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5107388a-db36-4dc9-9890-e5b713987d1b	attestable-at-semeval-2021-task-9-extending	null	null	https://aclanthology.org/2021.semeval-1.182/	https://aclanthology.org/2021.semeval-1.182.pdf	AttesTable at SemEval-2021 Task 9: Extending Statement Verification with Tables for Unknown Class, and Semantic Evidence Finding	This paper describes our approach for Task 9 of SemEval 2021: Statement Verification and Evidence Finding with Tables. We participated in both subtasks, namely statement verification and evidence finding. For the subtask of statement verification, we extend the TAPAS model to adapt to the ‘unknown’ class of statements by finetuning it on an augmented version of the task data. For the subtask of evidence finding, we finetune the DistilBERT model in a Siamese setting.	['Abhishek Rathi', 'Pratik Ratadiya', 'Aadish Jain', 'Harshit Varma']	2021-08-01	null	https://aclanthology.org/2021.semeval-1.182	https://aclanthology.org/2021.semeval-1.182.pdf	semeval-2021	['table-based-fact-verification']	['natural-language-processing']	[ 2.43494794e-01 3.19950879e-01 -4.13546652e-01 -3.41800094e-01 -9.89617288e-01 -7.27926314e-01 6.47541106e-01 8.18252742e-01 -3.55478972e-01 1.20922422e+00 9.65867415e-02 -1.03668189e+00 -1.21920891e-01 -4.15959328e-01 -1.14773178e+00 3.90541404e-02 -4.10182387e-01 4.51279551e-01 6.99673533e-01 1.24895714e-01 4.91256714e-01 2.16220930e-01 -1.02385092e+00 7.72836924e-01 6.78031147e-01 1.05968523e+00 -6.18227601e-01 7.73157954e-01 -7.95291141e-02 1.08181214e+00 -5.21062076e-01 -8.71191502e-01 2.92916745e-01 5.20891584e-02 -1.15855885e+00 -1.52729169e-01 9.99397457e-01 -1.55642360e-01 -7.24808425e-02 1.19129169e+00 -1.49865776e-01 1.79623410e-01 5.18178165e-01 -1.58304203e+00 -1.80761263e-01 1.21562064e+00 -4.40816581e-01 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6f853d5d-ba76-4e06-a94a-e7db0e1dc196	how-do-decoding-algorithms-distribute	2303.17006	null	https://arxiv.org/abs/2303.17006v1	https://arxiv.org/pdf/2303.17006v1.pdf	How do decoding algorithms distribute information in dialogue responses?	Humans tend to follow the Uniform Information Density (UID) principle by distributing information evenly in utterances. We study if decoding algorithms implicitly follow this UID principle, and under what conditions adherence to UID might be desirable for dialogue generation. We generate responses using different decoding algorithms with GPT-2 on the Persona-Chat dataset and collect human judgments on their quality using Amazon Mechanical Turk. We find that (i) surprisingly, model-generated responses follow the UID principle to a greater extent than human responses, and (ii) decoding algorithms that promote UID do not generate higher-quality responses. Instead, when we control for surprisal, non-uniformity of information density correlates with the quality of responses with very low/high surprisal. Our findings indicate that encouraging non-uniform responses is a potential solution to the ``likelihood trap'' problem (quality degradation in very high-likelihood text). Our dataset containing multiple candidate responses per dialog history along with human-annotated quality ratings is available at https://huggingface.co/datasets/saranya132/dialog_uid_gpt2.	['David Reitter', 'He He', 'Saranya Venkatraman']	2023-03-29	null	null	null	null	['dialogue-generation', 'dialogue-generation']	['natural-language-processing', 'speech']	[-1.10276587e-01 5.19992828e-01 -1.50387868e-01 -7.50781536e-01 -1.03486514e+00 -7.65265882e-01 7.46343434e-01 -2.91316435e-02 -3.72588873e-01 8.79355192e-01 8.94839406e-01 -2.52388060e-01 1.99001849e-01 -4.55308735e-01 -1.89523175e-01 -1.76224455e-01 6.04526758e-01 9.84902978e-01 -4.49523479e-02 -3.54434103e-01 3.45924467e-01 -2.29388863e-01 -9.35111761e-01 6.41043365e-01 1.00351107e+00 5.76287091e-01 1.25968650e-01 1.10423505e+00 -1.43359840e-01 1.12581205e+00 -9.37875509e-01 -7.99339175e-01 1.32429257e-01 -8.21977317e-01 -1.20866168e+00 6.88779503e-02 1.14610396e-01 -7.21545041e-01 -3.85214627e-01 7.39164650e-01 5.16127229e-01 2.80544281e-01 8.82600725e-01 -1.16551840e+00 -9.19722319e-01 9.89152312e-01 -4.99146841e-02 3.56192105e-02 9.38090205e-01 5.84044993e-01 1.22402346e+00 -6.11079335e-01 7.28138924e-01 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48baeabf-d60f-4713-9a03-b215dd83dd4d	cl-xabsa-contrastive-learning-for-cross	2204.00791	null	https://arxiv.org/abs/2204.00791v5	https://arxiv.org/pdf/2204.00791v5.pdf	CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis	As an extensive research in the field of natural language processing (NLP), aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment expressed in a text relative to the corresponding aspect. Unfortunately, most languages lack sufficient annotation resources, thus more and more recent researchers focus on cross-lingual aspect-based sentiment analysis (XABSA). However, most recent researches only concentrate on cross-lingual data alignment instead of model alignment. To this end, we propose a novel framework, CL-XABSA: Contrastive Learning for Cross-lingual Aspect-Based Sentiment Analysis. Based on contrastive learning, we close the distance between samples with the same label in different semantic spaces, thus achieving a convergence of semantic spaces of different languages. Specifically, we design two contrastive strategies, token level contrastive learning of token embeddings (TL-CTE) and sentiment level contrastive learning of token embeddings (SL-CTE), to regularize the semantic space of source and target language to be more uniform. Since our framework can receive datasets in multiple languages during training, our framework can be adapted not only for XABSA task but also for multilingual aspect-based sentiment analysis (MABSA). To further improve the performance of our model, we perform knowledge distillation technology leveraging data from unlabeled target language. In the distillation XABSA task, we further explore the comparative effectiveness of different data (source dataset, translated dataset, and code-switched dataset). The results demonstrate that the proposed method has a certain improvement in the three tasks of XABSA, distillation XABSA and MABSA. For reproducibility, our code for this paper is available at https://github.com/GKLMIP/CL-XABSA.	['Shengyi Jiang', 'Aimin Yang', 'Xiaotian Lin', 'Yingwen Fu', 'Nankai Lin']	2022-04-02	null	null	null	null	['aspect-based-sentiment-analysis']	['natural-language-processing']	[-1.96410462e-01 -4.39838976e-01 -2.52226710e-01 -5.76057076e-01 -8.33582222e-01 -6.81533456e-01 6.58059239e-01 2.32713938e-01 -5.62277019e-01 3.26609910e-01 2.03040481e-01 -4.73691583e-01 2.43481025e-01 -7.11474061e-01 -4.97091055e-01 -5.39543271e-01 6.14443600e-01 4.16067332e-01 -1.65788129e-01 -3.42272848e-01 9.53836367e-02 7.96435997e-02 -1.10995245e+00 4.23622519e-01 9.41097677e-01 8.50348890e-01 9.83673111e-02 1.40347451e-01 -8.08474302e-01 3.29909235e-01 -4.19012576e-01 -7.28690445e-01 1.80972859e-01 -4.25160021e-01 -7.84971774e-01 -4.63829674e-02 1.43961757e-01 4.82635200e-01 3.57091337e-01 1.24644661e+00 4.57922995e-01 -7.34377950e-02 5.69054186e-01 -1.48277819e+00 -8.71488810e-01 6.71028852e-01 -6.99289918e-01 -1.64945334e-01 2.85719365e-01 -1.35790929e-01 1.20791626e+00 -1.04178739e+00 4.26598400e-01 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98699cfd-fef2-4d5c-bf32-60cd200aed3e	sleep-posture-one-shot-learning-framework	2205.10778	null	https://arxiv.org/abs/2205.10778v1	https://arxiv.org/pdf/2205.10778v1.pdf	Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies	Sleep posture is linked to several health conditions such as nocturnal cramps and more serious musculoskeletal issues. However, in-clinic sleep assessments are often limited to vital signs (e.g. brain waves). Wearable sensors with embedded inertial measurement units have been used for sleep posture classification; nonetheless, previous works consider only few (commonly four) postures, which are inadequate for advanced clinical assessments. Moreover, posture learning algorithms typically require longitudinal data collection to function reliably, and often operate on raw inertial sensor readings unfamiliar to clinicians. This paper proposes a new framework for sleep posture classification based on a minimal set of joint angle measurements. The proposed framework is validated on a rich set of twelve postures in two experimental pipelines: computer animation to obtain synthetic postural data, and human participant pilot study using custom-made miniature wearable sensors. Through fusing raw geo-inertial sensor measurements to compute a filtered estimate of relative segment orientations across the wrist and ankle joints, the body posture can be characterised in a way comprehensible to medical experts. The proposed sleep posture learning framework offers plug-and-play posture classification by capitalising on a novel kinematic data augmentation method that requires only one training example per posture. Additionally, a new metric together with data visualisations are employed to extract meaningful insights from the postures dataset, demonstrate the added value of the data augmentation method, and explain the classification performance. The proposed framework attained promising overall accuracy as high as 100% on synthetic data and 92.7% on real data, on par with state of the art data-hungry algorithms available in the literature.	['Paolo Paoletti', 'Lyndon Mason', 'Andrew Hopkinson', 'Frans Coenen', 'Omar Elnaggar']	2022-05-22	null	null	null	null	['one-shot-learning']	['methodology']	[ 3.44505280e-01 3.74863118e-01 -3.14056501e-02 -3.87719452e-01 -4.00568783e-01 -1.75095111e-01 5.10124452e-02 2.83390969e-01 -7.04703391e-01 7.67695069e-01 2.15394929e-01 -1.66507050e-01 -2.65053272e-01 -2.38114387e-01 -3.27936083e-01 -5.68239093e-01 -2.88308859e-01 3.77262890e-01 8.91931802e-02 -4.51054186e-01 -1.73070971e-02 1.41558021e-01 -1.83529401e+00 -3.19955140e-01 8.03579926e-01 1.06333864e+00 1.28783863e-02 6.25490487e-01 5.40399432e-01 -3.02007139e-01 -6.40802741e-01 -5.67646921e-02 2.17060417e-01 -3.93185228e-01 -4.81417567e-01 8.31094682e-02 7.78543204e-02 -4.70804274e-02 5.12742221e-01 5.04830718e-01 9.05253768e-01 5.52704707e-02 2.62001157e-01 -9.98437881e-01 4.53130826e-02 -6.09126613e-02 -1.94972038e-01 4.88735050e-01 8.87305140e-01 3.39520156e-01 5.65100670e-01 -4.20025676e-01 4.94878381e-01 4.70721006e-01 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-1.37747908e+00 -3.97264302e-01 -1.31203759e+00 -4.35598552e-01 4.75111574e-01 3.90627533e-01 -1.13024688e+00 -9.50142145e-02 1.99488327e-01]	[13.551544189453125, 3.3707005977630615]
b828cc76-6957-4df3-9cd6-fa49278af0ea	causal-analysis-of-the-topcat-trial	2211.12983	null	https://arxiv.org/abs/2211.12983v1	https://arxiv.org/pdf/2211.12983v1.pdf	Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure	We describe the results of applying causal discovery methods on the data from a multi-site clinical trial, on the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT). The trial was inconclusive, with no clear benefits consistently shown for the whole cohort. However, there were questions regarding the reliability of the diagnosis and treatment protocol for a geographic subgroup of the cohort. With the inclusion of medical context in the form of domain knowledge, causal discovery is used to demonstrate regional discrepancies and to frame the regional transportability of the results. Furthermore, we show that, globally and especially for some subgroups, the treatment has significant causal effects, thus offering a more refined view of the trial results.	['Shlomo Ben-Haim', 'Javed Butler', 'Maksim Sipos', 'Andre Franca', 'Tamara Stemberga', 'Andrew R. Lawrence', 'Hana Chockler', "Tadhg O'Keeffe", 'Francesca E. D. Raimondi']	2022-11-23	null	null	null	null	['causal-discovery']	['knowledge-base']	[ 2.18277369e-02 1.69604793e-01 -8.04512739e-01 -3.68926883e-01 -5.86683154e-01 -5.81249356e-01 5.01168013e-01 6.47569299e-01 -3.21204126e-01 9.61467147e-01 9.79254305e-01 -9.35976267e-01 -9.33360636e-01 -6.08447552e-01 -4.19357181e-01 -3.89490336e-01 -5.17294466e-01 6.05675280e-01 -1.33412510e-01 4.71503399e-02 9.72843617e-02 2.88920105e-01 -4.73166108e-01 1.42482921e-01 5.91418982e-01 2.28564516e-01 -1.60327688e-01 3.76846820e-01 2.12055102e-01 6.38826489e-01 -3.34270030e-01 2.78574049e-01 1.06267899e-01 -8.61165822e-01 -6.87300742e-01 -2.01188624e-02 2.80738801e-01 -5.32741964e-01 -7.77046233e-02 2.54331499e-01 7.21224606e-01 -4.04639751e-01 4.88501579e-01 -6.82838559e-01 -2.40372509e-01 9.46291447e-01 -1.09673887e-02 4.29277092e-01 6.49028122e-02 3.22264105e-01 8.04498255e-01 -5.19554198e-01 9.20405149e-01 1.11137962e+00 4.66816574e-01 -1.44402152e-02 -1.38848579e+00 -6.18815660e-01 2.68534720e-01 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7.67250210e-02 8.12955946e-02 -5.44986904e-01 -4.19284403e-01 -1.80781782e-01]	[8.155743598937988, 5.67125129699707]
49f7fc01-23fc-4e22-bb74-607092a4a64a	residue-density-segmentation-for-monitoring	2102.04866	null	https://arxiv.org/abs/2102.04866v1	https://arxiv.org/pdf/2102.04866v1.pdf	Residue Density Segmentation for Monitoring and Optimizing Tillage Practices	"No-till" and cover cropping are often identified as the leading simple, best management practices for carbon sequestration in agriculture. However, the root of the problem is more complex, with the potential benefits of these approaches depending on numerous factors including a field's soil type(s), topography, and management history. Instead of using computer vision approaches to simply classify a field a still vs. no-till, we instead seek to identify the degree of residue coverage across afield through a probabilistic deep learning segmentation approach to enable more accurate analysis of carbon holding potential and realization. This approach will not only provide more precise insights into currently implemented practices, but also enable a more accurate identification process of fields with the greatest potential for adopting new practices to significantly impact carbon sequestration in agriculture.	['Naira Hovakimyan', 'Ivan Dozier', 'Jennifer Hobbs']	2021-02-09	null	null	null	null	['probabilistic-deep-learning']	['computer-vision']	[ 4.12913173e-01 -1.16494626e-01 -4.72870946e-01 5.30386344e-02 -1.05541483e-01 -8.45142782e-01 2.27062643e-01 8.29830527e-01 -5.07146083e-02 6.25441194e-01 2.34414443e-01 -1.32129252e+00 -2.64245540e-01 -1.18377340e+00 -5.59148729e-01 -8.36308360e-01 -3.49633209e-02 2.53770173e-01 -6.03233576e-02 -2.09415674e-01 2.80934751e-01 6.10845923e-01 -1.25452816e+00 6.81777298e-02 6.29606962e-01 5.11319399e-01 8.07098866e-01 5.55539191e-01 -1.74030080e-01 1.34220511e-01 -1.31298646e-01 2.73370683e-01 3.12235922e-01 -1.48945376e-01 -7.18686461e-01 5.50522208e-02 1.42729888e-02 -6.52316153e-01 1.38157353e-01 1.18830442e+00 5.80448210e-02 -2.48947769e-01 9.09704447e-01 -1.98253214e-01 -2.83310503e-01 7.70036638e-01 -1.05532789e+00 -5.47027476e-02 -8.87935534e-02 3.14196974e-01 7.81778932e-01 -3.23631734e-01 2.23941714e-01 1.09036040e+00 6.23768985e-01 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-7.29200125e-01 -2.00625211e-01 1.93576112e-01 -7.14941978e-01 2.06490066e-02 1.25950560e-01]	[9.297779083251953, -1.5360307693481445]
3db88695-4d00-41f6-8639-f9590942194f	optimizing-embedding-related-quantum	2011.00719	null	https://arxiv.org/abs/2011.00719v2	https://arxiv.org/pdf/2011.00719v2.pdf	Optimizing embedding-related quantum annealing parameters for reducing hardware bias	Quantum annealers have been designed to propose near-optimal solutions to NP-hard optimization problems. However, the accuracy of current annealers such as the ones of D-Wave Systems, Inc., is limited by environmental noise and hardware biases. One way to deal with these imperfections and to improve the quality of the annealing results is to apply a variety of pre-processing techniques such as spin reversal (SR), anneal offsets (AO), or chain weights (CW). Maximizing the effectiveness of these techniques involves performing optimizations over a large number of parameters, which would be too costly if needed to be done for each new problem instance. In this work, we show that the aforementioned parameter optimization can be done for an entire class of problems, given each instance uses a previously chosen fixed embedding. Specifically, in the training phase, we fix an embedding E of a complete graph onto the hardware of the annealer, and then run an optimization algorithm to tune the following set of parameter values: the set of bits to be flipped for SR, the specific qubit offsets for AO, and the distribution of chain weights, optimized over a set of training graphs randomly chosen from that class, where the graphs are embedded onto the hardware using E. In the testing phase, we estimate how well the parameters computed during the training phase work on a random selection of other graphs from that class. We investigate graph instances of varying densities for the Maximum Clique, Maximum Cut, and Graph Partitioning problems. Our results indicate that, compared to their default behavior, substantial improvements of the annealing results can be achieved by using the optimized parameters for SR, AO, and CW.	['Hristo N. Djidjev', 'Georg Hahn', 'Elijah Pelofske', 'Aaron Barbosa']	2020-11-02	null	null	null	null	['graph-partitioning']	['graphs']	[ 2.87485152e-01 8.50151330e-02 -3.69039066e-02 -1.59880772e-01 -6.33756697e-01 -6.92237735e-01 3.77377242e-01 4.20743018e-01 -6.35769248e-01 7.97853589e-01 -2.92195439e-01 -4.60708886e-01 -3.54707956e-01 -1.10429657e+00 -7.59308815e-01 -9.66026902e-01 -2.15732306e-01 8.79039645e-01 2.60544389e-01 -4.60031360e-01 6.80239022e-01 7.00405359e-01 -1.30638993e+00 -4.34401184e-01 7.91154623e-01 5.34291029e-01 2.13909000e-02 7.96266496e-01 1.86870664e-01 5.08562922e-02 -7.36310542e-01 -3.06153476e-01 3.84594858e-01 -4.97775555e-01 -7.46216714e-01 -1.08228408e-01 -1.44893095e-01 1.67921337e-03 -3.67604554e-01 1.25640213e+00 4.92502689e-01 2.90125787e-01 3.82701367e-01 -9.45441842e-01 1.62981786e-02 7.67168939e-01 -4.43914145e-01 1.26863778e-01 -1.51720131e-02 4.83452916e-01 1.07973254e+00 2.52010208e-02 7.17242360e-01 6.87626541e-01 3.72244149e-01 4.03887749e-01 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8f161728-1e76-48fb-925d-6e11b56335ef	displacenet-recognising-displaced-people-from	1905.02025	null	https://arxiv.org/abs/1905.02025v1	https://arxiv.org/pdf/1905.02025v1.pdf	DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level	Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters. The number of forcibly displaced people came at a record rate of 44,400 every day throughout 2017, raising the cumulative total to 68.5 million at the years end, overtaken the total population of the United Kingdom. Up to 85% of the forcibly displaced find refuge in low- and middle-income countries, calling for increased humanitarian assistance worldwide. To reduce the amount of manual labour required for human-rights-related image analysis, we introduce DisplaceNet, a novel model which infers potential displaced people from images by integrating the control level of the situation and conventional convolutional neural network (CNN) classifier into one framework for image classification. Experimental results show that DisplaceNet achieves up to 4% coverage-the proportion of a data set for which a classifier is able to produce a prediction-gain over the sole use of a CNN classifier. Our dataset, codes and trained models will be available online at https://github.com/GKalliatakis/DisplaceNet.	['Klaus McDonald-Maier', 'Shoaib Ehsan', 'Grigorios Kalliatakis', 'Maria Fasli']	2019-05-03	null	null	null	null	['displaced-people-recognition']	['computer-vision']	[ 8.40120241e-02 5.21405280e-01 -3.51321936e-01 -1.58284917e-01 -5.06810606e-01 -4.44648653e-01 6.71103179e-01 5.28906107e-01 -1.14378250e+00 8.31936955e-01 5.52478075e-01 -6.19943202e-01 3.98488902e-02 -1.23403239e+00 -3.97052377e-01 -5.49505949e-01 -1.16113608e-03 4.63339865e-01 -2.18725502e-01 -3.09553027e-01 -8.71117935e-02 8.58944356e-01 -1.18458700e+00 1.91813707e-02 8.55880737e-01 7.97977090e-01 -1.44112393e-01 3.37146223e-01 4.03730720e-01 6.18835688e-01 -4.04582739e-01 -5.58527648e-01 2.82552809e-01 -7.96779469e-02 -8.39035630e-01 -6.56581342e-01 2.10177273e-01 -9.54896510e-01 -6.89567447e-01 9.06062901e-01 5.89179337e-01 -1.96624547e-01 7.25059986e-01 -1.10701823e+00 -4.86548185e-01 5.21693468e-01 -5.20373106e-01 2.76244551e-01 4.20692384e-01 -3.68034318e-02 3.82052928e-01 -7.66312838e-01 7.61734426e-01 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10288f33-adef-438f-8fb2-cd57cd2d3620	on-training-locally-adaptive-cp	2306.04648	null	https://arxiv.org/abs/2306.04648v1	https://arxiv.org/pdf/2306.04648v1.pdf	On training locally adaptive CP	We address the problem of making Conformal Prediction (CP) intervals locally adaptive. Most existing methods focus on approximating the object-conditional validity of the intervals by partitioning or re-weighting the calibration set. Our strategy is new and conceptually different. Instead of re-weighting the calibration data, we redefine the conformity measure through a trainable change of variables, $A \to \phi_X(A)$, that depends explicitly on the object attributes, $X$. Under certain conditions and if $\phi_X$ is monotonic in $A$ for any $X$, the transformations produce prediction intervals that are guaranteed to be marginally valid and have $X$-dependent sizes. We describe how to parameterize and train $\phi_X$ to maximize the interval efficiency. Contrary to other CP-aware training methods, the objective function is smooth and can be minimized through standard gradient methods without approximations.	['Nicolo Colombo']	2023-06-05	null	null	null	null	['prediction-intervals']	['miscellaneous']	[ 1.67893991e-01 3.74298245e-01 -6.55265450e-01 -7.58717954e-01 -1.16353762e+00 -6.60930693e-01 6.88598678e-02 1.99799806e-01 -2.47610703e-01 1.12748373e+00 -2.17461482e-01 -5.64496934e-01 -5.85048437e-01 -1.10576451e+00 -9.30769503e-01 -6.76623881e-01 -2.66189665e-01 7.97504842e-01 1.80524647e-01 1.41990617e-01 3.46592695e-01 3.10800195e-01 -1.53289175e+00 5.50541431e-02 1.23451304e+00 1.33828366e+00 -4.54231828e-01 4.80721891e-01 3.01512945e-02 1.52169943e-01 -4.86712962e-01 -6.45533562e-01 3.97166461e-01 -6.38434470e-01 -9.05130267e-01 -2.38566101e-01 3.11779469e-01 6.88953623e-02 3.12374651e-01 1.10904765e+00 -2.60992267e-04 3.39537561e-01 1.04710877e+00 -1.26371968e+00 -5.83559394e-01 8.40168595e-01 -6.58154607e-01 5.05891591e-02 1.09285437e-01 -4.45455521e-01 9.93800700e-01 -5.30322433e-01 4.48133677e-01 8.34343255e-01 1.21819520e+00 3.41619045e-01 -1.61228490e+00 -8.98886025e-01 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73797ca1-a77d-4262-8abc-6d0050fecba4	a-high-precision-pipeline-for-financial	null	null	https://aclanthology.org/2020.coling-main.84	https://aclanthology.org/2020.coling-main.84.pdf	A High Precision Pipeline for Financial Knowledge Graph Construction	Motivated by applications such as question answering, fact checking, and data integration, there is significant interest in constructing knowledge graphs by extracting information from unstructured information sources, particularly text documents. Knowledge graphs have emerged as a standard for structured knowledge representation, whereby entities and their inter-relations are represented and conveniently stored as (subject,predicate,object) triples in a graph that can be used to power various downstream applications. The proliferation of financial news sources reporting on companies, markets, currencies, and stocks presents an opportunity for extracting valuable knowledge about this crucial domain. In this paper, we focus on constructing a knowledge graph automatically by information extraction from a large corpus of financial news articles. For that purpose, we develop a high precision knowledge extraction pipeline tailored for the financial domain. This pipeline combines multiple information extraction techniques with a financial dictionary that we built, all working together to produce over 342,000 compact extractions from over 288,000 financial news articles, with a precision of 78{\%} at the top-100 extractions.The extracted triples are stored in a knowledge graph making them readily available for use in downstream applications.	['Lanjun Wang', 'Zhefeng Wang', 'Baoxing Huai', 'Michael Simpson', 'Raymond Ng', 'Laks V.S. Lakshmanan', 'Sarah Elhammadi']	2020-12-01	null	null	null	coling-2020-8	['data-integration']	['knowledge-base']	[-3.20035815e-01 5.54624736e-01 -4.52451408e-01 -3.20599489e-02 -6.14164650e-01 -1.11961615e+00 7.21704364e-01 1.15902793e+00 -3.49337876e-01 8.47837448e-01 5.75688183e-01 -4.53695685e-01 -1.95191339e-01 -1.21086907e+00 -4.85958546e-01 8.92311633e-02 -1.39584035e-01 4.76588845e-01 5.33315539e-01 -2.63316900e-01 4.37488705e-01 4.01402354e-01 -1.36733460e+00 5.34112036e-01 7.11058378e-01 9.87942696e-01 -2.16419592e-01 1.10721380e-01 -7.45440245e-01 1.32756627e+00 -5.76382697e-01 -1.07060611e+00 1.19906805e-01 -1.20060876e-01 -9.66566801e-01 -3.17239225e-01 -1.51180178e-01 5.86465746e-02 -1.17482571e-02 9.06091511e-01 -6.19175248e-02 -1.39430851e-01 3.87229025e-01 -9.18064654e-01 -5.36990166e-01 1.15211630e+00 -4.15273428e-01 4.66192484e-01 6.22513175e-01 -3.63606721e-01 1.53570211e+00 -9.30625558e-01 1.23501408e+00 6.88700080e-01 5.12829542e-01 -2.24880531e-01 -6.29307687e-01 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4633ca22-912b-4b00-b0da-8dcebdcb8647	multizoo-multibench-a-standardized-toolkit	2306.16413	null	https://arxiv.org/abs/2306.16413v1	https://arxiv.org/pdf/2306.16413v1.pdf	MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learning	Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiZoo, a public toolkit consisting of standardized implementations of > 20 core multimodal algorithms and MultiBench, a large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas. Together, these provide an automated end-to-end machine learning pipeline that simplifies and standardizes data loading, experimental setup, and model evaluation. To enable holistic evaluation, we offer a comprehensive methodology to assess (1) generalization, (2) time and space complexity, and (3) modality robustness. MultiBench paves the way towards a better understanding of the capabilities and limitations of multimodal models, while ensuring ease of use, accessibility, and reproducibility. Our toolkits are publicly available, will be regularly updated, and welcome inputs from the community.	['Ruslan Salakhutdinov', 'Louis-Philippe Morency', 'Yun Cheng', 'Arav Agarwal', 'Xiang Fan', 'Yiwei Lyu', 'Paul Pu Liang']	2023-06-28	null	null	null	null	['multimodal-deep-learning']	['natural-language-processing']	[ 3.65260601e-01 -3.38252097e-01 -3.87542307e-01 -2.76125610e-01 -1.40164351e+00 -1.05883324e+00 6.80165946e-01 1.69257596e-01 -5.17467618e-01 5.63830614e-01 5.54475069e-01 -1.77855268e-01 -1.69317290e-01 -3.28743190e-01 -5.47235250e-01 -4.23851669e-01 -6.15137592e-02 2.65097350e-01 -1.51811570e-01 -3.96897420e-02 1.55977458e-01 2.79531240e-01 -1.48436761e+00 6.49845004e-01 5.85359514e-01 8.81890118e-01 -2.30844051e-01 8.32333386e-01 3.27809542e-01 4.79276270e-01 1.09011605e-02 -8.25692177e-01 -8.00443441e-02 -1.37933865e-01 -1.05245459e+00 -1.68385491e-01 7.02431142e-01 -6.96090460e-02 -2.78299779e-01 6.23463154e-01 9.51056421e-01 1.92019224e-01 4.80719030e-01 -1.42075300e+00 -6.25858366e-01 5.19377112e-01 -2.79064775e-01 -1.84722498e-01 6.46672785e-01 6.96486294e-01 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00a31581-c905-4399-b726-2927406b164a	digicall-a-benchmark-for-measuring-the	null	null	https://aclanthology.org/2022.finnlp-1.7/	https://aclanthology.org/2022.finnlp-1.7.pdf	DigiCall: A Benchmark for Measuring the Maturity of Digital Strategy through Company Earning Calls	Digital transformation reinvents companies, their vision and strategy, organizational structure, processes, capabilities, and culture, and enables the development of new or enhanced products and services delivered to customers more efficiently. Organizations, by formalizing their digital strategy attempt to plan for their digital transformations and accelerate their company growth. Understanding how successful a company is in its digital transformation starts with accurate measurement of its digital maturity levels. However, existing approaches to measuring organizations’ digital strategy have low accuracy levels and this leads to inconsistent results, and also does not provide resources (data) for future research to improve. In order to measure the digital strategy maturity of companies, we leverage the state-of-the-art NLP models on unstructured data (earning call transcripts), and reach the state-of-the-art levels (94%) for this task. We release 3.691 earning call transcripts and also annotated data set, labeled particularly for the digital strategy maturity by linguists. Our work provides an empirical baseline for research in industry and management science.	['T. Ravichandran', 'Kexuan Sun', 'Hilal Pataci']	2022-12-08	null	null	null	finnlp-emnlp-2022-12	['sentence-classification', 'part-of-speech-tagging', 'culture']	['natural-language-processing', 'natural-language-processing', 'speech']	[ 6.86143562e-02 3.30218762e-01 -7.35397756e-01 -1.49924785e-01 -8.62157583e-01 -1.08430564e+00 8.94484162e-01 1.92135394e-01 -4.56179008e-02 2.58048594e-01 7.84705818e-01 -3.54752451e-01 -1.20902970e-01 -7.49078572e-01 -3.84363502e-01 -1.63440630e-02 6.03794217e-01 6.69791877e-01 -1.41660795e-01 -2.11489871e-01 8.25932145e-01 3.57353508e-01 -9.73468184e-01 5.89597344e-01 4.67197120e-01 9.23007607e-01 1.89671248e-01 4.21819896e-01 -8.66936862e-01 1.04682791e+00 -2.22189486e-01 -8.70280921e-01 3.16904753e-01 7.42575899e-02 -1.04140460e+00 3.39171961e-02 2.00279489e-01 3.48997079e-02 8.92832056e-02 1.18668151e+00 1.28786517e-02 -8.25671494e-01 3.88786167e-01 -9.04199302e-01 -1.02340949e+00 1.20691061e+00 -5.87742329e-01 -1.17917046e-01 3.99091393e-01 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8b536bf7-69bf-41ae-8604-9b6e8b6b2b0b	characterizing-an-analogical-concept-memory	2006.01962	null	https://arxiv.org/abs/2006.01962v3	https://arxiv.org/pdf/2006.01962v3.pdf	Characterizing an Analogical Concept Memory for Architectures Implementing the Common Model of Cognition	Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. In this paper, we explore how computational models of analogical processing can be brought into these architectures to enable concept acquisition from examples obtained interactively. We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories. We frame the problem of concept learning as embedded within the larger context of interactive task learning (ITL) and embodied language processing (ELP). We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts that are useful not only in recognition of a concept in the environment but also in action selection. Our approach has been instantiated in an implemented cognitive system \textsc{Aileen} and evaluated on a simulated robotic domain.	['Matthew Shreve', 'Shiwali Mohan', 'Kent Evans', 'Matt Klenk', 'Aaron Ang', 'John Maxwell']	2020-06-02	null	null	null	null	['novel-concepts']	['reasoning']	[ 1.48610413e-01 3.31139982e-01 6.46521866e-01 -3.82387996e-01 2.77947664e-01 -8.75263393e-01 1.12101460e+00 5.47005653e-01 -5.67367733e-01 5.77632844e-01 -3.49064954e-02 -5.29018402e-01 -7.12466896e-01 -1.16149569e+00 -4.73030746e-01 -2.38057181e-01 -4.18393254e-01 8.17546427e-01 4.89462435e-01 -7.97035515e-01 7.43424237e-01 8.03016365e-01 -1.97938955e+00 5.15377343e-01 9.25623357e-01 6.92186832e-01 7.91789114e-01 4.01687771e-01 -4.71470237e-01 1.12306833e+00 -4.71717119e-01 1.16299540e-01 1.85776681e-01 -2.13943586e-01 -1.17236936e+00 -2.54530281e-01 -2.97770411e-01 8.63530636e-02 -4.87153884e-03 6.69656932e-01 9.08996016e-02 5.82088411e-01 7.90282547e-01 -9.72555876e-01 -8.55866849e-01 6.11580193e-01 4.03389752e-01 6.02056310e-02 6.81883633e-01 3.11172277e-01 2.94040471e-01 -1.00369763e+00 6.42806709e-01 1.60219133e+00 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4145c583-7437-4a06-ac9c-8aeb0771b643	gnn-at-the-edge-cost-efficient-graph-neural	2210.17281	null	https://arxiv.org/abs/2210.17281v1	https://arxiv.org/pdf/2210.17281v1.pdf	GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers	Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has extensively investigated multi-tier edge deployment for traditional deep learning models (e.g. CNNs, RNNs), the emerging Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions such as traffic flow forecasting and location-based social recommendation. To bridge this gap, this paper formally studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network. We build a comprehensive modeling framework that can capture a variety of different cost factors, based on which we formulate a cost-efficient graph layout optimization problem that is proved to be NP-hard. Instead of trivially applying traditional data placement wisdom, we theoretically reveal the structural property of quadratic submodularity implicated in GNN's unique computing pattern, which motivates our design of an efficient iterative solution exploiting graph cuts. Rigorous analysis shows that it provides parameterized constant approximation ratio, guaranteed convergence, and exact feasibility. To tackle potential graph topological evolution in GNN processing, we further devise an incremental update strategy and an adaptive scheduling algorithm for lightweight dynamic layout optimization. Evaluations with real-world datasets and various GNN benchmarks demonstrate that our approach achieves superior performance over de facto baselines with more than 95.8% cost eduction in a fast convergence speed.	['Xu Chen', 'Shuai Yu', 'Zhi Zhou', 'Peng Huang', 'Chongyu Yang', 'Liekang Zeng']	2022-10-31	null	null	null	null	['miscellaneous']	['miscellaneous']	[-2.29759052e-01 2.53359824e-01 -3.99780154e-01 -6.28756639e-03 8.21196940e-03 -5.20816386e-01 1.15284428e-01 9.11877006e-02 -1.33169413e-01 5.21318793e-01 -4.22910899e-01 -7.80967414e-01 -6.26389146e-01 -1.05665588e+00 -9.62024808e-01 -4.63365018e-01 -4.55341756e-01 4.03028071e-01 -5.64343147e-02 -1.95599258e-01 1.63078308e-01 7.13687778e-01 -1.27296400e+00 -4.78097707e-01 9.71039057e-01 1.25314569e+00 9.53343213e-02 5.72910011e-01 -1.15098678e-01 5.30614793e-01 -3.54510397e-01 -5.35749435e-01 7.22713470e-01 1.90277509e-02 -5.04936159e-01 3.63836344e-03 5.35213470e-01 -1.78242028e-01 -6.29492640e-01 9.94507492e-01 5.24431348e-01 1.77837331e-02 2.35167429e-01 -1.79934943e+00 -7.51866221e-01 8.24707747e-01 -8.17314565e-01 2.94878006e-01 -3.86090964e-01 -1.20391831e-01 1.15318179e+00 -4.67234075e-01 5.85017443e-01 7.40096688e-01 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92d5f8b2-7d0a-4e21-907d-26de597fd531	unitn-training-deep-convolutional-neural	null	null	https://aclanthology.org/S15-2079	https://aclanthology.org/S15-2079.pdf	UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification	null	['ro', 'Aless Moschitti', 'Aliaksei Severyn']	2015-06-01	null	null	null	semeval-2015-6	['twitter-sentiment-analysis']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 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637726dd-ed7d-4662-a114-56866dcdc338	structured-state-space-models-for-in-context	2303.03982	null	https://arxiv.org/abs/2303.03982v2	https://arxiv.org/pdf/2303.03982v2.pdf	Structured State Space Models for In-Context Reinforcement Learning	Structured state space sequence (S4) models have recently achieved state-of-the-art performance on long-range sequence modeling tasks. These models also have fast inference speeds and parallelisable training, making them potentially useful in many reinforcement learning settings. We propose a modification to a variant of S4 that enables us to initialise and reset the hidden state in parallel, allowing us to tackle reinforcement learning tasks. We show that our modified architecture runs asymptotically faster than Transformers and performs better than LSTM models on a simple memory-based task. Then, by leveraging the model's ability to handle long-range sequences, we achieve strong performance on a challenging meta-learning task in which the agent is given a randomly-sampled continuous control environment, combined with a randomly-sampled linear projection of the environment's observations and actions. Furthermore, we show the resulting model can adapt to out-of-distribution held-out tasks. Overall, the results presented in this paper suggest that the S4 models are a strong contender for the default architecture used for in-context reinforcement learning	['Feryal Behbahani', 'Satinder Singh', 'Jakob Foerster', 'Emilio Parisotto', 'Albert Gu', 'Yannick Schroecker', 'Chris Lu']	2023-03-07	null	null	null	null	['continuous-control']	['playing-games']	[ 1.48582935e-01 -3.19482312e-02 -3.85480136e-01 -1.86736852e-01 -5.32882750e-01 -5.23459375e-01 9.71030176e-01 -1.42074227e-01 -9.28788006e-01 9.60303962e-01 1.10665075e-01 -5.49772322e-01 2.28081997e-02 -5.68245590e-01 -1.07894826e+00 -8.48030388e-01 -4.10480738e-01 6.80405438e-01 4.49197471e-01 -5.80858767e-01 3.07498783e-01 3.41955721e-01 -1.48437905e+00 1.28213033e-01 3.43499064e-01 4.87573445e-01 7.95924187e-01 9.57239330e-01 1.31562173e-01 1.29695439e+00 -4.98082250e-01 2.20774516e-01 7.62289912e-02 -5.07388473e-01 -8.56290698e-01 -2.36746043e-01 4.51386310e-02 -4.53199595e-01 -5.58110297e-01 6.75295532e-01 4.62329775e-01 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8ba27f33-263f-43fb-9a32-fa553b6e8f80	deep-learning-based-gait-recognition-using	1811.00338	null	https://arxiv.org/abs/1811.00338v3	https://arxiv.org/pdf/1811.00338v3.pdf	Deep Learning-Based Gait Recognition Using Smartphones in the Wild	Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to capture gait dynamics. These inertial sensors are commonly integrated into smartphones and are widely used by the average person, which makes gait data convenient and inexpensive to collect. In this paper, we study gait recognition using smartphones in the wild. In contrast to traditional methods, which often require a person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under unconstrained conditions without knowing when, where, and how the user walks. To obtain good person identification and authentication performance, deep-learning techniques are presented to learn and model the gait biometrics based on walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones for a total of 118 subjects are used for evaluations. The experiments show that the proposed method achieves higher than 93.5\% and 93.7\% accuracies in person identification and authentication, respectively.	['Yi Zhao', 'Yanling Wang', 'Qin Zou', 'Qingquan Li', 'Qian Wang']	2018-11-01	null	null	null	null	['person-identification']	['computer-vision']	[-2.26618107e-02 -6.49190247e-01 -2.50540584e-01 -2.58487016e-01 -1.05807170e-01 9.99584571e-02 3.51913311e-02 -7.76865939e-03 -5.80046296e-01 6.86911643e-01 -2.15461254e-02 -1.81904688e-01 2.27830365e-01 -8.94368470e-01 -2.96001762e-01 -8.43058348e-01 -4.07397486e-02 -9.88910347e-02 -2.30457380e-01 2.48021800e-02 -5.39144501e-02 1.49654657e-01 -1.53544903e+00 -6.87015831e-01 8.12933683e-01 1.18783593e+00 -2.14476362e-01 4.53562856e-01 4.09828812e-01 1.07446946e-01 -6.28296375e-01 -4.10281122e-01 -2.46908553e-02 -2.54719049e-01 -2.57707447e-01 7.68575519e-02 -2.76829302e-02 -6.08802795e-01 -5.38719177e-01 9.21577573e-01 8.04128468e-01 6.16853647e-02 4.18225855e-01 -1.12974966e+00 -6.98265254e-01 9.74921286e-02 -7.44893670e-01 6.94013387e-02 6.96765721e-01 1.84782401e-01 5.58010221e-01 -4.48786646e-01 -7.47237280e-02 7.78670728e-01 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14178148-442a-4e35-97c1-d1346d17b79a	how-does-value-distribution-in-distributional	2209.14513	null	https://arxiv.org/abs/2209.14513v1	https://arxiv.org/pdf/2209.14513v1.pdf	How Does Value Distribution in Distributional Reinforcement Learning Help Optimization?	We consider the problem of learning a set of probability distributions from the Bellman dynamics in distributional reinforcement learning~(RL) that learns the whole return distribution compared with only its expectation in classical RL. Despite its success to obtain superior performance, we still have a poor understanding of how the value distribution in distributional RL works. In this study, we analyze the optimization benefits of distributional RL by leverage of additional value distribution information over classical RL in the Neural Fitted Z-Iteration~(Neural FZI) framework. To begin with, we demonstrate that the distribution loss of distributional RL has desirable smoothness characteristics and hence enjoys stable gradients, which is in line with its tendency to promote optimization stability. Furthermore, the acceleration effect of distributional RL is revealed by decomposing the return distribution. It turns out that distributional RL can perform favorably if the value distribution approximation is appropriate, measured by the variance of gradient estimates in each environment for any specific distributional RL algorithm. Rigorous experiments validate the stable optimization behaviors of distributional RL, contributing to its acceleration effects compared to classical RL. The findings of our research illuminate how the value distribution in distributional RL algorithms helps the optimization.	['Linglong Kong', 'Bei Jiang', 'Ke Sun']	2022-09-29	null	null	null	null	['distributional-reinforcement-learning']	['methodology']	[-3.25331718e-01 8.87529477e-02 -3.72160882e-01 -3.65621418e-01 -5.97057700e-01 -5.65765262e-01 3.35015893e-01 1.70038342e-01 -7.57183850e-01 8.37926984e-01 2.45020494e-01 -3.49075198e-01 -5.16643047e-01 -7.37183213e-01 -8.54590774e-01 -1.00983894e+00 -2.44968146e-01 2.58722544e-01 -4.46919173e-01 -4.20919150e-01 3.22594643e-01 5.07472336e-01 -1.54565001e+00 -3.24486852e-01 9.97766972e-01 8.17541480e-01 3.06562752e-01 5.96731842e-01 -1.00511968e-01 9.52710092e-01 -7.16267288e-01 -3.20577592e-01 3.19479734e-01 -3.77458394e-01 -4.13605005e-01 -4.84776199e-01 1.54204458e-01 -5.28525114e-01 -1.57191202e-01 1.32667661e+00 6.35288119e-01 5.58653533e-01 9.47118878e-01 -1.27403772e+00 -7.57854283e-01 9.35936034e-01 -6.46400690e-01 2.28836164e-01 3.54441926e-02 3.46871823e-01 1.52545476e+00 -4.26267505e-01 4.05233830e-01 1.46549284e+00 7.45524287e-01 5.94028294e-01 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e47ed1ec-2e96-4f11-9634-5cc29fb04da1	class-conditional-alignment-for-partial	2003.06722	null	https://arxiv.org/abs/2003.06722v1	https://arxiv.org/pdf/2003.06722v1.pdf	Class Conditional Alignment for Partial Domain Adaptation	Adversarial adaptation models have demonstrated significant progress towards transferring knowledge from a labeled source dataset to an unlabeled target dataset. Partial domain adaptation (PDA) investigates the scenarios in which the source domain is large and diverse, and the target label space is a subset of the source label space. The main purpose of PDA is to identify the shared classes between the domains and promote learning transferable knowledge from these classes. In this paper, we propose a multi-class adversarial architecture for PDA. The proposed approach jointly aligns the marginal and class-conditional distributions in the shared label space by minimaxing a novel multi-class adversarial loss function. Furthermore, we incorporate effective regularization terms to encourage selecting the most relevant subset of source domain classes. In the absence of target labels, the proposed approach is able to effectively learn domain-invariant feature representations, which in turn can enhance the classification performance in the target domain. Comprehensive experiments on three benchmark datasets Office-31, Office-Home, and Caltech-Office corroborate the effectiveness of the proposed approach in addressing different partial transfer learning tasks.	['Farhad Kamangar', 'Mohsen Kheirandishfard', 'Fariba Zohrizadeh']	2020-03-14	null	null	null	null	['partial-domain-adaptation']	['methodology']	[ 5.93124151e-01 8.06970745e-02 -2.81222165e-01 -5.46191812e-01 -1.04634809e+00 -7.86343753e-01 5.89917302e-01 -1.05861671e-01 -2.94095457e-01 1.08425617e+00 1.14112794e-02 5.91063090e-02 -1.26151964e-01 -8.19607377e-01 -8.44716311e-01 -8.27730775e-01 3.49063784e-01 5.37159681e-01 4.03312482e-02 -2.17416540e-01 -1.08414754e-01 5.00907421e-01 -1.06130362e+00 2.55025387e-01 1.07277763e+00 1.09617805e+00 -6.19273679e-03 2.62443542e-01 -2.12645307e-02 7.38110781e-01 -6.94531024e-01 -2.37982750e-01 5.25833011e-01 -4.43523705e-01 -8.52711976e-01 1.75919980e-01 5.84109604e-01 -5.21959364e-03 -1.43502578e-01 1.13985300e+00 4.54121590e-01 3.85915548e-01 1.09345734e+00 -1.44372666e+00 -7.32234538e-01 8.70833471e-02 -3.75979930e-01 4.77468818e-02 1.19857073e-01 -1.04653567e-01 7.74624407e-01 -7.89643228e-01 6.27252698e-01 1.14209294e+00 4.51435238e-01 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4e8c5d2f-24e9-4a4d-97e2-c293c0ef7e42	dialogue-act-recognition-for-text-based	null	null	https://aclanthology.org/W15-5952	https://aclanthology.org/W15-5952.pdf	Dialogue Act Recognition for Text-based Sinhala	null	['Surangika Ranathunga', 'B', 'Sudheera Palihakkara', 'Sahab', 'Chamika ara', 'Ahsan Shamsudeen', 'Dammina u']	2015-12-01	null	null	null	ws-2015-12	['meeting-summarization']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 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9db46eba-a32f-4b2a-8d8e-6c96cba786f4	q-attention-enabling-efficient-learning-for	2105.14829	null	https://arxiv.org/abs/2105.14829v2	https://arxiv.org/pdf/2105.14829v2.pdf	Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation	Despite the success of reinforcement learning methods, they have yet to have their breakthrough moment when applied to a broad range of robotic manipulation tasks. This is partly due to the fact that reinforcement learning algorithms are notoriously difficult and time consuming to train, which is exacerbated when training from images rather than full-state inputs. As humans perform manipulation tasks, our eyes closely monitor every step of the process with our gaze focusing sequentially on the objects being manipulated. With this in mind, we present our Attention-driven Robotic Manipulation (ARM) algorithm, which is a general manipulation algorithm that can be applied to a range of sparse-rewarded tasks, given only a small number of demonstrations. ARM splits the complex task of manipulation into a 3 stage pipeline: (1) a Q-attention agent extracts relevant pixel locations from RGB and point cloud inputs, (2) a next-best pose agent that accepts crops from the Q-attention agent and outputs poses, and (3) a control agent that takes the goal pose and outputs joint actions. We show that current learning algorithms fail on a range of RLBench tasks, whilst ARM is successful.	['Andrew J. Davison', 'Stephen James']	2021-05-31	null	null	null	null	['robot-task-planning']	['robots']	[ 5.00824094e-01 1.38660744e-01 -3.51535194e-02 -1.07203007e-01 -6.91807747e-01 -5.92177212e-01 5.52737653e-01 -1.33832306e-01 -5.27680516e-01 6.79134011e-01 -6.84258416e-02 -1.59236714e-01 -4.15120691e-01 -3.82142067e-01 -7.85490632e-01 -6.27225101e-01 -1.73110098e-01 7.86985278e-01 2.74073601e-01 -3.26035738e-01 5.66628933e-01 6.75349593e-01 -1.73316467e+00 -1.45118102e-01 3.13474774e-01 8.31678748e-01 5.68152547e-01 1.10155928e+00 3.49683791e-01 1.14778936e+00 -4.81895179e-01 2.11794332e-01 4.03330177e-01 -6.02434039e-01 -8.18487823e-01 1.73610106e-01 1.47415891e-01 -8.78553152e-01 -4.91179019e-01 7.75459766e-01 4.02498484e-01 5.48553407e-01 6.05785668e-01 -1.47551835e+00 -3.84829193e-01 4.36292380e-01 -5.67736745e-01 7.81632662e-02 3.86662155e-01 1.05803812e+00 8.85934234e-01 -6.38951838e-01 7.43168116e-01 1.41896021e+00 4.54746671e-02 5.15144229e-01 -1.13572717e+00 -3.14114511e-01 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3.90831053e-01 5.00111759e-01 -8.73873234e-01 -1.51860103e-01 -7.06089213e-02]	[4.644885063171387, 0.7178045511245728]
394e79e1-8c19-4194-82ed-246830b0dafe	optimization-algorithms-in-smart-grids-a	2301.07512	null	https://arxiv.org/abs/2301.07512v1	https://arxiv.org/pdf/2301.07512v1.pdf	Optimization Algorithms in Smart Grids: A Systematic Literature Review	Electrical smart grids are units that supply electricity from power plants to the users to yield reduced costs, power failures/loss, and maximized energy management. Smart grids (SGs) are well-known devices due to their exceptional benefits such as bi-directional communication, stability, detection of power failures, and inter-connectivity with appliances for monitoring purposes. SGs are the outcome of different modern applications that are used for managing data and security, i.e., modeling, monitoring, optimization, and/or Artificial Intelligence. Hence, the importance of SGs as a research field is increasing with every passing year. This paper focuses on novel features and applications of smart grids in domestic and industrial sectors. Specifically, we focused on Genetic algorithm, Particle Swarm Optimization, and Grey Wolf Optimization to study the efforts made up till date for maximized energy management and cost minimization in SGs. Therefore, we collected 145 research works (2011 to 2022) in this systematic literature review. This research work aims to figure out different features and applications of SGs proposed in the last decade and investigate the trends in popularity of SGs for different regions of world. Our finding is that the most popular optimization algorithm being used by researchers to bring forward new solutions for energy management and cost effectiveness in SGs is Particle Swarm Optimization. We also provide a brief overview of objective functions and parameters used in the solutions for energy and cost effectiveness as well as discuss different open research challenges for future research works.	['Ali Bou Nassif', 'Ala Altaweel', 'Sidra Aslam']	2023-01-16	null	null	null	null	['energy-management']	['time-series']	[-3.31341445e-01 -4.67389256e-01 -1.13314003e-01 5.76074049e-02 2.41635427e-01 -4.71138775e-01 2.58959234e-01 2.80176908e-01 2.41692245e-01 1.13433659e+00 -9.11820754e-02 -9.00511965e-02 -5.07140517e-01 -1.06258380e+00 2.16945097e-01 -1.37020743e+00 -2.33574688e-01 2.12754235e-01 -2.41135702e-01 -2.77042776e-01 5.16143620e-01 7.36957788e-01 -1.38370860e+00 -6.60361946e-01 1.25497556e+00 1.10984612e+00 6.48692548e-01 1.84797689e-01 3.59822541e-01 9.48387161e-02 -1.07785404e+00 3.01801682e-01 1.66211858e-01 -7.11565495e-01 -4.50540543e-01 -2.22834758e-02 -1.21887386e+00 1.14001453e-01 1.88510045e-01 1.33892477e+00 8.39668095e-01 3.52309018e-01 4.35679317e-01 -1.81421316e+00 -6.88355207e-01 5.83677649e-01 -7.48491466e-01 4.84507650e-01 2.07929254e-01 3.54155213e-01 6.99548841e-01 -1.52010128e-01 1.79954264e-02 6.53496265e-01 8.44084695e-02 4.80701104e-02 -7.06049621e-01 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-1.06144762e+00 1.34419009e-01 -2.95231014e-01 -6.17099583e-01 -5.76347172e-01 -3.37449402e-01]	[5.756521701812744, 2.6291842460632324]
ed70e332-808e-4a47-913f-7f5297a8915d	endmember-guided-unmixing-network-egu-net-a	2105.10194	null	https://arxiv.org/abs/2105.10194v1	https://arxiv.org/pdf/2105.10194v1.pdf	Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing	Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing, yet their ability to simultaneously generalize various spectral variabilities and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various spectral variabilities. Inspired by the powerful learning ability of deep learning, we attempt to develop a general deep learning approach for hyperspectral unmixing, by fully considering the properties of endmembers extracted from the hyperspectral imagery, called endmember-guided unmixing network (EGU-Net). Beyond the alone autoencoder-like architecture, EGU-Net is a two-stream Siamese deep network, which learns an additional network from the pure or nearly-pure endmembers to correct the weights of another unmixing network by sharing network parameters and adding spectrally meaningful constraints (e.g., non-negativity and sum-to-one) towards a more accurate and interpretable unmixing solution. Furthermore, the resulting general framework is not only limited to pixel-wise spectral unmixing but also applicable to spatial information modeling with convolutional operators for spatial-spectral unmixing. Experimental results conducted on three different datasets with the ground-truth of abundance maps corresponding to each material demonstrate the effectiveness and superiority of the EGU-Net over state-of-the-art unmixing algorithms. The codes will be available from the website: https://github.com/danfenghong/IEEE_TNNLS_EGU-Net.	['Bing Zhang', 'Uta Heiden', 'Jocelyn Chanussot', 'Naoto Yokoya', 'Jing Yao', 'Lianru Gao', 'Danfeng Hong']	2021-05-21	null	null	null	null	['hyperspectral-unmixing']	['computer-vision']	[ 3.24196011e-01 -4.95760202e-01 8.21390189e-03 -2.33793035e-01 -4.63837147e-01 -5.40416420e-01 2.88039178e-01 -2.85043478e-01 1.54091592e-03 7.70125747e-01 1.12496503e-01 -3.80974770e-01 -4.34479952e-01 -8.67423832e-01 -7.77218223e-01 -1.13038373e+00 -9.71056297e-02 3.65820438e-01 -6.59085095e-01 -2.50471294e-01 -4.34878647e-01 6.28749371e-01 -1.50768709e+00 1.18819706e-01 1.29388189e+00 1.05555201e+00 2.74512559e-01 3.78343403e-01 9.62834582e-02 3.96772295e-01 -2.55658403e-02 2.12782189e-01 6.07695699e-01 -3.90710741e-01 -4.16234434e-01 3.72344375e-01 6.54328048e-01 -5.20161033e-01 -6.33157551e-01 1.49644196e+00 4.40612972e-01 4.26099777e-01 6.07050836e-01 -1.17301476e+00 -7.81325340e-01 7.97521055e-01 -6.75802350e-01 -1.40710160e-01 -4.16959345e-01 2.78819710e-01 9.04822171e-01 -7.22667754e-01 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6640b634-9726-48d9-951b-73bccdcccd54	how-to-teach-dnns-to-pay-attention-to-the	2004.08250	null	https://arxiv.org/abs/2004.08250v1	https://arxiv.org/pdf/2004.08250v1.pdf	How to Teach DNNs to Pay Attention to the Visual Modality in Speech Recognition	Audio-Visual Speech Recognition (AVSR) seeks to model, and thereby exploit, the dynamic relationship between a human voice and the corresponding mouth movements. A recently proposed multimodal fusion strategy, AV Align, based on state-of-the-art sequence to sequence neural networks, attempts to model this relationship by explicitly aligning the acoustic and visual representations of speech. This study investigates the inner workings of AV Align and visualises the audio-visual alignment patterns. Our experiments are performed on two of the largest publicly available AVSR datasets, TCD-TIMIT and LRS2. We find that AV Align learns to align acoustic and visual representations of speech at the frame level on TCD-TIMIT in a generally monotonic pattern. We also determine the cause of initially seeing no improvement over audio-only speech recognition on the more challenging LRS2. We propose a regularisation method which involves predicting lip-related Action Units from visual representations. Our regularisation method leads to better exploitation of the visual modality, with performance improvements between 7% and 30% depending on the noise level. Furthermore, we show that the alternative Watch, Listen, Attend, and Spell network is affected by the same problem as AV Align, and that our proposed approach can effectively help it learn visual representations. Our findings validate the suitability of the regularisation method to AVSR and encourage researchers to rethink the multimodal convergence problem when having one dominant modality.	['George Sterpu', 'Naomi Harte', 'Christian Saam']	2020-04-17	null	null	null	null	['audio-visual-speech-recognition']	['speech']	[ 5.40425301e-01 5.58487512e-02 -6.65513128e-02 -5.42682670e-02 -8.87203038e-01 -4.53556567e-01 9.04454052e-01 -2.80021548e-01 -3.00826252e-01 2.74695486e-01 6.21181548e-01 -1.99983090e-01 -7.63708632e-03 1.26006499e-01 -5.84649324e-01 -9.07757819e-01 2.40856186e-01 9.43092704e-02 5.97852096e-03 -2.67412096e-01 2.08030179e-01 4.38174009e-01 -2.07322145e+00 6.29852593e-01 2.46416256e-01 1.09902132e+00 4.13437098e-01 1.09799337e+00 -2.04763517e-01 6.64469004e-01 -5.88748395e-01 -8.24998766e-02 9.90549177e-02 -6.45167947e-01 -6.57829881e-01 1.29659191e-01 7.36404240e-01 2.17873842e-01 -2.76389241e-01 7.58195877e-01 9.06365395e-01 1.37871012e-01 8.03080916e-01 -1.43755698e+00 -4.39583838e-01 3.46193522e-01 -5.68532825e-01 2.12184891e-01 6.53630316e-01 4.47428524e-01 1.11486006e+00 -8.71350944e-01 5.80087721e-01 1.60556483e+00 5.76187611e-01 8.43529582e-01 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8dec8a2e-37d6-480e-8e00-44e62b219955	active-gradual-machine-learning-for-entity	null	null	https://openreview.net/forum?id=xaWIeItQ7zb	https://openreview.net/pdf?id=xaWIeItQ7zb	Active Gradual Machine Learning for Entity Resolution	Recent work has shown that the task of entity resolution (ER) can be effectively performed by gradual machine learning (GML). GML begins with some easy instances, which can be automatically labeled by the machine with high accuracy, and then gradually labels more challenging instances by iterative knowledge conveyance in a factor graph. Without involving manual labeling effort, the current GML solution for ER is unsupervised. However, its performance is limited by inaccurate and insufficient knowledge conveyance. Therefore, there is a need to investigate how to improve knowledge conveyance by manual labeling effort.In this paper, we propose an active learning (AL) approach based on GML for ER. It iteratively generates new knowledge in the form of one-sided rules by manual label verification and instills them into a factor graph for improved knowledge conveyance. We first present a technique of knowledge discovery based on genetic mutations, which can generate effective knowledge rules with very small manual verification cost. Then, we demonstrate how to leverage the generated rules for improved knowledge conveyance by measuring their influence over label status by the metric of skyline distance. We have evaluated the performance of the proposed approach by a comparative study on real benchmark data. Our extensive experiments have shown that it can significantly improve the performance of unsupervised GML with very small manual cost; furthermore, it outperforms the state-of-the-art AL solutions for deep learning by considerable margins in terms of learning efficiency.	['Anonymous']	2022-01-16	null	null	null	acl-arr-january-2022-1	['entity-resolution']	['natural-language-processing']	[ 4.18961048e-01 6.13364458e-01 -5.47253013e-01 -3.93388659e-01 -9.63267446e-01 -4.52378064e-01 3.84890348e-01 3.48426342e-01 -4.74770665e-01 1.09247828e+00 -4.76753525e-02 -2.11633950e-01 -5.51086664e-01 -1.03775287e+00 -1.05469954e+00 -6.99373662e-01 -5.87514639e-02 6.54172182e-01 2.39629060e-01 -1.20824901e-02 -8.91507193e-02 3.48071456e-01 -1.45452368e+00 2.90748179e-01 1.29615629e+00 7.64248848e-01 -5.12632579e-02 3.11841637e-01 -5.22646368e-01 1.25784242e+00 -7.11430132e-01 -4.79042590e-01 1.64883345e-01 -1.20632358e-01 -9.47565317e-01 -1.44718990e-01 1.57844603e-01 1.56209618e-02 -5.37759624e-02 9.61609602e-01 5.53865016e-01 1.77818298e-01 6.55117452e-01 -1.12846458e+00 -6.52683258e-01 1.25623429e+00 -6.29424155e-01 -1.05010591e-01 2.02198207e-01 -1.80751428e-01 1.15622425e+00 -9.20298994e-01 7.91531742e-01 1.12776864e+00 6.62158728e-01 3.26859385e-01 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a7b3f551-c4d6-44e0-8883-c7fc70a8cb74	consistency-of-spectral-hypergraph	1505.01582	null	http://arxiv.org/abs/1505.01582v2	http://arxiv.org/pdf/1505.01582v2.pdf	Consistency of Spectral Hypergraph Partitioning under Planted Partition Model	Hypergraph partitioning lies at the heart of a number of problems in machine learning and network sciences. Many algorithms for hypergraph partitioning have been proposed that extend standard approaches for graph partitioning to the case of hypergraphs. However, theoretical aspects of such methods have seldom received attention in the literature as compared to the extensive studies on the guarantees of graph partitioning. For instance, consistency results of spectral graph partitioning under the stochastic block model are well known. In this paper, we present a planted partition model for sparse random non-uniform hypergraphs that generalizes the stochastic block model. We derive an error bound for a spectral hypergraph partitioning algorithm under this model using matrix concentration inequalities. To the best of our knowledge, this is the first consistency result related to partitioning non-uniform hypergraphs.	['Debarghya Ghoshdastidar', 'Ambedkar Dukkipati']	2015-05-07	null	null	null	null	['hypergraph-partitioning']	['graphs']	[ 2.94530720e-01 5.70765555e-01 -5.71868896e-01 -1.10098168e-01 -2.71477878e-01 -7.53544807e-01 -9.83192772e-02 2.58146495e-01 2.21910581e-01 7.99857438e-01 -2.36575007e-01 -4.64605451e-01 -8.43938470e-01 -9.69293952e-01 -5.18446982e-01 -8.15268755e-01 -3.63191485e-01 9.91063118e-01 3.42873394e-01 1.57296613e-01 2.70192251e-02 5.04548907e-01 -1.31265604e+00 4.52243835e-02 8.56821418e-01 3.31656694e-01 -1.92162022e-01 4.91929173e-01 -3.42410319e-02 2.12535441e-01 -2.79743403e-01 -4.70734030e-01 4.77810234e-01 -5.73335290e-01 -1.19270098e+00 5.73913813e-01 2.78091758e-01 3.39525342e-01 -6.37946188e-01 1.44385231e+00 3.48888993e-01 4.92637828e-02 6.33239210e-01 -1.58873677e+00 -4.61991608e-01 1.36151409e+00 -1.21354532e+00 1.39426693e-01 3.01551759e-01 -5.75337648e-01 1.36461020e+00 -1.69800341e-01 3.97439539e-01 1.09256411e+00 4.18078721e-01 1.62882641e-01 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652ed44c-4393-4acb-9c4e-1dc5a3246bbc	receiver-bandwidth-extension-beyond-nyquist	2210.07821	null	https://arxiv.org/abs/2210.07821v2	https://arxiv.org/pdf/2210.07821v2.pdf	Receiver Bandwidth Extension Beyond Nyquist Using Channel Bonding	Current and upcoming communication and sensing technologies require ever larger bandwidths. Channel bonding can be utilized to extend a receiver's instantaneous bandwidth beyond a single converter's Nyquist limit. Two potential joint front-end and converter design approaches are theoretically introduced, realized and evaluated in this paper. The Xilinx RFSoC platform with its 5 GSa/s analog to digital converters (ADCs) is used to implement both a hybrid coupler based in-phase/quadrature (I/Q) sampling and a time-interleaved sampling approach along with channel bonding. Both realizations are demonstrated to be able to reconstruct instantaneous bandwidths of 5 GHz with up to 49 dB image rejection ratio (IRR) typically within 4 to 8 dB the front-ends' theoretical limits.	['Alexander Ihlow', 'Maximilian Engelhardt', 'Michael Schubert', 'Carsten Andrich', 'Sebastian Giehl']	2022-10-14	null	null	null	null	['bandwidth-extension', 'bandwidth-extension']	['audio', 'speech']	[ 4.34994340e-01 1.75020605e-01 1.60235111e-02 -2.40317568e-01 -7.47966945e-01 -7.48409331e-01 3.70708942e-01 -6.57483339e-02 -2.73678273e-01 8.12399805e-01 2.11064168e-03 -5.15059054e-01 -3.57830167e-01 -7.02636719e-01 9.63664651e-02 -4.11721259e-01 -2.63088524e-01 1.84594050e-01 -9.70023870e-02 2.88513392e-01 -4.18269336e-02 4.66506124e-01 -1.29484737e+00 -3.88436988e-02 7.80948639e-01 1.55764914e+00 -2.52563775e-01 7.98076272e-01 5.14454469e-02 4.24391568e-01 -9.97487366e-01 -7.51197338e-04 2.15643540e-01 -4.22452837e-01 -1.69285342e-01 -5.10022759e-01 1.96462259e-01 -7.96492875e-01 -2.84698993e-01 7.25512683e-01 5.91819584e-01 -7.20210254e-01 5.76002181e-01 -8.44642639e-01 1.62724048e-01 8.11163008e-01 -4.18220848e-01 -3.14658619e-02 6.48778200e-01 2.30295569e-01 3.86383981e-01 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a5e085e1-4c8c-4d82-bf8c-7c6915352a99	decipherment-of-substitution-ciphers-with	null	null	https://aclanthology.org/d18-1102	https://aclanthology.org/d18-1102.pdf	Decipherment of Substitution Ciphers with Neural Language Models	null	['Anahita Mansouri Bigvand', 'Nishant Kambhatla', 'Anoop Sarkar']	2018-10-01	null	https://aclanthology.org/D18-1102	https://aclanthology.org/D18-1102.pdf	emnlp-2018-10	['decipherment']	['natural-language-processing']	[-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01 -8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01 -5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01 -2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01 -7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302e-01 8.47113907e-01 6.29996777e-01 -1.62287033e+00 5.77558100e-01 6.81926727e-01 5.49597681e-01 5.66466339e-02 1.04480565e+00 6.13576770e-02 1.59847176e+00 -6.05450153e-01 -4.56729174e-01 1.43710867e-01 -1.88022718e-01 -5.35770595e-01 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cbe9ba2e-4f82-4d2a-892b-e9c61cf03fa6	investigating-capsule-networks-with-dynamic	1804.00538	null	http://arxiv.org/abs/1804.00538v4	http://arxiv.org/pdf/1804.00538v4.pdf	Investigating Capsule Networks with Dynamic Routing for Text Classification	In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain "background" information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over strong baseline methods. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling.	['Jianbo Ye', 'Zhou Zhao', 'Zeyang Lei', 'Min Yang', 'Wei Zhao', 'Suofei Zhang']	2018-03-29	investigating-capsule-networks-with-dynamic-1	https://aclanthology.org/D18-1350	https://aclanthology.org/D18-1350.pdf	emnlp-2018-10	['subjectivity-analysis']	['natural-language-processing']	[-3.54809500e-02 9.05770659e-02 -5.82833171e-01 -4.70997155e-01 -4.88049597e-01 -7.19240844e-01 5.13542593e-01 3.56326193e-01 -5.88420592e-02 3.89752746e-01 7.59744585e-01 -2.50536680e-01 -1.14073372e-02 -5.21200120e-01 -7.62628734e-01 -4.50888366e-01 -2.85421818e-01 4.20824975e-01 -5.41731156e-02 2.21922934e-01 -1.60415143e-01 3.85518670e-02 -8.34713399e-01 8.11099172e-01 4.05012429e-01 9.98258471e-01 -1.89641938e-01 2.70802140e-01 -1.82513878e-01 1.26476765e+00 -7.95043588e-01 -3.62712830e-01 7.88879395e-02 -1.29933149e-01 -7.40034342e-01 2.85651445e-01 7.07892418e-01 -1.23774998e-01 -5.20825684e-01 1.24194181e+00 4.11224425e-01 -1.88741952e-01 5.66091537e-01 -9.65317786e-01 -8.44630599e-01 1.20800543e+00 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9bbbee83-da2a-4458-badb-bff0f27aef25	when-more-data-hurts-a-troubling-quirk-in	null	null	https://openreview.net/forum?id=53F2mLQQj8J	https://openreview.net/pdf?id=53F2mLQQj8J	When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems	In natural language understanding (NLU) production systems, the end users' evolving needs necessitate the addition of new abilities, indexed by discrete symbols, requiring additional training data and resulting in dynamic, ever-growing datasets. Dataset growth introduces new challenges: we find that when learning to map inputs to a new symbol from a fixed number of annotations, more data can in fact {\emph{reduce}} the model's performance on examples that involve this new symbol. We show that this trend holds for multiple models on two datasets for common NLU tasks: intent recognition and semantic parsing. We demonstrate that the performance decrease is largely associated with an effect we refer to as source signal dilution, which occurs when strong lexical cues in the training data become diluted as the dataset grows. Selectively dropping training examples to prevent source dilution often reverses the performance decrease, suggesting a direction for improving models. We release our code and models at \url{anonymous-link}.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['intent-recognition']	['natural-language-processing']	[ 6.09781742e-01 3.91868144e-01 -3.99768174e-01 -5.45224845e-01 -7.08758771e-01 -8.78121912e-01 4.67683375e-01 2.95084387e-01 -5.10386050e-01 6.76967978e-01 5.10239124e-01 -5.50917268e-01 1.78860515e-01 -6.08595133e-01 -1.06318164e+00 8.29630271e-02 1.57911301e-01 4.63332146e-01 1.45007342e-01 -1.92883849e-01 2.12958738e-01 -1.02780290e-01 -1.28109586e+00 4.40700561e-01 7.43652105e-01 6.04957998e-01 4.23675627e-01 6.42471015e-01 -5.26602924e-01 9.36886489e-01 -7.53553271e-01 -4.22337472e-01 3.41630995e-01 -4.94489461e-01 -9.18739080e-01 -2.61136740e-01 6.30600750e-01 -4.24291879e-01 -8.57737288e-03 9.13548887e-01 1.64570794e-01 1.73841771e-02 5.24063885e-01 -1.09545100e+00 -8.67250800e-01 1.12559426e+00 -3.95996869e-01 4.10336077e-01 3.73763084e-01 1.35509044e-01 1.39389682e+00 -7.56559372e-01 9.37044680e-01 1.20122612e+00 8.15331936e-01 7.52397537e-01 -1.24982393e+00 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2a36b5e5-a039-498d-9f08-f8b90078cab3	continuous-mixtures-of-tractable	2209.10584	null	https://arxiv.org/abs/2209.10584v3	https://arxiv.org/pdf/2209.10584v3.pdf	Continuous Mixtures of Tractable Probabilistic Models	Probabilistic models based on continuous latent spaces, such as variational autoencoders, can be understood as uncountable mixture models where components depend continuously on the latent code. They have proven to be expressive tools for generative and probabilistic modelling, but are at odds with tractable probabilistic inference, that is, computing marginals and conditionals of the represented probability distribution. Meanwhile, tractable probabilistic models such as probabilistic circuits (PCs) can be understood as hierarchical discrete mixture models, and thus are capable of performing exact inference efficiently but often show subpar performance in comparison to continuous latent-space models. In this paper, we investigate a hybrid approach, namely continuous mixtures of tractable models with a small latent dimension. While these models are analytically intractable, they are well amenable to numerical integration schemes based on a finite set of integration points. With a large enough number of integration points the approximation becomes de-facto exact. Moreover, for a finite set of integration points, the integration method effectively compiles the continuous mixture into a standard PC. In experiments, we show that this simple scheme proves remarkably effective, as PCs learnt this way set new state of the art for tractable models on many standard density estimation benchmarks.	['Robert Peharz', 'Cassio de Campos', 'Erik Quaeghebeur', 'Gennaro Gala', 'Alvaro H. C. Correia']	2022-09-21	null	null	null	null	['numerical-integration']	['miscellaneous']	[-2.37173259e-01 2.96231538e-01 -9.54862982e-02 6.60521984e-02 -1.14832044e+00 -8.59412432e-01 1.03529561e+00 -2.03085348e-01 -6.03188463e-02 8.56180012e-01 -5.89110814e-02 -3.27935308e-01 -2.84456074e-01 -1.01562262e+00 -1.04836535e+00 -1.19468677e+00 -3.95881906e-02 1.14457154e+00 -1.62801482e-02 2.67232656e-01 -1.31229639e-01 5.05387008e-01 -1.61676919e+00 -2.47360379e-01 6.72064781e-01 8.15481484e-01 -2.07686514e-01 8.29144239e-01 -4.05443549e-01 6.43986046e-01 -4.84909981e-01 -5.35770714e-01 -2.47374445e-01 -1.96175739e-01 -6.10536575e-01 -5.46720810e-02 2.10334621e-02 -4.19529319e-01 -2.78498441e-01 1.22708547e+00 -4.57052216e-02 9.01028216e-02 1.29477096e+00 -1.49078941e+00 -4.95077163e-01 9.42246437e-01 -2.84674674e-01 -1.40682593e-01 1.67193357e-02 -2.38308191e-01 1.04590964e+00 -7.13463306e-01 3.79086852e-01 1.35937345e+00 8.54639888e-01 3.07188869e-01 -1.81786609e+00 -4.14209217e-01 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72278a00-1d7c-487d-a73c-262ebdd99237	monolingual-and-cross-lingual-acceptability	2109.12053	null	https://arxiv.org/abs/2109.12053v1	https://arxiv.org/pdf/2109.12053v1.pdf	Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus	The development of automated approaches to linguistic acceptability has been greatly fostered by the availability of the English CoLA corpus, which has also been included in the widely used GLUE benchmark. However, this kind of research for languages other than English, as well as the analysis of cross-lingual approaches, has been hindered by the lack of resources with a comparable size in other languages. We have therefore developed the ItaCoLA corpus, containing almost 10,000 sentences with acceptability judgments, which has been created following the same approach and the same steps as the English one. In this paper we describe the corpus creation, we detail its content, and we present the first experiments on this new resource. We compare in-domain and out-of-domain classification, and perform a specific evaluation of nine linguistic phenomena. We also present the first cross-lingual experiments, aimed at assessing whether multilingual transformerbased approaches can benefit from using sentences in two languages during fine-tuning.	['Sara Tonelli', 'Elisa Leonardelli', 'Raffaele Guarasci', 'Daniela Trotta']	2021-09-24	null	https://aclanthology.org/2021.findings-emnlp.250	https://aclanthology.org/2021.findings-emnlp.250.pdf	findings-emnlp-2021-11	['linguistic-acceptability']	['natural-language-processing']	[-2.69627810e-01 1.51774973e-01 -7.44401244e-03 -6.20598495e-01 -1.00441360e+00 -8.03833008e-01 8.67059946e-01 6.76123381e-01 -7.54572809e-01 8.70554864e-01 3.80002677e-01 -3.92487854e-01 -1.15181088e-01 -6.20208263e-01 -3.35264385e-01 -3.61966640e-01 1.08950920e-01 7.72209466e-01 3.21738213e-01 -5.96131206e-01 2.48918861e-01 9.53403339e-02 -1.79887855e+00 4.95544285e-01 9.98869300e-01 7.89902508e-01 1.19387820e-01 8.45937803e-02 -3.32130224e-01 4.79415536e-01 -6.34834528e-01 -1.04531813e+00 -1.46629751e-01 -3.59279156e-01 -1.02160716e+00 -1.69981346e-01 2.41503239e-01 3.25258553e-01 7.42638707e-01 9.77935076e-01 5.25768042e-01 -1.37691200e-01 4.17530149e-01 -8.03480268e-01 -5.46496212e-01 1.11230707e+00 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2b7c1b77-2c6d-4595-9730-e4df328d5012	macro-micro-adversarial-network-for-human	1807.08260	null	http://arxiv.org/abs/1807.08260v2	http://arxiv.org/pdf/1807.08260v2.pdf	Macro-Micro Adversarial Network for Human Parsing	In human parsing, the pixel-wise classification loss has drawbacks in its low-level local inconsistency and high-level semantic inconsistency. The introduction of the adversarial network tackles the two problems using a single discriminator. However, the two types of parsing inconsistency are generated by distinct mechanisms, so it is difficult for a single discriminator to solve them both. To address the two kinds of inconsistencies, this paper proposes the Macro-Micro Adversarial Net (MMAN). It has two discriminators. One discriminator, Macro D, acts on the low-resolution label map and penalizes semantic inconsistency, e.g., misplaced body parts. The other discriminator, Micro D, focuses on multiple patches of the high-resolution label map to address the local inconsistency, e.g., blur and hole. Compared with traditional adversarial networks, MMAN not only enforces local and semantic consistency explicitly, but also avoids the poor convergence problem of adversarial networks when handling high resolution images. In our experiment, we validate that the two discriminators are complementary to each other in improving the human parsing accuracy. The proposed framework is capable of producing competitive parsing performance compared with the state-of-the-art methods, i.e., mIoU=46.81% and 59.91% on LIP and PASCAL-Person-Part, respectively. On a relatively small dataset PPSS, our pre-trained model demonstrates impressive generalization ability. The code is publicly available at https://github.com/RoyalVane/MMAN.	['Junqing Yu', 'Liang Zheng', 'Zhedong Zheng', 'Yawei Luo', 'Yi Yang', 'Tao Guan']	2018-07-22	macro-micro-adversarial-network-for-human-1	http://openaccess.thecvf.com/content_ECCV_2018/html/Yawei_Luo_Macro-Micro_Adversarial_Network_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Yawei_Luo_Macro-Micro_Adversarial_Network_ECCV_2018_paper.pdf	eccv-2018-9	['human-part-segmentation', 'human-parsing']	['computer-vision', 'computer-vision']	[ 2.95291901e-01 3.66507322e-01 8.76573008e-03 -2.74137169e-01 -1.02442777e+00 -5.98260522e-01 1.61262631e-01 -1.29717007e-01 -3.31299126e-01 8.18454266e-01 -6.02427945e-02 -1.48132533e-01 3.65235656e-01 -9.02792573e-01 -8.47373605e-01 -6.72429740e-01 4.47119385e-01 1.95180371e-01 4.89656746e-01 -3.38111296e-02 -1.09464072e-01 4.56832834e-02 -1.09276342e+00 4.03753281e-01 1.17718351e+00 1.10689235e+00 5.99034019e-02 4.28117335e-01 -2.85408109e-01 6.85165882e-01 -8.01477313e-01 -8.04921508e-01 2.88422406e-01 -5.18285394e-01 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bf2be6b4-3e8f-4839-91db-217a847a6c80	bo-icp-initialization-of-iterative-closest	2304.13114	null	https://arxiv.org/abs/2304.13114v1	https://arxiv.org/pdf/2304.13114v1.pdf	BO-ICP: Initialization of Iterative Closest Point Based on Bayesian Optimization	Typical algorithms for point cloud registration such as Iterative Closest Point (ICP) require a favorable initial transform estimate between two point clouds in order to perform a successful registration. State-of-the-art methods for choosing this starting condition rely on stochastic sampling or global optimization techniques such as branch and bound. In this work, we present a new method based on Bayesian optimization for finding the critical initial ICP transform. We provide three different configurations for our method which highlights the versatility of the algorithm to both find rapid results and refine them in situations where more runtime is available such as offline map building. Experiments are run on popular data sets and we show that our approach outperforms state-of-the-art methods when given similar computation time. Furthermore, it is compatible with other improvements to ICP, as it focuses solely on the selection of an initial transform, a starting point for all ICP-based methods.	['Christoffer Heckman', 'Andrew Beathard', 'Harel Biggie']	2023-04-25	null	null	null	null	['point-cloud-registration']	['computer-vision']	[ 1.84501141e-01 -2.36639827e-01 1.87843874e-01 -4.17211145e-01 -1.24471605e+00 -4.95777339e-01 6.97883010e-01 4.56613243e-01 -5.90231657e-01 6.66951716e-01 -3.19949687e-01 -2.44036913e-01 -3.03459942e-01 -9.12743807e-01 -8.76639485e-01 -6.05167925e-01 -1.04324192e-01 1.32672954e+00 7.42342710e-01 -1.80850178e-01 7.05478013e-01 9.78897095e-01 -1.59378123e+00 -2.56548464e-01 8.68562818e-01 8.72179925e-01 4.45355743e-01 4.37763810e-01 -2.40492001e-01 -4.31184143e-01 -2.21279353e-01 -1.10489123e-01 6.06842995e-01 -1.09900542e-01 -8.39317083e-01 -1.26395568e-01 3.47282261e-01 -1.03797488e-01 4.63794410e-01 8.29356313e-01 6.29018605e-01 3.46850067e-01 6.66672349e-01 -1.15720093e+00 3.20344239e-01 1.90386176e-01 -6.91809833e-01 2.77091190e-02 6.01923704e-01 -5.72318919e-02 7.77199805e-01 -1.16192055e+00 5.99875987e-01 9.62729037e-01 1.06174636e+00 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940a80c4-b718-42b9-8677-9f4c6889b62a	bytecover2-towards-dimensionality-reduction	null	null	https://ieeexplore.ieee.org/abstract/document/9747630	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9747630	BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION	Convolutional neural network (CNN)-based methods have dominated the recent research of cover song identification (CSI). A typical example is the ByteCover system we proposed, which has achieved state-of-the-art results on all the mainstream datasets of CSI. In this paper, we propose an upgraded version of ByteCover, termed ByteCover2, which further improves ByteCover in both identification performance and efficiency. Compared with ByteCover, ByteCover2 is designed with an additional PCA-FC module, which integrates the capability of principal component analysis (PCA) and fully-connected (FC) neural network for dimensionality reduction of the audio embedding, allowing ByteCover2 to perform CSI in a more precise and efficient way. We evaluated ByteCover2 on multiple datasets in different dimension sizes and training settings, where ByteCover2 beat all the compared methods including ByteCover, even with a dimension size of 128, which is 15 times smaller than that of ByteCover.	['Zejun Ma', 'Bilei Zhu', 'Zijie Wang', 'Ke Chen', 'Xingjian Du']	2022-04-27	null	null	null	icassp-2022-4	['cover-song-identification']	['music']	[ 2.59097647e-02 -2.75728911e-01 -2.15884633e-02 1.61008865e-01 -7.44356155e-01 -5.45105934e-01 3.65232676e-01 -5.97070120e-02 -4.15472120e-01 2.49347612e-01 3.55003357e-01 4.86518852e-02 -2.39088193e-01 -7.65907943e-01 -5.29489040e-01 -7.43326187e-01 -2.84214616e-01 2.97034800e-01 -9.33499485e-02 1.36417121e-01 -1.88743725e-01 1.70722961e-01 -1.89398575e+00 2.60690719e-01 3.82022858e-01 1.32796204e+00 -1.20188467e-01 8.02062690e-01 2.89334320e-02 5.15744209e-01 -6.36116087e-01 -6.12470627e-01 3.81255239e-01 -1.58356041e-01 -5.25335789e-01 -3.78796458e-01 4.30832386e-01 -3.03482801e-01 -7.30221212e-01 7.72063911e-01 7.53424942e-01 -1.86663643e-01 4.17235315e-01 -1.02396381e+00 -4.97966021e-01 1.12244892e+00 -4.28545147e-01 1.48858294e-01 -6.22371510e-02 2.33064294e-02 1.05838513e+00 -5.66692173e-01 1.23728484e-01 1.19447672e+00 1.29748416e+00 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7edf3c3f-837e-4de7-bb32-58880ce5d4f3	addressing-cold-start-problem-for-end-to-end	2306.14310	null	https://arxiv.org/abs/2306.14310v1	https://arxiv.org/pdf/2306.14310v1.pdf	Addressing Cold Start Problem for End-to-end Automatic Speech Scoring	Integrating automatic speech scoring/assessment systems has become a critical aspect of second-language speaking education. With self-supervised learning advancements, end-to-end speech scoring approaches have exhibited promising results. However, this study highlights the significant decrease in the performance of speech scoring systems in new question contexts, thereby identifying this as a cold start problem in terms of items. With the finding of cold-start phenomena, this paper seeks to alleviate the problem by following methods: 1) prompt embeddings, 2) question context embeddings using BERT or CLIP models, and 3) choice of the pretrained acoustic model. Experiments are conducted on TOEIC speaking test datasets collected from English-as-a-second-language (ESL) learners rated by professional TOEIC speaking evaluators. The results demonstrate that the proposed framework not only exhibits robustness in a cold-start environment but also outperforms the baselines for known content.	['Seungtaek Choi', 'Jungbae Park']	2023-06-25	null	null	null	null	['self-supervised-learning']	['computer-vision']	[-1.85235232e-01 -4.27106135e-02 7.15428367e-02 -5.57555676e-01 -1.59317100e+00 -6.60907388e-01 3.57982993e-01 1.45203367e-01 -7.39420712e-01 4.64103609e-01 7.56599784e-01 -3.76035750e-01 -1.23706654e-01 -2.71342248e-01 -2.48026967e-01 -4.71125275e-01 4.27262187e-01 3.94064784e-01 4.10907149e-01 -4.66933697e-01 2.21928209e-01 9.49374735e-02 -1.71496916e+00 3.88645768e-01 1.08220124e+00 6.39209628e-01 4.33741301e-01 9.39058006e-01 -3.27330887e-01 8.51274014e-01 -9.24958944e-01 -9.32697713e-01 -3.29461634e-01 -3.79794002e-01 -8.57627511e-01 -2.22635627e-01 7.52919257e-01 -3.20242763e-01 -2.00640500e-01 9.85276282e-01 1.02556503e+00 4.17253017e-01 3.05945843e-01 -7.06809700e-01 -1.00139117e+00 7.64291227e-01 1.79707468e-01 5.12209952e-01 5.50751507e-01 -1.29457116e-02 1.31458950e+00 -1.39695120e+00 2.14520425e-01 1.00987124e+00 5.53837299e-01 9.13372457e-01 -9.09281254e-01 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-4.13990587e-01 4.16712850e-01 3.59202832e-01 -4.46386576e-01 -8.13517094e-01 1.24496315e-03]	[14.451778411865234, 6.70559024810791]
d534ac6f-104d-4510-b24a-d08f9c483529	truedeep-a-systematic-approach-of-crack	2305.19088	null	https://arxiv.org/abs/2305.19088v1	https://arxiv.org/pdf/2305.19088v1.pdf	TrueDeep: A systematic approach of crack detection with less data	Supervised and semi-supervised semantic segmentation algorithms require significant amount of annotated data to achieve a good performance. In many situations, the data is either not available or the annotation is expensive. The objective of this work is to show that by incorporating domain knowledge along with deep learning architectures, we can achieve similar performance with less data. We have used publicly available crack segmentation datasets and shown that selecting the input images using knowledge can significantly boost the performance of deep-learning based architectures. Our proposed approaches have many fold advantages such as low annotation and training cost, and less energy consumption. We have measured the performance of our algorithm quantitatively in terms of mean intersection over union (mIoU) and F score. Our algorithms, developed with 23% of the overall data; have a similar performance on the test data and significantly better performance on multiple blind datasets.	['Akshit Achara', 'Ram Krishna Pandey']	2023-05-30	null	null	null	null	['crack-segmentation', 'semi-supervised-semantic-segmentation']	['computer-vision', 'computer-vision']	[ 9.26847905e-02 1.03873432e-01 -2.11697027e-01 -6.15988791e-01 -1.28119063e+00 -7.29564011e-01 -4.16240133e-02 -8.46306328e-03 -8.39444101e-01 6.24280870e-01 -9.13924575e-02 -1.68673843e-01 6.48245662e-02 -7.63528168e-01 -1.02627921e+00 -5.46004832e-01 2.09750786e-01 7.87178934e-01 6.50599539e-01 1.71564609e-01 3.89245450e-01 1.70035362e-01 -1.41187644e+00 1.20785534e-01 9.21877444e-01 1.11573970e+00 2.85844505e-01 7.10729659e-01 -1.92553520e-01 5.62389672e-01 -7.41278529e-01 -2.07849145e-01 3.81574720e-01 -1.39845237e-01 -1.39602983e+00 1.11280143e-01 5.80370128e-01 -2.57107884e-01 -1.25996947e-01 9.29205835e-01 8.76024961e-01 -5.22129722e-02 4.44108248e-01 -8.37076843e-01 -2.63123870e-01 6.75412059e-01 -7.32898831e-01 4.12937021e-03 2.13285312e-01 -1.13975331e-02 1.02694941e+00 -6.75504565e-01 5.52250624e-01 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5f15b481-fa6c-4273-8e93-b270dee83b44	target-detection-in-synthetic-aperture-radar	1804.04719	null	http://arxiv.org/abs/1804.04719v1	http://arxiv.org/pdf/1804.04719v1.pdf	Target detection in synthetic aperture radar imagery: a state-of-the-art survey	Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidian distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery.	[]	2018-04-12	null	null	null	null	['one-class-classifier']	['methodology']	[ 1.07584953e+00 -5.68542957e-01 1.64984190e-03 -4.05523092e-01 -1.04862297e+00 -6.53729618e-01 4.85725820e-01 -2.42394373e-01 -1.72479719e-01 4.93807524e-01 -1.07308097e-01 -6.12946451e-01 -6.58703446e-01 -4.95590150e-01 1.98776037e-01 -1.15003896e+00 -5.24686992e-01 -1.16913445e-01 -2.88327280e-02 -3.17239225e-01 1.63551524e-01 1.36871207e+00 -1.34323084e+00 2.15102732e-01 5.09840786e-01 1.40851080e+00 4.44718413e-02 8.71961951e-01 8.50560248e-01 7.28701711e-01 -6.59823596e-01 4.46672812e-02 6.84242845e-01 -4.04432803e-01 -1.66522309e-01 3.99221271e-01 4.61184591e-01 -1.56958550e-01 -3.12100142e-01 1.05753803e+00 4.58197057e-01 -1.43921450e-01 9.13681388e-01 -6.80857360e-01 -4.03152138e-01 6.45851791e-02 -6.21178925e-01 7.67394066e-01 -1.12414591e-01 1.81897447e-01 6.36086106e-01 -1.08024764e+00 -1.23513445e-01 1.01719427e+00 8.77076149e-01 -1.25249907e-01 -1.28346062e+00 -4.10426319e-01 -2.72572160e-01 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c942856a-f84a-4adc-b5ca-34dc06d84e22	local-temporal-bilinear-pooling-for-fine	1812.01922	null	https://arxiv.org/abs/1812.01922v3	https://arxiv.org/pdf/1812.01922v3.pdf	Local Temporal Bilinear Pooling for Fine-grained Action Parsing	Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to other work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced with neither information loss nor extra computation. We perform intensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art work on various datasets.	['Siyu Tang', 'Christian Jarvers', 'Yan Zhang', 'Heiko Neumann', 'Krikamol Muandet']	2018-12-05	local-temporal-bilinear-pooling-for-fine-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Local_Temporal_Bilinear_Pooling_for_Fine-Grained_Action_Parsing_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Local_Temporal_Bilinear_Pooling_for_Fine-Grained_Action_Parsing_CVPR_2019_paper.pdf	cvpr-2019-6	['action-parsing']	['natural-language-processing']	[ 2.66930789e-01 6.69130683e-02 -5.29084802e-01 -3.45539272e-01 -7.81337261e-01 -3.67998660e-01 3.67969811e-01 2.01635227e-01 -7.52834439e-01 7.83220708e-01 5.74458897e-01 -1.78163916e-01 -1.76910266e-01 -5.90070128e-01 -7.15695143e-01 -7.53889263e-01 -2.92533576e-01 -2.21987709e-01 6.72947466e-01 -3.11353467e-02 6.92889243e-02 3.30301702e-01 -9.70183492e-01 6.53635979e-01 4.62144464e-01 1.20323670e+00 1.68574139e-01 5.23707151e-01 2.44773716e-01 1.06959188e+00 -1.87725201e-01 -3.26293379e-01 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cba654f4-eb0c-4162-8939-a358fb229f4f	review-of-visual-saliency-detection-with	1803.03391	null	http://arxiv.org/abs/1803.03391v2	http://arxiv.org/pdf/1803.03391v2.pdf	Review of Visual Saliency Detection with Comprehensive Information	Visual saliency detection model simulates the human visual system to perceive the scene, and has been widely used in many vision tasks. With the acquisition technology development, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. Co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discission are conducted to provide a holistic overview of different saliency detection methods.	['Ming-Ming Cheng', 'Jianjun Lei', 'Huazhu Fu', 'Runmin Cong', 'Qingming Huang', 'Weisi Lin']	2018-03-09	null	null	null	null	['co-saliency-detection', 'video-saliency-detection']	['computer-vision', 'computer-vision']	[ 4.44061935e-01 -4.04037327e-01 -3.96680266e-01 -4.33227271e-02 -2.15876788e-01 -9.18415189e-02 1.81062818e-01 2.62919348e-02 -1.51559502e-01 3.86216283e-01 2.37274170e-01 1.48542583e-01 1.03255540e-01 -2.62281895e-01 -3.31992537e-01 -7.04923928e-01 2.19585016e-01 -5.80537379e-01 1.26155758e+00 -2.07448930e-01 8.03050041e-01 1.76625431e-01 -1.94071901e+00 -3.81478406e-02 7.86041141e-01 1.10960102e+00 1.29845119e+00 3.20065767e-01 -6.99720085e-02 9.09438729e-01 -4.53063399e-01 5.32750726e-01 -4.86230366e-02 -6.79434240e-01 -5.98728716e-01 3.87770414e-01 2.72476189e-02 -2.97768742e-01 -2.25018248e-01 1.18214512e+00 4.37210023e-01 1.52564779e-01 2.31727928e-01 -1.62935400e+00 -5.73464394e-01 2.07071453e-01 -8.75190914e-01 7.83905625e-01 6.10325396e-01 -9.86888036e-02 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e410b9cb-1cb9-47c7-ac59-19699ae05c7c	exploring-adaptor-grammars-for-native	null	null	https://aclanthology.org/D12-1064	https://aclanthology.org/D12-1064.pdf	Exploring Adaptor Grammars for Native Language Identification	null	['Sze-Meng Jojo Wong', 'Mark Johnson', 'Mark Dras']	2012-07-01	null	null	null	emnlp-2012-7	['native-language-identification']	['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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abe6b37a-31fc-48db-babf-d0ab68030588	on-the-need-and-applicability-of-causality	2207.04053	null	https://arxiv.org/abs/2207.04053v1	https://arxiv.org/pdf/2207.04053v1.pdf	On the Need and Applicability of Causality for Fair Machine Learning	Causal reasoning has an indispensable role in how humans make sense of the world and come to decisions in everyday life. While $20th$ century science was reserved from making causal claims as too strong and not achievable, the $21st$ century is marked by the return of causality encouraged by the mathematization of causal notions and the introduction of the non-deterministic concept of cause~\cite{illari2011look}. Besides its common use cases in epidemiology, political, and social sciences, causality turns out to be crucial in evaluating the fairness of automated decisions, both in a legal and everyday sense. We provide arguments and examples of why causality is particularly important for fairness evaluation. In particular, we point out the social impact of non-causal predictions and the legal anti-discrimination process that relies on causal claims. We conclude with a discussion about the challenges and limitations of applying causality in practical scenarios as well as possible solutions.	['Sami Zhioua', 'Rūta Binkytė']	2022-07-08	null	null	null	null	['epidemiology']	['medical']	[ 2.76784033e-01 5.58807194e-01 -4.85085934e-01 -4.12472010e-01 -6.44959137e-02 -5.55069804e-01 9.58550990e-01 6.71124458e-01 -8.05540383e-01 1.15052831e+00 5.91675520e-01 -9.54428136e-01 -6.98310256e-01 -6.78493917e-01 -4.47903365e-01 -4.16012615e-01 1.32671908e-01 2.16993749e-01 -2.40159079e-01 -1.04542531e-01 5.31448662e-01 4.89548862e-01 -1.12742627e+00 -3.16557378e-01 9.43888128e-01 5.19088984e-01 -4.57816750e-01 2.42385983e-01 -4.89346422e-02 1.19126177e+00 -4.68567431e-01 -1.25431395e+00 1.16912927e-02 -7.12582052e-01 -8.62558603e-01 -6.41679883e-01 5.87119907e-03 -4.43086028e-01 -1.35510802e-01 1.10515869e+00 3.57862413e-01 -8.58671442e-02 6.45588577e-01 -1.18206334e+00 -8.56087267e-01 9.63134348e-01 -5.62740207e-01 3.20248127e-01 2.61322320e-01 1.48104489e-01 9.87830758e-01 -5.49280308e-02 7.41016507e-01 1.40213621e+00 3.93829763e-01 3.31844687e-01 -1.04487062e+00 -9.28900003e-01 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dbe1e274-aaa2-4f6d-bd2e-85f48e801200	error-compensation-framework-for-flow-guided	2207.10391	null	https://arxiv.org/abs/2207.10391v1	https://arxiv.org/pdf/2207.10391v1.pdf	Error Compensation Framework for Flow-Guided Video Inpainting	The key to video inpainting is to use correlation information from as many reference frames as possible. Existing flow-based propagation methods split the video synthesis process into multiple steps: flow completion -> pixel propagation -> synthesis. However, there is a significant drawback that the errors in each step continue to accumulate and amplify in the next step. To this end, we propose an Error Compensation Framework for Flow-guided Video Inpainting (ECFVI), which takes advantage of the flow-based method and offsets its weaknesses. We address the weakness with the newly designed flow completion module and the error compensation network that exploits the error guidance map. Our approach greatly improves the temporal consistency and the visual quality of the completed videos. Experimental results show the superior performance of our proposed method with the speed up of x6, compared to the state-of-the-art methods. In addition, we present a new benchmark dataset for evaluation by supplementing the weaknesses of existing test datasets.	['Seon Joo Kim', 'Seoung Wug Oh', 'Jaeyeon Kang']	2022-07-21	null	null	null	null	['video-inpainting']	['computer-vision']	[ 2.00634584e-01 -3.72361958e-01 -1.43240228e-01 -4.62116338e-02 -2.47427195e-01 -1.25184044e-01 2.92530686e-01 -3.74834895e-01 -2.05890343e-01 9.95599747e-01 3.07723820e-01 -1.71319358e-02 -4.92751673e-02 -6.48087502e-01 -4.44924086e-01 -5.33755779e-01 -7.09889084e-02 -2.67690390e-01 6.09901607e-01 -4.06756364e-02 4.59398210e-01 3.65901560e-01 -1.24835587e+00 1.56302348e-01 1.03451443e+00 1.20997477e+00 9.73601863e-02 6.72018647e-01 -1.04725614e-01 1.31794262e+00 -4.29876566e-01 -2.19543725e-01 4.46003139e-01 -8.19833934e-01 -6.11731112e-01 1.79453343e-01 4.99492645e-01 -9.45301175e-01 -7.43081152e-01 9.36880231e-01 3.24539423e-01 3.43316704e-01 1.06448710e-01 -1.27377784e+00 -4.97880638e-01 2.42862940e-01 -8.07582498e-01 5.11245966e-01 3.50918770e-01 3.28598708e-01 6.76962972e-01 -9.59046364e-01 8.14208984e-01 1.08705807e+00 5.91005027e-01 5.56388378e-01 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5.43623865e-01 4.46270108e-01 -5.53023279e-01 -3.86329174e-01 4.40905571e-01]	[10.740038871765137, -1.4881021976470947]
9bef92a8-39e8-4bd0-a209-243f463503d4	the-impact-of-preprocessing-on-deep	1808.10032	null	http://arxiv.org/abs/1808.10032v1	http://arxiv.org/pdf/1808.10032v1.pdf	The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments	The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. Nowadays, one of the major research challenges relies on the recognition of iris images obtained in visible spectrum under unconstrained environments. In this scenario, the acquired iris are affected by capture distance, rotation, blur, motion blur, low contrast and specular reflection, creating noises that disturb the iris recognition systems. Besides delineating the iris region, usually preprocessing techniques such as normalization and segmentation of noisy iris images are employed to minimize these problems. But these techniques inevitably run into some errors. In this context, we propose the use of deep representations, more specifically, architectures based on VGG and ResNet-50 networks, for dealing with the images using (and not) iris segmentation and normalization. We use transfer learning from the face domain and also propose a specific data augmentation technique for iris images. Our results show that the approach using non-normalized and only circle-delimited iris images reaches a new state of the art in the official protocol of the NICE.II competition, a subset of the UBIRIS database, one of the most challenging databases on unconstrained environments, reporting an average Equal Error Rate (EER) of 13.98% which represents an absolute reduction of about 5%.	['Alceu S. Britto Jr.', 'Rayson Laroca', 'Luiz S. Oliveira', 'Luiz A. Zanlorensi', 'Eduardo Luz', 'David Menotti']	2018-08-29	null	null	null	null	['iris-segmentation']	['medical']	[ 4.03182775e-01 -9.71823707e-02 -6.81134290e-04 -2.40381628e-01 -1.72045186e-01 -4.06398535e-01 5.01782894e-01 -2.49364004e-02 -6.56731904e-01 5.16232371e-01 -1.61947886e-04 -7.76390508e-02 -3.59394073e-01 -4.53284889e-01 -4.90267515e-01 -9.32545066e-01 1.79942608e-01 2.83926189e-01 -6.00269437e-01 -1.85199585e-02 3.26189876e-01 9.46435511e-01 -2.05602551e+00 -1.76808566e-01 1.06084991e+00 9.22878325e-01 -5.73956609e-01 5.18588126e-01 6.64649159e-02 3.37932289e-01 -7.36116230e-01 -4.70253855e-01 4.30846691e-01 -4.75298852e-01 -8.11452031e-01 4.22202855e-01 9.65624273e-01 -3.05052400e-01 -2.55879220e-02 1.33048224e+00 7.32353926e-01 2.22833171e-01 4.70491379e-01 -5.79517901e-01 -5.99080026e-01 2.73085743e-01 -8.71823490e-01 2.58302838e-01 2.19953120e-01 5.38118899e-01 4.01515245e-01 -4.30177659e-01 4.44677621e-01 7.89871871e-01 4.53651339e-01 7.28072345e-01 -1.35868096e+00 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3c649f67-5f41-490a-a1c0-be60a7f0bbd7	multimodal-machine-learning-integrating	null	null	https://aclanthology.org/P17-5002	https://aclanthology.org/P17-5002.pdf	Multimodal Machine Learning: Integrating Language, Vision and Speech	Multimodal machine learning is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages. With the initial research on audio-visual speech recognition and more recently with image and video captioning projects, this research field brings some unique challenges for multimodal researchers given the heterogeneity of the data and the contingency often found between modalities.This tutorial builds upon a recent course taught at Carnegie Mellon University during the Spring 2016 semester (CMU course 11-777) and two tutorials presented at CVPR 2016 and ICMI 2016. The present tutorial will review fundamental concepts of machine learning and deep neural networks before describing the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation {\&} mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. The tutorial will also present state-of-the-art algorithms that were recently proposed to solve multimodal applications such as image captioning, video descriptions and visual question-answer. We will also discuss the current and upcoming challenges.	['Tadas Baltru{\\v{s}}aitis', 'Louis-Philippe Morency']	2017-07-01	null	null	null	acl-2017-7	['audio-visual-speech-recognition']	['speech']	[ 5.33521175e-01 -1.47051126e-01 -3.42044592e-01 -2.97253698e-01 -1.30915356e+00 -5.36810398e-01 8.24628890e-01 1.71059906e-01 -3.71786326e-01 5.90963185e-01 4.46264982e-01 -3.27469051e-01 -1.22931581e-02 2.92400103e-02 -6.84342861e-01 -4.18958932e-01 3.54566425e-02 5.18813431e-01 -3.66609514e-01 -4.70504910e-01 3.87022913e-01 1.93277821e-01 -2.04080558e+00 1.29198396e+00 2.72335172e-01 1.01607513e+00 2.18882933e-01 1.22919250e+00 -5.99638939e-01 1.19400656e+00 -5.66468477e-01 -5.13531685e-01 -4.73167539e-01 -5.38823903e-01 -1.42210901e+00 1.37548015e-01 8.69335234e-01 -1.69207230e-02 -4.95767027e-01 7.80061841e-01 6.34663582e-01 4.29371297e-01 5.39729118e-01 -1.91071630e+00 -8.43451977e-01 6.06414557e-01 -3.88586849e-01 4.46583815e-02 1.07206106e+00 -1.97328895e-01 8.88759971e-01 -9.81397212e-01 6.49167240e-01 1.55330527e+00 2.49088287e-01 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c8e5fc05-3a31-4724-810b-564b3253a9dc	learning-to-zoom-a-saliency-based-sampling	1809.03355	null	http://arxiv.org/abs/1809.03355v1	http://arxiv.org/pdf/1809.03355v1.pdf	Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks	We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task. Our differentiable layer can be added as a preprocessing block to existing task networks and trained altogether in an end-to-end fashion. The effect of the layer is to efficiently estimate how to sample from the original data in order to boost task performance. For example, for an image classification task in which the original data might range in size up to several megapixels, but where the desired input images to the task network are much smaller, our layer learns how best to sample from the underlying high resolution data in a manner which preserves task-relevant information better than uniform downsampling. This has the effect of creating distorted, caricature-like intermediate images, in which idiosyncratic elements of the image that improve task performance are zoomed and exaggerated. Unlike alternative approaches such as spatial transformer networks, our proposed layer is inspired by image saliency, computed efficiently from uniformly downsampled data, and degrades gracefully to a uniform sampling strategy under uncertainty. We apply our layer to improve existing networks for the tasks of human gaze estimation and fine-grained object classification. Code for our method is available in: http://github.com/recasens/Saliency-Sampler	['Antonio Torralba', 'Simon Stent', 'Adrià Recasens', 'Wojciech Matusik', 'Petr Kellnhofer']	2018-09-10	learning-to-zoom-a-saliency-based-sampling-1	http://openaccess.thecvf.com/content_ECCV_2018/html/Adria_Recasens_Learning_to_Zoom_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Adria_Recasens_Learning_to_Zoom_ECCV_2018_paper.pdf	eccv-2018-9	['caricature']	['computer-vision']	[ 5.05833805e-01 3.16876352e-01 -4.87993397e-02 -6.16150141e-01 -6.04644537e-01 -2.50848114e-01 3.91028643e-01 -8.59157816e-02 -5.72961986e-01 7.34909773e-01 3.17025989e-01 3.12968111e-03 1.27188519e-01 -6.03380203e-01 -1.15473402e+00 -6.77266240e-01 4.12199885e-01 2.62667567e-01 3.84368509e-01 -1.74143404e-01 4.72992569e-01 3.00191998e-01 -1.67506444e+00 4.89615500e-01 9.15598750e-01 1.01651204e+00 5.95685840e-01 5.43643177e-01 1.86767861e-01 7.63302088e-01 -5.31306803e-01 -2.29359269e-01 2.96669215e-01 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498fe39e-991b-48b6-ae35-f685e7c75e20	efficient-and-multiply-robust-risk-estimation	2306.16406	null	https://arxiv.org/abs/2306.16406v2	https://arxiv.org/pdf/2306.16406v2.pdf	Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift	Statistical machine learning methods often face the challenge of limited data available from the population of interest. One remedy is to leverage data from auxiliary source populations, which share some conditional distributions or are linked in other ways with the target domain. Techniques leveraging such \emph{dataset shift} conditions are known as \emph{domain adaptation} or \emph{transfer learning}. Despite extensive literature on dataset shift, limited works address how to efficiently use the auxiliary populations to improve the accuracy of risk evaluation for a given machine learning task in the target population. In this paper, we study the general problem of efficiently estimating target population risk under various dataset shift conditions, leveraging semiparametric efficiency theory. We consider a general class of dataset shift conditions, which includes three popular conditions -- covariate, label and concept shift -- as special cases. We allow for partially non-overlapping support between the source and target populations. We develop efficient and multiply robust estimators along with a straightforward specification test of these dataset shift conditions. We also derive efficiency bounds for two other dataset shift conditions, posterior drift and location-scale shift. Simulation studies support the efficiency gains due to leveraging plausible dataset shift conditions.	['Edgar Dobriban', 'Eric Tchetgen Tchetgen', 'Hongxiang Qiu']	2023-06-28	null	null	null	null	['transfer-learning']	['miscellaneous']	[ 4.58383441e-01 1.22310661e-01 -6.66601002e-01 -5.49725473e-01 -1.07316756e+00 -5.58730006e-01 3.94834757e-01 1.31548867e-01 -4.15944368e-01 1.11052024e+00 5.44572771e-02 -2.34091431e-01 -6.67121351e-01 -5.89704871e-01 -8.72589529e-01 -7.49925256e-01 1.82758197e-02 4.85719889e-01 -1.74878970e-01 2.89960116e-01 1.52712509e-01 1.97082013e-01 -1.06646335e+00 -2.48509333e-01 1.28742003e+00 7.18829215e-01 -1.57026336e-01 3.31705868e-01 3.38375777e-01 1.40193105e-01 -4.97821838e-01 -6.42111421e-01 6.07081532e-01 -2.59412378e-01 -2.34892637e-01 8.12339485e-02 3.28016013e-01 -3.32798332e-01 2.35406354e-01 1.14799905e+00 6.72984481e-01 -9.31680873e-02 1.16072130e+00 -1.61167669e+00 -5.88989854e-01 6.04381561e-01 -9.69456673e-01 3.33236068e-01 -1.26517236e-01 1.79070863e-03 6.36775017e-01 -6.68900013e-01 2.57457078e-01 1.11729372e+00 1.24745202e+00 3.72921497e-01 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7ebd9dfc-8799-4644-b361-de7302baa2db	foundationtts-text-to-speech-for-asr	2303.02939	null	https://arxiv.org/abs/2303.02939v3	https://arxiv.org/pdf/2303.02939v3.pdf	FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Model	Neural text-to-speech (TTS) generally consists of cascaded architecture with separately optimized acoustic model and vocoder, or end-to-end architecture with continuous mel-spectrograms or self-extracted speech frames as the intermediate representations to bridge acoustic model and vocoder, which suffers from two limitations: 1) the continuous acoustic frames are hard to predict with phoneme only, and acoustic information like duration or pitch is also needed to solve the one-to-many problem, which is not easy to scale on large scale and noise datasets; 2) to achieve diverse speech output based on continuous speech features, complex VAE or flow-based models are usually required. In this paper, we propose FoundationTTS, a new speech synthesis system with a neural audio codec for discrete speech token extraction and waveform reconstruction and a large language model for discrete token generation from linguistic (phoneme) tokens. Specifically, 1) we propose a hierarchical codec network based on vector-quantized auto-encoders with adversarial training (VQ-GAN), which first extracts continuous frame-level speech representations with fine-grained codec, and extracts a discrete token from each continuous speech frame with coarse-grained codec; 2) we jointly optimize speech token, linguistic tokens, speaker token together with a large language model and predict the discrete speech tokens autoregressively. Experiments show that FoundationTTS achieves a MOS gain of +0.14 compared to the baseline system. In ASR customization tasks, our method achieves 7.09\% and 10.35\% WERR respectively over two strong customized ASR baselines.	['Sheng Zhao', 'Edward Lin', 'Linquan Liu', 'Xu Tan', 'Lei He', 'Yanqing Liu', 'Ruiqing Xue']	2023-03-06	null	null	null	null	['speech-synthesis']	['speech']	[ 1.03983805e-01 -6.61916211e-02 1.35597505e-03 -2.69020826e-01 -1.29820204e+00 -5.08251667e-01 1.96587622e-01 -3.16533148e-01 -1.39793679e-01 4.21179086e-01 4.29675937e-01 -5.46211421e-01 7.30573952e-01 -5.14690578e-01 -7.74808288e-01 -5.73973060e-01 1.71156287e-01 2.41049882e-02 6.81535751e-02 -3.25180411e-01 -3.63575250e-01 1.91458017e-01 -1.36918318e+00 3.12186152e-01 8.21356356e-01 1.30128646e+00 4.58043069e-01 1.29942238e+00 -3.74494582e-01 8.00202727e-01 -1.06044865e+00 -4.32403743e-01 2.07885399e-01 -6.98645711e-01 -2.27188736e-01 -1.95270598e-01 1.80021793e-01 -4.71318811e-01 -5.97303987e-01 9.34143424e-01 9.47140038e-01 1.32262111e-01 7.40730226e-01 -9.02682483e-01 -8.25851142e-01 9.07021999e-01 -7.35690296e-02 -5.54944910e-02 9.53198224e-02 3.88972670e-01 9.87896144e-01 -1.08204401e+00 -1.66495293e-02 1.61316180e+00 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70371237-4e2f-4c72-839f-5dde2ea5e8bc	implicit-discourse-relation-identification	1907.03975	null	https://arxiv.org/abs/1907.03975v1	https://arxiv.org/pdf/1907.03975v1.pdf	Implicit Discourse Relation Identification for Open-domain Dialogues	Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system. Previous work primarily relies on a corpora of formal text which is inherently non-dialogic, i.e., news and journals. This data however is not suitable to handle the nuances of informal dialogue nor is it capable of navigating the plethora of valid topics present in open-domain dialogue. In this paper, we designed a novel discourse relation identification pipeline specifically tuned for open-domain dialogue systems. We firstly propose a method to automatically extract the implicit discourse relation argument pairs and labels from a dataset of dialogic turns, resulting in a novel corpus of discourse relation pairs; the first of its kind to attempt to identify the discourse relations connecting the dialogic turns in open-domain discourse. Moreover, we have taken the first steps to leverage the dialogue features unique to our task to further improve the identification of such relations by performing feature ablation and incorporating dialogue features to enhance the state-of-the-art model.	['Kevin K. Bowden', 'Marilyn Walker', 'Wen Cui', 'Mingyu Derek Ma', 'Jiaqi Wu']	2019-07-09	implicit-discourse-relation-identification-1	https://aclanthology.org/P19-1065	https://aclanthology.org/P19-1065.pdf	acl-2019-7	['implicit-discourse-relation-classification', 'implicit-relations']	['natural-language-processing', 'natural-language-processing']	[ 4.42214489e-01 9.51170325e-01 -6.90727979e-02 -1.66326940e-01 -6.56237364e-01 -9.86935556e-01 1.29022837e+00 3.73957843e-01 -2.05768749e-01 9.19399559e-01 7.26516843e-01 -5.46598554e-01 -1.88387766e-01 -5.61808467e-01 -6.49188319e-03 -2.32650831e-01 3.71872298e-02 8.60490859e-01 3.74699444e-01 -8.22168529e-01 3.08348328e-01 -1.56242950e-02 -1.26741552e+00 5.81585824e-01 9.48923349e-01 6.85733497e-01 -9.65504423e-02 6.73546255e-01 -5.42864859e-01 1.27399421e+00 -8.33004296e-01 -4.14391637e-01 -2.47258604e-01 -9.10363555e-01 -1.74625635e+00 1.19682017e-03 1.11926042e-01 -1.54427484e-01 -1.35620728e-01 5.84369242e-01 2.95747072e-01 1.39048547e-01 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6994810d-8b63-49fd-b9a9-52f6bed28b47	content-preserving-image-stitching-with-1	null	null	https://www.webofscience.com/wos/alldb/full-record/WOS:000655924400008	https://sci-hub.se/10.1109/TVCG.2020.2965097	Content-Preserving Image Stitching with Piecewise Rectangular Boundary Constraints	Abstract—This paper proposes an approach to content- preserving image stitching with regular boundary constraints, which aims to stitch multiple images to generate a panoramic image with a piecewise rectangular boundary. Existing methods treat image stitching and rectangling as two separate steps, which may result in suboptimal results as the stitching process is not aware of the further warping needs for rectangling. We address these limitations by formulating image stitching with regular boundaries in a uniﬁed optimization. Starting from the initial stitching results produced by the traditional warping- based optimization, we obtain the irregular boundary from the warped meshes by polygon Boolean operations which robustly handle arbitrary mesh compositions. By analyzing the irregular boundary, we construct a piecewise rectangular boundary. Based on this, we further incorporate line and regular boundary preservation constraints into the image stitching framework, and conduct iterative optimization to obtain an optimal piecewise rectangular boundary. Thus we can make the boundary of the stitching results as close as possible to a rectangle, while reducing unwanted distortions. We further extend our method to video stitching, by integrating the temporal coherence into the optimization. Experiments show that our method efﬁciently produces visually pleasing panoramas with regular boundaries and unnoticeable distortions.	['and Fang-Lue Zhang', 'IEEE', 'Member', 'Yu-Kun Lai', 'Yun Zhang']	2021-07-01	null	null	null	ieee-transactions-on-visualization-and-5	['image-stitching']	['computer-vision']	[ 8.83660555e-01 -9.79525521e-02 -3.50341722e-02 1.47026151e-01 -3.36092561e-01 -7.67400086e-01 4.73142356e-01 -1.92506224e-01 -1.11698806e-01 4.90987659e-01 2.31466144e-01 -8.25901479e-02 6.60433993e-02 -7.78833210e-01 -6.94116235e-01 -8.25898767e-01 2.26857856e-01 1.22022800e-01 3.25560212e-01 -1.20084867e-01 5.16840637e-01 4.49997962e-01 -1.32232022e+00 1.33172259e-01 1.43072522e+00 6.66485727e-01 -6.38631210e-02 6.31325483e-01 7.71880597e-02 2.91931987e-01 -6.34460270e-01 -2.23681495e-01 5.42749941e-01 -5.57304859e-01 -5.97579122e-01 9.79372799e-01 6.76741183e-01 -6.03718996e-01 -2.00679272e-01 1.25313067e+00 1.49956822e-01 1.38427362e-01 4.32230026e-01 -1.22549033e+00 -5.99728346e-01 1.96806490e-01 -1.35267508e+00 -3.76024246e-01 4.31628168e-01 6.90419152e-02 5.86722016e-01 -4.47934210e-01 7.38238692e-01 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51835522-2c0b-462c-bbaf-6b6de9a4d5e6	a-novel-framework-based-on-medical-concept	null	null	https://openreview.net/forum?id=syvKAodJ-Gj	https://openreview.net/pdf?id=syvKAodJ-Gj	A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge	Medical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes. Rare code problem, the medical codes with low occurrences, is prominent in medical code prediction. Recent studies employ deep neural networks and the external knowledge to tackle it. However, such approaches lack interpretability which is a vital issue in medical application. Moreover, due to the lengthy and noisy clinical notes, such approaches fail to achieve satisfactory results. Therefore, in this paper, we propose a novel framework based on medical concept driven attention to incorporate external knowledge for explainable medical code prediction. In specific, both the clinical notes and Wikipedia documents are aligned into topic space to extract medical concepts using topic modeling. Then, the medical concept-driven attention mechanism is applied to uncover the medical code related concepts which provide explanations for medical code prediction. Experimental results on the benchmark dataset show the superiority of the proposed framework over several state-of-the-art baselines.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['medical-code-prediction']	['medical']	[ 1.21991411e-01 4.15697157e-01 -2.65199006e-01 -4.38840538e-01 -8.17894220e-01 2.92750285e-03 -1.90929864e-02 6.75821066e-01 1.18189901e-02 6.13494754e-01 6.59908950e-01 -3.28577191e-01 -3.29912394e-01 -6.26712859e-01 -4.88319397e-01 -5.46793103e-01 -4.45491387e-05 5.87282956e-01 -1.98035195e-01 1.21911503e-01 2.36205891e-01 -4.39371943e-01 -1.17617035e+00 5.87586224e-01 1.49112260e+00 7.26717830e-01 2.70075619e-01 3.26794386e-01 -4.48309898e-01 9.43901300e-01 -2.57621169e-01 -5.26545286e-01 -2.25570410e-01 -5.59011698e-01 -6.79306209e-01 -7.39639997e-02 -8.41482431e-02 -2.34924838e-01 -1.31638169e-01 1.26707804e+00 2.95802414e-01 -2.28718668e-01 7.68848956e-01 -8.86699319e-01 -1.23382044e+00 6.97398603e-01 -4.07757491e-01 8.63619149e-02 3.42336625e-01 -1.84592694e-01 1.31042612e+00 -9.43576634e-01 4.92087781e-01 8.83471847e-01 8.91012192e-01 7.37636745e-01 -6.86711907e-01 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8fd283cc-3534-403a-a9ee-5ce1c3420018	texture-enhanced-light-field-super-resolution	2111.04069	null	https://arxiv.org/abs/2111.04069v2	https://arxiv.org/pdf/2111.04069v2.pdf	Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels	Despite the recent progress in light field super-resolution (LFSR) achieved by convolutional neural networks, the correlation information of light field (LF) images has not been sufficiently studied and exploited due to the complexity of 4D LF data. To cope with such high-dimensional LF data, most of the existing LFSR methods resorted to decomposing it into lower dimensions and subsequently performing optimization on the decomposed sub-spaces. However, these methods are inherently limited as they neglected the characteristics of the decomposition operations and only utilized a limited set of LF sub-spaces ending up failing to sufficiently extract spatio-angular features and leading to a performance bottleneck. To overcome these limitations, in this paper, we comprehensively discover the potentials of LF decomposition and propose a novel concept of decomposition kernels. In particular, we systematically unify the decomposition operations of various sub-spaces into a series of such decomposition kernels, which are incorporated into our proposed Decomposition Kernel Network (DKNet) for comprehensive spatio-angular feature extraction. The proposed DKNet is experimentally verified to achieve considerable improvements compared with the state-of-the-art methods. To further improve DKNet in producing more visually pleasing LFSR results, based on the VGG network, we propose a LFVGG loss to guide the Texture-Enhanced DKNet (TE-DKNet) to generate rich authentic textures and enhance LF images' visual quality significantly. We also propose an indirect evaluation metric by taking advantage of LF material recognition to objectively assess the perceptual enhancement brought by the LFVGG loss.	['Zhibo Chen', 'Yuk Ying Chung', 'Henry Wing Fung Yeung', 'Xiaoming Chen', 'Zexi Hu']	2021-11-07	null	null	null	null	['material-recognition']	['computer-vision']	[ 4.21398461e-01 -4.25033510e-01 8.23938996e-02 -5.03564738e-02 -3.59316766e-01 -1.20895855e-01 4.84684855e-01 -2.97441751e-01 -1.83376372e-01 8.58513892e-01 4.78233732e-02 -1.39410168e-01 -5.28977633e-01 -1.00472736e+00 -4.36261415e-01 -9.86149848e-01 -2.02462468e-02 -4.55387145e-01 2.63687223e-01 -7.53386319e-02 3.00429940e-01 7.14800060e-01 -1.89188445e+00 2.16853127e-01 1.09475851e+00 1.16604829e+00 4.05886859e-01 2.35826358e-01 -3.87139991e-02 5.34895241e-01 -2.29330987e-01 3.02337538e-02 2.66233921e-01 -4.48543012e-01 -5.33243239e-01 8.73810202e-02 3.78382534e-01 -4.18446153e-01 -4.15015161e-01 1.13873589e+00 5.17590940e-01 1.80915624e-01 4.25918788e-01 -6.41983867e-01 -7.60637522e-01 1.01148225e-01 -5.25807083e-01 3.25589955e-01 3.44109207e-01 1.46458328e-01 6.71916187e-01 -8.64254236e-01 7.52310932e-01 9.89293933e-01 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b8a19a8d-697e-4bf2-a6e4-da92cdb27079	can-differentiable-decision-trees-learn	2306.13004	null	https://arxiv.org/abs/2306.13004v2	https://arxiv.org/pdf/2306.13004v2.pdf	Can Differentiable Decision Trees Learn Interpretable Reward Functions?	There is an increasing interest in learning reward functions that model human intent and human preferences. However, many frameworks use blackbox learning methods that, while expressive, are difficult to interpret. We propose and evaluate a novel approach for learning expressive and interpretable reward functions from preferences using Differentiable Decision Trees (DDTs). Our experiments across several domains, including Cartpole, Visual Gridworld environments and Atari games, provide evidence that that the tree structure of our learned reward function is useful in determining the extent to which the reward function is aligned with human preferences. We experimentally demonstrate that using reward DDTs results in competitive performance when compared with larger capacity deep neural network reward functions. We also observe that the choice between soft and hard (argmax) output of reward DDT reveals a tension between wanting highly shaped rewards to ensure good RL performance, while also wanting simple, non-shaped rewards to afford interpretability.	['Daniel S. Brown', 'Akansha Kalra']	2023-06-22	null	null	null	null	['atari-games']	['playing-games']	[ 4.35093381e-02 4.87002343e-01 -4.29088622e-01 -8.23011637e-01 -5.53502262e-01 -7.22480774e-01 6.38405800e-01 -1.83512896e-01 -6.06860340e-01 9.74514484e-01 6.18521333e-01 -4.35300618e-01 -3.50145847e-01 -4.64853108e-01 -5.06248534e-01 -4.09011513e-01 -3.43389839e-01 6.53139532e-01 -3.03334713e-01 -3.56457621e-01 9.35186818e-02 2.63746083e-01 -1.31875217e+00 3.27682555e-01 9.20141816e-01 1.31943965e+00 -8.19056034e-02 8.32932174e-01 3.37591559e-01 1.52360058e+00 -5.11826038e-01 -4.45961088e-01 5.09314597e-01 -1.14448175e-01 -1.07286155e+00 -3.90269399e-01 3.64741907e-02 -7.55071819e-01 -4.47068363e-02 5.76634228e-01 3.42312902e-01 2.90898234e-01 7.34782338e-01 -1.38146579e+00 -7.83598721e-01 9.39110935e-01 -1.44462436e-01 -2.26276834e-02 1.39870629e-01 5.27466655e-01 1.70287657e+00 -2.16155097e-01 4.83197123e-01 1.46816921e+00 2.98776239e-01 7.35623538e-01 -1.37950647e+00 -5.03095210e-01 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3.95942599e-01 4.10804540e-01 -6.00715637e-01 4.99319052e-03 -3.83283705e-01]	[4.02855920791626, 1.7116193771362305]
edb59d14-3118-49c8-89cd-cbe96688a0a0	orthographic-feature-transform-for-monocular	1811.08188	null	http://arxiv.org/abs/1811.08188v1	http://arxiv.org/pdf/1811.08188v1.pdf	Orthographic Feature Transform for Monocular 3D Object Detection	3D object detection from monocular images has proven to be an enormously challenging task, with the performance of leading systems not yet achieving even 10\% of that of LiDAR-based counterparts. One explanation for this performance gap is that existing systems are entirely at the mercy of the perspective image-based representation, in which the appearance and scale of objects varies drastically with depth and meaningful distances are difficult to infer. In this work we argue that the ability to reason about the world in 3D is an essential element of the 3D object detection task. To this end, we introduce the orthographic feature transform, which enables us to escape the image domain by mapping image-based features into an orthographic 3D space. This allows us to reason holistically about the spatial configuration of the scene in a domain where scale is consistent and distances between objects are meaningful. We apply this transformation as part of an end-to-end deep learning architecture and achieve state-of-the-art performance on the KITTI 3D object benchmark.\footnote{We will release full source code and pretrained models upon acceptance of this manuscript for publication.	['Alex Kendall', 'Thomas Roddick', 'Roberto Cipolla']	2018-11-20	null	null	null	null	['3d-object-detection-from-monocular-images']	['computer-vision']	[ 1.41067207e-01 -2.66565889e-01 1.86336577e-01 -6.01508200e-01 -3.41030359e-01 -8.86860132e-01 7.17389345e-01 -3.91268618e-02 -5.11669815e-01 2.27420747e-01 -1.63619578e-01 -3.60515952e-01 4.16794196e-02 -6.40223503e-01 -7.88918853e-01 -4.81390268e-01 1.09967224e-01 6.24315858e-01 4.37545419e-01 -8.25424194e-02 3.54913533e-01 1.10217619e+00 -1.70263410e+00 1.51912138e-01 1.40190870e-01 1.15502298e+00 1.56774029e-01 7.98662841e-01 -6.21710420e-02 3.98226351e-01 -3.27572823e-01 -7.42665902e-02 7.19934762e-01 -1.08733885e-01 -5.07222414e-01 1.19710974e-01 9.79268909e-01 -4.69418526e-01 -5.48465312e-01 1.08580792e+00 4.16067600e-01 -1.77114382e-01 7.21943200e-01 -1.14767897e+00 -5.84657192e-01 -1.32666990e-01 -5.59074819e-01 2.48325735e-01 3.25204819e-01 1.50155678e-01 1.11763430e+00 -1.16853631e+00 6.42052114e-01 1.45067799e+00 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28bf5fc4-dbf3-4e92-b686-01b4d7d047f6	differentiable-multi-agent-actor-critic-for	2203.08257	null	https://arxiv.org/abs/2203.08257v2	https://arxiv.org/pdf/2203.08257v2.pdf	Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization	The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist's reasoning and conclusions, and it also aids the referring physician in confirming or excluding certain diagnoses. A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report. These tasks include acquisition of salient content from the report and generation of a concise, easily consumable IMPRESSIONS section. Prior research on radiology report summarization has focused on single-step end-to-end models -- which subsume the task of salient content acquisition. To fully explore the cascade structure and explainability of radiology report summarization, we introduce two innovations. First, we design a two-step approach: extractive summarization followed by abstractive summarization. Second, we additionally break down the extractive part into two independent tasks: extraction of salient (1) sentences and (2) keywords. Experiments on English radiology reports from two clinical sites show our novel approach leads to a more precise summary compared to single-step and to two-step-with-single-extractive-process baselines with an overall improvement in F1 score Of 3-4%.	['Oladimeji Farri', 'Hinrich Schuetze', 'Ning Liu', 'Sanjeev Kumar Karn']	2022-03-15	null	https://aclanthology.org/2022.acl-long.109	https://aclanthology.org/2022.acl-long.109.pdf	acl-2022-5	['extractive-summarization']	['natural-language-processing']	[ 7.87696779e-01 8.95748615e-01 -7.42496029e-02 -6.05430007e-01 -1.95189464e+00 -5.11392295e-01 4.44444180e-01 1.09102237e+00 -2.27339238e-01 8.31353188e-01 1.26630831e+00 -4.55994189e-01 -1.07138611e-01 -1.12641349e-01 -4.70495522e-01 -2.45420560e-01 1.33601213e-02 3.87285858e-01 1.13056842e-02 2.49667555e-01 4.76375967e-01 3.55838448e-01 -8.01788509e-01 8.42575490e-01 7.04370737e-01 6.91260457e-01 2.47041255e-01 1.23640299e+00 -2.61635017e-02 1.37879980e+00 -7.79241025e-01 -3.90587032e-01 -1.98095202e-01 -8.04658949e-01 -1.17681384e+00 5.61893165e-01 3.44323039e-01 -6.62086189e-01 -4.58145328e-02 7.35497832e-01 6.71337962e-01 -2.38634184e-01 9.61036265e-01 -5.11541784e-01 -3.76920760e-01 1.04095435e+00 -8.61439049e-01 6.16947174e-01 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f2ea10e6-9122-48db-b1ad-7e2d76a60e25	data-augmentation-using-random-image-cropping	1811.09030	null	https://arxiv.org/abs/1811.09030v2	https://arxiv.org/pdf/1811.09030v2.pdf	Data Augmentation using Random Image Cropping and Patching for Deep CNNs	Deep convolutional neural networks (CNNs) have achieved remarkable results in image processing tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. Recent CNN architectures with more parameters are rendering traditional data augmentation techniques insufficient. In this study, we propose a new data augmentation technique called random image cropping and patching (RICAP) which randomly crops four images and patches them to create a new training image. Moreover, RICAP mixes the class labels of the four images, resulting in an advantage similar to label smoothing. We evaluated RICAP with current state-of-the-art CNNs (e.g., the shake-shake regularization model) by comparison with competitive data augmentation techniques such as cutout and mixup. RICAP achieves a new state-of-the-art test error of $2.19\%$ on CIFAR-10. We also confirmed that deep CNNs with RICAP achieve better results on classification tasks using CIFAR-100 and ImageNet and an image-caption retrieval task using Microsoft COCO.	['Ryo Takahashi', 'Takashi Matsubara', 'Kuniaki Uehara']	2018-11-22	null	null	null	null	['image-cropping']	['computer-vision']	[ 1.78322762e-01 2.24799514e-01 -6.12927526e-02 -6.12274110e-01 -4.28730875e-01 -1.54458612e-01 5.34585297e-01 7.52152828e-03 -9.81032312e-01 6.58689916e-01 -1.77590683e-01 -2.85061777e-01 2.87817091e-01 -7.58418441e-01 -9.65051174e-01 -6.71974063e-01 1.03962637e-01 5.52493148e-02 -5.53396996e-03 -3.76267955e-02 -1.40391663e-01 4.26989138e-01 -1.63941252e+00 5.20357192e-01 9.52831507e-01 1.31629097e+00 1.13545991e-02 3.64270151e-01 -2.64701933e-01 7.74768770e-01 -7.13749468e-01 -6.11925781e-01 4.45364743e-01 -5.10703251e-02 -6.86837912e-01 3.84732755e-03 8.22370052e-01 -2.03882128e-01 -4.06942993e-01 1.01183355e+00 3.56000841e-01 1.00150555e-01 3.79899025e-01 -1.46478736e+00 -1.07750893e+00 5.82307875e-01 -5.79041958e-01 6.52336180e-02 -5.62549293e-01 4.54594716e-02 6.29243672e-01 -1.11275363e+00 2.23783657e-01 9.85505760e-01 9.63007152e-01 8.47274184e-01 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bf15c52d-e283-42e0-95ed-b8e39129537c	deep-unsupervised-active-learning-on	2111.04286	null	https://arxiv.org/abs/2111.04286v1	https://arxiv.org/pdf/2111.04286v1.pdf	Deep Unsupervised Active Learning on Learnable Graphs	Recently deep learning has been successfully applied to unsupervised active learning. However, current method attempts to learn a nonlinear transformation via an auto-encoder while ignoring the sample relation, leaving huge room to design more effective representation learning mechanisms for unsupervised active learning. In this paper, we propose a novel deep unsupervised Active Learning model via Learnable Graphs, named ALLG. ALLG benefits from learning optimal graph structures to acquire better sample representation and select representative samples. To make the learnt graph structure more stable and effective, we take into account $k$-nearest neighbor graph as a priori, and learn a relation propagation graph structure. We also incorporate shortcut connections among different layers, which can alleviate the well-known over-smoothing problem to some extent. To the best of our knowledge, this is the first attempt to leverage graph structure learning for unsupervised active learning. Extensive experiments performed on six datasets demonstrate the efficacy of our method.	['Guoren Wang', 'Ye Yuan', 'Xinchu Shi', 'Changsheng Li', 'Handong Ma']	2021-11-08	null	null	null	null	['graph-structure-learning']	['graphs']	[ 1.94549784e-01 7.57631540e-01 -7.10456967e-01 -5.73869765e-01 -5.37472427e-01 -2.45908603e-01 4.14101928e-01 3.05714786e-01 -2.36769080e-01 6.03357732e-01 2.31657952e-01 1.06519209e-02 -2.79368788e-01 -1.14510381e+00 -7.49436021e-01 -7.23254919e-01 -3.11090201e-01 2.12476015e-01 1.51311919e-01 1.29053041e-01 3.16227198e-01 4.54752505e-01 -1.04671264e+00 -4.73222211e-02 1.19107425e+00 7.38754928e-01 1.04001187e-01 8.12365487e-02 -3.57437372e-01 1.26656258e+00 -3.39501202e-01 -2.89530724e-01 2.74484634e-01 -4.10292923e-01 -9.61289883e-01 4.65187252e-01 3.12112123e-01 -6.10693805e-02 -5.12368441e-01 1.03179598e+00 2.54812419e-01 3.95034343e-01 6.43983543e-01 -1.03827202e+00 -7.05117404e-01 1.01281297e+00 -4.08042789e-01 5.75981382e-03 2.42493879e-02 2.11897075e-01 1.39840961e+00 -8.45140219e-01 6.43475533e-01 1.01939917e+00 4.80475754e-01 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f647cad8-09d2-4460-a618-0bfc1343a787	exploring-wasserstein-distance-across-concept	2207.11324	null	https://arxiv.org/abs/2207.11324v2	https://arxiv.org/pdf/2207.11324v2.pdf	Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching	Measuring the distance between ontological elements is fundamental for ontology matching. String-based distance metrics are notorious for shallow syntactic matching. In this exploratory study, we investigate Wasserstein distance targeting continuous space that can incorporate various types of information. We use a pre-trained word embeddings system to embed ontology element labels. We examine the effectiveness of Wasserstein distance for measuring similarity between ontologies, and discovering and refining matchings between individual elements. Our experiments with the OAEI conference track and MSE benchmarks achieved competitive results compared to the leading systems.	['Jane Greenberg', 'Alex Kalinowski', 'Yuan An']	2022-07-22	null	null	null	null	['ontology-matching']	['knowledge-base']	[ 1.27285033e-01 1.26451373e-01 -1.03519820e-01 -5.55263221e-01 -7.48140097e-01 -4.74619627e-01 3.54676068e-01 7.17028558e-01 -7.70434082e-01 1.00900702e-01 6.52927041e-01 -4.10553306e-01 -7.37785637e-01 -1.09623945e+00 -2.72182792e-01 3.30951288e-02 -5.09910703e-01 6.18903697e-01 1.90208539e-01 -4.17328954e-01 3.26589376e-01 4.37288314e-01 -1.75447357e+00 3.85732591e-01 8.01714599e-01 1.01633322e+00 -1.19484104e-01 4.36205477e-01 -7.32166052e-01 3.92737031e-01 -3.01286966e-01 -4.80673045e-01 2.90079385e-01 6.82233348e-02 -1.05065095e+00 -5.94338596e-01 6.55952036e-01 1.17799111e-01 -6.97727263e-01 1.33854365e+00 5.31286538e-01 3.69937122e-01 6.62782729e-01 -1.27056658e+00 -1.11163509e+00 7.46095300e-01 3.00474558e-02 5.87020516e-01 8.88725519e-01 -4.15943116e-01 1.74316490e+00 -1.00683415e+00 4.86625075e-01 1.57203734e+00 1.03303766e+00 4.44973588e-01 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f3bf3fdf-76be-48aa-affd-7db0a38099ee	despeckling-sentinel-1-grd-images-by-deep	2102.00692	null	https://arxiv.org/abs/2102.00692v1	https://arxiv.org/pdf/2102.00692v1.pdf	Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentation	This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy. Training the deep neural network on collections of Sentinel 1 GRD images leads to a despeckling algorithm that is robust to space-variant spatial correlations of speckle. Despeckled images improve the detection of structures like narrow rivers. We apply a detector based on exogenous information and a linear features detector and show that rivers are better segmented when the processing chain is applied to images pre-processed by our despeckling neural network.	['Florence Tupin', 'Loïc Denis', 'Emanuele Dalsasso', 'Nicolas Gasnier']	2021-02-01	null	null	null	null	['sar-image-despeckling']	['computer-vision']	[ 1.89598277e-01 -6.12231977e-02 4.70009148e-01 -4.90357101e-01 -4.75237995e-01 -7.06605136e-01 8.31363559e-01 -4.28479135e-01 -6.41176879e-01 4.00226206e-01 3.33343685e-01 -3.04761618e-01 -4.31340069e-01 -1.06335747e+00 -6.18991613e-01 -8.84600699e-01 -4.37530965e-01 1.25822574e-01 1.05516084e-01 -7.06005633e-01 -1.48241073e-01 1.27489293e+00 -1.17597449e+00 3.13865505e-02 4.47937191e-01 1.01226068e+00 8.50632340e-02 8.52797747e-01 3.27078551e-01 5.83024323e-01 -3.60183895e-01 3.64562929e-01 1.45993495e+00 -4.12699670e-01 -1.33916199e-01 6.69936165e-02 8.62487018e-01 -3.47709447e-01 -6.62450492e-01 1.27116501e+00 4.60686833e-01 6.52390420e-02 5.66310823e-01 -2.12777674e-01 -4.04432684e-01 4.73701090e-01 -4.46027577e-01 5.15982032e-01 -3.60262364e-01 2.05881506e-01 3.79908592e-01 -9.91082788e-01 7.94784427e-01 1.05361295e+00 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8acf052f-d8b4-4a05-bf27-4f10e5d7de9d	on-the-usefulness-of-synthetic-tabular-data	2306.15636	null	https://arxiv.org/abs/2306.15636v1	https://arxiv.org/pdf/2306.15636v1.pdf	On the Usefulness of Synthetic Tabular Data Generation	Despite recent advances in synthetic data generation, the scientific community still lacks a unified consensus on its usefulness. It is commonly believed that synthetic data can be used for both data exchange and boosting machine learning (ML) training. Privacy-preserving synthetic data generation can accelerate data exchange for downstream tasks, but there is not enough evidence to show how or why synthetic data can boost ML training. In this study, we benchmarked ML performance using synthetic tabular data for four use cases: data sharing, data augmentation, class balancing, and data summarization. We observed marginal improvements for the balancing use case on some datasets. However, we conclude that there is not enough evidence to claim that synthetic tabular data is useful for ML training.	['Sergül Aydöre', 'Dionysis Manousakas']	2023-06-27	null	null	null	null	['synthetic-data-generation', 'synthetic-data-generation', 'data-summarization']	['medical', 'miscellaneous', 'miscellaneous']	[ 1.85177550e-01 3.80270869e-01 -8.58662367e-01 -7.01213002e-01 -9.74101961e-01 -6.07014477e-01 8.37706804e-01 1.20647275e+00 -6.88295841e-01 1.51843488e+00 3.78638208e-01 -9.78274286e-01 2.33263806e-01 -8.52601171e-01 -9.96775985e-01 -6.19620740e-01 3.72220546e-01 5.61991572e-01 -2.57932305e-01 -6.98185563e-02 7.96400383e-02 5.05083084e-01 -1.35526729e+00 7.03817904e-01 1.09041893e+00 5.41529894e-01 -3.55082363e-01 3.37710381e-01 -2.65181839e-01 8.51780653e-01 -9.51871574e-01 -6.92917645e-01 4.72095430e-01 -5.64077914e-01 -6.17010415e-01 -4.28055584e-01 2.98038155e-01 -3.69186848e-01 1.30724400e-01 3.19555253e-01 7.66807437e-01 -3.71491730e-01 1.86192960e-01 -1.63907838e+00 -3.14673543e-01 7.22552836e-01 -3.95094246e-01 -1.08347386e-01 -1.06691606e-01 4.78517145e-01 9.07846332e-01 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317d8012-6d12-419f-992e-338bf4cf2483	prediction-then-correction-an-abductive	2304.14050	null	https://arxiv.org/abs/2304.14050v1	https://arxiv.org/pdf/2304.14050v1.pdf	Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation	Sequential recommender models typically generate predictions in a single step during testing, without considering additional prediction correction to enhance performance as humans would. To improve the accuracy of these models, some researchers have attempted to simulate human analogical reasoning to correct predictions for testing data by drawing analogies with the prediction errors of similar training data. However, there are inherent gaps between testing and training data, which can make this approach unreliable. To address this issue, we propose an \textit{Abductive Prediction Correction} (APC) framework for sequential recommendation. Our approach simulates abductive reasoning to correct predictions. Specifically, we design an abductive reasoning task that infers the most probable historical interactions from the future interactions predicted by a recommender, and minimizes the discrepancy between the inferred and true historical interactions to adjust the predictions.We perform the abductive inference and adjustment using a reversed sequential model in the forward and backward propagation manner of neural networks. Our APC framework is applicable to various differentiable sequential recommender models. We implement it on three backbone models and demonstrate its effectiveness. We release the code at https://github.com/zyang1580/APC.	['Fuli Feng', 'Chenxu Wang', 'Qifan Wang', 'Yang Zhang', 'Yulong Huang']	2023-04-27	null	null	null	null	['sequential-recommendation']	['miscellaneous']	[ 2.71863550e-01 3.47289920e-01 -2.75745749e-01 -8.55709612e-01 -9.40264575e-03 -5.24024189e-01 4.40856099e-01 -6.81133270e-02 -2.17126697e-01 8.06970000e-01 2.00848535e-01 -7.78315187e-01 -4.49345440e-01 -9.18282866e-01 -1.04161870e+00 -4.57958784e-04 3.21725577e-01 7.07752347e-01 2.64858961e-01 -4.48314905e-01 6.27139509e-01 4.29820120e-01 -1.63417280e+00 7.68644392e-01 1.21218657e+00 8.52202773e-01 -3.64242718e-02 7.02645540e-01 4.20815162e-02 1.25278568e+00 -4.62969542e-01 -1.00313938e+00 2.11372107e-01 -6.96910024e-01 -8.38802397e-01 -6.91664100e-01 1.64723516e-01 -5.41845918e-01 -2.40959495e-01 8.33525181e-01 1.18324265e-01 4.23864514e-01 5.61043024e-01 -1.31838942e+00 -1.14616656e+00 1.12528253e+00 -1.63733274e-01 1.83084011e-01 6.04985952e-01 7.24552274e-02 8.69842827e-01 -7.77700543e-01 2.92264551e-01 1.11179888e+00 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-1.04376113e+00 -7.74898410e-01 -1.49045557e-01 1.89253435e-01 -1.43958166e-01 -4.96154159e-01 -2.74306715e-01 -8.57343376e-02]	[9.885007858276367, 5.7705583572387695]
7c05f8eb-b82e-42bb-8c12-ec7a7f9368ff	aitlas-artificial-intelligence-toolbox-for	2201.08789	null	https://arxiv.org/abs/2201.08789v1	https://arxiv.org/pdf/2201.08789v1.pdf	AiTLAS: Artificial Intelligence Toolbox for Earth Observation	The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets. It can be easily applied for a variety of Earth Observation tasks, such as land use and cover classification, crop type prediction, localization of specific objects (semantic segmentation), etc. The main goal of AiTLAS is to facilitate better usability and adoption of novel AI methods (and models) by EO experts, while offering easy access and standardized format of EO datasets to AI experts which further allows benchmarking of various existing and novel AI methods tailored for EO data.	['Dragi Kocev', 'Nikola Simidjievski', 'Panče Panov', 'Ivan Kitanovski', 'Ivica Dimitrovski']	2022-01-21	null	null	null	null	['type-prediction']	['computer-code']	[-2.45892555e-02 -7.80960321e-02 -2.06106722e-01 -2.28010640e-01 8.84544924e-02 -6.93543792e-01 5.61300218e-01 4.36084479e-01 -1.94146380e-01 4.00849551e-01 -2.85115719e-01 -8.20982039e-01 -4.36210930e-01 -1.15411854e+00 -2.45426834e-01 -8.15796793e-01 -6.14944339e-01 6.21386468e-01 -1.77406166e-02 -5.76029956e-01 -1.43101662e-01 9.19391751e-01 -2.01700759e+00 1.19819917e-01 1.06491029e+00 1.17771912e+00 5.49209595e-01 7.27996528e-01 -5.17214239e-02 6.20190799e-01 -6.00870959e-02 3.48812163e-01 3.21920425e-01 1.20654926e-01 -1.01367426e+00 -7.53240213e-02 2.19653964e-01 2.97393417e-03 -9.55299139e-02 1.23315382e+00 1.85741276e-01 2.57076204e-01 9.93319511e-01 -1.35460830e+00 -3.94772321e-01 2.65823513e-01 -1.03778146e-01 5.37852049e-01 2.59515084e-02 7.58773014e-02 7.38620102e-01 -5.71078360e-01 6.42719805e-01 1.05826199e+00 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78063232-15e0-4423-86cc-03182b73a15f	attribute-controllable-beautiful-caucasian	2208.04517	null	https://arxiv.org/abs/2208.04517v1	https://arxiv.org/pdf/2208.04517v1.pdf	Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning	In recent years, image generation has made great strides in improving the quality of images, producing high-fidelity ones. Also, quite recently, there are architecture designs, which enable GAN to unsupervisedly learn the semantic attributes represented in different layers. However, there is still a lack of research on generating face images more consistent with human aesthetics. Based on EigenGAN [He et al., ICCV 2021], we build the techniques of reinforcement learning into the generator of EigenGAN. The agent tries to figure out how to alter the semantic attributes of the generated human faces towards more preferable ones. To accomplish this, we trained an aesthetics scoring model that can conduct facial beauty prediction. We also can utilize this scoring model to analyze the correlation between face attributes and aesthetics scores. Empirically, using off-the-shelf techniques from reinforcement learning would not work well. So instead, we present a new variant incorporating the ingredients emerging in the reinforcement learning communities in recent years. Compared to the original generated images, the adjusted ones show clear distinctions concerning various attributes. Experimental results using the MindSpore, show the effectiveness of the proposed method. Altered facial images are commonly more attractive, with significantly improved aesthetic levels.	['Chaoen Xiao', 'Qiang Deng', 'Xin Zhao', 'Le Zhang', 'Shu Zhao', 'Xin Jin']	2022-08-09	null	null	null	null	['facial-beauty-prediction']	['computer-vision']	[ 1.83328297e-02 5.34663260e-01 1.52734652e-01 -5.20825565e-01 -5.58470376e-02 -1.23103485e-01 5.13334811e-01 -4.69714701e-01 2.40534227e-02 8.21837246e-01 3.12609911e-01 3.43253553e-01 -2.60882564e-02 -1.09518230e+00 -4.90117401e-01 -6.90520644e-01 8.28711092e-02 1.69565842e-01 -6.00096703e-01 -5.23536384e-01 2.46364340e-01 4.65320677e-01 -1.84551156e+00 3.05982530e-01 8.27983201e-01 1.07230437e+00 -5.19969650e-02 2.34525979e-01 -2.82150090e-01 4.94400591e-01 -4.58726704e-01 -9.62844849e-01 4.39447790e-01 -8.29659045e-01 -6.14760280e-01 3.76948982e-01 2.06095248e-01 -2.02477694e-01 1.41323417e-01 1.11332417e+00 5.21705866e-01 -2.72605687e-01 8.25748622e-01 -1.48721921e+00 -1.08889401e+00 5.50522208e-01 -6.88721955e-01 -5.16253769e-01 4.48144734e-01 2.72627473e-01 1.02311230e+00 -6.56885207e-01 6.39262080e-01 1.45275307e+00 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80f9d09e-3c3a-4781-ab20-33a025d1cc8c	efficient-multi-task-auxiliary-learning	null	null	https://aclanthology.org/2021.emnlp-main.34	https://aclanthology.org/2021.emnlp-main.34.pdf	Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity	Multi-task auxiliary learning utilizes a set of relevant auxiliary tasks to improve the performance of a primary task. A common usage is to manually select multiple auxiliary tasks for multi-task learning on all data, which raises two issues: (1) selecting beneficial auxiliary tasks for a primary task is nontrivial; (2) when the auxiliary datasets are large, training on all data becomes time-expensive and impractical. Therefore, this paper focuses on addressing these problems and proposes a time-efficient sampling method to select the data that is most relevant to the primary task. The proposed method allows us to only train on the most beneficial sub-datasets from the auxiliary tasks, achieving efficient multi-task auxiliary learning. The experiments on three benchmark datasets (RTE, MRPC, STS-B) show that our method significantly outperforms random sampling and ST-DNN. Also, by applying our method, the model can surpass fully-trained MT-DNN on RTE, MRPC, STS-B, using only 50%, 66%, and 1% of data, respectively.	['Yun-Nung Chen', 'Tse-Hsuan Yang', 'Yi-Cheng Chen', 'Sheng-Siang Yin', 'Po-Nien Kung']	null	null	null	null	emnlp-2021-11	['auxiliary-learning']	['methodology']	[ 2.92407662e-01 -2.83422265e-02 -4.03452575e-01 -5.70362285e-02 -1.36545098e+00 -4.63268131e-01 5.12746274e-01 -1.65679231e-01 -8.57243419e-01 1.27899718e+00 3.12222868e-01 -3.55383337e-01 1.74541578e-01 -2.70775169e-01 -4.88275886e-01 -9.83500302e-01 4.93950754e-01 8.04694235e-01 3.59652340e-01 -3.09355378e-01 1.33330449e-01 1.35863433e-04 -1.50394619e+00 1.53503522e-01 1.21616161e+00 9.47143137e-01 1.07977736e+00 2.74703681e-01 -2.17122942e-01 6.13222897e-01 -7.99061179e-01 -2.38368109e-01 8.39964449e-02 -2.91547656e-01 -9.12491918e-01 -2.47980863e-01 1.11085176e-01 -1.10790744e-01 1.53871849e-01 7.63449132e-01 7.12540567e-01 1.62310734e-01 7.62816072e-01 -1.37556815e+00 -3.22894156e-01 5.56409717e-01 -4.70203340e-01 2.64076978e-01 -3.36288005e-01 -1.96921289e-01 9.95788455e-01 -1.01375413e+00 3.96401972e-01 1.14793098e+00 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723c9be9-3753-401a-8e19-3b7d471460d8	reghec-hand-eye-calibration-via-simultaneous	2304.14092	null	https://arxiv.org/abs/2304.14092v1	https://arxiv.org/pdf/2304.14092v1.pdf	RegHEC: Hand-Eye Calibration via Simultaneous Multi-view Point Clouds Registration of Arbitrary Object	RegHEC is a registration-based hand-eye calibration technique with no need for accurate calibration rig but arbitrary available objects, applicable for both eye-in-hand and eye-to-hand cases. It tries to find the hand-eye relation which brings multi-view point clouds of arbitrary scene into simultaneous registration under a common reference frame. RegHEC first achieves initial alignment of multi-view point clouds via Bayesian optimization, where registration problem is modeled as a Gaussian process over hand-eye relation and the covariance function is modified to be compatible with distance metric in 3-D motion space SE(3), then passes the initial guess of hand-eye relation to an Anderson Accelerated ICP variant for later fine registration and accurate calibration. RegHEC has little requirement on calibration object, it is applicable with sphere, cone, cylinder and even simple plane, which can be quite challenging for correct point cloud registration and sensor motion estimation using existing methods. While suitable for most 3-D vision guided tasks, RegHEC is especially favorable for robotic 3-D reconstruction, as calibration and multi-view point clouds registration of reconstruction target are unified into a single process. Our technique is verified with extensive experiments using varieties of arbitrary objects and real hand-eye system. We release an open-source C++ implementation of RegHEC.	['Min Tan', 'Fengshui Jing', 'Shiyu Xing']	2023-04-27	null	null	null	null	['point-cloud-registration', 'motion-estimation']	['computer-vision', 'computer-vision']	[-3.63383591e-01 -2.12265715e-01 2.12386534e-01 -2.06985772e-01 -6.39043510e-01 -6.24724209e-01 5.36678970e-01 -3.80182087e-01 -3.41396034e-01 2.71104842e-01 -3.61095458e-01 9.13028605e-03 -1.80996239e-01 -4.34589624e-01 -5.46452641e-01 -8.27700198e-01 7.13641942e-01 1.24048054e+00 4.62086022e-01 -3.18919659e-01 4.42083836e-01 9.22711134e-01 -1.45946777e+00 -4.99387145e-01 5.24686933e-01 6.71779275e-01 4.98727649e-01 5.35580933e-01 2.63198376e-01 5.32713300e-03 -1.16029158e-02 -4.74205554e-01 6.03444815e-01 2.14971572e-01 -5.44071794e-01 2.71163940e-01 4.48103070e-01 -5.17141111e-02 7.94921964e-02 1.22602105e+00 5.16695380e-01 -7.97654390e-02 7.86560893e-01 -1.34517658e+00 -5.20671189e-01 -3.78352582e-01 -1.16525662e+00 -9.60343629e-02 5.89647651e-01 -1.95350195e-03 4.88117635e-01 -1.18892610e+00 7.33421087e-01 1.12410164e+00 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80bc8588-8da2-4702-a9c8-7550cc5894f9	self-supervised-learning-for-panoptic	2209.04618	null	https://arxiv.org/abs/2209.04618v1	https://arxiv.org/pdf/2209.04618v1.pdf	Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species	Convolutional neural networks trained using manually generated labels are commonly used for semantic or instance segmentation. In precision agriculture, automated flower detection methods use supervised models and post-processing techniques that may not perform consistently as the appearance of the flowers and the data acquisition conditions vary. We propose a self-supervised learning strategy to enhance the sensitivity of segmentation models to different flower species using automatically generated pseudo-labels. We employ a data augmentation and refinement approach to improve the accuracy of the model predictions. The augmented semantic predictions are then converted to panoptic pseudo-labels to iteratively train a multi-task model. The self-supervised model predictions can be refined with existing post-processing approaches to further improve their accuracy. An evaluation on a multi-species fruit tree flower dataset demonstrates that our method outperforms state-of-the-art models without computationally expensive post-processing steps, providing a new baseline for flower detection applications.	['Henry Medeiros', 'Amy Tabb', 'Abubakar Siddique']	2022-09-10	null	null	null	null	['panoptic-segmentation']	['computer-vision']	[ 6.34664416e-01 -4.02515978e-02 -1.58418253e-01 -7.93956518e-01 -1.97627634e-01 -1.14937043e+00 5.83309591e-01 4.23720390e-01 -5.52254692e-02 2.32547522e-01 -5.86325347e-01 -2.49459282e-01 1.89183488e-01 -8.64831388e-01 -7.51655757e-01 -3.50738347e-01 7.57198706e-02 5.99402726e-01 4.31126058e-01 -7.34845474e-02 1.20169586e-02 6.40644312e-01 -1.78531659e+00 4.45329547e-01 7.46720552e-01 1.08417380e+00 8.04921627e-01 8.32489669e-01 -3.59647781e-01 3.06320518e-01 -4.33143914e-01 2.24283598e-02 5.26900470e-01 -1.73227027e-01 -7.58462071e-01 5.48807263e-01 4.37409550e-01 -2.73932338e-01 3.99418414e-01 9.99108195e-01 -4.46571782e-02 -2.31111720e-01 5.47494352e-01 -9.98583972e-01 -6.07469320e-01 6.24194443e-01 -6.10371351e-01 -1.82170972e-01 -2.24090874e-01 2.97276378e-01 7.19031751e-01 -7.53281474e-01 5.45212388e-01 1.25218952e+00 9.40032125e-01 2.24979714e-01 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a24ff8bf-3d09-4a21-b6f7-007e351aebaf	visually-grounded-continual-learning-of	2005.00785	null	https://arxiv.org/abs/2005.00785v5	https://arxiv.org/pdf/2005.00785v5.pdf	Visually Grounded Continual Learning of Compositional Phrases	Humans acquire language continually with much more limited access to data samples at a time, as compared to contemporary NLP systems. To study this human-like language acquisition ability, we present VisCOLL, a visually grounded language learning task, which simulates the continual acquisition of compositional phrases from streaming visual scenes. In the task, models are trained on a paired image-caption stream which has shifting object distribution; while being constantly evaluated by a visually-grounded masked language prediction task on held-out test sets. VisCOLL compounds the challenges of continual learning (i.e., learning from continuously shifting data distribution) and compositional generalization (i.e., generalizing to novel compositions). To facilitate research on VisCOLL, we construct two datasets, COCO-shift and Flickr-shift, and benchmark them using different continual learning methods. Results reveal that SoTA continual learning approaches provide little to no improvements on VisCOLL, since storing examples of all possible compositions is infeasible. We conduct further ablations and analysis to guide future work.	['Xisen Jin', 'Arka Sadhu', 'Xiang Ren', 'Ram Nevatia', 'Junyi Du']	2020-05-02	null	https://aclanthology.org/2020.emnlp-main.158	https://aclanthology.org/2020.emnlp-main.158.pdf	emnlp-2020-11	['grounded-language-learning']	['natural-language-processing']	[ 5.35149872e-01 -7.38632157e-02 -1.06111050e-01 -3.72955203e-01 -9.71623540e-01 -9.50836182e-01 7.92543352e-01 3.27991605e-01 -3.15728694e-01 5.18330097e-01 1.48789585e-01 -4.93968934e-01 3.81051868e-01 -5.27145505e-01 -1.23040354e+00 -6.12161636e-01 -3.62092376e-01 6.74528480e-01 1.33574739e-01 -1.58084169e-01 2.15507254e-01 3.28623980e-01 -1.68380034e+00 1.02071297e+00 4.99950737e-01 6.55762136e-01 5.16106963e-01 9.91991878e-01 -3.33837152e-01 1.24309361e+00 -4.38234061e-01 -2.81716853e-01 2.56916434e-01 -7.27167547e-01 -7.98279762e-01 3.40034723e-01 1.24989998e+00 -3.63116890e-01 -1.55569911e-01 9.40418541e-01 3.20442617e-01 1.61750719e-01 7.39969611e-01 -1.35372102e+00 -1.46556568e+00 9.23505604e-01 -4.63979512e-01 2.66484886e-01 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2.70826042e-01 7.01345503e-01 -7.81990215e-02 4.09635991e-01 3.51663738e-01 -8.14271808e-01 -5.03672838e-01 -6.19521260e-01 -3.55038375e-01 9.03008401e-01 9.34989154e-01 -2.53355622e-01 -3.59153569e-01 7.00586140e-01]	[10.79304313659668, 1.8316060304641724]
97fc5ce1-b157-4f0e-a0de-181b6a99efe1	unifying-and-personalizing-weakly-supervised	2304.05635	null	https://arxiv.org/abs/2304.05635v1	https://arxiv.org/pdf/2304.05635v1.pdf	Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation	Federated learning (FL) enables multiple sites to collaboratively train powerful deep models without compromising data privacy and security. The statistical heterogeneity (e.g., non-IID data and domain shifts) is a primary obstacle in FL, impairing the generalization performance of the global model. Weakly supervised segmentation, which uses sparsely-grained (i.e., point-, bounding box-, scribble-, block-wise) supervision, is increasingly being paid attention to due to its great potential of reducing annotation costs. However, there may exist label heterogeneity, i.e., different annotation forms across sites. In this paper, we propose a novel personalized FL framework for medical image segmentation, named FedICRA, which uniformly leverages heterogeneous weak supervision via adaptIve Contrastive Representation and Aggregation. Concretely, to facilitate personalized modeling and to avoid confusion, a channel selection based site contrastive representation module is employed to adaptively cluster intra-site embeddings and separate inter-site ones. To effectively integrate the common knowledge from the global model with the unique knowledge from each local model, an adaptive aggregation module is applied for updating and initializing local models at the element level. Additionally, a weakly supervised objective function that leverages a multiscale tree energy loss and a gated CRF loss is employed to generate more precise pseudo-labels and further boost the segmentation performance. Through extensive experiments on two distinct medical image segmentation tasks of different modalities, the proposed FedICRA demonstrates overwhelming performance over other state-of-the-art personalized FL methods. Its performance even approaches that of fully supervised training on centralized data. Our code and data are available at https://github.com/llmir/FedICRA.	['Xiaoying Tang', 'Kenneth K. Y. Wong', 'Yixiang Liu', 'Jiewei Wu', 'Li Lin']	2023-04-12	null	null	null	null	['weakly-supervised-segmentation']	['computer-vision']	[ 2.51420349e-01 -4.26303595e-02 -6.47692680e-01 -5.82415342e-01 -1.25169277e+00 -5.89779675e-01 1.42225713e-01 1.88014761e-01 -4.03087974e-01 5.98000407e-01 2.41058961e-01 -2.21209869e-01 5.91041967e-02 -5.81509888e-01 -6.81842029e-01 -1.11401665e+00 2.85706818e-01 3.63418043e-01 8.43649879e-02 2.57152528e-01 -3.36478740e-01 2.41931349e-01 -9.35938895e-01 2.87552476e-01 1.14232397e+00 1.11477339e+00 1.99979097e-01 1.45159379e-01 -2.53458232e-01 4.96548563e-01 -2.86832899e-01 -4.72558171e-01 3.11999947e-01 -1.44729838e-01 -7.15082169e-01 1.38539702e-01 4.95050222e-01 -3.40882123e-01 -1.71603397e-01 1.18097782e+00 6.29708409e-01 -9.28986669e-02 2.99667060e-01 -9.70984638e-01 -5.57876170e-01 7.88858712e-01 -8.01133752e-01 -3.64919491e-02 -1.80682912e-01 1.18235841e-01 9.50859845e-01 -6.24302745e-01 6.07512832e-01 7.02504754e-01 6.86902463e-01 6.81012094e-01 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cdb45b44-b6fe-4a14-9a6f-e295e68fbe24	a-deep-learning-approach-to-predict-blood	2108.00099	null	https://arxiv.org/abs/2108.00099v1	https://arxiv.org/pdf/2108.00099v1.pdf	A Deep Learning Approach to Predict Blood Pressure from PPG Signals	Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body's vital (life-sustaining) functions. BP is difficult to continuously monitor using a sphygmomanometer (i.e. a blood pressure cuff), especially in everyday-setting. However, other health signals which can be easily and continuously acquired, such as photoplethysmography (PPG), show some similarities with the Aortic Pressure waveform. Based on these similarities, in recent years several methods were proposed to predict BP from the PPG signal. Building on these results, we propose an advanced personalized data-driven approach that uses a three-layer deep neural network to estimate BP based on PPG signals. Different from previous work, the proposed model analyzes the PPG signal in time-domain and automatically extracts the most critical features for this specific application, then uses a variation of recurrent neural networks called Long-Short-Term-Memory (LSTM) to map the extracted features to the BP value associated with that time window. Experimental results on two separate standard hospital datasets, yielded absolute errors mean and absolute error standard deviation for systolic and diastolic BP values outperforming prior works.	['Marco Levorato', 'Ali Tazarv']	2021-07-30	null	null	null	null	['photoplethysmography-ppg']	['medical']	[ 8.52929279e-02 5.16019575e-02 4.60244082e-02 -6.19257033e-01 -1.83349773e-01 1.34523943e-01 -1.48228168e-01 -4.91855852e-02 -2.00153887e-01 1.05938530e+00 4.62570876e-01 -3.83469820e-01 -2.85067171e-01 -9.17959750e-01 -1.64855495e-01 -6.34627879e-01 -5.65863371e-01 1.80285256e-02 -1.13095738e-01 -1.30692527e-01 3.66947412e-01 2.90134788e-01 -1.13545072e+00 -1.27262071e-01 9.71075892e-01 1.34357083e+00 -3.71037513e-01 8.40403557e-01 -1.89816773e-01 6.15977824e-01 -7.22127438e-01 1.49623767e-01 3.85240227e-01 -7.32706249e-01 -3.80444974e-01 -3.77322614e-01 4.95691240e-01 -5.50001860e-01 -3.82747680e-01 7.65364766e-01 1.11318421e+00 4.30373289e-02 -2.09457390e-02 -6.43597841e-01 -6.99659288e-01 6.28468573e-01 -2.33534783e-01 5.82441986e-01 4.73436378e-02 1.46744281e-01 2.53224164e-01 -4.41603035e-01 -1.17541552e-01 8.10650706e-01 1.04263484e+00 5.14770865e-01 -1.19856834e+00 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4.53794301e-01 6.27038777e-01 -3.16080898e-01 -4.37672794e-01 -1.66604474e-01]	[14.068920135498047, 2.9638867378234863]
a431a143-fb00-4895-a948-fe22ca73baa1	clartts-an-open-source-classical-arabic-text	2303.00069	null	https://arxiv.org/abs/2303.00069v1	https://arxiv.org/pdf/2303.00069v1.pdf	ClArTTS: An Open-Source Classical Arabic Text-to-Speech Corpus	At present, Text-to-speech (TTS) systems that are trained with high-quality transcribed speech data using end-to-end neural models can generate speech that is intelligible, natural, and closely resembles human speech. These models are trained with relatively large single-speaker professionally recorded audio, typically extracted from audiobooks. Meanwhile, due to the scarcity of freely available speech corpora of this kind, a larger gap exists in Arabic TTS research and development. Most of the existing freely available Arabic speech corpora are not suitable for TTS training as they contain multi-speaker casual speech with variations in recording conditions and quality, whereas the corpus curated for speech synthesis are generally small in size and not suitable for training state-of-the-art end-to-end models. In a move towards filling this gap in resources, we present a speech corpus for Classical Arabic Text-to-Speech (ClArTTS) to support the development of end-to-end TTS systems for Arabic. The speech is extracted from a LibriVox audiobook, which is then processed, segmented, and manually transcribed and annotated. The final ClArTTS corpus contains about 12 hours of speech from a single male speaker sampled at 40100 kHz. In this paper, we describe the process of corpus creation and provide details of corpus statistics and a comparison with existing resources. Furthermore, we develop two TTS systems based on Grad-TTS and Glow-TTS and illustrate the performance of the resulting systems via subjective and objective evaluations. The corpus will be made publicly available at www.clartts.com for research purposes, along with the baseline TTS systems demo.	['Hanan Aldarmaki', 'Sara Abedalmonem Mohammad Shatnawi', 'Atharva Kulkarni', 'Ajinkya Kulkarni']	2023-02-28	null	null	null	null	['speech-synthesis']	['speech']	[-1.59406904e-02 2.54318774e-01 3.85519683e-01 -5.41720331e-01 -1.31234312e+00 -7.31324315e-01 4.65888262e-01 -2.28528194e-02 -1.97122186e-01 4.46515620e-01 4.56477940e-01 -5.90615511e-01 2.18000203e-01 -1.47999614e-01 -3.54858935e-01 -5.14320076e-01 2.45867902e-03 6.48580432e-01 -7.45471101e-03 -6.99322164e-01 -9.80363637e-02 2.27020711e-01 -1.43173432e+00 5.10019362e-01 8.30470026e-01 9.45934355e-01 6.16875410e-01 1.06875813e+00 1.42663333e-03 4.88769472e-01 -1.26699305e+00 -4.12536889e-01 -1.20041385e-01 -6.93728745e-01 -8.19422841e-01 2.25562468e-01 4.10029590e-02 -4.33361828e-01 -2.36832023e-01 6.18725479e-01 8.93698275e-01 2.89013296e-01 5.45805275e-01 -8.48577857e-01 -7.49586642e-01 9.28393364e-01 3.09384972e-01 1.20631114e-01 5.92950106e-01 -3.86694190e-03 9.81273353e-01 -1.09342074e+00 4.43771273e-01 1.37822926e+00 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40c9653c-b575-4bc0-87c2-45913976265a	degae-a-new-pretraining-paradigm-for-low	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Liu_DegAE_A_New_Pretraining_Paradigm_for_Low-Level_Vision_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_DegAE_A_New_Pretraining_Paradigm_for_Low-Level_Vision_CVPR_2023_paper.pdf	DegAE: A New Pretraining Paradigm for Low-Level Vision	Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acquisition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (DegAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tailored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively.	['Chao Dong', 'Yu Qiao', 'Xiangtao Kong', 'Jinjin Gu', 'Jingwen He', 'Yihao Liu']	2023-01-01	null	null	null	cvpr-2023-1	['philosophy']	['miscellaneous']	[ 3.90602767e-01 2.49292869e-02 -2.79333871e-02 -3.45816255e-01 -4.16101515e-01 -1.99211001e-01 5.64089656e-01 -1.61325395e-01 -7.29005158e-01 4.19989049e-01 2.13610336e-01 -2.15704963e-01 -1.02695636e-03 -6.52028441e-01 -8.12828660e-01 -7.66916633e-01 1.70635790e-01 -2.47748211e-01 2.95982450e-01 -3.77741665e-01 1.64551213e-01 5.40070653e-01 -2.14589071e+00 5.42317748e-01 1.02624273e+00 1.02806127e+00 4.70857859e-01 9.56913888e-01 3.57953519e-01 6.98508441e-01 -7.73930788e-01 -3.32613170e-01 4.98779297e-01 -5.67345917e-01 -7.37970829e-01 3.72717142e-01 1.02677608e+00 -3.66414368e-01 -4.53015745e-01 1.33312988e+00 6.16503000e-01 -4.49517928e-02 6.50545597e-01 -8.80201101e-01 -8.69185925e-01 2.72948951e-01 -5.30113101e-01 5.60578644e-01 -4.39880714e-02 3.51449609e-01 7.69415796e-01 -9.85705197e-01 -1.13325134e-01 1.13491046e+00 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2ac27c7a-3ecb-4e8a-b214-a3575357ac7f	190206443	1902.06443	null	http://arxiv.org/abs/1902.06443v1	http://arxiv.org/pdf/1902.06443v1.pdf	Sparse residual tree and forest	Sparse residual tree (SRT) is an adaptive exploration method for multivariate scattered data approximation. It leads to sparse and stable approximations in areas where the data is sufficient or redundant, and points out the possible local regions where data refinement is needed. Sparse residual forest (SRF) is a combination of SRT predictors to further improve the approximation accuracy and stability according to the error characteristics of SRTs. The hierarchical parallel SRT algorithm is based on both tree decomposition and adaptive radial basis function (RBF) explorations, whereby for each child a sparse and proper RBF refinement is added to the approximation by minimizing the norm of the residual inherited from its parent. The convergence results are established for both SRTs and SRFs. The worst case time complexity of SRTs is $\mathcal{O}(N\log_2N)$ for the initial work and $\mathcal{O}(\log_2N)$ for each prediction, meanwhile, the worst case storage requirement is $\mathcal{O}(N\log_2N)$, where the $N$ data points can be arbitrary distributed. Numerical experiments are performed for several illustrative examples.	['Xin Xu', 'Xiaopeng Luo']	2019-02-18	null	null	null	null	['tree-decomposition']	['graphs']	[-5.46244644e-02 7.82495290e-02 -1.06811926e-01 -1.34592608e-01 -6.43548429e-01 1.80336282e-01 -3.06344241e-01 4.17655200e-01 -7.70969167e-02 1.02870739e+00 -1.41316533e-01 -2.91983843e-01 -6.01718783e-01 -1.09721959e+00 -4.88370061e-01 -9.36497033e-01 -6.10009670e-01 5.98220229e-01 2.51029819e-01 -2.39381135e-01 3.33609611e-01 7.28167892e-01 -1.61757350e+00 -1.76461771e-01 1.43077898e+00 1.73552155e+00 2.58778483e-01 3.18407804e-01 -2.78565377e-01 1.00375140e+00 -3.59655857e-01 1.77218512e-01 4.60034788e-01 -1.88655302e-01 -5.94193339e-01 -4.59078588e-02 -4.25098687e-02 1.39049798e-01 -1.64298818e-01 9.90270615e-01 3.02742600e-01 5.67228079e-01 4.36492473e-01 -9.93565321e-01 -1.80832714e-01 4.03728336e-01 -1.05262256e+00 2.19411448e-01 -8.02524481e-03 -3.13356549e-01 3.12251747e-01 -1.31926370e+00 2.79713511e-01 1.08019555e+00 8.77135336e-01 -1.88535526e-01 -1.40216267e+00 -8.83851767e-01 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fbf50a39-0a78-4045-a114-c01cc8f59da1	hyperparameter-free-losses-for-model-based	1908.09001	null	https://arxiv.org/abs/1908.09001v1	https://arxiv.org/pdf/1908.09001v1.pdf	Hyperparameter-Free Losses for Model-Based Monocular Reconstruction	This work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM). We dispense with the hyperparameters used in other works by exploiting geometry, so that the shape of the object and the camera pose are jointly optimized in a sole term expression. This simplification reduces the optimization time and its complexity. Moreover, we propose a novel implicit regularization technique based on random virtual projections that does not require additional 2D or 3D annotations. Our experiments suggest that minimizing a shape reprojection error together with the proposed implicit regularization is especially suitable for applications that require precise alignment between geometry and image spaces, such as augmented reality. We evaluate our losses on a large scale dataset with 3D ground truth and publish our implementations to facilitate reproducibility and public benchmarking in this field.	['Xavier Giró-i-Nieto', 'Guillermo Ruiz', 'Eduard Ramon', 'Thomas Batard']	2019-08-16	null	null	null	null	['single-view-3d-reconstruction']	['computer-vision']	[ 6.42588269e-03 3.01920384e-01 1.57054082e-01 -2.35292435e-01 -6.49193347e-01 -6.13275707e-01 3.89747888e-01 -9.13306102e-02 -3.59030962e-01 4.60779816e-01 -1.62466578e-02 -6.08932823e-02 -9.50751305e-02 -5.93598962e-01 -9.15740132e-01 -5.52831173e-01 3.59043539e-01 6.51596606e-01 7.76945427e-02 -8.19002688e-02 1.79300115e-01 8.16967070e-01 -1.22631049e+00 -4.12363827e-01 7.47708380e-01 8.65871072e-01 3.20501536e-01 3.01423967e-01 2.47081429e-01 1.53808504e-01 -1.17609161e-03 -6.51974499e-01 7.95586407e-01 -7.62761524e-03 -5.05500913e-01 5.62588334e-01 7.78689623e-01 -3.54291707e-01 -1.58282146e-01 9.29920435e-01 6.73305631e-01 1.34796575e-01 5.14361739e-01 -9.28860724e-01 -2.61096060e-01 -1.82446331e-01 -7.18106568e-01 -4.98060286e-01 2.95026153e-01 4.64174338e-02 8.03949833e-01 -1.03792644e+00 9.03560579e-01 1.02617180e+00 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bdc5dff9-ff70-4aa3-918b-a6c1fd8120b2	unbalanced-optimal-transport-for-unbalanced	2306.04116	null	https://arxiv.org/abs/2306.04116v1	https://arxiv.org/pdf/2306.04116v1.pdf	Unbalanced Optimal Transport for Unbalanced Word Alignment	Monolingual word alignment is crucial to model semantic interactions between sentences. In particular, null alignment, a phenomenon in which words have no corresponding counterparts, is pervasive and critical in handling semantically divergent sentences. Identification of null alignment is useful on its own to reason about the semantic similarity of sentences by indicating there exists information inequality. To achieve unbalanced word alignment that values both alignment and null alignment, this study shows that the family of optimal transport (OT), i.e., balanced, partial, and unbalanced OT, are natural and powerful approaches even without tailor-made techniques. Our extensive experiments covering unsupervised and supervised settings indicate that our generic OT-based alignment methods are competitive against the state-of-the-arts specially designed for word alignment, remarkably on challenging datasets with high null alignment frequencies.	['Sho Yokoi', 'Han Bao', 'Yuki Arase']	2023-06-07	null	null	null	null	['word-alignment', 'semantic-textual-similarity', 'semantic-similarity']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 3.77820432e-01 1.51852608e-01 -5.02147913e-01 -4.35520530e-01 -6.20421588e-01 -7.98683524e-01 5.72344124e-01 4.16734964e-01 -4.38488126e-01 6.34254873e-01 5.22656381e-01 -4.39263284e-01 -1.88614950e-01 -3.83892566e-01 -3.62473667e-01 -4.78205562e-01 3.52038771e-01 8.00125241e-01 3.04116700e-02 -7.79772103e-01 6.16004229e-01 4.83280383e-02 -1.34666002e+00 2.45995656e-01 1.11204636e+00 3.85176659e-01 3.77134353e-01 3.87226224e-01 -7.29811132e-01 4.66074407e-01 -4.34096992e-01 -8.38247001e-01 1.92067698e-01 -6.19492173e-01 -1.20444715e+00 -1.81603432e-01 5.65699458e-01 3.29719126e-01 1.13238953e-01 1.46059406e+00 5.63517213e-01 -4.86731380e-02 6.63467407e-01 -1.21663177e+00 -5.64297974e-01 1.19700325e+00 -3.94547522e-01 7.00692296e-01 5.84834754e-01 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f8904bdf-7b39-4c42-be40-d0c6fd8db963	dir-as-decoupling-individual-identification	2304.02110	null	https://arxiv.org/abs/2304.02110v1	https://arxiv.org/pdf/2304.02110v1.pdf	DIR-AS: Decoupling Individual Identification and Temporal Reasoning for Action Segmentation	Fully supervised action segmentation works on frame-wise action recognition with dense annotations and often suffers from the over-segmentation issue. Existing works have proposed a variety of solutions such as boundary-aware networks, multi-stage refinement, and temporal smoothness losses. However, most of them take advantage of frame-wise supervision, which cannot effectively tackle the evaluation metrics with different granularities. In this paper, for the desirable large receptive field, we first develop a novel local-global attention mechanism with temporal pyramid dilation and temporal pyramid pooling for efficient multi-scale attention. Then we decouple two inherent goals in action segmentation, ie, (1) individual identification solved by frame-wise supervision, and (2) temporal reasoning tackled by action set prediction. Afterward, an action alignment module fuses these different granularity predictions, leading to more accurate and smoother action segmentation. We achieve state-of-the-art accuracy, eg, 82.8% (+2.6%) on GTEA and 74.7% (+1.2%) on Breakfast, which demonstrates the effectiveness of our proposed method, accompanied by extensive ablation studies. The code will be made available later.	['Haibin Ling', 'Peiyao Wang']	2023-04-04	null	null	null	null	['action-recognition-in-videos', 'action-segmentation']	['computer-vision', 'computer-vision']	[ 5.05582690e-01 1.05199054e-01 -4.25736248e-01 -3.14394981e-01 -8.73222888e-01 -1.74303085e-01 2.97723621e-01 -6.24038577e-02 -4.91178155e-01 5.68658173e-01 3.83758932e-01 1.25920773e-01 -5.97019568e-02 -4.25741583e-01 -4.78775293e-01 -7.39645720e-01 1.36343732e-01 3.70498486e-02 6.25361323e-01 -1.62992589e-02 2.32900485e-01 2.79162228e-01 -1.47142124e+00 5.00568926e-01 1.13015068e+00 1.22236824e+00 1.00291073e-01 5.67295372e-01 6.18861914e-02 1.09344995e+00 -4.03981566e-01 -2.28727594e-01 2.06003442e-01 -4.96508747e-01 -9.30301785e-01 3.48345906e-01 3.29421043e-01 -5.75162649e-01 -1.56605870e-01 9.22279060e-01 3.05802405e-01 3.29222202e-01 2.16303170e-01 -1.14305341e+00 -4.48709995e-01 4.81690228e-01 -8.97200942e-01 3.07410620e-02 8.55384991e-02 3.52411896e-01 1.10900366e+00 -5.22026360e-01 3.93840194e-01 1.22938693e+00 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437fb2ba-72c5-4b39-9d66-206b283fd084	video-salient-object-detection-via-fully	1702.00871	null	http://arxiv.org/abs/1702.00871v3	http://arxiv.org/pdf/1702.00871v3.pdf	Video Salient Object Detection via Fully Convolutional Networks	This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: (1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data, and (2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further propose a novel data augmentation technique that simulates video training data from existing annotated image datasets, which enables our network to learn diverse saliency information and prevents overfitting with the limited number of training videos. Leveraging our synthetic video data (150K video sequences) and real videos, our deep video saliency model successfully learns both spatial and temporal saliency cues, thus producing accurate spatiotemporal saliency estimate. We advance the state-of-the-art on the DAVIS dataset (MAE of .06) and the FBMS dataset (MAE of .07), and do so with much improved speed (2fps with all steps).	['Wenguan Wang', 'Jianbing Shen', 'Ling Shao']	2017-02-02	null	null	null	null	['video-salient-object-detection']	['computer-vision']	[ 3.79766911e-01 -1.38204753e-01 -3.73661876e-01 -4.72295694e-02 -6.04949236e-01 -1.90704674e-01 3.87864470e-01 -2.81759650e-01 -3.29290628e-01 7.84948647e-01 2.60395974e-01 -1.47443816e-01 1.68039933e-01 -4.24380362e-01 -1.07405210e+00 -4.35432911e-01 -3.80444825e-01 -3.55134368e-01 1.06256878e+00 -1.03765339e-01 3.99492025e-01 1.52263371e-02 -1.91403866e+00 7.13895038e-02 1.12269676e+00 1.36462247e+00 6.74357533e-01 8.46666336e-01 2.62088895e-01 1.40641630e+00 -2.17613444e-01 2.01667026e-01 3.03653538e-01 -4.13308710e-01 -8.17318857e-01 1.45122483e-01 6.32404268e-01 -7.49615312e-01 -7.42164791e-01 8.73983800e-01 1.57685891e-01 3.73388112e-01 1.00005955e-01 -1.51454282e+00 -7.55374134e-01 4.16659266e-01 -7.42677629e-01 8.27614427e-01 1.42647341e-01 3.09263438e-01 7.79802978e-01 -9.61561322e-01 6.36679173e-01 9.48023081e-01 3.46998692e-01 4.74631608e-01 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1714fd98-0d37-4072-9685-a6c59a2c74fb	controllable-list-wise-ranking-for-universal	1911.10566	null	https://arxiv.org/abs/1911.10566v2	https://arxiv.org/pdf/1911.10566v2.pdf	Controllable List-wise Ranking for Universal No-reference Image Quality Assessment	No-reference image quality assessment (NR-IQA) has received increasing attention in the IQA community since reference image is not always available. Real-world images generally suffer from various types of distortion. Unfortunately, existing NR-IQA methods do not work with all types of distortion. It is a challenging task to develop universal NR-IQA that has the ability of evaluating all types of distorted images. In this paper, we propose a universal NR-IQA method based on controllable list-wise ranking (CLRIQA). First, to extend the authentically distorted image dataset, we present an imaging-heuristic approach, in which the over-underexposure is formulated as an inverse of Weber-Fechner law, and fusion strategy and probabilistic compression are adopted, to generate the degraded real-world images. These degraded images are label-free yet associated with quality ranking information. We then design a controllable list-wise ranking function by limiting rank range and introducing an adaptive margin to tune rank interval. Finally, the extended dataset and controllable list-wise ranking function are used to pre-train a CNN. Moreover, in order to obtain an accurate prediction model, we take advantage of the original dataset to further fine-tune the pre-trained network. Experiments evaluated on four benchmark datasets (i.e. LIVE, CSIQ, TID2013, and LIVE-C) show that the proposed CLRIQA improves the state of the art by over 9% in terms of overall performance. The code and model are publicly available at https://github.com/GZHU-Image-Lab/CLRIQA.	['Guopu Zhu', 'Yuan-Gen Wang', 'Fu-Zhao Ou', 'Sam Kwong', 'Jin Li']	2019-11-24	null	null	null	null	['no-reference-image-quality-assessment']	['computer-vision']	[ 3.76230508e-01 -4.48117733e-01 8.32652301e-02 -3.85297954e-01 -1.19022405e+00 -4.89583880e-01 3.32423657e-01 -2.60628521e-01 -1.96381718e-01 6.10848844e-01 1.39569134e-01 -2.50545144e-01 -2.67620653e-01 -8.02679777e-01 -6.82388008e-01 -9.01377022e-01 9.41828713e-02 -2.09850110e-02 8.07588100e-02 -1.26429632e-01 2.36069098e-01 3.69046807e-01 -1.39831555e+00 2.20961288e-01 1.17273641e+00 1.30116081e+00 3.53883624e-01 5.44256032e-01 5.46328962e-01 6.73978508e-01 -6.35102153e-01 -4.58052069e-01 5.67986190e-01 -5.90532601e-01 -5.85945487e-01 1.72394916e-01 3.64950508e-01 -6.53113544e-01 -4.92964178e-01 1.21002364e+00 8.35767031e-01 1.30737633e-01 5.65942645e-01 -1.20745969e+00 -8.74334216e-01 2.80114561e-01 -5.16207099e-01 2.31132761e-01 2.48165965e-01 3.55840385e-01 8.55977952e-01 -7.96271622e-01 3.89143109e-01 1.14704621e+00 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613d92f9-bf47-4618-85b2-c4fd0cf49196	pac-bayesian-like-error-bound-for-a-class-of	2212.14838	null	https://arxiv.org/abs/2212.14838v1	https://arxiv.org/pdf/2212.14838v1.pdf	PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models	In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various consequences of this error bound.	['Mihaly Petreczky', 'Rafal Wisniewski', 'Zheng-Hua Tan', 'John Leth', 'Deividas Eringis']	2022-12-30	null	null	null	null	['econometrics']	['miscellaneous']	[ 1.08973324e-01 7.61539564e-02 -2.57342100e-01 -2.01103479e-01 -5.89993000e-01 -3.75450909e-01 8.31548989e-01 -3.24053228e-01 -2.45524988e-01 1.04018319e+00 -8.35177451e-02 -5.65103710e-01 -6.29624605e-01 -5.26198566e-01 -6.03113532e-01 -1.18241763e+00 -1.72693748e-02 5.08263171e-01 2.71302700e-01 1.53076231e-01 -1.03101768e-01 5.92119813e-01 -1.25224221e+00 -5.06006777e-01 5.47135472e-01 1.02929711e+00 1.14553675e-01 7.98563004e-01 2.87509173e-01 6.85590327e-01 -5.02530932e-01 2.91068684e-02 2.21175671e-01 -5.03771663e-01 -2.73656934e-01 -3.38778496e-01 -1.65524110e-01 2.00065672e-02 -7.52617061e-01 1.30412388e+00 2.83312738e-01 2.68654674e-01 1.15267611e+00 -1.19739652e+00 -1.93817869e-01 7.20811427e-01 1.91196963e-01 2.99273193e-01 -2.29687974e-01 2.05942154e-01 8.65859449e-01 -5.24069846e-01 2.14051008e-01 1.37203550e+00 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03fd6f16-3c03-4e4b-ad10-7584ef956a12	time-masking-leveraging-temporal-information	1907.11315	null	https://arxiv.org/abs/1907.11315v1	https://arxiv.org/pdf/1907.11315v1.pdf	Time Masking: Leveraging Temporal Information in Spoken Dialogue Systems	In a spoken dialogue system, dialogue state tracker (DST) components track the state of the conversation by updating a distribution of values associated with each of the slots being tracked for the current user turn, using the interactions until then. Much of the previous work has relied on modeling the natural order of the conversation, using distance based offsets as an approximation of time. In this work, we hypothesize that leveraging the wall-clock temporal difference between turns is crucial for finer-grained control of dialogue scenarios. We develop a novel approach that applies a {\it time mask}, based on the wall-clock time difference, to the associated slot embeddings and empirically demonstrate that our proposed approach outperforms existing approaches that leverage distance offsets, on both an internal benchmark dataset as well as DSTC2.	['Rylan Conway', 'Lambert Mathias']	2019-07-25	time-masking-leveraging-temporal-information-1	https://aclanthology.org/W19-5907	https://aclanthology.org/W19-5907.pdf	ws-2019-9	['video-salient-object-detection']	['computer-vision']	[-8.54182094e-02 3.57125610e-01 -3.27595472e-01 -7.41802394e-01 -3.43642622e-01 -9.91721749e-01 1.19277692e+00 3.50243896e-01 -5.88797450e-01 6.17970526e-01 8.91351342e-01 -4.26042616e-01 -6.50931001e-02 -5.42753816e-01 1.25981063e-01 -2.11199135e-01 -2.29466990e-01 9.25826252e-01 4.26386267e-01 -7.46046424e-01 4.28789705e-01 1.77988723e-01 -1.18724799e+00 2.43635982e-01 4.77882087e-01 8.95761371e-01 -2.54057497e-01 9.58891392e-01 -5.73829591e-01 7.66106308e-01 -9.22461152e-01 -3.38210352e-03 1.44690648e-01 -5.02098680e-01 -1.08299327e+00 1.22255618e-02 -7.44362250e-02 -5.40503025e-01 -7.87404358e-01 4.27072436e-01 3.23450863e-01 7.71208167e-01 3.65671784e-01 -1.34585547e+00 5.95112145e-02 9.05245602e-01 9.02369618e-04 3.41393083e-01 7.54252791e-01 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aa8dcc41-d320-49c2-9606-5bb77b2919a6	structured-reordering-for-modeling-latent	2106.03257	null	https://arxiv.org/abs/2106.03257v3	https://arxiv.org/pdf/2106.03257v3.pdf	Structured Reordering for Modeling Latent Alignments in Sequence Transduction	Despite success in many domains, neural models struggle in settings where train and test examples are drawn from different distributions. In particular, in contrast to humans, conventional sequence-to-sequence (seq2seq) models fail to generalize systematically, i.e., interpret sentences representing novel combinations of concepts (e.g., text segments) seen in training. Traditional grammar formalisms excel in such settings by implicitly encoding alignments between input and output segments, but are hard to scale and maintain. Instead of engineering a grammar, we directly model segment-to-segment alignments as discrete structured latent variables within a neural seq2seq model. To efficiently explore the large space of alignments, we introduce a reorder-first align-later framework whose central component is a neural reordering module producing {\it separable} permutations. We present an efficient dynamic programming algorithm performing exact marginal inference of separable permutations, and, thus, enabling end-to-end differentiable training of our model. The resulting seq2seq model exhibits better systematic generalization than standard models on synthetic problems and NLP tasks (i.e., semantic parsing and machine translation).	['Ivan Titov', 'Mirella Lapata', 'Bailin Wang']	2021-06-06	null	http://proceedings.neurips.cc/paper/2021/hash/6f46dd176364ccec308c2760189a4605-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/6f46dd176364ccec308c2760189a4605-Paper.pdf	neurips-2021-12	['systematic-generalization']	['reasoning']	[ 1.02039015e+00 5.00044823e-01 -1.06944978e-01 -6.45809531e-01 -1.09598827e+00 -1.17163491e+00 4.93410707e-01 -1.09835356e-01 -2.35303193e-01 9.58434105e-01 4.18076152e-03 -7.84491420e-01 -4.34482768e-02 -8.00555527e-01 -1.28617680e+00 -6.23391807e-01 8.79653394e-02 9.37168121e-01 -2.07960203e-01 1.17553331e-01 2.38329574e-01 1.35788947e-01 -1.39059937e+00 4.26997900e-01 1.06279361e+00 3.31242591e-01 4.59862798e-01 8.50026309e-01 -4.04946476e-01 8.49121511e-02 -6.47930562e-01 -5.35980225e-01 2.23380908e-01 -9.36296761e-01 -8.10402334e-01 8.71788412e-02 5.54645240e-01 6.62804674e-03 1.78351831e-02 1.19311786e+00 1.77414238e-01 1.50736570e-01 5.75275958e-01 -9.40592945e-01 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a2ffc7b4-fa45-4cf1-97e4-6cb6772eed71	robust-person-following-under-severe-indoor	null	null	https://ieeexplore.ieee.org/abstract/document/9649857	https://ieeexplore.ieee.org/abstract/document/9649857	Robust Person Following Under Severe Indoor Illumination Changes for Mobile Robots: Online Color-Based Identification Update	Tracking a specific person in environments with non-uniform illumination is a difficult task for mobile robots. Image information such as color is essential to identify a target person. However, the information is not reliable under severe illumination changes unless the system can accommodate these changes over time. In this paper, we propose a robust identifier that has been combined with a deep learning technique to accommodate varying illumination in the ambient lighting of a scene. Moreover, an enhanced online update strategy for the person identification model is used to deal with the challenge of drifting the target person's appearance changes during tracking. Using the proposed method, the system achieves a successfully tracked rate above 90% on real-world video sequences in which variations in illumination are dominant. We confirmed the effectiveness of the proposed method through target-following experiments using five different clothing colors in a real indoor environment where the lighting conditions change extremely.	['Redhwan Algabri']	2021-12-28	null	null	null	conference-2021-12	['person-identification']	['computer-vision']	[ 1.08149178e-01 -8.13529432e-01 4.12299663e-01 -3.50427836e-01 -6.45846054e-02 -6.14318907e-01 2.94958681e-01 -4.49204803e-01 -8.65972698e-01 8.45262170e-01 -2.80632645e-01 3.16076785e-01 2.84187198e-01 -2.98103541e-01 -7.23204017e-01 -9.26874161e-01 1.06465966e-01 2.29044095e-01 2.21012264e-01 3.95609513e-02 1.19313868e-02 4.25615817e-01 -1.69167709e+00 -3.38897973e-01 7.56669462e-01 7.29635417e-01 4.47450846e-01 7.12993860e-01 1.97716191e-01 1.01699583e-01 -8.69812429e-01 -1.48614869e-01 5.51003218e-01 6.92836344e-02 2.47727223e-02 5.25347650e-01 6.64405823e-01 -4.73772138e-01 -2.93951333e-01 1.27874339e+00 6.92669690e-01 1.75951079e-01 3.06073695e-01 -1.20537138e+00 -4.86048609e-01 -5.00439443e-02 -7.61592686e-01 1.92817077e-01 5.01497030e-01 3.37311625e-01 8.61794204e-02 -5.57700694e-01 3.04037452e-01 1.35418117e+00 8.75957310e-01 8.39078546e-01 -7.91488409e-01 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5.00020564e-01 5.59409916e-01 -8.56020510e-01 -8.01882073e-02 3.26097399e-01]	[6.760674476623535, -1.6836971044540405]
26f3b7b6-919d-45dd-a082-c842a5b37753	trans4trans-efficient-transformer-for	2107.03172	null	https://arxiv.org/abs/2107.03172v2	https://arxiv.org/pdf/2107.03172v2.pdf	Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World	Common fully glazed facades and transparent objects present architectural barriers and impede the mobility of people with low vision or blindness, for instance, a path detected behind a glass door is inaccessible unless it is correctly perceived and reacted. However, segmenting these safety-critical objects is rarely covered by conventional assistive technologies. To tackle this issue, we construct a wearable system with a novel dual-head Transformer for Transparency (Trans4Trans) model, which is capable of segmenting general and transparent objects and performing real-time wayfinding to assist people walking alone more safely. Especially, both decoders created by our proposed Transformer Parsing Module (TPM) enable effective joint learning from different datasets. Besides, the efficient Trans4Trans model composed of symmetric transformer-based encoder and decoder, requires little computational expenses and is readily deployed on portable GPUs. Our Trans4Trans model outperforms state-of-the-art methods on the test sets of Stanford2D3D and Trans10K-v2 datasets and obtains mIoU of 45.13% and 75.14%, respectively. Through various pre-tests and a user study conducted in indoor and outdoor scenarios, the usability and reliability of our assistive system have been extensively verified.	['Rainer Stiefelhagen', 'Karin Müller', 'Kunyu Peng', 'Angela Constantinescu', 'Kailun Yang', 'Jiaming Zhang']	2021-07-07	null	null	null	null	['transparent-objects']	['computer-vision']	[ 1.80327687e-02 4.97809164e-02 1.25294074e-01 -2.49414638e-01 -5.17933369e-01 -2.48804346e-01 1.70310497e-01 -4.26220149e-01 -4.36164916e-01 6.88919663e-01 1.95843458e-01 -6.78976357e-01 1.50512502e-01 -8.60414863e-01 -6.23675883e-01 -4.08145756e-01 1.21683754e-01 2.06349753e-02 5.04662156e-01 -2.63356000e-01 -3.22096705e-01 -1.05448253e-01 -1.72433257e+00 1.78468481e-01 1.45187533e+00 1.09371030e+00 6.87696517e-01 6.08058274e-01 3.23061854e-01 4.09343839e-01 -3.63833934e-01 -6.94685042e-01 1.52327046e-01 3.08813453e-01 -4.62378651e-01 -7.36532882e-02 6.05369389e-01 -9.49592650e-01 -5.89219332e-01 6.28028333e-01 9.46963668e-01 -1.52257979e-01 4.67957973e-01 -9.84738111e-01 -7.29955912e-01 -1.43093169e-01 -5.67910671e-01 -5.71338683e-02 7.29910910e-01 3.22258651e-01 4.92034137e-01 -5.63556552e-01 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aeec4c6d-c5c6-4700-b8df-4bb3ab73f017	audio-defect-detection-in-music-with-deep	2202.05718	null	https://arxiv.org/abs/2202.05718v1	https://arxiv.org/pdf/2202.05718v1.pdf	Audio Defect Detection in Music with Deep Networks	With increasing amounts of music being digitally transferred from production to distribution, automatic means of determining media quality are needed. Protection mechanisms in digital audio processing tools have not eliminated the need of production entities located downstream the distribution chain to assess audio quality and detect defects inserted further upstream. Such analysis often relies on the received audio and scarce meta-data alone. Deliberate use of artefacts such as clicks in popular music as well as more recent defects stemming from corruption in modern audio encodings call for data-centric and context sensitive solutions for detection. We present a convolutional network architecture following end-to-end encoder decoder configuration to develop detectors for two exemplary audio defects. A click detector is trained and compared to a traditional signal processing method, with a discussion on context sensitivity. Additional post-processing is used for data augmentation and workflow simulation. The ability of our models to capture variance is explored in a detector for artefacts from decompression of corrupted MP3 compressed audio. For both tasks we describe the synthetic generation of artefacts for controlled detector training and evaluation. We evaluate our detectors on the large open-source Free Music Archive (FMA) and genre-specific datasets.	['Axel Roebel', 'Rémi Mignot', 'Daniel Wolff']	2022-02-11	null	null	null	null	['defect-detection']	['computer-vision']	[ 5.98991334e-01 -3.62119079e-01 5.45037270e-01 -1.63349628e-01 -1.36118841e+00 -6.62329257e-01 1.44821197e-01 6.53887391e-01 -4.03700590e-01 2.00622231e-01 6.05535150e-01 1.55884102e-02 -3.60811293e-01 -5.45047998e-01 -6.83181226e-01 -5.09850718e-02 -5.17811477e-01 4.74066660e-02 4.35053408e-01 -1.26766086e-01 4.49597687e-01 1.38181597e-01 -2.01914620e+00 9.93272901e-01 2.27457330e-01 1.19502270e+00 3.31704885e-01 1.47038770e+00 2.98126429e-01 6.19215727e-01 -1.32209444e+00 -9.87922251e-02 2.50896543e-01 -3.69060695e-01 -5.18550873e-01 2.04007644e-02 4.07970369e-01 -4.14032251e-01 -1.31588414e-01 7.45388031e-01 1.22391367e+00 -3.33491772e-01 2.99110651e-01 -1.07680404e+00 -9.80672389e-02 1.14898336e+00 -2.67451834e-02 4.91607338e-01 7.85234571e-01 3.52428645e-01 1.10844564e+00 -7.53134429e-01 5.29966712e-01 8.77420068e-01 1.08611655e+00 -1.69230372e-01 -1.11730897e+00 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b01dd9b3-4474-496a-be49-6e703e22dfbb	promptda-label-guided-data-augmentation-for	2205.09229	null	https://arxiv.org/abs/2205.09229v3	https://arxiv.org/pdf/2205.09229v3.pdf	PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners	Recent advances in large pre-trained language models (PLMs) lead to impressive gains in natural language understanding (NLU) tasks with task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on sufficient labeled training instances, which are usually hard to obtain. Prompt-based tuning on PLMs has shown to be powerful for various downstream few-shot tasks. Existing works studying prompt-based tuning for few-shot NLU tasks mainly focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs. In addition, conventional data augmentation strategies such as synonym substitution, though widely adopted in low-resource scenarios, only bring marginal improvements for prompt-based few-shot learning. Thus, an important research question arises: how to design effective data augmentation methods for prompt-based few-shot tuning? To this end, considering the label semantics are essential in prompt-based tuning, we propose a novel label-guided data augmentation framework PromptDA, which exploits the enriched label semantic information for data augmentation. Extensive experiment results on few-shot text classification tasks demonstrate the superior performance of the proposed framework by effectively leveraging label semantics and data augmentation for natural language understanding. Our code is available at https://github.com/canyuchen/PromptDA.	['Kai Shu', 'Canyu Chen']	2022-05-18	null	null	null	null	['few-shot-text-classification']	['natural-language-processing']	[ 5.15939534e-01 1.22259796e-01 -7.37277925e-01 -6.29085600e-01 -7.22985744e-01 -2.03923613e-01 6.77798331e-01 3.32221597e-01 -5.83436787e-01 7.12129653e-01 4.88116026e-01 -1.86372951e-01 1.41637608e-01 -8.06560457e-01 -3.65495682e-01 -5.61937213e-01 6.41140103e-01 5.88245928e-01 -1.51401013e-01 -5.22534668e-01 7.96050578e-02 -2.04077557e-01 -1.41897833e+00 1.20914288e-01 1.11785865e+00 7.12426126e-01 3.91644716e-01 3.68217915e-01 -7.36391962e-01 4.77798343e-01 -2.69565523e-01 -1.47891939e-01 1.96668562e-02 -5.61930776e-01 -8.35163295e-01 5.72963208e-02 5.54934852e-02 -3.03289115e-01 -1.27305731e-01 8.92471373e-01 5.66039562e-01 7.55030751e-01 6.81685030e-01 -1.11562598e+00 -8.45633566e-01 9.71440673e-01 -5.50899088e-01 2.90144056e-01 6.22086674e-02 2.06651464e-01 1.42439270e+00 -1.13808799e+00 3.69623840e-01 1.25648487e+00 2.90407032e-01 1.05289042e+00 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039e1900-03e3-4b04-bff6-4315bb572159	shapley-variable-importance-cloud-for-machine	2212.08370	null	https://arxiv.org/abs/2212.08370v1	https://arxiv.org/pdf/2212.08370v1.pdf	Shapley variable importance cloud for machine learning models	Current practice in interpretable machine learning often focuses on explaining the final model trained from data, e.g., by using the Shapley additive explanations (SHAP) method. The recently developed Shapley variable importance cloud (ShapleyVIC) extends the current practice to a group of "nearly optimal models" to provide comprehensive and robust variable importance assessments, with estimated uncertainty intervals for a more complete understanding of variable contributions to predictions. ShapleyVIC was initially developed for applications with traditional regression models, and the benefits of ShapleyVIC inference have been demonstrated in real-life prediction tasks using the logistic regression model. However, as a model-agnostic approach, ShapleyVIC application is not limited to such scenarios. In this work, we extend ShapleyVIC implementation for machine learning models to enable wider applications, and propose it as a useful complement to the current SHAP analysis to enable more trustworthy applications of these black-box models.	['Nan Liu', 'Mingxuan Liu', 'Yilin Ning']	2022-12-16	null	null	null	null	['interpretable-machine-learning']	['methodology']	[ 2.71524012e-01 7.68723369e-01 -8.29574585e-01 -4.36619788e-01 -6.52082086e-01 -4.59279805e-01 6.54633462e-01 1.42634660e-01 1.49507374e-01 1.34108901e+00 1.41235247e-01 -8.45834792e-01 -4.84030455e-01 -4.75582689e-01 -6.44201875e-01 -5.02158403e-01 1.17433488e-01 7.25676775e-01 -4.65530872e-01 -8.78601335e-03 4.48074758e-01 2.92086095e-01 -1.49001300e+00 1.72626168e-01 9.87628043e-01 7.75340140e-01 -2.04491824e-01 5.03824413e-01 6.26209844e-03 1.13353288e+00 -4.34165537e-01 -8.39841425e-01 1.22057796e-01 -4.56355184e-01 -5.81367373e-01 -6.70728445e-01 4.00625281e-02 -3.43793899e-01 2.32187346e-01 7.32463896e-01 -1.71856374e-01 -2.98400819e-02 1.15242243e+00 -2.07033229e+00 -1.11836624e+00 1.18380392e+00 -5.99392056e-01 -7.84086064e-02 -6.31237775e-02 6.09739944e-02 1.64117754e+00 -5.88911176e-01 3.35640222e-01 1.27014887e+00 7.45357752e-01 6.79127812e-01 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509f8244-bda8-4fe9-be94-8a27f09554b3	bone-marrow-cell-recognition-training-deep	2110.12647	null	https://arxiv.org/abs/2110.12647v1	https://arxiv.org/pdf/2110.12647v1.pdf	Bone Marrow Cell Recognition: Training Deep Object Detection with A New Loss Function	For a long time, bone marrow cell morphology examination has been an essential tool for diagnosing blood diseases. However, it is still mainly dependent on the subjective diagnosis of experienced doctors, and there is no objective quantitative standard. Therefore, it is crucial to study a robust bone marrow cell detection algorithm for a quantitative automatic analysis system. Currently, due to the dense distribution of cells in the bone marrow smear and the diverse cell classes, the detection of bone marrow cells is difficult. The existing bone marrow cell detection algorithms are still insufficient for the automatic analysis system of bone marrow smears. This paper proposes a bone marrow cell detection algorithm based on the YOLOv5 network, trained by minimizing a novel loss function. The classification method of bone marrow cell detection tasks is the basis of the proposed novel loss function. Since bone marrow cells are classified according to series and stages, part of the classes in adjacent stages are similar. The proposed novel loss function considers the similarity between bone marrow cell classes, increases the penalty for prediction errors between dissimilar classes, and reduces the penalty for prediction errors between similar classes. The results show that the proposed loss function effectively improves the algorithm's performance, and the proposed bone marrow cell detection algorithm has achieved better performance than other cell detection algorithms.	['Jie Li', 'Qiongxiong Ma', 'Zhihao Su', 'Rui Fan', 'Jintao Cheng', 'Dehao Huang']	2021-10-25	null	null	null	null	['cell-detection']	['computer-vision']	[-1.75489813e-01 -3.96961421e-01 -1.40675694e-01 -1.84341550e-01 -4.98154968e-01 5.56013621e-02 2.09223330e-01 8.14827383e-01 -5.92868209e-01 7.68505096e-01 -3.08482319e-01 1.19062260e-01 1.97482258e-01 -1.10654211e+00 3.04495782e-01 -1.18251240e+00 2.79177368e-01 9.42398965e-01 6.22148693e-01 9.50263813e-02 2.89380521e-01 9.28297997e-01 -1.10090292e+00 1.84889898e-01 6.77313149e-01 9.54569280e-01 1.79766372e-01 7.01786935e-01 -5.34708440e-01 9.27462637e-01 -7.79837012e-01 1.36680985e-02 -1.13777786e-01 -7.39024758e-01 -4.12482321e-01 3.02926097e-02 -3.29246759e-01 -1.54368550e-01 -2.85539888e-02 9.61012483e-01 7.67271519e-01 2.20309757e-02 1.22867513e+00 -9.50866699e-01 -1.24119669e-01 2.25749761e-01 -6.57192409e-01 5.81003070e-01 1.44553334e-01 1.83776114e-02 5.98718166e-01 -8.16890538e-01 4.19006675e-01 1.01038337e+00 6.06709361e-01 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c86aad72-a7c6-4c97-824b-8019c92c02ab	spatiotemporal-deep-learning-model-for	1911.12919	null	https://arxiv.org/abs/1911.12919v1	https://arxiv.org/pdf/1911.12919v1.pdf	Spatiotemporal deep learning model for citywide air pollution interpolation and prediction	Recently, air pollution is one of the most concerns for big cities. Predicting air quality for any regions and at any time is a critical requirement of urban citizens. However, air pollution prediction for the whole city is a challenging problem. The reason is, there are many spatiotemporal factors affecting air pollution throughout the city. Collecting as many of them could help us to forecast air pollution better. In this research, we present many spatiotemporal datasets collected over Seoul city in Korea, which is currently much suffered by air pollution problem as well. These datasets include air pollution data, meteorological data, traffic volume, average driving speed, and air pollution indexes of external areas which are known to impact Seoul's air pollution. To the best of our knowledge, traffic volume and average driving speed data are two new datasets in air pollution research. In addition, recent research in air pollution has tried to build models to interpolate and predict air pollution in the city. Nevertheless, they mostly focused on predicting air quality in discrete locations or used hand-crafted spatial and temporal features. In this paper, we propose the usage of Convolutional Long Short-Term Memory (ConvLSTM) model \cite{b16}, a combination of Convolutional Neural Networks and Long Short-Term Memory, which automatically manipulates both the spatial and temporal features of the data. Specially, we introduce how to transform the air pollution data into sequences of images which leverages the using of ConvLSTM model to interpolate and predict air quality for the entire city at the same time. We prove that our approach is suitable for spatiotemporal air pollution problems and also outperforms other related research.	['Sang Kyun Cha', 'Tien-Cuong Bui', 'Van-Duc Le']	2019-11-29	null	null	null	null	['air-pollution-prediction']	['miscellaneous']	[-3.11889350e-01 -9.61072624e-01 -5.92327751e-02 -1.91857219e-01 -7.05710649e-01 -2.22081065e-01 2.93793321e-01 1.75275300e-02 -4.68280524e-01 8.69320214e-01 1.29523352e-01 -6.59017920e-01 -5.48118830e-01 -1.61394310e+00 -6.41991317e-01 -8.27520251e-01 1.95084512e-01 8.31505936e-03 8.40764269e-02 -1.64962769e-01 -1.25640467e-01 7.24403799e-01 -1.51741982e+00 4.92745787e-02 1.08300257e+00 8.93260419e-01 3.36368710e-01 5.99898040e-01 -3.74436229e-01 2.54958808e-01 -5.77044070e-01 9.01320428e-02 3.96949500e-01 -9.80863646e-02 -5.58894932e-01 -1.37046397e-01 3.59823287e-01 -9.88051593e-02 -4.75121915e-01 9.86519456e-01 3.39195520e-01 6.22899950e-01 2.24672645e-01 -1.09884214e+00 -7.15339422e-01 5.03122397e-02 -2.11844772e-01 7.31698573e-01 -3.05385381e-01 6.34517729e-01 6.51508033e-01 -4.88381624e-01 -1.94591090e-01 9.51288700e-01 5.51456153e-01 6.64279610e-02 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3.83454591e-01 7.96291828e-01 -5.28008819e-01 -3.31395417e-01 -1.92362100e-01]	[6.263516902923584, 2.473785161972046]
17d3b4cd-26f8-42ff-93d0-28381ad218a8	probabilistic-forecasting-methods-for-system	2210.09399	null	https://arxiv.org/abs/2210.09399v1	https://arxiv.org/pdf/2210.09399v1.pdf	Probabilistic Forecasting Methods for System-Level Electricity Load Forecasting	Load forecasts have become an integral part of energy security. Due to the various influencing factors that can be considered in such a forecast, there is also a wide range of models that attempt to integrate these parameters into a system in various ways. Due to the growing importance of probabilistic load forecast models, different approaches are presented in this analysis. The focus is on different models from the short-term sector. After that, another model from the long-term sector is presented. Then, the presented models are put in relation to each other and examined with reference to advantages and disadvantages. Afterwards, the presented papers are analyzed with focus on their comparability to each other. Finally, an outlook on further areas of development in the literature will be discussed.	['Philipp Giese']	2022-10-17	null	null	null	null	['load-forecasting']	['miscellaneous']	[-4.03607160e-01 -1.10970467e-01 -4.59485352e-01 -2.18172058e-01 -1.17597863e-01 -6.66984200e-01 9.65868533e-01 1.50179490e-01 9.01835337e-02 9.24958825e-01 2.72266120e-01 -5.66256464e-01 -4.33809429e-01 -1.11637723e+00 1.34181648e-01 -1.03191018e+00 -3.89345852e-03 4.82543677e-01 5.00738733e-02 -3.17524582e-01 3.29289734e-01 9.20559764e-01 -1.73182547e+00 -2.08774120e-01 9.64651406e-01 9.61096048e-01 2.99302489e-01 2.36475110e-01 -3.88460122e-02 5.76771677e-01 -8.56279492e-01 -8.34636837e-02 -1.55895039e-01 -3.67115051e-01 -4.39404786e-01 -5.67600727e-02 -8.23439837e-01 -2.10096557e-02 2.69171447e-01 9.66547608e-01 4.07989115e-01 2.89268792e-01 1.06129324e+00 -1.39370954e+00 -1.55219063e-01 7.58524716e-01 -2.06820130e-01 3.79312217e-01 5.46697557e-01 -2.00671703e-01 6.09150290e-01 -6.73429787e-01 5.61864786e-02 6.95703208e-01 4.35948163e-01 -4.90197912e-02 -1.09283626e+00 -5.04498720e-01 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-2.81529337e-01 -1.43226430e-01 -8.01376224e-01 -7.38484323e-01 -4.50702548e-01]	[6.056568622589111, 2.8236916065216064]
2dcdd5d3-fe67-4cbc-a082-8a276fb5e02e	deep-reinforcement-learning-for-contact-rich	2008.13223	null	https://arxiv.org/abs/2008.13223v2	https://arxiv.org/pdf/2008.13223v2.pdf	Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives	In recent years, industrial robots have been installed in various industries to handle advanced manufacturing and high precision tasks. However, further integration of industrial robots is hampered by their limited flexibility, adaptability and decision making skills compared to human operators. Assembly tasks are especially challenging for robots since they are contact-rich and sensitive to even small uncertainties. While reinforcement learning (RL) offers a promising framework to learn contact-rich control policies from scratch, its applicability to high-dimensional continuous state-action spaces remains rather limited due to high brittleness and sample complexity. To address those issues, we propose different pruning methods that facilitate convergence and generalization. In particular, we divide the task into free and contact-rich sub-tasks, perform the control in Cartesian rather than joint space, and parameterize the control policy. Those pruning methods are naturally implemented within the framework of dynamic movement primitives (DMP). To handle contact-rich tasks, we extend the DMP framework by introducing a coupling term that acts like the human wrist and provides active compliance under contact with the environment. We demonstrate that the proposed method can learn insertion skills that are invariant to space, size, shape, and closely related scenarios, while handling large uncertainties. Finally we demonstrate that the learned policy can be easily transferred from simulations to real world and achieve similar performance on UR5e robot.	['Oren Spector', 'Miriam Zacksenhouse']	2020-08-30	null	null	null	null	['industrial-robots']	['robots']	[ 2.10551187e-01 3.44768226e-01 -2.87124574e-01 1.04194671e-01 -1.85686007e-01 -4.13573861e-01 3.72428089e-01 2.17804126e-02 -4.39607382e-01 1.01580048e+00 -5.49393237e-01 -1.96713120e-01 -8.01812410e-01 -6.81189418e-01 -7.50398040e-01 -8.31903696e-01 -3.52429867e-01 6.52731240e-01 3.02010953e-01 -4.11259860e-01 1.29266068e-01 8.00708532e-01 -1.40268743e+00 -3.80216211e-01 1.06206548e+00 8.69331539e-01 8.65077078e-01 1.53704643e-01 3.89418572e-01 3.53454858e-01 -4.69592750e-01 3.58143657e-01 5.38490117e-01 4.38697077e-02 -5.60176849e-01 3.51441681e-01 -4.43878382e-01 -4.70878370e-02 -1.22327901e-01 8.92520487e-01 4.47524458e-01 6.80470288e-01 5.39331079e-01 -1.40627337e+00 -1.29494131e-01 4.71197933e-01 -4.62562501e-01 -4.10317004e-01 2.27658361e-01 3.14935446e-01 4.33567166e-01 -4.65218991e-01 6.24546528e-01 1.34500146e+00 4.54587311e-01 4.79921460e-01 -1.00972331e+00 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1.32246092e-01 2.05690518e-01 -1.48927867e+00 -2.60186773e-02 -3.74884129e-01]	[4.760690212249756, 1.423081636428833]
0fda7fda-9a22-4bed-90cf-7f0c6adbd894	universal-domain-adaptation-through-self	2002.07953	null	https://arxiv.org/abs/2002.07953v3	https://arxiv.org/pdf/2002.07953v3.pdf	Universal Domain Adaptation through Self Supervision	Unsupervised domain adaptation methods traditionally assume that all source categories are present in the target domain. In practice, little may be known about the category overlap between the two domains. While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori. We propose a more universally applicable domain adaptation framework that can handle arbitrary category shift, called Domain Adaptative Neighborhood Clustering via Entropy optimization (DANCE). DANCE combines two novel ideas: First, as we cannot fully rely on source categories to learn features discriminative for the target, we propose a novel neighborhood clustering technique to learn the structure of the target domain in a self-supervised way. Second, we use entropy-based feature alignment and rejection to align target features with the source, or reject them as unknown categories based on their entropy. We show through extensive experiments that DANCE outperforms baselines across open-set, open-partial and partial domain adaptation settings. Implementation is available at https://github.com/VisionLearningGroup/DANCE.	['Kate Saenko', 'Kuniaki Saito', 'Donghyun Kim', 'Stan Sclaroff']	2020-02-19	null	http://proceedings.neurips.cc/paper/2020/hash/bb7946e7d85c81a9e69fee1cea4a087c-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/bb7946e7d85c81a9e69fee1cea4a087c-Paper.pdf	neurips-2020-12	['universal-domain-adaptation', 'partial-domain-adaptation']	['computer-vision', 'methodology']	[ 6.54592961e-02 -9.95083377e-02 -4.20426309e-01 -6.23318613e-01 -6.75791860e-01 -1.11740994e+00 6.27485573e-01 -1.05867662e-01 -4.31383431e-01 6.95168972e-01 2.89648831e-01 1.11254372e-01 -8.51104385e-04 -4.60183203e-01 -5.50783992e-01 -8.01132321e-01 3.09620291e-01 8.31469297e-01 4.27164197e-01 -1.34980068e-01 2.26723120e-01 1.39754251e-01 -1.56613636e+00 2.00862482e-01 9.83954489e-01 7.51167297e-01 1.28943250e-01 3.89284492e-01 -1.26043372e-02 1.62288442e-01 -3.91888797e-01 -1.71775762e-02 7.03730464e-01 -6.03659868e-01 -8.19449604e-01 3.05591971e-01 5.53639829e-01 -1.72100246e-01 -1.40363306e-01 1.14188695e+00 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e47a5917-b01a-40c1-94fb-17b61efac3af	multi-vqg-generating-engaging-questions-for	2211.07441	null	https://arxiv.org/abs/2211.07441v2	https://arxiv.org/pdf/2211.07441v2.pdf	Multi-VQG: Generating Engaging Questions for Multiple Images	Generating engaging content has drawn much recent attention in the NLP community. Asking questions is a natural way to respond to photos and promote awareness. However, most answers to questions in traditional question-answering (QA) datasets are factoids, which reduce individuals' willingness to answer. Furthermore, traditional visual question generation (VQG) confines the source data for question generation to single images, resulting in a limited ability to comprehend time-series information of the underlying event. In this paper, we propose generating engaging questions from multiple images. We present MVQG, a new dataset, and establish a series of baselines, including both end-to-end and dual-stage architectures. Results show that building stories behind the image sequence enables models to generate engaging questions, which confirms our assumption that people typically construct a picture of the event in their minds before asking questions. These results open up an exciting challenge for visual-and-language models to implicitly construct a story behind a series of photos to allow for creativity and experience sharing and hence draw attention to downstream applications.	["Ting-Hao 'Kenneth' Haung", 'Lun-Wei Ku', 'Vicent Chen', 'Min-Hsuan Yeh']	2022-11-14	null	null	null	null	['question-generation']	['natural-language-processing']	[ 4.25129205e-01 6.42410159e-01 2.29084000e-01 -4.61255580e-01 -8.63564789e-01 -8.90362144e-01 1.04629374e+00 1.89692676e-01 -1.20557338e-01 6.80356979e-01 9.55937266e-01 -1.48432106e-01 3.48357439e-01 -9.63802516e-01 -6.23235583e-01 -9.16761085e-02 3.65750015e-01 1.71935722e-01 -1.23963401e-01 -2.60794759e-01 2.42516220e-01 -1.84165001e-01 -1.40489554e+00 9.39065814e-01 6.55889153e-01 6.86947703e-01 1.57391027e-01 8.89191985e-01 -5.51559389e-01 1.47259831e+00 -8.41296077e-01 -8.94558668e-01 -1.65503472e-01 -1.13690639e+00 -1.15487576e+00 3.62940013e-01 9.27610040e-01 -4.94038939e-01 -4.36218619e-01 4.64374512e-01 6.31690264e-01 4.22896385e-01 5.33619940e-01 -1.28133976e+00 -1.54323113e+00 3.85462910e-01 -3.22916508e-01 1.38503209e-01 1.05705690e+00 6.16122782e-01 1.14429522e+00 -8.29610944e-01 9.90472257e-01 1.49078691e+00 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ddeb909c-d187-4f49-89bf-810f792c4fc7	crowd-counting-with-sparse-annotation	2304.06021	null	https://arxiv.org/abs/2304.06021v1	https://arxiv.org/pdf/2304.06021v1.pdf	Crowd Counting with Sparse Annotation	This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image. We argue that sparse labeling can reduce the redundancy of full annotation and capture more diverse information from distant individuals that is not fully captured by Partial Annotation methods. Besides, we propose a point-based Progressive Point Matching network (PPM) to better explore the crowd from the whole image with sparse annotation, which includes a Proposal Matching Network (PMN) and a Performance Restoration Network (PRN). The PMN generates pseudo-point samples using a basic point classifier, while the PRN refines the point classifier with the pseudo points to maximize performance. Our experimental results show that PPM outperforms previous semi-supervised crowd counting methods with the same amount of annotation by a large margin and achieves competitive performance with state-of-the-art fully-supervised methods.	['Tong Zhang', 'Wei Ke', 'Fei Wang', 'Qing Liu', 'Zhengzheng Wang', 'Shiwei Zhang']	2023-04-12	null	null	null	null	['crowd-counting']	['computer-vision']	[-4.18948010e-02 1.67975873e-01 -1.52297569e-02 -1.72405913e-01 -5.19033372e-01 -2.14646608e-01 4.40751433e-01 1.65367797e-01 -6.05468273e-01 8.06150675e-01 3.47857475e-01 5.05724609e-01 5.00449419e-01 -7.06817448e-01 -5.83922684e-01 -4.71232712e-01 1.90284103e-01 1.05011225e+00 9.90012348e-01 -2.01452062e-01 2.03631431e-01 2.69551218e-01 -1.70060670e+00 9.21703689e-03 1.09528184e+00 6.65038824e-01 2.60605782e-01 4.04776633e-01 -3.70731115e-01 9.17343199e-01 -9.20754611e-01 -7.21892118e-01 4.50202733e-01 -7.01279119e-02 -7.91123271e-01 4.19599324e-01 7.44140267e-01 -4.55126435e-01 -3.07426840e-01 1.15080142e+00 6.34096503e-01 3.14819127e-01 4.92546469e-01 -1.18369591e+00 -4.51379597e-01 3.40908080e-01 -1.19863212e+00 8.25642347e-02 5.56146979e-01 1.13629304e-01 6.07810438e-01 -1.17401111e+00 5.43900311e-01 1.47853911e+00 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b9a5bdae-ac75-4d3a-87f0-316c8c85ed4a	computational-protein-design-using-andor	1412.3138	null	http://arxiv.org/abs/1412.3138v2	http://arxiv.org/pdf/1412.3138v2.pdf	Computational Protein Design Using AND/OR Branch-and-Bound Search	The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design. In this paper, we propose a new protein design algorithm based on the AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional branch-and-bound search algorithm, to solve this combinatorial optimization problem. By integrating with a powerful heuristic function, AOBB is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the design process. Tests on real protein data show that our new protein design algorithm is able to solve many prob- lems that were previously unsolvable by the traditional exact search algorithms, and for the problems that can be solved with traditional provable algorithms, our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation (GMEC) solution.	['Jianyang Zeng', 'Yuexin Wu', 'Yichao Zhou']	2014-12-08	null	null	null	null	['protein-design']	['medical']	[ 2.54676372e-01 1.47363037e-01 -1.00216590e-01 -6.27836511e-02 -4.36428368e-01 -8.04734528e-01 -1.89377844e-01 5.87217212e-01 -3.20875347e-01 1.22999084e+00 -3.18530262e-01 -9.00901139e-01 -2.36815572e-01 -7.70101964e-01 -9.57668126e-01 -8.91940832e-01 -9.26054493e-02 6.88974082e-01 3.76307070e-01 -3.86409730e-01 4.76867586e-01 7.82447875e-01 -1.09162772e+00 1.08276688e-01 8.64907086e-01 7.49445558e-01 4.44850266e-01 3.56954038e-01 -2.09845901e-01 9.67413113e-02 -1.81043819e-01 -3.15125883e-02 2.00460225e-01 -6.46601081e-01 -1.01245391e+00 -1.89798310e-01 1.25746548e-01 2.14870468e-01 1.42084733e-01 8.72359276e-01 5.61623752e-01 6.13039173e-02 4.07644302e-01 -7.21149385e-01 -1.09167978e-01 1.05506599e-01 -5.77952921e-01 -5.76557256e-02 4.68698859e-01 1.14897914e-01 1.09434855e+00 -6.21712267e-01 1.00842893e+00 9.12583172e-01 3.38912159e-01 3.93176615e-01 -1.71360064e+00 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-4.73912179e-01 -1.00759314e-02 1.26284614e-01 -5.72847426e-01 -6.81347474e-02 -2.09285989e-01]	[4.853063106536865, 5.514769554138184]
4f40dda9-2ab3-439d-9720-4b85d988d849	towards-zero-shot-sign-language-recognition	2201.05914	null	https://arxiv.org/abs/2201.05914v1	https://arxiv.org/pdf/2201.05914v1.pdf	Towards Zero-shot Sign Language Recognition	This paper tackles the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign classes to recognize the instances of unseen sign classes. In this context, readily available textual sign descriptions and attributes collected from sign language dictionaries are utilized as semantic class representations for knowledge transfer. For this novel problem setup, we introduce three benchmark datasets with their accompanying textual and attribute descriptions to analyze the problem in detail. Our proposed approach builds spatiotemporal models of body and hand regions. By leveraging the descriptive text and attribute embeddings along with these visual representations within a zero-shot learning framework, we show that textual and attribute based class definitions can provide effective knowledge for the recognition of previously unseen sign classes. We additionally introduce techniques to analyze the influence of binary attributes in correct and incorrect zero-shot predictions. We anticipate that the introduced approaches and the accompanying datasets will provide a basis for further exploration of zero-shot learning in sign language recognition.	['Nazli Ikizler-Cinbis', 'Ramazan Gokberk Cinbis', 'Yunus Can Bilge']	2022-01-15	null	null	null	null	['sign-language-recognition']	['computer-vision']	[ 3.20892453e-01 -8.69087800e-02 -6.85229361e-01 -7.35196769e-01 -8.02577376e-01 -4.24902231e-01 8.09116542e-01 -3.77301812e-01 -4.13705915e-01 3.69139850e-01 6.56074464e-01 4.61510643e-02 -2.53036320e-01 -6.04337394e-01 -4.12098080e-01 -6.91087961e-01 -5.73668489e-03 3.05121899e-01 2.59498626e-01 -1.42077416e-01 2.42166534e-01 4.61462587e-01 -2.23612618e+00 5.28276622e-01 5.64075232e-01 9.15011823e-01 -1.42610848e-01 6.79155886e-01 -3.61863792e-01 1.26262951e+00 -2.77993858e-01 -2.71929175e-01 2.33541086e-01 -3.75290006e-01 -5.76251924e-01 8.64765868e-02 1.06973314e+00 -5.68314731e-01 -7.72356093e-01 7.50514805e-01 6.35323763e-01 5.07683754e-01 1.11515462e+00 -1.32875299e+00 -7.62938380e-01 1.57741472e-01 -4.78057191e-02 1.39143541e-02 2.10698113e-01 5.16752481e-01 1.23967147e+00 -9.03166056e-01 1.12099516e+00 9.41821754e-01 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c247d892-da76-40a9-a428-36876c7506e0	vihealthbert-pre-trained-language-models-for-1	null	null	https://aclanthology.org/2022.lrec-1.35	https://aclanthology.org/2022.lrec-1.35.pdf	ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining	Pre-trained language models have become crucial to achieving competitive results across many Natural Language Processing (NLP) problems. For monolingual pre-trained models in low-resource languages, the quantity has been significantly increased. However, most of them relate to the general domain, and there are limited strong baseline language models for domain-specific. We introduce ViHealthBERT, the first domain-specific pre-trained language model for Vietnamese healthcare. The performance of our model shows strong results while outperforming the general domain language models in all health-related datasets. Moreover, we also present Vietnamese datasets for the healthcare domain for two tasks are Acronym Disambiguation (AD) and Frequently Asked Questions (FAQ) Summarization. We release our ViHealthBERT to facilitate future research and downstream application for Vietnamese NLP in domain-specific. Our dataset and code are available in https://github.com/demdecuong/vihealthbert.	['Steven Quoc Hung Truong', 'Trung Huu Bui', 'Huy Duc Ta', 'Vu Hoang', 'Vu Hoang Tran', 'Nguyen Minh']	null	null	null	null	lrec-2022-6	['vietnamese-datasets']	['natural-language-processing']	[-2.80334167e-02 2.58296579e-01 -6.20736063e-01 -3.98892760e-01 -1.51420999e+00 -5.53543925e-01 4.79501367e-01 7.27102757e-01 -7.84190178e-01 1.04286671e+00 1.04359066e+00 -3.61332864e-01 3.33696827e-02 -4.41854686e-01 -2.34728694e-01 -2.72427142e-01 2.23417327e-01 9.45396721e-01 -3.17459464e-01 -5.80925584e-01 -1.76525056e-01 1.08689494e-01 -3.52585971e-01 6.66913331e-01 1.15846503e+00 9.15859565e-02 3.13176125e-01 6.38166368e-01 -2.32434630e-01 9.27832007e-01 -5.07338405e-01 -4.95359182e-01 -1.80517435e-01 -4.64523464e-01 -1.22522855e+00 -4.80371088e-01 2.46839076e-01 -8.30061287e-02 -2.33467937e-01 7.48473287e-01 9.22795832e-01 -1.15490019e-01 7.42253602e-01 -8.77461791e-01 -7.50485659e-01 9.79424775e-01 -3.46019894e-01 3.15494388e-01 7.04532683e-01 7.75573701e-02 1.10273659e+00 -7.98104405e-01 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7ac0f3f2-b201-4197-ac68-049ea8ad1a49	i2l-meshnet-image-to-lixel-prediction-network-1	2008.03713	null	https://arxiv.org/abs/2008.03713v2	https://arxiv.org/pdf/2008.03713v2.pdf	I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image	Most of the previous image-based 3D human pose and mesh estimation methods estimate parameters of the human mesh model from an input image. However, directly regressing the parameters from the input image is a highly non-linear mapping because it breaks the spatial relationship between pixels in the input image. In addition, it cannot model the prediction uncertainty, which can make training harder. To resolve the above issues, we propose I2L-MeshNet, an image-to-lixel (line+pixel) prediction network. The proposed I2L-MeshNet predicts the per-lixel likelihood on 1D heatmaps for each mesh vertex coordinate instead of directly regressing the parameters. Our lixel-based 1D heatmap preserves the spatial relationship in the input image and models the prediction uncertainty. We demonstrate the benefit of the image-to-lixel prediction and show that the proposed I2L-MeshNet outperforms previous methods. The code is publicly available https://github.com/mks0601/I2L-MeshNet_RELEASE.	['Kyoung Mu Lee', 'Gyeongsik Moon']	2020-08-09	i2l-meshnet-image-to-lixel-prediction-network	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/397_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520732.pdf	eccv-2020-8	['3d-human-reconstruction']	['computer-vision']	[-5.72467409e-02 5.10316014e-01 -2.53189534e-01 -3.94028723e-01 -7.06708252e-01 -1.58446297e-01 1.05579846e-01 -1.55955657e-01 -2.35328466e-01 6.09393656e-01 8.25758353e-02 2.12962367e-02 6.56289840e-03 -8.03488255e-01 -1.31751764e+00 -2.41467074e-01 2.14758009e-01 7.84005284e-01 4.79249626e-01 1.62144974e-01 6.38543367e-02 4.64356095e-01 -1.29858696e+00 9.44700241e-02 4.92622167e-01 1.16401756e+00 1.48923650e-01 5.55561841e-01 1.01210929e-01 1.66761979e-01 -4.90202121e-02 -2.60899186e-01 4.12166536e-01 -2.19119713e-01 -6.14014149e-01 -2.36446351e-01 8.23539257e-01 -2.86373764e-01 -5.48047483e-01 1.06784487e+00 3.47687453e-01 -2.35230133e-01 9.39734519e-01 -1.30084503e+00 -2.24521533e-01 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65c2947c-3ff5-4d1e-937a-4e8cc2e33bd7	tfusion-transformer-based-n-to-one-multimodal	2208.12776	null	https://arxiv.org/abs/2208.12776v2	https://arxiv.org/pdf/2208.12776v2.pdf	SFusion: Self-attention based N-to-One Multimodal Fusion Block	People perceive the world with different senses, such as sight, hearing, smell, and touch. Processing and fusing information from multiple modalities enables Artificial Intelligence to understand the world around us more easily. However, when there are missing modalities, the number of available modalities is different in diverse situations, which leads to an N-to-One fusion problem. To solve this problem, we propose a self-attention based fusion block called SFusion. Different from preset formulations or convolution based methods, the proposed block automatically learns to fuse available modalities without synthesizing or zero-padding missing ones. Specifically, the feature representations extracted from upstream processing model are projected as tokens and fed into self-attention module to generate latent multimodal correlations. Then, a modal attention mechanism is introduced to build a shared representation, which can be applied by the downstream decision model. The proposed SFusion can be easily integrated into existing multimodal analysis networks. In this work, we apply SFusion to different backbone networks for human activity recognition and brain tumor segmentation tasks. Extensive experimental results show that the SFusion block achieves better performance than the competing fusion strategies. Our code is available at https://github.com/scut-cszcl/SFusion.	['Jianlong Zhou', 'Rui Li', 'Jia Wei', 'Zecheng Liu']	2022-08-26	null	null	null	null	['brain-tumor-segmentation']	['medical']	[ 3.67105067e-01 -4.81576882e-02 -2.79435843e-01 -2.86274999e-01 -6.31673634e-01 -2.19981164e-01 5.37236571e-01 3.37531269e-02 -4.27233875e-01 5.25766671e-01 4.71337974e-01 -8.33576173e-02 3.34648341e-02 -6.62223697e-01 -3.01716447e-01 -9.44197655e-01 5.90146065e-01 -8.48079938e-03 -4.37066369e-02 -6.32362142e-02 6.35054335e-02 7.90517926e-02 -1.32586074e+00 6.55455709e-01 1.05185306e+00 1.09197831e+00 3.75785917e-01 3.91734183e-01 -4.36072230e-01 7.93614626e-01 -2.94620663e-01 -3.65344644e-01 -2.89226830e-01 -4.74589199e-01 -6.65848911e-01 1.77232444e-01 -7.51651451e-02 -1.98610395e-01 -3.73617589e-01 1.29441249e+00 4.73065794e-01 1.61586940e-01 5.82872272e-01 -1.40651095e+00 -7.74703503e-01 8.18529248e-01 -7.82878876e-01 1.81310117e-01 4.25073981e-01 1.87448099e-01 7.06301212e-01 -6.90059245e-01 3.69303316e-01 1.42733073e+00 3.35805165e-03 6.28171086e-01 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-1.15887356e+00 -3.47847134e-01 2.90476114e-01 4.98967707e-01 -7.65455723e-01 -6.05139770e-02 1.05783261e-01]	[13.102859497070312, 4.97509765625]
3e4ce589-6607-4250-a457-2be76f1c46c0	an-empirical-model-of-large-batch-training	1812.06162	null	http://arxiv.org/abs/1812.06162v1	http://arxiv.org/pdf/1812.06162v1.pdf	An Empirical Model of Large-Batch Training	In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency. However the limits of this massive data parallelism seem to differ from domain to domain, ranging from batches of tens of thousands in ImageNet to batches of millions in RL agents that play the game Dota 2. To our knowledge there is limited conceptual understanding of why these limits to batch size differ or how we might choose the correct batch size in a new domain. In this paper, we demonstrate that a simple and easy-to-measure statistic called the gradient noise scale predicts the largest useful batch size across many domains and applications, including a number of supervised learning datasets (MNIST, SVHN, CIFAR-10, ImageNet, Billion Word), reinforcement learning domains (Atari and Dota), and even generative model training (autoencoders on SVHN). We find that the noise scale increases as the loss decreases over a training run and depends on the model size primarily through improved model performance. Our empirically-motivated theory also describes the tradeoff between compute-efficiency and time-efficiency, and provides a rough model of the benefits of adaptive batch-size training.	['OpenAI Dota Team', 'Jared Kaplan', 'Dario Amodei', 'Sam McCandlish']	2018-12-14	null	null	null	null	['dota-2']	['playing-games']	[-3.64919662e-01 -4.44959477e-02 7.47516155e-02 -5.68241060e-01 -4.38021362e-01 -4.33526546e-01 8.01807344e-01 -1.49931267e-01 -8.62078011e-01 8.76151800e-01 -2.05672514e-02 -3.87167007e-01 -1.33324489e-01 -6.93283021e-01 -6.62934244e-01 -7.47469187e-01 -2.16219783e-01 8.87614489e-01 3.29461992e-01 -2.41852269e-01 1.09914139e-01 4.03636724e-01 -1.40458333e+00 2.26683281e-02 3.93043429e-01 1.04548943e+00 4.05449182e-01 7.25306273e-01 1.46975249e-01 9.83694673e-01 -6.65160239e-01 -2.91381985e-01 5.87843239e-01 -6.17637813e-01 -8.72217417e-01 8.30915645e-02 1.65212587e-01 -5.38096547e-01 -3.01342100e-01 7.53363252e-01 7.12784767e-01 3.76345277e-01 4.93197441e-01 -1.07982862e+00 -4.12116200e-01 8.07971418e-01 -3.80859256e-01 6.63383663e-01 -4.88897651e-01 4.12452459e-01 9.44036663e-01 -5.84883332e-01 6.45533979e-01 1.16074550e+00 7.39194095e-01 6.81838155e-01 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57f7e735-9829-452e-b244-5b2ed2c71c90	explaining-the-black-box-smoothly-a	2101.04230	null	https://arxiv.org/abs/2101.04230v3	https://arxiv.org/pdf/2101.04230v3.pdf	Explaining the Black-box Smoothly- A Counterfactual Approach	We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., saliency maps) that assess feature importance do not explain "how" imaging features in important anatomical regions are relevant to the classification decision. Our framework explains the decision for a target class by gradually "exaggerating" the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation. We used counterfactual explanations from our framework to audit a classifier trained on a chest x-ray dataset with multiple labels. We proposed clinically-relevant quantitative metrics such as cardiothoracic ratio and the score of a healthy costophrenic recess to evaluate our explanations. We conducted a human-grounded experiment with diagnostic radiology residents to compare different styles of explanations (no explanation, saliency map, cycleGAN explanation, and our counterfactual explanation) by evaluating different aspects of explanations: (1) understandability, (2) classifier's decision justification, (3) visual quality, (d) identity preservation, and (5) overall helpfulness of an explanation to the users. Our results show that our counterfactual explanation was the only explanation method that significantly improved the users' understanding of the classifier's decision compared to the no-explanation baseline. Our metrics established a benchmark for evaluating model explanation methods in medical images. Our explanations revealed that the classifier relied on clinically relevant radiographic features for its diagnostic decisions, thus making its decision-making process more transparent to the end-user.	['Kayhan Batmanghelich', 'Brian Pollack', 'Motahhare Eslami', 'Stephen Wallace', 'Sumedha Singla']	2021-01-11	null	null	null	null	['counterfactual-explanation']	['miscellaneous']	[ 6.42918944e-01 1.08804262e+00 -3.56100321e-01 -4.57854003e-01 -5.12074053e-01 -4.60400373e-01 4.39305961e-01 3.33885819e-01 -2.23305792e-01 8.19147706e-01 5.43910205e-01 -7.06980228e-01 -8.29486996e-02 -4.48664933e-01 -8.08735311e-01 -5.83541572e-01 1.63663402e-01 4.00825918e-01 -5.36582209e-02 6.96968362e-02 5.08945286e-01 4.24862236e-01 -1.42012036e+00 7.41652906e-01 1.13650835e+00 6.84064984e-01 3.36080343e-01 5.75895667e-01 7.57161975e-02 9.76144016e-01 -5.68372488e-01 -7.03649998e-01 9.17788595e-02 -1.01967919e+00 -1.12591422e+00 1.06749460e-01 3.58710498e-01 -3.79001498e-01 -2.47460335e-01 1.11908770e+00 3.46304953e-01 -1.39915258e-01 7.90238857e-01 -1.38696492e+00 -1.13177121e+00 7.39347816e-01 -3.78962040e-01 3.22105080e-01 3.03324819e-01 4.79744196e-01 6.79369867e-01 -5.43591380e-01 8.36484730e-01 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790fa410-d731-4c48-ac3c-d0271611c525	deep-learning-for-video-based-person-re	2303.11332	null	https://arxiv.org/abs/2303.11332v1	https://arxiv.org/pdf/2303.11332v1.pdf	Deep Learning for Video-based Person Re-Identification: A Survey	Video-based person re-identification (video re-ID) has lately fascinated growing attention due to its broad practical applications in various areas, such as surveillance, smart city, and public safety. Nevertheless, video re-ID is quite difficult and is an ongoing stage due to numerous uncertain challenges such as viewpoint, occlusion, pose variation, and uncertain video sequence, etc. In the last couple of years, deep learning on video re-ID has continuously achieved surprising results on public datasets, with various approaches being developed to handle diverse problems in video re-ID. Compared to image-based re-ID, video re-ID is much more challenging and complex. To encourage future research and challenges, this first comprehensive paper introduces a review of up-to-date advancements in deep learning approaches for video re-ID. It broadly covers three important aspects, including brief video re-ID methods with their limitations, major milestones with technical challenges, and architectural design. It offers comparative performance analysis on various available datasets, guidance to improve video re-ID with valuable thoughts, and exciting research directions.	['Khawar Islam']	2023-03-21	null	null	null	null	['person-re-identification']	['computer-vision']	[-1.22243427e-01 -7.46058166e-01 -1.45593509e-01 -4.15122002e-01 -5.91444016e-01 -2.35621214e-01 5.46924531e-01 -3.07709187e-01 -3.87871236e-01 6.60898864e-01 4.76733983e-01 2.91478008e-01 3.26612256e-02 -2.11861014e-01 -4.32798713e-01 -5.13529658e-01 -5.94105665e-03 3.09238672e-01 -8.54425598e-03 -1.36106268e-01 1.12197742e-01 3.34095329e-01 -1.72045600e+00 1.33164719e-01 3.36246938e-01 8.83598387e-01 -1.52373210e-01 6.95121109e-01 3.16902488e-01 7.38398969e-01 -8.33099008e-01 -6.92028642e-01 1.60916492e-01 -3.71914774e-01 -6.35000646e-01 1.04833722e-01 8.62162650e-01 -8.15791786e-01 -9.26346123e-01 1.10598457e+00 1.00796604e+00 4.56664503e-01 3.44085276e-01 -1.56639099e+00 -9.51201737e-01 6.45905510e-02 -5.70911586e-01 4.67486173e-01 7.64841497e-01 -9.18592215e-02 5.69583714e-01 -8.12127769e-01 3.15276831e-01 1.22852683e+00 1.12285542e+00 9.73574102e-01 -4.99514878e-01 -8.89917433e-01 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