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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5884c871-aed2-4bf1-8b64-b0d4d65845fc | aaa-fair-evaluation-for-abuse-detection | null | null | https://dl.acm.org/doi/abs/10.1145/3447535.3462484 | https://dl.acm.org/doi/pdf/10.1145/3447535.3462484 | AAA: Fair Evaluation for Abuse Detection Systems Wanted | User-generated web content is rife with abusive language that can harm others and discourage participation. Thus, a primary research aim is to develop abuse detection systems that can be used to alert and support human moderators of online communities. Such systems are notoriously hard to develop and evaluate. Even whe... | ['Roberto Navigli', 'Rocco Tripodi', 'Björn Ross', 'Michele Bevilacqua', 'Agostina Calabrese'] | 2021-06-21 | null | null | null | acm-web-science-2021-6 | ['abuse-detection'] | ['natural-language-processing'] | [ 1.82174072e-02 -2.48401016e-01 -4.05108631e-01 -3.83736521e-01
-5.17749906e-01 -7.63234675e-01 8.63499939e-01 4.33111459e-01
-6.53046250e-01 7.41904557e-01 7.80898556e-02 -5.92505336e-01
1.61088817e-02 -8.29565823e-01 -3.78034413e-01 -5.63508645e-02
-2.60858238e-01 3.37791711e-01 2.06837222e-01 -4.20115530... | [8.6823148727417, 10.465112686157227] |
d242db40-fb74-429c-9486-6adfa982d862 | unsupervised-parallel-corpus-mining-on-web | 2009.08595 | null | https://arxiv.org/abs/2009.08595v1 | https://arxiv.org/pdf/2009.08595v1.pdf | Unsupervised Parallel Corpus Mining on Web Data | With a large amount of parallel data, neural machine translation systems are able to deliver human-level performance for sentence-level translation. However, it is costly to label a large amount of parallel data by humans. In contrast, there is a large-scale of parallel corpus created by humans on the Internet. The maj... | ['Zihang Dai', 'Yiming Yang', 'Guokun Lai'] | 2020-09-18 | null | null | null | null | ['parallel-corpus-mining'] | ['natural-language-processing'] | [ 1.91008478e-01 -4.09647733e-01 -4.50547218e-01 -2.87828594e-01
-1.48210955e+00 -6.93189085e-01 6.05217636e-01 -2.58105490e-02
-8.32404852e-01 8.99773479e-01 4.36514197e-03 -6.72967255e-01
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2.49258906e-01 1.07015526e+00 -9.49028358e-02 -5.77993453... | [11.568166732788086, 10.311986923217773] |
c1c372b1-78a2-49a5-a649-e5028bbe78f0 | generalizing-a-person-retrieval-model-hetero | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Zhun_Zhong_Generalizing_A_Person_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhun_Zhong_Generalizing_A_Person_ECCV_2018_paper.pdf | Generalizing A Person Retrieval Model Hetero- and Homogeneously | Person re-identification (re-ID) poses unique challenges for unsupervised domain adaptation (UDA) in that classes in the source and target sets (domains) are entirely different and that image variations are largely caused by cameras. Given a labeled source training set and an unlabeled target training set, we aim to im... | ['Zhun Zhong', 'Shaozi Li', 'Yi Yang', 'Liang Zheng'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['person-retrieval'] | ['computer-vision'] | [ 7.41225109e-02 -1.52607098e-01 -3.81923527e-01 -4.71694559e-01
-6.79549932e-01 -7.94698417e-01 8.42424393e-01 -4.18585658e-01
-4.59279865e-01 8.79296899e-01 1.17837436e-01 2.91585326e-01
8.89317393e-02 -5.58622181e-01 -8.54116499e-01 -5.49201131e-01
3.18783194e-01 8.28667402e-01 9.37243402e-02 -1.47783220... | [14.755678176879883, 1.0423527956008911] |
e738d847-5f8a-4d2c-97d5-911a34a193f3 | a-zero-shot-framework-for-sketch-based-image | 1807.11724 | null | http://arxiv.org/abs/1807.11724v1 | http://arxiv.org/pdf/1807.11724v1.pdf | A Zero-Shot Framework for Sketch-based Image Retrieval | Sketch-based image retrieval (SBIR) is the task of retrieving images from a
natural image database that correspond to a given hand-drawn sketch. Ideally,
an SBIR model should learn to associate components in the sketch (say, feet,
tail, etc.) with the corresponding components in the image having similar shape
character... | ['Ashish Mishra', 'Anurag Mittal', 'Shiva Krishna Reddy', 'Sasi Kiran Yelamarthi'] | 2018-07-31 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.33815277e-01 -3.95851701e-01 -1.69950575e-01 -4.16062146e-01
-1.06842732e+00 -6.22755289e-01 1.00100732e+00 -2.84485728e-01
-4.70780768e-02 4.75321889e-01 -1.73436925e-02 1.89337566e-01
-3.13928187e-01 -9.83790696e-01 -9.38744843e-01 -7.41527557e-01
4.16169614e-01 9.46383357e-01 2.15077683e-01 -1.80610180... | [11.627346992492676, 0.6364580392837524] |
e11c2bbf-886a-408a-9032-ac2885511e99 | understanding-parameter-sharing-in | 2306.0938 | null | https://arxiv.org/abs/2306.09380v1 | https://arxiv.org/pdf/2306.09380v1.pdf | Understanding Parameter Sharing in Transformers | Parameter sharing has proven to be a parameter-efficient approach. Previous work on Transformers has focused on sharing parameters in different layers, which can improve the performance of models with limited parameters by increasing model depth. In this paper, we study why this approach works from two perspectives. Fi... | ['Jingbo Zhu', 'Tong Xiao', 'Xiaohui Wang', 'Zhexi Zhang', 'Mingxuan Wang', 'Ye Lin'] | 2023-06-15 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [-2.17146724e-02 3.01781535e-01 -4.59749788e-01 -2.33163863e-01
-3.01256448e-01 -4.26829398e-01 3.10004920e-01 -3.44257080e-03
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1.11774586e-01 7.08880603e-01 4.39220101e-01 -3.48879904... | [8.681144714355469, 3.637951374053955] |
d7efe3c0-7409-468e-a0e0-13aa3f1251d2 | segment-fusion-hierarchical-context-fusion | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Thyagharajan_Segment-Fusion_Hierarchical_Context_Fusion_for_Robust_3D_Semantic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Thyagharajan_Segment-Fusion_Hierarchical_Context_Fusion_for_Robust_3D_Semantic_Segmentation_CVPR_2022_paper.pdf | Segment-Fusion: Hierarchical Context Fusion for Robust 3D Semantic Segmentation | 3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR. Several state-of-the-art semantic segmentation models suffer from the part-misclassification problem, wherein parts of the same object are labelled incorrectly. Previ... | ['Sreenivas Subramoney', 'Om Ji Omer', 'Prashant Laddha', 'Benjamin Ummenhofer', 'Anirud Thyagharajan'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['robust-3d-semantic-segmentation'] | ['computer-vision'] | [ 6.20460033e-01 7.39569187e-01 -2.19534039e-01 -7.83896685e-01
-6.78806722e-01 -3.95774841e-01 3.94383669e-01 4.63080764e-01
-1.19015567e-01 3.26987892e-01 -3.15249950e-01 -2.79161662e-01
-3.14906329e-01 -8.35979283e-01 -7.74201930e-01 -4.98255402e-01
1.79254651e-01 8.76967072e-01 8.67856443e-01 -2.08674282... | [8.16026496887207, -2.906137466430664] |
df84bcb5-1674-4acd-b488-7e16912b187f | unified-smoke-and-fire-detection-in-an | 2202.07954 | null | https://arxiv.org/abs/2202.07954v1 | https://arxiv.org/pdf/2202.07954v1.pdf | Unified smoke and fire detection in an evolutionary framework with self-supervised progressive data augment | Few researches have studied simultaneous detection of smoke and flame accompanying fires due to their different physical natures that lead to uncertain fluid patterns. In this study, we collect a large image data set to re-label them as a multi-label image classification problem so as to identify smoke and flame simult... | ['helin sun', 'zhongyan lu', 'Hongyong Wang', 'Su Yang', 'Hang Zhang'] | 2022-02-16 | null | null | null | null | ['multi-label-image-classification', 'self-learning', 'fire-detection'] | ['computer-vision', 'natural-language-processing', 'time-series'] | [ 5.52353680e-01 -5.36243677e-01 1.52802214e-01 -3.89593579e-02
1.76665280e-02 -7.06286311e-01 4.04839993e-01 -2.42836729e-01
-3.99855286e-01 4.13723111e-01 -2.64368266e-01 -5.93396090e-03
-4.32350524e-02 -8.71303082e-01 -4.93634939e-01 -1.12707496e+00
4.36784655e-01 3.23372453e-01 6.45707786e-01 -5.39173074... | [10.854293823242188, -1.2221972942352295] |
a2958e12-be4e-4b3f-a96e-546a6ca06c49 | cs4ml-a-general-framework-for-active-learning | 2306.00945 | null | https://arxiv.org/abs/2306.00945v1 | https://arxiv.org/pdf/2306.00945v1.pdf | CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions | We introduce a general framework for active learning in regression problems. Our framework extends the standard setup by allowing for general types of data, rather than merely pointwise samples of the target function. This generalization covers many cases of practical interest, such as data acquired in transform domain... | ['Nick Dexter', 'Juan M. Cardenas', 'Ben Adcock'] | 2023-06-01 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 5.56382060e-01 2.40555078e-01 -2.25796953e-01 -3.28124583e-01
-1.23512685e+00 -3.53257746e-01 4.71492738e-01 3.06233913e-01
-7.47225761e-01 1.05094039e+00 -1.28577977e-01 -1.29877508e-01
-5.33046007e-01 -7.99421787e-01 -9.53412592e-01 -1.25961804e+00
-3.77405673e-01 7.64262199e-01 -7.48871565e-02 -1.00684583... | [6.855490684509277, 4.083220481872559] |
d42dea73-1962-4db3-b425-72ec2a22d8bd | sharpness-aware-minimization-an-implicit | 2302.11836 | null | https://arxiv.org/abs/2302.11836v3 | https://arxiv.org/pdf/2302.11836v3.pdf | On Statistical Properties of Sharpness-Aware Minimization: Provable Guarantees | Sharpness-Aware Minimization (SAM) is a recent optimization framework aiming to improve the deep neural network generalization, through obtaining flatter (i.e. less sharp) solutions. As SAM has been numerically successful, recent papers have studied the theoretical aspects of the framework and have shown SAM solutions ... | ['Rahul Mazumder', 'Kayhan Behdin'] | 2023-02-23 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-3.00482810e-02 3.03324431e-01 -1.42875031e-01 -3.18948656e-01
-5.62347889e-01 -2.76972413e-01 7.37749040e-02 -2.12274701e-03
-6.39320314e-01 9.94280577e-01 -1.06730826e-01 -2.94255853e-01
-4.84269828e-01 -4.87552345e-01 -1.01948869e+00 -1.10103917e+00
-1.55996799e-01 1.21751474e-02 -2.84788102e-01 -8.32743421... | [7.791507244110107, 3.837566375732422] |
88f95762-c772-4ab0-af9e-820042f6f5cf | select-answer-and-explain-interpretable-multi | 1911.00484 | null | https://arxiv.org/abs/1911.00484v4 | https://arxiv.org/pdf/1911.00484v4.pdf | Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents | Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences. In this paper, we propose an effective and interpretable Select, Answer and Explain (... | ['Bo-Wen Zhou', 'Guangtao Wang', 'Xiaodong He', 'Ming Tu', 'Jing Huang', 'Kevin Huang'] | 2019-11-01 | null | null | null | null | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 5.39180875e-01 3.09116155e-01 -6.99867159e-02 -8.21577668e-01
-1.52740848e+00 -4.30410177e-01 2.45156020e-01 7.27178812e-01
-4.31604058e-01 8.89391720e-01 6.90711856e-01 -2.93451458e-01
-4.55023110e-01 -2.24899158e-01 -7.59117723e-01 -1.17745779e-01
4.38674480e-01 9.88735318e-01 4.28813368e-01 -3.06425244... | [11.314056396484375, 8.009955406188965] |
e9445d9c-61c0-4735-8bbd-3e2a235589c5 | boosting-image-based-mutual-gaze-detection | 2010.07811 | null | https://arxiv.org/abs/2010.07811v2 | https://arxiv.org/pdf/2010.07811v2.pdf | Boosting Image-based Mutual Gaze Detection using Pseudo 3D Gaze | Mutual gaze detection, i.e., predicting whether or not two people are looking at each other, plays an important role in understanding human interactions. In this work, we focus on the task of image-based mutual gaze detection, and propose a simple and effective approach to boost the performance by using an auxiliary 3D... | ['Bradley Green', 'Yukun Zhu', 'Xuhui Jia', 'Raviteja Vemulapalli', 'Ching-Hui Chen', 'Bardia Doosti'] | 2020-10-15 | null | null | null | null | ['mutual-gaze'] | ['computer-vision'] | [ 1.16848432e-01 3.34468096e-01 -1.09107532e-01 -6.62185550e-01
-2.91242301e-01 -1.40687615e-01 3.88120621e-01 -7.43930861e-02
-5.83897412e-01 2.32503965e-01 -1.09782971e-01 -1.03644408e-01
1.51609749e-01 -1.16215460e-01 -7.95959651e-01 -7.50934541e-01
1.23346999e-01 1.02650061e-01 2.88979888e-01 6.06376193... | [14.127790451049805, 0.0425601527094841] |
dda13201-e8d4-4763-adce-04663093def1 | secure-deep-learning-based-distributed | 2307.01559 | null | https://arxiv.org/abs/2307.01559v1 | https://arxiv.org/pdf/2307.01559v1.pdf | Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones | Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog computational paradigm, we can offload part of the computation to the fog; however, this poses security concerns if the fog node, or the communi... | ['Daniele Palossi', 'Alessandro Giusti', 'Elia Cereda'] | 2023-07-04 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [-4.91839051e-01 6.15763724e-01 1.46392852e-01 1.01231873e-01
-5.78592271e-02 -7.32134879e-01 8.96987766e-02 -3.56499910e-01
-6.14142179e-01 8.01995218e-01 -6.43870413e-01 -3.17327410e-01
3.11244335e-02 -1.18011332e+00 -1.08533192e+00 -5.84908009e-01
-4.18627322e-01 4.46253181e-01 6.51789188e-01 -1.61251277... | [8.1185302734375, 2.36484694480896] |
473deff2-2a11-41f1-82a0-67d0cbb1b082 | efficient-multi-task-scene-analysis-with-rgb | 2306.05242 | null | https://arxiv.org/abs/2306.05242v1 | https://arxiv.org/pdf/2306.05242v1.pdf | Efficient Multi-Task Scene Analysis with RGB-D Transformers | Scene analysis is essential for enabling autonomous systems, such as mobile robots, to operate in real-world environments. However, obtaining a comprehensive understanding of the scene requires solving multiple tasks, such as panoptic segmentation, instance orientation estimation, and scene classification. Solving thes... | ['Horst-Michael Gross', 'Leonard Rabes', 'Robin Schmidt', 'Daniel Seichter', 'Söhnke Benedikt Fischedick'] | 2023-06-08 | null | null | null | null | ['panoptic-segmentation', 'scene-classification'] | ['computer-vision', 'computer-vision'] | [ 1.89618960e-01 -3.95843863e-01 1.07276358e-01 -5.06192923e-01
-3.17891866e-01 -7.37914741e-01 2.76590288e-01 -1.20150827e-01
-8.19672823e-01 4.19094294e-01 -6.16698802e-01 -7.63174713e-01
7.32009485e-02 -9.20527875e-01 -1.24421847e+00 -4.77497667e-01
9.14400518e-02 2.75170535e-01 5.66168189e-01 -1.98072597... | [8.539623260498047, -2.282526969909668] |
1c9a6bba-c51a-4ed2-8e2a-66e1b5d67a2d | float-factorized-learning-of-object | 2203.16168 | null | https://arxiv.org/abs/2203.16168v1 | https://arxiv.org/pdf/2203.16168v1.pdf | FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing | Multi-object multi-part scene parsing is a challenging task which requires detecting multiple object classes in a scene and segmenting the semantic parts within each object. In this paper, we propose FLOAT, a factorized label space framework for scalable multi-object multi-part parsing. Our framework involves independe... | ['Ravikiran Sarvadevabhatla', 'Pradeep Shenoy', 'Pranav Gupta', 'Rishubh Singh'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Singh_FLOAT_Factorized_Learning_of_Object_Attributes_for_Improved_Multi-Object_Multi-Part_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Singh_FLOAT_Factorized_Learning_of_Object_Attributes_for_Improved_Multi-Object_Multi-Part_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-parsing', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.99549991e-01 1.81863621e-01 -1.34233847e-01 -5.31415880e-01
-1.24265170e+00 -7.87534535e-01 1.26111448e-01 1.86225906e-01
-3.76570314e-01 3.84513408e-01 -2.26831302e-01 6.20344393e-02
1.14992909e-01 -5.14687538e-01 -1.07990873e+00 -3.70188445e-01
3.14690232e-01 7.16494799e-01 1.01412237e+00 9.01282728... | [9.34360122680664, 0.5362877249717712] |
b173ad43-22a7-4772-a49a-8ec5f0ecb6b5 | disproving-xai-myths-with-formal-methods | 2306.01744 | null | https://arxiv.org/abs/2306.01744v1 | https://arxiv.org/pdf/2306.01744v1.pdf | Disproving XAI Myths with Formal Methods -- Initial Results | The advances in Machine Learning (ML) in recent years have been both impressive and far-reaching. However, the deployment of ML models is still impaired by a lack of trust in how the best-performing ML models make predictions. The issue of lack of trust is even more acute in the uses of ML models in high-risk or safety... | ['Joao Marques-Silva'] | 2023-05-13 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-1.61511809e-01 8.42638552e-01 -2.65145689e-01 -5.13325155e-01
-2.61364043e-01 -2.65863359e-01 6.88918352e-01 3.54287177e-01
1.08246788e-01 8.48008215e-01 7.87711218e-02 -8.35154712e-01
-3.08940321e-01 -5.41827202e-01 -8.50800216e-01 -3.05508912e-01
-1.67713722e-03 6.18525803e-01 -2.39721596e-01 -1.60565972... | [8.859902381896973, 6.065372467041016] |
4a79ea37-6b0f-465d-942a-1b09c3848525 | multi-objective-resource-optimization-of | 2202.02139 | null | https://arxiv.org/abs/2202.02139v1 | https://arxiv.org/pdf/2202.02139v1.pdf | Multi Objective Resource Optimization of Wireless Network Based on Cross Domain Virtual Network Embedding | The rapid development of virtual network architecture makes it possible for wireless network to be widely used. With the popularity of artificial intelligence (AI) industry in daily life, efficient resource allocation of wireless network has become a problem. Especially when network users request wireless network resou... | ['Peiying Zhang', 'Qifeng Sun', 'Youxiang Duan', 'Tao Dong', 'Chao Wang'] | 2022-02-03 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 8.14237744e-02 -1.41468763e-01 -6.36491418e-01 4.91074994e-02
5.21586180e-01 -3.35367799e-01 -2.29988813e-01 -3.06866288e-01
-5.40036023e-01 1.19886196e+00 -3.92744541e-01 -5.75713694e-01
-8.22873354e-01 -1.07039416e+00 3.21044207e-01 -4.16765749e-01
-4.06240582e-01 5.07352829e-01 4.59825562e-04 -1.35698140... | [5.881754398345947, 1.703986644744873] |
3265d984-d497-4917-9d6b-f8a99ceaf5aa | detecting-attended-visual-targets-in-video | 2003.02501 | null | https://arxiv.org/abs/2003.02501v2 | https://arxiv.org/pdf/2003.02501v2.pdf | Detecting Attended Visual Targets in Video | We address the problem of detecting attention targets in video. Our goal is to identify where each person in each frame of a video is looking, and correctly handle the case where the gaze target is out-of-frame. Our novel architecture models the dynamic interaction between the scene and head features and infers time-va... | ['James M. Rehg', 'Nataniel Ruiz', 'Eunji Chong', 'Yongxin Wang'] | 2020-03-05 | detecting-attended-visual-targets-in-video-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chong_Detecting_Attended_Visual_Targets_in_Video_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chong_Detecting_Attended_Visual_Targets_in_Video_CVPR_2020_paper.pdf | cvpr-2020-6 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 3.78705114e-01 2.24068109e-02 -4.01607901e-01 -3.87080789e-01
-3.43976259e-01 -3.34567398e-01 1.90101311e-01 -2.73784071e-01
-3.84486049e-01 4.01857138e-01 2.74648994e-01 9.89301130e-02
7.53187835e-02 3.14973384e-01 -7.42849410e-01 -6.40921533e-01
-2.32908860e-01 -1.26895830e-01 3.83325249e-01 3.23866874... | [14.04182243347168, 0.08404454588890076] |
af723e39-f07a-4be9-8907-64c9c9377f2a | gated-multimodal-units-for-information-fusion | 1702.01992 | null | http://arxiv.org/abs/1702.01992v1 | http://arxiv.org/pdf/1702.01992v1.pdf | Gated Multimodal Units for Information Fusion | This paper presents a novel model for multimodal learning based on gated
neural networks. The Gated Multimodal Unit (GMU) model is intended to be used
as an internal unit in a neural network architecture whose purpose is to find
an intermediate representation based on a combination of data from different
modalities. Th... | ['Fabio A. González', 'Manuel Montes-y-Gómez', 'Thamar Solorio', 'John Arevalo'] | 2017-02-07 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 3.80044192e-01 -1.44846171e-01 -4.15644795e-01 -5.73979795e-01
-9.33774531e-01 -7.38714278e-01 6.02999926e-01 3.43739301e-01
-3.56594890e-01 6.63523734e-01 4.18030709e-01 -1.85035005e-01
7.08201677e-02 -3.63560230e-01 -7.92230189e-01 -7.68112838e-01
2.52713233e-01 4.43280786e-01 -2.18856812e-01 -2.18894437... | [13.226664543151855, 5.112884521484375] |
017681ce-1dd5-40ce-8c22-defe51d13312 | aidroid-when-heterogeneous-information | 1811.01027 | null | https://arxiv.org/abs/1811.01027v2 | https://arxiv.org/pdf/1811.01027v2.pdf | AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection | The explosive growth and increasing sophistication of Android malware call for new defensive techniques that are capable of protecting mobile users against novel threats. In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level s... | ['Shifu Hou', 'Wenqiang Wan', 'Qi Xiong', 'Lingwei Chen', 'Fudong Shao', 'Yanfang Ye', 'Jiabin Wang', 'Jingwei Lei'] | 2018-11-02 | null | null | null | null | ['android-malware-detection', 'mobile-security'] | ['miscellaneous', 'miscellaneous'] | [ 2.08482146e-01 -2.83526808e-01 -5.84844828e-01 -9.65575799e-02
-3.34443718e-01 -8.61165643e-01 4.80485678e-01 -9.39308405e-02
-1.55815324e-02 3.62234950e-01 6.47760034e-02 -8.41447234e-01
-2.59121120e-01 -8.57751787e-01 -7.07440078e-01 -2.57201880e-01
-3.74328732e-01 2.13068366e-01 5.18992543e-01 -5.02687134... | [14.414848327636719, 9.674386024475098] |
8b92b8f9-b5b4-4cf3-9e10-a5992e4a8d19 | fuzzy-labeling-semantics-for-quantitative | 2207.07339 | null | https://arxiv.org/abs/2207.07339v1 | https://arxiv.org/pdf/2207.07339v1.pdf | Fuzzy Labeling Semantics for Quantitative Argumentation | The topic of evaluating argument strength in various quantitative argumentation systems has received increasing attention in the field of abstract argumentation. However, the existing gradual semantics on argument strength considers acceptability degree alone, which may be not sufficient to evaluate arguments in practi... | ['Yuping Shen', 'Zongshun Wang'] | 2022-07-15 | null | null | null | null | ['abstract-argumentation', 'abstract-argumentation'] | ['natural-language-processing', 'reasoning'] | [ 2.24017836e-02 4.21625584e-01 -2.44728476e-01 -6.18298769e-01
5.76122552e-02 -8.45558524e-01 5.94601512e-01 7.95867920e-01
-3.46947461e-01 8.12863886e-01 7.27995560e-02 -5.97252131e-01
-7.48905241e-01 -1.28000164e+00 -2.05103010e-01 -3.93096864e-01
1.80989414e-01 3.14297765e-01 3.27236056e-01 -9.93628919... | [8.8605318069458, 6.837864398956299] |
14e1e875-16db-4b2f-bb49-6481f872367d | a-topological-nomenclature-for-3d-shape | 1909.12887 | null | https://arxiv.org/abs/1909.12887v2 | https://arxiv.org/pdf/1909.12887v2.pdf | A Topological Nomenclature for 3D Shape Analysis in Connectomics | One of the essential tasks in connectomics is the morphology analysis of neurons and organelles like mitochondria to shed light on their biological properties. However, these biological objects often have tangled parts or complex branching patterns, which make it hard to abstract, categorize, and manipulate their morph... | ['Won-Dong Jang', 'Abhimanyu Talwar', 'Zudi Lin', 'Xueying Wang', 'Jinglin Zhao', 'Jeff W. Lichtman', 'Donglai Wei', 'Bowen Zheng', 'Yuesong Wu', 'Hanspeter Pfister'] | 2019-09-27 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-1.59347832e-01 4.51505631e-02 5.38402610e-02 -2.10817814e-01
-2.06480265e-01 -1.08762634e+00 3.33188295e-01 3.36123407e-01
-3.75363380e-01 6.95326567e-01 -7.56463856e-02 -1.88941374e-01
-1.58320576e-01 -6.17644012e-01 -6.47104263e-01 -8.53469670e-01
-6.48775920e-02 6.99745953e-01 1.06525280e-01 1.11520357... | [14.275208473205566, -3.13678240776062] |
785fe652-9d5b-4c94-9d57-09726b020d2a | text-is-not-enough-integrating-visual | 2109.05778 | null | https://arxiv.org/abs/2109.05778v2 | https://arxiv.org/pdf/2109.05778v2.pdf | Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation | Open-domain dialogue generation in natural language processing (NLP) is by default a pure-language task, which aims to satisfy human need for daily communication on open-ended topics by producing related and informative responses. In this paper, we point out that hidden images, named as visual impressions (VIs), can be... | ['Xiaofang Zhao', 'Yonghao Song', 'Xin Shen', 'Haolan Zhan', 'Lei Shen'] | 2021-09-13 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 3.89942229e-01 4.98242319e-01 1.38855159e-01 -5.94963849e-01
-8.86344135e-01 -5.13090134e-01 8.38873982e-01 -1.64870635e-01
-3.37486237e-01 7.22965479e-01 5.79685688e-01 -2.11040124e-01
7.29548693e-01 -8.49565744e-01 -5.71105897e-01 -4.38421398e-01
6.37463331e-01 6.46372736e-01 1.88243270e-01 -4.48538929... | [10.993925094604492, 1.4120914936065674] |
663e5b6d-9938-4ec7-9eca-a8ae4222f2d5 | temporal-coherent-and-graph-optimized | 1804.06253 | null | http://arxiv.org/abs/1804.06253v1 | http://arxiv.org/pdf/1804.06253v1.pdf | Temporal Coherent and Graph Optimized Manifold Ranking for Visual Tracking | Recently, weighted patch representation has been widely studied for
alleviating the impact of background information included in bounding box to
improve visual tracking results. However, existing weighted patch
representation models generally exploit spatial structure information among
patches in each frame separately ... | ['Jin Tang', 'Bin Luo', 'Doudou Lin', 'Bo Jiang'] | 2018-04-17 | null | null | null | null | ['graph-ranking'] | ['graphs'] | [ 1.00685477e-01 -3.33247334e-01 -5.38775802e-01 -1.19151697e-02
-3.87781858e-01 -4.67630208e-01 2.77721196e-01 2.54130870e-01
-1.27617776e-01 5.02841949e-01 3.10605347e-01 2.94367552e-01
-4.40771401e-01 -6.87277734e-01 -5.55174053e-01 -8.18733335e-01
-3.18390690e-02 -4.22152847e-01 1.00274181e+00 -7.95201585... | [6.423912525177002, -2.1602861881256104] |
0d074735-3c2a-4e38-904d-428e12ffeeea | self-supervised-human-mesh-recovery-with | 2209.04596 | null | https://arxiv.org/abs/2209.04596v1 | https://arxiv.org/pdf/2209.04596v1.pdf | Self-supervised Human Mesh Recovery with Cross-Representation Alignment | Fully supervised human mesh recovery methods are data-hungry and have poor generalizability due to the limited availability and diversity of 3D-annotated benchmark datasets. Recent progress in self-supervised human mesh recovery has been made using synthetic-data-driven training paradigms where the model is trained fro... | ['Ziyan Wu', 'David Doermann', 'Terrence Chen', 'Srikrishna Karanam', 'Benjamin Planche', 'Meng Zheng', 'Xuan Gong'] | 2022-09-10 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [ 3.90083581e-01 4.51710165e-01 -4.53473210e-01 -1.76514953e-01
-1.23300958e+00 -1.33029088e-01 3.73280942e-01 6.00472800e-02
2.49526259e-02 6.48361802e-01 2.85348028e-01 5.39793134e-01
9.55659151e-02 -7.57509530e-01 -1.00293720e+00 -4.28867549e-01
6.91685230e-02 9.98313367e-01 2.91763902e-01 -3.58166665... | [7.171933650970459, -1.325011134147644] |
7a833aa3-e2d1-45d5-8242-0854d8e65cea | neural-kaleidoscopic-space-sculpting | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ahn_Neural_Kaleidoscopic_Space_Sculpting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ahn_Neural_Kaleidoscopic_Space_Sculpting_CVPR_2023_paper.pdf | Neural Kaleidoscopic Space Sculpting | We introduce a method that recovers full-surround 3D reconstructions from a single kaleidoscopic image using a neural surface representation. Full-surround 3D reconstruction is critical for many applications, such as augmented and virtual reality. A kaleidoscope, which uses a single camera and multiple mirrors, is ... | ['Aswin C. Sankaranarayanan', 'Ioannis Gkioulekas', 'Michael De Zeeuw', 'Byeongjoo Ahn'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 4.5046493e-01 -3.4640020e-01 1.8918566e-01 -2.1667333e-01
-3.4058321e-01 -6.8882507e-01 3.6328146e-01 -4.1839263e-01
-1.7582750e-01 4.8745605e-01 2.2645822e-02 -1.3265924e-01
4.1549993e-01 -6.9237828e-01 -7.2272766e-01 -7.7203000e-01
8.2452136e-01 2.8382051e-01 4.0484017e-01 -3.8716570e-02
1.7368859e-01... | [9.427943229675293, -2.7291018962860107] |
342b373d-0152-455c-afce-acbf87141c07 | motifretro-exploring-the-combinability | 2305.15153 | null | https://arxiv.org/abs/2305.15153v1 | https://arxiv.org/pdf/2305.15153v1.pdf | MotifRetro: Exploring the Combinability-Consistency Trade-offs in retrosynthesis via Dynamic Motif Editing | Is there a unified framework for graph-based retrosynthesis prediction? Through analysis of full-, semi-, and non-template retrosynthesis methods, we discovered that they strive to strike an optimal balance between combinability and consistency: \textit{Should atoms be combined as motifs to simplify the molecular editi... | ['Stan Z. Li', 'Cheng Tan', 'Xingran Chen', 'Zhangyang Gao'] | 2023-05-20 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 2.35663876e-01 -1.42269686e-01 -6.13052964e-01 -7.13948533e-02
-1.69475988e-01 -1.07880557e+00 5.12400806e-01 2.01885968e-01
-1.35627002e-01 6.05338931e-01 2.87547242e-02 -7.62886345e-01
-2.11235182e-03 -8.70729685e-01 -7.12577045e-01 -6.40812695e-01
1.40015855e-01 2.77971834e-01 6.61083817e-01 -3.89059097... | [4.495567321777344, 6.109785079956055] |
6e582e6c-4634-493e-91bd-9562abba163a | using-large-pre-trained-language-model-to | 2212.01217 | null | https://arxiv.org/abs/2212.01217v1 | https://arxiv.org/pdf/2212.01217v1.pdf | Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device | This paper proposes a possible method using natural language processing that might assist in the FDA medical device marketing process. Actual device descriptions are taken and matched with the device description in FDA Title 21 of CFR to determine their corresponding device type. Both pre-trained word embeddings such a... | ['Zongzhe Xu'] | 2022-11-03 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 4.92977738e-01 4.05275464e-01 -3.99047405e-01 -4.68977362e-01
-9.69144285e-01 -9.17575717e-01 3.21284592e-01 9.71193671e-01
-4.04110521e-01 4.41783726e-01 3.95678818e-01 -8.60909820e-01
-3.02750319e-01 -6.11685336e-01 -5.13840914e-01 -1.00465693e-01
2.69029409e-01 6.50864840e-01 -2.25644067e-01 2.95197695... | [8.365416526794434, 8.657524108886719] |
f412f1ea-e5ee-476e-93ac-60536b11e10c | an-effective-approach-to-unsupervised-machine | 1902.01313 | null | https://arxiv.org/abs/1902.01313v2 | https://arxiv.org/pdf/1902.01313v2.pdf | An Effective Approach to Unsupervised Machine Translation | While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of exis... | ['Mikel Artetxe', 'Gorka Labaka', 'Eneko Agirre'] | 2019-02-04 | an-effective-approach-to-unsupervised-machine-1 | https://aclanthology.org/P19-1019 | https://aclanthology.org/P19-1019.pdf | acl-2019-7 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.93254572e-01 1.43248335e-01 -4.58261430e-01 -3.17986459e-01
-1.56058061e+00 -7.86879063e-01 1.05609000e+00 -3.17275785e-02
-6.49921000e-01 1.17031562e+00 2.11264208e-01 -7.94821739e-01
3.35731447e-01 -3.36097956e-01 -9.52350259e-01 -4.52255726e-01
4.69466835e-01 1.32089365e+00 -1.98769912e-01 -5.55059433... | [11.589447975158691, 10.348690032958984] |
4771548f-1b92-4f97-a83f-89b222fdfaf0 | sold2-self-supervised-occlusion-aware-line | 2104.03362 | null | https://arxiv.org/abs/2104.03362v2 | https://arxiv.org/pdf/2104.03362v2.pdf | SOLD2: Self-supervised Occlusion-aware Line Description and Detection | Compared to feature point detection and description, detecting and matching line segments offer additional challenges. Yet, line features represent a promising complement to points for multi-view tasks. Lines are indeed well-defined by the image gradient, frequently appear even in poorly textured areas and offer robust... | ['Marc Pollefeys', 'Martin R. Oswald', 'Viktor Larsson', 'Juan-Ting Lin', 'Rémi Pautrat'] | 2021-04-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_Line_Description_and_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_Line_Description_and_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['line-detection', 'wireframe-parsing'] | ['computer-vision', 'computer-vision'] | [-7.97843486e-02 -3.51091921e-01 -2.67311871e-01 -3.85444820e-01
-9.86281335e-01 -1.02182281e+00 7.64336109e-01 4.80318367e-01
-3.22390646e-01 3.47409844e-01 -1.45345420e-01 2.14304954e-01
4.89985757e-02 -6.48595393e-01 -8.16776752e-01 -3.15041482e-01
-8.13995898e-02 5.60393929e-01 5.36860883e-01 -2.84679830... | [8.158283233642578, -2.0482399463653564] |
5db111f8-9d9a-4bb1-a833-2430fa441863 | analyzing-and-improving-the-robustness-of | null | null | https://ieeexplore.ieee.org/document/9679972 | https://ieeexplore.ieee.org/document/9679972 | Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual Explanations | Recent studies have revealed that Machine Learning (ML) models are vulnerable to adversarial perturbations. Such perturbations can be intentionally or accidentally added to the original inputs, evading the classifier's behavior to misclassify the crafted samples. A widely-used solution is to retrain the model using dat... | ['Ingrid Chieh Yu', 'Peyman Rasouli'] | 2021-12-13 | null | null | null | 20th-ieee-international-conference-on-machine-1 | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.01622444e-01 4.87845570e-01 -3.33573312e-01 -2.23434046e-01
-3.61090243e-01 -9.98548329e-01 8.03000689e-01 2.42689952e-01
-9.52536985e-02 1.02361107e+00 -1.66376844e-01 -6.90936625e-01
-2.81054586e-01 -8.89910221e-01 -1.13785553e+00 -9.07708347e-01
-7.82141834e-02 1.53194815e-01 -4.36550565e-02 -7.03517720... | [5.753055572509766, 7.646125316619873] |
9a83d95a-5354-42d3-9f2b-e306c57188fe | towards-listening-to-10-people-simultaneously | 2010.11871 | null | https://arxiv.org/abs/2010.11871v2 | https://arxiv.org/pdf/2010.11871v2.pdf | Towards Listening to 10 People Simultaneously: An Efficient Permutation Invariant Training of Audio Source Separation Using Sinkhorn's Algorithm | In neural network-based monaural speech separation techniques, it has been recently common to evaluate the loss using the permutation invariant training (PIT) loss. However, the ordinary PIT requires to try all $N!$ permutations between $N$ ground truths and $N$ estimates. Since the factorial complexity explodes very r... | ['Hideyuki Tachibana'] | 2020-10-22 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 8.48346129e-02 -1.25932127e-01 2.48955805e-02 -2.43126214e-01
-1.12309039e+00 -2.56380498e-01 -9.31237191e-02 -4.55959380e-01
-5.42055488e-01 6.98811233e-01 -1.78215444e-01 -3.92078012e-01
-3.75090748e-01 -6.07719183e-01 -8.61113906e-01 -8.27813447e-01
-3.22215736e-01 2.07873672e-01 -1.94398686e-03 -1.90571751... | [15.242488861083984, 5.64982795715332] |
bbf3e2fa-4961-4ebe-b393-712fa277e6fd | geometric-scene-parsing-with-hierarchical | 1604.01931 | null | http://arxiv.org/abs/1604.01931v2 | http://arxiv.org/pdf/1604.01931v2.pdf | Geometric Scene Parsing with Hierarchical LSTM | This paper addresses the problem of geometric scene parsing, i.e.
simultaneously labeling geometric surfaces (e.g. sky, ground and vertical
plane) and determining the interaction relations (e.g. layering, supporting,
siding and affinity) between main regions. This problem is more challenging
than the traditional semant... | ['Xiaobai Liu', 'Ruimao Zhang', 'Liang Lin', 'Zhanglin Peng', 'Xiaodan Liang'] | 2016-04-07 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 6.19683623e-01 3.91607761e-01 4.54214513e-02 -6.38736188e-01
-1.07373261e+00 -3.02381903e-01 2.88116157e-01 4.60859202e-02
-5.37433242e-03 1.24331065e-01 2.14661345e-01 -3.24384391e-01
8.96443203e-02 -1.10067821e+00 -1.16086626e+00 -7.10890114e-01
-1.16453230e-01 4.68771428e-01 5.45549572e-01 1.75592210... | [9.581010818481445, 0.3583662807941437] |
d8d38ee5-358d-4334-bd9d-7f3b96fa6f8d | interactive-fashion-content-generation-using | 2306.05182 | null | https://arxiv.org/abs/2306.05182v1 | https://arxiv.org/pdf/2306.05182v1.pdf | Interactive Fashion Content Generation Using LLMs and Latent Diffusion Models | Fashionable image generation aims to synthesize images of diverse fashion prevalent around the globe, helping fashion designers in real-time visualization by giving them a basic customized structure of how a specific design preference would look in real life and what further improvements can be made for enhanced custom... | ['Nevasini Sasikumar', 'Krishna Sri Ipsit Mantri'] | 2023-05-15 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 1.43163234e-01 2.88701300e-02 2.37122215e-02 -4.12698686e-01
-3.80123019e-01 -7.74553895e-01 7.24650800e-01 -1.42127842e-01
2.42571846e-01 4.03868556e-01 7.33399749e-01 2.31490582e-02
6.17207475e-02 -9.45963085e-01 -5.87116599e-01 -4.70685333e-01
3.88864160e-01 5.10215878e-01 -2.82892555e-01 -3.17971021... | [11.45038890838623, -0.2499590516090393] |
e7f3e41d-469b-4020-81c2-8b346020b7bc | analyzing-input-and-output-representations-1 | 1903.03369 | null | https://arxiv.org/abs/1903.03369v4 | https://arxiv.org/pdf/1903.03369v4.pdf | Analyzing Input and Output Representations for Speech-Driven Gesture Generation | This paper presents a novel framework for automatic speech-driven gesture generation, applicable to human-agent interaction including both virtual agents and robots. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning. Our... | ['Hedvig Kjellström', 'Gustav Eje Henter', 'Taras Kucherenko', 'Dai Hasegawa', 'Naoshi Kaneko'] | 2019-03-08 | analyzing-input-and-output-representations | null | null | arxiv-2019-3 | ['gesture-generation'] | ['robots'] | [ 2.77790397e-01 2.81748086e-01 2.11357772e-02 -2.24235058e-01
-6.95705414e-01 -4.20420051e-01 8.72254193e-01 -6.41167641e-01
-4.77059901e-01 3.65622848e-01 8.91805053e-01 9.22408104e-02
2.26504341e-01 -5.62757552e-01 -5.98263264e-01 -9.28781331e-01
-5.75309508e-02 4.48015869e-01 1.96374655e-01 -3.65373552... | [5.610669136047363, -0.10460688918828964] |
597ee8b3-d5f3-4ad1-a8c0-07a0f6b58255 | low-resource-learning-with-knowledge-graphs-a | 2112.10006 | null | https://arxiv.org/abs/2112.10006v6 | https://arxiv.org/pdf/2112.10006v6.pdf | Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey | Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision. As sufficient labeled training data are not always ready due to e.g., continuously emerging prediction targets and costly sample annotation in real world applications,... | ['Huajun Chen', 'Wen Zhang', 'Jeff Z. Pan', 'Zhuo Chen', 'Yuan He', 'Ian Horrocks', 'Yuxia Geng', 'Jiaoyan Chen'] | 2021-12-18 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [ 3.42763603e-01 3.18728000e-01 -7.19639182e-01 -5.91193616e-01
-4.61022377e-01 4.98096049e-02 2.94004321e-01 2.51411855e-01
-4.99133587e-01 9.44936275e-01 -8.54490697e-02 -1.47555903e-01
-3.22529525e-01 -1.08881545e+00 -5.17472923e-01 -6.95954323e-01
2.78685719e-01 6.16852343e-01 1.97455481e-01 -5.52460924... | [10.027558326721191, 3.219343423843384] |
321a7b78-0040-49bd-823d-e83861aac801 | cross-modal-search-method-of-technology-video | 2210.05243 | null | https://arxiv.org/abs/2210.05243v1 | https://arxiv.org/pdf/2210.05243v1.pdf | Cross-modal Search Method of Technology Video based on Adversarial Learning and Feature Fusion | Technology videos contain rich multi-modal information. In cross-modal information search, the data features of different modalities cannot be compared directly, so the semantic gap between different modalities is a key problem that needs to be solved. To address the above problems, this paper proposes a novel Feature ... | ['Ang Li', 'Meiyu Liang', 'Junping Du', 'Xiangbin Liu'] | 2022-10-11 | null | null | null | null | ['text-to-video-search'] | ['natural-language-processing'] | [ 1.80060759e-01 -6.97979510e-01 -1.32209823e-01 -4.71627451e-02
-9.62273657e-01 -7.09308803e-01 7.13032305e-01 -2.94856727e-01
-2.66495168e-01 3.43776494e-01 3.72766674e-01 3.40334564e-01
-5.39923847e-01 -7.55575538e-01 -4.02442962e-01 -8.48343134e-01
2.59187698e-01 8.87774676e-02 2.80123800e-01 -3.14762890... | [11.177681922912598, 1.1961500644683838] |
324e1360-d736-49a8-82b6-d573526d336b | wearable-seld-dataset-dataset-for-sound-event | 2202.08458 | null | https://arxiv.org/abs/2202.08458v1 | https://arxiv.org/pdf/2202.08458v1.pdf | Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head | Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction. Deep neural networks (DNNs) are utilized to associate them with the sound signals observed by a microphone array. Although ambisonic microphones are popular in the literature of SELD, they might limit the ... | ['Yasuhiro Oikawa', 'Shoichiro Saito', 'Kohei Yatabe', 'Masahiro Yasuda', 'Kento Nagatomo'] | 2022-02-17 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [-6.63609281e-02 -4.82322037e-01 4.16288704e-01 -3.47330123e-01
-2.67297715e-01 -2.08991334e-01 -6.48519099e-02 -2.09321886e-01
-3.28452349e-01 4.23607051e-01 4.73856151e-01 -1.48821145e-01
2.51367778e-01 -7.50232041e-01 -5.92915595e-01 -5.81516325e-01
-5.67630753e-02 -3.59300047e-01 3.45838010e-01 2.39218339... | [15.020191192626953, 5.622372627258301] |
437088a7-11c3-4a71-9560-4f70e6825a67 | towards-selection-of-text-to-speech-data-to | 2306.00998 | null | https://arxiv.org/abs/2306.00998v1 | https://arxiv.org/pdf/2306.00998v1.pdf | Towards Selection of Text-to-speech Data to Augment ASR Training | This paper presents a method for selecting appropriate synthetic speech samples from a given large text-to-speech (TTS) dataset as supplementary training data for an automatic speech recognition (ASR) model. We trained a neural network, which can be optimised using cross-entropy loss or Arcface loss, to measure the sim... | ['Ozlem Kalinli', 'Jay Mahadeokar', 'Yuan Shangguan', 'Gil Keren', 'Chunyang Wu', 'Leda Sari', 'Shuo Liu'] | 2023-05-30 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 7.04251945e-01 3.64930063e-01 -7.98061192e-02 -6.52490437e-01
-1.30551767e+00 -4.47624862e-01 6.86867654e-01 -2.33020157e-01
-5.04041672e-01 6.63963318e-01 3.09576392e-01 -6.86218917e-01
2.90266186e-01 -1.99293941e-01 -5.75851381e-01 -5.12755156e-01
1.88643768e-01 4.28318977e-01 -4.06189635e-02 -2.79102325... | [14.49475383758545, 6.61788272857666] |
da0f7e55-0c58-4a91-9c82-f31f1bffdea7 | adapting-neural-link-predictors-for-complex | 2301.12313 | null | https://arxiv.org/abs/2301.12313v3 | https://arxiv.org/pdf/2301.12313v3.pdf | Adapting Neural Link Predictors for Data-Efficient Complex Query Answering | Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to address this problem by designing architectures trained end-to-end for the complex query answering task ... | ['Isabelle Augenstein', 'Michael Cochez', 'Daniel Daza', 'Pasquale Minervini', 'Erik Arakelyan'] | 2023-01-29 | null | null | null | null | ['complex-query-answering'] | ['knowledge-base'] | [ 8.96200314e-02 6.43708527e-01 -1.89202636e-01 -5.29644012e-01
-1.11209846e+00 -6.21889472e-01 2.29991719e-01 3.16755027e-01
-5.54994941e-01 7.72219718e-01 -2.51573473e-01 -7.92422533e-01
-4.34111089e-01 -1.24819934e+00 -1.22053325e+00 1.49089977e-01
-1.02554247e-01 1.02392900e+00 5.48832834e-01 -6.16917074... | [9.478941917419434, 7.714141845703125] |
c1ca1bfc-8e76-4f38-818e-fde4f8af0b7b | doubly-contrastive-end-to-end-semantic | 2211.11131 | null | https://arxiv.org/abs/2211.11131v1 | https://arxiv.org/pdf/2211.11131v1.pdf | Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather | Road scene understanding tasks have recently become crucial for self-driving vehicles. In particular, real-time semantic segmentation is indispensable for intelligent self-driving agents to recognize roadside objects in the driving area. As prior research works have primarily sought to improve the segmentation performa... | ['Jong-Hwan Kim', 'Jongoh Jeong'] | 2022-11-21 | null | null | null | null | ['road-scene-understanding', 'real-time-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.90067703e-01 -1.40675828e-01 -6.84900442e-03 -6.25559747e-01
-3.55377853e-01 -2.40571856e-01 2.43558303e-01 -1.38356969e-01
-8.48273635e-01 5.99949956e-01 -6.69564903e-01 -7.82926917e-01
4.78546917e-01 -9.11794364e-01 -9.46062803e-01 -6.08267248e-01
3.27455997e-03 4.06422168e-01 9.01319146e-01 -4.48914647... | [8.990496635437012, -0.8834424614906311] |
371728d0-ff24-44b4-a19a-ec3bd073f4e8 | leveraging-patient-similarity-and-time-series | 1704.07498 | null | http://arxiv.org/abs/1704.07498v3 | http://arxiv.org/pdf/1704.07498v3.pdf | Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models | Patient time series classification faces challenges in high degrees of
dimensionality and missingness. In light of patient similarity theory, this
study explores effective temporal feature engineering and reduction, missing
value imputation, and change point detection methods that can afford
similarity-based classifica... | ['Samir AbdelRahman', 'Mohammad Amin Morid', 'Olivia R. Liu Sheng'] | 2017-04-25 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 1.69754058e-01 -6.24023557e-01 -2.74764925e-01 -4.63101417e-01
-5.70937514e-01 -2.52078772e-01 5.13272248e-02 6.72205389e-01
-2.25855976e-01 8.53281200e-01 6.33700132e-01 -3.29531491e-01
-1.30554569e+00 -5.39122701e-01 4.65055071e-02 -6.22026205e-01
-7.85412371e-01 5.24489880e-01 -4.01633084e-01 -2.61226296... | [7.952169418334961, 6.130927562713623] |
e2868c59-b422-4d11-a26a-bf3603d6fa16 | open-retrieval-conversational-question | 2005.11364 | null | https://arxiv.org/abs/2005.11364v1 | https://arxiv.org/pdf/2005.11364v1.pdf | Open-Retrieval Conversational Question Answering | Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and conversational question answering, where an answer is either selected from a given candidate set or extracted from a given passage. These simplific... | ['W. Bruce Croft', 'Cen Chen', 'Liu Yang', 'Mohit Iyyer', 'Minghui Qiu', 'Chen Qu'] | 2020-05-22 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-4.79586460e-02 1.47599965e-01 -2.43412703e-01 -2.10954174e-01
-1.68670154e+00 -9.85312462e-01 9.32728410e-01 2.25180492e-01
-4.12316799e-01 6.25869215e-01 8.63585532e-01 -5.48571825e-01
-1.99977770e-01 -6.01444304e-01 -4.33133692e-01 -3.54975194e-01
1.42028660e-01 8.72386813e-01 4.65077043e-01 -6.86089754... | [12.060586929321289, 7.835334300994873] |
a28e5852-74bd-4a38-9549-856de2d7ab2b | structured-training-for-neural-network | 1506.06158 | null | http://arxiv.org/abs/1506.06158v1 | http://arxiv.org/pdf/1506.06158v1.pdf | Structured Training for Neural Network Transition-Based Parsing | We present structured perceptron training for neural network transition-based
dependency parsing. We learn the neural network representation using a gold
corpus augmented by a large number of automatically parsed sentences. Given
this fixed network representation, we learn a final layer using the structured
perceptron ... | ['Slav Petrov', 'Chris Alberti', 'Michael Collins', 'David Weiss'] | 2015-06-19 | structured-training-for-neural-network-1 | https://aclanthology.org/P15-1032 | https://aclanthology.org/P15-1032.pdf | ijcnlp-2015-7 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 1.66201994e-01 1.06386983e+00 -5.30434966e-01 -9.51034904e-01
-1.03393507e+00 -5.29802203e-01 3.28931101e-02 1.75538868e-01
-7.51332700e-01 1.04108334e+00 4.12322849e-01 -9.10409033e-01
3.87103707e-01 -6.72391236e-01 -6.96208596e-01 -3.06522667e-01
-2.74110317e-01 7.64172614e-01 7.55190700e-02 -2.56400496... | [10.330048561096191, 9.664861679077148] |
346cddde-2b34-4b92-bb15-b1a0b5ae389f | optimizing-drug-design-by-merging-generative | 2305.06334 | null | https://arxiv.org/abs/2305.06334v1 | https://arxiv.org/pdf/2305.06334v1.pdf | Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks | Traditional drug discovery programs are being transformed by the advent of machine learning methods. Among these, Generative AI methods (GM) have gained attention due to their ability to design new molecules and enhance specific properties of existing ones. However, current GM methods have limitations, such as low affi... | ['Victor Guallar', 'Soumya Ray', 'Ajay S Yekkirala', 'Laura Malo', 'Julia Vilalta Mor', 'Yang Ming Zhu', 'Lucía Díaz', 'Marek Orzechowski', 'Alexis Molina', 'Isaac Filella-Merce'] | 2023-05-04 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.56323868e-01 1.59416676e-01 -2.32732937e-01 -2.28261463e-02
-7.90747881e-01 -8.54770482e-01 5.28432965e-01 4.62094665e-01
-1.82675377e-01 1.46157634e+00 9.07551348e-02 -4.09796000e-01
-2.21946508e-01 -8.27232420e-01 -8.07017207e-01 -1.14182055e+00
5.10778874e-02 6.30268455e-01 -6.10977784e-02 -1.70508415... | [4.981445789337158, 5.707146644592285] |
89b727df-d4a4-485d-94e1-6f60db729759 | learning-and-memorizing-representative | 2001.01349 | null | https://arxiv.org/abs/2001.01349v1 | https://arxiv.org/pdf/2001.01349v1.pdf | Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation | 3D point cloud semantic and instance segmentation is crucial and fundamental for 3D scene understanding. Due to the complex structure, point sets are distributed off balance and diversely, which appears as both category imbalance and pattern imbalance. As a result, deep networks can easily forget the non-dominant cases... | ['Chunhua Shen', 'Zhi Tian', 'Dong Gong', 'Tong He'] | 2020-01-06 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3039_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630545.pdf | eccv-2020-8 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-1.52523980e-01 -2.48804912e-01 -7.45589808e-02 -5.96047044e-01
-2.11077631e-01 -2.37023920e-01 1.81438908e-01 2.62005568e-01
-3.09621960e-01 5.21184385e-01 -1.51500821e-01 4.50928546e-02
-3.00171852e-01 -8.85718048e-01 -7.16641605e-01 -7.62672961e-01
8.46203789e-03 6.78234577e-01 4.43744630e-01 5.43703958... | [7.895294666290283, -3.2336583137512207] |
476b5c6e-7c82-4dd5-95a1-799fba1622b8 | oops-did-i-just-say-that-testing-and | 2305.02626 | null | https://arxiv.org/abs/2305.02626v1 | https://arxiv.org/pdf/2305.02626v1.pdf | "Oops, Did I Just Say That?" Testing and Repairing Unethical Suggestions of Large Language Models with Suggest-Critique-Reflect Process | As the popularity of large language models (LLMs) soars across various applications, ensuring their alignment with human values has become a paramount concern. In particular, given that LLMs have great potential to serve as general-purpose AI assistants in daily life, their subtly unethical suggestions become a serious... | ['Shuai Wang', 'Ao Sun', 'Zongjie Li', 'Pingchuan Ma'] | 2023-05-04 | null | null | null | null | ['moral-scenarios'] | ['miscellaneous'] | [ 1.39297202e-01 5.74021697e-01 1.59600880e-02 -3.58049631e-01
-6.85583770e-01 -7.90185452e-01 5.07903636e-01 -7.41795078e-02
-4.68481541e-01 7.56015837e-01 -2.53384739e-01 -9.52742040e-01
4.61887419e-02 -4.48018044e-01 -7.03447163e-01 -3.34964454e-01
4.09061044e-01 3.03376287e-01 -3.56818289e-02 -1.40042603... | [10.29927921295166, 7.6158246994018555] |
07d54cee-d462-4509-bd0f-35beb13de91e | effects-of-human-dynamics-on-epidemic | 1605.00899 | null | http://arxiv.org/abs/1605.00899v1 | http://arxiv.org/pdf/1605.00899v1.pdf | Effects of human dynamics on epidemic spreading in C\^{o}te d'Ivoire | Understanding and predicting outbreaks of contagious diseases are crucial to
the development of society and public health, especially for underdeveloped
countries. However, challenging problems are encountered because of complex
epidemic spreading dynamics influenced by spatial structure and human dynamics
(including b... | [] | 2016-04-30 | null | null | null | null | ['human-dynamics'] | ['computer-vision'] | [-1.51952282e-01 -1.08335465e-01 -9.97836217e-02 2.46127173e-01
5.39806545e-01 -3.24508816e-01 4.66839671e-01 1.96811602e-01
-4.62699682e-01 6.83966279e-01 8.42556804e-02 -6.35949194e-01
-4.76012975e-01 -9.81723905e-01 -7.91377574e-02 -9.80498314e-01
-8.37623477e-01 6.01988196e-01 2.76784956e-01 -5.65332592... | [5.955801486968994, 4.436418533325195] |
158dd09f-b5b6-4884-b8d1-355c5ef05ec3 | efficient-liver-segmentation-with-3d-cnn | 2208.13271 | null | https://arxiv.org/abs/2208.13271v1 | https://arxiv.org/pdf/2208.13271v1.pdf | Efficient liver segmentation with 3D CNN using computed tomography scans | The liver is one of the most critical metabolic organs in vertebrates due to its vital functions in the human body, such as detoxification of the blood from waste products and medications. Liver diseases due to liver tumors are one of the most common mortality reasons around the globe. Hence, detecting liver tumors in ... | ['Mohammed Sallah', 'Mohammed Elmogy', 'Ahmed Elgarayhi', 'Nabila Eladawi', 'Yasmeen Al-Saeed', 'Khaled Humady'] | 2022-08-28 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-6.35582805e-01 -1.19261369e-01 -2.17561364e-01 -1.48510128e-01
1.50698617e-01 -4.17011648e-01 1.11930780e-01 2.20025599e-01
-4.53077883e-01 5.21593571e-01 1.71053559e-01 -4.13581759e-01
2.45151162e-01 -7.93186784e-01 -2.40871832e-02 -8.94738913e-01
-1.58521980e-01 5.41691303e-01 8.40816647e-02 3.90393734... | [14.493050575256348, -2.7156295776367188] |
3d9fd0fc-13e4-4d28-8257-2a88666cee36 | robust-face-alignment-using-a-mixture-of | 1511.04404 | null | http://arxiv.org/abs/1511.04404v2 | http://arxiv.org/pdf/1511.04404v2.pdf | Robust Face Alignment Using a Mixture of Invariant Experts | Face alignment, which is the task of finding the locations of a set of facial
landmark points in an image of a face, is useful in widespread application
areas. Face alignment is particularly challenging when there are large
variations in pose (in-plane and out-of-plane rotations) and facial expression.
To address this ... | ['Tim K. Marks', 'Salil Tambe', 'Oncel Tuzel'] | 2015-11-13 | null | null | null | null | ['robust-face-alignment'] | ['computer-vision'] | [ 8.87231752e-02 -1.99576840e-01 -3.32509995e-01 -7.95711696e-01
-5.29234111e-01 -6.66448295e-01 4.86721188e-01 -5.33275962e-01
-2.52944201e-01 1.43578231e-01 -4.15758137e-03 3.52959603e-01
2.16470286e-01 -4.04994190e-01 -6.99581981e-01 -7.38376796e-01
2.80603796e-01 4.98715907e-01 -1.78339064e-01 -2.37774640... | [13.358819007873535, 0.29944318532943726] |
f0d53d93-219c-4b9c-8cb7-206dda7ef046 | fine-grained-noise-control-for-multispeaker | 2204.0507 | null | https://arxiv.org/abs/2204.05070v2 | https://arxiv.org/pdf/2204.05070v2.pdf | Fine-grained Noise Control for Multispeaker Speech Synthesis | A text-to-speech (TTS) model typically factorizes speech attributes such as content, speaker and prosody into disentangled representations.Recent works aim to additionally model the acoustic conditions explicitly, in order to disentangle the primary speech factors, i.e. linguistic content, prosody and timbre from any r... | ['Pirros Tsiakoulis', 'Aimilios Chalamandaris', 'Gunu Jho', 'June Sig Sung', 'Spyros Raptis', 'Konstantinos Markopoulos', 'Konstantinos Klapsas', 'Nikolaos Ellinas', 'Georgios Vamvoukakis', 'Karolos Nikitaras'] | 2022-04-11 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [-6.73372820e-02 1.80620208e-01 5.54400356e-03 -3.16083491e-01
-9.63603020e-01 -5.64922869e-01 6.86419904e-01 -1.59072995e-01
-3.75656858e-02 6.33541346e-01 9.88098145e-01 -1.17419057e-01
2.77480841e-01 -3.98724645e-01 -6.07497752e-01 -8.30910385e-01
3.43977243e-01 1.11379586e-01 -4.04819280e-01 -2.13821709... | [14.988808631896973, 6.527825832366943] |
25b7747c-7643-416e-953a-7cc96ae90285 | automatic-context-window-composition-for | 1805.10498 | null | http://arxiv.org/abs/1805.10498v1 | http://arxiv.org/pdf/1805.10498v1.pdf | Automatic context window composition for distant speech recognition | Distant speech recognition is being revolutionized by deep learning, that has
contributed to significantly outperform previous HMM-GMM systems. A key aspect
behind the rapid rise and success of DNNs is their ability to better manage
large time contexts. With this regard, asymmetric context windows that embed
more past ... | ['Mirco Ravanelli', 'Maurizio Omologo'] | 2018-05-26 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 2.71758139e-02 -4.23646688e-01 3.56218100e-01 -2.42442802e-01
-3.15998077e-01 -2.45663404e-01 6.82094038e-01 -1.01343296e-01
-6.68103993e-01 6.89430892e-01 3.68340373e-01 -4.74411249e-01
-4.57274579e-02 -4.56415653e-01 -2.86235332e-01 -9.82313454e-01
-1.17034100e-01 -1.40221968e-01 3.29949409e-01 -1.39563635... | [14.842227935791016, 5.873052597045898] |
13ad7a38-a4b2-44d2-8808-a9af2c40f830 | appt-asymmetric-parallel-point-transformer | 2303.17815 | null | https://arxiv.org/abs/2303.17815v1 | https://arxiv.org/pdf/2303.17815v1.pdf | APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding | Transformer-based networks have achieved impressive performance in 3D point cloud understanding. However, most of them concentrate on aggregating local features, but neglect to directly model global dependencies, which results in a limited effective receptive field. Besides, how to effectively incorporate local and glo... | ['Deng Cai', 'Binbin Lin', 'Boxi Wu', 'Wenxiao Wang', 'Zheng Yang', 'Zhihao Chi', 'Tu Zheng', 'Hengjia Li'] | 2023-03-31 | null | null | null | null | ['3d-shape-retrieval', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-3.04107428e-01 -3.31204012e-02 -3.02714091e-02 -3.92894864e-01
-4.66685504e-01 -6.56364501e-01 4.98431504e-01 9.24763307e-02
-6.59620464e-02 2.12630332e-02 -1.34216189e-01 -1.05409905e-01
-2.55794406e-01 -1.06091547e+00 -8.82723093e-01 -4.94356990e-01
1.34495586e-01 7.59521186e-01 5.75956702e-01 -1.64066240... | [7.970794677734375, -3.461402177810669] |
a6314247-80ef-4fef-8022-cf29fa05cb5c | leveraging-sequence-embedding-and | 2112.00344 | null | https://arxiv.org/abs/2112.00344v1 | https://arxiv.org/pdf/2112.00344v1.pdf | Leveraging Sequence Embedding and Convolutional Neural Network for Protein Function Prediction | The capability of accurate prediction of protein functions and properties is essential in the biotechnology industry, e.g. drug development and artificial protein synthesis, etc. The main challenges of protein function prediction are the large label space and the lack of labeled training data. Our method leverages unsu... | ['Min Sun', 'Jia-Hua Wu', 'Po-Han Chi', 'Wei-Cheng Tseng'] | 2021-12-01 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 3.74067307e-01 -1.05393670e-01 -2.08215430e-01 -2.60915369e-01
-3.20608079e-01 -9.21531320e-01 2.68856548e-02 3.87036264e-01
-4.65597957e-01 1.28192115e+00 -6.30735140e-03 -4.57687765e-01
8.61673132e-02 -5.64719856e-01 -7.77548254e-01 -9.76880252e-01
1.10310018e-01 3.35904658e-01 1.45494074e-01 -4.87909503... | [4.74404239654541, 5.64155387878418] |
0cee356a-2a4d-45bb-bed4-f846fb22daf5 | efficient-zero-shot-event-extraction-with | 2211.05156 | null | https://arxiv.org/abs/2211.05156v2 | https://arxiv.org/pdf/2211.05156v2.pdf | Efficient Zero-shot Event Extraction with Context-Definition Alignment | Event extraction (EE) is the task of identifying interested event mentions from text. Conventional efforts mainly focus on the supervised setting. However, these supervised models cannot generalize to event types out of the pre-defined ontology. To fill this gap, many efforts have been devoted to the zero-shot EE probl... | ['Dong Yu', 'Wenlin Yao', 'Hongming Zhang'] | 2022-11-09 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 3.20729494e-01 3.84370118e-01 -4.68248576e-01 -5.32031894e-01
-8.28796327e-01 -5.68777561e-01 6.42670870e-01 5.86034477e-01
-6.06665969e-01 6.08281434e-01 4.77426231e-01 -2.32500091e-01
-2.16542378e-01 -1.07537067e+00 -6.75299048e-01 -4.71511781e-01
6.65433146e-03 4.08550322e-01 4.89525288e-01 -1.72089070... | [9.149552345275879, 9.104650497436523] |
8d59c634-756f-4ce7-818d-ebc481e784de | analysis-of-chatgpt-on-source-code | 2306.00597 | null | https://arxiv.org/abs/2306.00597v2 | https://arxiv.org/pdf/2306.00597v2.pdf | Analysis of ChatGPT on Source Code | This paper explores the use of Large Language Models (LLMs) and in particular ChatGPT in programming, source code analysis, and code generation. LLMs and ChatGPT are built using machine learning and artificial intelligence techniques, and they offer several benefits to developers and programmers. While these models can... | ['Ahmed R. Sadik', 'Jibesh Patra', 'Frank Joublin', 'Antonello Ceravola'] | 2023-06-01 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-4.58040684e-01 3.76905620e-01 -2.67023593e-01 -2.06452414e-01
-4.93123472e-01 -2.97469497e-01 2.31742248e-01 6.37689650e-01
3.13691765e-01 3.24482560e-01 -1.07209496e-01 -6.63610816e-01
-4.74540628e-02 -6.00958288e-01 -3.60478818e-01 1.38772994e-01
-2.76001066e-01 2.76654750e-01 1.55113950e-01 -1.51730835... | [7.825216770172119, 7.645615100860596] |
52095f56-60d3-4b09-aa3b-754c3c0e11e5 | boundary-guided-context-aggregation-for | 2110.14587 | null | https://arxiv.org/abs/2110.14587v1 | https://arxiv.org/pdf/2110.14587v1.pdf | Boundary Guided Context Aggregation for Semantic Segmentation | The recent studies on semantic segmentation are starting to notice the significance of the boundary information, where most approaches see boundaries as the supplement of semantic details. However, simply combing boundaries and the mainstream features cannot ensure a holistic improvement of semantics modeling. In contr... | ['Di Huang', 'Hongyu Yang', 'Haoxiang Ma'] | 2021-10-27 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 2.46667102e-01 1.04171753e-01 -1.98977321e-01 -5.74886203e-01
-2.79178619e-01 -1.94409639e-01 4.71669018e-01 4.15499091e-01
-3.05034310e-01 4.59600836e-01 2.95833915e-01 8.98911729e-02
-2.27882415e-01 -9.31860030e-01 -4.52177376e-01 -9.18443441e-01
2.10457623e-01 5.55548295e-02 7.68757343e-01 -2.04637945... | [9.539497375488281, -0.6345651149749756] |
d16b47d8-a903-43c7-b8a5-c9189c1eeeb3 | learning-to-recover-reasoning-chains-for | 2004.02393 | null | https://arxiv.org/abs/2004.02393v1 | https://arxiv.org/pdf/2004.02393v1.pdf | Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games | We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i.e., the question-answer pairs. We propose a cooperative game approach to deal with this problem, in which how the evidence passages are selected and how the selected passages are connected are handled by two models that... | ['Jun-Jie Huang', 'Xiaodan Zhu', 'Xiaoxiao Guo', 'Yufei Feng', 'Shiyu Chang', 'Wenhan Xiong', 'Murray Campbell', 'Mo Yu', 'Michael Greenspan'] | 2020-04-06 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [-1.72166541e-01 6.33828282e-01 -4.12089944e-01 -5.06813467e-01
-1.55600035e+00 -7.27865815e-01 2.79263586e-01 2.68973261e-01
-3.55614722e-01 1.27783990e+00 1.92788601e-01 -2.65556693e-01
-4.22645062e-01 -6.83944285e-01 -9.46305692e-01 -4.23879534e-01
-1.07986115e-01 1.14907908e+00 1.04451334e+00 -3.22527796... | [10.975397109985352, 7.9300737380981445] |
921d31f9-c69d-4125-82c2-20a24c447747 | learning-long-term-dependencies-in | 2006.04418 | null | https://arxiv.org/abs/2006.04418v4 | https://arxiv.org/pdf/2006.04418v4.pdf | Learning Long-Term Dependencies in Irregularly-Sampled Time Series | Recurrent neural networks (RNNs) with continuous-time hidden states are a natural fit for modeling irregularly-sampled time series. These models, however, face difficulties when the input data possess long-term dependencies. We prove that similar to standard RNNs, the underlying reason for this issue is the vanishing o... | ['Ramin Hasani', 'Mathias Lechner'] | 2020-06-08 | null | http://proceedings.neurips.cc/paper/2020/hash/fa733611ef13bd333ebfbab7eed14b63-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/fa733611ef13bd333ebfbab7eed14b63-Paper.pdf | neurips-2020-12 | ['sequential-image-classification'] | ['computer-vision'] | [-1.95623890e-01 2.42088623e-02 -4.83318679e-02 7.51659786e-03
-3.10071141e-01 -4.06663835e-01 4.16760147e-01 -4.16606426e-01
-3.31446141e-01 6.61536098e-01 2.81076226e-02 -5.59427142e-01
-1.98291894e-02 -4.79801595e-01 -7.77900755e-01 -8.85007739e-01
-2.37961099e-01 2.37767547e-01 -1.44399345e-01 -3.13131690... | [7.309007167816162, 3.370670795440674] |
9a680f3a-700b-437d-a220-c48f579a7e44 | a-step-towards-interpretable-multi-hop | null | null | https://aclanthology.org/2022.lrec-1.485 | https://aclanthology.org/2022.lrec-1.485.pdf | A STEP towards Interpretable Multi-Hop Reasoning:Bridge Phrase Identification and Query Expansion | We propose an unsupervised method for the identification of bridge phrases in multi-hop question answering (QA). Our method constructs a graph of noun phrases from the question and the available context, and applies the Steiner tree algorithm to identify the minimal sub-graph that connects all question phrases. Nodes i... | ['Mihai Surdeanu', 'Fan Luo'] | null | null | null | null | lrec-2022-6 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 2.04261672e-02 7.00840354e-01 -2.83757865e-01 -2.51522452e-01
-1.46324742e+00 -8.13128889e-01 1.74561515e-01 9.38589692e-01
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-6.99016690e-01 -9.29324806e-01 -5.65724134e-01 -2.63747901e-01
1.80207565e-01 9.03460741e-01 1.13640130e+00 -6.05935454... | [10.869186401367188, 7.953239440917969] |
e5a98501-bda2-4a9e-b2f1-12639cf070b2 | reusable-phrase-extraction-based-on-syntactic | null | null | https://aclanthology.org/2020.ccl-1.108 | https://aclanthology.org/2020.ccl-1.108.pdf | Reusable Phrase Extraction Based on Syntactic Parsing | Academic Phrasebank is an important resource for academic writers. Student writers use the phrases of Academic Phrasebank organizing their research article to improve their writing ability. Due to the limited size of Academic Phrasebank, it can not meet all the academic writing needs. There are still a large number of ... | ['Christoph Zähner', 'Xiaojing Bai', 'Zan Hongying', 'Xuemin Duan'] | null | null | null | null | ccl-2020-10 | ['constituency-parsing'] | ['natural-language-processing'] | [-3.63636553e-01 3.51981103e-01 -4.35050488e-01 -7.07816407e-02
-5.65573931e-01 -7.16831744e-01 3.85589093e-01 2.75427848e-01
-3.63274425e-01 9.13999081e-01 5.36623538e-01 -7.46996582e-01
1.08570419e-01 -8.61140609e-01 -2.45389923e-01 -3.09958369e-01
9.29544866e-01 2.98019528e-01 1.70919269e-01 -4.07890290... | [10.476665496826172, 10.022411346435547] |
83170b79-0d06-41ae-a0d4-5897a2ce17e0 | collaborative-unsupervised-visual | 2108.06492 | null | https://arxiv.org/abs/2108.06492v1 | https://arxiv.org/pdf/2108.06492v1.pdf | Collaborative Unsupervised Visual Representation Learning from Decentralized Data | Unsupervised representation learning has achieved outstanding performances using centralized data available on the Internet. However, the increasing awareness of privacy protection limits sharing of decentralized unlabeled image data that grows explosively in multiple parties (e.g., mobile phones and cameras). As such,... | ['Shuai Yi', 'Shuai Zhang', 'Yonggang Wen', 'Xin Gan', 'Weiming Zhuang'] | 2021-08-14 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhuang_Collaborative_Unsupervised_Visual_Representation_Learning_From_Decentralized_Data_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhuang_Collaborative_Unsupervised_Visual_Representation_Learning_From_Decentralized_Data_ICCV_2021_paper.pdf | iccv-2021-1 | ['federated-unsupervised-learning'] | ['methodology'] | [ 8.98178890e-02 4.82456982e-01 -3.55515659e-01 -5.76297939e-01
-6.85186744e-01 -9.09583509e-01 3.75738829e-01 -2.93431878e-01
-5.90518594e-01 7.22147703e-01 3.31077017e-02 -1.75153300e-01
2.25028425e-01 -5.61363816e-01 -9.55493271e-01 -9.24510479e-01
-2.99155712e-04 4.31167692e-01 1.73338830e-01 4.58103478... | [5.868849754333496, 6.444414138793945] |
3e30cc61-6f12-4b1c-87c5-b080379d3aa4 | sf-net-single-frame-supervision-for-temporal | 2003.06845 | null | https://arxiv.org/abs/2003.06845v6 | https://arxiv.org/pdf/2003.06845v6.pdf | SF-Net: Single-Frame Supervision for Temporal Action Localization | In this paper, we study an intermediate form of supervision, i.e., single-frame supervision, for temporal action localization (TAL). To obtain the single-frame supervision, the annotators are asked to identify only a single frame within the temporal window of an action. This can significantly reduce the labor cost of o... | ['Gourab Kundu', 'Linchao Zhu', 'Fan Ma', 'Shengxin Zha', 'Matt Feiszli', 'Zheng Shou', 'Yi Yang'] | 2020-03-15 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2314_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490409.pdf | eccv-2020-8 | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 4.93964434e-01 1.44263938e-01 -7.99440920e-01 -3.93658817e-01
-9.98507857e-01 -5.44859409e-01 3.02187920e-01 -6.83713332e-02
-4.44141448e-01 6.61510110e-01 1.05106615e-01 1.04382761e-01
3.93734425e-01 -3.71828347e-01 -8.13177645e-01 -8.29555094e-01
1.29480526e-01 7.12133199e-02 7.83996940e-01 2.56996453... | [8.544309616088867, 0.5902863144874573] |
69dc378e-fd8a-49a6-b817-e72859538a57 | heterogeneous-molecular-graph-neural-networks | 2009.1271 | null | https://arxiv.org/abs/2009.12710v1 | https://arxiv.org/pdf/2009.12710v1.pdf | Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties | As they carry great potential for modeling complex interactions, graph neural network (GNN)-based methods have been widely used to predict quantum mechanical properties of molecules. Most of the existing methods treat molecules as molecular graphs in which atoms are modeled as nodes. They characterize each atom's chemi... | ['Zeren Shui', 'George Karypis'] | 2020-09-26 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 2.19002321e-01 1.78428426e-01 -6.24800026e-01 -2.88566202e-01
-6.68352144e-03 -2.88854271e-01 5.32535672e-01 6.69555247e-01
4.23960574e-02 8.27939928e-01 1.92517698e-01 -6.60684705e-01
4.48601842e-02 -1.23695612e+00 -1.08441460e+00 -8.56628835e-01
-4.05354291e-01 4.60675597e-01 9.77133363e-02 -4.14170653... | [5.153307914733887, 5.826826095581055] |
4ed320c3-c78f-47f6-9a31-3f8eefda8c7c | learning-a-structured-latent-space-for | 2203.1558 | null | https://arxiv.org/abs/2203.15580v1 | https://arxiv.org/pdf/2203.15580v1.pdf | Learning a Structured Latent Space for Unsupervised Point Cloud Completion | Unsupervised point cloud completion aims at estimating the corresponding complete point cloud of a partial point cloud in an unpaired manner. It is a crucial but challenging problem since there is no paired partial-complete supervision that can be exploited directly. In this work, we propose a novel framework, which le... | ['Hongsheng Li', 'Xiaogang Wang', 'Qiang Wang', 'Chao Zhang', 'Kwan-Yee Lin', 'Yingjie Cai'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cai_Learning_a_Structured_Latent_Space_for_Unsupervised_Point_Cloud_Completion_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cai_Learning_a_Structured_Latent_Space_for_Unsupervised_Point_Cloud_Completion_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-completion'] | ['computer-vision'] | [-7.17585683e-02 -9.43804309e-02 -3.06421131e-01 -6.04745567e-01
-1.03992438e+00 -5.80570579e-01 5.92898130e-01 -1.17123961e-01
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-2.97813982e-01 -6.09749138e-01 -9.29244220e-01 -6.80886626e-01
3.51613581e-01 9.42010641e-01 1.31540954e-01 2.49215186... | [8.250185012817383, -3.4549224376678467] |
4136e42b-f712-48af-bdd7-067bc27be315 | exploring-self-attention-for-crop-type | 2210.13167 | null | https://arxiv.org/abs/2210.13167v1 | https://arxiv.org/pdf/2210.13167v1.pdf | Exploring Self-Attention for Crop-type Classification Explainability | Automated crop-type classification using Sentinel-2 satellite time series is essential to support agriculture monitoring. Recently, deep learning models based on transformer encoders became a promising approach for crop-type classification. Using explainable machine learning to reveal the inner workings of these models... | ['Xiao Xiang Zhu', 'Dario Augusto Borges Oliveira', 'Ribana Roscher', 'Ivica Obadic'] | 2022-10-24 | null | null | null | null | ['type'] | ['speech'] | [ 3.24556559e-01 3.47293854e-01 -4.58159357e-01 -4.10597742e-01
-1.45209104e-01 -8.21492910e-01 3.36549640e-01 8.97086203e-01
2.88875580e-01 4.39563364e-01 3.24035823e-01 -8.44672084e-01
-4.67857093e-01 -1.06872511e+00 -1.19011235e+00 -7.49749660e-01
-4.22801703e-01 -1.34422928e-01 -6.48403347e-01 -6.58554137... | [9.414546966552734, -1.5713423490524292] |
d3b1407e-f43b-47c7-9894-a3553debc055 | social-interactions-with-endogenous-group | 2306.01544 | null | https://arxiv.org/abs/2306.01544v1 | https://arxiv.org/pdf/2306.01544v1.pdf | Social Interactions with Endogenous Group Formation | This paper explores the identification and estimation of social interaction models with endogenous group formation. We characterize group formation using a two-sided many-to-one matching model, where individuals select groups based on their preferences, while groups rank individuals according to their qualifications, a... | ['Xiaoting Sun', 'Shuyang Sheng'] | 2023-06-02 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-1.60487548e-01 3.34442943e-01 -6.99929595e-01 -4.30284590e-01
-2.48222470e-01 -4.38669473e-01 2.90763348e-01 1.15655199e-01
-5.51150739e-01 1.07449770e+00 2.99202293e-01 -4.52163935e-01
-5.50060272e-01 -1.03311551e+00 -5.43580115e-01 -5.65468431e-01
5.09482285e-04 7.64561415e-01 -2.64928728e-01 1.32906705... | [7.910217761993408, 5.190557479858398] |
31f99021-703d-45b6-8329-4108f8e9da94 | put-at-semeval-2016-task-4-the-abc-of-twitter | null | null | https://aclanthology.org/S16-1018 | https://aclanthology.org/S16-1018.pdf | PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis | null | ['Mateusz Lango', 'Dariusz Brzezinski', 'Jerzy Stefanowski'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.284582614898682, 3.654465675354004] |
b574064a-f504-451e-ab22-6f0c420e2e49 | affective-manifolds-modeling-machine-s-mind | 2208.13386 | null | https://arxiv.org/abs/2208.13386v1 | https://arxiv.org/pdf/2208.13386v1.pdf | Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A Human | After the development of different machine learning and manifold learning algorithms, it may be a good time to put them together to make a powerful mind for machine. In this work, we propose affective manifolds as components of a machine's mind. Every affective manifold models a characteristic group of mind and contain... | ['Benyamin Ghojogh'] | 2022-08-29 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-2.82343537e-01 4.01490569e-01 3.38406749e-02 -6.19396687e-01
1.87849086e-02 -3.90018255e-01 4.47203636e-01 -3.42539176e-02
-8.22155252e-02 2.56952047e-01 -9.93073825e-03 1.60620481e-01
1.41233811e-02 -7.51809716e-01 -3.79922569e-01 -6.52573884e-01
-1.63840309e-01 2.88147926e-01 -3.55669320e-01 -4.30745602... | [13.006738662719727, 5.694408893585205] |
1c2e44eb-7b63-4c91-b114-4cd4aeebb9cd | recurrent-pixel-embedding-for-instance | 1712.08273 | null | http://arxiv.org/abs/1712.08273v1 | http://arxiv.org/pdf/1712.08273v1.pdf | Recurrent Pixel Embedding for Instance Grouping | We introduce a differentiable, end-to-end trainable framework for solving
pixel-level grouping problems such as instance segmentation consisting of two
novel components. First, we regress pixels into a hyper-spherical embedding
space so that pixels from the same group have high cosine similarity while
those from differ... | ['Charless Fowlkes', 'Shu Kong'] | 2017-12-22 | recurrent-pixel-embedding-for-instance-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Kong_Recurrent_Pixel_Embedding_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Kong_Recurrent_Pixel_Embedding_CVPR_2018_paper.pdf | cvpr-2018-6 | ['object-proposal-generation'] | ['computer-vision'] | [ 4.19873625e-01 7.53404856e-01 -7.93487132e-02 -4.30489093e-01
-1.13359177e+00 -4.06359076e-01 3.07338417e-01 2.75819421e-01
-5.99257529e-01 3.79637510e-01 -1.38602003e-01 -1.62182540e-01
-2.34580651e-01 -6.49480462e-01 -9.41221774e-01 -9.49306726e-01
-1.31290555e-01 6.23240232e-01 4.15852547e-01 3.05198699... | [9.539765357971191, 0.4792215824127197] |
f1135f64-82d3-4ea9-9871-76e77cee7f88 | learn-to-resolve-conversational-dependency-a | 2106.11575 | null | https://arxiv.org/abs/2106.11575v1 | https://arxiv.org/pdf/2106.11575v1.pdf | Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering | One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do not explicitly train QA models on how to resolve the dependency, and thus these models are limited in understanding human dialogues. In this p... | ['Jaewoo Kang', 'Jungsoo Park', 'Hyunjae Kim', 'Gangwoo Kim'] | 2021-06-22 | null | https://aclanthology.org/2021.acl-long.478 | https://aclanthology.org/2021.acl-long.478.pdf | acl-2021-5 | ['question-rewriting'] | ['natural-language-processing'] | [-1.06928078e-02 5.50534070e-01 1.70971602e-01 -6.13981903e-01
-9.65893209e-01 -7.80282855e-01 5.95776916e-01 8.90491158e-02
-2.37239569e-01 1.04360878e+00 6.82207406e-01 -4.98027653e-01
6.29367074e-04 -7.72554159e-01 -2.53428191e-01 -1.19209029e-01
3.29251498e-01 8.63953471e-01 3.58473241e-01 -1.00992322... | [12.262101173400879, 8.020942687988281] |
a12534ad-64a3-4716-9aae-38c6f67a782c | finite-time-analysis-of-minimax-q-learning | 2306.057 | null | https://arxiv.org/abs/2306.05700v2 | https://arxiv.org/pdf/2306.05700v2.pdf | Finite-Time Analysis of Minimax Q-Learning for Two-Player Zero-Sum Markov Games: Switching System Approach | The objective of this paper is to investigate the finite-time analysis of a Q-learning algorithm applied to two-player zero-sum Markov games. Specifically, we establish a finite-time analysis of both the minimax Q-learning algorithm and the corresponding value iteration method. To enhance the analysis of both value ite... | ['Donghwan Lee'] | 2023-06-09 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 7.79071897e-02 3.21547508e-01 -3.88741195e-01 2.17238635e-01
-7.10426569e-01 -7.46666014e-01 2.99069464e-01 3.90366822e-01
-5.90759397e-01 1.00392163e+00 -2.94383407e-01 -7.15537667e-01
-8.80047441e-01 -5.73536754e-01 -3.70067626e-01 -6.74120188e-01
-5.87840974e-01 1.81343526e-01 -3.35797928e-02 -3.74408334... | [4.187510967254639, 2.617335796356201] |
33c95b59-7ba7-4fff-9fe7-d1cd884c2e56 | automated-identification-of-tree-species-by | 2210.0929 | null | https://arxiv.org/abs/2210.09290v1 | https://arxiv.org/pdf/2210.09290v1.pdf | Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks | Identification of tree species plays a key role in forestry related tasks like forest conservation, disease diagnosis and plant production. There had been a debate regarding the part of the tree to be used for differentiation, whether it should be leaves, fruits, flowers or bark. Studies have proven that bark is of utm... | ['Sahil Faizal'] | 2022-10-03 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.45744780e-01 4.19955775e-02 -8.99339914e-02 -1.70539305e-01
1.56651095e-01 -6.22292161e-01 6.75734997e-01 3.67481202e-01
-2.26442188e-01 7.13444352e-01 2.15131771e-02 -5.65454602e-01
-5.74995100e-01 -8.83163095e-01 9.60063860e-02 -6.55467868e-01
-4.92860466e-01 4.29588169e-01 4.38782096e-01 -2.04462763... | [9.343017578125, -1.42855703830719] |
60fbe48e-abfe-48ca-99ee-54f1ebad6f8c | indoor-scene-generation-from-a-collection-of | 2108.09022 | null | https://arxiv.org/abs/2108.09022v1 | https://arxiv.org/pdf/2108.09022v1.pdf | Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images | We present a method for creating 3D indoor scenes with a generative model learned from a collection of semantic-segmented depth images captured from different unknown scenes. Given a room with a specified size, our method automatically generates 3D objects in a room from a randomly sampled latent code. Different from e... | ['Xin Tong', 'Bin Zhou', 'Yu-Xiao Guo', 'Ming-Jia Yang'] | 2021-08-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Indoor_Scene_Generation_From_a_Collection_of_Semantic-Segmented_Depth_Images_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Indoor_Scene_Generation_From_a_Collection_of_Semantic-Segmented_Depth_Images_ICCV_2021_paper.pdf | iccv-2021-1 | ['scene-generation'] | ['computer-vision'] | [ 4.87246156e-01 2.29307935e-01 4.88366872e-01 -6.26359165e-01
-6.76908016e-01 -9.48798835e-01 5.03284037e-01 -3.61991853e-01
9.18842852e-03 4.85142559e-01 1.87092602e-01 -2.06310824e-01
4.36866850e-01 -1.29653347e+00 -1.30567801e+00 -5.23689270e-01
3.88089508e-01 7.79290199e-01 1.96512025e-02 2.58798420... | [9.07532024383545, -3.217766523361206] |
21bda1c6-8d1d-421f-b10d-a13cd729a768 | kuaipedia-a-large-scale-multi-modal-short | 2211.00732 | null | https://arxiv.org/abs/2211.00732v2 | https://arxiv.org/pdf/2211.00732v2.pdf | Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia | Online encyclopedias, such as Wikipedia, have been well-developed and researched in the last two decades. One can find any attributes or other information of a wiki item on a wiki page edited by a community of volunteers. However, the traditional text, images and tables can hardly express some aspects of an wiki item. ... | ['Bing Qin', 'Zhongyuan Wang', 'Yangqiu Song', 'Ming Liu', 'Ruiji Fu', 'Zepeng Zhai', 'Yuzhou Zhang', 'Haojie Pan'] | 2022-10-28 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [-5.30278981e-01 -1.98746607e-01 -6.48068190e-01 -5.38478121e-02
-8.28829765e-01 -9.19476390e-01 3.32786977e-01 2.31698781e-01
-3.10077101e-01 7.82779157e-01 6.17741883e-01 1.38187066e-01
-5.32260314e-02 -1.02823818e+00 -1.03765202e+00 -1.90243602e-01
1.78742297e-02 6.89450651e-02 1.14618629e-01 -4.86605436... | [10.124886512756348, 0.8491062521934509] |
609a1c76-ae33-4792-b2f2-ad50b2066262 | a-parallel-english-serbian-bulgarian | null | null | https://aclanthology.org/2022.clib-1.17 | https://aclanthology.org/2022.clib-1.17.pdf | A Parallel English - Serbian - Bulgarian - Macedonian Lexicon of Named Entities | This paper describes the creation of a parallel multilingual lexicon of named entities from English to three South Slavic languages: Serbian, Bulgarian and Macedonian, with Wikipedia as a source. The basics of the proposed methodology are well known. This methodology provides a cheap opportunity to build multilingual l... | ['Aleksandar Petrovski'] | null | null | null | null | clib-2022-9 | ['miscellaneous'] | ['miscellaneous'] | [-6.96953237e-01 1.40656427e-01 -1.33166447e-01 -2.12478593e-01
-5.26824653e-01 -8.93415332e-01 8.62352550e-01 5.88119149e-01
-1.02634752e+00 1.45506394e+00 2.09374189e-01 -2.58284599e-01
-5.46899438e-02 -1.02087522e+00 -3.51425260e-01 -1.28300935e-01
2.00612262e-01 8.01350713e-01 2.94942170e-01 -4.08251673... | [9.690922737121582, 9.623844146728516] |
a85560ad-52b3-4882-ad01-0113da9fa92b | progressively-normalized-self-attention | 2105.08468 | null | https://arxiv.org/abs/2105.08468v2 | https://arxiv.org/pdf/2105.08468v2.pdf | Progressively Normalized Self-Attention Network for Video Polyp Segmentation | Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features. However, due to their limited receptive fields, CNNs can not fully exploit the global temporal and spatial information in successive video frames, resulting in false-positive segmentation results. I... | ['Ling Shao', 'Debesh Jha', 'Huazhu Fu', 'Geng Chen', 'Deng-Ping Fan', 'Yu-Cheng Chou', 'Ge-Peng Ji'] | 2021-05-18 | null | null | null | null | ['video-polyp-segmentation'] | ['computer-vision'] | [ 2.90291607e-01 -1.31540522e-01 -4.42875504e-01 -1.78765103e-01
-4.14934129e-01 -3.07826966e-01 1.69730872e-01 -1.05927229e-01
-4.58511621e-01 3.14844728e-01 4.22488153e-02 -3.35009545e-01
5.55530250e-01 -7.44491875e-01 -1.03849137e+00 -4.25028622e-01
-9.33975056e-02 -3.71802568e-01 9.17973399e-01 3.98475565... | [9.299015045166016, -0.04466262087225914] |
35a5d822-80dc-40f7-8cc9-2876d156da13 | fashion-cut-unsupervised-domain-adaptation | 2305.0558 | null | https://arxiv.org/abs/2305.05580v1 | https://arxiv.org/pdf/2305.05580v1.pdf | Fashion CUT: Unsupervised domain adaptation for visual pattern classification in clothes using synthetic data and pseudo-labels | Accurate product information is critical for e-commerce stores to allow customers to browse, filter, and search for products. Product data quality is affected by missing or incorrect information resulting in poor customer experience. While machine learning can be used to correct inaccurate or missing information, achie... | ["Noel E. O'Connor", 'Philip Kelly', 'Martina Naughton', 'Alex Martinelli', 'Enric Moreu'] | 2023-05-09 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 6.42674387e-01 4.32455540e-03 -2.81672716e-01 -1.00084245e+00
-8.13538790e-01 -8.39486003e-01 2.04564691e-01 1.11664392e-01
-2.83331871e-01 6.79349363e-01 -1.41632065e-01 -9.46434513e-02
3.77846360e-01 -8.85755897e-01 -1.01697135e+00 -3.80644858e-01
5.96630156e-01 8.36809993e-01 8.79229158e-02 -1.00875005... | [9.907181739807129, 1.595145583152771] |
9830d336-1a53-4307-8364-1509277b2154 | relationformer-a-unified-framework-for-image | 2203.10202 | null | https://arxiv.org/abs/2203.10202v1 | https://arxiv.org/pdf/2203.10202v1.pdf | Relationformer: A Unified Framework for Image-to-Graph Generation | A comprehensive representation of an image requires understanding objects and their mutual relationship, especially in image-to-graph generation, e.g., road network extraction, blood-vessel network extraction, or scene graph generation. Traditionally, image-to-graph generation is addressed with a two-stage approach con... | ['Bjoern Menze', 'Volker Tresp', 'Georgios Kaissis', 'Sahand Sharifzadeh', 'Jiazhen Pan', 'Hongwei Li', 'Ivan Ezhov', 'Johannes Paetzold', 'Bastian Wittmann', 'Rajat Koner', 'Suprosanna Shit'] | 2022-03-19 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 3.12324643e-01 4.29128975e-01 -2.64210701e-01 -5.08445442e-01
-5.70232987e-01 -3.75341356e-01 8.34531307e-01 3.83497477e-01
1.27996162e-01 3.67710739e-01 1.21575579e-01 -3.15284692e-02
-3.17810863e-01 -1.16441572e+00 -7.63819098e-01 -3.30217391e-01
-5.36969863e-02 4.06014323e-01 6.39183640e-01 -1.55201983... | [10.303046226501465, 1.6414434909820557] |
1df59c62-7091-4a73-9299-8b8dcba89f32 | meetingbank-a-benchmark-dataset-for-meeting | 2305.17529 | null | https://arxiv.org/abs/2305.17529v1 | https://arxiv.org/pdf/2305.17529v1.pdf | MeetingBank: A Benchmark Dataset for Meeting Summarization | As the number of recorded meetings increases, it becomes increasingly important to utilize summarization technology to create useful summaries of these recordings. However, there is a crucial lack of annotated meeting corpora for developing this technology, as it can be hard to collect meetings, especially when the top... | ['Fei Liu', 'Hassan Foroosh', 'Franck Dernoncourt', 'Hanieh Deilamsalehy', 'Tim Ganter', 'Yebowen Hu'] | 2023-05-27 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 2.70077407e-01 2.94498205e-01 -1.17597193e-01 -3.63735229e-01
-1.64367652e+00 -9.80118155e-01 5.26749134e-01 7.86520600e-01
-2.43518919e-01 1.05133235e+00 1.14554214e+00 -1.56204745e-01
1.03038400e-01 -2.79785663e-01 -2.39808902e-01 -2.59165823e-01
3.55175972e-01 4.06523466e-01 -4.50271778e-02 -1.80810347... | [12.616031646728516, 9.416236877441406] |
058e78ac-e6e2-4e27-bbf3-d075d36ccd2c | revisiting-the-stability-of-stochastic | null | null | https://openreview.net/forum?id=oQyb8NrFzu | https://openreview.net/pdf?id=oQyb8NrFzu | Revisiting the Stability of Stochastic Gradient Descent: A Tightness Analysis | The technique of algorithmic stability has been used to capture the generalization power of several learning models, especially those trained with stochastic gradient descent (SGD). This paper investigates the tightness of the algorithmic stability bounds for SGD given by~\cite{hardt2016train}. We show that the analysi... | ['Mayank Goswami', 'Chao Chen', 'Vamsi Pritham Pingali', 'Wenjia Zhang', 'Samuel Bald', 'Yikai Zhang'] | 2021-01-01 | null | null | null | null | ['exponential-degradation'] | ['time-series'] | [-1.62489891e-01 3.41379166e-01 -4.68218029e-02 -4.42425638e-01
-7.53523588e-01 -5.80511868e-01 3.65779437e-02 8.12521353e-02
-7.51684189e-01 8.60210717e-01 -1.92085326e-01 -4.56524581e-01
-4.93009478e-01 -3.49916786e-01 -1.10203016e+00 -1.09482884e+00
-3.73208910e-01 1.83258951e-01 1.02134138e-01 -2.73198664... | [7.753108978271484, 3.826568841934204] |
68662ba5-ab87-4c07-9f23-df7dbf90086b | considering-nested-tree-structure-in-sentence | null | null | https://aclanthology.org/2021.emnlp-main.330 | https://aclanthology.org/2021.emnlp-main.330.pdf | Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer | Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. However, constructing a coherent and informative summary is difficult using a pre-trained BERT-based encoder since it is not explicitly trained for representing the information of sentence... | ['Manabu Okumura', 'Hidetaka Kamigaito', 'Naoki Kobayashi', 'Jingun Kwon'] | null | null | null | null | emnlp-2021-11 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 1.87396929e-01 5.75554073e-01 -2.13448852e-01 -4.29009467e-01
-1.12663364e+00 -5.74605048e-01 5.75330913e-01 3.59246105e-01
-3.50051850e-01 9.12335455e-01 1.48796880e+00 2.60743778e-02
1.60323814e-01 -6.32601619e-01 -6.64602935e-01 -3.16932321e-01
-2.79159267e-02 2.83260643e-01 3.24207582e-02 -3.43439728... | [12.525839805603027, 9.494762420654297] |
985ffd40-4b1c-4756-8dfd-da54c0778b86 | transferring-a-semantic-representation-for | 1706.03725 | null | http://arxiv.org/abs/1706.03725v1 | http://arxiv.org/pdf/1706.03725v1.pdf | Transferring a Semantic Representation for Person Re-Identification and Search | Learning semantic attributes for person re-identification and
description-based person search has gained increasing interest due to
attributes' great potential as a pose and view-invariant representation.
However, existing attribute-centric approaches have thus far underperformed
state-of-the-art conventional approache... | ['Zhiyuan Shi', 'Timothy M. Hospedales', 'Tao Xiang'] | 2017-06-12 | transferring-a-semantic-representation-for-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Shi_Transferring_a_Semantic_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Shi_Transferring_a_Semantic_2015_CVPR_paper.pdf | cvpr-2015-6 | ['person-search'] | ['computer-vision'] | [ 2.29692429e-01 -1.72602192e-01 -1.79012462e-01 -7.09833086e-01
-8.42189789e-01 -6.11654639e-01 1.11546087e+00 4.40104127e-01
-7.18363285e-01 7.06629932e-01 4.35909003e-01 4.49424893e-01
-2.12632284e-01 -5.24125338e-01 -2.65888900e-01 -4.77208763e-01
3.58338118e-01 1.18023264e+00 1.98235348e-01 -1.03270583... | [14.654324531555176, 0.9955406188964844] |
9d3833db-c17c-472d-9f86-10aaf0671473 | efficient-and-accurate-multi-scale | 2102.12135 | null | https://arxiv.org/abs/2102.12135v1 | https://arxiv.org/pdf/2102.12135v1.pdf | Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing | Single image dehazing is a challenging ill-posed problem that has drawn significant attention in the last few years. Recently, convolutional neural networks have achieved great success in image dehazing. However, it is still difficult for these increasingly complex models to recover accurate details from the hazy image... | ['Guixu Zhang', 'Aiwen Jiang', 'Faming Fang', 'Juncheng Li', 'Qiaosi Yi'] | 2021-02-24 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 2.22595453e-01 -5.89103103e-01 1.99539393e-01 -2.77248900e-02
-2.35305995e-01 4.14605699e-02 1.57003522e-01 -1.67320400e-01
-1.12657584e-01 4.42289650e-01 4.16449346e-02 1.56069323e-01
-5.65832436e-01 -9.97837961e-01 -2.87702173e-01 -9.81672585e-01
-1.13421589e-01 -4.63477522e-01 6.67791605e-01 -3.28143895... | [10.904335021972656, -2.9747817516326904] |
bc634f2b-3d58-483b-a72d-a85c6cea8ae2 | multimodal-brain-age-estimation-using | 2307.04639 | null | https://arxiv.org/abs/2307.04639v1 | https://arxiv.org/pdf/2307.04639v1.pdf | Multimodal brain age estimation using interpretable adaptive population-graph learning | Brain age estimation is clinically important as it can provide valuable information in the context of neurodegenerative diseases such as Alzheimer's. Population graphs, which include multimodal imaging information of the subjects along with the relationships among the population, have been used in literature along with... | ['Daniel Rueckert', 'Alexander Hammers', 'Rolandos Alexandros Potamias', 'Vasileios Baltatzis', 'Kyriaki-Margarita Bintsi'] | 2023-07-10 | null | null | null | null | ['age-estimation', 'graph-learning', 'graph-construction', 'age-estimation'] | ['computer-vision', 'graphs', 'graphs', 'miscellaneous'] | [ 2.19921917e-01 4.25773352e-01 1.72896951e-01 -5.39307594e-01
-1.91952884e-01 -1.96209788e-01 4.81833577e-01 5.37758946e-01
-6.70916975e-01 7.37661481e-01 3.03661704e-01 -1.62662551e-01
-2.05875516e-01 -8.54800344e-01 -5.45950174e-01 -8.48572671e-01
-6.23329043e-01 7.08179474e-01 1.23087116e-01 5.62479272... | [12.382786750793457, 3.360534191131592] |
a77654fe-b5dd-4f6d-acbe-972a7d03c2c3 | 2305-14562 | 2305.14562 | null | https://arxiv.org/abs/2305.14562v1 | https://arxiv.org/pdf/2305.14562v1.pdf | GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing | Careful placement of a computational application within a target device cluster is critical for achieving low application completion time. The problem is challenging due to its NP-hardness and combinatorial nature. In recent years, learning-based approaches have been proposed to learn a placement policy that can be app... | ['Carlee Joe-Wong', 'Bob Iannucci', 'Yanqi Zhou', 'James Laudon', 'Aviral Shrivastava', 'Harshul Singh', 'Edward Andert', 'Chaoran Zhang', 'Yi Hu'] | 2023-05-23 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 1.40721068e-01 1.61220171e-02 -8.24718297e-01 2.02172603e-02
-7.55818963e-01 -8.09644580e-01 -3.58147979e-01 2.98801184e-01
3.56116109e-02 5.62477410e-01 -2.24255562e-01 -8.67012441e-01
-5.51970422e-01 -7.07496166e-01 -1.15185654e+00 -6.92552030e-01
-3.41462284e-01 9.45372820e-01 3.07960212e-01 7.84270316... | [5.471680641174316, 3.0657191276550293] |
acbad825-6c4c-4c38-8b7d-743c7ce6ebd8 | socrates-a-stereo-camera-trap-for-monitoring | 2209.0907 | null | https://arxiv.org/abs/2209.09070v2 | https://arxiv.org/pdf/2209.09070v2.pdf | SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity | The development and application of modern technology is an essential basis for the efficient monitoring of species in natural habitats and landscapes to trace the development of ecosystems, species communities, and populations, and to analyze reasons of changes. For estimating animal abundance using methods such as cam... | ['Hjalmar S. Kühl', 'Volker Steinhage', 'Timm Haucke'] | 2022-09-19 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-7.14408979e-03 -6.52109087e-01 -9.36087593e-02 -1.12582989e-01
5.61353005e-02 -6.59777164e-01 3.40982169e-01 5.09828389e-01
-1.01611924e+00 5.50790608e-01 3.01452484e-02 -4.80126649e-01
1.97863922e-01 -9.52754200e-01 -4.33266252e-01 -4.47177768e-01
-4.83769119e-01 2.64148980e-01 4.15304452e-01 -5.41047491... | [8.612133979797363, -1.1187125444412231] |
d7422b6c-4faf-4fa4-b5f5-0e76d904beb0 | word-embedding-for-response-to-text-1 | 1908.01969 | null | https://arxiv.org/abs/1908.01969v1 | https://arxiv.org/pdf/1908.01969v1.pdf | Word Embedding for Response-To-Text Assessment of Evidence | Manually grading the Response to Text Assessment (RTA) is labor intensive. Therefore, an automatic method is being developed for scoring analytical writing when the RTA is administered in large numbers of classrooms. Our long-term goal is to also use this scoring method to provide formative feedback to students and tea... | ['Haoran Zhang', 'Diane Litman'] | 2019-08-06 | word-embedding-for-response-to-text | https://aclanthology.org/P17-3013 | https://aclanthology.org/P17-3013.pdf | acl-2017-7 | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 1.03861451e-01 1.95951208e-01 -1.07690096e-01 -5.85579574e-01
-9.09723580e-01 -6.24853253e-01 5.12720406e-01 7.54725695e-01
-4.07438576e-01 5.74527442e-01 4.59107667e-01 -6.29024506e-01
-3.30923527e-01 -7.06581593e-01 -2.02833802e-01 -7.14736581e-02
6.93541765e-01 2.34307379e-01 4.55280930e-01 -2.57834822... | [11.285449981689453, 9.256958961486816] |
409cced2-12f6-41cf-875c-f8c33061d234 | camil-context-aware-multiple-instance | 2305.05314 | null | https://arxiv.org/abs/2305.05314v1 | https://arxiv.org/pdf/2305.05314v1.pdf | CAMIL: Context-Aware Multiple Instance Learning for Whole Slide Image Classification | Cancer diagnoses typically involve human pathologists examining whole slide images (WSIs) of tissue section biopsies to identify tumor cells and their subtypes. However, artificial intelligence (AI)-based models, particularly weakly supervised approaches, have recently emerged as viable alternatives. Weakly supervised ... | ['Chris Bakal', 'Mat De Vries', 'Chen Jin', 'Avi Arampatzis', 'Olga Fourkioti'] | 2023-05-09 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 6.11347079e-01 5.17280340e-01 -6.30091548e-01 -8.30020234e-02
-1.27624309e+00 -5.04185677e-01 5.26578546e-01 6.86732888e-01
-2.41435423e-01 7.22443402e-01 2.56052405e-01 -5.06882727e-01
-6.32674322e-02 -6.92347646e-01 -5.67679048e-01 -1.34939706e+00
7.98632279e-02 5.71408451e-01 2.41793916e-01 3.40313703... | [15.103022575378418, -2.890406608581543] |
73226b40-a692-4bc2-a19d-647af110a2a8 | temporal-consistency-loss-for-high-resolution | 2104.09259 | null | https://arxiv.org/abs/2104.09259v1 | https://arxiv.org/pdf/2104.09259v1.pdf | Temporal Consistency Loss for High Resolution Textured and Clothed 3DHuman Reconstruction from Monocular Video | We present a novel method to learn temporally consistent 3D reconstruction of clothed people from a monocular video. Recent methods for 3D human reconstruction from monocular video using volumetric, implicit or parametric human shape models, produce per frame reconstructions giving temporally inconsistent output and li... | ['Adrian Hilton', 'Armin Mustafa', 'Akin Caliskan'] | 2021-04-19 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [-1.23801611e-01 -5.17948925e-01 -1.23593271e-01 -1.71139762e-01
-3.19521517e-01 -2.23535225e-01 3.52015018e-01 -6.15603149e-01
-1.07240617e-01 8.02353203e-01 1.50047183e-01 4.80756730e-01
1.58279851e-01 -3.37262303e-01 -8.21768939e-01 -5.06844282e-01
-1.71035767e-01 6.60559237e-01 2.75125474e-01 2.23831460... | [7.207547664642334, -1.2036736011505127] |
1a176d9b-fd92-41e5-8bb6-ac33196836d2 | linguistic-generalization-and | 1904.00157 | null | https://arxiv.org/abs/1904.00157v3 | https://arxiv.org/pdf/1904.00157v3.pdf | Linguistic generalization and compositionality in modern artificial neural networks | In the last decade, deep artificial neural networks have achieved astounding performance in many natural language processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely assumed that humans handle linguistic productivity by means of algebraic... | ['Marco Baroni'] | 2019-03-30 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 2.74993092e-01 3.09825629e-01 -1.30947396e-01 -3.33001554e-01
4.60373640e-01 -8.31006289e-01 8.64112258e-01 2.01393262e-01
-4.74352419e-01 3.39467257e-01 5.08033156e-01 -1.02204382e+00
-4.15773004e-01 -1.03561342e+00 -4.35497314e-01 -2.84392267e-01
-1.90069377e-01 4.78664607e-01 8.49530250e-02 -8.66777599... | [10.4725980758667, 8.884968757629395] |
14199ad6-b984-416b-93ea-8a5210e0e2fe | full-pulse-tomographic-reconstruction-with | 1802.02242 | null | http://arxiv.org/abs/1802.02242v1 | http://arxiv.org/pdf/1802.02242v1.pdf | Full-pulse Tomographic Reconstruction with Deep Neural Networks | Plasma tomography consists in reconstructing the 2D radiation profile in a
poloidal cross-section of a fusion device, based on line-integrated
measurements along several lines of sight. The reconstruction process is
computationally intensive and, in practice, only a few reconstructions are
usually computed per pulse. I... | ['Horácio Fernandes', 'Pedro J. Carvalho', 'Diogo R. Ferreira'] | 2018-02-02 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [-3.20303351e-01 -2.45252639e-01 4.62009817e-01 -3.18461329e-01
-5.12896419e-01 -3.68282527e-01 8.04903209e-01 1.83290839e-01
-2.11576641e-01 1.02193034e+00 9.43538472e-02 -5.00473022e-01
-2.93058027e-02 -1.01093733e+00 -4.65384990e-01 -7.86069691e-01
-2.38493398e-01 1.20668411e+00 -2.91297466e-01 -1.13501400... | [6.518337249755859, 3.458902597427368] |
a6e2366d-7025-45b3-bf6f-c860833e97b6 | monte-carlo-inference-for-semiparametric | 2306.05498 | null | https://arxiv.org/abs/2306.05498v1 | https://arxiv.org/pdf/2306.05498v1.pdf | Monte Carlo inference for semiparametric Bayesian regression | Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbe... | ['Bohan Wu', 'Daniel R. Kowal'] | 2023-06-08 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 5.48197515e-02 -4.00825232e-01 -2.42581442e-01 -3.67679179e-01
-8.48602474e-01 -5.77515543e-01 4.69677210e-01 -6.06965926e-03
-2.51686275e-01 1.28810287e+00 -3.97848040e-01 -5.47384560e-01
-5.92735112e-01 -8.27704132e-01 -6.12457335e-01 -9.27623868e-01
-2.05045529e-02 6.48912966e-01 4.37777257e-03 4.21689749... | [7.069799423217773, 4.2025017738342285] |
2b8686be-cbf8-4ea8-893c-889ee3ac4f90 | lost-in-the-middle-how-language-models-use | 2307.03172 | null | https://arxiv.org/abs/2307.03172v1 | https://arxiv.org/pdf/2307.03172v1.pdf | Lost in the Middle: How Language Models Use Long Contexts | While recent language models have the ability to take long contexts as input, relatively little is known about how well the language models use longer context. We analyze language model performance on two tasks that require identifying relevant information within their input contexts: multi-document question answering ... | ['Percy Liang', 'Fabio Petroni', 'Michele Bevilacqua', 'Ashwin Paranjape', 'John Hewitt', 'Kevin Lin', 'Nelson F. Liu'] | 2023-07-06 | null | null | null | null | ['retrieval', 'question-answering'] | ['methodology', 'natural-language-processing'] | [ 1.63125515e-01 -3.75388980e-01 -4.37745243e-01 -3.26024860e-01
-1.15931571e+00 -1.06489182e+00 8.19763184e-01 9.16123748e-01
-9.50038612e-01 4.52718705e-01 5.94776869e-01 -8.02533925e-01
-2.28551894e-01 -5.72346210e-01 -4.57147300e-01 1.80426450e-03
3.82748805e-02 4.39948708e-01 6.25135243e-01 -3.04048657... | [11.242130279541016, 8.06555461883545] |
f792d43e-cbce-4135-9eae-9669cc7bcd42 | rpnet-a-deep-learning-approach-for-robust-r | 2004.08103 | null | https://arxiv.org/abs/2004.08103v1 | https://arxiv.org/pdf/2004.08103v1.pdf | RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG | Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applications including Heart Rate Variability (HRV) analysis and Cardio Vascular Disease(CVD) diagnosis. Although there have been numerous approaches that have successfully addressed the problem, there has been a notable dip in t... | ['Vignesh R', 'Jayaraj Joseph', 'Sricharan Vijayarangan', 'Preejith SP', 'Mohansankar Sivaprakasam', 'Balamurali Murugesan'] | 2020-04-17 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.07281822e-01 -1.59692004e-01 8.26628581e-02 -1.91662878e-01
-6.76856518e-01 -1.88390553e-01 8.67278054e-02 1.58730686e-01
-2.10056484e-01 6.52565479e-01 -4.59281132e-02 -5.54301776e-02
-3.09964389e-01 -4.56484586e-01 -6.31884187e-02 -6.34031951e-01
-4.97213632e-01 -6.91506490e-02 -2.18131170e-01 -1.83372498... | [14.29918098449707, 3.2790777683258057] |
a57ab889-011a-4d3d-b075-8a7a496da8df | eggs-eigen-gap-guided-search-making-subspace | 2107.12183 | null | https://arxiv.org/abs/2107.12183v4 | https://arxiv.org/pdf/2107.12183v4.pdf | A Simple Approach to Automated Spectral Clustering | The performance of spectral clustering heavily relies on the quality of affinity matrix. A variety of affinity-matrix-construction (AMC) methods have been proposed but they have hyperparameters to determine beforehand, which requires strong experience and leads to difficulty in real applications, especially when the in... | ['Mingbo Zhao', 'Zhao Zhang', 'Haijun Zhang', 'Yiheng Tu', 'Jicong Fan'] | 2021-07-23 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 2.47948915e-02 -6.70263767e-01 4.26113196e-02 -2.31402367e-02
-8.83202374e-01 -5.23741722e-01 3.88392173e-02 8.41920450e-02
-3.82345647e-01 4.10139710e-01 -2.15557039e-01 -2.24011093e-02
-4.86024320e-01 -4.82306123e-01 -3.53336245e-01 -1.16273570e+00
-6.57886788e-02 5.63612878e-01 5.84021151e-01 -6.93411231... | [7.604463577270508, 4.675248146057129] |
d102fa34-89e4-4be8-bb16-2750ec2fec3d | end-to-end-audio-visual-scene-aware-dialog | 1806.08409 | null | http://arxiv.org/abs/1806.08409v2 | http://arxiv.org/pdf/1806.08409v2.pdf | End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features | Dialog systems need to understand dynamic visual scenes in order to have
conversations with users about the objects and events around them. Scene-aware
dialog systems for real-world applications could be developed by integrating
state-of-the-art technologies from multiple research areas, including:
end-to-end dialog te... | ['Irfan Essa', 'Raphael Gontijo Lopes', 'Tim K. Marks', 'Vincent Cartillier', 'Huda Alamri', 'Takaaki Hori', 'Jue Wang', 'Dhruv Batra', 'Gordon Wichern', 'Devi Parikh', 'Chiori Hori', 'Anoop Cherian', 'Abhishek Das'] | 2018-06-21 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 3.93279716e-02 -1.22633064e-02 3.00548404e-01 -8.98271263e-01
-1.00426519e+00 -8.42682660e-01 7.65248299e-01 -3.43046993e-01
-2.31474385e-01 5.26888311e-01 8.58659327e-01 1.06825896e-01
6.76889181e-01 -3.41481507e-01 -7.23527491e-01 -2.94823855e-01
4.33916271e-01 7.68170416e-01 3.61006230e-01 -4.39667791... | [10.879088401794434, 1.220104694366455] |
3b1bb6b2-d3c6-4ee6-9b01-cefbe41e1f1e | latentgaze-cross-domain-gaze-estimation | 2209.10171 | null | https://arxiv.org/abs/2209.10171v1 | https://arxiv.org/pdf/2209.10171v1.pdf | LatentGaze: Cross-Domain Gaze Estimation through Gaze-Aware Analytic Latent Code Manipulation | Although recent gaze estimation methods lay great emphasis on attentively extracting gaze-relevant features from facial or eye images, how to define features that include gaze-relevant components has been ambiguous. This obscurity makes the model learn not only gaze-relevant features but also irrelevant ones. In partic... | ['Seok Bong Yoo', 'Youngju Na', 'Hee Hyeon Kim', 'Jun-Seok Yun', 'Isack Lee'] | 2022-09-21 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 4.33230549e-01 1.88744009e-01 -1.60728768e-01 -6.29562557e-01
-5.52701473e-01 -3.65684271e-01 4.01870668e-01 -6.79760754e-01
-1.27522811e-01 8.07322383e-01 1.12797379e-01 3.40953469e-02
-8.53226483e-02 -3.66540670e-01 -7.03311265e-01 -8.74785542e-01
4.01143104e-01 -2.84105450e-01 -2.59939194e-01 -7.21064284... | [14.06788444519043, 0.017367567867040634] |
0412b030-d299-4f03-a6ce-d3682027d08e | cross-encoder-for-unsupervised-gaze | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Sun_Cross-Encoder_for_Unsupervised_Gaze_Representation_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sun_Cross-Encoder_for_Unsupervised_Gaze_Representation_Learning_ICCV_2021_paper.pdf | Cross-Encoder for Unsupervised Gaze Representation Learning | In order to train 3D gaze estimators without too many annotations, we propose an unsupervised learning framework, Cross-Encoder, to leverage the unlabeled data to learn suitable representation for gaze estimation. To address the issue that the feature of gaze is always intertwined with the appearance of the eye, Cr... | ['Xilin Chen', 'Shiguang Shan', 'Jiabei Zeng', 'Yunjia Sun'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['gaze-estimation'] | ['computer-vision'] | [ 6.58904836e-02 2.49152258e-01 -4.53481495e-01 -6.22735500e-01
-3.02888960e-01 -2.01974526e-01 3.99588048e-01 -5.17784715e-01
-2.49298915e-01 4.70374227e-01 1.97200701e-01 1.81822315e-01
-5.73151931e-02 -2.10377574e-01 -8.60354841e-01 -7.57912457e-01
2.44928554e-01 -1.26712114e-01 5.28980792e-02 1.44937068... | [14.117530822753906, 0.031076705083251] |
25cb36a5-1879-42e6-ac40-59e524d154b5 | leveraging-long-and-short-term-information-in | 1712.09059 | null | http://arxiv.org/abs/1712.09059v5 | http://arxiv.org/pdf/1712.09059v5.pdf | Leveraging Long and Short-term Information in Content-aware Movie Recommendation | Movie recommendation systems provide users with ranked lists of movies based
on individual's preferences and constraints. Two types of models are commonly
used to generate ranking results: long-term models and session-based models.
While long-term models represent the interactions between users and movies that
are supp... | ['Chen Xiaojun', 'Zhao Zhou', 'Yang Min', 'Ye Jianbo', 'Wang Benyou', 'Chai Haixia', 'Zhao Wei'] | 2018-06-26 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-4.74722907e-02 -5.10373354e-01 -2.70931154e-01 -8.17454636e-01
-4.28185582e-01 -9.92837906e-01 6.88899815e-01 -3.75466049e-01
-2.86638111e-01 7.43768871e-01 5.25353611e-01 1.92293953e-02
-3.29925954e-01 -9.21243906e-01 -6.95096970e-01 -5.78368127e-01
-2.09254205e-01 3.68157059e-01 2.83489138e-01 -7.31255114... | [10.128472328186035, 5.630187511444092] |
505ddd5e-0556-4044-922e-7a24b89eb131 | unreal-unlabeled-nodes-retrieval-and-labeling | 2303.10371 | null | https://arxiv.org/abs/2303.10371v1 | https://arxiv.org/pdf/2303.10371v1.pdf | UNREAL:Unlabeled Nodes Retrieval and Labeling for Heavily-imbalanced Node Classification | Extremely skewed label distributions are common in real-world node classification tasks. If not dealt with appropriately, it significantly hurts the performance of GNNs in minority classes. Due to its practical importance, there have been a series of recent research devoted to this challenge. Existing over-sampling tec... | ['Zengfeng Huang', 'Min Zhou', 'Bisheng Li', 'Shengzhong Zhang', 'Liang Yan'] | 2023-03-18 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 9.25315768e-02 2.55148411e-01 -5.16726375e-01 -4.19296533e-01
-3.84450912e-01 -7.00488627e-01 4.33840454e-01 1.41824275e-01
-2.30436884e-02 7.48075664e-01 -1.01888180e-01 -2.57451922e-01
-6.79362863e-02 -1.11483610e+00 -3.85299981e-01 -8.82811129e-01
-1.21850304e-01 5.91218054e-01 2.21006617e-01 -2.17422381... | [7.304291248321533, 6.009034633636475] |
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