paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1e88523a-13d3-4b4d-8854-e8999341204a | environment-invariant-linear-least-squares | 2303.03092 | null | https://arxiv.org/abs/2303.03092v1 | https://arxiv.org/pdf/2303.03092v1.pdf | Environment Invariant Linear Least Squares | This paper considers a multiple environments linear regression model in which data from multiple experimental settings are collected. The joint distribution of the response variable and covariate may vary across different environments, yet the conditional expectation of $y$ given the unknown set of important variables ... | ['Tong Zhang', 'Yihong Gu', 'Cong Fang', 'Jianqing Fan'] | 2023-03-06 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 2.57017076e-01 -2.02669248e-01 -5.80295444e-01 -3.12621534e-01
-6.90917432e-01 -4.79764253e-01 7.79701993e-02 -1.92993537e-01
-5.99876106e-01 1.03198600e+00 -6.03968836e-02 -2.72631645e-01
-7.95133591e-01 -4.21901286e-01 -1.07372344e+00 -8.78583550e-01
-1.29976481e-01 8.33644439e-03 -5.90411603e-01 4.10511881... | [7.789970874786377, 5.00166654586792] |
12651707-e14d-4ac8-a9f5-bbcca864d0b0 | predicting-the-presence-of-reasoning-markers | null | null | https://aclanthology.org/2022.argmining-1.13 | https://aclanthology.org/2022.argmining-1.13.pdf | Predicting the Presence of Reasoning Markers in Argumentative Text | This paper proposes a novel task in Argument Mining, which we will refer to as Reasoning Marker Prediction. We reuse the popular Persuasive Essays Corpus (Stab and Gurevych, 2014). Instead of using this corpus for Argument Structure Parsing, we use a simple heuristic method to identify text spans which we can identify ... | ['Rob Gaizauskas', 'Jonathan Clayton'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 3.01096618e-01 9.57460999e-01 -6.22734845e-01 -1.42263785e-01
-1.09977400e+00 -7.61222899e-01 9.34332252e-01 5.80080628e-01
-3.38418543e-01 9.74947274e-01 7.25822926e-01 -1.18381345e+00
-1.40211120e-01 -7.39502370e-01 -7.15602934e-01 1.41190588e-01
3.32375675e-01 4.54884291e-01 4.58607823e-01 -1.29862726... | [9.685820579528809, 9.519012451171875] |
d88c4aa3-fefc-48c9-9d19-2353cc9360a4 | nemo-learning-3d-neural-motion-fields-from | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_NeMo_Learning_3D_Neural_Motion_Fields_From_Multiple_Video_Instances_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_NeMo_Learning_3D_Neural_Motion_Fields_From_Multiple_Video_Instances_CVPR_2023_paper.pdf | NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action | The task of reconstructing 3D human motion has wide-ranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware, and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than Mo... | ['Serena Yeung', 'Karen Liu', 'Jeffrey Gu', 'João Pedro Araújo', 'Maria Xenochristou', 'Zhenzhen Weng', 'Kuan-Chieh Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['keypoint-detection', '3d-reconstruction', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.91563383e-01 -4.45666164e-01 -5.50278187e-01 2.69808918e-01
-8.26497436e-01 -5.46998918e-01 3.48671407e-01 -6.61481678e-01
-3.75957936e-01 3.82765472e-01 6.93010569e-01 1.54669836e-01
2.88887978e-01 -5.26328683e-01 -9.92443323e-01 -4.75527257e-01
-4.04957943e-02 3.32507014e-01 5.52491009e-01 -3.10259879... | [7.296175479888916, -0.7045865654945374] |
235106ff-5246-4677-a5e2-2abb918cff02 | improving-word-translation-via-two-stage-1 | 2203.08307 | null | https://arxiv.org/abs/2203.08307v3 | https://arxiv.org/pdf/2203.08307v3.pdf | Improving Word Translation via Two-Stage Contrastive Learning | Word translation or bilingual lexicon induction (BLI) is a key cross-lingual task, aiming to bridge the lexical gap between different languages. In this work, we propose a robust and effective two-stage contrastive learning framework for the BLI task. At Stage C1, we propose to refine standard cross-lingual linear maps... | ['Ivan Vulić', 'Anna Korhonen', 'Nigel Collier', 'Fangyu Liu', 'Yaoyiran Li'] | 2022-03-15 | null | https://aclanthology.org/2022.acl-long.299 | https://aclanthology.org/2022.acl-long.299.pdf | acl-2022-5 | ['multilingual-word-embeddings', 'pretrained-multilingual-language-models', 'self-learning', 'multilingual-nlp'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.14365242e-01 -2.08823234e-01 -7.17695713e-01 -3.95036876e-01
-1.39111650e+00 -8.19659293e-01 8.34013641e-01 8.30108523e-02
-6.08120799e-01 7.14870811e-01 4.38308895e-01 -6.41295910e-01
1.98432580e-01 -3.47624809e-01 -9.20327008e-01 -3.51619303e-01
1.02105699e-01 6.04898512e-01 -9.88344550e-02 -5.55760741... | [11.08339786529541, 10.050939559936523] |
35186373-3599-4f93-949f-c6d9b8e0c297 | symbolic-music-structure-analysis-with-graph | 2303.13881 | null | https://arxiv.org/abs/2303.13881v1 | https://arxiv.org/pdf/2303.13881v1.pdf | Symbolic Music Structure Analysis with Graph Representations and Changepoint Detection Methods | Music Structure Analysis is an open research task in Music Information Retrieval (MIR). In the past, there have been several works that attempt to segment music into the audio and symbolic domains, however, the identification and segmentation of the music structure at different levels is still an open research problem ... | ['Jose R. Beltran', 'Sonia Rubio Llamas', 'Carlos Hernandez-Olivan'] | 2023-03-24 | null | null | null | null | ['music-generation', 'music-generation', 'music-classification', 'music-information-retrieval'] | ['audio', 'music', 'music', 'music'] | [ 4.26480472e-01 -2.07260996e-01 2.10056990e-01 1.29064038e-01
-5.58572352e-01 -1.15657139e+00 3.42737287e-01 4.35468316e-01
-2.07065448e-01 1.77041695e-01 7.98655208e-03 2.34820112e-03
-7.22183466e-01 -6.66726291e-01 -3.29408586e-01 -5.55518627e-01
-5.26641071e-01 4.92161602e-01 5.69157183e-01 -2.82684505... | [15.898263931274414, 5.257133483886719] |
9a5cfa19-d7e7-4e67-9854-a9116199226e | camera-pose-estimation-with-unknown-principal | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Larsson_Camera_Pose_Estimation_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Larsson_Camera_Pose_Estimation_CVPR_2018_paper.pdf | Camera Pose Estimation With Unknown Principal Point | To estimate the 6-DoF extrinsic pose of a pinhole camera with partially unknown intrinsic parameters is a critical sub-problem in structure-from-motion and camera localization. In most of existing camera pose estimation solvers, the principal point is assumed to be in the image center. Unfortunately, this assumption is... | ['Yinqiang Zheng', 'Viktor Larsson', 'Zuzana Kukelova'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['camera-localization'] | ['computer-vision'] | [ 1.24272667e-02 3.06046661e-02 6.91943839e-02 2.62101471e-01
-6.57502115e-01 -8.17971051e-01 3.74436498e-01 -3.62466365e-01
-2.58633971e-01 4.56833899e-01 -3.56758147e-01 -2.43146747e-01
-1.65082306e-01 -6.08168244e-02 -1.05386651e+00 -5.75942039e-01
2.51754075e-01 6.36693954e-01 2.61364967e-01 8.83699358... | [7.98431921005249, -2.3806135654449463] |
b53a89d3-3d7f-460c-ac5f-c54e9ff1f4a5 | rgb-d-saliency-detection-via-cascaded-mutual | 2109.07246 | null | https://arxiv.org/abs/2109.07246v2 | https://arxiv.org/pdf/2109.07246v2.pdf | RGB-D Saliency Detection via Cascaded Mutual Information Minimization | Existing RGB-D saliency detection models do not explicitly encourage RGB and depth to achieve effective multi-modal learning. In this paper, we introduce a novel multi-stage cascaded learning framework via mutual information minimization to "explicitly" model the multi-modal information between RGB image and depth data... | ['Ling Shao', 'Nick Barnes', 'Yiran Zhong', 'Xin Yu', 'Yuchao Dai', 'Deng-Ping Fan', 'Jing Zhang'] | 2021-09-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_RGB-D_Saliency_Detection_via_Cascaded_Mutual_Information_Minimization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_RGB-D_Saliency_Detection_via_Cascaded_Mutual_Information_Minimization_ICCV_2021_paper.pdf | iccv-2021-1 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 1.65325999e-02 7.77550042e-02 -3.90226752e-01 -6.22748494e-01
-8.01188409e-01 -2.34012589e-01 3.91083986e-01 -1.16034642e-01
-2.86439717e-01 4.33105648e-01 3.53394657e-01 -1.69541240e-01
8.88323560e-02 -6.24271572e-01 -9.07651603e-01 -6.28840864e-01
2.68202960e-01 -5.91302849e-02 4.73121583e-01 -3.04280341... | [9.669017791748047, -0.7799323201179504] |
47028e04-9c9d-413d-b917-a891c6b1c542 | 190600365 | 1906.00365 | null | https://arxiv.org/abs/1906.00365v1 | https://arxiv.org/pdf/1906.00365v1.pdf | User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation | A huge amount of user generated content related to movies is created with the popularization of web 2.0. With these continues exponential growth of data, there is an inevitable need for recommender systems as people find it difficult to make informed and timely decisions. Movie recommendation systems assist users to fi... | ['Supunmali Ahangama', 'Tharindu Ranasinghe', 'Lasitha Uyangoda'] | 2019-06-02 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.69608086e-01 -2.92314500e-01 -1.02347648e-02 -6.61831737e-01
-9.91539583e-02 -6.04104459e-01 3.97428155e-01 4.65811461e-01
-5.97844183e-01 5.25081336e-01 3.00265342e-01 -1.86852455e-01
-5.54279864e-01 -9.72269714e-01 9.58526880e-02 -2.74347782e-01
9.45211351e-02 4.92894083e-01 6.24665320e-01 -6.17351174... | [10.063214302062988, 5.854092121124268] |
e44504e1-e435-45a4-b45a-3a065ef719a1 | joint-extraction-of-entities-and-overlapping | null | null | https://www.aaai.org/ojs/index.php/AAAI/article/view/4591 | https://www.aaai.org/ojs/index.php/AAAI/article/view/4591/4469 | Joint extraction of entities and overlapping relations using position-attentive sequence labeling | Joint entity and relation extraction is to detect entity and relation using a single model. In this paper, we present a novel unified joint extraction model which directly tags entity and relation labels according to a query word position p, i.e., detecting entity at p, and identifying entities at other positions that ... | ['Xinyan Xiao', 'Qiaoqiao She', 'Yajuan Lyu', 'Dai Dai', 'Shan Dou', 'Haifeng Wang'] | 2019-07-17 | null | null | null | aaai-2019-2019-7 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 2.93632336e-02 4.08327430e-01 -3.29018742e-01 -3.30372870e-01
-8.98749650e-01 -5.79080105e-01 3.84072363e-01 6.04641140e-01
-4.94219661e-01 7.26049721e-01 2.38636687e-01 -2.66108006e-01
-1.26200840e-02 -1.10646856e+00 -4.22699451e-01 -3.40478897e-01
-1.66644618e-01 6.64646626e-01 6.00962281e-01 -1.53790638... | [9.262514114379883, 8.671828269958496] |
f002935a-8b2c-441e-aa84-0b1381e0a47f | provably-scale-covariant-networks-from | 1903.00289 | null | https://arxiv.org/abs/1903.00289v2 | https://arxiv.org/pdf/1903.00289v2.pdf | Provably scale-covariant networks from oriented quasi quadrature measures in cascade | This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional ... | ['Tony Lindeberg'] | 2019-03-01 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.48399973e-01 2.32639849e-01 3.15886855e-01 -5.04759789e-01
-7.18361810e-02 -3.71601105e-01 8.61914754e-01 -4.96773943e-02
-4.67785329e-01 5.87414920e-01 4.92415689e-02 -1.41210287e-04
-5.56845069e-01 -8.69583368e-01 -3.49058181e-01 -9.26649511e-01
-5.44434905e-01 2.90300339e-01 5.42689145e-01 -7.01372325... | [9.242876052856445, 2.3170294761657715] |
f9202821-162c-461a-b8de-9c2b2be79d55 | continuous-multimodal-emotion-recognition | 1709.05861 | null | http://arxiv.org/abs/1709.05861v2 | http://arxiv.org/pdf/1709.05861v2.pdf | Continuous Multimodal Emotion Recognition Approach for AVEC 2017 | This paper reports the analysis of audio and visual features in predicting
the continuous emotion dimensions under the seventh Audio/Visual Emotion
Challenge (AVEC 2017), which was done as part of a B.Tech. 2nd year internship
project. For visual features we used the HOG (Histogram of Gradients) features,
Fisher encodi... | ['Abhinav Dhall', 'Narotam Singh', 'Nittin Singh'] | 2017-09-18 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-9.42674875e-02 -1.72331125e-01 3.10922235e-01 -5.22341847e-01
-1.10130775e+00 -3.97818357e-01 6.26305878e-01 3.72469515e-01
-5.64094186e-01 2.98026532e-01 5.79121768e-01 4.86114264e-01
-2.67589781e-02 -4.07541424e-01 -4.83694822e-01 -5.29916525e-01
-7.91609704e-01 -1.32192820e-01 -1.70816947e-02 -9.86017808... | [13.347771644592285, 5.049518585205078] |
92f7695c-6871-45cf-9ae1-35c0c35c3d03 | deep-reinforcement-learning-for-chinese-zero | 1806.03711 | null | http://arxiv.org/abs/1806.03711v2 | http://arxiv.org/pdf/1806.03711v2.pdf | Deep Reinforcement Learning for Chinese Zero pronoun Resolution | Deep neural network models for Chinese zero pronoun resolution learn semantic
information for zero pronoun and candidate antecedents, but tend to be
short-sighted---they often make local decisions. They typically predict
coreference chains between the zero pronoun and one single candidate antecedent
one link at a time,... | ['Wei-Nan Zhang', 'William Yang Wang', 'Ting Liu', 'Qingyu Yin', 'Yu Zhang'] | 2018-06-10 | deep-reinforcement-learning-for-chinese-zero-1 | https://aclanthology.org/P18-1053 | https://aclanthology.org/P18-1053.pdf | acl-2018-7 | ['chinese-zero-pronoun-resolution'] | ['natural-language-processing'] | [ 1.40165482e-02 5.76832771e-01 -5.69393337e-01 -6.52098298e-01
-7.88274169e-01 -4.92931634e-01 4.83283818e-01 3.28372151e-01
-8.80703628e-01 1.11370969e+00 6.61835372e-01 -3.17896992e-01
-2.19344765e-01 -1.11373460e+00 -6.38138533e-01 -3.99808973e-01
-1.58586457e-01 1.22113848e+00 8.69523883e-02 -7.17629075... | [9.571394920349121, 9.462135314941406] |
589556db-a028-47cc-8445-49f507c1fda2 | comparison-of-automatic-prostate-zones | 2207.09483 | null | https://arxiv.org/abs/2207.09483v1 | https://arxiv.org/pdf/2207.09483v1.pdf | Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures | Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide. The main problem that specialists face during the diagnosis of prostate cancer is the localization of Regions of Interest (ROI) containing a tumor tissue. Currently, the segmentation of this RO... | ['Christian Mata', 'Gerardo Rodriguez-Hernandez', 'Miguel Gonzalez-Mendoza', 'Gilberto Ochoa-Ruiz', 'Pablo Cesar Quihui-Rubio'] | 2022-07-19 | null | null | null | null | ['prostate-zones-segmentation'] | ['computer-vision'] | [ 1.50304720e-01 3.95210028e-01 -2.19479203e-02 -2.55814135e-01
-7.53784180e-01 -4.53942060e-01 5.29940128e-01 7.12441444e-01
-7.41812944e-01 7.78868914e-01 -1.63081475e-02 -1.94328818e-02
-2.78902918e-01 -7.62387931e-01 -3.40564728e-01 -9.15089726e-01
-4.26648915e-01 8.19962263e-01 -9.91234332e-02 2.88734287... | [14.627787590026855, -2.54910945892334] |
e9c3eafd-bb60-4f17-a65a-50669d786f04 | irregular-change-detection-in-sparse-bi | 2306.15416 | null | https://arxiv.org/abs/2306.15416v2 | https://arxiv.org/pdf/2306.15416v2.pdf | Irregular Change Detection in Sparse Bi-Temporal Point Clouds using Learned Place Recognition Descriptors and Point-to-Voxel Comparison | Change detection and irregular object extraction in 3D point clouds is a challenging task that is of high importance not only for autonomous navigation but also for updating existing digital twin models of various industrial environments. This article proposes an innovative approach for change detection in 3D point clo... | ['George Nikolakopoulos', 'Anton Koval', 'Nikolaos Stathoulopoulos'] | 2023-06-27 | null | null | null | null | ['change-detection', 'autonomous-navigation'] | ['computer-vision', 'computer-vision'] | [ 2.02818349e-01 -4.51189965e-01 3.90908778e-01 -2.05510676e-01
-4.74569201e-01 -3.87450308e-01 5.84486663e-01 6.62192225e-01
-2.45435610e-01 2.78210908e-01 -5.54273963e-01 -6.86081871e-02
-4.21272635e-01 -1.15236795e+00 -7.54969597e-01 -8.24495494e-01
-4.76015180e-01 6.86269403e-01 4.43912059e-01 -3.40147942... | [8.136676788330078, -2.810939311981201] |
3f18eae5-eae5-47b9-b6cd-f8516aeae13a | designing-perceptual-puzzles-by | 2204.12301 | null | https://arxiv.org/abs/2204.12301v1 | https://arxiv.org/pdf/2204.12301v1.pdf | Designing Perceptual Puzzles by Differentiating Probabilistic Programs | We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search efficiently, we design a differentiable probabilistic programming language, whose API exposes MCMC infere... | ['Jonathan Ragan-Kelley', 'Joshua Tenenbaum', 'Tzu-Mao Li', 'Kartik Chandra'] | 2022-04-26 | null | null | null | null | ['color-constancy', 'probabilistic-programming'] | ['computer-vision', 'methodology'] | [ 1.98594883e-01 4.91708100e-01 2.37256512e-01 -3.76317769e-01
-2.71571994e-01 -7.46732771e-01 8.68337750e-01 -6.69543505e-01
-1.48016021e-01 3.98794055e-01 -5.94835877e-02 -6.56043291e-01
2.78413147e-01 -7.07432091e-01 -9.44593310e-01 -4.86097783e-01
9.39370245e-02 2.76809573e-01 1.29553312e-02 1.01668477... | [10.502659797668457, 0.18920865654945374] |
c8fc0a23-dba9-4a3f-ae21-96d58bb1302f | linkage-based-face-clustering-via-graph | 1903.11306 | null | http://arxiv.org/abs/1903.11306v3 | http://arxiv.org/pdf/1903.11306v3.pdf | Linkage Based Face Clustering via Graph Convolution Network | In this paper, we present an accurate and scalable approach to the face
clustering task. We aim at grouping a set of faces by their potential
identities. We formulate this task as a link prediction problem: a link exists
between two faces if they are of the same identity. The key idea is that we
find the local context ... | ['Ya-Li Li', 'Shengjin Wang', 'Zhongdao Wang', 'Liang Zheng'] | 2019-03-27 | linkage-based-face-clustering-via-graph-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Linkage_Based_Face_Clustering_via_Graph_Convolution_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Linkage_Based_Face_Clustering_via_Graph_Convolution_Network_CVPR_2019_paper.pdf | cvpr-2019-6 | ['face-clustering'] | ['computer-vision'] | [-2.37082034e-01 9.89723355e-02 -1.23675680e-02 -8.06358635e-01
-2.41407260e-01 -4.81141299e-01 6.29279017e-01 8.57204571e-02
3.17310244e-01 1.16935678e-01 8.33506584e-02 2.36401111e-01
-3.31510782e-01 -8.90528321e-01 -8.02608430e-01 -6.54588997e-01
-4.80018437e-01 7.63728499e-01 -5.45403585e-02 2.07694590... | [13.483120918273926, 1.061877965927124] |
49601e95-6aca-490d-a6e1-57e7af6837ee | image-harmonization-by-matching-regional | 2204.04715 | null | https://arxiv.org/abs/2204.04715v1 | https://arxiv.org/pdf/2204.04715v1.pdf | Image Harmonization by Matching Regional References | To achieve visual consistency in composite images, recent image harmonization methods typically summarize the appearance pattern of global background and apply it to the global foreground without location discrepancy. However, for a real image, the appearances (illumination, color temperature, saturation, hue, texture,... | ['Chun-Le Guo', 'Zhi Chai', 'Ruiqi Wu', 'Zheng Lin', 'Zhao Zhang', 'Ziyue Zhu'] | 2022-04-10 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.59303391e-01 -4.17783737e-01 -6.85486421e-02 -1.38144106e-01
-7.03056455e-02 -5.79446673e-01 1.87521234e-01 -2.95252055e-01
1.70072779e-01 5.79734921e-01 -4.42217290e-02 -2.29698457e-02
4.42340463e-01 -7.42460251e-01 -4.06825364e-01 -1.01758456e+00
6.04905486e-01 -1.31248802e-01 9.15349960e-01 -1.67613178... | [11.203394889831543, -1.2015609741210938] |
cac1aa60-1911-4902-a04a-8ca4f77adb29 | domain-relevant-embeddings-for-medical | 1910.04192 | null | https://arxiv.org/abs/1910.04192v2 | https://arxiv.org/pdf/1910.04192v2.pdf | Domain-Relevant Embeddings for Medical Question Similarity | The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-question pairs could enable questions to be answered more effectively. While many research efforts have focused on the problem of general questio... | ['Clara McCreery', 'Xavier Amatriain', 'Anitha Kannan', 'Namit Katariya', 'Manish Chablani'] | 2019-10-09 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [ 2.74159372e-01 4.04698133e-01 -8.94259810e-02 -7.08255827e-01
-1.32053363e+00 -4.42996860e-01 1.68159738e-01 7.92809129e-01
-6.89935088e-01 5.29197454e-01 2.14926094e-01 -7.02947617e-01
-3.91185462e-01 -6.43244922e-01 -4.05171551e-02 -4.53496799e-02
3.75258625e-01 7.20525444e-01 2.50652283e-01 -3.61375362... | [8.966144561767578, 8.555458068847656] |
089afe1f-14e7-4f1e-afca-39f0805bf058 | clustering-similar-amendments-at-the-italian | null | null | https://aclanthology.org/2022.parlaclarin-1.7 | https://aclanthology.org/2022.parlaclarin-1.7.pdf | Clustering Similar Amendments at the Italian Senate | In this paper we describe an experiment for the application of text clustering techniques to dossiers of amendments to proposed legislation discussed in the Italian Senate. The aim is to assist the Senate staff in the detection of groups of amendments similar in their textual formulation in order to schedule their simu... | ['Giuseppe Briotti', 'Roberto Battistoni', 'Carlo Marchetti', 'Tommaso Agnoloni'] | null | null | null | null | parlaclarin-lrec-2022-6 | ['text-clustering'] | ['natural-language-processing'] | [ 3.09934109e-01 3.72702591e-02 7.81332701e-02 -3.47822309e-01
-5.66720843e-01 -7.73173034e-01 6.68111563e-01 7.43487954e-01
-6.72345698e-01 6.30964279e-01 4.51652944e-01 -9.34028864e-01
-7.17391372e-01 -6.30095482e-01 1.08796410e-01 -5.72480619e-01
3.45490515e-01 5.45021892e-01 2.96327442e-01 -3.13523918... | [9.551831245422363, 8.81094741821289] |
c67b333e-206c-4f29-bdc5-e9658d970460 | softgroup-for-3d-instance-segmentation-on | 2203.01509 | null | https://arxiv.org/abs/2203.01509v1 | https://arxiv.org/pdf/2203.01509v1.pdf | SoftGroup for 3D Instance Segmentation on Point Clouds | Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation followed by grouping. The hard predictions are made when performing semantic segmentation such that each point is associated with a single class. However, the errors stemming from hard decision propagate into grouping that results ... | ['Chang D. Yoo', 'Xuan Thanh Nguyen', 'Tung M. Luu', 'Kookhoi Kim', 'Thang Vu'] | 2022-03-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Vu_SoftGroup_for_3D_Instance_Segmentation_on_Point_Clouds_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Vu_SoftGroup_for_3D_Instance_Segmentation_on_Point_Clouds_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 3.49006534e-01 7.24383712e-01 -3.26196164e-01 -5.24301946e-01
-9.07484591e-01 -3.28602880e-01 4.10795838e-01 1.00710280e-01
-2.56939143e-01 3.70421380e-01 -3.46799463e-01 -2.21463218e-01
3.01680505e-01 -7.45319009e-01 -8.32448959e-01 -5.13241231e-01
2.56371908e-02 7.77689457e-01 1.08208346e+00 2.63629198... | [9.149585723876953, -0.557399570941925] |
face111b-108a-4b18-8429-c634881bb6fa | multi-layered-graph-based-multi-document | 1405.7975 | null | http://arxiv.org/abs/1405.7975v1 | http://arxiv.org/pdf/1405.7975v1.pdf | Multi-layered graph-based multi-document summarization model | Multi-document summarization is a process of automatic generation of a
compressed version of the given collection of documents. Recently, the
graph-based models and ranking algorithms have been actively investigated by
the extractive document summarization community. While most work to date
focuses on homogeneous conne... | ['Ercan Canhasi'] | 2014-05-17 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.38056970e-01 5.56025147e-01 -4.04701471e-01 -2.47628525e-01
-6.66687906e-01 -6.91556275e-01 7.17984080e-01 1.20261896e+00
2.58078035e-02 9.78581548e-01 1.19530964e+00 1.73552275e-01
-5.45758486e-01 -6.86815023e-01 -1.89052522e-01 -3.16200823e-01
-2.41073146e-01 4.82524306e-01 3.23189586e-01 -5.27635038... | [12.521642684936523, 9.564602851867676] |
3058a2da-adda-4dc2-ac7a-cfef7e7fba20 | rapgen-an-approach-for-fixing-code | 2306.17077 | null | https://arxiv.org/abs/2306.17077v1 | https://arxiv.org/pdf/2306.17077v1.pdf | RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot | Performance bugs are non-functional bugs that can even manifest in well-tested commercial products. Fixing these performance bugs is an important yet challenging problem. In this work, we address this challenge and present a new approach called Retrieval-Augmented Prompt Generation (RAPGen). Given a code snippet with a... | ['Neel Sundaresan', 'Roshanak Zilouchian Moghaddam', 'Spandan Garg'] | 2023-06-29 | null | null | null | null | ['retrieval'] | ['methodology'] | [-4.82602492e-02 -2.80122906e-02 -2.42337376e-01 -3.35309468e-02
-1.35968900e+00 -7.71595597e-01 -1.01424009e-01 5.30881405e-01
1.79770902e-01 4.42702562e-01 8.69514570e-02 -7.04206467e-01
3.69124636e-02 -3.92879725e-01 -9.82958853e-01 1.69842526e-01
7.95977339e-02 -5.78210466e-02 6.01832449e-01 -4.05065268... | [7.599742889404297, 7.69386625289917] |
f6fcfad2-9ade-4dcb-85f5-96b3cb3a177b | a-report-on-the-complex-word-identification | 1804.09132 | null | http://arxiv.org/abs/1804.09132v1 | http://arxiv.org/pdf/1804.09132v1.pdf | A Report on the Complex Word Identification Shared Task 2018 | We report the findings of the second Complex Word Identification (CWI) shared
task organized as part of the BEA workshop co-located with NAACL-HLT'2018. The
second CWI shared task featured multilingual and multi-genre datasets divided
into four tracks: English monolingual, German monolingual, Spanish monolingual,
and a... | ['Anaïs Tack', 'Sanja Štajner', 'Lucia Specia', 'Gustavo H. Paetzold', 'Seid Muhie Yimam', 'Marcos Zampieri', 'Chris Biemann', 'Shervin Malmasi'] | 2018-04-24 | a-report-on-the-complex-word-identification-1 | https://aclanthology.org/W18-0507 | https://aclanthology.org/W18-0507.pdf | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.66821781e-02 -2.30413899e-01 -4.99563187e-01 -3.37359875e-01
-1.30053949e+00 -1.10325277e+00 9.76398170e-01 2.58525163e-01
-9.96056139e-01 8.56903195e-01 4.09882367e-01 -5.11027753e-01
5.75334430e-02 1.06783032e-01 -3.81596029e-01 -3.94826829e-02
1.35134906e-01 7.84195662e-01 -2.60717809e-01 -3.98843437... | [10.562594413757324, 10.438438415527344] |
2530fffc-80ce-4e4b-af64-5bb60a2cf1cd | neural-network-interpretation-via-fine | 1805.08969 | null | https://arxiv.org/abs/1805.08969v3 | https://arxiv.org/pdf/1805.08969v3.pdf | Semantic Network Interpretation | Network interpretation as an effort to reveal the features learned by a network remains largely visualization-based. In this paper, our goal is to tackle semantic network interpretation at both filter and decision level. For filter-level interpretation, we represent the concepts a filter encodes with a probability dist... | ['Pei Guo', 'Ryan Farrell'] | 2018-05-23 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 4.39850658e-01 4.62064952e-01 -1.10749997e-01 -3.02786589e-01
2.55166918e-01 -6.46830797e-01 8.78677309e-01 7.07767546e-01
2.47093022e-01 6.47385538e-01 8.26542914e-01 -7.33408511e-01
-8.90706539e-01 -7.95709014e-01 -2.90987194e-01 -4.44913983e-01
-4.10089552e-01 8.87046158e-02 -4.49730344e-02 -2.54019964... | [8.781341552734375, 5.547600269317627] |
3260490f-6c93-43a7-9e39-78bfdba3fa6b | identifying-shared-decodable-concepts-in-the | 2306.03375 | null | https://arxiv.org/abs/2306.03375v1 | https://arxiv.org/pdf/2306.03375v1.pdf | Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models | We introduce a method that takes advantage of high-quality pretrained multimodal representations to explore fine-grained semantic networks in the human brain. Previous studies have documented evidence of functional localization in the brain, with different anatomical regions preferentially activating for different type... | ['Alona Fyshe', 'Joel Zylberberg', 'Alex Murphy', 'Cory Efird'] | 2023-06-06 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 3.84908676e-01 8.41061249e-02 2.89606035e-01 -4.35158432e-01
-3.54341477e-01 -7.48904526e-01 7.19899118e-01 6.80234581e-02
-6.47236228e-01 3.22486639e-01 3.73263329e-01 4.94532324e-02
-3.77559155e-01 -5.63825607e-01 -6.06097400e-01 -6.56109512e-01
-2.79869139e-01 3.34049016e-01 -2.75697298e-02 -9.61170495... | [10.612300872802734, 2.5026071071624756] |
b6834e1d-210c-413c-a167-d2468380eff0 | us-rule-discovering-utility-driven-sequential | 2111.15020 | null | https://arxiv.org/abs/2111.15020v1 | https://arxiv.org/pdf/2111.15020v1.pdf | US-Rule: Discovering Utility-driven Sequential Rules | Utility-driven mining is an important task in data science and has many applications in real life. High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining. HUSPM aims to discover all sequential patterns with high utility. However, the existing algorithms of HUSPM can not provide an accurate ... | ['Philip S. Yu', 'Jian Weng', 'Wensheng Gan', 'Gengsen Huang'] | 2021-11-29 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 2.08129793e-01 -2.53520519e-01 -5.05024552e-01 -2.10809305e-01
-2.19904054e-02 4.88015935e-02 1.62108541e-01 2.88447171e-01
-1.92765236e-01 1.13034320e+00 4.77005690e-02 -4.83306020e-01
-6.85978353e-01 -1.17565835e+00 -1.63910732e-01 -4.92602378e-01
-2.63407707e-01 4.59021509e-01 7.83759356e-01 -1.39528468... | [8.304604530334473, 6.288039207458496] |
8b7b04bb-c299-4568-8311-9c5685de9cc8 | vision-transformer-with-attention-map | 2306.10875 | null | https://arxiv.org/abs/2306.10875v1 | https://arxiv.org/pdf/2306.10875v1.pdf | Vision Transformer with Attention Map Hallucination and FFN Compaction | Vision Transformer(ViT) is now dominating many vision tasks. The drawback of quadratic complexity of its token-wise multi-head self-attention (MHSA), is extensively addressed via either token sparsification or dimension reduction (in spatial or channel). However, the therein redundancy of MHSA is usually overlooked and... | ['Jingdong Wang', 'Fu Li', 'Dongliang He', 'Zhichao Zhou', 'Haiyang Xu'] | 2023-06-19 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 1.68779984e-01 4.14051861e-01 4.50846761e-01 3.15871951e-03
-4.45163488e-01 5.69631197e-02 4.61638123e-01 -2.65084922e-01
-2.82177567e-01 6.64382875e-01 4.57438111e-01 -2.61049628e-01
-5.25353402e-02 -5.84491730e-01 -7.95728028e-01 -8.98236275e-01
3.93723249e-01 1.16599053e-01 1.92627251e-01 -1.12314403... | [10.905305862426758, -1.5693751573562622] |
1d701d68-34ab-4f1d-99d3-040fd5f43520 | simulated-car-racing-championship-competition | 1304.1672 | null | http://arxiv.org/abs/1304.1672v2 | http://arxiv.org/pdf/1304.1672v2.pdf | Simulated Car Racing Championship: Competition Software Manual | This manual describes the competition software for the Simulated Car Racing
Championship, an international competition held at major conferences in the
field of Evolutionary Computation and in the field of Computational
Intelligence and Games. It provides an overview of the architecture, the
instructions to install the... | ['Luigi Cardamone', 'Pier Luca Lanzi', 'Daniele Loiacono'] | 2013-04-05 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-1.32426605e-01 5.85816056e-02 1.52242426e-02 -1.20753773e-01
4.76051629e-01 -5.64502776e-01 3.05881232e-01 -5.93909442e-01
-2.59618819e-01 6.40663564e-01 -4.72520351e-01 -1.45086974e-01
-1.45821080e-01 -8.31374943e-01 -4.48982537e-01 -7.83632874e-01
-1.60804600e-01 5.25976241e-01 7.81707585e-01 -1.27797508... | [3.557682514190674, 1.572199821472168] |
a5b8707e-4433-4071-92c0-1c7184786e88 | compmix-a-benchmark-for-heterogeneous | 2306.12235 | null | https://arxiv.org/abs/2306.12235v2 | https://arxiv.org/pdf/2306.12235v2.pdf | CompMix: A Benchmark for Heterogeneous Question Answering | Fact-centric question answering (QA) often requires access to multiple, heterogeneous, information sources. By jointly considering several sources like a knowledge base (KB), a text collection, and tables from the web, QA systems can enhance their answer coverage and confidence. However, existing QA benchmarks are most... | ['Gerhard Weikum', 'Rishiraj Saha Roy', 'Philipp Christmann'] | 2023-06-21 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [-4.98011649e-01 3.44948441e-01 -2.42616013e-01 -4.57925171e-01
-1.78648031e+00 -1.05923104e+00 6.12836719e-01 6.14308834e-01
-2.95986891e-01 1.10977757e+00 6.90112054e-01 -3.41940552e-01
-1.32010370e-01 -1.08061802e+00 -6.26235783e-01 2.77509749e-01
6.57201827e-01 1.09657264e+00 8.03359628e-01 -8.16934407... | [10.794936180114746, 7.954294681549072] |
6e87c896-39d2-4f7e-af3c-46ddb6c1f60b | metalogic-logical-reasoning-explanations-with | 2210.12487 | null | https://arxiv.org/abs/2210.12487v1 | https://arxiv.org/pdf/2210.12487v1.pdf | MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure | In this paper, we propose a comprehensive benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Current explanation datasets often employ synthetic data with simple reasoning structures. Therefore, it cannot express more complex reasoning processes, such as the rebuttal to a re... | ['Dong Yu', 'ChangShui Zhang', 'Xiaodan Liang', 'Ruixin Hong', 'Hongming Zhang', 'Yinya Huang'] | 2022-10-22 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 2.12016031e-01 1.04878104e+00 -6.63380027e-02 -3.49291772e-01
7.02243149e-02 -3.19626987e-01 9.18636203e-01 3.19768220e-01
3.10847402e-01 7.74321377e-01 2.31244609e-01 -9.52124894e-01
-4.42625314e-01 -1.22407269e+00 -6.89067543e-01 2.69451644e-03
1.89323246e-01 7.57958055e-01 5.48112631e-01 -4.05656546... | [9.422871589660645, 7.405421257019043] |
d091cedc-74e9-4ea8-8924-c396627d62c8 | exploration-by-distributional-reinforcement | 1805.01907 | null | http://arxiv.org/abs/1805.01907v2 | http://arxiv.org/pdf/1805.01907v2.pdf | Exploration by Distributional Reinforcement Learning | We propose a framework based on distributional reinforcement learning and
recent attempts to combine Bayesian parameter updates with deep reinforcement
learning. We show that our proposed framework conceptually unifies multiple
previous methods in exploration. We also derive a practical algorithm that
achieves efficien... | ['Yunhao Tang', 'Shipra Agrawal'] | 2018-05-04 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.35521615e-01 -2.81808600e-02 -6.34778082e-01 -2.17642069e-01
-1.05493736e+00 -3.36516708e-01 6.17139459e-01 -2.00153559e-01
-8.01576495e-01 1.39003336e+00 1.59318283e-01 -4.81959522e-01
-5.32768428e-01 -7.65795767e-01 -6.74721062e-01 -7.04778314e-01
-3.62959772e-01 8.09743166e-01 1.75028685e-02 -2.54860729... | [4.103651523590088, 2.0842244625091553] |
a06061a0-bdbc-4d51-a821-89f9af09a8a2 | boundless-generative-adversarial-networks-for | 1908.07007 | null | https://arxiv.org/abs/1908.07007v1 | https://arxiv.org/pdf/1908.07007v1.pdf | Boundless: Generative Adversarial Networks for Image Extension | Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literature, it is challenging to directly apply the state-of-the-art inpainting methods to image extension as they tend to generate blurry or repet... | ['William T. Freeman', 'Aaron Sarna', 'Aaron Maschinot', 'Piotr Teterwak', 'David Belanger', 'Ce Liu', 'Dilip Krishnan'] | 2019-08-19 | boundless-generative-adversarial-networks-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Teterwak_Boundless_Generative_Adversarial_Networks_for_Image_Extension_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Teterwak_Boundless_Generative_Adversarial_Networks_for_Image_Extension_ICCV_2019_paper.pdf | iccv-2019-10 | ['uncropping'] | ['computer-vision'] | [ 9.89076257e-01 2.11149633e-01 5.44907898e-02 -2.90540397e-01
-4.35794681e-01 -5.29544413e-01 6.21680737e-01 -7.24575520e-01
-1.10713625e-02 1.07004404e+00 -1.36461863e-02 -1.99213177e-01
2.41808653e-01 -8.05711746e-01 -1.13848460e+00 -4.39084709e-01
2.72339046e-01 1.11280717e-01 -1.28911957e-01 -4.17793661... | [11.62878131866455, -0.7123055458068848] |
4e4465c2-c1dc-46a1-8a2d-6ac630345d06 | customizing-general-purpose-foundation-models | 2306.05642 | null | https://arxiv.org/abs/2306.05642v1 | https://arxiv.org/pdf/2306.05642v1.pdf | Customizing General-Purpose Foundation Models for Medical Report Generation | Medical caption prediction which can be regarded as a task of medical report generation (MRG), requires the automatic generation of coherent and accurate captions for the given medical images. However, the scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-sca... | ['Tong Zhang', 'Yuexian Zou', 'Asif Raza', 'Bang Yang'] | 2023-06-09 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 5.00299931e-01 7.92505801e-01 5.19399047e-02 -4.03311312e-01
-1.63438892e+00 -2.92934090e-01 6.04105473e-01 -2.64897346e-01
-2.69334584e-01 4.74892706e-01 3.96963239e-01 -4.47657764e-01
3.52937073e-01 -2.21485555e-01 -9.03583407e-01 -2.71128267e-01
2.33645186e-01 7.59090483e-01 -3.20656478e-01 -1.47000030... | [11.195854187011719, 1.014467716217041] |
1cc9f574-9e52-4fda-9b38-968f95a92595 | ssim-a-deep-learning-approach-for-recovering | null | null | https://ieeexplore.ieee.org/document/8681112 | https://www.ivivan.com/papers/IOTJ2019.pdf | SSIM -A Deep Learning Approach for Recovering Missing Time Series Sensor Data | Missing data are unavoidable in wireless sensor networks, due to issues such as network communication outage, sensor maintenance or failure, etc. Although a plethora of methods have been proposed for imputing sensor data, limitations still exist. Firstly, most methods give poor estimates when a consecutive number of da... | ['Xiang ; Peter', 'Thorburn ; Wei', 'Zhang ; Peter', 'Yi-Fan', 'Fitch'] | 2019-04-03 | null | null | null | ieee-internet-of-things-journal-2019-4 | ['small-data'] | ['computer-vision'] | [ 7.16644645e-01 -3.83917809e-01 -2.54614472e-01 -4.42623198e-01
-5.36722958e-01 -1.17073573e-01 9.30058435e-02 9.88698080e-02
-5.21006346e-01 1.24030530e+00 1.04294948e-01 -2.74290383e-01
-4.09919024e-01 -1.08295596e+00 -9.63298202e-01 -1.00952077e+00
-2.30842233e-01 1.06937230e-01 -3.04707557e-01 -8.48681629... | [6.965930461883545, 3.0762813091278076] |
2efd2e73-5619-4e49-a301-8f84514f8f03 | gpt-too-a-language-model-first-approach-for | 2005.09123 | null | https://arxiv.org/abs/2005.09123v2 | https://arxiv.org/pdf/2005.09123v2.pdf | GPT-too: A language-model-first approach for AMR-to-text generation | Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we propose an alternative approach that combines a strong pre-trained lan... | ['Md. Arafat Sultan', 'Young-suk Lee', 'Salim Roukos', 'Ramon Fernandez Astudillo', 'Manuel Mager', 'Tahira Naseem', 'Radu Florian'] | 2020-05-18 | gpt-too-a-language-model-first-approach-for-1 | https://aclanthology.org/2020.acl-main.167 | https://aclanthology.org/2020.acl-main.167.pdf | acl-2020-6 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 6.65357828e-01 6.73784733e-01 -2.31850237e-01 -2.86174238e-01
-1.08982778e+00 -6.53191447e-01 1.18210244e+00 2.19851024e-02
-2.23505259e-01 9.78724062e-01 7.52855062e-01 -6.66402817e-01
2.56113172e-01 -8.20080042e-01 -7.09685743e-01 5.31652421e-02
1.48255646e-01 6.12549365e-01 1.76210508e-01 -5.02953231... | [10.462472915649414, 8.528085708618164] |
3cf33623-90fb-453a-8176-2a0bd89045a1 | you-are-what-you-talk-about-inducing | 2302.00493 | null | https://arxiv.org/abs/2302.00493v1 | https://arxiv.org/pdf/2302.00493v1.pdf | You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis | Expressing attitude or stance toward entities and concepts is an integral part of human behavior and personality. Recently, evaluative language data has become more accessible with social media's rapid growth, enabling large-scale opinion analysis. However, surprisingly little research examines the relationship between... | ['Jan Šnajder', 'Iva Vukojević', 'Josip Jukić'] | 2023-02-01 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-5.53682804e-01 4.98340607e-01 -4.60134357e-01 -5.46273887e-01
-3.19771111e-01 -5.13400316e-01 6.80186927e-01 8.71154249e-01
-5.18347979e-01 4.67645377e-01 8.06550682e-01 -4.62189503e-02
-5.02806455e-02 -8.18982959e-01 1.71833068e-01 -7.32508823e-02
1.20979033e-01 6.25248790e-01 -3.30281764e-01 -6.97213039... | [9.316204071044922, 10.191021919250488] |
0ba836ec-41fb-456f-ac1d-de0514b14abe | watch-or-listen-robust-audio-visual-speech | 2303.08536 | null | https://arxiv.org/abs/2303.08536v2 | https://arxiv.org/pdf/2303.08536v2.pdf | Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability Scoring | This paper deals with Audio-Visual Speech Recognition (AVSR) under multimodal input corruption situations where audio inputs and visual inputs are both corrupted, which is not well addressed in previous research directions. Previous studies have focused on how to complement the corrupted audio inputs with the clean vis... | ['Yong Man Ro', 'Jeongsoo Choi', 'Minsu Kim', 'Joanna Hong'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hong_Watch_or_Listen_Robust_Audio-Visual_Speech_Recognition_With_Visual_Corruption_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hong_Watch_or_Listen_Robust_Audio-Visual_Speech_Recognition_With_Visual_Corruption_CVPR_2023_paper.pdf | cvpr-2023-1 | ['audio-visual-speech-recognition'] | ['speech'] | [ 1.65537104e-01 -1.56543046e-01 2.39132270e-02 -8.45573917e-02
-1.14860892e+00 -2.72485137e-01 6.39311433e-01 -1.45217953e-02
1.46650784e-02 6.16499364e-01 3.42414737e-01 -9.87607911e-02
-4.07931358e-02 -2.60108948e-01 -6.55533910e-01 -7.09061682e-01
2.36865565e-01 2.42572781e-02 1.57083198e-01 -9.99677852... | [14.350041389465332, 5.173609256744385] |
74e31854-4f1d-4bc1-862f-ebebed627b75 | addressing-the-issue-of-stochastic | 2211.08669 | null | https://arxiv.org/abs/2211.08669v1 | https://arxiv.org/pdf/2211.08669v1.pdf | Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning | Multi-objective reinforcement learning (MORL) is a relatively new field which builds on conventional Reinforcement Learning (RL) to solve multi-objective problems. One of common algorithm is to extend scalar value Q-learning by using vector Q values in combination with a utility function, which captures the user's pref... | ['Kewen Ding'] | 2022-11-16 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-8.33221078e-02 -3.00021730e-02 -3.60878170e-01 1.44078389e-01
-1.06624949e+00 -5.59728980e-01 4.93173629e-01 2.34410897e-01
-8.51016343e-01 1.27720666e+00 3.68608572e-02 -4.83439237e-01
-6.30265296e-01 -6.60431087e-01 -4.13634092e-01 -8.34724009e-01
-3.96624058e-01 4.96790767e-01 1.12389490e-01 -3.58487338... | [4.181626319885254, 2.477006673812866] |
6c830322-728c-4672-8617-a3ca1d5ff6e0 | quantum-split-neural-network-learning-using | 2211.06524 | null | https://arxiv.org/abs/2211.06524v2 | https://arxiv.org/pdf/2211.06524v2.pdf | Quantum Split Neural Network Learning using Cross-Channel Pooling | In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing. Among these emerging areas, quantum federated learning (QFL) has gained particular attention due to the integration of quantum ne... | ['Joongheon Kim', 'Hankyul Baek', 'Won Joon Yun'] | 2022-11-12 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 2.69968718e-01 -1.22307226e-01 -2.07400367e-01 -2.88307160e-01
-1.05501497e+00 -3.42851758e-01 4.65336025e-01 3.47862273e-01
-6.42401934e-01 1.03554189e+00 -2.61609524e-01 -3.50357890e-01
-3.67099673e-01 -8.99575531e-01 -5.27980268e-01 -1.03624821e+00
-1.73137233e-01 -2.90679693e-01 -3.19292456e-01 -1.58855498... | [5.565455436706543, 5.009954452514648] |
07b5959a-2206-42c2-91e3-9f2123cfba83 | supporting-future-electrical-utilities-using | 2303.00428 | null | https://arxiv.org/abs/2303.00428v1 | https://arxiv.org/pdf/2303.00428v1.pdf | Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms | Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation. This necessitates the development of near real-time power system algorithms, demanding lower computational complexity regarding the power s... | ['Dejan Vukobratovic', 'Dragisa Miskovic', 'Mile Mitrovic', 'Gorana Gojic', 'Ognjen Kundacina'] | 2023-03-01 | null | null | null | null | ['energy-management'] | ['time-series'] | [-4.32482034e-01 -3.25058490e-01 -7.42318183e-02 1.80148594e-02
-1.05364420e-01 -3.56692016e-01 2.84988403e-01 3.35851192e-01
-1.99194580e-01 9.09851134e-01 -3.48285913e-01 -4.83691990e-01
-7.01015353e-01 -8.33227754e-01 1.07511185e-01 -1.02088344e+00
-3.66823256e-01 5.34340084e-01 -5.75771391e-01 -2.51736671... | [5.969096660614014, 2.630262613296509] |
91bed09f-5aeb-429c-98f1-2b1be7ba651e | transcribing-educational-videos-using-whisper | 2307.03200 | null | https://arxiv.org/abs/2307.03200v1 | https://arxiv.org/pdf/2307.03200v1.pdf | Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos | Videos are increasingly being used for e-learning, and transcripts are vital to enhance the learning experience. The costs and delays of generating transcripts can be alleviated by automatic speech recognition (ASR) systems. In this article, we quantify the transcripts generated by whisper for 25 educational videos and... | ['Ashwin Rao'] | 2023-07-04 | null | null | null | null | ['speech-recognition', 'automatic-speech-recognition'] | ['speech', 'speech'] | [ 2.87892759e-01 1.52478755e-01 -1.14430889e-01 -2.76233792e-01
-1.25150084e+00 -8.08401108e-01 4.53699559e-01 -1.77215841e-02
-1.38191655e-01 8.37669253e-01 6.92055643e-01 -4.92742330e-01
3.43012661e-01 -3.72723520e-01 -7.54656136e-01 -4.04853255e-01
1.09240495e-01 -4.77447689e-01 -1.38088375e-01 -2.93814540... | [14.580162048339844, 6.77963924407959] |
06ccf98f-0488-4807-a414-3fbd3434a44e | investigating-emergent-goal-like-behaviour-in | 2305.07970 | null | https://arxiv.org/abs/2305.07970v1 | https://arxiv.org/pdf/2305.07970v1.pdf | Investigating Emergent Goal-Like Behaviour in Large Language Models Using Experimental Economics | In this study, we investigate the capacity of large language models (LLMs), specifically GPT-3.5, to operationalise natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior in social dilemmas. Our focus is on the iterated Prisoner's Dilemma, a classic example of a non-zero-sum... | ['Yvan I. Russell', 'Steve Phelps'] | 2023-05-13 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-1.43336564e-01 4.16159302e-01 1.63433269e-01 -2.65183479e-01
2.02707559e-01 -3.90326679e-01 7.02323079e-01 1.16415352e-01
-8.81761849e-01 6.89636171e-01 2.52546161e-01 -5.67455232e-01
-4.95124936e-01 -5.36606610e-01 -1.25413397e-02 -6.25402272e-01
-5.83050430e-01 5.12817919e-01 -2.39966035e-01 -7.96110928... | [3.822049140930176, 2.083042860031128] |
7361ce0d-a14b-4fd1-83f4-86986ba6b037 | one-model-to-edit-them-all-free-form-text | 2210.07883 | null | https://arxiv.org/abs/2210.07883v2 | https://arxiv.org/pdf/2210.07883v2.pdf | One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations | Free-form text prompts allow users to describe their intentions during image manipulation conveniently. Based on the visual latent space of StyleGAN[21] and text embedding space of CLIP[34], studies focus on how to map these two latent spaces for text-driven attribute manipulations. Currently, the latent mapping betwee... | ['Ziyang Yuan', 'Jue Wang', 'Qifeng Chen', 'Chun Yuan', 'Xintong Han', 'Yibing Song', 'Hongyu Liu', 'Yiming Zhu'] | 2022-10-14 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 4.49286014e-01 -1.58427916e-02 -4.25952584e-01 -2.63186902e-01
-4.89779532e-01 -8.07436883e-01 9.86208141e-01 -5.35937488e-01
-1.59826756e-01 2.18870848e-01 4.33866054e-01 -1.63997531e-01
2.70314097e-01 -7.25326419e-01 -7.95241952e-01 -6.96275711e-01
5.14329970e-01 -1.10469889e-02 -2.99935162e-01 -1.12671167... | [11.717001914978027, -0.2682258188724518] |
ddcfbd04-38bb-4d44-8742-45439427ebbe | training-compact-models-for-low-resource | 1910.06294 | null | https://arxiv.org/abs/1910.06294v2 | https://arxiv.org/pdf/1910.06294v2.pdf | Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models | Training models on low-resource named entity recognition tasks has been shown to be a challenge, especially in industrial applications where deploying updated models is a continuous effort and crucial for business operations. In such cases there is often an abundance of unlabeled data, while labeled data is scarce or u... | ['Shira Guskin', 'Peter Izsak', 'Moshe Wasserblat'] | 2019-10-14 | null | null | null | null | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 3.85774463e-01 2.25728020e-01 -5.12272477e-01 -6.99079990e-01
-1.03309798e+00 -4.80255693e-01 3.62245262e-01 5.42603694e-02
-6.22439921e-01 8.82977903e-01 5.58969714e-02 -7.42583811e-01
3.82375002e-01 -5.98646343e-01 -8.24795127e-01 -2.78407216e-01
2.85975896e-02 8.60540450e-01 -3.25244777e-02 2.82083213... | [9.996768951416016, 8.85450267791748] |
da835cdb-4d51-48f2-81e1-7065554b2ccd | variational-learning-across-domains-with-1 | null | null | https://openreview.net/forum?id=ryfwFJWioX | https://openreview.net/pdf?id=ryfwFJWioX | Variational learning across domains with triplet information | The work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint distribution of objects from different domains. We extend the VBTAs objective function by the relativ... | ['Anonymous'] | 2018-10-22 | null | null | null | null | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [ 1.62170902e-02 -1.27881914e-01 -5.30292392e-02 -6.56424165e-01
-8.14458132e-01 -4.20535207e-01 1.22574115e+00 -8.91388118e-01
6.62974790e-02 9.78994608e-01 2.74445742e-01 1.28060356e-01
2.79514045e-02 -8.83150339e-01 -9.56065953e-01 -9.30988014e-01
8.45202446e-01 9.99903560e-01 -2.79213101e-01 -8.12655017... | [11.551261901855469, -0.19769784808158875] |
c4fec90b-c980-443d-99a3-cd8e5f222bcd | phonetic-spelling-algorithm-implementations | null | null | https://www.jstatsoft.org/article/view/v095i08 | https://www.jstatsoft.org/index.php/jss/article/view/v095i08/v95i08.pdf | Phonetic Spelling Algorithm Implementations for R | The phonics package provides several functions for indexing words by their English language pronunciation. Over nearly one hundred years, many different algorithms have been developed to support word and name indexing. From Soundex, developed in the early 20th century and predating the digital computer, through to mode... | ['James P. Howard II'] | 2020-10-07 | null | null | null | journal-of-statistical-software-2020-10 | ['record-linking'] | ['natural-language-processing'] | [-2.88998604e-01 -6.99209869e-01 -5.47235370e-01 -3.99255008e-01
-9.36103761e-01 -1.04117322e+00 7.30310857e-01 2.39639357e-02
-6.79758728e-01 3.91293436e-01 4.53860909e-01 -5.99599957e-01
-3.07929844e-01 -7.43819296e-01 3.06209996e-02 -1.57657981e-01
4.02678162e-01 6.44061983e-01 2.02736005e-01 -3.10463250... | [10.553783416748047, 10.412281036376953] |
856afeca-91e2-43cc-814f-d7320c46b81b | a-probabilistic-generative-model-for-tracking | 2302.08673 | null | https://arxiv.org/abs/2302.08673v1 | https://arxiv.org/pdf/2302.08673v1.pdf | A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability | Knowledge tracing aims to track students' knowledge status over time to predict students' future performance accurately. Markov chain-based knowledge tracking (MCKT) models can track knowledge concept mastery probability over time. However, as the number of tracked knowledge concepts increases, the time complexity of M... | ['Ge Yu', 'Minghe Yu', 'Fan Li', 'Tiancheng Zhang', 'Hengyu Liu'] | 2023-02-17 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [-5.24857976e-02 -1.58378884e-01 -4.92662400e-01 -4.12084430e-01
-4.16521072e-01 -5.55659771e-01 1.95205640e-02 2.84525692e-01
-2.07533687e-01 6.32507801e-01 1.49216965e-01 -6.02340996e-01
-8.14640284e-01 -1.17596269e+00 -6.71933174e-01 -2.38296464e-01
1.50451690e-01 5.66747963e-01 3.86727929e-01 -6.49531314... | [10.147987365722656, 7.098105430603027] |
9db35c05-9218-41e1-a786-ecf4e3d86b62 | the-treasure-beneath-multiple-annotations-an | 2303.11828 | null | https://arxiv.org/abs/2303.11828v1 | https://arxiv.org/pdf/2303.11828v1.pdf | The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector | Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voting process, ignoring the inherent ambiguity of edges and labeling bias of annotators. In this paper, we propose a novel uncertainty-aware ed... | ['Haibin Ling', 'Li Huang', 'Qingji Guan', 'Mengyang Pu', 'Yaping Huang', 'Caixia Zhou'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_The_Treasure_Beneath_Multiple_Annotations_An_Uncertainty-Aware_Edge_Detector_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_The_Treasure_Beneath_Multiple_Annotations_An_Uncertainty-Aware_Edge_Detector_CVPR_2023_paper.pdf | cvpr-2023-1 | ['edge-detection'] | ['computer-vision'] | [-1.82365745e-01 3.15677702e-01 -2.85544157e-01 -6.26869440e-01
-9.34794307e-01 -3.54825348e-01 5.36419339e-02 -3.17218415e-02
-4.60672170e-01 7.53455937e-01 2.39161476e-01 5.72350472e-02
2.40460545e-01 -5.70378602e-01 -6.55926287e-01 -7.42082357e-01
2.45222986e-01 1.56586379e-01 4.39346075e-01 3.45802337... | [9.326353073120117, 1.1667159795761108] |
7136f607-7626-4c51-885f-c6391839fffe | dilated-recurrent-neural-networks | 1710.02224 | null | http://arxiv.org/abs/1710.02224v3 | http://arxiv.org/pdf/1710.02224v3.pdf | Dilated Recurrent Neural Networks | Learning with recurrent neural networks (RNNs) on long sequences is a
notoriously difficult task. There are three major challenges: 1) complex
dependencies, 2) vanishing and exploding gradients, and 3) efficient
parallelization. In this paper, we introduce a simple yet effective RNN
connection structure, the DilatedRNN... | ['Mark Hasegawa-Johnson', 'Wei Tan', 'Shiyu Chang', 'Michael Witbrock', 'Yang Zhang', 'Xiaoxiao Guo', 'Wei Han', 'Xiaodong Cui', 'Thomas S. Huang', 'Mo Yu'] | 2017-10-05 | dilated-recurrent-neural-networks-1 | http://papers.nips.cc/paper/6613-dilated-recurrent-neural-networks | http://papers.nips.cc/paper/6613-dilated-recurrent-neural-networks.pdf | neurips-2017-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 7.77909830e-02 -1.80521056e-01 -1.33041620e-01 -1.96349416e-02
-5.16006589e-01 -4.64883536e-01 4.01886731e-01 -4.45351750e-01
-6.25453651e-01 8.08371842e-01 3.53037775e-01 -4.54194844e-01
6.18480239e-03 -4.44157928e-01 -4.02016282e-01 -8.38911057e-01
5.05905151e-02 1.75400823e-01 2.05346853e-01 -4.01417792... | [10.83569049835205, 6.454974174499512] |
0185683b-1ce1-4557-8b84-276a8485b5de | a-large-cross-modal-video-retrieval-dataset | 2305.03347 | null | https://arxiv.org/abs/2305.03347v1 | https://arxiv.org/pdf/2305.03347v1.pdf | A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension | Most existing cross-modal language-to-video retrieval (VR) research focuses on single-modal input from video, i.e., visual representation, while the text is omnipresent in human environments and frequently critical to understand video. To study how to retrieve video with both modal inputs, i.e., visual and text semanti... | ['Xiang Bai', 'Mike Zheng Shou', 'Hong Zhou', 'Jiahong Li', 'Zhuang Li', 'Yuzhong Zhao', 'Weijia Wu'] | 2023-05-05 | null | null | null | null | ['video-retrieval', 'reading-comprehension'] | ['computer-vision', 'natural-language-processing'] | [ 2.44038757e-02 -7.00558364e-01 -4.33060467e-01 -8.38616639e-02
-1.10989714e+00 -8.81559134e-01 6.83598995e-01 -2.54251026e-02
-1.63662195e-01 2.23394856e-01 5.84591389e-01 -4.25162941e-01
-7.72658437e-02 -2.94906348e-01 -7.18121111e-01 -5.42520106e-01
4.15995866e-01 1.06137529e-01 2.02570438e-01 -2.68051237... | [10.32767105102539, 0.9135299921035767] |
5f155859-6241-4612-96aa-4c816b79fd59 | pre-training-and-fine-tuning-neural-topic | null | null | https://openreview.net/forum?id=8ds7iPDUrls | https://openreview.net/pdf?id=8ds7iPDUrls | Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge | Recent years have witnessed growing interests in incorporating external knowledge such as pre-trained word embeddings (PWEs) or pre-trained language models (PLMs) into neural topic modeling. However, we found that employing PWEs and PLMs for topic modeling only achieved limited performance improvements but with huge co... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['topic-models'] | ['natural-language-processing'] | [-1.30689442e-01 3.81725281e-01 -3.36550266e-01 -3.89748454e-01
-5.39033651e-01 2.42688376e-02 9.42046463e-01 7.47555345e-02
-5.71271956e-01 6.10140800e-01 3.63297433e-01 -2.30648220e-01
3.99182774e-02 -1.22829986e+00 -5.73258758e-01 -4.95042741e-01
6.33499119e-04 6.12213016e-01 5.43287516e-01 1.48983166... | [10.431922912597656, 6.958074569702148] |
1ddc8c4f-3eae-4509-8c27-57c1dfa68c6d | does-image-anonymization-impact-computer | 2306.05135 | null | https://arxiv.org/abs/2306.05135v1 | https://arxiv.org/pdf/2306.05135v1.pdf | Does Image Anonymization Impact Computer Vision Training? | Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models o... | ['Frank Lindseth', 'Håkon Hukkelås'] | 2023-06-08 | null | null | null | null | ['pose-estimation', 'face-anonymization'] | ['computer-vision', 'computer-vision'] | [ 3.45560402e-01 1.60224095e-01 -6.28427416e-02 -5.31976223e-01
-6.41832292e-01 -9.41661119e-01 5.00232041e-01 -3.32148150e-02
-6.85012162e-01 3.80703002e-01 2.41709109e-02 -1.92724526e-01
2.93826699e-01 -4.83545274e-01 -1.05498981e+00 -3.54687721e-01
2.84461319e-01 1.00907400e-01 -2.22323775e-01 2.84902185... | [12.762171745300293, 0.7951474189758301] |
5b1470cd-736a-4a1a-bedd-d8be3ee26f73 | applications-of-gaussian-processes-at-extreme | 2303.14291 | null | https://arxiv.org/abs/2303.14291v1 | https://arxiv.org/pdf/2303.14291v1.pdf | Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes | In many areas of the observational and experimental sciences data is scarce. Data observation in high-energy astrophysics is disrupted by celestial occlusions and limited telescope time while data derived from laboratory experiments in synthetic chemistry and materials science is time and cost-intensive to collect. On ... | ['Ryan-Rhys Griffiths'] | 2023-03-24 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.95974821e-01 1.96124800e-02 -1.48655381e-02 2.14183778e-01
5.22653051e-02 -4.69241560e-01 8.85974109e-01 3.41648422e-02
-2.36116216e-01 9.84111786e-01 -3.26782048e-01 -3.58316660e-01
-3.94539237e-01 -7.27827787e-01 -6.25236630e-01 -1.26384521e+00
3.05773437e-01 9.34141040e-01 1.63501650e-01 1.71351597... | [6.275209426879883, 3.7829325199127197] |
c586b4f5-497f-435c-8797-33693a9ddcef | point-cloud-completion-by-skip-attention | 2005.03871 | null | https://arxiv.org/abs/2005.03871v2 | https://arxiv.org/pdf/2005.03871v2.pdf | Point Cloud Completion by Skip-attention Network with Hierarchical Folding | Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. Previous methods usually predict the complete point cloud based on the global shape representation extracted from the incomplete input. However, the global representation often suffers from the informati... | ['Yu-Shen Liu', 'Zhizhong Han', 'Tianyang Li', 'Xin Wen'] | 2020-05-08 | point-cloud-completion-by-skip-attention-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.pdf | cvpr-2020-6 | ['point-cloud-completion'] | ['computer-vision'] | [-7.70788640e-02 2.52421737e-01 8.63375142e-02 -3.85947317e-01
-9.82581258e-01 -4.61244464e-01 3.67186069e-01 -1.43062351e-02
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1.61993220e-01 6.89500511e-01 1.79582983e-01 -2.94806659... | [8.297978401184082, -3.633700132369995] |
c292e42e-5e1c-4165-8e4f-072ddde98b63 | maximizing-classification-accuracy-in-native | null | null | https://aclanthology.org/W13-1714 | https://aclanthology.org/W13-1714.pdf | Maximizing Classification Accuracy in Native Language Identification | null | ['Yves Bestgen', 'Steve Pepper', 'Scott Jarvis'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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084b2b31-99e6-45e7-8046-6f99dfbff110 | automated-detection-and-diagnosis-of-diabetic | 2107.00115 | null | https://arxiv.org/abs/2107.00115v1 | https://arxiv.org/pdf/2107.00115v1.pdf | Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey | Diabetic Retinopathy (DR) is a leading cause of vision loss in the world,. In the past few Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, Artificial Intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment a... | ['J. Jothi Balaji', 'Arya Sarkar', 'Hoda Kherdfallah', 'Vasudevan Lakshminarayanan'] | 2021-06-30 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 1.50105506e-01 -1.73629761e-01 -5.09114802e-01 -1.11789323e-01
-4.80028212e-01 -2.21762136e-01 1.63179547e-01 1.44207180e-01
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-3.03640962e-01 -5.88086128e-01 -6.18010908e-02 -6.87090993e-01
1.15976430e-01 2.66573042e-01 2.19274294e-02 3.88124883... | [15.819196701049805, -3.974520206451416] |
89c96dba-eb5e-47fb-80d6-78b7e0a19799 | assemblenet-searching-for-multi-stream-neural | 1905.13209 | null | https://arxiv.org/abs/1905.13209v4 | https://arxiv.org/pdf/1905.13209v4.pdf | AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures | Learning to represent videos is a very challenging task both algorithmically and computationally. Standard video CNN architectures have been designed by directly extending architectures devised for image understanding to include the time dimension, using modules such as 3D convolutions, or by using two-stream design to... | ['Mingxing Tan', 'Michael S. Ryoo', 'Anelia Angelova', 'AJ Piergiovanni'] | 2019-05-30 | assemblenet-searching-for-multi-stream-neural-1 | https://openreview.net/forum?id=SJgMK64Ywr | https://openreview.net/pdf?id=SJgMK64Ywr | iclr-2020-1 | ['multimodal-activity-recognition'] | ['computer-vision'] | [-1.13475785e-01 -1.06100067e-01 -5.77137507e-02 -3.60426009e-01
-4.96983938e-02 -8.54800284e-01 8.54597807e-01 -2.75555134e-01
-3.81330580e-01 4.21767324e-01 4.05804366e-01 -2.78416812e-01
-5.93198910e-02 -7.94808865e-01 -9.64028656e-01 -5.43611586e-01
-5.87009847e-01 2.38163248e-01 2.93985218e-01 -2.60057300... | [8.722445487976074, 0.5567145347595215] |
1ff85bd7-26ff-4945-9611-be7ac305c15a | veda-uneven-light-image-enhancement-via-a | 2305.16072 | null | https://arxiv.org/abs/2305.16072v1 | https://arxiv.org/pdf/2305.16072v1.pdf | VEDA: Uneven light image enhancement via a vision-based exploratory data analysis model | Uneven light image enhancement is a highly demanded task in many industrial image processing applications. Many existing enhancement methods using physical lighting models or deep-learning techniques often lead to unnatural results. This is mainly because: 1) the assumptions and priors made by the physical lighting mod... | ['Qingsong Zhu', 'Zhenming Peng', 'Shuhang Wang', 'Tian Pu'] | 2023-05-25 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 5.65455496e-01 -5.26201189e-01 2.44092569e-01 -1.10236220e-01
-2.21001461e-01 -8.78977254e-02 4.34220165e-01 -1.75611734e-01
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-1.67216614e-01 -7.10613489e-01 -5.01059592e-01 -1.18479311e+00
3.73997033e-01 -4.84401464e-01 3.14720273e-01 -2.29638293... | [10.878376007080078, -2.4570162296295166] |
8c7d8724-1553-43fe-8634-18317690a370 | semi-supervised-hyperspectral-image-1 | null | null | https://ieeexplore.ieee.org/abstract/document/9849704 | https://ieeexplore.ieee.org/abstract/document/9849704 | Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework | Deep neural networks (DNNs) show impressive performance for hyperspectral image (HSI) classification when abundant labeled samples are available. The problem is that HSI sample annotation is extremely costly and the budget for this task is usually limited. To reduce the reliance on labeled samples, deep semi-supervised... | ['Azam Asilian Bidgoli', 'Pedram Ghamisi', 'Shahryar Rahnamayan', 'Majid Seydgar'] | 2022-08-05 | null | null | null | journal-2022-8 | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 4.93526518e-01 -2.02880085e-01 -3.56484056e-01 -8.62385035e-01
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3.87428373e-01 4.95695591e-01 -1.69805452e-01 5.14197409... | [9.864294052124023, -1.480352520942688] |
780daa83-8d2a-44ae-b0ad-52d7b0eb00b9 | semi-supervised-video-salient-object | 1908.04051 | null | https://arxiv.org/abs/1908.04051v2 | https://arxiv.org/pdf/1908.04051v2.pdf | Semi-Supervised Video Salient Object Detection Using Pseudo-Labels | Deep learning-based video salient object detection has recently achieved great success with its performance significantly outperforming any other unsupervised methods. However, existing data-driven approaches heavily rely on a large quantity of pixel-wise annotated video frames to deliver such promising results. In thi... | ['Pengxiang Yan', 'Liang Lin', 'Chuan Wang', 'Tianshui Chen', 'Guanbin Li', 'Zhen Li', 'Yuan Xie'] | 2019-08-12 | semi-supervised-video-salient-object-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Yan_Semi-Supervised_Video_Salient_Object_Detection_Using_Pseudo-Labels_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yan_Semi-Supervised_Video_Salient_Object_Detection_Using_Pseudo-Labels_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-salient-object-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.54015285e-01 -5.15209958e-02 -4.10163701e-01 -3.54146749e-01
-6.54423654e-01 -4.56895456e-02 4.43157077e-01 -5.05052134e-02
-3.51919174e-01 7.97835171e-01 4.95566905e-01 2.33635336e-01
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-4.69938144e-02 -2.53344059e-01 1.02076209e+00 -1.05842322... | [9.68359661102295, -0.3243969678878784] |
3d3db3a4-c299-472f-851f-ec94933e04aa | an-open-access-database-for-the-evaluation-of | null | null | https://pubmed.ncbi.nlm.nih.gov/30708353/ | https://www.researchgate.net/publication/330815510_An_open_access_database_for_the_evaluation_of_respiratory_sound_classification_algorithms | An open access database for the evaluation of respiratory sound classification algorithms | ###Objective: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of suc... | ['Paulo de Carvalho', 'Ioanna Chouvarda', 'Rui Pedro Paiva', 'Nicos Maglaveras', 'Alda Marques', 'Cristina Jácome', 'Ana Oliveira', 'Pantelis Natsiavas', 'Evangelos Kaimakamis', 'Eleni Perantoni', 'Ioannis M Vogiatzis', 'Tatjana L Turukalo', 'Nikša Jakovljevic', 'Yasemin P Kahya', 'Sezer Ulukaya', 'Gorkem Serbes', 'Luí... | 2019-05-22 | null | null | null | physiological-measurement-2019-5 | ['sound-classification'] | ['audio'] | [ 2.32327193e-01 -1.76103801e-01 2.14567125e-01 -1.53167263e-01
-1.01798582e+00 -4.31184411e-01 -2.77424726e-04 3.75814140e-01
-3.49168360e-01 6.46971762e-01 1.80110440e-01 1.08501881e-01
-2.79930711e-01 -4.48392153e-01 -2.95039713e-01 -7.49302685e-01
-2.25170236e-02 2.59938896e-01 6.58434749e-01 1.25885829... | [14.503633499145508, 3.865450382232666] |
90463757-e915-4cb6-a1e6-3d9572b01146 | image-fine-grained-inpainting | 2002.02609 | null | https://arxiv.org/abs/2002.02609v2 | https://arxiv.org/pdf/2002.02609v2.pdf | Image Fine-grained Inpainting | Image inpainting techniques have shown promising improvement with the assistance of generative adversarial networks (GANs) recently. However, most of them often suffered from completed results with unreasonable structure or blurriness. To mitigate this problem, in this paper, we present a one-stage model that utilizes ... | ['Zheng Hui', 'Xiumei Wang', 'Jie Li', 'Xinbo Gao'] | 2020-02-07 | null | null | null | null | ['fine-grained-image-inpainting', 'facial-inpainting'] | ['computer-vision', 'computer-vision'] | [ 2.63675660e-01 2.31441632e-01 2.00932682e-03 -3.29019099e-01
-7.96976328e-01 -4.35761720e-01 3.38344276e-01 -4.25588310e-01
-1.36968717e-01 9.56051707e-01 1.19623117e-01 1.21962644e-01
6.41825423e-02 -1.04779065e+00 -9.75211740e-01 -8.79826784e-01
4.24668342e-01 -1.83360100e-01 1.09256834e-01 -1.35286018... | [11.420530319213867, -1.1985783576965332] |
92776f7a-64d9-431b-9a3e-8e68b146f52d | semantic-distance-a-new-metric-for-asr | 2104.02138 | null | https://arxiv.org/abs/2104.02138v1 | https://arxiv.org/pdf/2104.02138v1.pdf | Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding | Word Error Rate (WER) has been the predominant metric used to evaluate the performance of automatic speech recognition (ASR) systems. However, WER is sometimes not a good indicator for downstream Natural Language Understanding (NLU) tasks, such as intent recognition, slot filling, and semantic parsing in task-oriented ... | ['Michael L. Seltzer', 'Ozlem Kalinli', 'Christian Fuegen', 'Ching-Feng Yeh', 'Duc Le', 'Abhinav Arora', 'Suyoun Kim'] | 2021-04-05 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 3.62785906e-01 2.51695782e-01 6.86413795e-02 -8.62029314e-01
-8.07149708e-01 -3.56407613e-01 5.86773515e-01 2.61423290e-01
-8.40988755e-01 5.25294125e-01 7.02682436e-01 -7.34156191e-01
1.92822129e-01 -7.26307869e-01 -2.92965531e-01 -3.26602846e-01
5.49796641e-01 3.30786973e-01 1.91029608e-01 -2.95056075... | [13.710975646972656, 7.132658004760742] |
1d197899-fdec-420e-8178-8eb52b8763ab | anomaly-segmentation-for-high-resolution | 2301.13422 | null | https://arxiv.org/abs/2301.13422v2 | https://arxiv.org/pdf/2301.13422v2.pdf | Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors | Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications. However, it is a challenging task due to the complex distribution and the irregular shapes... | ['Yanfei Zhong', 'Shaoyu Wang', 'Hengwei Zhao', 'Xinyu Wang', 'Jingtao Li'] | 2023-01-31 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 3.54937732e-01 -2.95122743e-01 3.89034420e-01 -3.28371584e-01
-4.10322785e-01 -1.67964652e-01 4.81239557e-01 1.64781585e-01
-1.40371948e-01 1.78334460e-01 -2.09118560e-01 -6.48097470e-02
-4.03890908e-01 -1.12477732e+00 -2.04537854e-01 -1.10253382e+00
-2.75775045e-01 2.73385793e-01 3.28092575e-01 -5.13028324... | [9.794463157653809, -1.3453320264816284] |
eade902e-8145-4a4e-8903-a56350869b0f | connecting-the-dots-document-level-neural | 1909.00228 | null | https://arxiv.org/abs/1909.00228v1 | https://arxiv.org/pdf/1909.00228v1.pdf | Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs | Document-level relation extraction is a complex human process that requires logical inference to extract relationships between named entities in text. Existing approaches use graph-based neural models with words as nodes and edges as relations between them, to encode relations across sentences. These models are node-ba... | ['Makoto Miwa', 'Fenia Christopoulou', 'Sophia Ananiadou'] | 2019-08-31 | connecting-the-dots-document-level-neural-1 | https://aclanthology.org/D19-1498 | https://aclanthology.org/D19-1498.pdf | ijcnlp-2019-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 4.82413113e-01 6.93094015e-01 -5.66811144e-01 -6.44688725e-01
-1.83339313e-01 -2.00882658e-01 5.39919972e-01 1.04414046e+00
-2.23209530e-01 9.27666128e-01 1.17387503e-01 -5.76380014e-01
-5.22333205e-01 -1.52451515e+00 -6.96370304e-01 -2.49168605e-01
-5.24446130e-01 4.25776333e-01 -4.88103777e-02 -1.17830209... | [8.809809684753418, 8.659205436706543] |
fe005c5a-b738-4785-8849-1b2515e7bce0 | incremental-embedding-for-temporal-networks | 1904.03423 | null | https://arxiv.org/abs/1904.03423v2 | https://arxiv.org/pdf/1904.03423v2.pdf | FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings | Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on graphs have centered on both homogeneous and heterogeneous graphs, where the latter presenting the challenges associated with multi-typed n... | ['Tomasz Kajdanowicz', 'Maciej Falkiewicz', 'Piotr Bielak', 'Nitesh V. Chawla', 'Kamil Tagowski'] | 2019-04-06 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [ 2.76023954e-01 2.82772332e-01 -3.10153931e-01 -8.72352067e-03
-5.65484405e-01 -6.04389966e-01 6.61650240e-01 5.48159838e-01
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-6.68955028e-01 4.57717896e-01 1.73104554e-01 -3.52238774... | [7.130212306976318, 6.118068218231201] |
8a4c6711-a321-48b0-a6cf-2ffcc1510bad | event-driven-learning-of-systematic | 2010.15586 | null | https://arxiv.org/abs/2010.15586v1 | https://arxiv.org/pdf/2010.15586v1.pdf | Event-Driven Learning of Systematic Behaviours in Stock Markets | It is reported that financial news, especially financial events expressed in news, provide information to investors' long/short decisions and influence the movements of stock markets. Motivated by this, we leverage financial event streams to train a classification neural network that detects latent event-stock linkages... | ['Xianchao Wu'] | 2020-10-23 | null | https://aclanthology.org/2020.findings-emnlp.220 | https://aclanthology.org/2020.findings-emnlp.220.pdf | findings-of-the-association-for-computational | ['open-information-extraction'] | ['natural-language-processing'] | [-7.69098938e-01 -1.35220379e-01 -4.32089597e-01 -2.77610689e-01
-6.59804344e-01 -7.99945116e-01 1.13880777e+00 3.04538220e-01
-2.30710924e-01 8.80405903e-01 8.52164626e-01 -4.79274511e-01
-5.22290766e-02 -1.31393170e+00 -7.37611711e-01 4.05546390e-02
-4.76519316e-01 4.18888748e-01 4.54263330e-01 -6.44503012... | [4.406068801879883, 4.293079376220703] |
766e7649-07c2-4213-b775-7b6de6c922ad | power-bundle-adjustment-for-large-scale-3d | 2204.12834 | null | https://arxiv.org/abs/2204.12834v4 | https://arxiv.org/pdf/2204.12834v4.pdf | Power Bundle Adjustment for Large-Scale 3D Reconstruction | We introduce Power Bundle Adjustment as an expansion type algorithm for solving large-scale bundle adjustment problems. It is based on the power series expansion of the inverse Schur complement and constitutes a new family of solvers that we call inverse expansion methods. We theoretically justify the use of power seri... | ['Tin Chon Chan', 'Daniel Cremers', 'Nikolaus Demmel', 'Simon Weber'] | 2022-04-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Weber_Power_Bundle_Adjustment_for_Large-Scale_3D_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Weber_Power_Bundle_Adjustment_for_Large-Scale_3D_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['distributed-optimization'] | ['methodology'] | [-3.25832009e-01 1.28141074e-02 5.10903656e-01 1.04439020e-01
-8.24930906e-01 -6.55576229e-01 1.59433603e-01 2.36808553e-01
-5.75365067e-01 5.21948755e-01 3.94249707e-02 -2.57024705e-01
-4.66780663e-02 -5.71172178e-01 -8.56969535e-01 -7.39464700e-01
2.89628282e-02 6.82833850e-01 9.84686241e-02 -5.41004360... | [8.023833274841309, -2.4281182289123535] |
aacf351d-ff8e-4c2f-a09d-25c9307455ff | hyper-connected-transformer-network-for-co | 2210.15808 | null | https://arxiv.org/abs/2210.15808v1 | https://arxiv.org/pdf/2210.15808v1.pdf | Hyper-Connected Transformer Network for Co-Learning Multi-Modality PET-CT Features | [18F]-Fluorodeoxyglucose (FDG) positron emission tomography - computed tomography (PET-CT) has become the imaging modality of choice for diagnosing many cancers. Co-learning complementary PET-CT imaging features is a fundamental requirement for automatic tumor segmentation and for developing computer aided cancer diagn... | ['Jinman Kim', 'Michael Fulham', 'David Dagan Feng', 'Shaoli Song', 'Qiufang Liu', 'Xiaohang Fu', 'Lei Bi'] | 2022-10-28 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 5.05593359e-01 2.29513958e-01 -6.47617459e-01 -4.42304194e-01
-1.22149134e+00 -3.04876149e-01 5.30501902e-01 2.47503817e-01
-6.05965853e-01 5.65392613e-01 2.69758433e-01 -4.08833325e-01
-1.83483019e-01 -8.29667568e-01 -4.66431826e-01 -9.60363984e-01
-3.09724566e-02 6.47184610e-01 2.76489645e-01 3.60796191... | [14.651469230651855, -2.495818614959717] |
8efe0fa5-ef5f-4315-af58-0ccd2c7f3a20 | decoupled-pyramid-correlation-network-for | 2205.13199 | null | https://arxiv.org/abs/2205.13199v1 | https://arxiv.org/pdf/2205.13199v1.pdf | Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images | Purpose: Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent ad... | ['Zhiqiang He', 'Yang Zhang', 'Zhongchao shi', 'Cheng Zhong', 'Siyun Wang', 'Jiang Tian', 'Yang Liu', 'Jiawei Yang', 'Yao Zhang'] | 2022-05-26 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-1.36206537e-01 -6.90484270e-02 -1.85658768e-01 -2.93717474e-01
-9.10753429e-01 -3.41310650e-01 4.25250947e-01 5.10363400e-01
-3.66706192e-01 2.80843675e-01 3.55631769e-01 -2.05905139e-01
-1.75545827e-01 -7.24465430e-01 -3.73316109e-01 -1.09700406e+00
-3.63617688e-01 1.21496335e-01 3.53565812e-01 1.29299998... | [14.573902130126953, -2.6445536613464355] |
5f4e8583-2fab-4d64-b2b5-84dbc0bc1c7c | privileged-prior-information-distillation-for | 2211.14036 | null | https://arxiv.org/abs/2211.14036v1 | https://arxiv.org/pdf/2211.14036v1.pdf | Privileged Prior Information Distillation for Image Matting | Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we propose a novel framework named Privileged Prior Information Distill... | ['Yong Tang', 'Chuang Zhang', 'Ming Wu', 'Xin Huang', 'Han Huang', 'Cheng Lu', 'Bo Xu', 'Jiake Xie', 'Cheng Lyu'] | 2022-11-25 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 3.05586159e-01 2.47781873e-01 -1.96370572e-01 -4.82993513e-01
-5.64533353e-01 -3.65306765e-01 5.14545023e-01 -3.69549125e-01
-4.13611621e-01 6.18557870e-01 -8.77443030e-02 -4.16539133e-01
-1.91532135e-01 -9.01048720e-01 -1.33557510e+00 -1.07513106e+00
6.26714408e-01 3.95444661e-01 4.18294430e-01 -8.14495981... | [10.614057540893555, -0.9538940191268921] |
32c9d61c-a1de-4229-9f02-c0dc61c5e7ca | lore-logical-location-regression-network-for | 2303.03730 | null | https://arxiv.org/abs/2303.03730v1 | https://arxiv.org/pdf/2303.03730v1.pdf | LORE: Logical Location Regression Network for Table Structure Recognition | Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. Recent methods solve this problem by predicting the adjacency relations of detected cell boxes, or learning to generate the corresponding markup sequences from the table images. However, they either count on addit... | ['Zhi Yu', 'Cong Yao', 'Liangcheng Li', 'Qi Zheng', 'Jiajun Bu', 'Rujiao Long', 'Feiyu Gao', 'Hangdi Xing'] | 2023-03-07 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 2.71891475e-01 9.44864526e-02 -6.05445623e-01 -3.14339608e-01
-1.23917580e+00 -7.60069251e-01 4.32344377e-01 3.91039908e-01
-1.65799148e-02 8.19639027e-01 2.37174511e-01 -6.59428120e-01
1.31895587e-01 -9.17714477e-01 -1.22594273e+00 -2.39786088e-01
2.48640075e-01 6.63381279e-01 2.04822809e-01 -9.96212810... | [11.700206756591797, 3.0475382804870605] |
4bea12d9-94fd-4a38-afc9-141f5c60b20b | elaborative-simplification-as-implicit | 2305.10387 | null | https://arxiv.org/abs/2305.10387v1 | https://arxiv.org/pdf/2305.10387v1.pdf | Elaborative Simplification as Implicit Questions Under Discussion | Automated text simplification, a technique useful for making text more accessible to people such as children and emergent bilinguals, is often thought of as a monolingual translation task from complex sentences to simplified sentences using encoder-decoder models. This view fails to account for elaborative simplificati... | ['Junyi Jessy Li', 'Kyle Mahowald', 'William Sheffield', 'Yating Wu'] | 2023-05-17 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 3.29427302e-01 8.79911721e-01 1.67777818e-02 -4.02910620e-01
-5.47701895e-01 -5.65766990e-01 1.01203120e+00 4.94800717e-01
-1.93223178e-01 6.84235513e-01 1.34336555e+00 -4.47955459e-01
2.24716023e-01 -6.87039375e-01 -4.06524897e-01 -1.44288272e-01
6.23141289e-01 3.75465512e-01 -3.69106382e-01 -7.25833893... | [11.525687217712402, 9.45781135559082] |
4f5abead-ad56-43bc-b3a8-eb49f77483e8 | natural-language-to-code-translation-with | 2204.11454 | null | https://arxiv.org/abs/2204.11454v2 | https://arxiv.org/pdf/2204.11454v2.pdf | Natural Language to Code Translation with Execution | Generative models of code, pretrained on large corpora of programs, have shown great success in translating natural language to code (Chen et al., 2021; Austin et al., 2021; Li et al., 2022, inter alia). While these models do not explicitly incorporate program semantics (i.e., execution results) during training, they a... | ['Sida I. Wang', 'Luke Zettlemoyer', 'Marjan Ghazvininejad', 'Daniel Fried', 'Freda Shi'] | 2022-04-25 | null | null | null | null | ['code-translation'] | ['computer-code'] | [ 1.16012603e-01 -1.97160035e-01 -6.66205347e-01 -5.09634614e-01
-1.39967239e+00 -7.57192314e-01 4.73091245e-01 6.00497127e-02
-1.74956629e-03 3.46605152e-01 1.72065079e-01 -8.52904439e-01
3.75919789e-01 -8.80281746e-01 -1.24190331e+00 -7.96395168e-02
1.27652124e-01 2.98952222e-01 5.39937802e-02 9.02008265... | [7.787455081939697, 7.791131973266602] |
1829e20d-7c17-4dd2-acde-e23e6525a767 | long-term-person-re-identification-with | null | null | https://dl.acm.org/doi/abs/10.1145/3503161.3548327 | https://dl.acm.org/doi/pdf/10.1145/3503161.3548327 | Long-Term Person Re-identification with Dramatic Appearance Change: Algorithm and Benchmark | For person re-identification (Re-ID) task, most of previous studies assumed that the pedestrians do not change their appearances. The works on cross-appearance Re-ID, including datasets and algorithms, are still few. Therefore, this paper contributes a cross-season appearance change Re-ID dataset, namely NKUP+, includi... | ['Kai Wang', 'Yanfeng Jiang', 'Tao Li', 'Zhi Ma', 'Mengmeng Liu'] | 2022-10-01 | null | null | null | mm-22-proceedings-of-the-30th-acm | ['person-re-identification', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [-2.68645734e-01 -6.81099296e-01 1.34987205e-01 -5.80559433e-01
-3.81267041e-01 -3.63366306e-01 3.98297787e-01 -3.10236990e-01
-5.36169887e-01 5.83958030e-01 -3.76591682e-02 3.32322955e-01
3.92040700e-01 -6.82315409e-01 -8.04541528e-01 -7.10398078e-01
2.22502455e-01 2.57856429e-01 2.91472167e-01 -2.16981187... | [14.652663230895996, 0.9316099882125854] |
aa3d1a4b-ed69-4b11-854e-304d6d2e7ae9 | joint-transmission-map-estimation-and | 1708.00581 | null | http://arxiv.org/abs/1708.00581v2 | http://arxiv.org/pdf/1708.00581v2.pdf | Joint Transmission Map Estimation and Dehazing using Deep Networks | Single image haze removal is an extremely challenging problem due to its
inherent ill-posed nature. Several prior-based and learning-based methods have
been proposed in the literature to solve this problem and they have achieved
superior results. However, most of the existing methods assume constant
atmospheric light m... | ['He Zhang', 'Vishal M. Patel', 'Vishwanath Sindagi'] | 2017-08-02 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 3.72236252e-01 -4.14577395e-01 4.89424825e-01 -3.79551560e-01
-6.09544992e-01 1.06361330e-01 2.73171484e-01 -2.48515859e-01
-4.58486259e-01 6.81727767e-01 -1.07613511e-01 5.53671224e-03
-1.48991570e-01 -8.36221516e-01 -5.99012315e-01 -1.26187575e+00
1.13148391e-01 -1.18365824e-01 5.44046998e-01 -2.25716695... | [10.909829139709473, -3.1489548683166504] |
551fba41-d403-4efd-9e93-44405a21ed34 | cross-corpora-language-recognition-a | 2105.04639 | null | https://arxiv.org/abs/2105.04639v2 | https://arxiv.org/pdf/2105.04639v2.pdf | Cross-Corpora Language Recognition: A Preliminary Investigation with Indian Languages | In this paper, we conduct one of the very first studies for cross-corpora performance evaluation in the spoken language identification (LID) problem. Cross-corpora evaluation was not explored much in LID research, especially for the Indian languages. We have selected three Indian spoken language corpora: IIITH-ILSC, LD... | ['Md Sahidullah', 'Goutam Saha', 'Spandan Dey'] | 2021-05-10 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-3.96177173e-01 -4.00264621e-01 1.82056472e-01 -1.60846576e-01
-1.39730704e+00 -6.31798744e-01 4.50644255e-01 -1.43528387e-01
-6.41242087e-01 6.18654668e-01 5.52719593e-01 -2.07972154e-01
7.65321329e-02 1.93059891e-01 -3.48919779e-02 -7.98573852e-01
7.01792017e-02 3.05246916e-02 -1.33412942e-01 -9.01730433... | [14.25996208190918, 6.474227428436279] |
d49fb7c4-fd67-44b0-a6c7-b393e9db20fe | audio-diffusion-model-for-speech-synthesis-a | 2303.13336 | null | https://arxiv.org/abs/2303.13336v2 | https://arxiv.org/pdf/2303.13336v2.pdf | A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI | Generative AI has demonstrated impressive performance in various fields, among which speech synthesis is an interesting direction. With the diffusion model as the most popular generative model, numerous works have attempted two active tasks: text to speech and speech enhancement. This work conducts a survey on audio di... | ['In So Kweon', 'Sung-Ho Bae', 'Maryam Qamar', 'Mengchun Zhang', 'Sheng Zheng', 'Chaoning Zhang', 'Chenshuang Zhang'] | 2023-03-23 | null | null | null | null | ['text-to-speech-synthesis', 'speech-enhancement', 'speech-synthesis'] | ['speech', 'speech', 'speech'] | [ 4.05373096e-01 2.74869919e-01 1.12126440e-01 -2.10810438e-01
-8.38905215e-01 -3.82174492e-01 9.49690580e-01 -2.66546190e-01
-1.15672871e-01 4.68173802e-01 9.18157935e-01 -1.34285435e-01
-1.18767321e-02 -6.70815766e-01 -1.20496802e-01 -1.02334881e+00
1.43760294e-01 1.40019551e-01 -1.54169342e-02 -3.79653752... | [14.970852851867676, 6.08493709564209] |
b21237a5-6b58-4738-ae6e-537dc262b2bb | classification-of-fib-sem-tomography-images | 2207.14114 | null | https://arxiv.org/abs/2207.14114v1 | https://arxiv.org/pdf/2207.14114v1.pdf | Classification of FIB/SEM-tomography images for highly porous multiphase materials using random forest classifiers | FIB/SEM tomography represents an indispensable tool for the characterization of three-dimensional nanostructures in battery research and many other fields. However, contrast and 3D classification/reconstruction problems occur in many cases, which strongly limits the applicability of the technique especially on porous m... | ['Ingo Manke', 'John Banhart', 'Volker Schmidt', 'Joachim R. Binder', 'Nicole Bohn', 'Amalia Wagner', 'Matthias Neumann', 'André Hilger', 'Markus Osenberg'] | 2022-07-28 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [ 4.70075697e-01 -4.84799862e-01 1.21300146e-01 -1.81237943e-02
-4.61155862e-01 -2.22116992e-01 5.60363591e-01 5.02493978e-01
-7.12326348e-01 1.06816423e+00 -4.41731155e-01 -2.02664912e-01
-4.37633544e-02 -1.02426362e+00 -6.17197752e-01 -1.21947539e+00
2.93403327e-01 1.23376894e+00 8.39955986e-01 2.58280605... | [12.917928695678711, -2.7854762077331543] |
0853a49f-ce0b-4778-8029-379f9ec1d57f | mrnet-a-multi-scale-residual-network-for-eeg | 2101.02538 | null | https://arxiv.org/abs/2101.02538v1 | https://arxiv.org/pdf/2101.02538v1.pdf | MRNet: a Multi-scale Residual Network for EEG-based Sleep Staging | Sleep staging based on electroencephalogram (EEG) plays an important role in the clinical diagnosis and treatment of sleep disorders. In order to emancipate human experts from heavy labeling work, deep neural networks have been employed to formulate automated sleep staging systems recently. However, EEG signals lose co... | ['Xue Jiang'] | 2021-01-07 | null | null | null | null | ['sleep-staging', 'eeg-based-sleep-staging'] | ['medical', 'time-series'] | [ 2.55913883e-02 -3.32120717e-01 -3.26994546e-02 -6.30888402e-01
3.42742689e-02 4.40280661e-02 -7.38849789e-02 -8.42484534e-02
-5.45861006e-01 6.43764019e-01 4.68013547e-02 1.20551594e-01
-2.78317034e-01 -4.48357940e-01 1.84226222e-02 -8.38256061e-01
7.72485584e-02 -3.49027291e-02 3.75765383e-01 -5.14654852... | [13.477811813354492, 3.5081372261047363] |
20a701f4-778c-476d-ba2e-93eb6b96c775 | hgcc-enhancing-hyperbolic-graph-convolution | 2304.02961 | null | https://arxiv.org/abs/2304.02961v1 | https://arxiv.org/pdf/2304.02961v1.pdf | HGCC: Enhancing Hyperbolic Graph Convolution Networks on Heterogeneous Collaborative Graph for Recommendation | Due to the naturally power-law distributed nature of user-item interaction data in recommendation tasks, hyperbolic space modeling has recently been introduced into collaborative filtering methods. Among them, hyperbolic GCN combines the advantages of GCN and hyperbolic space and achieves a surprising performance. Howe... | ['Ning Wu', 'Lu Zhang'] | 2023-04-06 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-6.82961166e-01 -1.06301658e-01 -4.92990576e-03 -2.11655721e-01
-1.44905746e-01 -4.86222655e-01 4.19225007e-01 1.67188093e-01
-1.88256383e-01 1.43999636e-01 6.74141705e-01 -3.97847801e-01
-8.27048898e-01 -1.03922856e+00 -2.78342754e-01 -8.49032700e-01
-8.56209844e-02 3.11749220e-01 5.29617488e-01 -4.46073174... | [10.236273765563965, 5.646207809448242] |
2120dd1f-8543-4e0a-89e1-9b899577637b | tropical-cyclone-track-forecasting-using | 1910.10566 | null | https://arxiv.org/abs/1910.10566v2 | https://arxiv.org/pdf/1910.10566v2.pdf | Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data | The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of... | ['Balázs Kégl', 'Christina Kumler-Bonfanti', 'Sophie Giffard-Roisin', 'Guillaume Charpiat', 'Mo Yang', 'Claire Monteleoni'] | 2019-10-23 | null | null | null | null | ['tropical-cyclone-track-forecasting'] | ['time-series'] | [-5.59458971e-01 -6.84114814e-01 -1.32291481e-01 -7.25187719e-01
-6.42130524e-02 -4.91673589e-01 1.14359045e+00 -1.66317776e-01
-1.98159412e-01 9.56088901e-01 3.25499684e-01 -8.46888840e-01
-7.17755966e-03 -1.07137716e+00 -2.35928789e-01 -1.01570714e+00
-6.10402405e-01 2.44179919e-01 -9.11920667e-02 -9.61101234... | [6.569606781005859, 2.922863721847534] |
67d781a6-9c29-4808-8aec-4fd6e3b67d75 | music-source-separation-in-the-waveform-1 | 1911.13254 | null | https://arxiv.org/abs/1911.13254v2 | https://arxiv.org/pdf/1911.13254v2.pdf | Music Source Separation in the Waveform Domain | Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song. Such components include voice, bass, drums and any other accompaniments.Contrarily to many audio synthesis tasks where the best performances are achieved b... | ['Léon Bottou', 'Nicolas Usunier', 'Alexandre Défossez', 'Francis Bach'] | 2019-11-27 | null | https://openreview.net/forum?id=HJx7uJStPH | https://openreview.net/pdf?id=HJx7uJStPH | null | ['audio-generation', 'music-source-separation'] | ['audio', 'music'] | [ 3.68188143e-01 -2.73865908e-01 1.72348514e-01 1.13613106e-01
-1.28258216e+00 -8.68042886e-01 4.40224111e-01 -6.37909397e-02
-1.40625238e-01 6.63633585e-01 3.80620599e-01 -1.35336280e-01
-1.39806256e-01 -4.27708805e-01 -6.30601704e-01 -8.29588473e-01
-1.30130082e-01 8.45512971e-02 4.04759571e-02 -2.62711018... | [15.492226600646973, 5.641339302062988] |
a1098ef2-89df-4e66-8ce5-95643236b473 | world-from-blur | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Qiu_World_From_Blur_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Qiu_World_From_Blur_CVPR_2019_paper.pdf | World From Blur | What can we tell from a single motion-blurred image? We show in this paper that a 3D scene can be revealed. Unlike prior methods that focus on producing a deblurred image, we propose to estimate and take advantage of the hidden message of a blurred image, the relative motion trajectory, to restore the 3D scene collap... | [' Dacheng Tao', ' Stephen J. Maybank', ' Xinchao Wang', 'Jiayan Qiu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 4.55785453e-01 2.33147591e-02 4.58748102e-01 -3.79677534e-01
-4.11558181e-01 -5.83133101e-01 6.37251556e-01 -8.08532238e-01
-3.80132487e-03 6.40360773e-01 7.59845018e-01 -1.66212335e-01
-9.37002599e-02 -1.98016018e-01 -9.53366637e-01 -7.03729272e-01
1.60503179e-01 -8.38746801e-02 6.07395452e-03 2.52786696... | [11.3829927444458, -2.519827127456665] |
44d7b0d8-15f4-4378-b480-6d645f37f0cf | fisher-discriminant-triplet-and-contrastive | 2004.04674 | null | https://arxiv.org/abs/2004.04674v1 | https://arxiv.org/pdf/2004.04674v1.pdf | Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks | Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese concept is topology-agnostic and can use any neural network as its backbone. The two most popular loss functions for training these networks are... | ['Sobhan Shafiei', 'Milad Sikaroudi', 'Fakhri Karray', 'Mark Crowley', 'H. R. Tizhoosh', 'Benyamin Ghojogh'] | 2020-04-05 | null | null | null | null | ['histopathological-image-classification', 'classification-of-breast-cancer-histology'] | ['medical', 'medical'] | [-2.99518127e-02 -4.94538903e-01 -2.14579403e-01 -6.73768759e-01
-7.94131339e-01 -3.16672415e-01 4.59735096e-01 -8.98892246e-03
-6.72469854e-01 8.66975248e-01 3.84164415e-02 -1.74252704e-01
-5.80508947e-01 -3.19860905e-01 -3.71466041e-01 -1.04501164e+00
-3.47522855e-01 2.89486676e-01 2.88169861e-01 -1.66266918... | [15.070104598999023, -2.6317520141601562] |
72d6ba9c-bf35-4577-8155-2556e562eeb9 | a-named-entity-recognition-corpus-for | null | null | https://aclanthology.org/2022.lrec-1.385 | https://aclanthology.org/2022.lrec-1.385.pdf | A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment | Named Entity Recognition (NER) is an important task in information extraction. However, due to the lack of labelled corpora, biomedical NER has scarcely been studied in Vietnamese compared to English. To address this situation, we have constructed VietBioNER, a labelled NER corpus of Vietnamese academic biomedical text... | ['Nhung Nguyen', 'Phuong N.V Nguyen', 'Uyen Phan'] | null | null | null | null | lrec-2022-6 | ['one-shot-learning'] | ['methodology'] | [ 1.62738830e-01 1.73616812e-01 -3.29238653e-01 -7.15663955e-02
-9.55489039e-01 -3.35873723e-01 7.84272015e-01 8.37983131e-01
-1.13442278e+00 1.11759174e+00 6.07010305e-01 -2.10919932e-01
-5.11999950e-02 -7.33683705e-01 -6.26304969e-02 -7.30913579e-01
1.12171777e-01 6.46335185e-01 1.27648205e-01 -9.61314980... | [8.48655891418457, 8.739355087280273] |
2774b0bc-317b-41cd-a1f1-67b596c02811 | constrained-policy-optimization-for | 2209.08429 | null | https://arxiv.org/abs/2209.08429v1 | https://arxiv.org/pdf/2209.08429v1.pdf | Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems | Recently, self-learning methods based on user satisfaction metrics and contextual bandits have shown promising results to enable consistent improvements in conversational AI systems. However, directly targeting such metrics by off-policy bandit learning objectives often increases the risk of making abrupt policy change... | ['Sungjin Lee', 'Mohammad Kachuee'] | 2022-09-17 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 9.77820829e-02 1.42513111e-01 -8.71586919e-01 -5.25117218e-01
-8.30314517e-01 -4.22437817e-01 3.92464906e-01 8.93390030e-02
-4.70778197e-01 1.24917412e+00 3.19726557e-01 -2.58125335e-01
-4.77164716e-01 -5.60688674e-01 -5.12173355e-01 -5.65183759e-01
-1.61008045e-01 9.03903663e-01 -2.91228797e-02 -3.34790438... | [4.457081317901611, 2.9362542629241943] |
454922b4-2b71-4030-889e-8d00f87873be | data-driven-input-reconstruction-and | 2203.02827 | null | https://arxiv.org/abs/2203.02827v1 | https://arxiv.org/pdf/2203.02827v1.pdf | Data-driven input reconstruction and experimental validation | This paper addresses a data-driven input reconstruction problem based on Willems' Fundamental Lemma in which unknown input estimators (UIEs) are constructed directly from historical I/O data. Given only output measurements, the inputs are estimated by the UIE, which is shown to asymptotically converge to the true input... | ['Colin N. Jones', 'Yingzhao Lian', 'Jicheng Shi'] | 2022-03-05 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 3.16988200e-01 2.57173449e-01 -3.62379640e-01 2.19415978e-01
-4.00543034e-01 -5.26470542e-01 2.28541210e-01 1.43836781e-01
-1.77454993e-01 1.13326025e+00 -1.83816820e-01 -5.64491928e-01
-4.21012551e-01 -5.17636418e-01 -8.93747807e-01 -6.86615169e-01
2.59560853e-01 -3.96709293e-01 -3.87041867e-01 1.55900167... | [5.2108988761901855, 2.5553841590881348] |
29795d9b-ed3e-4ce0-9b75-34d0c6ad603f | convkn-at-semeval-2016-task-3-answer-and | null | null | https://aclanthology.org/S16-1138 | https://aclanthology.org/S16-1138.pdf | ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora | null | ['Kateryna Tymoshenko', 'ro', "Alberto Barr{\\'o}n-Cede{\\~n}o", 'Giovanni Da San Martino', 'Shafiq Joty', 'Antonio Uva', 'Salvatore Romeo', 'Fahad Al Obaidli', 'Daniele Bonadiman', 'Aless Moschitti'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['question-selection'] | ['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... | [-7.285930633544922, 3.4464855194091797] |
7977cdc4-ed8d-4ba8-a16e-f10fd9c1036f | onix-an-x-ray-deep-learning-tool-for-3d | 2203.00682 | null | https://arxiv.org/abs/2203.00682v1 | https://arxiv.org/pdf/2203.00682v1.pdf | ONIX: an X-ray deep-learning tool for 3D reconstructions from sparse views | Three-dimensional (3D) X-ray imaging techniques like tomography and confocal microscopy are crucial for academic and industrial applications. These approaches access 3D information by scanning the sample with respect to the X-ray source. However, the scanning process limits the temporal resolution when studying dynamic... | ['Pablo Villanueva-Perez', 'Tobias Ritschel', 'Zisheng Yao', 'Yuhe Zhang'] | 2022-03-01 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 3.14084888e-01 -3.53918254e-01 -4.76962999e-02 -7.75730312e-02
-6.44651413e-01 -3.43892694e-01 5.19637227e-01 -1.23089842e-01
-5.84926367e-01 6.20234013e-01 -7.65297040e-02 -3.96581501e-01
-4.55669463e-01 -7.13587224e-01 -6.94661081e-01 -9.73094165e-01
-1.00285068e-01 1.03451633e+00 2.25705400e-01 4.95108701... | [12.972589492797852, -2.786046028137207] |
fd81fa74-d3c8-45ea-9159-ea0614bb97cd | treeprompt-learning-to-compose-tree-prompts | 2305.11497 | null | https://arxiv.org/abs/2305.11497v1 | https://arxiv.org/pdf/2305.11497v1.pdf | TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding | Prompt tuning has achieved great success in transferring the knowledge from large pretrained vision-language models into downstream tasks, and has dominated the performance on visual grounding (VG). However, almost all existing prompt tuning paradigms suffer from poor interpretability. In this paper, we argue that thei... | ['Long Chen', 'Jian Shao', 'Lei Chen', 'Jun Xiao', 'Chenchi Zhang'] | 2023-05-19 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [ 3.93701404e-01 4.09188747e-01 -9.43460390e-02 -5.46837151e-01
-3.05880994e-01 -8.15068960e-01 8.09265137e-01 1.58765778e-01
-2.31600106e-01 3.05102140e-01 5.95049858e-01 -7.47971356e-01
1.94522828e-01 -7.59589016e-01 -7.78440714e-01 -4.12570298e-01
7.12826312e-01 4.07348275e-01 -1.30159467e-01 -2.31136143... | [10.699524879455566, 1.7319468259811401] |
b75a1181-3dac-4bd1-86c1-43d2c453cad2 | comprehensive-review-of-deep-learning-based | 2203.03311 | null | https://arxiv.org/abs/2203.03311v3 | https://arxiv.org/pdf/2203.03311v3.pdf | Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis | Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed p... | ['Lipeng Ma', 'Xing Hu', 'Tao Ma', 'Yikang Li', 'Zhijun Li', 'Wenming Chen', 'Weidong Yang', 'Ben Fei'] | 2022-03-07 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.97905988e-01 -4.24199760e-01 1.69316754e-01 -1.75452024e-01
-2.85045117e-01 -1.86977416e-01 6.82860732e-01 -2.73307651e-01
1.96849361e-01 4.11556393e-01 -3.51430595e-01 -3.00560504e-01
-1.82753712e-01 -9.26135242e-01 -5.03851771e-01 -8.38223815e-01
-4.70751375e-02 4.69883978e-01 -3.08010485e-02 -1.44399449... | [8.16666316986084, -3.48230242729187] |
d8985245-6c20-4277-b6e6-bd7bc36ff185 | docee-a-large-scale-and-fine-grained-1 | null | null | https://aclanthology.org/2022.naacl-main.291 | https://aclanthology.org/2022.naacl-main.291.pdf | DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction | Event extraction aims to identify an event and then extract the arguments participating in the event. Despite the great success in sentence-level event extraction, events are more naturally presented in the form of documents, with event arguments scattered in multiple sentences. However, a major barrier to promote docu... | ['Juanzi Li', 'Lei Hou', 'Siyu Chen', 'Jiangqi Zhu', 'Yixin Cao', 'Meihuan Han', 'Shuai Wang', 'Bin Xu', 'Meihan Tong'] | null | null | null | null | naacl-2022-7 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 1.47176608e-01 4.30063963e-01 -1.60223231e-01 -2.46437684e-01
-1.17851985e+00 -8.80627573e-01 7.97529936e-01 7.53278732e-01
-6.74665093e-01 9.84567285e-01 7.19384909e-01 -1.20598525e-01
6.48550615e-02 -7.12509692e-01 -6.64456189e-01 -1.94556311e-01
-8.36500004e-02 4.62476820e-01 4.30834740e-01 -6.40987903... | [9.094639778137207, 9.253984451293945] |
d29b4cbb-209c-4702-98b7-6ac7fab8e8b1 | drug-response-prediction-by-inferring-pathway | 1606.03623 | null | http://arxiv.org/abs/1606.03623v1 | http://arxiv.org/pdf/1606.03623v1.pdf | Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization | A key goal of computational personalized medicine is to systematically
utilize genomic and other molecular features of samples to predict drug
responses for a previously unseen sample. Such predictions are valuable for
developing hypotheses for selecting therapies tailored for individual patients.
This is especially va... | ['Olli Kallioniemi', 'Astrid Murumägi', 'Disha Malani', 'Muhammad Ammad-Ud-Din', 'Suleiman A. Khan', 'Tero Aittokallio', 'Samuel Kaski'] | 2016-06-11 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 5.34084201e-01 -4.75777239e-01 -7.42661655e-01 -1.04756676e-01
-9.44164097e-01 -5.29515684e-01 2.32804343e-01 7.04328001e-01
-2.87915319e-01 1.19900548e+00 2.64052689e-01 -4.66238439e-01
-5.65879405e-01 -6.32257342e-01 -5.35899758e-01 -1.05538213e+00
1.04523838e-01 6.56915426e-01 -9.25608724e-02 -9.68983769... | [5.7476396560668945, 5.70820951461792] |
c26b6de7-be92-4916-a89d-d253acb2ced3 | dias-a-comprehensive-benchmark-for-dsa | 2306.12153 | null | https://arxiv.org/abs/2306.12153v1 | https://arxiv.org/pdf/2306.12153v1.pdf | DIAS: A Comprehensive Benchmark for DSA-sequence Intracranial Artery Segmentation | Automatic segmentation of the intracranial artery (IA) in digital subtraction angiography (DSA) sequence is an essential step in diagnosing IA-related diseases and guiding neuro-interventional surgery. However, the lack of publicly available datasets has impeded research in this area. In this paper, we release DIAS, an... | ['Ruisheng Su', 'Yiming Deng', 'Feng Gao', 'Huihua Yang', 'Xipeng Pan', 'Wenyi Zhao', 'Haoyuan Li', 'Weijin Xu', 'Lemeng Wang', 'Tong Tian', 'Wentao Liu'] | 2023-06-21 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 2.13762045e-01 -8.15511248e-06 -4.14767414e-01 -6.15039051e-01
-1.00509703e+00 -6.24834359e-01 3.44356775e-01 -2.68220842e-01
-1.08115770e-01 4.92357790e-01 2.28419900e-01 -8.11822891e-01
1.28115388e-02 -4.32346374e-01 -5.62659204e-01 -8.29946518e-01
5.94357699e-02 6.00323558e-01 4.08646792e-01 2.56239295... | [14.601573944091797, -2.168219804763794] |
0de46beb-8b62-49c4-9fb7-59c0015e9ddc | predicting-user-engagement-in-twitter-with | 1412.7990 | null | http://arxiv.org/abs/1412.7990v1 | http://arxiv.org/pdf/1412.7990v1.pdf | Predicting User Engagement in Twitter with Collaborative Ranking | Collaborative Filtering (CF) is a core component of popular web-based
services such as Amazon, YouTube, Netflix, and Twitter. Most applications use
CF to recommend a small set of items to the user. For instance, YouTube
presents to a user a list of top-n videos she would likely watch next based on
her rating and viewin... | ['Michele Berlingerio', 'Ernesto Diaz-Aviles', 'Yiannis Gkoufas', 'Stefano Braghin', 'Fabio Pinelli', 'Hoang Thanh Lam', 'Francesco Calabrese'] | 2014-12-26 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-2.90290236e-01 -4.40021753e-01 -6.58664882e-01 -6.16400123e-01
-5.88039696e-01 -8.16867709e-01 5.74370861e-01 4.18930560e-01
-5.47170401e-01 2.32808620e-01 8.21598709e-01 -1.59402609e-01
-3.48860472e-01 -7.15096295e-01 -4.05443370e-01 -2.29106918e-01
-1.69914305e-01 1.88140959e-01 2.77603596e-01 -1.88160077... | [10.108098030090332, 5.742865085601807] |
e961d9e9-33f0-4f5d-bba2-e55d31cfe308 | edgenet-semantic-scene-completion-from-rgb-d | 1908.02893 | null | https://arxiv.org/abs/1908.02893v2 | https://arxiv.org/pdf/1908.02893v2.pdf | EdgeNet: Semantic Scene Completion from a Single RGB-D Image | Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view. Previous works on Semantic Scene Completion from RGB-D data used either only depth or depth with colour by projecting the 2D image into the... | ['Hansung Kim', 'Aloisio Dourado', 'Adrian Hilton', 'Teofilo Emidio de Campos'] | 2019-08-08 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 6.31518781e-01 2.49686658e-01 3.40321034e-01 -7.24912465e-01
-4.27373886e-01 2.99322233e-03 5.31136870e-01 2.00740844e-01
-5.40311754e-01 4.09977019e-01 3.44983727e-01 1.21333832e-02
5.61987869e-02 -8.51727545e-01 -6.61515355e-01 -3.05738449e-01
-4.02162164e-01 5.69062829e-01 1.97214887e-01 -1.58693623... | [8.479293823242188, -2.8715667724609375] |
e5920979-c738-41e6-a900-e71e3450c643 | 3d-shape-synthesis-for-conceptual-design-and | 1904.07964 | null | http://arxiv.org/abs/1904.07964v1 | http://arxiv.org/pdf/1904.07964v1.pdf | 3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders | We propose a data-driven 3D shape design method that can learn a generative
model from a corpus of existing designs, and use this model to produce a wide
range of new designs. The approach learns an encoding of the samples in the
training corpus using an unsupervised variational autoencoder-decoder
architecture, withou... | ['Zhangsihao Yang', 'Tomotake Furuhata', 'Soji Yamakawa', 'Levent Burak Kara', 'Suyash Nigam', 'Haoliang Jiang', 'Wentai Zhang', 'Kenji Shimada'] | 2019-04-16 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 1.69202328e-01 3.51803780e-01 1.18842155e-01 -1.52660102e-01
-6.33309782e-01 -6.16463721e-01 6.53990805e-01 1.44141037e-02
1.54864356e-01 6.73175633e-01 2.91514456e-01 -8.50903913e-02
-3.65482152e-01 -1.18296552e+00 -8.45785201e-01 -7.24136651e-01
6.54475391e-02 9.70474899e-01 -2.60482818e-01 -3.21231931... | [5.830658435821533, 3.3423311710357666] |
257e02d2-28b9-46e2-b96a-9b6281ca09b2 | aaformer-auto-aligned-transformer-for-person | 2104.00921 | null | https://arxiv.org/abs/2104.00921v2 | https://arxiv.org/pdf/2104.00921v2.pdf | AAformer: Auto-Aligned Transformer for Person Re-Identification | In person re-identification, extracting part-level features from person images has been verified to be crucial. Most of existing CNN-based methods only locate the human parts coarsely, or rely on pre-trained human parsing models and fail in locating the identifiable non-human parts (e.g., knapsack). In this paper, we i... | ['Ming Tang', 'Jinqiao Wang', 'Jing Liu', 'Honglin Qiao', 'Gaopan Huang', 'YaoWei Wang', 'Shiliang Zhang', 'Haiyun Guo', 'Kuan Zhu'] | 2021-04-02 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [-1.60831556e-01 3.25094648e-02 4.15906869e-02 -2.99550980e-01
-8.22878182e-01 -4.75644797e-01 4.51337337e-01 -6.21622093e-02
-3.85143012e-01 3.25321078e-01 2.87886709e-01 4.98226106e-01
2.76600141e-02 -5.82446694e-01 -7.42294490e-01 -7.42520750e-01
3.19009840e-01 6.41720176e-01 2.63992071e-01 1.55386822... | [14.744356155395508, 0.815386950969696] |
00ed8731-c79d-422f-a6c2-3161a8869d9f | safari-sparsity-enabled-federated-learning | 2204.02321 | null | https://arxiv.org/abs/2204.02321v1 | https://arxiv.org/pdf/2204.02321v1.pdf | SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications | Federated learning (FL) enables edge devices to collaboratively learn a model in a distributed fashion. Many existing researches have focused on improving communication efficiency of high-dimensional models and addressing bias caused by local updates. However, most of FL algorithms are either based on reliable communic... | ['Xiao-Ping Zhang', 'Tian Lan', 'Wenbo Ding', 'Yang Liu', 'Le Liang', 'Meilin Yang', 'Zihao Zhao', 'Yuzhu Mao'] | 2022-04-05 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-1.96961358e-01 -4.75159995e-02 -3.13230157e-01 -2.87780225e-01
-5.83730459e-01 -3.06165636e-01 1.14707239e-01 7.15401247e-02
-7.35638514e-02 9.41312730e-01 2.63927191e-01 3.26277614e-02
-5.43626189e-01 -6.68489039e-01 -8.37460756e-01 -9.26143408e-01
-4.33923930e-01 4.97982085e-01 -1.61530733e-01 7.58413151... | [5.946261405944824, 5.679646968841553] |
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