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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cea6af7c-fd30-493d-884f-85d7eaf76b4b | genre-classification-using-balanced-winnow-in | null | null | https://aclanthology.org/W14-6304 | https://aclanthology.org/W14-6304.pdf | Genre classification using Balanced Winnow in the DEFT 2014 challenge | null | ["Eva D{'}hondt"] | 2014-07-01 | null | null | null | jeptalnrecital-2014-7 | ['genre-classification'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.34540319442749, 3.8507180213928223] |
df95ed04-36a4-45b5-9acd-b49cdd88acc5 | automl-for-neuromorphic-computing-and | 2302.13210 | null | https://arxiv.org/abs/2302.13210v1 | https://arxiv.org/pdf/2302.13210v1.pdf | AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures | In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures. Through the integration of a parallel asynchronous model-based search approach with a simulation framework to simulate spiking architectures, we are able to efficiently explore the configuration s... | ['Sandeep Madireddy', 'Angel Yanguas-Gil'] | 2023-02-26 | null | null | null | null | ['automl'] | ['methodology'] | [ 3.03293705e-01 -2.12413698e-01 5.37724614e-01 -1.43208250e-01
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-6.46283507e-01 8.95190537e-01 -4.46438879e-01 -3.00192505e-01
-3.68939757e-01 -7.16364086e-01 -5.43709278e-01 -6.77967489e-01
-2.13389933e-01 5.01330733e-01 4.23122585e-01 -4.70385700... | [8.00538444519043, 2.8683652877807617] |
2bf52b21-facb-47a2-8af0-adf5dd7b6350 | eqmotion-equivariant-multi-agent-motion | 2303.10876 | null | https://arxiv.org/abs/2303.10876v2 | https://arxiv.org/pdf/2303.10876v2.pdf | EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning | Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geometric transformations and invariance of agent interaction is a critical and fundamental principle. However, such equivariance and invariance p... | ['Yanfeng Wang', 'Xinchao Wang', 'Yu Guang Wang', 'Siheng Chen', 'Yuhong Tan', 'Robby T. Tan', 'Chenxin Xu'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_EqMotion_Equivariant_Multi-Agent_Motion_Prediction_With_Invariant_Interaction_Reasoning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_EqMotion_Equivariant_Multi-Agent_Motion_Prediction_With_Invariant_Interaction_Reasoning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-prediction', 'trajectory-prediction', 'human-pose-forecasting'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.19801918e-01 -2.53375202e-01 -2.90138364e-01 -1.92429066e-01
-1.74593017e-01 -4.42740947e-01 8.61057937e-01 -2.75583506e-01
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-1.79051071e-01 4.07119900e-01 3.35318834e-01 -5.40421724... | [7.389370441436768, -0.16982536017894745] |
46c2b53c-8748-4347-ad3d-7792833de0c6 | bs-gat-behavior-similarity-based-graph | 2304.07226 | null | https://arxiv.org/abs/2304.07226v1 | https://arxiv.org/pdf/2304.07226v1.pdf | BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection | With the development of the Internet of Things (IoT), network intrusion detection is becoming more complex and extensive. It is essential to investigate an intelligent, automated, and robust network intrusion detection method. Graph neural networks based network intrusion detection methods have been proposed. However, ... | ['Xin He', 'Jie Li', 'Zhijie Han', 'Yalu Wang'] | 2023-04-07 | null | null | null | null | ['graph-construction', 'network-intrusion-detection'] | ['graphs', 'miscellaneous'] | [-1.53262377e-01 -3.88120174e-01 -1.69929311e-01 -2.20815465e-01
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8.62822980e-02 2.39836589e-01 6.11784101e-01 -3.20150942... | [7.102431774139404, 5.773742198944092] |
83678da1-bacb-4773-a6fc-80cb3fb61504 | animediffusion-anime-face-line-drawing | 2303.11137 | null | https://arxiv.org/abs/2303.11137v1 | https://arxiv.org/pdf/2303.11137v1.pdf | AnimeDiffusion: Anime Face Line Drawing Colorization via Diffusion Models | It is a time-consuming and tedious work for manually colorizing anime line drawing images, which is an essential stage in cartoon animation creation pipeline. Reference-based line drawing colorization is a challenging task that relies on the precise cross-domain long-range dependency modelling between the line drawing ... | ['Ping Li', 'Tong-Yee Lee', 'Xueting Liu', 'P. Y. Mok', 'Xiangqiao Meng', 'Yu Cao'] | 2023-03-20 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 7.37614855e-02 -9.10398737e-02 1.57296687e-01 -2.82806784e-01
-5.44347525e-01 -6.46205604e-01 5.72995961e-01 -8.10212076e-01
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3.70018989e-01 5.25202572e-01 -2.48303283e-02 -5.44291437... | [11.766214370727539, -0.541267991065979] |
f9e2af5c-d094-4c86-8af3-16854e5f9d75 | s-2-sql-injecting-syntax-to-question-schema-1 | 2203.06958 | null | https://arxiv.org/abs/2203.06958v1 | https://arxiv.org/pdf/2203.06958v1.pdf | S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers | The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based encoder has been successfully used in this task but does not model the question syntax well. In this paper, we propose S$^2$SQL, injecting S... | ['Yongbin Li', 'Jian Sun', 'Bowen Li', 'Bowen Qin', 'Lihan Wang', 'Ruiying Geng', 'Binyuan Hui'] | 2022-03-14 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 8.77349228e-02 4.99896020e-01 -3.45360607e-01 -7.35963523e-01
-7.54047990e-01 -6.69716954e-01 2.91875094e-01 2.48552173e-01
-9.05196518e-02 3.19219343e-02 2.62709111e-01 -9.21077490e-01
7.60322586e-02 -1.28905082e+00 -1.14564335e+00 3.35251302e-01
1.03760235e-01 5.29878199e-01 6.49763525e-01 -5.84874868... | [9.964105606079102, 7.867325305938721] |
e5a428f4-ae3f-4d1a-a706-54adbb03eb30 | learning-open-information-extraction-of | 1905.07471 | null | https://arxiv.org/abs/1905.07471v1 | https://arxiv.org/pdf/1905.07471v1.pdf | Learning Open Information Extraction of Implicit Relations from Reading Comprehension Datasets | The relationship between two entities in a sentence is often implied by word order and common sense, rather than an explicit predicate. For example, it is evident that "Fed chair Powell indicates rate hike" implies (Powell, is a, Fed chair) and (Powell, works for, Fed). These tuples are just as significant as the expli... | ['Theodore Christakis', 'Jacob Beckerman'] | 2019-05-15 | null | null | null | null | ['open-information-extraction', 'implicit-relations'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.97825903e-01 1.07794631e+00 -3.41411620e-01 -6.90888524e-01
-9.54422176e-01 -6.54753089e-01 4.11736786e-01 8.88008833e-01
-3.92213613e-01 1.28894377e+00 8.27507019e-01 -7.99708128e-01
-3.28581601e-01 -1.04412150e+00 -8.79933000e-01 2.40080625e-01
2.17861250e-01 7.27696776e-01 -5.62680624e-02 -4.43565756... | [9.867053031921387, 8.752729415893555] |
6a82542f-737e-4d7c-9e96-12bb0939a7af | multi-target-backdoor-attacks-for-code-pre | 2306.08350 | null | https://arxiv.org/abs/2306.08350v1 | https://arxiv.org/pdf/2306.08350v1.pdf | Multi-target Backdoor Attacks for Code Pre-trained Models | Backdoor attacks for neural code models have gained considerable attention due to the advancement of code intelligence. However, most existing works insert triggers into task-specific data for code-related downstream tasks, thereby limiting the scope of attacks. Moreover, the majority of attacks for pre-trained models ... | ['Yang Liu', 'Tianwei Zhang', 'Xiaofei Xie', 'Kangjie Chen', 'Shangqing Liu', 'Yanzhou Li'] | 2023-06-14 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 1.84317142e-01 8.50697085e-02 -2.27048174e-01 -2.34899685e-01
-9.34146404e-01 -1.05795932e+00 4.64194685e-01 1.54795885e-01
-8.25690851e-02 1.24184586e-01 1.43520758e-01 -9.03029382e-01
2.60751396e-01 -6.78907454e-01 -7.09867001e-01 -5.23365140e-01
-2.57753134e-01 -1.74391076e-01 3.04967165e-01 -4.03428793... | [6.770562648773193, 7.8755784034729] |
219340f5-e59b-4bc3-95c4-a2cc9c28ffa4 | lesion-net-skin-lesion-segmentation-using | 2012.14249 | null | https://arxiv.org/abs/2012.14249v1 | https://arxiv.org/pdf/2012.14249v1.pdf | Lesion Net -- Skin Lesion Segmentation Using Coordinate Convolution and Deep Residual Units | Skin lesions segmentation is an important step in the process of automated diagnosis of the skin melanoma. However, the accuracy of segmenting melanomas skin lesions is quite a challenging task due to less data for training, irregular shapes, unclear boundaries, and different skin colors. Our proposed approach helps in... | ['Priya Kansal', 'Sabari Nathan'] | 2020-12-28 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 4.66925502e-01 -3.01136691e-02 -1.15899712e-01 -1.76098198e-01
-7.09689260e-01 -5.80968857e-01 4.13548172e-01 7.16167688e-02
-7.12276042e-01 5.89395821e-01 -1.01316586e-01 -3.64645362e-01
-4.39072661e-02 -5.41570604e-01 -4.02556270e-01 -9.10310924e-01
5.72402924e-02 -2.21562147e-01 3.25510055e-01 1.56953916... | [15.591521263122559, -2.900707244873047] |
f230c5a9-2263-4863-8ada-5bfc2e7ecadb | macd-r-cnn-an-abnormal-cell-nucleus-detection | null | null | https://ieeexplore.ieee.org/document/9179730 | https://ieeexplore.ieee.org/document/9179730 | MACD R-CNN: An Abnormal Cell Nucleus Detection Method | The detection of abnormal cell nuclei is a key technique of the cytopathic automatic screening system, which directly determines the performance of the system. Although the Mask R-CNN which combines target detection and semantic segmentation has achieved good performance in general target detection tasks, the performan... | ['Yongjun He', 'Feng Cao', 'Jian Zhang', 'Baoyan Ma'] | 2020-07-28 | null | null | null | null | ['medical-object-detection', 'cell-detection'] | ['computer-vision', 'computer-vision'] | [-3.49303447e-02 -9.79139507e-02 1.44461423e-01 -2.28197023e-01
-2.77148426e-01 -3.20292681e-01 1.33758426e-01 6.61392584e-02
-5.56889832e-01 4.86953169e-01 -2.45551486e-02 -1.42387718e-01
6.32286012e-01 -1.13141263e+00 -3.30455095e-01 -9.74381506e-01
3.59013230e-01 2.99457818e-01 8.40620935e-01 -1.00612938... | [14.89055061340332, -3.0761687755584717] |
c58d0c10-6e38-4495-93cf-510d2b37bb55 | learning-object-placements-for-relational | 2001.08481 | null | https://arxiv.org/abs/2001.08481v2 | https://arxiv.org/pdf/2001.08481v2.pdf | Learning Object Placements For Relational Instructions by Hallucinating Scene Representations | Robots coexisting with humans in their environment and performing services for them need the ability to interact with them. One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user. In this wor... | ['Johan Vertens', 'Oier Mees', 'Alp Emek', 'Wolfram Burgard'] | 2020-01-23 | null | null | null | null | ['spatial-relation-recognition', 'scene-generation', 'auxiliary-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [-3.54472175e-02 4.90726888e-01 5.91152310e-02 -5.30766308e-01
-1.36189684e-01 -3.08845013e-01 5.58877110e-01 1.86966248e-02
-3.13880622e-01 5.94820023e-01 1.85871627e-02 -8.32687020e-02
-1.34169638e-01 -9.04129267e-01 -1.09888256e+00 -4.48455244e-01
-1.68442741e-01 7.06786156e-01 3.53244871e-01 -1.95997864... | [4.8603515625, 0.42210426926612854] |
d4038c53-52ad-4115-a6d7-132204b88cbb | actor-centric-relation-network | 1807.10982 | null | http://arxiv.org/abs/1807.10982v1 | http://arxiv.org/pdf/1807.10982v1.pdf | Actor-Centric Relation Network | Current state-of-the-art approaches for spatio-temporal action localization
rely on detections at the frame level and model temporal context with 3D
ConvNets. Here, we go one step further and model spatio-temporal relations to
capture the interactions between human actors, relevant objects and scene
elements essential ... | ['Rahul Sukthankar', 'Kevin Murphy', 'Cordelia Schmid', 'Carl Vondrick', 'Chen Sun', 'Abhinav Shrivastava'] | 2018-07-28 | actor-centric-relation-network-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Chen_Sun_Actor-centric_Relation_Network_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Chen_Sun_Actor-centric_Relation_Network_ECCV_2018_paper.pdf | eccv-2018-9 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 2.64884114e-01 -7.03262240e-02 -3.56585860e-01 -2.47835249e-01
-3.46176505e-01 -3.22140515e-01 1.27131462e+00 5.05832374e-01
-6.91479504e-01 2.98447073e-01 6.60804451e-01 1.60636231e-01
-5.45962930e-01 -6.62688971e-01 -3.05033863e-01 -3.85186523e-01
-5.15799463e-01 4.21842515e-01 7.82086492e-01 -3.35552812... | [8.28431224822998, 0.6414133906364441] |
b39800d0-a8ae-4c00-bdf1-4bfed5802de6 | generalizing-through-forgetting-domain | 2209.09485 | null | https://arxiv.org/abs/2209.09485v2 | https://arxiv.org/pdf/2209.09485v2.pdf | Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes | Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extraction approaches that can handle clinical language variation across different institutions and specialties are needed. In this paper, we present... | ['Mari Ostendorf', 'Meliha Yetisgen', 'Kevin Lybarger', 'Sitong Zhou'] | 2022-09-20 | null | null | null | null | ['event-extraction', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.60379225e-01 1.38728082e-01 -6.00526392e-01 -5.53004384e-01
-1.09848642e+00 -7.38082230e-01 1.31061196e-01 8.63090336e-01
-6.43352747e-01 1.03273690e+00 4.51186597e-01 -5.56606054e-01
-2.59595871e-01 -5.36139905e-01 -2.14557514e-01 -3.37834001e-01
-2.10333848e-03 6.84560776e-01 1.59205839e-01 7.09603541... | [8.566466331481934, 8.748542785644531] |
98e44961-765c-488f-9805-ee5ef3ef7827 | blind-signal-dereverberation-for-machine | 2210.00117 | null | https://arxiv.org/abs/2210.00117v1 | https://arxiv.org/pdf/2210.00117v1.pdf | Blind Signal Dereverberation for Machine Speech Recognition | We present a method to remove unknown convolutive noise introduced to speech by reverberations of recording environments, utilizing some amount of training speech data from the reverberant environment, and any available non-reverberant speech data. Using Fourier transform computed over long temporal windows, which idea... | ['Hynek Hermansky', 'Samik Sadhu'] | 2022-09-30 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 4.77739125e-01 -6.43657506e-01 1.14498997e+00 -2.34115407e-01
-9.86888885e-01 -8.48036289e-01 1.74796879e-01 -2.32538283e-01
-5.67229331e-01 7.27899849e-01 7.69863188e-01 -5.25916636e-01
3.48015502e-02 -4.58585203e-01 -3.39058399e-01 -8.31875384e-01
-2.71012098e-01 -6.48045540e-01 -2.18530953e-01 -3.86224866... | [15.088521003723145, 5.887970447540283] |
14a1e200-0027-4791-b73c-d0d59f8442ef | goal-randomization-for-playing-text-based | null | null | https://openreview.net/forum?id=KdcLdLuIjQT | https://openreview.net/pdf?id=KdcLdLuIjQT | Goal Randomization for Playing Text-based Games without a Reward Function | Playing text-based games requires language understanding and sequential decision making. The objective of a reinforcement learning agent is to behave so as to maximise the sum of a suitable scalar reward function. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using v... | ['Chengqi Zhang', 'Ling Chen', 'Yali Du', 'Yunqiu Xu', 'Meng Fang'] | 2021-09-29 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 9.95076150e-02 3.93486738e-01 2.02807471e-01 -3.57310064e-02
-5.86653590e-01 -6.44549429e-01 8.55689347e-01 1.18328921e-01
-8.84287119e-01 1.11879611e+00 -1.06366448e-01 -2.56288022e-01
-3.00612777e-01 -9.43064272e-01 -6.22180998e-01 -6.52351081e-01
-3.02393049e-01 7.80232012e-01 3.85509700e-01 -9.09448087... | [3.8721959590911865, 1.5524119138717651] |
7d7d4590-e342-4a6a-b14e-3eddb4268f35 | see-your-heart-psychological-states | 2302.10276 | null | https://arxiv.org/abs/2302.10276v2 | https://arxiv.org/pdf/2302.10276v2.pdf | See Your Heart: Psychological states Interpretation through Visual Creations | In psychoanalysis, generating interpretations to one's psychological state through visual creations is facing significant demands. The two main tasks of existing studies in the field of computer vision, sentiment/emotion classification and affective captioning, can hardly satisfy the requirement of psychological interp... | ['Kaiqi Huang', 'Shiyu Zhang', 'Xiaotang Chen', 'Xiaokun Feng', 'Likun Yang'] | 2023-02-11 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 3.98006439e-01 5.34736872e-01 1.31994829e-01 -4.77800727e-01
-1.76514193e-01 -5.62205017e-01 7.75040627e-01 -7.87274726e-03
-1.18970871e-01 5.65550506e-01 3.79313052e-01 -1.88944310e-01
-3.77180241e-02 -4.24538583e-01 -5.57522058e-01 -3.90767157e-01
5.36979020e-01 5.52930117e-01 -5.03088772e-01 -6.20152473... | [10.733591079711914, 1.5528507232666016] |
78c59faa-e7bd-4ab9-b4a3-1ad848aa9eeb | improving-heterogeneous-face-recognition-with | 1709.02848 | null | http://arxiv.org/abs/1709.02848v2 | http://arxiv.org/pdf/1709.02848v2.pdf | Improving Heterogeneous Face Recognition with Conditional Adversarial Networks | Heterogeneous face recognition between color image and depth image is a much
desired capacity for real world applications where shape information is looked
upon as merely involved in gallery. In this paper, we propose a cross-modal
deep learning method as an effective and efficient workaround for this
challenge. Specif... | ['Liming Chen', 'Zhixin Shu', 'Wuming Zhang', 'Dimitris Samaras'] | 2017-09-08 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 1.94069162e-01 -2.04256717e-02 -2.51280498e-02 -5.52822292e-01
-1.15885890e+00 -5.42251706e-01 6.76976383e-01 -6.07622206e-01
-4.52301688e-02 2.17851624e-01 -6.40767291e-02 -1.20665707e-01
2.86337346e-01 -1.02019513e+00 -8.10389459e-01 -8.12956095e-01
2.74314404e-01 6.54494286e-01 -2.39961579e-01 -1.69540182... | [13.194001197814941, 0.36579272150993347] |
44c8350c-669f-4401-aeca-81f9e4afbd27 | patt-lite-lightweight-patch-and-attention | 2306.09626 | null | https://arxiv.org/abs/2306.09626v1 | https://arxiv.org/pdf/2306.09626v1.pdf | PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition | Facial Expression Recognition (FER) is a machine learning problem that deals with recognizing human facial expressions. While existing work has achieved performance improvements in recent years, FER in the wild and under challenging conditions remains a challenge. In this paper, a lightweight patch and attention networ... | ['Thian Song Ong', 'Chin Poo Lee', 'Kian Ming Lim', 'Jia Le Ngwe'] | 2023-06-16 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 8.61183256e-02 -1.23636469e-01 -5.11977710e-02 -4.85020459e-01
-4.83341366e-01 1.65151134e-01 3.02562088e-01 -5.62712848e-01
-4.36040819e-01 5.46011269e-01 -6.90809414e-02 2.73149908e-01
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2.69137993e-02 -2.69019336e-01 -2.64633179e-01 -5.15837967... | [13.59060287475586, 1.633973240852356] |
685950cd-f9d7-4c67-9fdb-3c214ef98195 | a-novel-joint-points-and-silhouette-based | 2012.06109 | null | https://arxiv.org/abs/2012.06109v1 | https://arxiv.org/pdf/2012.06109v1.pdf | A novel joint points and silhouette-based method to estimate 3D human pose and shape | This paper presents a novel method for 3D human pose and shape estimation from images with sparse views, using joint points and silhouettes, based on a parametric model. Firstly, the parametric model is fitted to the joint points estimated by deep learning-based human pose estimation. Then, we extract the correspondenc... | ['Magnus Oskarsson', 'Anders Heyden', 'Zhongguo Li'] | 2020-12-11 | null | null | null | null | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-3.99016887e-01 1.10024497e-01 -5.46308868e-02 -3.73411477e-01
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1.76342502e-02 9.82625723e-01 6.40520155e-02 -1.98194996... | [7.004018783569336, -1.1093746423721313] |
e3ea51f9-147b-4afe-926f-6b04604e7ecd | weakly-supervised-video-emotion-detection-and | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Weakly_Supervised_Video_Emotion_Detection_and_Prediction_via_Cross-Modal_Temporal_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Weakly_Supervised_Video_Emotion_Detection_and_Prediction_via_Cross-Modal_Temporal_CVPR_2023_paper.pdf | Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network | Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-m... | ['Jufeng Yang', 'Lijuan Wang', 'Zhicheng Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-emotion-detection', 'video-emotion-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.84172884e-02 -2.53905714e-01 -4.67640221e-01 -3.94321650e-01
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-2.05685616e-01 -4.80534762e-01 -2.94224955e-02 -5.13775907... | [10.02846908569336, 0.4981241822242737] |
86ec2783-2d4a-467c-97b3-2a61cc1890bd | infocnf-efficient-conditional-continuous | null | null | https://openreview.net/forum?id=SJgvl6EFwH | https://openreview.net/pdf?id=SJgvl6EFwH | InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers | Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation. However, conditioning CNFs on signals of interest for conditional image generation and downstream predictive tasks is inefficient due to the high-... | ['Anima Anandkumar', 'Richard G. Baraniuk', 'Animesh Garg', 'Tan M. Nguyen'] | 2019-09-25 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 7.55638182e-02 1.77933881e-03 -2.53339652e-02 -4.86826450e-01
-7.89388955e-01 -5.52979052e-01 6.28559947e-01 -3.98975313e-01
-3.14533442e-01 9.02476966e-01 2.61264443e-01 -2.97905177e-01
7.67726591e-03 -6.34777367e-01 -7.14629650e-01 -9.01614606e-01
1.68742873e-02 4.90819156e-01 8.15021172e-02 4.72382873... | [11.3145170211792, 0.01879049837589264] |
c8457e8f-f3ea-468b-a130-ae39bf1d1d3f | quality-estimation-of-machine-translated | 2306.15399 | null | https://arxiv.org/abs/2306.15399v1 | https://arxiv.org/pdf/2306.15399v1.pdf | Quality Estimation of Machine Translated Texts based on Direct Evidence from Training Data | Current Machine Translation systems achieve very good results on a growing variety of language pairs and data sets. However, it is now well known that they produce fluent translation outputs that often can contain important meaning errors. Quality Estimation task deals with the estimation of quality of translations pro... | ['Narayana Murthy Kavi', 'Vibhuti Kumari'] | 2023-06-27 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 2.16861248e-01 4.48824652e-03 -3.73788655e-01 -4.98857379e-01
-1.42092466e+00 -7.74577796e-01 1.10341358e+00 1.07563332e-01
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5.03056049e-01 1.14390957e+00 -5.85077889e-02 -7.63412356... | [11.560418128967285, 10.30076789855957] |
0bdb508b-7ef1-42ee-886f-a5aa30fec58c | meta-distant-transfer-learning-for-pre | null | null | https://aclanthology.org/2021.emnlp-main.768 | https://aclanthology.org/2021.emnlp-main.768.pdf | Meta Distant Transfer Learning for Pre-trained Language Models | With the wide availability of Pre-trained Language Models (PLMs), multi-task fine-tuning across domains has been extensively applied. For tasks related to distant domains with different class label sets, PLMs may memorize non-transferable knowledge for the target domain and suffer from negative transfer. Inspired by me... | ['Yin Zhang', 'Fei Yang', 'Jun Huang', 'Minghui Qiu', 'Haojie Pan', 'Chengyu Wang'] | null | null | null | null | emnlp-2021-11 | ['implicit-relations'] | ['natural-language-processing'] | [ 8.12795535e-02 -1.47682428e-01 -6.60996735e-01 -5.69538593e-01
-1.09973192e+00 -4.80265439e-01 8.01547289e-01 -8.28253925e-02
-5.87124169e-01 9.31592584e-01 1.51181787e-01 1.58782136e-02
-1.19583920e-01 -5.76490819e-01 -6.89948440e-01 -4.07827020e-01
1.71281606e-01 6.17731333e-01 3.13621432e-01 -2.00400144... | [10.841300964355469, 7.998851776123047] |
eeefa8e1-cb4c-4b91-855b-18c18facfae3 | neural-re-rendering-for-full-frame-video | 2102.06205 | null | https://arxiv.org/abs/2102.06205v4 | https://arxiv.org/pdf/2102.06205v4.pdf | Hybrid Neural Fusion for Full-frame Video Stabilization | Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views. In this work, we present a frame synthesis algorithm to achieve full-frame video stabilization. We first estimate dense warp fields from neighboring frames and ... | ['Jia-Bin Huang', 'Yung-Yu Chuang', 'Ming-Hsuan Yang', 'Wei-Sheng Lai', 'Yu-Lun Liu'] | 2021-02-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Hybrid_Neural_Fusion_for_Full-Frame_Video_Stabilization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Hybrid_Neural_Fusion_for_Full-Frame_Video_Stabilization_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-stabilization'] | ['computer-vision'] | [-3.17139104e-02 -3.34193408e-01 -2.09244683e-01 -9.07368809e-02
-5.95185280e-01 -4.43484396e-01 3.75256687e-01 -4.37763870e-01
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1.47773409e-02 -5.12885094e-01 5.88656068e-01 -1.01396203... | [10.65101432800293, -1.421170949935913] |
d711eb4a-d157-4624-9828-b11d5571b555 | posterior-sampling-with-cnn-based-plug-and | 2212.14595 | null | https://arxiv.org/abs/2212.14595v1 | https://arxiv.org/pdf/2212.14595v1.pdf | Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion | Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data. It is therefore of paramount importanc... | ['Matteo Ravasi', 'Nick Luiken', 'Miguel Corrales', 'Juan Romero', 'Tariq Alkhalifah', 'Muhammad Izzatullah'] | 2022-12-30 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 2.11284876e-01 1.13271931e-02 3.85939062e-01 -2.86681533e-01
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-1.78910524e-01 6.23774827e-01 1.15149476e-01 -1.86609179... | [6.800649642944336, 3.2874581813812256] |
65bafda3-bfdf-44c2-ad2e-aeff365b467b | transition-based-dependency-parsing-using-two | null | null | https://aclanthology.org/D15-1215 | https://aclanthology.org/D15-1215.pdf | Transition-based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks | null | ['Xuanjing Huang', 'Xinchi Chen', 'Yaqian Zhou', 'Xipeng Qiu', 'Chenxi Zhu'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.370517253875732, 3.570150852203369] |
8f7e1069-c982-4593-8c06-3c6cbbc839b6 | capsule-based-persianarabic-robust | 1912.03634 | null | https://arxiv.org/abs/1912.03634v2 | https://arxiv.org/pdf/1912.03634v2.pdf | Capsule-Based Persian/Arabic Robust Handwritten Digit Recognition Using EM Routing | In this paper, the problem of handwritten digit recognition has been addressed. However, the underlying language is Persian/Arabic, and the system with which this task is a capsule network (CapsNet) has recently emerged as a more advanced architecture than its ancestor, namely CNN (Convolutional Neural Network). The tr... | ['Rahil Mahdian Toroghi', 'Ali Ghofrani'] | 2019-12-08 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.60769269e-01 -3.22073877e-01 -4.81959544e-02 -2.38718942e-01
5.98726869e-02 -7.93803394e-01 9.80226934e-01 -4.09343779e-01
-5.43989897e-01 6.65587068e-01 -1.65484101e-01 -4.67374951e-01
-9.86607373e-03 -6.47318840e-01 -2.40570694e-01 -7.28245556e-01
-2.29369700e-01 5.17491698e-01 -9.84198451e-02 -3.57708693... | [11.830846786499023, 2.6395301818847656] |
a361b3b4-86eb-4661-8321-7bf3c29d3099 | hierarchical-relational-learning-for-few-shot | 2209.01205 | null | https://arxiv.org/abs/2209.01205v3 | https://arxiv.org/pdf/2209.01205v3.pdf | Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion | Knowledge graphs (KGs) are known for their large scale and knowledge inference ability, but are also notorious for the incompleteness associated with them. Due to the long-tail distribution of the relations in KGs, few-shot KG completion has been proposed as a solution to alleviate incompleteness and expand the coverag... | ['Jie Yin', 'Bala Rajaratnam', 'Jianyuan Guo', 'Han Wu'] | 2022-09-02 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-8.51168782e-02 5.02974510e-01 -6.10277176e-01 -4.83692408e-01
-6.50610626e-01 -1.44319892e-01 3.45849097e-01 3.74247730e-01
2.35838741e-01 9.67244685e-01 2.95940757e-01 4.32935916e-02
-6.91316426e-01 -1.12982178e+00 -7.07705677e-01 -6.27851248e-01
-1.29301652e-01 7.88787842e-01 2.93543637e-01 -3.76046807... | [8.827446937561035, 7.96291446685791] |
8ff2ffcb-169c-4401-83d5-f60a94864437 | ji-yu-zhi-shi-qian-yi-de-qing-gan-yuan-yin | null | null | https://aclanthology.org/2022.ccl-1.45 | https://aclanthology.org/2022.ccl-1.45.pdf | 基于知识迁移的情感-原因对抽取(Emotion-Cause Pair Extraction Based on Knowledge-Transfer) | “现有的情感瘭原因对抽取模型均没有通过加入外部知识来提升情感瘭原因对的抽取效果。本文提出基于知识迁移的情感瘭原因对抽取模型瘨癅癃癐癅瘭癋癔瘩,采用知识库获取文本的显性知识编码;随后引入外部情感分类语料库迁移得到子句的隐性知识编码;最后拼接两个知识编码,加入情感瘨原因瘩子句预测概率及相对位置,搭配癔癲癡癮癳癦癯癲癭癥癲机制融合上下文,并采用窗口机制优化计算压力,实现情感瘭原因对抽取。在癅癃癐癅数据集上的实验结果显示,本文提出的方法超过当前最先进的模型癅癃癐癅瘭瘲癄。” | ['Guoqiong Liao', 'Xiping Liu', 'Changxuan Wan', 'Qizhi Wan', 'Dexi Liu', 'Fengyuan Zhao'] | null | null | null | null | ccl-2022-10 | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [-0.768306 -0.81891614 0.63639235 0.39404333 0.28725553 -1.3841403
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1.0594453 1.2486392 0.85... | [-3.3161284923553467, 6.907734394073486] |
345b9449-13ff-404a-bce6-9183c3d0bdbb | direct-speech-to-speech-translation-with | 2107.05604 | null | https://arxiv.org/abs/2107.05604v2 | https://arxiv.org/pdf/2107.05604v2.pdf | Direct speech-to-speech translation with discrete units | We present a direct speech-to-speech translation (S2ST) model that translates speech from one language to speech in another language without relying on intermediate text generation. We tackle the problem by first applying a self-supervised discrete speech encoder on the target speech and then training a sequence-to-seq... | ['Sravya Popuri', 'Wei-Ning Hsu', 'Juan Pino', 'Yun Tang', 'Qing He', 'Yossi Adi', 'Adam Polyak', 'Xutai Ma', 'Jiatao Gu', 'Changhan Wang', 'Peng-Jen Chen', 'Ann Lee'] | 2021-07-12 | null | https://aclanthology.org/2022.acl-long.235 | https://aclanthology.org/2022.acl-long.235.pdf | acl-2022-5 | ['speech-to-speech-translation'] | ['speech'] | [ 7.68633902e-01 6.60788596e-01 -1.35156110e-01 -4.72035438e-01
-1.58333182e+00 -6.23773754e-01 9.16961133e-01 -4.55059111e-01
-1.12412078e-02 8.05951357e-01 5.30915141e-01 -8.71899426e-01
7.60045171e-01 -4.35142905e-01 -1.02157581e+00 -5.22497535e-01
5.70562840e-01 5.95599771e-01 -1.44152995e-02 -2.12463692... | [14.564943313598633, 7.070451259613037] |
e8825ca2-588d-4439-b3b9-ea1d805670e0 | scene-coordinate-regression-forests-for | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Shotton_Scene_Coordinate_Regression_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Shotton_Scene_Coordinate_Regression_2013_CVPR_paper.pdf | Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images | We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image. Our approach employs a regression forest that is capable of inferring an estimate of each pixel's correspondence to 3D points in the scene's world coordinate frame. The forest uses only simp... | ['Christopher Zach', 'Shahram Izadi', 'Jamie Shotton', 'Andrew Fitzgibbon', 'Ben Glocker', 'Antonio Criminisi'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['camera-relocalization'] | ['computer-vision'] | [ 5.61554849e-01 1.49399554e-02 1.03592323e-02 -2.33389035e-01
-1.05366421e+00 -7.88521707e-01 6.79493785e-01 3.16434950e-02
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2.28957102e-01 -7.24561810e-01 -8.10985446e-01 -6.34344697e-01
2.80408889e-01 9.32572126e-01 3.88527542e-01 1.81453019... | [7.721721172332764, -2.3892359733581543] |
3b5e8c08-ec5d-487a-88e3-cf1496ecc014 | the-event-storyline-corpus-a-new-benchmark | null | null | https://aclanthology.org/W17-2711 | https://aclanthology.org/W17-2711.pdf | The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction | This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news d... | ['Tommaso Caselli', 'Piek Vossen'] | 2017-08-01 | null | null | null | ws-2017-8 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 1.88278332e-01 2.73774922e-01 -7.22554922e-01 -5.13346732e-01
-1.05740023e+00 -6.83598042e-01 1.75603330e+00 7.71773636e-01
-2.58083403e-01 9.67014551e-01 1.34361458e+00 8.78440887e-02
-2.05378175e-01 -7.26168990e-01 -8.16348612e-01 -4.06770110e-01
-6.13313973e-01 3.98218542e-01 6.70530677e-01 -9.96228307... | [9.114920616149902, 9.398785591125488] |
bf2fcfb0-d6b0-487a-8837-5c9cf0b2a9e3 | instance-as-identity-a-generic-online | 2208.03079 | null | https://arxiv.org/abs/2208.03079v2 | https://arxiv.org/pdf/2208.03079v2.pdf | Instance As Identity: A Generic Online Paradigm for Video Instance Segmentation | Modeling temporal information for both detection and tracking in a unified framework has been proved a promising solution to video instance segmentation (VIS). However, how to effectively incorporate the temporal information into an online model remains an open problem. In this work, we propose a new online VIS paradig... | ['Yunchao Wei', 'Yi Yang', 'Xin Yu', 'Zongxin Yang', 'Feng Zhu'] | 2022-08-05 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-2.94785291e-01 -4.56305116e-01 -5.68844438e-01 -7.73304179e-02
-7.03250170e-01 -8.50625873e-01 6.26969218e-01 -1.40746415e-01
-6.88337147e-01 4.70624775e-01 -1.45674646e-01 -1.49633810e-01
1.16775103e-01 -4.88016874e-01 -8.35102499e-01 -3.53698730e-01
-1.90560043e-01 2.23346964e-01 8.15207720e-01 1.83862567... | [9.2128267288208, 0.07630570977926254] |
c385699d-58a5-498d-9a36-ac99f393e0d5 | monte-carlo-tree-search-algorithms-for-risk | 2211.13032 | null | https://arxiv.org/abs/2211.13032v2 | https://arxiv.org/pdf/2211.13032v2.pdf | Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning | In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from a single execution of a policy. In these settings, making decisions based on the average future returns is not suitable. For example, in a medical setting a patient may only have one opportunity to treat thei... | ['Patrick Mannion', 'Enda Howley', 'Diederik M. Roijers', 'Mathieu Reymond', 'Conor F. Hayes'] | 2022-11-23 | null | null | null | null | ['multi-objective-reinforcement-learning', 'thompson-sampling'] | ['methodology', 'methodology'] | [ 6.30925894e-02 2.93783516e-01 -6.91542685e-01 -1.62698850e-01
-9.95942652e-01 -3.00140649e-01 4.67967451e-01 4.62361246e-01
-8.67522955e-01 1.35747552e+00 2.23956048e-01 -6.82548702e-01
-6.98857903e-01 -1.19481170e+00 -4.43839520e-01 -6.48872614e-01
-3.77437323e-01 9.86484408e-01 2.15463638e-02 9.04827937... | [4.26634407043457, 2.618459939956665] |
fdd2d2dd-e74d-4eae-aa8a-45d8ccd42eee | towards-multimodal-emotion-recognition-in | 1909.02764 | null | https://arxiv.org/abs/1909.02764v2 | https://arxiv.org/pdf/1909.02764v2.pdf | Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning | The recognition of emotions by humans is a complex process which considers multiple interacting signals such as facial expressions and both prosody and semantic content of utterances. Commonly, research on automatic recognition of emotions is, with few exceptions, limited to one modality. We describe an in-car experime... | ['Sebastian Zepf', 'Deniz Cevher', 'Roman Klinger'] | 2019-09-06 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-7.77324438e-02 1.10986590e-01 1.99956015e-01 -9.29583788e-01
-1.05041921e+00 -4.98672277e-01 3.80469114e-01 -1.46004543e-01
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1.83120251e-01 -1.89457268e-01 -3.15342128e-01 -3.14531833e-01
1.89394563e-01 4.33251411e-02 -1.70682639e-01 -6.19102716... | [13.540190696716309, 5.681424617767334] |
b8e20720-99ba-4d9d-b584-5e04eada7859 | exposing-deepfake-with-pixel-wise-ar-and-ppg | 2110.15561 | null | https://arxiv.org/abs/2110.15561v1 | https://arxiv.org/pdf/2110.15561v1.pdf | Exposing Deepfake with Pixel-wise AR and PPG Correlation from Faint Signals | Deepfake poses a serious threat to the reliability of judicial evidence and intellectual property protection. In spite of an urgent need for Deepfake identification, existing pixel-level detection methods are increasingly unable to resist the growing realism of fake videos and lack generalization. In this paper, we pro... | ['Jun Yang', 'Maoyu Mao'] | 2021-10-29 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 8.55552852e-02 -1.72986537e-01 6.20312393e-02 -2.08974048e-01
9.51572731e-02 -2.53158510e-01 2.64340818e-01 -6.11903369e-01
-1.72172815e-01 7.13602841e-01 -1.65047780e-01 -1.06273018e-01
1.26700446e-01 -8.42011392e-01 -4.44507927e-01 -1.06550932e+00
-1.05080530e-01 -6.28836215e-01 -1.61457106e-01 -1.46395415... | [12.722497940063477, 1.1312812566757202] |
19d3f894-fead-45fb-afe4-c1a3933a2385 | equivariant-and-invariant-grounding-for-video | 2207.12783 | null | https://arxiv.org/abs/2207.12783v1 | https://arxiv.org/pdf/2207.12783v1.pdf | Equivariant and Invariant Grounding for Video Question Answering | Video Question Answering (VideoQA) is the task of answering the natural language questions about a video. Producing an answer requires understanding the interplay across visual scenes in video and linguistic semantics in question. However, most leading VideoQA models work as black boxes, which make the visual-linguisti... | ['Tat-Seng Chua', 'Junbin Xiao', 'Xiang Wang', 'Yicong Li'] | 2022-07-26 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 1.82432562e-01 4.09233183e-01 -1.14918865e-01 -5.12467861e-01
-2.75056124e-01 -8.18040490e-01 7.32999682e-01 -1.77789599e-01
1.09779730e-01 1.44703612e-01 5.97306192e-01 -4.90569562e-01
3.21870930e-02 -5.80782175e-01 -1.01605570e+00 -4.49660152e-01
3.22715074e-01 4.30670306e-02 3.51384342e-01 -3.35224152... | [10.50045394897461, 1.341585636138916] |
9d9c8168-687a-4bd9-ada2-90390eb5616d | application-of-data-encryption-in-chinese | 2208.14627 | null | https://arxiv.org/abs/2208.14627v1 | https://arxiv.org/pdf/2208.14627v1.pdf | Application of Data Encryption in Chinese Named Entity Recognition | Recently, with the continuous development of deep learning, the performance of named entity recognition tasks has been dramatically improved. However, the privacy and the confidentiality of data in some specific fields, such as biomedical and military, cause insufficient data to support the training of deep neural netw... | ['Weizhi Xu', 'Hui Yu', 'Han Zhao', 'Yang Cao', 'Yanfang Geng', 'Shengyu Fan', 'Jikun Dong', 'Kaifang Long'] | 2022-08-31 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-2.84708478e-03 -1.90495983e-01 1.08491585e-01 -6.47647560e-01
-2.67397583e-01 -4.30371344e-01 4.42957282e-01 1.11556895e-01
-1.06844187e+00 9.94319737e-01 2.94226974e-01 -4.27275002e-01
1.06471397e-01 -1.00350356e+00 -5.27446866e-01 -9.06023681e-01
5.02298027e-02 -2.58370876e-01 -2.24817246e-01 8.88622552... | [5.934537410736084, 6.907309532165527] |
061d4f38-3fc8-4407-89a5-ce7b8a8a7bd4 | transformer-transforms-salient-object | 2104.10127 | null | https://arxiv.org/abs/2104.10127v5 | https://arxiv.org/pdf/2104.10127v5.pdf | Generative Transformer for Accurate and Reliable Salient Object Detection | Transformer, which originates from machine translation, is particularly powerful at modeling long-range dependencies. Currently, the transformer is making revolutionary progress in various vision tasks, leading to significant performance improvements compared with the convolutional neural network (CNN) based frameworks... | ['Nick Barnes', 'Deng-Ping Fan', 'Xinyu Tian', 'Yunqiu Lv', 'Aixuan Li', 'Yuchao Dai', 'Zhexiong Wan', 'Jing Zhang', 'Yuxin Mao'] | 2021-04-20 | null | null | null | null | ['camouflaged-object-segmentation'] | ['computer-vision'] | [ 3.90115321e-01 2.49187812e-01 9.74388979e-03 -6.35473058e-02
-6.55918121e-01 -3.38264078e-01 7.29025006e-01 -5.19890249e-01
6.84856549e-02 7.61024594e-01 5.91066713e-03 -1.92210749e-01
2.20354125e-01 -1.03628206e+00 -9.35973585e-01 -1.11708033e+00
3.48441809e-01 3.52912575e-01 1.76055938e-01 1.95479050... | [11.046823501586914, -0.22286400198936462] |
6f37b384-7496-47fe-badb-95a13b902d02 | post-mortem-iris-recognition-with-deep | 1901.01708 | null | https://arxiv.org/abs/1901.01708v2 | https://arxiv.org/pdf/1901.01708v2.pdf | Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation | This paper proposes the first known to us iris recognition methodology designed specifically for post-mortem samples. We propose to use deep learning-based iris segmentation models to extract highly irregular iris texture areas in post-mortem iris images. We show how to use segmentation masks predicted by neural networ... | ['Adam Czajka', 'Piotr Maciejewicz', 'Mateusz Trokielewicz'] | 2019-01-07 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 1.41445786e-01 2.00621020e-02 5.78013286e-02 -1.47335127e-01
-4.56175387e-01 -3.24175656e-01 3.07035774e-01 1.94510192e-01
-7.06552625e-01 4.84489292e-01 -1.60147354e-01 -2.97439992e-01
-5.21226585e-01 -4.84490514e-01 -2.27638707e-01 -9.31746900e-01
-1.79879591e-01 4.82928187e-01 -4.73840982e-01 3.16053659... | [3.745671510696411, -3.630659580230713] |
8ee31cb4-03c6-44b8-9a2e-98ce0030c290 | how-do-you-correct-run-on-sentences-its-not | 1809.08298 | null | http://arxiv.org/abs/1809.08298v1 | http://arxiv.org/pdf/1809.08298v1.pdf | How do you correct run-on sentences it's not as easy as it seems | Run-on sentences are common grammatical mistakes but little research has
tackled this problem to date. This work introduces two machine learning models
to correct run-on sentences that outperform leading methods for related tasks,
punctuation restoration and whole-sentence grammatical error correction. Due to
the limit... | ['Kostiantyn Omelianchuk', 'Courtney Napoles', 'Joel Tetreault', 'Junchao Zheng'] | 2018-09-21 | how-do-you-correct-run-on-sentences-its-not-1 | https://aclanthology.org/W18-6105 | https://aclanthology.org/W18-6105.pdf | ws-2018-11 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 2.66367376e-01 7.41318047e-01 2.38979205e-01 -8.77718687e-01
-1.38467264e+00 -2.42382377e-01 7.23255575e-02 7.56117284e-01
-7.51464844e-01 1.10098338e+00 5.24814963e-01 -5.73864162e-01
4.57447529e-01 -3.44396561e-01 -1.01042163e+00 -1.86682877e-03
2.02212751e-01 2.23558992e-01 5.82047701e-02 -3.59163612... | [11.115102767944336, 10.661858558654785] |
2da72127-c7b1-4ea1-bd3e-3fda2ee7e3bf | augpt-dialogue-with-pre-trained-language | 2102.05126 | null | https://arxiv.org/abs/2102.05126v3 | https://arxiv.org/pdf/2102.05126v3.pdf | AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models | Attention-based pre-trained language models such as GPT-2 brought considerable progress to end-to-end dialogue modelling. However, they also present considerable risks for task-oriented dialogue, such as lack of knowledge grounding or diversity. To address these issues, we introduce modified training objectives for lan... | ['Ondřej Dušek', 'Tomáš Nekvinda', 'Vojtěch Hudeček', 'Jonáš Kulhánek'] | 2021-02-09 | null | https://aclanthology.org/2021.nlp4convai-1.19 | https://aclanthology.org/2021.nlp4convai-1.19.pdf | emnlp-nlp4convai-2021-11 | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [ 1.21543258e-02 6.85201108e-01 -2.40432531e-01 -5.25544703e-01
-1.07465434e+00 -5.95586658e-01 8.80544662e-01 7.54041523e-02
-7.20824182e-01 1.02857649e+00 7.46152699e-01 -3.94357890e-01
2.80344218e-01 -3.80320996e-01 -2.29258120e-01 -2.04489324e-02
3.03331345e-01 1.11622822e+00 1.51999788e-02 -8.65305245... | [12.693939208984375, 8.075217247009277] |
86913762-de1c-401a-a474-9559fb62fb9f | challenging-america-modeling-language-in-1 | null | null | https://aclanthology.org/2022.findings-naacl.56 | https://aclanthology.org/2022.findings-naacl.56.pdf | Challenging America: Modeling language in longer time scales | The aim of the paper is to apply, for historical texts, the methodology used commonly to solve various NLP tasks defined for contemporary data, i.e. pre-train and fine-tune large Transformer models. This paper introduces an ML challenge, named Challenging America (ChallAm), based on OCR-ed excerpts from historical news... | ['Piotr Wierzchon', 'Krzysztof Jurkiewicz', 'Karol Kaczmarek', 'Krzysztof Jassem', 'Filip Graliński', 'Jakub Pokrywka'] | null | null | null | null | findings-naacl-2022-7 | ['cloze-test'] | ['natural-language-processing'] | [ 2.84375131e-01 4.31659818e-02 -2.58875966e-01 -1.61364600e-01
-1.65897417e+00 -1.22120726e+00 9.71760690e-01 3.28918904e-01
-6.07653618e-01 7.66293585e-01 5.79083800e-01 -5.19100130e-01
-1.90485958e-02 -3.65229398e-01 -8.46450031e-01 -5.17809868e-01
3.04463476e-01 5.81924438e-01 -1.92367762e-01 -2.01514706... | [10.739883422851562, 10.068394660949707] |
fd5d3838-2af7-439c-8a80-4eb2dbd3261f | pali-a-jointly-scaled-multilingual-language | 2209.06794 | null | https://arxiv.org/abs/2209.06794v4 | https://arxiv.org/pdf/2209.06794v4.pdf | PaLI: A Jointly-Scaled Multilingual Language-Image Model | Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface perfo... | ['Radu Soricut', 'Neil Houlsby', 'Xiaohua Zhai', 'Anelia Angelova', 'Andreas Steiner', 'Carlos Riquelme', 'Burcu Karagol Ayan', 'Chao Jia', 'Mojtaba Seyedhosseini', 'Weicheng Kuo', 'James Bradbury', 'Ashish Thapliyal', 'Linting Xue', 'Gaurav Mishra', 'Hassan Akbari', 'Keran Rong', 'Nan Ding', 'Joan Puigcerver', 'Alexan... | 2022-09-14 | null | null | null | null | ['zero-shot-transfer-image-classification', 'visual-reasoning', 'few-shot-image-classification', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'reasoning'] | [ 2.12910101e-02 1.33878002e-02 1.48830548e-01 -2.75438160e-01
-9.69942451e-01 -7.64205098e-01 1.03957915e+00 -2.65610158e-01
-6.26209736e-01 1.38884872e-01 3.24943155e-01 -6.53009295e-01
6.65664375e-01 -3.72071564e-01 -1.09298944e+00 -8.56784284e-02
4.35182154e-01 6.58580303e-01 1.11695804e-01 -1.37495130... | [10.889486312866211, 1.5993415117263794] |
e58a91a6-4946-470f-81f7-55ac60ba074c | pairwise-heuristic-sequence-alignment | 2010.13478 | null | https://arxiv.org/abs/2010.13478v1 | https://arxiv.org/pdf/2010.13478v1.pdf | Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning | Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used for comparative analysis of biological genomes. However, the traditional sequence alignment method is considerably complicated in proportion to the seq... | ['Dong Ho Cho', 'Gyu Bum Han', 'Hye In Seo', 'Dong Jin Ji', 'Yong Joon Song'] | 2020-10-26 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 3.23411256e-01 -5.57071388e-01 -3.43759991e-02 -2.51308948e-01
-3.87810469e-01 -5.05859494e-01 7.59323537e-02 2.11528063e-01
-4.92049277e-01 8.70757401e-01 -2.04465434e-01 -2.92480528e-01
-2.66653020e-02 -8.62375557e-01 -6.02411807e-01 -1.13441420e+00
1.38187438e-01 4.63133425e-01 1.23075522e-01 -3.19819808... | [4.734225273132324, 5.401484489440918] |
6d5bca79-300e-4186-96dd-c083d7801c78 | mars-entry-trajectory-planning-with-range | 2201.09455 | null | https://arxiv.org/abs/2201.09455v1 | https://arxiv.org/pdf/2201.09455v1.pdf | Mars Entry Trajectory Planning with Range Discretization and Successive Convexification | This paper develops a sequential convex programming approach for Mars entry trajectory planning by range discretization. To improve the accuracy of numerical integration, the range of entry trajectory is selected as the independent variable rather than time or energy. A dilation factor is employed to normalize the entr... | ['Ming Xin', 'Shuang Li', 'Xu Liu'] | 2022-01-24 | null | null | null | null | ['numerical-integration', 'trajectory-planning'] | ['miscellaneous', 'robots'] | [-1.81121141e-01 1.21333532e-01 -5.81399679e-01 -1.43445894e-01
-3.31831187e-01 -5.89054406e-01 1.31811544e-01 1.33229420e-01
-4.89273757e-01 9.18259442e-01 -2.29728758e-01 -3.62016678e-01
-5.90362310e-01 -7.66243160e-01 -5.09417176e-01 -1.06997013e+00
-1.12708677e-02 6.79876208e-02 -3.29429150e-01 -5.56455135... | [5.184669494628906, 2.257227897644043] |
5a26ec24-a830-4126-8361-e1ab6b9410f9 | zebrapose-coarse-to-fine-surface-encoding-for | 2203.09418 | null | https://arxiv.org/abs/2203.09418v2 | https://arxiv.org/pdf/2203.09418v2.pdf | ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation | Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improveme... | ['Federico Tombari', 'Didier Stricker', 'Benjamin Busam', 'Nassir Navab', 'Jason Rambach', 'Torben Fetzer', 'Mahdi Saleh', 'Yongzhi Su'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-to-3d'] | ['computer-vision'] | [ 5.04193082e-02 6.31610230e-02 -2.46948481e-01 -3.74587506e-01
-8.72501254e-01 -5.89270711e-01 5.98075688e-01 8.00703466e-03
-1.52640969e-01 3.84831786e-01 2.07922578e-01 5.89222610e-01
-3.14750336e-02 -7.33269989e-01 -9.67332959e-01 -4.15145516e-01
2.15921864e-01 1.17439139e+00 4.65995193e-01 1.44829080... | [7.54421329498291, -2.676823854446411] |
d055e562-a615-496c-9934-af952a2fe4b9 | tablenet-deep-learning-model-for-end-to-end | 2001.01469 | null | https://arxiv.org/abs/2001.01469v1 | https://arxiv.org/pdf/2001.01469v1.pdf | TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images | With the widespread use of mobile phones and scanners to photograph and upload documents, the need for extracting the information trapped in unstructured document images such as retail receipts, insurance claim forms and financial invoices is becoming more acute. A major hurdle to this objective is that these images of... | ['Lovekesh Vig', 'Monika Sharma', 'Vishwanath D', 'Shubham Paliwal', 'Rohit Rahul'] | 2020-01-06 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 3.11385423e-01 6.31654710e-02 -1.97195694e-01 -3.92183930e-01
-1.09792840e+00 -8.78692627e-01 4.83536094e-01 5.35721481e-01
-2.77068675e-01 5.24664223e-01 2.16123194e-01 -8.15311521e-02
1.34669930e-01 -8.15003991e-01 -8.39115858e-01 -2.96304375e-01
-9.83298346e-02 8.07962298e-01 1.66317776e-01 -1.44721091... | [11.6953763961792, 3.0090458393096924] |
03f7b817-90b8-4b64-9058-5fc50346e001 | skipconvnet-skip-convolutional-neural-network | 2007.09131 | null | https://arxiv.org/abs/2007.09131v1 | https://arxiv.org/pdf/2007.09131v1.pdf | SkipConvNet: Skip Convolutional Neural Network for Speech Dereverberation using Optimally Smoothed Spectral Mapping | The reliability of using fully convolutional networks (FCNs) has been successfully demonstrated by recent studies in many speech applications. One of the most popular variants of these FCNs is the `U-Net', which is an encoder-decoder network with skip connections. In this study, we propose `SkipConvNet' where we replac... | ['Jing Huang', 'Wei Xue', 'John H. L. Hansen', 'Shahram Ghorbani', 'Wei Xia', 'Vinay Kothapally'] | 2020-07-17 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 1.07238658e-01 8.94803256e-02 3.15784335e-01 -4.82870013e-01
-7.10721016e-01 -2.27770552e-01 4.89439577e-01 -1.06364168e-01
-4.43586528e-01 5.17566085e-01 3.66806537e-01 -5.23557961e-01
1.38770878e-01 -2.45351851e-01 -6.32356644e-01 -6.74661756e-01
-1.40971109e-01 -5.28716743e-01 1.31130755e-01 -2.73957193... | [14.811861038208008, 6.007406234741211] |
779f4b52-be4c-4718-92dd-0001f2b042e0 | efficient-graph-deep-learning-in-tensorflow | 2101.11552 | null | https://arxiv.org/abs/2101.11552v1 | https://arxiv.org/pdf/2101.11552v1.pdf | Efficient Graph Deep Learning in TensorFlow with tf_geometric | We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries for building Graph Neural Networks (GNNs) as well as implementations of popular GNNs. The kernel libraries consist of infrastructures for bui... | ['Changsheng Xu', 'Huaiwen Zhang', 'Quan Zhao', 'Youze Wang', 'Quan Fang', 'Shengsheng Qian', 'Jun Hu'] | 2021-01-27 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-7.99626768e-01 1.17636845e-01 -3.10491979e-01 -3.75729799e-01
1.13327347e-01 -1.87404662e-01 2.43804514e-01 2.85709471e-01
-1.33342864e-02 3.31577599e-01 -3.06746453e-01 -5.97032845e-01
-1.84969530e-01 -1.81095231e+00 -5.60628414e-01 -6.57451451e-01
-4.85212654e-01 4.60636228e-01 1.95963129e-01 -2.11701572... | [6.989870071411133, 5.8950090408325195] |
8450ba3b-9104-482d-8b35-0dd32f8d7904 | cwid-hi-a-dataset-for-complex-word | null | null | https://aclanthology.org/2022.lrec-1.604 | https://aclanthology.org/2022.lrec-1.604.pdf | CWID-hi: A Dataset for Complex Word Identification in Hindi Text | Text simplification is a method for improving the accessibility of text by converting complex sentences into simple sentences. Multiple studies have been done to create datasets for text simplification. However, most of these datasets focus on high-resource languages only. In this work, we proposed a complex word datas... | ['Ravi Shekhar', 'Dhanya Pramod', 'Gayatri Venugopal'] | null | null | null | null | lrec-2022-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-6.4717107e-02 5.2677250e-01 3.7780430e-02 -6.1281121e-01
-6.5972859e-01 -5.5818808e-01 3.0858520e-01 4.0117192e-01
-5.2044010e-01 7.3307467e-01 8.8852537e-01 -3.5307297e-01
1.5662141e-01 -5.9289527e-01 -2.2726090e-01 -9.1824159e-02
7.6858020e-01 6.4989722e-01 -4.7168307e-02 -7.2688758e-01
3.5240734e-01... | [10.854703903198242, 10.382070541381836] |
e147cef4-4e29-4ed6-983e-00e49e12bb2b | stochastic-marginal-likelihood-gradients | 2306.03968 | null | https://arxiv.org/abs/2306.03968v1 | https://arxiv.org/pdf/2306.03968v1.pdf | Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels | Selecting hyperparameters in deep learning greatly impacts its effectiveness but requires manual effort and expertise. Recent works show that Bayesian model selection with Laplace approximations can allow to optimize such hyperparameters just like standard neural network parameters using gradients and on the training d... | ['Bernhard Schölkopf', 'Gunnar Rätsch', 'Mark van der Wilk', 'Tycho F. A. van der Ouderaa', 'Alexander Immer'] | 2023-06-06 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [-4.03613389e-01 2.09844634e-01 -3.06874424e-01 -6.66963875e-01
-1.02531505e+00 -6.36955619e-01 4.68589962e-01 1.69122647e-02
-1.08598387e+00 6.70033336e-01 -1.07498236e-01 -4.51859176e-01
-1.49567232e-01 -4.52279299e-01 -8.61721396e-01 -7.35092819e-01
-8.76536816e-02 4.19184059e-01 3.40856820e-01 3.60323787... | [7.47553014755249, 3.800049066543579] |
6dce0ddd-567d-4789-966e-d5d8dc166e51 | unsupervised-continual-learning-and-self | 1904.02021 | null | https://arxiv.org/abs/1904.02021v6 | https://arxiv.org/pdf/1904.02021v6.pdf | Unsupervised Progressive Learning and the STAM Architecture | We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed. To solve the UPL problem we ... | ['Constantine Dovrolis', 'Seth Baer', 'James Smith', 'Cameron Taylor'] | 2019-04-03 | null | https://openreview.net/forum?id=Skxw-REFwS | https://openreview.net/pdf?id=Skxw-REFwS | null | ['online-clustering'] | ['computer-vision'] | [ 2.98204154e-01 9.00646523e-02 1.35831498e-02 -3.89122784e-01
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-8.02726328e-01 7.79033959e-01 5.71110129e-01 6.95683062... | [9.846476554870605, 3.3681342601776123] |
48b68d94-d9ef-43f0-8db9-ff5afa28417e | template-kernels-for-dependency-parsing | null | null | https://aclanthology.info/papers/N15-1163/n15-1163 | https://www.aclweb.org/anthology/N15-1163 | Template Kernels for Dependency Parsing | null | ['Hillel Taub-Tabib', 'Amir Globerson', 'Yoav Goldberg'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['transition-based-dependency-parsing'] | ['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
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-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391993522644043, 15.869195938110352] |
5bc0853b-208e-4943-8285-b03912028d3e | s4t-source-free-domain-adaptation-for | 2107.10140 | null | https://arxiv.org/abs/2107.10140v2 | https://arxiv.org/pdf/2107.10140v2.pdf | AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation | Most modern approaches for domain adaptive semantic segmentation rely on continued access to source data during adaptation, which may be infeasible due to computational or privacy constraints. We focus on source-free domain adaptation for semantic segmentation, wherein a source model must adapt itself to a new target d... | ['Judy Hoffman', 'Deeksha Kartik', 'Shivam Khare', 'Viraj Prabhu'] | 2021-07-21 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 6.83006465e-01 3.28161031e-01 -4.42419797e-01 -7.81158388e-01
-1.32835031e+00 -7.86335409e-01 3.71040136e-01 -2.25142967e-02
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3.68413597e-01 1.10689247e+00 7.19327986e-01 2.57897586... | [9.738381385803223, 1.3005521297454834] |
18fa0f8d-a8dc-43e0-8baf-ed19bad1bec5 | towards-coupling-full-disk-and-active-region | 2209.07406 | null | https://arxiv.org/abs/2209.07406v1 | https://arxiv.org/pdf/2209.07406v1.pdf | Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting | Solar flare prediction is a central problem in space weather forecasting and has captivated the attention of a wide spectrum of researchers due to recent advances in both remote sensing as well as machine learning and deep learning approaches. The experimental findings based on both machine and deep learning models rev... | ['Berkay Aydin', 'Manolis K. Georgoulis', 'Rafal A. Angryk', 'Anli Ji', 'Chetraj Pandey'] | 2022-08-11 | null | null | null | null | ['weather-forecasting', 'solar-flare-prediction'] | ['miscellaneous', 'time-series'] | [-1.33770213e-01 -2.49943987e-01 1.71559691e-01 -4.21020448e-01
-6.98543489e-01 -4.70754266e-01 5.78866839e-01 5.22161424e-02
2.09904835e-02 1.19163084e+00 -2.63826102e-01 -4.52337563e-01
-6.33215427e-01 -1.08734620e+00 -8.45476806e-01 -8.21143270e-01
-2.70999253e-01 4.91398871e-01 9.53647345e-02 -6.54923081... | [6.587123394012451, 2.7968106269836426] |
fb70f391-3984-4e8a-b16a-76f0303b3d5b | can-ner-convolutional-attention-network | 1904.02141 | null | https://arxiv.org/abs/1904.02141v3 | https://arxiv.org/pdf/1904.02141v3.pdf | CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition | Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors an... | ['Börje F. Karlsson', 'Yuying Zhu', 'Guoxin Wang'] | 2019-04-03 | can-ner-convolutional-attention-network-for | https://aclanthology.org/N19-1342 | https://aclanthology.org/N19-1342.pdf | naacl-2019-6 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-3.08653086e-01 -1.73499793e-01 6.96856976e-02 -2.46141866e-01
-5.61788738e-01 -4.79532033e-01 2.33634382e-01 1.67318955e-01
-1.22794151e+00 7.26858318e-01 4.50347096e-01 -5.60606837e-01
5.74283838e-01 -8.73039126e-01 -3.30608398e-01 -3.73284727e-01
1.94431722e-01 2.13311255e-01 3.33615988e-01 -2.83608496... | [9.821562767028809, 9.845317840576172] |
bfa41185-6c54-40cc-b454-d8f48e4675b2 | tdls-a-top-down-layer-searching-algorithm-for | 2108.04238 | null | https://arxiv.org/abs/2108.04238v2 | https://arxiv.org/pdf/2108.04238v2.pdf | TDLS: A Top-Down Layer Searching Algorithm for Generating Counterfactual Visual Explanation | Explanation of AI, as well as fairness of algorithms' decisions and the transparency of the decision model, are becoming more and more important. And it is crucial to design effective and human-friendly techniques when opening the black-box model. Counterfactual conforms to the human way of thinking and provides a huma... | ['Caleb Chen Cao', 'Haocheng Han', 'Cong Wang'] | 2021-08-08 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.10289150e-01 6.04288220e-01 -1.02555864e-01 -5.85147679e-01
2.96931654e-01 -4.46229517e-01 9.68541741e-01 -2.05478325e-01
-1.90857470e-01 9.64829564e-01 3.22302759e-01 -7.69845665e-01
-1.84606045e-01 -6.04475975e-01 -8.82211506e-01 -4.57156032e-01
-1.87234268e-01 3.14843267e-01 -1.83970422e-01 -1.12647198... | [8.819191932678223, 5.530403137207031] |
50aef59d-f018-43ca-bf6e-f9608ef02c53 | wastewater-catchment-areas-in-great-britain | null | null | https://www.essoar.org/doi/10.1002/essoar.10510612.2 | https://www.essoar.org/pdfjs/10.1002/essoar.10510612.2 | Wastewater catchment areas in Great Britain | Wastewater catchment area data are essential for wastewater treatment capacity planning and have recently become critical for operationalising wastewater-based epidemiology (WBE) for COVID-19. Owing to the privatised nature of the water industry in the United Kingdom, the required catchment area datasets are not readil... | ['Andrew Singer', 'Barbara Kasprzyk-Hordern', 'Sarah Bunney', 'Till Hoffmann'] | 2022-02-25 | null | null | null | essoar-2022-2 | ['epidemiology'] | ['medical'] | [ 2.21890211e-01 2.85633802e-02 2.07162589e-01 1.23951524e-01
-7.41850019e-01 -2.44919196e-01 5.45388103e-01 7.50652850e-01
-6.72125220e-01 8.81265640e-01 1.13561094e+00 -1.00550711e+00
-7.50706553e-01 -1.38659370e+00 -9.42528844e-02 -8.38479221e-01
2.34636609e-02 3.83063048e-01 -3.06336075e-01 -1.71879113... | [5.9490838050842285, 4.113972187042236] |
62148378-1f95-40a7-8a61-22b28b104506 | evolution-of-3gpp-standards-towards-true | 2306.04012 | null | https://arxiv.org/abs/2306.04012v1 | https://arxiv.org/pdf/2306.04012v1.pdf | Evolution of 3GPP Standards Towards True Extended Reality (XR) Support in 6G Networks | Extended reality (XR) is a key innovation of 5G-advanced and beyond networks. The diverse XR use-cases, including virtual reality, augmented reality, and mixed reality, transform the way humans interact with surrounding environments. Thus, XR technology enables true immersive experiences of novel services spanning, e.g... | ['Morris Repeta', 'Ali A. Esswie'] | 2023-06-06 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-4.80901673e-02 3.66080850e-02 -3.76417398e-01 -1.86425164e-01
-2.12408036e-01 -5.94867289e-01 -1.07645527e-01 -5.58870316e-01
4.14318591e-01 1.21242797e+00 8.29634741e-02 -1.18088555e+00
-4.58205640e-01 -5.99955022e-01 1.39499083e-02 -6.10602856e-01
-6.83272243e-01 -1.96881399e-01 -2.12087885e-01 -5.10568440... | [6.2747063636779785, 1.2340259552001953] |
263f74f0-3bf6-4279-996d-83b52dfba635 | viser-video-specific-surface-embeddings-for | null | null | http://proceedings.neurips.cc/paper/2021/hash/a11f9e533f28593768ebf87075ab34f2-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a11f9e533f28593768ebf87075ab34f2-Paper.pdf | ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction | We introduce ViSER, a method for recovering articulated 3D shapes and dense3D trajectories from monocular videos. Previous work on high-quality reconstruction of dynamic 3D shapes typically relies on multiple camera views, strong category-specific priors, or 2D keypoint supervision. We show that none of these are requ... | ['Deva Ramanan', 'Ce Liu', 'Forrester Cole', 'Daniel Vlasic', 'Varun Jampani', 'Deqing Sun', 'Gengshan Yang'] | 2021-12-01 | null | https://openreview.net/forum?id=-JJy-Hw8TFB | https://openreview.net/pdf?id=-JJy-Hw8TFB | neurips-2021-12 | ['3d-shape-reconstruction-from-videos'] | ['computer-vision'] | [-1.22314326e-01 -2.48940796e-01 -2.40700364e-01 -3.15873355e-01
-7.55661488e-01 -7.65272439e-01 6.00098908e-01 -3.10187489e-01
-3.29813123e-01 2.85322994e-01 3.51412058e-01 3.47645313e-01
2.08014488e-01 -3.62678558e-01 -1.24312425e+00 -4.43255335e-01
-1.29927546e-01 6.68351173e-01 1.93406701e-01 2.01989025... | [8.600089073181152, -2.719856023788452] |
41231cea-1f5e-4bea-ae26-c9e85bda615a | a-comprehensive-study-of-real-time-object | 2208.10895 | null | https://arxiv.org/abs/2208.10895v2 | https://arxiv.org/pdf/2208.10895v2.pdf | A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey | Deep neural network based object detectors are continuously evolving and are used in a multitude of applications, each having its own set of requirements. While safety-critical applications need high accuracy and reliability, low-latency tasks need resource and energy-efficient networks. Real-time detectors, which are ... | ['Bahram Zonooz', 'Senthilkumar Kathiresan', 'Omar Magdy', 'Ratnajit Mukherjee', 'Shruthi Gowda', 'Elahe Arani'] | 2022-08-23 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 9.15656686e-02 -2.72673011e-01 -3.44444394e-01 -1.69117942e-01
-2.78463244e-01 -5.88336170e-01 3.37554038e-01 -1.89081430e-01
-5.14903784e-01 4.02442366e-01 -2.84611553e-01 -5.94976187e-01
-1.69938192e-01 -8.00813437e-01 -6.34728670e-01 -6.63240194e-01
-4.28297222e-01 -1.69962227e-01 6.33442044e-01 -1.10396385... | [8.220820426940918, 2.551231861114502] |
e13a107e-ce5a-45d6-98e6-d7e9efd0332c | robust-leave-one-out-cross-validation-for | 2209.09190 | null | https://arxiv.org/abs/2209.09190v1 | https://arxiv.org/pdf/2209.09190v1.pdf | Robust leave-one-out cross-validation for high-dimensional Bayesian models | Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive due to the need to fit the model multiple times. In the Bayesian context, importance sampling provides a possible solution but classical appro... | ['Giacomo Zanella', 'Luca Silva'] | 2022-09-19 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [-2.12905519e-02 -1.72277063e-01 -2.84213513e-01 -5.21095335e-01
-1.36865973e+00 -4.04951304e-01 4.74013627e-01 3.90044272e-01
-3.79280776e-01 1.04738367e+00 -6.09219849e-01 -4.19346333e-01
-5.64680934e-01 -6.64008796e-01 -5.12814045e-01 -8.98852646e-01
-1.69885568e-02 7.46258140e-01 3.62534702e-01 4.83082473... | [7.345010280609131, 4.216913223266602] |
74022fa9-719d-4611-9618-41dd8dfeebfe | towards-transparent-application-of-machine | 2105.12700 | null | https://arxiv.org/abs/2105.12700v2 | https://arxiv.org/pdf/2105.12700v2.pdf | Towards Transparent Application of Machine Learning in Video Processing | Machine learning techniques for more efficient video compression and video enhancement have been developed thanks to breakthroughs in deep learning. The new techniques, considered as an advanced form of Artificial Intelligence (AI), bring previously unforeseen capabilities. However, they typically come in the form of r... | ['Marta Mrak', 'Fiona Rivera', 'Maria Santamaria', 'Marc Gorriz Blanch', 'Luka Murn'] | 2021-05-26 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 2.60441393e-01 2.46219397e-01 -2.84625351e-01 -4.46573466e-01
-2.44968578e-01 -1.86007038e-01 5.82998574e-01 4.21374887e-02
-3.67496163e-01 7.03363955e-01 -7.42533728e-02 -4.82193291e-01
-2.53183216e-01 -6.95785999e-01 -9.50989604e-01 -7.08189428e-01
-4.33925480e-01 2.11830586e-01 1.00075349e-01 -3.59796673... | [11.362703323364258, -1.5937501192092896] |
822ca871-95f2-4e09-9dcc-977e97ec7bba | acute-eval-improved-dialogue-evaluation-with | 1909.03087 | null | https://arxiv.org/abs/1909.03087v1 | https://arxiv.org/pdf/1909.03087v1.pdf | ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons | While dialogue remains an important end-goal of natural language research, the difficulty of evaluation is an oft-quoted reason why it remains troublesome to make real progress towards its solution. Evaluation difficulties are actually two-fold: not only do automatic metrics not correlate well with human judgments, but... | ['Jason Weston', 'Stephen Roller', 'Margaret Li'] | 2019-09-06 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 4.06976993e-04 3.49133730e-01 9.12498757e-02 -7.34845400e-01
-1.12897539e+00 -1.16200709e+00 5.23946702e-01 2.80142844e-01
-6.58478141e-01 9.84867573e-01 4.12792563e-01 -4.86290604e-01
-8.35210383e-02 -2.68590838e-01 1.22841336e-01 -3.47208947e-01
1.21783644e-01 8.18904161e-01 4.02143389e-01 -3.83869380... | [12.781791687011719, 8.145469665527344] |
c1dd7d5d-b45f-4080-b93b-0c21f1e3794d | towards-weakly-supervised-text-spotting-using | 2202.05508 | null | https://arxiv.org/abs/2202.05508v2 | https://arxiv.org/pdf/2202.05508v2.pdf | Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer | Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components. Existing methods usually have a distinct separation between the detection and recognition branches, requiring exact annotations for the two tasks. We... | ['Pietro Perona', 'R. Manmatha', 'Yarin Bar', 'Sharon Fogel', 'Inbal Lavi', 'Yair Kittenplon'] | 2022-02-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kittenplon_Towards_Weakly-Supervised_Text_Spotting_Using_a_Multi-Task_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kittenplon_Towards_Weakly-Supervised_Text_Spotting_Using_a_Multi-Task_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['text-spotting'] | ['computer-vision'] | [ 5.05405128e-01 -2.20554203e-01 -2.52435803e-01 -4.43372428e-01
-1.49239719e+00 -5.65503597e-01 7.42775261e-01 7.08935931e-02
-4.92862880e-01 2.43922874e-01 2.73523539e-01 -3.03604424e-01
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5.34381211e-01 7.85792887e-01 3.81393522e-01 1.54644713... | [11.95043659210205, 2.2600815296173096] |
5950d3f0-812d-4427-a2a2-65e102b3e6da | beyond-the-model-data-pre-processing-attack | 2305.03963 | null | https://arxiv.org/abs/2305.03963v2 | https://arxiv.org/pdf/2305.03963v2.pdf | Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps | The increasing popularity of deep learning (DL) models and the advantages of computing, including low latency and bandwidth savings on smartphones, have led to the emergence of intelligent mobile applications, also known as DL apps, in recent years. However, this technological development has also given rise to several... | ['Helei Cui', 'Shuo Huang', 'Yujin Huang', 'Ye Sang'] | 2023-05-06 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 4.52851877e-02 -2.62874544e-01 -3.47451270e-01 6.57643005e-02
-3.39278251e-01 -6.51339769e-01 1.89571723e-01 -4.28065024e-02
-1.46641910e-01 3.40330690e-01 -3.34497720e-01 -9.38601375e-01
2.57559985e-01 -6.68966293e-01 -9.66707408e-01 -4.61950362e-01
-1.10981852e-01 -3.31547707e-01 4.83038038e-01 2.95665681... | [14.416658401489258, 9.679444313049316] |
7ab38718-4923-4b47-8094-14040025f3e0 | learning-to-match-mathematical-statements | 2102.02110 | null | https://arxiv.org/abs/2102.02110v1 | https://arxiv.org/pdf/2102.02110v1.pdf | Learning to Match Mathematical Statements with Proofs | We introduce a novel task consisting in assigning a proof to a given mathematical statement. The task is designed to improve the processing of research-level mathematical texts. Applying Natural Language Processing (NLP) tools to research level mathematical articles is both challenging, since it is a highly specialized... | ['Shay B. Cohen', 'Maximin Coavoux'] | 2021-02-03 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 2.62542158e-01 7.19127357e-02 -1.93020090e-01 -2.41966203e-01
-1.29266798e+00 -8.59009206e-01 8.89734387e-01 6.65449202e-01
-3.88268530e-01 5.66585779e-01 3.02821755e-01 -1.11596441e+00
-2.84702450e-01 -6.12009287e-01 -1.13134694e+00 -1.11637935e-01
7.83478916e-02 7.16159999e-01 2.29558032e-02 -1.73123136... | [9.479763984680176, 7.360867023468018] |
cb4bb5c4-e64b-4a0a-b637-6f38e60b11d3 | towards-knowledge-intensive-text-to-sql | 2301.01067 | null | https://arxiv.org/abs/2301.01067v1 | https://arxiv.org/pdf/2301.01067v1.pdf | Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge | In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. ... | ['Jian-Guang Lou', 'Min-Yen Kan', 'Dechen Zhan', 'Wanxiang Che', 'Dingzirui Wang', 'Mingyang Pan', 'Xuqi Liu', 'Yan Gao', 'Longxu Dou'] | 2023-01-03 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [-2.51097947e-01 6.65751398e-01 -2.54390210e-01 -8.53464723e-01
-1.08120358e+00 -1.16787183e+00 9.43450257e-02 3.77552718e-01
-2.12079599e-01 9.80964184e-01 2.37727001e-01 -8.33021283e-01
-9.27270949e-02 -1.52108502e+00 -1.11102784e+00 3.95868272e-01
2.76042879e-01 9.18694139e-01 7.31810749e-01 -4.96014863... | [10.045818328857422, 7.858875274658203] |
db8cd67b-0171-4ad9-8ad3-5f768e4114b8 | vita-clip-video-and-text-adaptive-clip-via | 2304.03307 | null | https://arxiv.org/abs/2304.03307v1 | https://arxiv.org/pdf/2304.03307v1.pdf | Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting | Adopting contrastive image-text pretrained models like CLIP towards video classification has gained attention due to its cost-effectiveness and competitive performance. However, recent works in this area face a trade-off. Finetuning the pretrained model to achieve strong supervised performance results in low zero-shot ... | ['Mubarak Shah', 'Fahad Shahbaz Khan', 'Salman Khan', 'Muzammal Naseer', 'Syed Talal Wasim'] | 2023-04-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wasim_Vita-CLIP_Video_and_Text_Adaptive_CLIP_via_Multimodal_Prompting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wasim_Vita-CLIP_Video_and_Text_Adaptive_CLIP_via_Multimodal_Prompting_CVPR_2023_paper.pdf | cvpr-2023-1 | ['zero-shot-action-recognition', 'video-classification', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.34806287e-01 -2.12088630e-01 -6.31528676e-01 -4.66156512e-01
-9.52701986e-01 -2.54422873e-01 5.81638336e-01 8.36092904e-02
-5.41317403e-01 3.54632437e-01 3.75860721e-01 -8.63372236e-02
1.00518510e-01 -4.01589334e-01 -8.02173078e-01 -8.31658185e-01
2.94015408e-01 7.66059086e-02 5.06154597e-01 -1.31255224... | [9.498207092285156, 0.9284477233886719] |
2cd9b356-be64-4a1f-afb3-848b878e2daa | dynamic-facet-selection-by-maximizing-graded | null | null | https://aclanthology.org/2021.internlp-1.5 | https://aclanthology.org/2021.internlp-1.5.pdf | Dynamic Facet Selection by Maximizing Graded Relevance | Dynamic faceted search (DFS), an interactive query refinement technique, is a form of Human–computer information retrieval (HCIR) approach. It allows users to narrow down search results through facets, where the facets-documents mapping is determined at runtime based on the context of user query instead of pre-indexing... | ['Nandana Mihindukulasooriya', 'Alfio Gliozzo', 'Nicolas Rodolfo Fauceglia', 'Ruchi Mahindru', 'Yu Deng', 'Md Faisal Mahbub Chowdhury', 'Michael Glass'] | null | null | null | null | acl-internlp-2021-8 | ['document-ranking'] | ['natural-language-processing'] | [ 2.73709536e-01 3.28155428e-01 -3.26036155e-01 -1.78002253e-01
-1.06518900e+00 -8.28168809e-01 9.04878676e-01 -1.36704594e-01
-1.64485350e-01 7.46815383e-01 6.39044881e-01 -1.02954596e-01
-6.98785484e-01 -7.95785725e-01 -2.23582610e-01 2.58057583e-02
-1.79927394e-01 1.09975207e+00 6.58172131e-01 -2.88492382... | [11.514640808105469, 7.527489185333252] |
17a0b9b2-7668-4ee5-ac4f-b05a046333d9 | multitask-learning-for-fine-grained-twitter | 1707.03569 | null | http://arxiv.org/abs/1707.03569v1 | http://arxiv.org/pdf/1707.03569v1.pdf | Multitask Learning for Fine-Grained Twitter Sentiment Analysis | Traditional sentiment analysis approaches tackle problems like ternary
(3-category) and fine-grained (5-category) classification by learning the tasks
separately. We argue that such classification tasks are correlated and we
propose a multitask approach based on a recurrent neural network that benefits
by jointly learn... | ['Massih-Reza Amini', 'Simon Moura', 'Georgios Balikas'] | 2017-07-12 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-2.69555300e-02 -2.48714924e-01 -3.25382143e-01 -9.09530818e-01
-1.21530652e+00 -4.42652881e-01 8.01121116e-01 2.18390152e-01
-5.33186674e-01 7.07213581e-01 3.85930419e-01 -2.41318956e-01
-3.66458297e-01 -5.23504376e-01 -2.64444530e-01 -5.91203928e-01
7.70066008e-02 4.72253054e-01 -8.63494575e-02 -5.49302042... | [11.361628532409668, 6.916584491729736] |
fd627d51-7368-43be-813b-2ff64ccb6809 | hybrid-optimization-algorithm-for-large-scale | 1509.06254 | null | http://arxiv.org/abs/1509.06254v1 | http://arxiv.org/pdf/1509.06254v1.pdf | Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition | In this paper we present a hybrid approach for automatic composition of Web
services that generates semantic input-output based compositions with optimal
end-to-end QoS, minimizing the number of services of the resulting composition.
The proposed approach has four main steps: 1) generation of the composition
graph for ... | ['Manuel Mucientes', 'Pablo Rodriguez-Mier', 'Manuel Lama'] | 2015-09-21 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 2.45284393e-01 -9.55056623e-02 7.52766803e-02 -4.99571115e-01
-5.70151269e-01 -7.72838116e-01 4.73465294e-01 2.54955471e-01
-7.44955149e-03 4.05038744e-01 3.32808912e-01 -1.38376608e-01
-7.64711022e-01 -8.79743338e-01 -1.26218840e-01 -6.28727913e-01
-2.20617667e-01 1.00844765e+00 5.36721051e-01 -4.61876988... | [8.59494400024414, 6.941981792449951] |
d97484ee-8f88-4738-8f75-34a72479e7ee | radiomics-enhanced-deep-multi-task-learning | 2211.05409 | null | https://arxiv.org/abs/2211.05409v1 | https://arxiv.org/pdf/2211.05409v1.pdf | Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer | Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regi... | ['Jinman Kim', 'Dagan Feng', 'Lei Bi', 'Mingyuan Meng'] | 2022-11-10 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 6.93293512e-02 3.29294920e-01 -4.64925379e-01 -4.33782279e-01
-1.37680769e+00 -1.74162522e-01 1.77079156e-01 3.81690592e-01
-4.94740933e-01 7.52442598e-01 4.55352694e-01 -4.24459815e-01
-1.85095474e-01 -6.00606441e-01 -1.38223007e-01 -1.11855435e+00
1.04019351e-01 7.66615808e-01 2.32406065e-01 1.05195194... | [14.944982528686523, -2.5295827388763428] |
193aa77e-1224-4d50-b69a-0baa23986458 | learning-to-parallelize-with-openmp-by | 2305.05779 | null | https://arxiv.org/abs/2305.05779v1 | https://arxiv.org/pdf/2305.05779v1.pdf | Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation | Detecting parallelizable code regions is a challenging task, even for experienced developers. Numerous recent studies have explored the use of machine learning for code analysis and program synthesis, including parallelization, in light of the success of machine learning in natural language processing. However, applyin... | ['Ali Jannesari', 'Nesreen K. Ahmed', 'Hung Phan', 'Quazi Ishtiaque Mahmud', 'Le Chen'] | 2023-05-09 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-1.76510736e-01 -3.58068794e-01 -7.35571504e-01 -1.89020440e-01
-9.43316162e-01 -3.43053401e-01 3.49452406e-01 7.16805935e-01
-1.59234092e-01 2.45073121e-02 -5.70108257e-02 -8.91012967e-01
3.06300282e-01 -8.44645500e-01 -6.20344162e-01 -2.33645216e-01
-5.70230722e-01 -3.68371569e-02 4.36589122e-01 -1.74086299... | [7.580098628997803, 7.850558280944824] |
c9abf6c6-53f9-428f-b17d-e50ceaa0c7b7 | codewithzichao-dravidianlangtech-eacl2021-1 | null | null | https://aclanthology.org/2021.dravidianlangtech-1.52 | https://aclanthology.org/2021.dravidianlangtech-1.52.pdf | Codewithzichao@DravidianLangTech-EACL2021: Exploring Multimodal Transformers for Meme Classification in Tamil Language | This paper describes our submission to shared task on Meme Classification for Tamil Language. To address this task, we explore a multimodal transformer for meme classification in Tamil language. According to the characteristics of the image and text, we use different pretrained models to encode the image and text so as... | ['Zichao Li'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['meme-classification'] | ['natural-language-processing'] | [-2.53470868e-01 -2.79366434e-01 7.77787939e-02 -2.65532941e-01
-4.86138761e-01 -3.66908580e-01 8.42251420e-01 9.37561542e-02
-7.23777533e-01 5.00444770e-01 4.09684151e-01 -1.72186866e-01
6.81022108e-01 -5.55247009e-01 -5.28480589e-01 -4.33108449e-01
2.46068150e-01 3.83886516e-01 4.65311185e-02 -2.71643102... | [8.442843437194824, 10.673723220825195] |
fb7b716e-fc03-4638-b21d-d0f5c95284b3 | predicting-3d-human-dynamics-from-video | 1908.04781 | null | https://arxiv.org/abs/1908.04781v2 | https://arxiv.org/pdf/1908.04781v2.pdf | Predicting 3D Human Dynamics from Video | Given a video of a person in action, we can easily guess the 3D future motion of the person. In this work, we present perhaps the first approach for predicting a future 3D mesh model sequence of a person from past video input. We do this for periodic motions such as walking and also actions like bowling and squatting s... | ['Jason Y. Zhang', 'Panna Felsen', 'Angjoo Kanazawa', 'Jitendra Malik'] | 2019-08-13 | predicting-3d-human-dynamics-from-video-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_Predicting_3D_Human_Dynamics_From_Video_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Predicting_3D_Human_Dynamics_From_Video_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-human-dynamics', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [ 1.08251445e-01 7.67924404e-03 -2.48258054e-01 -4.90887851e-01
-3.76742005e-01 -3.76972556e-01 1.01958656e+00 -4.68491822e-01
-3.65020365e-01 4.31245774e-01 6.11480832e-01 -1.10678978e-01
1.82032332e-01 -5.80318153e-01 -7.35401213e-01 -3.65636230e-01
-3.32283139e-01 5.31960845e-01 1.37413800e-01 -1.70675173... | [7.54067325592041, -0.12043020129203796] |
6df0a9b9-9a20-4d73-b106-927284681987 | 3d-mininet-learning-a-2d-representation-from | 2002.10893 | null | https://arxiv.org/abs/2002.10893v5 | https://arxiv.org/pdf/2002.10893v5.pdf | 3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation | LIDAR semantic segmentation, which assigns a semantic label to each 3D point measured by the LIDAR, is becoming an essential task for many robotic applications such as autonomous driving. Fast and efficient semantic segmentation methods are needed to match the strong computational and temporal restrictions of many of t... | ['Iñigo Alonso', 'Luis Riazuelo', 'Luis Montesano', 'Ana C. Murillo'] | 2020-02-25 | null | null | null | null | ['real-time-3d-semantic-segmentation', 'lidar-semantic-segmentation', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.67441839e-01 1.89884827e-01 -1.96576491e-01 -8.23477924e-01
-4.73692328e-01 -4.38788444e-01 6.09047472e-01 -1.25567764e-01
-7.45412290e-01 1.88747987e-01 -2.00588197e-01 -2.67526478e-01
-5.46523044e-03 -1.01731837e+00 -8.07813466e-01 -2.57579952e-01
1.52173191e-01 1.00044572e+00 8.99896562e-01 -1.26616538... | [8.077986717224121, -2.772526741027832] |
f9fabf8b-5112-4d68-9d79-eb03164e504a | bme-submission-for-sigmorphon-2021-shared | null | null | https://aclanthology.org/2021.sigmorphon-1.27 | https://aclanthology.org/2021.sigmorphon-1.27.pdf | BME Submission for SIGMORPHON 2021 Shared Task 0. A Three Step Training Approach with Data Augmentation for Morphological Inflection | We present the BME submission for the SIGMORPHON 2021 Task 0 Part 1, Generalization Across Typologically Diverse Languages shared task. We use an LSTM encoder-decoder model with three step training that is first trained on all languages, then fine-tuned on each language family and finally fine-tuned on individual langu... | ['Judit Ács', 'Dorina Lakatos', 'Botond Barta', 'Gábor Szolnok'] | null | null | null | null | acl-sigmorphon-2021-8 | ['morphological-inflection'] | ['natural-language-processing'] | [-5.25128767e-02 -3.78159340e-03 -3.26016188e-01 -4.09166038e-01
-8.59783351e-01 -7.16680169e-01 8.81118059e-01 -2.79778764e-02
-9.10710335e-01 1.01999021e+00 4.20344681e-01 -6.27128124e-01
3.80957752e-01 -4.64400738e-01 -9.02260661e-01 -1.92918614e-01
-1.81774214e-01 9.51196253e-01 5.69965839e-02 -4.60469127... | [10.803682327270508, 9.733196258544922] |
b55e3b36-6d56-4c7f-bc38-0652b216a6d8 | process-knowledge-infused-ai-towards-user | 2206.13349 | null | https://arxiv.org/abs/2206.13349v1 | https://arxiv.org/pdf/2206.13349v1.pdf | Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety | AI systems have been widely adopted across various domains in the real world. However, in high-value, sensitive, or safety-critical applications such as self-management for personalized health or food recommendation with a specific purpose (e.g., allergy-aware recipe recommendations), their adoption is unlikely. Firstl... | ['Vedant Khandelwal', 'Revathy Venkataraman', 'Kaushik Roy', 'Manas Gaur', 'Amit Sheth'] | 2022-06-09 | null | null | null | null | ['food-recommendation'] | ['miscellaneous'] | [ 2.73559928e-01 6.10425234e-01 -3.86720687e-01 -7.95175314e-01
-5.21056019e-02 -5.60555518e-01 -2.39950791e-01 9.39965725e-01
-5.96214868e-02 6.11390829e-01 2.17782438e-01 -7.98127770e-01
-3.60421985e-01 -9.56451952e-01 -2.98954576e-01 -1.48212597e-01
2.94533879e-01 7.00159371e-01 -7.68292472e-02 -4.59335089... | [8.994616508483887, 6.170406341552734] |
1c4fe427-06f6-4bf5-b8ca-d91076bdc4b3 | a-transformer-based-network-for-deformable | 2202.12104 | null | https://arxiv.org/abs/2202.12104v3 | https://arxiv.org/pdf/2202.12104v3.pdf | A Transformer-based Network for Deformable Medical Image Registration | Deformable medical image registration plays an important role in clinical diagnosis and treatment. Recently, the deep learning (DL) based image registration methods have been widely investigated and showed excellent performance in computational speed. However, these methods cannot provide enough registration accuracy b... | ['Xuming Zhang', 'Wen Qian', 'Yibo Wang'] | 2022-02-24 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 2.87560429e-02 -3.79415482e-01 8.68544281e-02 -5.08497953e-01
-5.52616179e-01 1.11552160e-02 4.61060911e-01 3.70918005e-03
-5.19157469e-01 5.04125655e-01 2.62043685e-01 2.84735531e-01
-4.55497980e-01 -8.52801383e-01 -1.28530934e-01 -1.04608166e+00
-1.46493047e-01 4.87864822e-01 3.67629409e-01 -2.74338722... | [14.10042667388916, -2.5498595237731934] |
606516e0-bafd-45d4-8810-86479fc55520 | safe-exploration-in-finite-markov-decision | 1606.04753 | null | http://arxiv.org/abs/1606.04753v2 | http://arxiv.org/pdf/1606.04753v2.pdf | Safe Exploration in Finite Markov Decision Processes with Gaussian Processes | In classical reinforcement learning, when exploring an environment, agents
accept arbitrary short term loss for long term gain. This is infeasible for
safety critical applications, such as robotics, where even a single unsafe
action may cause system failure. In this paper, we address the problem of
safely exploring fin... | ['Andreas Krause', 'Matteo Turchetta', 'Felix Berkenkamp'] | 2016-06-15 | safe-exploration-in-finite-markov-decision-1 | http://papers.nips.cc/paper/6358-safe-exploration-in-finite-markov-decision-processes-with-gaussian-processes | http://papers.nips.cc/paper/6358-safe-exploration-in-finite-markov-decision-processes-with-gaussian-processes.pdf | neurips-2016-12 | ['safe-exploration'] | ['robots'] | [ 3.33680809e-01 6.60468757e-01 8.79013985e-02 1.44001380e-01
-8.10533464e-01 -7.26219475e-01 5.74435234e-01 3.76287907e-01
-6.14794374e-01 1.06911075e+00 -3.18897575e-01 -5.42187929e-01
-5.46337068e-01 -1.04411805e+00 -1.06615317e+00 -1.00164402e+00
-6.74444795e-01 7.10528195e-01 2.74007469e-01 -9.01105106... | [4.698989391326904, 2.132805585861206] |
44f58b4f-2af1-431f-bd97-5f193cec642f | shared-representation-learning-for | 1406.1247 | null | https://arxiv.org/abs/1406.1247v1 | https://arxiv.org/pdf/1406.1247v1.pdf | Shared Representation Learning for Heterogeneous Face Recognition | After intensive research, heterogenous face recognition is still a challenging problem. The main difficulties are owing to the complex relationship between heterogenous face image spaces. The heterogeneity is always tightly coupled with other variations, which makes the relationship of heterogenous face images highly n... | ['Zhen Lei', 'Stan Z. Li', 'Shengcai Liao', 'Dong Yi'] | 2014-06-05 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [-1.31569654e-01 -3.67356539e-01 -7.39969462e-02 -4.50283945e-01
-6.27696276e-01 2.52221897e-02 5.67591906e-01 -9.20135498e-01
-5.71464654e-03 5.05001545e-01 -1.65424848e-04 5.57609022e-01
-2.01263815e-01 -7.72543728e-01 -6.22607172e-01 -1.46598196e+00
1.49844393e-01 5.42871714e-01 -1.70309842e-01 -1.28428429... | [13.155386924743652, 0.49778324365615845] |
a9ee074b-c7f2-4ebc-bbc7-39e8d635a8d8 | color-deconvolution-applied-to-domain | 2305.07404 | null | https://arxiv.org/abs/2305.07404v1 | https://arxiv.org/pdf/2305.07404v1.pdf | Color Deconvolution applied to Domain Adaptation in HER2 histopathological images | Breast cancer early detection is crucial for improving patient outcomes. The Institut Catal\`a de la Salut (ICS) has launched the DigiPatICS project to develop and implement artificial intelligence algorithms to assist with the diagnosis of cancer. In this paper, we propose a new approach for facing the color normaliza... | ['Montse Pardàs', 'Ferran Marqués', 'David Anglada-Rotger'] | 2023-05-12 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 3.04647684e-01 2.19674140e-01 2.25519225e-01 -3.33978571e-02
-7.39161789e-01 -3.83622795e-01 1.95766687e-01 3.21560912e-02
-3.84260446e-01 6.10827923e-01 -3.80355477e-01 -2.89537251e-01
4.38672692e-01 -9.62322116e-01 -5.24743557e-01 -1.14293408e+00
3.84099633e-01 4.36555713e-01 1.27802327e-01 -2.87325561... | [14.948232650756836, -3.037447214126587] |
7765e4e5-453d-44d5-9d50-e95fd4a651f5 | discriminator-free-unsupervised-domain | 2301.10611 | null | https://arxiv.org/abs/2301.10611v1 | https://arxiv.org/pdf/2301.10611v1.pdf | Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification | In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed. Over the last two years, some attempts have been made for introducing adversarial-based UDA methods in the context of MLIC. However, these methods w... | ['Djamila Aouada', 'Arunkumar Rathinam', 'Anis Kacem', 'Enjie Ghorbel', 'Indel Pal Singh'] | 2023-01-25 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.52671140e-01 6.60157651e-02 -1.16604611e-01 -3.13370287e-01
-9.22253370e-01 -5.53021252e-01 6.60331428e-01 1.67764723e-01
-6.28975213e-01 7.48613238e-01 -5.51146746e-01 -9.57675278e-02
-8.42527524e-02 -5.76815248e-01 -4.98951167e-01 -1.07438862e+00
4.70399201e-01 2.66247511e-01 1.62509888e-01 4.21497114... | [10.004617691040039, 3.310990571975708] |
b7dddff1-3305-4c53-ae1d-9c89d9fec328 | machine-learning-prediction-for-mean-motion | 2201.06743 | null | https://arxiv.org/abs/2201.06743v1 | https://arxiv.org/pdf/2201.06743v1.pdf | Machine learning prediction for mean motion resonance behaviour -- The planar case | Most recently, machine learning has been used to study the dynamics of integrable Hamiltonian systems and the chaotic 3-body problem. In this work, we consider an intermediate case of regular motion in a non-integrable system: the behaviour of objects in the 2:3 mean motion resonance with Neptune. We show that, given i... | ['Nikolaos Georgakarakos', 'Zhihong Jeff Xia', 'Jian Li', 'Xin Li'] | 2022-01-18 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.43090624e-01 2.18967304e-01 -3.88001949e-02 2.37662226e-01
-5.65580130e-01 -4.32138711e-01 7.15069890e-01 -2.41513908e-01
-5.15363157e-01 7.84986377e-01 -2.20299184e-01 -3.28937918e-01
-1.70167714e-01 -8.92382443e-01 -4.35668737e-01 -1.34335577e+00
-3.54073912e-01 1.12367499e+00 2.78427958e-01 -5.97051620... | [6.731052875518799, 3.4248557090759277] |
d711d262-a535-4ceb-b47a-6edd7ed0ddab | sf-tmn-slowfast-temporal-modeling-network-for | 2306.08859 | null | https://arxiv.org/abs/2306.08859v1 | https://arxiv.org/pdf/2306.08859v1.pdf | SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition | Automatic surgical phase recognition is one of the key technologies to support Video-Based Assessment (VBA) systems for surgical education. Utilizing temporal information is crucial for surgical phase recognition, hence various recent approaches extract frame-level features to conduct full video temporal modeling. For ... | ['Amer Ghanem', 'Svetlana Petculescu', 'Bharti Goel', 'Mohammad Hasan Sarhan', 'Bokai Zhang'] | 2023-06-15 | null | null | null | null | ['surgical-phase-recognition', 'action-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.35469753e-01 -5.87198697e-02 -8.89020443e-01 -1.24393567e-01
-8.80264997e-01 -2.41811395e-01 3.51810545e-01 4.00391445e-02
-8.49276960e-01 3.49763453e-01 3.86017203e-01 -5.42151213e-01
-6.37007713e-01 -4.87259001e-01 -6.20721817e-01 -8.60330224e-01
-3.92689556e-01 1.35103092e-01 4.57776189e-01 -8.83776024... | [14.080973625183105, -3.3419675827026367] |
4e72ed48-dea6-4bce-b4a4-82ecac5ee4f8 | contextual-knowledge-learning-for-dialogue | 2305.18200 | null | https://arxiv.org/abs/2305.18200v1 | https://arxiv.org/pdf/2305.18200v1.pdf | Contextual Knowledge Learning For Dialogue Generation | Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. The context, comprising utterances from previous dialogue exchanges, is used as a source of content for response generation and as a means of selecting external kno... | ['Ke Zhou', 'Natasa Milic-Frayling', 'Wen Zheng'] | 2023-05-29 | null | null | null | null | ['dialogue-generation', 'response-generation', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.78443211e-01 1.85854137e-01 -2.67311573e-01 -5.21807671e-01
-8.22836876e-01 -6.81712985e-01 1.11781204e+00 2.76393682e-01
-5.27793109e-01 9.58689094e-01 1.03318000e+00 -9.74854529e-02
1.92574456e-01 -6.74232543e-01 -2.21865028e-01 -4.33208674e-01
3.43507290e-01 4.98606771e-01 1.65715009e-01 -6.26397669... | [12.535841941833496, 8.042560577392578] |
ae1fd3a7-5d20-460b-9c49-2748da933bc1 | neuro-reachability-of-networked-microgrids | 2101.05159 | null | https://arxiv.org/abs/2101.05159v1 | https://arxiv.org/pdf/2101.05159v1.pdf | Neuro-Reachability of Networked Microgrids | A neural ordinary differential equations network (ODE-Net)-enabled reachability method (Neuro-Reachability) is devised for the dynamic verification of networked microgrids (NMs) with unidentified subsystems and heterogeneous uncertainties. Three new contributions are presented: 1) An ODENet-enabled dynamic model discov... | ['Peng Zhang', 'Yifan Zhou'] | 2021-01-13 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-6.96325719e-01 1.36788338e-01 2.87093371e-01 2.86635756e-01
7.66234621e-02 -7.97237158e-01 6.45003259e-01 -1.53402120e-01
6.04708195e-01 9.10653830e-01 -4.90775295e-02 -5.58459699e-01
-8.66484940e-01 -5.92078924e-01 -5.69151580e-01 -8.59188855e-01
-7.97582626e-01 2.74186790e-01 -3.32497060e-02 -5.82248807... | [5.556403160095215, 2.5470850467681885] |
bd798895-66b7-46f5-81ed-17af054e1ea2 | x-paste-revisit-copy-paste-at-scale-with-clip | 2212.03863 | null | https://arxiv.org/abs/2212.03863v2 | https://arxiv.org/pdf/2212.03863v2.pdf | X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion | Copy-Paste is a simple and effective data augmentation strategy for instance segmentation. By randomly pasting object instances onto new background images, it creates new training data for free and significantly boosts the segmentation performance, especially for rare object categories. Although diverse, high-quality o... | ['Nenghai Yu', 'Weiming Zhang', 'Qi Chu', 'Wenbo Zhou', 'Ce Liu', 'Lu Yuan', 'Fang Wen', 'Dong Chen', 'Dongdong Chen', 'Jianmin Bao', 'Dianmo Sheng', 'Hanqing Zhao'] | 2022-12-07 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 4.01480317e-01 6.88609183e-02 -2.69967943e-01 -2.91576058e-01
-9.89245713e-01 -5.18166125e-01 5.46140313e-01 -2.36305758e-01
-4.14048940e-01 2.81108201e-01 -2.99970716e-01 -1.49603948e-01
3.80101919e-01 -5.79694986e-01 -9.31831479e-01 -5.64887524e-01
1.76810533e-01 5.13886988e-01 7.52793729e-01 -8.93066302... | [9.562602043151855, 0.3549972474575043] |
bec037da-4036-4d91-be72-4a48aabb6f1f | trimming-feature-extraction-and-inference-for | 2105.10302 | null | https://arxiv.org/abs/2105.10302v1 | https://arxiv.org/pdf/2105.10302v1.pdf | Trimming Feature Extraction and Inference for MCU-based Edge NILM: a Systematic Approach | Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features fro... | ['Luca Benini', 'Andrea Acquaviva', 'Davide Brunelli', 'Enrico Tabanelli'] | 2021-05-21 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-8.94960389e-02 -5.60496867e-01 -2.80305505e-01 -3.93251389e-01
-1.09408104e+00 -5.14952242e-01 5.29816568e-01 6.07212424e-01
-8.88734683e-03 4.73514616e-01 -3.14892113e-01 -3.88984978e-01
-1.65709123e-01 -9.72859323e-01 -2.67045051e-01 -9.29036796e-01
-3.36920649e-01 5.53917468e-01 -1.19667582e-01 2.44205296... | [5.986401557922363, 2.611996650695801] |
405bd3fd-d788-49ba-85ca-14a4a203cdae | pistol-pupil-invisible-supportive-tool-to | 2201.06799 | null | https://arxiv.org/abs/2201.06799v2 | https://arxiv.org/pdf/2201.06799v2.pdf | Pistol: Pupil Invisible Supportive Tool to extract Pupil, Iris, Eye Opening, Eye Movements, Pupil and Iris Gaze Vector, and 2D as well as 3D Gaze | This paper describes a feature extraction and gaze estimation software, named \textit{Pistol} that can be used with Pupil Invisible projects and other eye trackers in the future. In offline mode, our software extracts multiple features from the eye including, the pupil and iris ellipse, eye aperture, pupil vector, iris... | ['Shahram Eivazi', 'Daniel Weber', 'Wolfgang Fuhl'] | 2022-01-18 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-3.80205750e-01 -2.01440305e-02 1.60928965e-01 -3.43389243e-01
-9.35582072e-02 -5.93279421e-01 -5.36071472e-02 -2.70282865e-01
-2.82085180e-01 3.75827998e-01 6.93786591e-02 -4.00884539e-01
-1.43754736e-01 -6.52529076e-02 -1.87115893e-01 -6.00362539e-01
3.22422907e-02 -7.61085749e-02 -4.15898822e-02 1.03227369... | [14.041686058044434, 0.17864525318145752] |
b6fb4373-85ec-45de-90c8-06ca90f56587 | fed-nilm-a-federated-learning-based-non | 2105.11085 | null | https://arxiv.org/abs/2105.11085v2 | https://arxiv.org/pdf/2105.11085v2.pdf | Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection | Non-intrusive load monitoring (NILM) is essential for understanding customer's power consumption patterns and may find wide applications like carbon emission reduction and energy conservation. The training of NILM models requires massive load data containing different types of appliances. However, inadequate load data ... | ['Fushuan Wen', 'Guolong Liu', 'Junhua Zhao', 'Caomingzhe Si', 'Haijin Wang'] | 2021-05-24 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-2.32342348e-01 -5.54306395e-02 -6.86497033e-01 -6.62138641e-01
-3.58631253e-01 -3.23037624e-01 4.81663764e-01 -3.33252922e-02
-1.55153111e-01 6.06206477e-01 -1.98450640e-01 -2.88773119e-01
-1.18538722e-01 -1.08675146e+00 -4.61973071e-01 -8.43575239e-01
-2.34897994e-02 3.89384359e-01 -4.01089072e-01 3.17241132... | [5.860482692718506, 2.7576022148132324] |
868b1345-ec7d-45d7-9add-7ed9ab594310 | representer-theorems-for-metric-and | 2304.03720 | null | https://arxiv.org/abs/2304.03720v1 | https://arxiv.org/pdf/2304.03720v1.pdf | Representer Theorems for Metric and Preference Learning: A Geometric Perspective | We explore the metric and preference learning problem in Hilbert spaces. We obtain a novel representer theorem for the simultaneous task of metric and preference learning. Our key observation is that the representer theorem can be formulated with respect to the norm induced by the inner product inherent in the problem ... | ['Peyman Morteza'] | 2023-04-07 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 1.27587646e-01 1.16456658e-01 1.21951126e-01 -4.90965366e-01
-9.42756474e-01 -6.67629242e-01 3.63816947e-01 2.22513676e-01
-3.70756805e-01 6.23828351e-01 3.03231895e-01 -1.08130753e-01
-6.68151617e-01 -4.27082509e-01 -4.58074182e-01 -7.72826791e-01
-3.08704525e-01 4.92794514e-02 -4.34040397e-01 -3.26993257... | [7.556622505187988, 4.040106296539307] |
cb359ccb-597f-41b1-bd6b-e2ed3ee7d7f9 | lipnet-end-to-end-sentence-level-lipreading | 1611.01599 | null | http://arxiv.org/abs/1611.01599v2 | http://arxiv.org/pdf/1611.01599v2.pdf | LipNet: End-to-End Sentence-level Lipreading | Lipreading is the task of decoding text from the movement of a speaker's
mouth. Traditional approaches separated the problem into two stages: designing
or learning visual features, and prediction. More recent deep lipreading
approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman,
2016a). However, exi... | ['Brendan Shillingford', 'Shimon Whiteson', 'Nando de Freitas', 'Yannis M. Assael'] | 2016-11-05 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.30040282e-01 2.35429276e-02 -5.80982864e-01 -2.65939385e-01
-1.10686767e+00 -2.98138142e-01 6.39169574e-01 -2.71868199e-01
-4.77782696e-01 5.18527150e-01 7.84524381e-01 -4.28923547e-01
6.01501226e-01 2.06828967e-01 -6.71704233e-01 -3.65483254e-01
5.57415932e-02 -2.31725350e-01 -6.33564293e-02 2.57006049... | [14.34388542175293, 5.0149688720703125] |
8541981f-c1d8-4728-b9d5-cf3f28777b23 | fml-based-prediction-agent-and-its | 1704.04719 | null | http://arxiv.org/abs/1704.04719v1 | http://arxiv.org/pdf/1704.04719v1.pdf | FML-based Prediction Agent and Its Application to Game of Go | In this paper, we present a robotic prediction agent including a darkforest
Go engine, a fuzzy markup language (FML) assessment engine, an FML-based
decision support engine, and a robot engine for game of Go application. The
knowledge base and rule base of FML assessment engine are constructed by
referring the informat... | ['Sheng-Chi Yang', 'Chia-Hsiu Kao', 'Mei-Hui Wang', 'Chang-Shing Lee', 'Yusuke Nojima', 'Nan Shuo', 'Ryosuke Saga', 'Naoyuki Kubota'] | 2017-04-16 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-5.37222385e-01 4.47507024e-01 -3.26226205e-01 -3.31423432e-01
-3.43069643e-01 -5.11313856e-01 5.08711040e-01 -1.16632223e-01
-4.13179308e-01 7.83136308e-01 -2.69297719e-01 -4.33865219e-01
-5.66233873e-01 -1.22268724e+00 -4.77014214e-01 -1.35301188e-01
1.78797081e-01 8.15789461e-01 8.84803236e-01 -8.58590543... | [3.9622879028320312, 1.2554353475570679] |
74f774f4-a73c-44c7-8a18-5c4d59f305f2 | know-what-i-don-t-know-handling-ambiguous-and | 2212.08902 | null | https://arxiv.org/abs/2212.08902v2 | https://arxiv.org/pdf/2212.08902v2.pdf | Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL | The task of text-to-SQL aims to convert a natural language question into its corresponding SQL query within the context of relational tables. Existing text-to-SQL parsers generate a "plausible" SQL query for an arbitrary user question, thereby failing to correctly handle problematic user questions. To formalize this pr... | ['Jian-Guang Lou', 'Zhoujun Li', 'Yan Gao', 'Bing Wang'] | 2022-12-17 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-2.44648661e-02 5.97460866e-01 -8.22200477e-02 -8.29075873e-01
-1.48012567e+00 -8.20444286e-01 3.98212641e-01 3.22156906e-01
1.26646861e-01 7.25245595e-01 2.60827243e-01 -9.41667020e-01
-9.20340940e-02 -9.32330132e-01 -1.03590333e+00 3.01018476e-01
4.19673592e-01 6.43622637e-01 3.65345091e-01 -2.39071816... | [9.851316452026367, 7.827001094818115] |
37341671-5b07-48c7-88e4-5e5c9738c8aa | defect-detection-approaches-based-on | 2303.11971 | null | https://arxiv.org/abs/2303.11971v1 | https://arxiv.org/pdf/2303.11971v1.pdf | Defect Detection Approaches Based on Simulated Reference Image | This work is addressing the problem of defect anomaly detection based on a clean reference image. Specifically, we focus on SEM semiconductor defects in addition to several natural image anomalies. There are well-known methods to create a simulation of an artificial reference image by its defect specimen. In this work,... | ['Boris Sherman', 'Ran Badanes', 'Yotam Ben Shoshan', 'Nati Ofir'] | 2023-03-21 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 6.25816524e-01 3.49027872e-01 6.66694224e-01 -8.24736729e-02
-4.73094702e-01 3.37094873e-01 6.54485643e-01 1.72734931e-01
2.18366887e-02 2.28609517e-01 -1.18684851e-01 6.24265261e-02
1.85261533e-01 -7.35943139e-01 -6.60693347e-01 -8.31036568e-01
1.83571100e-01 5.69474399e-01 5.60504794e-01 -3.21424991... | [7.488670825958252, 1.9968661069869995] |
90812db2-5ecd-424b-9744-787bcbd59f87 | 1st-place-solution-for-psg-competition-with | 2302.02651 | null | https://arxiv.org/abs/2302.02651v1 | https://arxiv.org/pdf/2302.02651v1.pdf | 1st Place Solution for PSG competition with ECCV'22 SenseHuman Workshop | Panoptic Scene Graph (PSG) generation aims to generate scene graph representations based on panoptic segmentation instead of rigid bounding boxes. Existing PSG methods utilize one-stage paradigm which simultaneously generates scene graphs and predicts semantic segmentation masks or two-stage paradigm that first adopt a... | ['Haofan Wang', 'Xiaofeng Guo', 'Qixun Wang'] | 2023-02-06 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 5.79313099e-01 5.46072662e-01 -3.74563754e-01 -5.76499820e-01
-5.09113193e-01 -4.65653211e-01 5.58027506e-01 -1.42745618e-02
1.78812612e-02 5.49030364e-01 9.77336839e-02 -2.70420551e-01
-9.48296189e-02 -1.07248771e+00 -6.39146090e-01 -4.53159809e-01
2.20176950e-01 6.74003482e-01 5.13909221e-01 -6.23024590... | [10.24645709991455, 1.664866328239441] |
241bd592-0bb0-4c04-b5e1-c081b8d1027d | how-domain-terminology-affects-meeting | 2011.00692 | null | https://arxiv.org/abs/2011.00692v2 | https://arxiv.org/pdf/2011.00692v2.pdf | How Domain Terminology Affects Meeting Summarization Performance | Meetings are essential to modern organizations. Numerous meetings are held and recorded daily, more than can ever be comprehended. A meeting summarization system that identifies salient utterances from the transcripts to automatically generate meeting minutes can help. It empowers users to rapidly search and sift throu... | ['Fei Liu', 'Alec Kerrigan', 'Dillon Burns', 'Xiaojin Dai', 'Alexander Roustai', 'Jia Jin Koay'] | 2020-11-02 | null | https://aclanthology.org/2020.coling-main.499 | https://aclanthology.org/2020.coling-main.499.pdf | coling-2020-8 | ['meeting-summarization'] | ['natural-language-processing'] | [ 5.61466098e-01 5.44069648e-01 -1.45351827e-01 -3.08932126e-01
-1.52342808e+00 -8.48368526e-01 7.30805516e-01 7.43844211e-01
-7.93253109e-02 1.07103252e+00 1.33555555e+00 -4.98054689e-03
4.24105339e-02 -1.32720932e-01 -2.28504807e-01 -6.54156283e-02
2.91546345e-01 5.15188456e-01 -3.14380914e-01 -4.61009085... | [12.611522674560547, 9.394533157348633] |
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