-named-entity-recognition', 'cross-lingual-ner']	['natural-language-processing', 'natural-language-processing']	[-1.56339154e-01 -8.10058266e-02 -8.43220502e-02 -4.38227385e-01 -8.88308764e-01 -9.37202215e-01 6.52289271e-01 -1.71432719e-02 -1.00324631e+00 8.61396968e-01 2.18209237e-01 -4.63915169e-01 3.56368840e-01 -7.17989862e-01 -7.67015815e-01 -3.75247806e-01 2.89286733e-01 4.84387279e-01 -1.37021825e-01 -3.70202839e-01 -3.71473759e-01 6.28422379e-01 -5.48954844e-01 -8.04117396e-02 1.02079713e+00 3.93509835e-01 -5.56304976e-02 3.33671719e-01 -3.97930801e-01 5.31197906e-01 -5.21919668e-01 -8.85278940e-01 4.48262632e-01 -2.87119478e-01 -7.47316420e-01 -3.49005163e-01 3.44771713e-01 5.71681634e-02 -3.62261742e-01 1.01097465e+00 5.78706920e-01 1.08349755e-01 3.20870757e-01 -8.21344793e-01 -9.41769183e-01 8.84848595e-01 -3.13557625e-01 -1.19788051e-02 -8.98487866e-02 -6.12680465e-02 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3c240b6c-01a0-49f3-926c-033a6d462a7c	bridging-gap-between-image-pixels-and	2107.13757	null	https://arxiv.org/abs/2107.13757v3	https://arxiv.org/pdf/2107.13757v3.pdf	Bridging Gap between Image Pixels and Semantics via Supervision: A Survey	The fact that there exists a gap between low-level features and semantic meanings of images, called the semantic gap, is known for decades. Resolution of the semantic gap is a long standing problem. The semantic gap problem is reviewed and a survey on recent efforts in bridging the gap is made in this work. Most importantly, we claim that the semantic gap is primarily bridged through supervised learning today. Experiences are drawn from two application domains to illustrate this point: 1) object detection and 2) metric learning for content-based image retrieval (CBIR). To begin with, this paper offers a historical retrospective on supervision, makes a gradual transition to the modern data-driven methodology and introduces commonly used datasets. Then, it summarizes various supervision methods to bridge the semantic gap in the context of object detection and metric learning.	['C. -C. Jay Kuo', 'Jiali Duan']	2021-07-29	null	null	null	null	['content-based-image-retrieval']	['computer-vision']	[ 6.22190714e-01 -1.11328937e-01 -6.99369371e-01 -7.28328168e-01 -8.95370603e-01 -1.68786064e-01 6.43829346e-01 4.39648122e-01 -3.46283704e-01 3.76146823e-01 8.27888399e-02 -2.25799698e-02 -8.29464793e-01 -5.23851871e-01 -2.45079130e-01 -5.33811629e-01 -7.77764320e-02 9.22216475e-02 1.51839599e-01 -1.52196750e-01 8.70746195e-01 4.00950998e-01 -2.23536229e+00 5.41247427e-01 5.00367820e-01 1.27702308e+00 3.39557081e-01 1.82993621e-01 -4.89646316e-01 1.01826823e+00 -5.72168529e-01 -4.80180323e-01 1.28963128e-01 -3.59399825e-01 -1.29936635e+00 5.39598107e-01 6.07475400e-01 -1.20917462e-01 -1.24985345e-01 1.21729517e+00 2.67619729e-01 -4.52882461e-02 7.47834802e-01 -1.75866103e+00 -8.98413479e-01 2.27686375e-01 -5.13605595e-01 3.56419384e-01 3.80921543e-01 -6.33832276e-01 1.45301867e+00 -1.08398199e+00 6.11959636e-01 1.33173478e+00 5.80212474e-01 5.10791659e-01 -8.21780145e-01 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7.32895613e-01 5.42312741e-01 -1.00178909e+00 -5.36449134e-01 4.92264122e-01]	[9.9149169921875, 2.293501615524292]
a8271712-2428-4b06-857c-57ac9d4e1652	adaptive-low-complexity-sequential-inference	1409.8185	null	http://arxiv.org/abs/1409.8185v3	http://arxiv.org/pdf/1409.8185v3.pdf	Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models	We develop a sequential low-complexity inference procedure for Dirichlet process mixtures of Gaussians for online clustering and parameter estimation when the number of clusters are unknown a-priori. We present an easily computable, closed form parametric expression for the conditional likelihood, in which hyperparameters are recursively updated as a function of the streaming data assuming conjugate priors. Motivated by large-sample asymptotics, we propose a novel adaptive low-complexity design for the Dirichlet process concentration parameter and show that the number of classes grow at most at a logarithmic rate. We further prove that in the large-sample limit, the conditional likelihood and data predictive distribution become asymptotically Gaussian. We demonstrate through experiments on synthetic and real data sets that our approach is superior to other online state-of-the-art methods.	['Keith W. Forsythe', 'Theodoros Tsiligkaridis']	2014-09-29	adaptive-low-complexity-sequential-inference-1	http://papers.nips.cc/paper/6035-adaptive-low-complexity-sequential-inference-for-dirichlet-process-mixture-models	http://papers.nips.cc/paper/6035-adaptive-low-complexity-sequential-inference-for-dirichlet-process-mixture-models.pdf	neurips-2015-12	['online-clustering']	['computer-vision']	[-2.66049236e-01 -8.01918432e-02 3.73144783e-02 -3.51344764e-01 -9.89587188e-01 -4.43812668e-01 4.90511984e-01 4.43148226e-01 -5.13573945e-01 5.81763625e-01 -1.46391734e-01 -1.45474911e-01 -1.68389156e-01 -6.80281818e-01 -7.34862864e-01 -9.87165987e-01 -4.26393032e-01 1.42551303e+00 4.08760697e-01 5.42979360e-01 9.82128233e-02 2.82339931e-01 -1.47498584e+00 -4.92410481e-01 8.15733314e-01 7.11214006e-01 1.76539551e-02 1.14257419e+00 3.98252718e-02 9.05307978e-02 -5.12950361e-01 -1.28397211e-01 1.47313774e-01 -2.57936597e-01 -4.74456012e-01 5.29543400e-01 -2.61089742e-01 -3.15830022e-01 5.36023900e-02 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2a98a539-c4d7-4290-a867-921b771540a7	joint-convolutional-analysis-and-synthesis	null	null	http://openaccess.thecvf.com/content_iccv_2017/html/Gu_Joint_Convolutional_Analysis_ICCV_2017_paper.html	http://openaccess.thecvf.com/content_ICCV_2017/papers/Gu_Joint_Convolutional_Analysis_ICCV_2017_paper.pdf	Joint Convolutional Analysis and Synthesis Sparse Representation for Single Image Layer Separation	Analysis sparse representation (ASR) and synthesis sparse representation (SSR) are two representative approaches for sparsity-based image modeling. An image is described mainly by the non-zero coefficients in SSR, while it is characterized by the indices of zeros in ASR. To exploit the complementary representation mechanisms of ASR and SSR, we integrate the two models and propose a joint convolutional analysis and synthesis (JCAS) sparse representation model. The convolutional implementation is adopted to more effectively exploit the image global information. In JCAS, a single image is decomposed into two layers, one is approximated by ASR to represent image large-scale structures, and the other by SSR to represent image fine-scale textures. The synthesis dictionary is adaptively learned in JCAS to describe the texture patterns for different single image layer separation tasks. We evaluate the proposed JCAS model on a variety of applications, including rain streak removal, high dynamic range image tone mapping, etc. The results show that our JCAS method outperforms state-ofthe-arts in those applications in terms of both quantitative measure and visual perception quality.	['Lei Zhang', 'WangMeng Zuo', 'Shuhang Gu', 'Deyu Meng']	2017-10-01	null	null	null	iccv-2017-10	['tone-mapping']	['computer-vision']	[ 3.68378788e-01 -5.23534417e-01 -4.09945399e-02 -1.27842396e-01 -4.95656967e-01 -6.90376759e-02 3.40725482e-01 -2.96485245e-01 2.90018082e-01 3.37919474e-01 4.06597733e-01 2.61687458e-01 1.20447077e-01 -7.87395895e-01 -5.51744461e-01 -1.00081575e+00 1.41954750e-01 -4.30930912e-01 3.39924157e-01 -5.13709426e-01 2.07419127e-01 6.43908262e-01 -1.87733090e+00 6.41380250e-01 7.56310880e-01 1.29757750e+00 5.22946656e-01 5.88822186e-01 -1.89824671e-01 1.41661835e+00 -4.04608667e-01 3.71890724e-01 1.89250886e-01 -6.10318899e-01 -1.54182121e-01 2.63612241e-01 4.00721192e-01 -1.82268158e-01 -7.38253951e-01 1.33662653e+00 3.64690006e-01 1.66897506e-01 4.59467500e-01 -7.88369775e-01 -9.25584853e-01 2.30590343e-01 -1.00675035e+00 3.54851097e-01 1.11737698e-01 -2.21101493e-01 6.05048060e-01 -1.02817941e+00 4.63003039e-01 1.35951519e+00 4.90350306e-01 2.94745881e-02 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c83de78f-e00c-4745-aaed-f9e9950689f4	transcc-transformer-based-multiple-illuminant	2211.08772	null	https://arxiv.org/abs/2211.08772v2	https://arxiv.org/pdf/2211.08772v2.pdf	MIMT: Multi-Illuminant Color Constancy via Multi-Task Learning	The assumption of a uniform light color distribution, which holds true in single light color scenes, is no longer applicable in scenes that have multiple light colors. The spatial variability in multiple light colors causes the color constancy problem to be more challenging and requires the extraction of local surface/light information. Motivated by this, we introduce a multi-task learning method to estimate multiple light colors from a single input image. To have better cues of the local surface/light colors under multiple light color conditions, we design a multi-task learning framework with achromatic-pixel detection and surface-color similarity prediction as our auxiliary tasks. These tasks facilitate the acquisition of local light color information and surface color correlations. Moreover, to ensure that our model maintains the constancy of surface colors regardless of the variations of light colors, we also preserve local surface color features in our model. We demonstrate that our model achieves 47.1% improvement compared to a state-of-the-art multi-illuminant color constancy method on a multi-illuminant dataset (LSMI). While single light colors are not our main focus, our model also maintains a robust performance on the single illuminant dataset (NUS-8) and provides 18.5% improvement on the state-of-the-art single color constancy method.	['Robby T. Tan', 'Michael S. Brown', 'Jikai Wang', 'Shuwei Li']	2022-11-16	null	null	null	null	['color-constancy', 'edge-detection']	['computer-vision', 'computer-vision']	[ 2.96728969e-01 -1.08254075e+00 1.53529286e-01 -3.55207980e-01 -7.50283718e-01 -7.21876025e-01 2.51554161e-01 -3.77043992e-01 -2.77071059e-01 5.36222875e-01 -4.34666514e-01 -8.23494419e-02 2.93604493e-01 -5.93764484e-01 -7.67119586e-01 -1.11868238e+00 4.62259024e-01 -1.48237690e-01 2.69079685e-01 -9.88926515e-02 4.14544404e-01 2.87054032e-01 -1.71049643e+00 4.19423223e-01 9.68405426e-01 1.16497171e+00 4.23665583e-01 1.02977228e+00 -2.16659695e-01 3.74744385e-01 -2.83946484e-01 7.07207073e-04 6.93665028e-01 -4.38265651e-01 -2.24709973e-01 1.70937747e-01 1.31896508e+00 -3.30238730e-01 9.85242352e-02 1.23358083e+00 4.75763917e-01 3.96594219e-02 5.27874947e-01 -1.29553819e+00 -9.34067428e-01 -3.88666093e-01 -1.11121047e+00 -1.36973292e-01 -2.18216050e-02 1.73055783e-01 1.19370615e+00 -9.06446278e-01 1.44658849e-01 9.02177632e-01 3.61965299e-01 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baddf940-24a2-44ea-afee-4477b1adef08	calibrenet-calibration-networks-for	2011.05723	null	https://arxiv.org/abs/2011.05723v1	https://arxiv.org/pdf/2011.05723v1.pdf	CalibreNet: Calibration Networks for Multilingual Sequence Labeling	Lack of training data in low-resource languages presents huge challenges to sequence labeling tasks such as named entity recognition (NER) and machine reading comprehension (MRC). One major obstacle is the errors on the boundary of predicted answers. To tackle this problem, we propose CalibreNet, which predicts answers in two steps. In the first step, any existing sequence labeling method can be adopted as a base model to generate an initial answer. In the second step, CalibreNet refines the boundary of the initial answer. To tackle the challenge of lack of training data in low-resource languages, we dedicatedly develop a novel unsupervised phrase boundary recovery pre-training task to enhance the multilingual boundary detection capability of CalibreNet. Experiments on two cross-lingual benchmark datasets show that the proposed approach achieves SOTA results on zero-shot cross-lingual NER and MRC tasks.	['Daxin Jiang', 'Wanli Zuo', 'Ming Gong', 'Jian Pei', 'Linjun Shou', 'Shining Liang']	2020-11-11	null	null	null	null	['cross-lingual-ner']	['natural-language-processing']	[ 5.56204677e-01 5.85291050e-02 9.49467183e-04 -3.41557890e-01 -1.14204335e+00 -8.49799573e-01 2.29955852e-01 2.82664180e-01 -7.75759757e-01 9.76518452e-01 2.45125756e-01 -6.01453424e-01 2.53159046e-01 -7.13401079e-01 -7.05135942e-01 -1.21106341e-01 7.25117445e-01 5.93011081e-01 5.67536712e-01 -3.64312172e-01 4.38183814e-01 -2.21274436e-01 -1.23311174e+00 5.39399326e-01 1.59978426e+00 3.72424871e-01 4.13881749e-01 5.73262393e-01 -9.01370168e-01 5.52967608e-01 -4.79234427e-01 -5.57132304e-01 -2.66609248e-02 -6.46004319e-01 -1.22119963e+00 -2.27650657e-01 1.76447317e-01 2.43532166e-01 2.78078198e-01 1.03657603e+00 6.22033954e-01 1.34081423e-01 3.25087905e-01 -7.56683111e-01 -4.48249400e-01 8.33101094e-01 -2.64891922e-01 3.00955266e-01 6.75795674e-01 6.01109453e-02 9.50654447e-01 -1.12588334e+00 8.95981014e-01 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13d4c867-8900-414b-84de-846d6a2fd8ec	fully-point-wise-convolutional-neural-network	1801.06302	null	http://arxiv.org/abs/1801.06302v3	http://arxiv.org/pdf/1801.06302v3.pdf	Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images	Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise ($1*1$) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10$\sim$1/100 network parameters and computational cost while achieving comparable performance.	['Chang Wen Chen', 'Yang Wang', 'Jing Zhang', 'Yang Cao', 'Chenglin Wen']	2018-01-19	null	null	null	null	['color-constancy']	['computer-vision']	[ 1.58565253e-01 -2.47891665e-01 1.37839004e-01 -4.88044381e-01 -8.81765783e-02 -1.94733277e-01 1.83568224e-01 -4.15984280e-02 -5.17381847e-01 6.25125110e-01 -1.20955132e-01 -6.28370419e-02 -8.18187594e-02 -6.76085949e-01 -8.83712411e-01 -1.10620117e+00 -7.91845694e-02 -5.62911928e-01 9.16132852e-02 -8.75533298e-02 2.80676156e-01 3.71610284e-01 -1.50140262e+00 9.69438776e-02 1.08015084e+00 1.25334048e+00 1.61205292e-01 4.91242468e-01 -1.56847209e-01 8.22086453e-01 -4.60305095e-01 -2.13104337e-01 3.85365874e-01 -4.99004513e-01 -1.51129186e-01 2.27865279e-01 6.83289170e-01 -2.75784969e-01 -5.90934157e-01 1.51281333e+00 3.53806674e-01 4.42230374e-01 4.87446576e-01 -1.15753484e+00 -1.28504848e+00 2.62090862e-01 -9.89018440e-01 2.04770997e-01 -4.74500269e-01 2.96859056e-01 8.04326475e-01 -6.46402478e-01 -7.82928914e-02 1.12546849e+00 6.30862057e-01 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ac516fdf-a6f8-420a-a747-7387453d9661	out-of-distribution-detection-without-class	2112.07662	null	https://arxiv.org/abs/2112.07662v2	https://arxiv.org/pdf/2112.07662v2.pdf	Out-of-Distribution Detection Without Class Labels	Out-of-distribution detection seeks to identify novelties, samples that deviate from the norm. The task has been found to be quite challenging, particularly in the case where the normal data distribution consists of multiple semantic classes (e.g., multiple object categories). To overcome this challenge, current approaches require manual labeling of the normal images provided during training. In this work, we tackle multi-class novelty detection without class labels. Our simple but effective solution consists of two stages: we first discover "pseudo-class" labels using unsupervised clustering. Then using these pseudo-class labels, we are able to use standard supervised out-of-distribution detection methods. We verify the performance of our method by a favorable comparison to the state-of-the-art, and provide extensive analysis and ablations.	['Yedid Hoshen', 'Ron Abutbul', 'Niv Cohen']	2021-12-14	null	null	null	null	['physical-video-anomaly-detection', 'image-clustering']	['computer-vision', 'computer-vision']	[ 4.67962921e-01 -2.80788243e-02 -9.17771012e-02 -3.20213884e-01 -8.12355757e-01 -8.28188360e-01 7.79556811e-01 3.45704347e-01 -3.41536462e-01 5.32707930e-01 -2.49773815e-01 4.35984246e-02 -2.23953381e-01 -4.69167769e-01 -5.31895220e-01 -6.92067802e-01 1.60190508e-01 5.88851631e-01 3.01255703e-01 1.36439979e-01 4.49734300e-01 5.24418890e-01 -2.12366796e+00 4.47238356e-01 7.54039288e-01 8.82369637e-01 -1.01663753e-01 3.58215511e-01 -3.28664362e-01 2.41413042e-01 -8.70104730e-01 -1.71468258e-01 2.10155934e-01 -5.83909690e-01 -5.71727872e-01 1.89554349e-01 6.03947997e-01 1.40925730e-02 2.85620093e-01 1.59430802e+00 4.26057130e-01 1.69369459e-01 1.13725507e+00 -1.32448864e+00 -3.62920821e-01 3.01035404e-01 -6.53945327e-01 3.04894805e-01 2.04228744e-01 -7.43545592e-02 8.80347490e-01 -1.14485502e+00 7.74088204e-01 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4.22352910e-01 -7.23569989e-01 -5.95880389e-01 -8.72368217e-01 -8.20263028e-01 8.35942507e-01 3.54443341e-01 -8.81869435e-01 -8.15619886e-01 1.37694433e-01]	[9.373537063598633, 2.9280481338500977]
6224099c-a435-47f5-8cd8-722f6715faf9	a-gaussian-mixture-model-representation-of	1710.00075	null	http://arxiv.org/abs/1710.00075v2	http://arxiv.org/pdf/1710.00075v2.pdf	A Gaussian mixture model representation of endmember variability in hyperspectral unmixing	Hyperspectral unmixing while considering endmember variability is usually performed by the normal compositional model (NCM), where the endmembers for each pixel are assumed to be sampled from unimodal Gaussian distributions. However, in real applications, the distribution of a material is often not Gaussian. In this paper, we use Gaussian mixture models (GMM) to represent the endmember variability. We show, given the GMM starting premise, that the distribution of the mixed pixel (under the linear mixing model) is also a GMM (and this is shown from two perspectives). The first perspective originates from the random variable transformation and gives a conditional density function of the pixels given the abundances and GMM parameters. With proper smoothness and sparsity prior constraints on the abundances, the conditional density function leads to a standard maximum a posteriori (MAP) problem which can be solved using generalized expectation maximization. The second perspective originates from marginalizing over the endmembers in the GMM, which provides us with a foundation to solve for the endmembers at each pixel. Hence, our model can not only estimate the abundances and distribution parameters, but also the distinct endmember set for each pixel. We tested the proposed GMM on several synthetic and real datasets, and showed its potential by comparing it to current popular methods.	['Yuan Zhou', 'Paul D. Gader', 'Anand Rangarajan']	2017-09-29	null	null	null	null	['hyperspectral-unmixing']	['computer-vision']	[ 3.90178800e-01 -3.26940924e-01 1.40106782e-01 2.27495581e-02 -3.66121352e-01 -4.66196954e-01 6.80164695e-01 -1.70128763e-01 -1.20982140e-01 8.52410436e-01 -5.28892782e-03 -1.27291858e-01 4.71607633e-02 -9.62312520e-01 -7.50290632e-01 -1.50477362e+00 3.22120816e-01 3.54925483e-01 -2.74626404e-01 1.58995822e-01 -5.72158471e-02 4.17825937e-01 -1.74853325e+00 -1.14911199e-01 1.32302499e+00 8.34411919e-01 3.95170540e-01 4.08633351e-01 -3.09530348e-01 6.95544600e-01 -5.84770679e-01 1.50056809e-01 4.59832907e-01 -5.62144697e-01 -3.46646726e-01 6.65205538e-01 4.13646698e-01 -2.04264581e-01 1.21160792e-02 1.71468341e+00 -8.79035052e-03 4.95287597e-01 1.23925757e+00 -1.13724279e+00 -3.93140137e-01 5.68711102e-01 -8.69145513e-01 -6.26380920e-01 -3.50633144e-01 -2.43254919e-02 6.58702075e-01 -6.87719345e-01 2.51260102e-01 1.23062837e+00 3.89698684e-01 6.96224719e-02 -1.31795979e+00 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e9c1200b-50bf-43e1-ace5-e5a95916b0b3	armas-active-reconstruction-of-missing-audio	2111.10891	null	https://arxiv.org/abs/2111.10891v3	https://arxiv.org/pdf/2111.10891v3.pdf	ARMAS: Active Reconstruction of Missing Audio Segments	Digital audio signal reconstruction of a lost or corrupt segment using deep learning algorithms has been explored intensively in recent years. Nevertheless, prior traditional methods with linear interpolation, phase coding and tone insertion techniques are still in vogue. However, we found no research work on reconstructing audio signals with the fusion of dithering, steganography, and machine learning regressors. Therefore, this paper proposes the combination of steganography, halftoning (dithering), and state-of-the-art shallow (RF- Random Forest regression) and deep learning (LSTM- Long Short-Term Memory) methods. The results (including comparing the SPAIN, Autoregressive, deep learning-based, graph-based, and other methods) are evaluated with three different metrics. The observations from the results show that the proposed solution is effective and can enhance the reconstruction of audio signals performed by the side information (e.g., Latent representation and learning for audio inpainting) steganography provides. Moreover, this paper proposes a novel framework for reconstruction from heavily compressed embedded audio data using halftoning (i.e., dithering) and machine learning, which we termed the HCR (halftone-based compression and reconstruction). This work may trigger interest in optimising this approach and/or transferring it to different domains (i.e., image reconstruction). Compared to existing methods, we show improvement in the inpainting performance in terms of signal-to-noise (SNR), the objective difference grade (ODG) and the Hansen's audio quality metric.	['Abbas Cheddad', 'Zohra Cheddad']	2021-11-21	null	null	null	null	['audio-inpainting']	['audio']	[ 7.53572464e-01 6.96729794e-02 7.58256316e-02 3.22573215e-01 -1.04759169e+00 -4.68185432e-02 3.96242321e-01 -5.14506221e-01 -1.65953338e-01 7.40428567e-01 4.31659371e-01 -3.53041142e-01 -1.38988374e-02 -8.20791006e-01 -8.10201645e-01 -1.07253194e+00 -4.00167972e-01 -1.62261158e-01 -1.12499803e-01 -3.28474343e-01 2.20756292e-01 1.33948743e-01 -1.40333211e+00 6.06028914e-01 6.48226917e-01 1.01498580e+00 2.04449028e-01 9.12861228e-01 2.61764526e-01 1.10271025e+00 -7.97805786e-01 -3.21178764e-01 1.03508100e-01 -9.80066657e-01 -4.29673940e-01 -1.76529944e-01 -2.26741463e-01 -4.89799976e-01 -6.13836765e-01 8.02356124e-01 8.74208510e-01 -2.76753783e-01 3.70133847e-01 -9.28009808e-01 -3.92526954e-01 7.29311347e-01 -4.92116868e-01 5.02854958e-03 5.31579375e-01 9.73963663e-02 5.32358646e-01 -8.22338223e-01 5.03852725e-01 1.17192817e+00 1.01203787e+00 1.40679434e-01 -1.04213703e+00 -1.13929415e+00 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569a6db6-f64e-406d-9235-0a48a97fb28e	registration-of-multiresolution-remote	null	null	https://ieeexplore.ieee.org/document/9264687	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9264687	Registration of Multiresolution Remote Sensing Images Based on L2-Siamese Model	The registration of multiresolution optical remote sensing images has been widely used in image fusion, change detection, and image stitching. However, traditional registration methods achieve poor accuracy in the registration of multiresolution remote sensing images. In this study, we propose a framework for generating deep features via a deep residual encoder (DRE) fused with shallow features for multiresolution remote sensing image registration. Through an L2 normalization Siamese network (L2-Siamese) based on the DRE, the multiscale loss function is used to learn the attribute characteristics and distance characteristics of two key points and obtain the trained feature extractor. Finally, the DRE is used to extract the deep features of the key points and their neighbors, which are concatenated with the shallow features into a fusion feature vector to complete the image registration. We performed comprehensive experiments on four sets of multiresolution optical remote sensing images and two sets of synthetic aperture radar images. The results demonstrate that the proposed registration model can achieve subpixel registration. The relative registration accuracy improved by 1.6%-7.5%, whereas the overall performance improved by 4.5%-14.1%.	['Zenglin Hong', 'Jianhua Yang', 'Jinbao Liu', 'Bochuan Hou', 'Rongbo Fan']	2020-11-19	null	null	null	ieee-journal-of-selected-topics-in-applied-4	['image-stitching']	['computer-vision']	[ 4.99563664e-01 -7.22584069e-01 2.22993508e-01 -4.80991453e-01 -1.33145225e+00 -3.12482804e-01 5.33889115e-01 -5.09324707e-02 -6.10840321e-01 4.53533500e-01 1.59027934e-01 3.45190793e-01 -3.78028661e-01 -9.00438547e-01 -4.76387322e-01 -1.09821224e+00 -1.90270618e-02 -6.07910119e-02 -2.39867970e-01 -3.36285621e-01 2.69013584e-01 8.40842485e-01 -1.44163632e+00 -8.82581547e-02 9.75799143e-01 1.10407245e+00 2.20156491e-01 3.31217229e-01 4.37498450e-01 1.72489941e-01 -3.83560121e-01 2.29579657e-01 7.24665880e-01 -1.90854058e-01 -3.71484101e-01 1.65935218e-01 8.03570807e-01 -3.88035119e-01 -4.61659104e-01 1.32026839e+00 7.65578032e-01 4.11078900e-01 4.48901564e-01 -8.40372622e-01 -9.05650437e-01 3.51437837e-01 -1.08068216e+00 3.08946669e-01 1.50126508e-02 -3.90539994e-04 8.79053533e-01 -8.88887703e-01 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1.76766261e-01 1.21322739e+00 -9.61666048e-01 -6.37327135e-01 -8.07585001e-01 -7.95171320e-01 3.70752305e-01 7.11076736e-01 -3.54254276e-01 -3.48583221e-01 3.66725884e-02]	[10.31439208984375, -1.7872838973999023]
37f7e102-1237-4614-bfc6-f80b44b364d6	xgboost-a-scalable-tree-boosting-system	1603.02754	null	http://arxiv.org/abs/1603.02754v3	http://arxiv.org/pdf/1603.02754v3.pdf	XGBoost: A Scalable Tree Boosting System	Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.	['Carlos Guestrin', 'Tianqi Chen']	2016-03-09	null	null	null	null	['humor-detection']	['natural-language-processing']	[-6.81955159e-01 -4.26448703e-01 -9.76009786e-01 -7.69708991e-01 -1.12476563e+00 -8.31991732e-02 4.45122719e-01 7.86247909e-01 -7.73549378e-02 6.50813222e-01 4.86553013e-01 -5.59237719e-01 -3.29791941e-02 -9.10923958e-01 -5.83764315e-01 -2.53237963e-01 -2.62684405e-01 5.82672954e-01 1.52965084e-01 4.57929745e-02 2.19638690e-01 7.41140470e-02 -1.82929647e+00 8.85616660e-01 7.98492968e-01 1.40611923e+00 -3.24841827e-01 6.73051000e-01 -4.60913032e-01 1.18775988e+00 -1.78794935e-01 -6.45295262e-01 3.95268500e-01 -2.37817820e-02 -6.90144658e-01 -5.21909773e-01 9.57414985e-01 -6.58486724e-01 -1.51995704e-01 4.86143261e-01 4.21827585e-01 -2.78411470e-02 4.95174438e-01 -1.25799119e+00 -2.63379693e-01 9.50938225e-01 -8.78018558e-01 2.73269594e-01 5.42431101e-02 -1.73377723e-01 1.20936811e+00 -1.01498675e+00 2.50045866e-01 1.20896518e+00 1.10020161e+00 4.34108600e-02 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22bb0625-4572-431e-be4e-5c18859fde30	can-we-predict-new-facts-with-open-knowledge	null	null	https://aclanthology.org/2020.acl-main.209	https://aclanthology.org/2020.acl-main.209.pdf	Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction	Open Information Extraction systems extract ({``}subject text{''}, {``}relation text{''}, {``}object text{''}) triples from raw text. Some triples are textual versions of facts, i.e., non-canonicalized mentions of entities and relations. In this paper, we investigate whether it is possible to infer new facts directly from the open knowledge graph without any canonicalization or any supervision from curated knowledge. For this purpose, we propose the open link prediction task,i.e., predicting test facts by completing ({``}subject text{''}, {``}relation text{''}, ?) questions. An evaluation in such a setup raises the question if a correct prediction is actually a new fact that was induced by reasoning over the open knowledge graph or if it can be trivially explained. For example, facts can appear in different paraphrased textual variants, which can lead to test leakage. To this end, we propose an evaluation protocol and a methodology for creating the open link prediction benchmark OlpBench. We performed experiments with a prototypical knowledge graph embedding model for openlink prediction. While the task is very challenging, our results suggests that it is possible to predict genuinely new facts, which can not be trivially explained.	['Rainer Gemulla', 'Samuel Broscheit', 'Kiril Gashteovski', 'Yanjie Wang']	2020-07-01	null	null	null	acl-2020-6	['open-knowledge-graph-embedding', 'open-information-extraction']	['knowledge-base', 'natural-language-processing']	[ 6.37637638e-03 1.02351952e+00 -2.93112636e-01 -1.82083443e-01 -4.14275229e-01 -9.31226313e-01 5.09853542e-01 6.81747079e-01 -7.18908533e-02 1.30819368e+00 2.54295409e-01 -8.18090141e-01 -4.44605172e-01 -1.32519114e+00 -1.46055806e+00 -1.55279055e-01 -5.94166182e-02 7.80806184e-01 6.85378671e-01 -3.04016858e-01 -1.59066096e-01 4.26244959e-02 -1.47770274e+00 7.05493867e-01 8.13317835e-01 8.00058305e-01 -2.42541492e-01 4.40831691e-01 -4.44202602e-01 1.14396477e+00 -6.03264630e-01 -1.53890038e+00 1.19736105e-01 -1.62884638e-01 -1.52963150e+00 -4.06338781e-01 6.83460176e-01 6.65037856e-02 -4.08090234e-01 1.15043473e+00 -6.53468189e-04 -9.64289680e-02 6.12942576e-01 -1.42044473e+00 -8.21395457e-01 1.12809634e+00 -5.10894619e-02 3.72334838e-01 7.10031211e-01 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33cb657c-7b82-432b-8af4-a415841d80ec	an-early-study-on-intelligent-analysis-of	2005.00096	null	https://arxiv.org/abs/2005.00096v2	https://arxiv.org/pdf/2005.00096v2.pdf	An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety	The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of .69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease.	['Björn W. Schuller', 'Yoshiharu Yamamoto', 'Shuo Liu', 'Meishu Song', 'Xiao Li', 'Huaiyuan Zheng', 'Zijiang Yang', 'Wei Ji', 'Kun Qian', 'Juan Liu', 'Zixing Zhang', 'Zhao Ren', 'Tomoya Koike', 'Jing Han']	2020-04-30	null	null	null	null	['sleep-quality-prediction']	['medical']	[ 2.05354795e-01 6.40118793e-02 3.23643923e-01 -1.87936798e-01 -5.49668431e-01 -2.72582650e-01 3.69470268e-01 7.62181342e-01 -5.04495323e-01 5.21211445e-01 4.96011257e-01 -1.13280408e-01 -4.90403175e-01 -4.54847634e-01 2.98231602e-01 -5.93213499e-01 -3.55536431e-01 6.47742391e-01 -1.03906557e-01 -7.21286684e-02 -2.67647542e-02 4.51106250e-01 -1.20067704e+00 3.28662813e-01 4.84829068e-01 8.27102423e-01 4.23902243e-01 8.39719772e-01 3.13067496e-01 6.59641802e-01 -9.50446188e-01 1.56449988e-01 -2.77236909e-01 -6.39987469e-01 -7.13233411e-01 -1.71593577e-01 -8.29363227e-01 -3.16137731e-01 2.84628481e-01 4.53534871e-01 8.41281295e-01 -1.14712052e-01 5.52733004e-01 -1.04876947e+00 3.76743346e-01 1.06303953e-01 4.90095504e-02 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bf60f78e-5b63-4242-9774-4abee9c08af6	detie-multilingual-open-information	2206.12514	null	https://arxiv.org/abs/2206.12514v1	https://arxiv.org/pdf/2206.12514v1.pdf	DetIE: Multilingual Open Information Extraction Inspired by Object Detection	State of the art neural methods for open information extraction (OpenIE) usually extract triplets (or tuples) iteratively in an autoregressive or predicate-based manner in order not to produce duplicates. In this work, we propose a different approach to the problem that can be equally or more successful. Namely, we present a novel single-pass method for OpenIE inspired by object detection algorithms from computer vision. We use an order-agnostic loss based on bipartite matching that forces unique predictions and a Transformer-based encoder-only architecture for sequence labeling. The proposed approach is faster and shows superior or similar performance in comparison with state of the art models on standard benchmarks in terms of both quality metrics and inference time. Our model sets the new state of the art performance of 67.7% F1 on CaRB evaluated as OIE2016 while being 3.35x faster at inference than previous state of the art. We also evaluate the multilingual version of our model in the zero-shot setting for two languages and introduce a strategy for generating synthetic multilingual data to fine-tune the model for each specific language. In this setting, we show performance improvement 15% on multilingual Re-OIE2016, reaching 75% F1 for both Portuguese and Spanish languages. Code and models are available at https://github.com/sberbank-ai/DetIE.	['Sergey Nikolenko', 'Andrey Chertok', 'Mikhail Stepnov', 'Dmitriy Salikhov', 'Elena Tutubalina', 'Ilya Shenbin', 'Valentin Malykh', 'Anton Alekseev', 'Michael Vasilkovsky']	2022-06-24	null	null	null	null	['open-information-extraction', 'multilingual-nlp']	['natural-language-processing', 'natural-language-processing']	[ 1.46655291e-02 1.31589949e-01 -5.79490662e-02 -2.97714710e-01 -1.30087042e+00 -8.10910523e-01 6.20738268e-01 1.50304750e-01 -5.47638059e-01 1.09309292e+00 1.21449143e-01 -4.30949003e-01 3.92194353e-02 -8.69763076e-01 -1.28989863e+00 -2.33930409e-01 1.41110003e-01 9.36509788e-01 3.31914350e-02 -4.19395983e-01 -1.37842134e-01 2.16610208e-01 -1.68359840e+00 6.01941705e-01 1.03511882e+00 8.23975027e-01 9.54302996e-02 7.47633815e-01 -3.42911303e-01 6.29888773e-01 -5.22453785e-01 -1.08278680e+00 4.16055262e-01 -1.98200852e-01 -1.08228624e+00 -4.77634192e-01 7.06479013e-01 4.79095578e-02 -2.57789940e-01 9.66051042e-01 6.77336335e-01 -1.50517911e-01 5.26717365e-01 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2a63257b-c02a-406e-b978-935c4d5a40fc	video-processing-from-electro-optical-sensors	1611.05842	null	http://arxiv.org/abs/1611.05842v1	http://arxiv.org/pdf/1611.05842v1.pdf	Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey	We present a survey on maritime object detection and tracking approaches, which are essential for the development of a navigational system for autonomous ships. The electro-optical (EO) sensor considered here is a video camera that operates in the visible or the infrared spectra, which conventionally complement radar and sonar and have demonstrated effectiveness for situational awareness at sea has demonstrated its effectiveness over the last few years. This paper provides a comprehensive overview of various approaches of video processing for object detection and tracking in the maritime environment. We follow an approach-based taxonomy wherein the advantages and limitations of each approach are compared. The object detection system consists of the following modules: horizon detection, static background subtraction and foreground segmentation. Each of these has been studied extensively in maritime situations and has been shown to be challenging due to the presence of background motion especially due to waves and wakes. The main processes involved in object tracking include video frame registration, dynamic background subtraction, and the object tracking algorithm itself. The challenges for robust tracking arise due to camera motion, dynamic background and low contrast of tracked object, possibly due to environmental degradation. The survey also discusses multisensor approaches and commercial maritime systems that use EO sensors. The survey also highlights methods from computer vision research which hold promise to perform well in maritime EO data processing. Performance of several maritime and computer vision techniques is evaluated on newly proposed Singapore Maritime Dataset.	['D. K. Prasad', 'L. Rachmawati', 'E. Rajabaly', 'C. Quek', 'D. Rajan']	2016-11-17	null	null	null	null	['foreground-segmentation']	['computer-vision']	[ 3.66161913e-01 -8.24556589e-01 7.42927074e-01 6.46151826e-02 -3.74028474e-01 -8.18065345e-01 6.40323520e-01 -6.94044456e-02 -9.33468640e-01 4.43186849e-01 -2.30311081e-01 -1.61291972e-01 -3.30666602e-01 -3.78941745e-01 -1.42789558e-01 -1.31209397e+00 -3.07494104e-01 -1.26067251e-01 6.15674734e-01 -5.23717046e-01 2.73785949e-01 8.33844781e-01 -1.64434838e+00 -5.41569851e-02 -5.19536547e-02 9.39575970e-01 3.19441944e-01 1.34570837e+00 2.17612743e-01 5.27605116e-01 -7.65546024e-01 1.39546841e-01 8.22925329e-01 -4.23310906e-01 8.23875219e-02 3.30848336e-01 5.58430552e-01 -3.04017905e-02 -3.82438928e-01 1.23067188e+00 5.91556728e-01 6.53147936e-01 4.12638307e-01 -1.17368698e+00 2.07526073e-01 -3.20493102e-01 -4.44817811e-01 1.27155304e+00 2.64921635e-01 7.69007802e-02 6.73688352e-02 -8.02869856e-01 3.34506691e-01 8.82993340e-01 8.29709232e-01 5.15452981e-01 -4.39554304e-01 -5.39261937e-01 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-6.05490580e-02 7.21055627e-01 -8.06100070e-01 -9.12788868e-01 5.34893870e-01]	[8.797280311584473, -0.921929657459259]
f74eb3a0-4802-4e3a-aa59-8d75a1675b5c	musicface-music-driven-expressive-singing	2303.14044	null	https://arxiv.org/abs/2303.14044v1	https://arxiv.org/pdf/2303.14044v1.pdf	MusicFace: Music-driven Expressive Singing Face Synthesis	It is still an interesting and challenging problem to synthesize a vivid and realistic singing face driven by music signal. In this paper, we present a method for this task with natural motions of the lip, facial expression, head pose, and eye states. Due to the coupling of the mixed information of human voice and background music in common signals of music audio, we design a decouple-and-fuse strategy to tackle the challenge. We first decompose the input music audio into human voice stream and background music stream. Due to the implicit and complicated correlation between the two-stream input signals and the dynamics of the facial expressions, head motions and eye states, we model their relationship with an attention scheme, where the effects of the two streams are fused seamlessly. Furthermore, to improve the expressiveness of the generated results, we propose to decompose head movements generation into speed generation and direction generation, and decompose eye states generation into the short-time eye blinking generation and the long-time eye closing generation to model them separately. We also build a novel SingingFace Dataset to support the training and evaluation of this task, and to facilitate future works on this topic. Extensive experiments and user study show that our proposed method is capable of synthesizing vivid singing face, which is better than state-of-the-art methods qualitatively and quantitatively.	['Ming Zeng', 'Xiaohu Guo', 'Yiwei Ding', 'Yinglin Zheng', 'Jintai Wang', 'Hengda Li', 'Wenjin Deng', 'PengFei Liu']	2023-03-24	null	null	null	null	['face-generation']	['computer-vision']	[-9.71210003e-02 -7.73667991e-02 1.44652486e-01 -9.55934227e-02 -4.71528113e-01 -4.19896513e-01 4.82786059e-01 -8.21701825e-01 2.97439516e-01 3.38029623e-01 4.09202099e-01 3.53994846e-01 1.94952130e-01 -2.50334203e-01 -4.07594442e-01 -8.60022128e-01 2.70532578e-01 -2.47686550e-01 -3.11655831e-02 -3.36414397e-01 -1.68152347e-01 1.93871751e-01 -2.20403624e+00 3.97899121e-01 8.40874970e-01 1.06471300e+00 5.39264940e-02 6.44883335e-01 -7.57221133e-02 8.41812968e-01 -6.26464725e-01 -1.86272159e-01 -2.97469534e-02 -9.14091229e-01 -2.38818258e-01 3.05149198e-01 4.35815990e-01 -1.97952151e-01 -2.33996451e-01 8.97851408e-01 1.03876078e+00 1.99087754e-01 4.84662235e-01 -1.39364958e+00 -3.24504346e-01 3.07547659e-01 -6.83999658e-01 -2.43153915e-01 5.71589708e-01 3.06854904e-01 7.72755921e-01 -9.34857309e-01 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ba5ead7e-c506-4761-bb3a-8b970b5f3ac8	exploring-conversation-topics-in	null	null	https://doi.org/10.1016/j.chb.2022.107326	https://doi.org/10.1016/j.chb.2022.107326	Exploring conversation topics in conversational artificial intelligence–Based social mediated communities of practice	This study utilized ecologically valid social media data to identify motivations and relevant topics regarding the interaction with conversational artificial intelligence (AI) in a natural setting through investigating user conversations on Reddit, a social mediated community of practice. By applying the latent Dirichlet allocation approach, this study extracted conversation topics in six subreddit communities among users of AI–powered virtual assistants (Apple Siri, Amazon Alexa, and Google Assistant) and the corresponding smart speakers (Apple HomePod, Amazon Echo, and Google Home), and investigated changes in the conversation topics over time. Findings showed six themes of conversation topics, i.e., functional gratification, hedonic gratification, social gratification, settings, problems encountered, and connections between devices. A large volatility of the conversation topics over time was found. The results implied that members in subreddit communities share their motivations for interacting with conversational AI and collaboratively discuss the relevant issues and problem solving to learn how to practice better.	['Zhihuai Lin', 'Yu-Leung Ng']	2022-05-11	null	null	null	computers-in-human-behavior-2022-5	['topic-models']	['natural-language-processing']	[-3.59214395e-01 9.22625840e-01 -1.51492301e-02 -2.91312128e-01 -3.75650660e-03 -4.57358092e-01 8.93784642e-01 -1.25843763e-01 2.01190308e-01 2.15608791e-01 9.13972259e-01 6.05034307e-02 -2.30827495e-01 -3.21680874e-01 5.97593375e-02 -5.60080111e-01 1.06207885e-01 6.45766556e-01 -3.74369830e-01 -3.72548997e-01 4.63661790e-01 -5.73380589e-02 -1.82088625e+00 3.91494006e-01 7.60511816e-01 3.63924533e-01 2.75982589e-01 5.24282932e-01 -7.52651870e-01 1.13368309e+00 -7.93160141e-01 -4.92731452e-01 -9.57732648e-02 -3.25594991e-01 -6.44089520e-01 4.35489118e-01 -6.44508824e-02 -6.94759429e-01 -1.88522890e-01 4.28974271e-01 3.21511745e-01 1.49260953e-01 5.24510384e-01 -1.90168858e+00 -8.32348645e-01 1.18332481e+00 -5.12528658e-01 2.22422004e-01 7.80388653e-01 1.70946494e-01 7.72708416e-01 -5.39706051e-01 9.55925047e-01 1.78285182e+00 5.26305795e-01 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-8.13402057e-01 3.46064419e-01 -1.41645920e+00 -7.01428533e-01 3.38722199e-01 -8.72899964e-02 -5.77400804e-01 -7.94322550e-01 -2.81547010e-01]	[12.424490928649902, 7.819214344024658]
98b9ddf2-d8f4-4290-9818-50a6f34ba460	searching-inhibitors-for-three-important	2004.08095	null	https://arxiv.org/abs/2004.08095v3	https://arxiv.org/pdf/2004.08095v3.pdf	Searching inhibitors for three important proteins of COVID-19 through molecular docking studies	The lack of recommended drugs or vaccines to deal with the COVID-19 is the main concern of this pandemic. The approved drugs for similar health problems, drugs under clinical trials, and molecules from medicinal plants extracts are investigated randomly to deal with the COVID-19 infection. Molecular docking, one of the best approach to search therapeutically potent drugs/molecules in real time with possible hope to apply on COVID-19. In this communication, molecular docking studies of 18 ligands were carried out with the three therapeutic target proteins of SARS-CoV-2, i.e., RNA-dependent RNA polymerase (RdRp), angiotensin-converting enzyme 2 (ACE2) and spike glycoprotein (SGp). The obtained results revealed that the phytochemicals showed better dock score in compared to the drugs paracetmol and hydroxychloroquine. Combining the dock score and medicinal properties, we believe the terpenoids based phytochemicals limonin and scopadulcic acid B can be further explored for potential use against COVID-19.	['Suban K Sahoo', 'Seshu Vardhan']	2020-04-17	null	null	null	null	['molecular-docking']	['medical']	[ 2.46780217e-02 -2.78617233e-01 -9.90575179e-02 1.72099233e-01 -1.57344136e-02 -7.18026042e-01 2.08243504e-01 3.80685776e-01 -3.05126518e-01 1.43199432e+00 -6.36061803e-02 -3.46383423e-01 -3.92200202e-02 -3.84803921e-01 -1.05112679e-01 -1.07337153e+00 -5.78655839e-01 6.32387221e-01 -1.55662537e-01 -2.88709551e-01 3.56525868e-01 9.48038936e-01 -8.17305684e-01 -2.11978108e-01 1.43767643e+00 2.56490618e-01 3.15124124e-01 1.82203963e-01 -5.60701732e-03 2.37301141e-01 -5.01520574e-01 -6.35418966e-02 4.44216989e-02 -3.76306236e-01 -2.89971948e-01 -6.15719676e-01 -3.24528545e-01 -3.25887382e-01 5.37917614e-01 8.09486866e-01 4.89339501e-01 -5.17993122e-02 1.25344121e+00 -1.01063192e+00 -3.52169454e-01 -2.43000716e-01 -6.56326175e-01 1.31794572e-01 4.00711566e-01 1.00640394e-01 2.60511726e-01 -1.18082738e+00 7.45155394e-01 1.09518194e+00 6.18158817e-01 6.35654867e-01 -8.47666442e-01 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c2e8a2e9-109d-46ab-89df-3f0ba026afff	rst-discourse-parsing-with-second-stage-edu	null	null	https://aclanthology.org/2022.acl-long.294	https://aclanthology.org/2022.acl-long.294.pdf	RST Discourse Parsing with Second-Stage EDU-Level Pre-training	Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing.Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i.e. element discourse unit (EDU).To this end, we propose a second-stage EDU-level pre-training approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models.Concretely, the two tasks are (1) next EDU prediction (NEP) and (2) discourse marker prediction (DMP).We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation.Experimental results on a benckmark dataset show that our method is highly effective,leading a 2.1-point improvement in F1-score.All codes and pre-trained models will be released publicly to facilitate future studies.	['Min Zhang', 'Guohong Fu', 'Meishan Zhang', 'Nan Yu']	null	null	null	null	acl-2022-5	['discourse-parsing', 'discourse-marker-prediction']	['natural-language-processing', 'natural-language-processing']	[ 7.50392497e-01 7.26070940e-01 -5.66259086e-01 -3.58651996e-01 -1.18389022e+00 -4.19278026e-01 7.62045920e-01 3.71986896e-01 -4.49113488e-01 7.06077635e-01 6.82698607e-01 -8.44004571e-01 5.30074239e-01 -8.38843167e-01 -9.51563120e-01 -3.01263630e-01 -2.25521927e-03 2.71322727e-01 4.19241130e-01 -3.83882374e-01 5.37539184e-01 -6.93458617e-02 -1.08210230e+00 8.59917939e-01 1.08131528e+00 4.56481487e-01 4.57253218e-01 1.00575578e+00 -5.33676505e-01 1.38527417e+00 -7.62235880e-01 -3.36900830e-01 -4.67629790e-01 -6.38369799e-01 -1.27226710e+00 -1.74538657e-01 8.81758109e-02 -2.50440687e-01 -2.47030675e-01 8.87334347e-01 2.02800155e-01 3.76245171e-01 5.42633951e-01 -4.50994134e-01 -8.76653671e-01 1.18112743e+00 -6.60461962e-01 2.99929053e-01 3.15012485e-01 4.32770625e-02 1.26787305e+00 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7f1a8270-fe33-4c0f-b084-e0b8db9a33f8	underwater-fish-detection-with-weak-multi	1905.10708	null	https://arxiv.org/abs/1905.10708v2	https://arxiv.org/pdf/1905.10708v2.pdf	Underwater Fish Detection with Weak Multi-Domain Supervision	Given a sufficiently large training dataset, it is relatively easy to train a modern convolution neural network (CNN) as a required image classifier. However, for the task of fish classification and/or fish detection, if a CNN was trained to detect or classify particular fish species in particular background habitats, the same CNN exhibits much lower accuracy when applied to new/unseen fish species and/or fish habitats. Therefore, in practice, the CNN needs to be continuously fine-tuned to improve its classification accuracy to handle new project-specific fish species or habitats. In this work we present a labelling-efficient method of training a CNN-based fish-detector (the Xception CNN was used as the base) on relatively small numbers (4,000) of project-domain underwater fish/no-fish images from 20 different habitats. Additionally, 17,000 of known negative (that is, missing fish) general-domain (VOC2012) above-water images were used. Two publicly available fish-domain datasets supplied additional 27,000 of above-water and underwater positive/fish images. By using this multi-domain collection of images, the trained Xception-based binary (fish/not-fish) classifier achieved 0.17% false-positives and 0.61% false-negatives on the project's 20,000 negative and 16,000 positive holdout test images, respectively. The area under the ROC curve (AUC) was 99.94%.	['Mangalam Sankupellay', 'Dmitry A. Konovalov', 'Simone Marini', 'Michael Bradley', 'Marcus Sheaves', 'Alzayat Saleh']	2019-05-26	null	null	null	null	['fish-detection']	['computer-vision']	[ 1.12310223e-01 -1.53500006e-01 5.13283372e-01 -3.84208411e-01 -3.58501226e-01 -8.06229472e-01 2.47444227e-01 2.12037936e-01 -1.14263773e+00 5.07155597e-01 -2.81000584e-01 -1.18509248e-01 2.32515797e-01 -1.14252508e+00 -9.53866839e-01 -8.79460871e-01 -5.08329749e-01 1.41284779e-01 4.59541053e-01 -6.19811900e-02 2.17343032e-01 7.32730389e-01 -1.98332703e+00 1.70194060e-01 2.85150230e-01 8.59441161e-01 4.68457490e-01 1.06637895e+00 1.39269084e-01 1.75735086e-01 -7.86124766e-01 -4.63796973e-01 6.24578238e-01 -2.07107201e-01 -3.74072462e-01 -2.27541059e-01 9.03380096e-01 -6.26516819e-01 -1.66216671e-01 1.29326534e+00 6.62693918e-01 -8.01629126e-02 4.86687332e-01 -9.40036416e-01 -3.36651027e-01 7.20485330e-01 -3.38524312e-01 5.49544156e-01 -2.41837546e-01 9.35322344e-01 5.66013277e-01 -8.79182220e-01 1.48415104e-01 1.16272128e+00 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0450c395-c3f2-4f86-afeb-da8730b1d4e6	6d-camera-relocalization-in-visually	2207.06333	null	https://arxiv.org/abs/2207.06333v1	https://arxiv.org/pdf/2207.06333v1.pdf	6D Camera Relocalization in Visually Ambiguous Extreme Environments	We propose a novel method to reliably estimate the pose of a camera given a sequence of images acquired in extreme environments such as deep seas or extraterrestrial terrains. Data acquired under these challenging conditions are corrupted by textureless surfaces, image degradation, and presence of repetitive and highly ambiguous structures. When naively deployed, the state-of-the-art methods can fail in those scenarios as confirmed by our empirical analysis. In this paper, we attempt to make camera relocalization work in these extreme situations. To this end, we propose: (i) a hierarchical localization system, where we leverage temporal information and (ii) a novel environment-aware image enhancement method to boost the robustness and accuracy. Our extensive experimental results demonstrate superior performance in favor of our method under two extreme settings: localizing an autonomous underwater vehicle and localizing a planetary rover in a Mars-like desert. In addition, our method achieves comparable performance with state-of-the-art methods on the indoor benchmark (7-Scenes dataset) using only 20% training data.	['Leonidas J. Guibas', 'Yueqi Duan', 'Yanchao Yang', 'Fei Xia', 'Tolga Birdal', 'Yang Zheng']	2022-07-13	null	null	null	null	['camera-relocalization']	['computer-vision']	[ 4.46940333e-01 -1.73610985e-01 4.99093026e-01 -4.08373684e-01 -7.40089178e-01 -7.81670213e-01 5.17939925e-01 -3.60814393e-01 -7.63767540e-01 6.00912452e-01 -1.84312701e-01 -1.20483570e-01 -7.00888038e-02 -5.03312886e-01 -9.79195356e-01 -7.74096787e-01 -4.18288410e-01 2.48404890e-01 5.23600698e-01 -3.97445112e-01 4.01161134e-01 3.35242450e-01 -1.72436655e+00 -3.27083200e-01 1.08516026e+00 8.37520659e-01 6.09749198e-01 7.01533675e-01 5.13460755e-01 5.03394961e-01 -2.07731947e-01 -7.98483044e-02 5.65061510e-01 -4.64001596e-02 -4.02476013e-01 3.98070395e-01 9.04371321e-01 -5.49823344e-01 -1.80885673e-01 1.26951575e+00 4.06291932e-01 1.68745384e-01 3.38893920e-01 -6.82651281e-01 -9.03334375e-03 -5.34054525e-02 -7.38882065e-01 1.21352814e-01 5.13340235e-01 2.89009865e-02 5.80891848e-01 -1.05211711e+00 6.21252179e-01 1.03109682e+00 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b114d385-1026-4d16-8439-40049e169fb0	taylor-lagrange-neural-ordinary-differential	2201.05715	null	https://arxiv.org/abs/2201.05715v2	https://arxiv.org/pdf/2201.05715v2.pdf	Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs	Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown continuous-time dynamical systems from data. However, every forward evaluation of a NODE requires numerical integration of the neural network used to capture the system dynamics, making their training prohibitively expensive. Existing works rely on off-the-shelf adaptive step-size numerical integration schemes, which often require an excessive number of evaluations of the underlying dynamics network to obtain sufficient accuracy for training. By contrast, we accelerate the evaluation and the training of NODEs by proposing a data-driven approach to their numerical integration. The proposed Taylor-Lagrange NODEs (TL-NODEs) use a fixed-order Taylor expansion for numerical integration, while also learning to estimate the expansion's approximation error. As a result, the proposed approach achieves the same accuracy as adaptive step-size schemes while employing only low-order Taylor expansions, thus greatly reducing the computational cost necessary to integrate the NODE. A suite of numerical experiments, including modeling dynamical systems, image classification, and density estimation, demonstrate that TL-NODEs can be trained more than an order of magnitude faster than state-of-the-art approaches, without any loss in performance.	['Ufuk Topcu', 'Sylvie Putot', 'Eric Goubault', 'Cyrus Neary', 'Franck Djeumou']	2022-01-14	null	null	null	null	['numerical-integration']	['miscellaneous']	[-4.26130623e-01 -1.14732012e-01 7.15039903e-03 -1.75756980e-02 -3.62552792e-01 -2.39085779e-01 3.14518511e-01 1.05524950e-01 -6.68098569e-01 8.65820885e-01 -8.47095609e-01 -5.34877539e-01 -4.93782980e-04 -6.60357237e-01 -7.18493998e-01 -1.01775181e+00 -3.07462841e-01 5.97742438e-01 1.15423612e-01 -2.07593262e-01 -8.75943899e-02 6.18850827e-01 -1.27105129e+00 -6.69105887e-01 9.48516726e-01 1.54343534e+00 -2.23018065e-01 9.07951415e-01 -7.28853866e-02 9.81412768e-01 -3.74519169e-01 2.17927084e-03 2.30635807e-01 -4.43662345e-01 -3.31049591e-01 -1.88260093e-01 2.33040869e-01 -6.77384615e-01 -5.79282939e-01 1.13092661e+00 4.50788975e-01 4.51152354e-01 9.56994116e-01 -1.10969067e+00 -7.28207648e-01 1.53934836e-01 -2.89931715e-01 3.17365199e-01 -3.23718846e-01 1.86462894e-01 4.98957515e-01 -9.07497883e-01 2.24624991e-01 9.04688120e-01 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0f2b604e-9331-42b7-b31d-11a6585bae93	a-privacy-preserving-energy-management-system	2207.04359	null	https://arxiv.org/abs/2207.04359v1	https://arxiv.org/pdf/2207.04359v1.pdf	A Privacy-Preserving Energy Management System for Cooperative Multi-Microgrid Networks	This paper presents an Energy Management System (EMS) that considers power exchanges between a set of interconnected microgrids (MGs) and the main grid, in the context of Multi-MG (MMG) systems. The model is first formulated as a centralized optimization problem, which is then decomposed into subproblems corresponding to each MG, using Lagrangian relaxation, and solved through a distributed approach using a subgradient method. The proposed model determines the power exchanges minimizing the operation cost of each MG, considering grid constraints and preserving the privacy of each MG by not revealing their generation cost and demand information. The distributed approach is validated with respect to the centralized problem, and various case studies are presented to demonstrate the performance of the proposed approach, comparing the costs of the MGs operating individually and cooperatively. The results show that all MGs in the MMG system improve their cost as consequence of the power exchanges, thus demonstrating the advantages of interconnecting MGs.	['Claudio A. Cañizares', 'Mehrdad Pirnia', 'Carlos Ceja-Espinosa']	2022-07-10	null	null	null	null	['energy-management']	['time-series']	[-5.71504056e-01 3.62773448e-01 1.99624822e-01 1.97398379e-01 -3.02023739e-01 -9.70500648e-01 2.76772767e-01 3.11595827e-01 -1.41571328e-01 1.29372907e+00 -1.85296178e-01 1.83335856e-01 -3.88581604e-01 -8.80049467e-01 -2.94991415e-02 -1.47051013e+00 -4.90292698e-01 4.14047390e-01 -4.79440600e-01 8.80892649e-02 5.04273809e-02 4.82610375e-01 -9.47240353e-01 -6.28768027e-01 9.88197148e-01 1.05061197e+00 2.47222111e-01 -6.67888299e-03 8.49778831e-01 3.38030547e-01 -1.18459070e+00 -3.30073908e-02 7.05154240e-01 -3.03080946e-01 -4.90865737e-01 6.61724269e-01 -8.26736987e-01 -6.03219569e-01 2.42069364e-04 1.51370621e+00 4.67831016e-01 3.05725634e-01 3.06412101e-01 -2.13462925e+00 -4.09524441e-01 5.87981880e-01 -7.21560299e-01 -2.67304927e-01 8.91776308e-02 -4.83116619e-02 1.26227701e+00 -1.00902438e-01 4.68912363e-01 6.29890144e-01 -1.99854612e-01 -1.33288667e-01 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-4.08244520e-01 -1.83430851e+00 -1.90377474e-01 -1.10606037e-01 1.78316310e-02 -8.63552630e-01 -3.03937018e-01]	[5.702943801879883, 2.502811908721924]
770a4f75-8081-4a48-a809-39576a79d621	cross-modal-coherence-modeling-for-caption	null	null	https://aclanthology.org/2020.acl-main.583	https://aclanthology.org/2020.acl-main.583.pdf	Cross-modal Coherence Modeling for Caption Generation	We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning. Using an annotation protocol specifically devised for capturing image{--}caption coherence relations, we annotate 10,000 instances from publicly-available image{--}caption pairs. We introduce a new task for learning inferences in imagery and text, coherence relation prediction, and show that these coherence annotations can be exploited to learn relation classifiers as an intermediary step, and also train coherence-aware, controllable image captioning models. The results show a dramatic improvement in the consistency and quality of the generated captions with respect to information needs specified via coherence relations.	['Matthew Stone', 'Malihe Alikhani', 'Radu Soricut', 'Piyush Sharma', 'Shengjie Li']	2020-07-01	null	null	null	acl-2020-6	['controllable-image-captioning']	['computer-vision']	[ 7.01004803e-01 1.02969158e+00 -3.79934698e-01 -7.99087286e-01 -1.06282115e+00 -3.85398597e-01 1.00037658e+00 2.63480365e-01 9.30273458e-02 8.36783469e-01 9.98936296e-01 1.27370164e-01 -1.21258803e-01 -4.04807985e-01 -1.00072491e+00 -2.65768677e-01 -1.96313828e-01 7.63684273e-01 -1.60932571e-01 -9.90194604e-02 1.00251110e-02 -7.83167183e-02 -1.50513470e+00 9.11242664e-01 6.83079123e-01 8.77788901e-01 2.81867146e-01 7.75587857e-01 2.03155994e-01 1.59937930e+00 -3.60761076e-01 -5.72894812e-01 -3.59788716e-01 -7.20583558e-01 -1.64962435e+00 5.51592290e-01 6.85542762e-01 -8.83245990e-02 -4.93726969e-01 9.02351737e-01 -5.63524887e-02 -3.01760733e-01 6.09937131e-01 -1.42651200e+00 -9.57223952e-01 1.00804806e+00 -1.57218307e-01 2.09713846e-01 8.43901813e-01 2.31189832e-01 1.44698751e+00 -2.39043713e-01 1.13358068e+00 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038b6275-2fda-42c2-9f8c-678754f9be32	deciding-monotone-duality-and-identifying	1212.1881	null	http://arxiv.org/abs/1212.1881v3	http://arxiv.org/pdf/1212.1881v3.pdf	Deciding Monotone Duality and Identifying Frequent Itemsets in Quadratic Logspace	The monotone duality problem is defined as follows: Given two monotone formulas f and g in iredundant DNF, decide whether f and g are dual. This problem is the same as duality testing for hypergraphs, that is, checking whether a hypergraph H consists of precisely all minimal transversals of a simple hypergraph G. By exploiting a recent problem-decomposition method by Boros and Makino (ICALP 2009), we show that duality testing for hypergraphs, and thus for monotone DNFs, is feasible in DSPACE[log^2 n], i.e., in quadratic logspace. As the monotone duality problem is equivalent to a number of problems in the areas of databases, data mining, and knowledge discovery, the results presented here yield new complexity results for those problems, too. For example, it follows from our results that whenever for a Boolean-valued relation (whose attributes represent items), a number of maximal frequent itemsets and a number of minimal infrequent itemsets are known, then it can be decided in quadratic logspace whether there exist additional frequent or infrequent itemsets.	['Georg Gottlob']	2012-12-09	null	null	null	null	['problem-decomposition']	['miscellaneous']	[ 2.57532120e-01 6.08054996e-01 -5.54841220e-01 -2.06802368e-01 -3.15244853e-01 -8.89541805e-01 -1.20527476e-01 5.96103370e-01 1.17921650e-01 1.13743401e+00 -1.05548792e-01 -6.02077842e-01 -7.12639570e-01 -1.37607050e+00 -1.19817984e+00 -5.90134382e-01 -6.63442850e-01 1.07505846e+00 1.67423666e-01 -2.23752901e-01 1.21512070e-01 3.31286490e-01 -1.82131410e+00 3.20068955e-01 5.81685543e-01 1.16737020e+00 -2.80362815e-01 7.11455464e-01 6.26060963e-02 4.62286592e-01 -4.04562652e-02 -4.81981844e-01 5.81863582e-01 -5.45931876e-01 -1.19807351e+00 4.51973200e-01 4.85399961e-01 6.19065426e-02 -4.31728274e-01 1.34187305e+00 -1.03077084e-01 -1.58073947e-01 1.93247750e-01 -1.68754423e+00 -4.61943626e-01 1.11639106e+00 -8.43837917e-01 3.02389324e-01 6.55511737e-01 -2.22522348e-01 1.54107392e+00 -4.31229860e-01 4.67719406e-01 1.09466588e+00 2.64939964e-01 9.38589126e-02 -1.48639750e+00 -6.25563204e-01 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-1.60091415e-01 1.65083632e-01 -3.57766241e-01 -2.96663642e-01 -3.20633739e-01]	[6.833073139190674, 5.102703094482422]
99cfb626-3f15-4143-bea9-c92efd5371e1	neighborhood-preserved-sparse-representation	1601.07336	null	http://arxiv.org/abs/1601.07336v1	http://arxiv.org/pdf/1601.07336v1.pdf	Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices	Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years. However, the existing SRC type methods apply only to vector data in Euclidean space. As such, there is still no satisfactory approach to conduct classification task for symmetric positive definite (SPD) matrices which is very useful in computer vision. To address this problem, in this paper, a neighborhood preserved kernel SRC method is proposed on SPD manifolds. Specifically, by embedding the SPD matrices into a Reproducing Kernel Hilbert Space (RKHS), the proposed method can perform classification on SPD manifolds through an appropriate Log-Euclidean kernel. Through exploiting the geodesic distance between SPD matrices, our method can effectively characterize the intrinsic local Riemannian geometry within data so as to well unravel the underlying sub-manifold structure. Despite its simplicity, experimental results on several famous database demonstrate that the proposed method achieves better classification results than the state-of-the-art approaches.	['Ming Yin', 'Shengli Xie', 'Yun Zhang', 'Yi Guo', 'Junbin Gao']	2016-01-27	null	null	null	null	['sparse-representation-based-classification']	['computer-vision']	[-1.43530011e-01 -3.46069306e-01 -1.34359151e-01 -1.50807589e-01 -5.65548956e-01 -3.24171931e-01 5.27353466e-01 -2.58477002e-01 -8.28580931e-02 3.60446960e-01 9.03062448e-02 -1.65090695e-01 -3.74555498e-01 -5.34415245e-01 -2.07610995e-01 -9.62087750e-01 -4.55600694e-02 -3.05745065e-01 1.32945642e-01 -9.48332772e-02 3.43666613e-01 6.11373246e-01 -1.30926144e+00 -3.21286649e-01 8.77170861e-01 7.61363864e-01 2.03481913e-01 -1.12138819e-02 1.26178682e-01 5.42949855e-01 -1.09900832e-01 -1.19251154e-01 2.34016284e-01 -4.64420795e-01 -5.90807974e-01 2.89304614e-01 1.36630476e-01 1.50111318e-01 -7.51777887e-01 1.15629363e+00 1.36218831e-01 3.52819413e-01 9.30732250e-01 -1.10744298e+00 -9.67541873e-01 9.52037871e-02 -6.07597411e-01 2.30577588e-01 2.48263896e-01 -2.67441064e-01 1.09880126e+00 -1.32336962e+00 2.99114019e-01 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fc46d7c9-2fc1-4a7f-9d50-8f317218d012	consistent-video-style-transfer-via	null	null	https://doi.org/10.1109/TIP.2020.3024018	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9204808	Consistent Video Style Transfer via Relaxation and Regularization	In recent years, neural style transfer has attracted more and more attention, especially for image style transfer. However, temporally consistent style transfer for videos is still a challenging problem. Existing methods, either relying on a significant amount of video data with optical flows or using single-frame regularizers, fail to handle strong motions or complex variations, therefore have limited performance on real videos. In this article, we address the problem by jointly considering the intrinsic properties of stylization and temporal consistency. We first identify the cause of the conflict between style transfer and temporal consistency, and propose to reconcile this contradiction by relaxing the objective function, so as to make the stylization loss term more robust to motions. Through relaxation, style transfer is more robust to inter-frame variation without degrading the subjective effect. Then, we provide a novel formulation and understanding of temporal consistency. Based on the formulation, we analyze the drawbacks of existing training strategies and derive a new regularization. We show by experiments that the proposed regularization can better balance the spatial and temporal performance. Based on relaxation and regularization, we design a zero-shot video style transfer framework. Moreover, for better feature migration, we introduce a new module to dynamically adjust inter-channel distributions. Quantitative and qualitative results demonstrate the superiority of our method over state-of-the-art style transfer methods.	['Jiaying Liu', 'Jizheng Xu', 'Shuai Yang', 'Wenjing Wang']	2020-09-23	null	null	null	null	['video-style-transfer']	['computer-vision']	[ 1.03014521e-01 -4.70950693e-01 -3.02457154e-01 -1.86083019e-01 -2.56468534e-01 -4.36781585e-01 4.04685408e-01 -3.88902068e-01 -2.71778524e-01 8.15865278e-01 1.53124347e-01 5.77820539e-02 -1.89238220e-01 -6.46536052e-01 -6.98670387e-01 -7.33741224e-01 3.58538419e-01 -2.73512155e-01 4.70884681e-01 -2.05389515e-01 1.76308081e-01 1.87130347e-01 -1.11071754e+00 -2.20023096e-02 1.17160356e+00 9.78059113e-01 2.90220171e-01 1.37275711e-01 -2.17064336e-01 5.67763984e-01 -3.77601922e-01 -2.19046682e-01 1.86577961e-01 -8.99124205e-01 -6.82210684e-01 3.90098244e-01 2.87861854e-01 -3.30570579e-01 -4.42458928e-01 1.12148666e+00 4.69612777e-01 1.98921353e-01 4.72845644e-01 -1.26800156e+00 -8.34868014e-01 1.22172527e-01 -7.53328383e-01 2.20448405e-01 1.57966644e-01 2.57652134e-01 7.50021160e-01 -7.06013680e-01 6.58052146e-01 1.29155934e+00 5.34295678e-01 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7a8a59a7-7f8a-4aed-9ea1-992d6b9c3d44	managed-geo-distributed-feature-store	2305.20077	null	https://arxiv.org/abs/2305.20077v1	https://arxiv.org/pdf/2305.20077v1.pdf	Managed Geo-Distributed Feature Store: Architecture and System Design	Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process. They are building feature engineering pipelines as part of MLOps life cycle to transform data from various data sources and materialize the same for future consumption. Without feature stores, different teams across various business groups would maintain the above process independently, which can lead to conflicting and duplicated features in the system. Data scientists find it hard to search for and reuse existing features and it is painful to maintain version control. Furthermore, feature correctness violations related to online (inferencing) - offline (training) skews and data leakage are common. Although the machine learning community has extensively discussed the need for feature stores and their purpose, this paper aims to capture the core architectural components that make up a managed feature store and to share the design learning in building such a system.	['Vivienne Tang', 'Shail Paragbhai Shah', 'Sethu Raman', 'Runhan Li', 'Qianjun Xu', 'Mickey Zhang', 'Feng Pan', 'Bhala Ranganathan', 'Anya Li']	2023-05-31	null	null	null	null	['feature-engineering']	['methodology']	[-2.83042043e-01 -2.12573987e-02 -2.81976014e-01 -6.94480419e-01 -4.47211772e-01 -8.45581770e-01 6.04572967e-02 4.69835192e-01 1.93713680e-01 2.70803660e-01 -2.55799025e-01 -5.38608670e-01 -1.40891373e-01 -7.01415956e-01 -4.39170361e-01 -1.16664749e-02 5.03148437e-02 2.93938994e-01 7.28940070e-02 -3.00113738e-01 6.72949016e-01 5.29539883e-01 -1.82312727e+00 5.43447971e-01 4.56784844e-01 9.33875203e-01 -1.86719149e-01 3.56080234e-01 -3.39751065e-01 6.26153767e-01 -7.82355249e-01 -6.15056038e-01 7.64027953e-01 -1.26159370e-01 -8.81760180e-01 2.37877235e-01 3.91290635e-01 -2.94230551e-01 1.75548136e-01 7.51892984e-01 8.43981802e-02 -4.89243329e-01 3.86084355e-02 -2.07964087e+00 -4.38041329e-01 5.56462884e-01 -6.20052397e-01 -1.52326711e-02 2.45712057e-01 3.24140936e-01 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1375ac5c-9fc1-47c8-aeb7-2f6b185c1dda	beyond-disentangled-representations-an	2004.05085	null	https://arxiv.org/abs/2004.05085v2	https://arxiv.org/pdf/2004.05085v2.pdf	LIAAD: Lightweight Attentive Angular Distillation for Large-scale Age-Invariant Face Recognition	Disentangled representations have been commonly adopted to Age-invariant Face Recognition (AiFR) tasks. However, these methods have reached some limitations with (1) the requirement of large-scale face recognition (FR) training data with age labels, which is limited in practice; (2) heavy deep network architectures for high performance; and (3) their evaluations are usually taken place on age-related face databases while neglecting the standard large-scale FR databases to guarantee robustness. This work presents a novel Lightweight Attentive Angular Distillation (LIAAD) approach to Large-scale Lightweight AiFR that overcomes these limitations. Given two high-performance heavy networks as teachers with different specialized knowledge, LIAAD introduces a learning paradigm to efficiently distill the age-invariant attentive and angular knowledge from those teachers to a lightweight student network making it more powerful with higher FR accuracy and robust against age factor. Consequently, LIAAD approach is able to take the advantages of both FR datasets with and without age labels to train an AiFR model. Far apart from prior distillation methods mainly focusing on accuracy and compression ratios in closed-set problems, our LIAAD aims to solve the open-set problem, i.e. large-scale face recognition. Evaluations on LFW, IJB-B and IJB-C Janus, AgeDB and MegaFace-FGNet with one million distractors have demonstrated the efficiency of the proposed approach on light-weight structure. This work also presents a new longitudinal face aging (LogiFace) database \footnote{This database will be made available} for further studies in age-related facial problems in future.	['Ngan Le', 'Thanh-Dat Truong', 'Kha Gia Quach', 'Tien D. Bui', 'Khoa Luu', 'Chi Nhan Duong']	2020-04-09	null	null	null	null	['age-invariant-face-recognition']	['computer-vision']	[ 2.41041072e-02 2.92199373e-01 -5.63708544e-02 -5.77466846e-01 -5.03952563e-01 4.60208990e-02 3.15741599e-01 -5.40878773e-01 -4.40707356e-01 9.15445685e-01 -4.82382402e-02 -5.60729317e-02 -5.34649670e-01 -7.01389432e-01 -5.61319292e-01 -8.44699502e-01 -4.05925512e-01 5.09932756e-01 -4.39711243e-01 -2.27263540e-01 -1.96731344e-01 9.26465452e-01 -2.13777709e+00 5.26041575e-02 9.07542527e-01 1.36138535e+00 -6.46477863e-02 3.17834646e-01 3.14399213e-01 4.68242347e-01 -4.75696951e-01 -8.44404221e-01 4.34920162e-01 -1.01211399e-01 -6.53692603e-01 -2.60016531e-01 1.28981745e+00 -6.38381660e-01 -4.12984252e-01 6.89391434e-01 1.09987032e+00 1.30581707e-01 5.72393298e-01 -1.57575524e+00 -8.68759871e-01 5.59234440e-01 -8.94952655e-01 2.36607939e-01 2.26404563e-01 -7.24454075e-02 5.99805772e-01 -1.09085965e+00 2.41962269e-01 1.68239427e+00 7.97229350e-01 1.10434842e+00 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098f4a23-6065-498c-a72a-bdec34f0dbd2	an-empirical-study-of-uniform-architecture	2302.04112	null	https://arxiv.org/abs/2302.04112v1	https://arxiv.org/pdf/2302.04112v1.pdf	An Empirical Study of Uniform-Architecture Knowledge Distillation in Document Ranking	Although BERT-based ranking models have been commonly used in commercial search engines, they are usually time-consuming for online ranking tasks. Knowledge distillation, which aims at learning a smaller model with comparable performance to a larger model, is a common strategy for reducing the online inference latency. In this paper, we investigate the effect of different loss functions for uniform-architecture distillation of BERT-based ranking models. Here "uniform-architecture" denotes that both teacher and student models are in cross-encoder architecture, while the student models include small-scaled pre-trained language models. Our experimental results reveal that the optimal distillation configuration for ranking tasks is much different than general natural language processing tasks. Specifically, when the student models are in cross-encoder architecture, a pairwise loss of hard labels is critical for training student models, whereas the distillation objectives of intermediate Transformer layers may hurt performance. These findings emphasize the necessity of carefully designing a distillation strategy (for cross-encoder student models) tailored for document ranking with pairwise training samples.	['Yutao Zhu', 'Jie Liu', 'Xiongfeng Zheng', 'Xiyuan Liu', 'Xubo Qin']	2023-02-08	null	null	null	null	['document-ranking']	['natural-language-processing']	[-1.05252437e-01 5.22992648e-02 -2.11507604e-01 -7.58400261e-01 -1.26304924e+00 -6.38970494e-01 6.17315829e-01 2.39789516e-01 -5.94158053e-01 6.91756129e-01 9.86141041e-02 -5.04681170e-01 -1.42499536e-01 -6.36722744e-01 -8.78246367e-01 -5.01249552e-01 -1.93757817e-01 9.63823259e-01 2.40667969e-01 -3.08454841e-01 -8.66001695e-02 2.72379994e-01 -1.36734176e+00 3.44607711e-01 1.04288173e+00 1.06956422e+00 3.90297711e-01 7.73174942e-01 -9.76531953e-02 9.89113092e-01 -7.68365741e-01 -1.09688449e+00 2.87093014e-01 -1.96850404e-01 -9.96129036e-01 -7.70250976e-01 8.55353653e-01 -7.07135499e-01 -4.18212861e-01 1.03700554e+00 6.60060287e-01 2.17872992e-01 7.05168128e-01 -9.25343692e-01 -6.72817469e-01 1.24412251e+00 -2.48881578e-01 1.23814091e-01 -6.36842772e-02 -3.56390804e-01 1.64594138e+00 -7.30696201e-01 1.86066821e-01 1.26605284e+00 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92ead198-2189-491a-b5d0-3dc43a817d12	robust-representation-learning-with-feedback	2101.12463	null	https://arxiv.org/abs/2101.12463v3	https://arxiv.org/pdf/2101.12463v3.pdf	Robust Representation Learning with Feedback for Single Image Deraining	A deraining network can be interpreted as a conditional generator that aims at removing rain streaks from image. Most existing image deraining methods ignore model errors caused by uncertainty that reduces embedding quality. Unlike existing image deraining methods that embed low-quality features into the model directly, we replace low-quality features by latent high-quality features. The spirit of closed-loop feedback in the automatic control field is borrowed to obtain latent high-quality features. A new method for error detection and feature compensation is proposed to address model errors. Extensive experiments on benchmark datasets as well as specific real datasets demonstrate that the proposed method outperforms recent state-of-the-art methods. Code is available at: \\ https://github.com/LI-Hao-SJTU/DerainRLNet	['Hao Li', 'Chenghao Chen']	2021-01-29	null	http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.pdf	cvpr-2021-1	['single-image-deraining']	['computer-vision']	[ 2.39493728e-01 1.93032682e-01 1.81809347e-02 -5.10842204e-01 -8.16015422e-01 -9.18436423e-02 3.37666869e-01 -5.53198338e-01 -1.49832562e-01 8.86730850e-01 9.94225442e-02 -9.79373679e-02 2.18823358e-01 -7.55340397e-01 -1.03007770e+00 -9.37520087e-01 4.82832752e-02 -3.65642160e-01 -1.40041098e-01 7.81818181e-02 5.11914976e-02 1.85345337e-01 -1.38151896e+00 5.36470748e-02 1.27507412e+00 9.22919273e-01 3.86998534e-01 8.76603365e-01 5.87873816e-01 7.53500283e-01 -4.51024503e-01 3.10716778e-02 6.00697219e-01 -6.04060590e-01 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9bbe4623-f73c-4806-8ad1-7dc419d70cd8	mme-a-comprehensive-evaluation-benchmark-for	2306.13394	null	https://arxiv.org/abs/2306.13394v2	https://arxiv.org/pdf/2306.13394v2.pdf	MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models	Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image. However, it is difficult for these case studies to fully reflect the performance of MLLM, lacking a comprehensive evaluation. In this paper, we fill in this blank, presenting the first MLLM Evaluation benchmark MME. It measures both perception and cognition abilities on a total of 14 subtasks. In order to avoid data leakage that may arise from direct use of public datasets for evaluation, the annotations of instruction-answer pairs are all manually designed. The concise instruction design allows us to fairly compare MLLMs, instead of struggling in prompt engineering. Besides, with such an instruction, we can also easily carry out quantitative statistics. A total of 12 advanced MLLMs are comprehensively evaluated on our MME, which not only suggests that existing MLLMs still have a large room for improvement, but also reveals the potential directions for the subsequent model optimization.	['Rongrong Ji', 'Xing Sun', 'Ke Li', 'Xiawu Zheng', 'Jinrui Yang', 'Wei Lin', 'Zhenyu Qiu', 'Xu Lin', 'Mengdan Zhang', 'Yulei Qin', 'Yunhang Shen', 'Peixian Chen', 'Chaoyou Fu']	2023-06-23	null	null	null	null	['benchmarking', 'prompt-engineering', 'benchmarking']	['miscellaneous', 'natural-language-processing', 'robots']	[ 6.59872219e-02 8.27266648e-02 1.05147930e-02 -1.98181301e-01 -1.15366936e+00 -6.53250337e-01 4.91585433e-01 4.22983617e-01 -5.33409417e-01 4.86138701e-01 3.17604899e-01 -4.36635554e-01 -4.80267704e-02 -4.23336595e-01 -8.03521752e-01 -3.28027576e-01 1.82467178e-01 2.78790116e-01 8.29065517e-02 -4.48723823e-01 4.88040477e-01 1.37598980e-02 -1.60776091e+00 6.10872805e-01 1.02571452e+00 6.72429740e-01 4.95563895e-01 8.11515331e-01 -1.70333758e-01 1.35115874e+00 -9.65365171e-01 -6.76485121e-01 -2.92561322e-01 -2.73197353e-01 -8.82826746e-01 -2.23844588e-01 8.15535009e-01 -3.31202537e-01 -4.63432372e-02 8.73621047e-01 6.46246910e-01 2.75600404e-01 4.00696307e-01 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8ed6cd28-868b-4033-8be8-70387b86e2d8	on-the-effectiveness-of-parameter-efficient	2211.15583	null	https://arxiv.org/abs/2211.15583v1	https://arxiv.org/pdf/2211.15583v1.pdf	On the Effectiveness of Parameter-Efficient Fine-Tuning	Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always yields an entirely new model for each task. Currently, many research works propose to only fine-tune a small portion of the parameters while keeping most of the parameters shared across different tasks. These methods achieve surprisingly good performance and are shown to be more stable than their corresponding fully fine-tuned counterparts. However, such kind of methods is still not well understood. Some natural questions arise: How does the parameter sparsity lead to promising performance? Why is the model more stable than the fully fine-tuned models? How to choose the tunable parameters? In this paper, we first categorize the existing methods into random approaches, rule-based approaches, and projection-based approaches based on how they choose which parameters to tune. Then, we show that all of the methods are actually sparse fine-tuned models and conduct a novel theoretical analysis of them. We indicate that the sparsity is actually imposing a regularization on the original model by controlling the upper bound of the stability. Such stability leads to better generalization capability which has been empirically observed in a lot of recent research works. Despite the effectiveness of sparsity grounded by our theory, it still remains an open problem of how to choose the tunable parameters. To better choose the tunable parameters, we propose a novel Second-order Approximation Method (SAM) which approximates the original problem with an analytically solvable optimization function. The tunable parameters are determined by directly optimizing the approximation function. The experimental results show that our proposed SAM model outperforms many strong baseline models and it also verifies our theoretical analysis.	['Nigel Collier', 'Lidong Bing', 'Wai Lam', 'Anthony Man-Cho So', 'Haoran Yang', 'Zihao Fu']	2022-11-28	null	null	null	null	['natural-questions']	['miscellaneous']	[ 6.22029556e-03 2.26319749e-02 -4.62898582e-01 -4.92197186e-01 -6.86601758e-01 -5.90713859e-01 5.72109699e-01 -4.08859491e-01 -2.25727633e-01 7.42967367e-01 2.98945069e-01 -2.07247123e-01 -4.42768544e-01 -5.75645447e-01 -7.10690200e-01 -8.73532832e-01 4.46841657e-01 4.94602263e-01 3.39785814e-01 -4.20005947e-01 4.08180445e-01 1.84567019e-01 -1.31033015e+00 1.51230440e-01 1.18503189e+00 7.37637341e-01 4.75728840e-01 1.30320594e-01 -2.22115844e-01 1.29400223e-01 -3.78204703e-01 -3.09036791e-01 4.93331492e-01 -2.92669564e-01 -7.54543781e-01 -5.37072960e-03 2.67577082e-01 9.81734321e-02 1.07517712e-01 1.17849672e+00 2.04691350e-01 2.76978612e-01 5.84460914e-01 -1.03900290e+00 -7.90169418e-01 7.53463805e-01 -4.83412117e-01 1.72789454e-01 1.17073823e-02 -2.35466927e-01 1.05807793e+00 -9.17373717e-01 4.36176658e-01 1.24948859e+00 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-1.02618206e+00 -9.63266492e-01 -4.42279905e-01 2.49069974e-01 -3.35240811e-02 -9.56464171e-01 -5.73875964e-01 6.17672615e-02]	[10.094400405883789, 3.2885499000549316]
0d21c33e-9547-4644-8e71-e8fdf264d13c	self-supervised-activity-representation	2305.00619	null	https://arxiv.org/abs/2305.00619v1	https://arxiv.org/pdf/2305.00619v1.pdf	Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study	In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data. Analyzing this ever-increasing data presents several challenges, including limited access to annotated data and a constantly changing environment. Recent advancements in self-supervised learning have been utilized as a pre-training step to enhance the performance of conventional supervised models to address the absence of labelled datasets. This research examines the impact of using a self-supervised representation learning model for time series classification tasks in which data is incrementally available. We proposed and evaluated a workflow in which a model learns to extract informative features using a corpus of unlabeled time series data and then conducts classification on labelled data using features extracted by the model. We analyzed the effect of varying the size, distribution, and source of the unlabeled data on the final classification performance across four public datasets, including various types of sensors in diverse applications.	['Flora D. Salim', 'Van Nguyen', 'Hao Xue', 'Shohreh Deldari', 'Jason Liu']	2023-05-01	null	null	null	null	['time-series-classification']	['time-series']	[ 8.65903199e-01 5.48105463e-02 -4.04264063e-01 -6.82402015e-01 -7.13226199e-01 -6.54015720e-01 6.43913329e-01 4.04718667e-01 -4.45025563e-01 7.42878437e-01 3.26535821e-01 -2.94555396e-01 -3.90950963e-02 -7.17853546e-01 -6.52503610e-01 -4.65000898e-01 -1.89000085e-01 1.25845596e-01 2.20625445e-01 -9.05830786e-03 -3.17926556e-02 2.06829205e-01 -1.99858236e+00 2.18990669e-01 6.56521499e-01 1.17861116e+00 -1.02892530e-03 5.32949090e-01 -2.61277586e-01 7.75766492e-01 -4.55630422e-01 2.10413411e-01 3.26364309e-01 -2.06502393e-01 -4.97347057e-01 1.91478387e-01 -1.38807729e-01 -5.50868697e-02 1.46679536e-01 3.30281436e-01 4.88852829e-01 -9.11303759e-02 4.42188531e-01 -1.26531327e+00 -1.16360113e-01 6.93043828e-01 -2.18848750e-01 5.46407819e-01 3.94144028e-01 -6.92836046e-02 8.12575519e-01 -4.41915512e-01 4.44935888e-01 4.36254501e-01 1.04703069e+00 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f21b6871-d1ce-4c47-9ec1-d2c80eaef749	online-computation-of-terminal-ingredients-in	2207.09216	null	https://arxiv.org/abs/2207.09216v1	https://arxiv.org/pdf/2207.09216v1.pdf	Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking	A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller is computed offline. Unlike many standard existing schemes, this scheme yields large feasible regions without performing offline centralized computations. Although the resulting optimal control problem (OCP) is a semidefinite program (SDP), an SDP scalability method based on diagonal dominance is used to approximate the derived SDP by a second-order cone program. The OCPs of the proposed scheme and its approximation are amenable to distributed optimization. Both schemes are evaluated using a power network example and compared to a scheme where the terminal controller is reconfigured online as well. It is found that fixing the terminal controller results in better performance, noticeable reduction in computational cost and similar feasible region compared to the case in which this controller is reconfigured online.	['John Lygeros', 'Annika Eichler', 'Goran Banjac', 'Ahmed Aboudonia']	2022-07-19	null	null	null	null	['distributed-optimization']	['methodology']	[ 1.53640851e-01 4.18701708e-01 -5.53982556e-01 2.19456986e-01 -5.84938884e-01 -1.13409340e+00 4.18120250e-02 2.21092582e-01 -2.64681820e-02 1.15059853e+00 -3.81718308e-01 -5.45956671e-01 -7.43093610e-01 -3.64636660e-01 -5.53949058e-01 -1.00517464e+00 -4.17639405e-01 7.05201864e-01 -1.00068957e-01 -1.59186557e-01 -1.98428910e-02 6.73624098e-01 -6.66595221e-01 -5.59628189e-01 9.94590044e-01 1.46269882e+00 1.90125220e-02 5.33749223e-01 5.91414511e-01 4.66249198e-01 -2.06902489e-01 -3.74606880e-03 1.06177402e+00 -1.30913913e-01 -4.28284317e-01 6.78674757e-01 -8.82033333e-02 -4.81947333e-01 -1.29391357e-01 1.28814888e+00 5.23856878e-01 4.10698831e-01 3.79305273e-01 -1.86435890e+00 -3.00046951e-01 2.60315686e-01 -7.63395071e-01 -1.59708261e-01 1.74454495e-01 1.80640161e-01 1.16814888e+00 -4.06915635e-01 6.63971543e-01 9.78880584e-01 5.55110693e-01 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1cbdbbe2-9606-49d9-91d9-c6e10f1438e2	aligning-open-ie-relations-and-kb-relations	null	null	https://aclanthology.org/W19-0412	https://aclanthology.org/W19-0412.pdf	Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding	Open Information Extraction (Open IE) aims at generating entity-relation-entity triples from a large amount of text, aiming at capturing key semantics of the text. Given a triple, the relation expresses the type of semantic relation between the entities. Although relations from an Open IE system are more extensible than those used in a traditional Information Extraction system and a Knowledge Base (KB) such as Knowledge Graphs, the former lacks in semantics; an Open IE relation is simply a sequence of words, whereas a KB relation has a predefined meaning. As a way to provide a meaning to an Open IE relation, we attempt to align it with one of the predefined set of relations used in a KB. Our approach is to use a Siamese network that compares two sequences of word embeddings representing an Open IE relation and a predefined KB relation. In order to make the approach practical, we automatically generate a training dataset using a distant supervision approach instead of relying on a hand-labeled dataset. Our experiment shows that the proposed method can capture the relational semantics better than the recent approaches.	['Sung-Hyon Myaeng', 'Rifki Afina Putri', 'Giwon Hong']	2019-05-01	null	null	null	ws-2019-5	['open-information-extraction']	['natural-language-processing']	[ 5.22991940e-02 7.80432045e-01 -3.57960582e-01 -3.51179600e-01 -4.00659710e-01 -4.49949533e-01 7.14949548e-01 5.72536469e-01 -4.66764510e-01 8.90227973e-01 3.61792773e-01 -3.66387606e-01 -2.91511923e-01 -1.40481973e+00 -7.36065805e-01 -3.15214634e-01 6.46846741e-02 7.73767889e-01 5.06822824e-01 -6.07452273e-01 -6.57540709e-02 2.73719370e-01 -1.40648139e+00 2.72033006e-01 8.66540968e-01 8.79758656e-01 1.84961542e-01 1.32056519e-01 -9.09883738e-01 9.51103151e-01 -3.54348868e-01 -9.48289454e-01 9.57385674e-02 -3.96474212e-01 -1.52267551e+00 -2.79918104e-01 9.09790993e-02 4.04708713e-01 -9.88637060e-02 1.24464440e+00 4.95924354e-02 2.43578274e-02 8.41309607e-01 -1.38163126e+00 -8.39275897e-01 1.03172457e+00 -8.49930197e-02 -5.03656790e-02 7.19717681e-01 -5.53910792e-01 1.56323326e+00 -6.75409257e-01 1.04108608e+00 9.53398466e-01 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