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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
14d93748-9f8e-4bf9-88a6-129788c735e4 | online-adaptive-personalization-for-face-anti | 2207.12272 | null | https://arxiv.org/abs/2207.12272v1 | https://arxiv.org/pdf/2207.12272v1.pdf | Online Adaptive Personalization for Face Anti-spoofing | Face authentication systems require a robust anti-spoofing module as they can be deceived by fabricating spoof images of authorized users. Most recent face anti-spoofing methods rely on optimized architectures and training objectives to alleviate the distribution shift between train and test users. However, in real onl... | ['Fatih Porikli', 'Bence Major', 'Debasmit Das', 'Davide Belli'] | 2022-07-04 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 5.92774332e-01 -1.54495478e-01 -1.86761871e-01 -3.93799812e-01
-1.17810443e-04 -7.32520521e-01 3.92474264e-01 -4.57177609e-01
-4.78273124e-01 3.91576320e-01 -2.24422142e-01 -4.28527266e-01
-3.06683239e-02 -4.31965470e-01 -6.97814167e-01 -6.58210576e-01
-4.11138862e-01 1.84151366e-01 1.09975673e-02 -1.55852795... | [13.009915351867676, 1.1142455339431763] |
63a27cd4-cdc3-485c-9c33-b53b6a60ede5 | learning-spatial-context-with-graph-neural | 2104.02385 | null | https://arxiv.org/abs/2104.02385v1 | https://arxiv.org/pdf/2104.02385v1.pdf | Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping | Bottom-up approaches for image-based multi-person pose estimation consist of two stages: (1) keypoint detection and (2) grouping of the detected keypoints to form person instances. Current grouping approaches rely on learned embedding from only visual features that completely ignore the spatial configuration of human p... | ['Gim Hee Lee', 'Jiahao Lin'] | 2021-04-06 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-3.23677182e-01 -9.77506787e-02 -7.50853568e-02 -3.44188452e-01
-5.29652655e-01 -4.43425804e-01 5.12819231e-01 4.78795379e-01
-2.78092146e-01 2.29155391e-01 3.57671410e-01 2.82835215e-01
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-6.11284599e-02 6.44565225e-01 1.95117936e-01 -7.48663992... | [7.111317157745361, -0.812706470489502] |
d37ed4ca-fe73-4c8f-b421-2fb9c79e6e5a | deformable-part-descriptors-for-fine-grained | null | null | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhang_Deformable_Part_Descriptors_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhang_Deformable_Part_Descriptors_2013_ICCV_paper.pdf | Deformable Part Descriptors for Fine-grained Recognition and Attribute Prediction | Recognizing objects in fine-grained domains can be
extremely challenging due to the subtle differences between subcategories. Discriminative markings are often
highly localized, leading traditional object recognition approaches to struggle with the large pose variation often
present in these domains. Pose-normalizat... | ['Trevor Darrell', 'Forrest Iandola', 'Ryan Farrell', 'Ning Zhang'] | 2013-12-01 | null | null | null | iccv-2013-12 | ['fine-grained-image-classification'] | ['computer-vision'] | [ 2.69521505e-01 -2.44244099e-01 -3.55105877e-01 -7.72400022e-01
-9.42527711e-01 -9.92391348e-01 6.35974288e-01 1.16192400e-01
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-1.52948111e-01 -3.20041686e-01 -8.83847654e-01 -6.32168949e-01
9.38428342e-02 7.91831732e-01 2.64621675e-01 2.38722652... | [7.711299896240234, -2.7853105068206787] |
7a291d83-25b7-4987-b8ce-4892b8cdb743 | advancing-topic-segmentation-and-outline | 2305.14790 | null | https://arxiv.org/abs/2305.14790v1 | https://arxiv.org/pdf/2305.14790v1.pdf | Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark | Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings. Such a process unveils the discourse topic structure of a document that benefits quickly grasping and understanding the overall context of the document from a higher level. However,... | ['Haizhou Li', 'Qiaoming Zhu', 'Peifeng Li', 'Xiaomin Chu', 'Weihao Liu', 'Feng Jiang'] | 2023-05-24 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.93082833e-01 7.54653096e-01 -3.75134379e-01 -3.29934478e-01
-1.03933942e+00 -7.18128741e-01 9.49480116e-01 5.47123373e-01
-8.02474543e-02 6.70207083e-01 7.92248428e-01 -4.52151746e-01
5.36874607e-02 -8.78033578e-01 -3.93703699e-01 -5.52320957e-01
1.31788608e-02 5.14087260e-01 4.58225071e-01 -8.96019191... | [10.785045623779297, 9.380377769470215] |
3058714b-e574-4b11-be0c-1468fff2fff3 | compact-representation-of-uncertainty-in-1 | 2002.11661 | null | https://arxiv.org/abs/2002.11661v3 | https://arxiv.org/pdf/2002.11661v3.pdf | Data Structures & Algorithms for Exact Inference in Hierarchical Clustering | Hierarchical clustering is a fundamental task often used to discover meaningful structures in data, such as phylogenetic trees, taxonomies of concepts, subtypes of cancer, and cascades of particle decays in particle physics. Typically approximate algorithms are used for inference due to the combinatorial number of poss... | ['Andrew McCallum', 'Andrew Mcgregor', 'Kyle Cranmer', 'Ji-Ah Lee', 'Nicholas Monath', 'Patrick Flaherty', 'Craig S. Greenberg', 'Sebastian Macaluso'] | 2020-02-26 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.56991541e-01 -6.25757203e-02 2.61324309e-02 -3.54671001e-01
-8.88507426e-01 -6.92219913e-01 4.62389946e-01 8.66099060e-01
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-6.13387167e-01 -8.35842431e-01 -7.61930346e-01 -1.08690369e+00
-6.02883399e-01 1.12671041e+00 5.57043076e-01 1.95127413... | [7.0871663093566895, 5.005406856536865] |
1357ac88-8cec-4e39-b2eb-c986b2edcd1f | automatic-test-suite-generation-for-key | 2012.06511 | null | https://arxiv.org/abs/2012.06511v2 | https://arxiv.org/pdf/2012.06511v2.pdf | Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper) | Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detectio... | ['Jun Wang', 'Thomas Stifter', 'Lionel C. Briand', 'Donghwan Shin', 'Fitash Ul Haq'] | 2020-12-11 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 6.03427999e-02 1.45701319e-02 -1.32754326e-01 -4.63233888e-01
-7.06612766e-01 -3.86168271e-01 3.23501796e-01 -1.00493044e-01
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-3.64185303e-01 -5.69053888e-01 -7.85542786e-01 -7.58127391e-01
1.29084900e-01 5.57923794e-01 3.98192763e-01 -3.98595124... | [7.952764511108398, -0.7208189964294434] |
76d5177e-88a5-4389-9266-ac31829ef0d0 | solving-occlusion-in-terrain-mapping-with | 2109.07150 | null | https://arxiv.org/abs/2109.07150v2 | https://arxiv.org/pdf/2109.07150v2.pdf | Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning | Accurate and complete terrain maps enhance the awareness of autonomous robots and enable safe and optimal path planning. Rocks and topography often create occlusions and lead to missing elevation information in the Digital Elevation Map (DEM). Currently, these occluded areas are either fully avoided during motion plann... | ['Marco Hutter', 'Martin Azkarate', 'Levin Gerdes', 'Takahiro Miki', 'Maximilian Stölzle'] | 2021-09-15 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 5.39570272e-01 5.93486071e-01 2.33714268e-01 -5.53880215e-01
-7.17264593e-01 -4.31679189e-01 5.53160489e-01 2.30660751e-01
-6.01673007e-01 1.27602732e+00 -1.67934954e-01 -4.02934194e-01
-2.41295040e-01 -1.51052904e+00 -8.77116919e-01 -3.32033485e-01
-8.49056721e-01 1.31546926e+00 4.24751639e-01 -6.68521881... | [7.89056396484375, -2.265960693359375] |
d92040e2-0d71-45ff-a7df-8ac48000c338 | bas-an-answer-selection-method-using-bert | 1911.01528 | null | https://arxiv.org/abs/1911.01528v4 | https://arxiv.org/pdf/1911.01528v4.pdf | BAS: An Answer Selection Method Using BERT Language Model | In recent years, Question Answering systems have become more popular and widely used by users. Despite the increasing popularity of these systems, the their performance is not even sufficient for textual data and requires further research. These systems consist of several parts that one of them is the Answer Selection ... | ['Mohammad Ali Nematbakhsh', 'Afsaneh Fatemi', 'Jamshid Mozafari'] | 2019-11-04 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-1.44405603e-01 -1.38830231e-03 2.50210375e-01 -6.20346546e-01
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-3.16941947e-01 -8.30583632e-01 -2.29968876e-01 4.06786576e-02
2.82230556e-01 5.28386593e-01 8.84703994e-01 -8.88470471... | [11.458236694335938, 8.07802963256836] |
7068e7c0-2ab0-4ec1-939c-0a4a88471048 | explore-and-exploit-the-diverse-knowledge-in | 2306.02595 | null | https://arxiv.org/abs/2306.02595v1 | https://arxiv.org/pdf/2306.02595v1.pdf | Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization | The proliferation of pretrained models, as a result of advancements in pretraining techniques, has led to the emergence of a vast zoo of publicly available models. Effectively utilizing these resources to obtain models with robust out-of-distribution generalization capabilities for downstream tasks has become a crucial... | ['ZhiMing Ma', 'Zhenguo Li', 'Fengwei Zhou', 'Tianyang Hu', 'Yimeng Chen'] | 2023-06-05 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 1.66942894e-01 -3.15728694e-01 -3.89639884e-01 -5.83914518e-01
-6.00045919e-01 -8.86428654e-01 6.16208792e-01 3.35551858e-01
-4.27713126e-01 5.34413278e-01 2.59499520e-01 -2.45316938e-01
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-1.57527953e-01 8.94214213e-02 2.82124758e-01 -3.70789558... | [9.76560115814209, 3.1769721508026123] |
683fe8d2-8f92-491d-99e4-75a51d8ed996 | carla-gear-a-dataset-generator-for-a | 2206.04365 | null | https://arxiv.org/abs/2206.04365v1 | https://arxiv.org/pdf/2206.04365v1.pdf | CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models | Adversarial examples represent a serious threat for deep neural networks in several application domains and a huge amount of work has been produced to investigate them and mitigate their effects. Nevertheless, no much work has been devoted to the generation of datasets specifically designed to evaluate the adversarial ... | ['Giorgio Buttazzo', 'Alessandro Biondi', "Gianluca D'Amico", 'Giulio Rossolini', 'Federico Nesti'] | 2022-06-09 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.19555676e-01 3.00372601e-01 6.21066928e-01 -2.60594100e-01
-3.24537724e-01 -7.52231061e-01 9.73734915e-01 -1.23963133e-01
-4.40757543e-01 5.13672709e-01 -4.41813111e-01 -5.32166600e-01
-1.93473753e-02 -1.04500854e+00 -1.13566303e+00 -9.30332124e-01
-2.23752543e-01 4.07006204e-01 5.64173400e-01 -4.89736110... | [5.486487865447998, 7.803900241851807] |
9d727c5c-79df-447e-9aa8-72d34859147f | fusion-of-an-ensemble-of-augmented-image | 1803.06554 | null | http://arxiv.org/abs/1803.06554v1 | http://arxiv.org/pdf/1803.06554v1.pdf | Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection | A significant challenge in object detection is accurate identification of an
object's position in image space, whereas one algorithm with one set of
parameters is usually not enough, and the fusion of multiple algorithms and/or
parameters can lead to more robust results. Herein, a new computational
intelligence fusion ... | ['John E. Ball', 'Pan Wei', 'Derek T. Anderson'] | 2018-03-17 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 1.76015183e-01 -4.01871800e-01 2.68771827e-01 -4.10582304e-01
-6.30315602e-01 -1.54544413e-01 4.14917856e-01 5.08404732e-01
-7.28729963e-01 5.42606592e-01 -5.61985731e-01 -3.34978461e-01
-3.38497281e-01 -4.32538331e-01 -5.48910022e-01 -9.32528913e-01
2.56120801e-01 2.78538465e-01 4.76847202e-01 -2.39443839... | [8.104242324829102, -0.8744815587997437] |
834a0b01-acef-450a-a031-dd05f1371337 | long-term-visual-localization-with-mobile | 2304.07691 | null | https://arxiv.org/abs/2304.07691v1 | https://arxiv.org/pdf/2304.07691v1.pdf | Long-term Visual Localization with Mobile Sensors | Despite the remarkable advances in image matching and pose estimation, image-based localization of a camera in a temporally-varying outdoor environment is still a challenging problem due to huge appearance disparity between query and reference images caused by illumination, seasonal and structural changes. In this work... | ['Xiaowei Zhou', 'Guofeng Zhang', 'Maojun Zhang', 'Haomin Liu', 'Zhen Peng', 'Zehong Shen', 'Long Wang', 'Yu Liu', 'Shen Yan'] | 2023-04-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yan_Long-Term_Visual_Localization_With_Mobile_Sensors_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yan_Long-Term_Visual_Localization_With_Mobile_Sensors_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-based-localization', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [ 2.38515511e-01 -5.60871065e-01 -7.26623014e-02 -3.92133236e-01
-8.67816210e-01 -8.85314405e-01 4.02738094e-01 -4.31446135e-01
-4.75853145e-01 2.69176275e-01 -2.35519543e-01 -9.06055346e-02
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1.77873865e-01 2.68150389e-01 5.81965327e-01 -2.49631405... | [7.62559175491333, -2.176687717437744] |
62092da1-cf35-435c-9bd7-74f6fd62f303 | minimax-analysis-for-inverse-risk-in | 2112.00213 | null | https://arxiv.org/abs/2112.00213v2 | https://arxiv.org/pdf/2112.00213v2.pdf | Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression | We study a minimax risk of estimating inverse functions on a plane, while keeping an estimator is also invertible. Learning invertibility from data and exploiting an invertible estimator are used in many domains, such as statistics, econometrics, and machine learning. Although the consistency and universality of invert... | ['Masaaki Imaizumi', 'Akifumi Okuno'] | 2021-12-01 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 3.86771262e-01 5.68063676e-01 -2.65706718e-01 -4.36383635e-01
-9.15972054e-01 -7.65656352e-01 2.26353154e-01 -2.74509221e-01
-3.46229762e-01 7.84835219e-01 -9.02289227e-02 -3.74515980e-01
-4.86661434e-01 -7.10346282e-01 -1.18597138e+00 -8.40176940e-01
-4.14695263e-01 2.17150092e-01 -7.90253952e-02 2.99165025... | [7.280307292938232, 4.154984474182129] |
fc9f8875-bf8a-4690-9d13-a3317cb67993 | traffic-sign-classification-using-deep | 1511.02992 | null | http://arxiv.org/abs/1511.02992v2 | http://arxiv.org/pdf/1511.02992v2.pdf | Traffic Sign Classification Using Deep Inception Based Convolutional Networks | In this work, we propose a novel deep network for traffic sign classification
that achieves outstanding performance on GTSRB surpassing all previous methods.
Our deep network consists of spatial transformer layers and a modified version
of inception module specifically designed for capturing local and global
features t... | ['Mrinal Haloi'] | 2015-11-10 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [-1.93672478e-01 -3.69766384e-01 -2.72053033e-01 -6.57308400e-01
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-2.90193111e-02 -8.74139011e-01 -6.04396999e-01 -6.35334015e-01
1.11614086e-01 2.15106800e-01 8.40808988e-01 -4.92851734... | [7.999164581298828, -0.7865582704544067] |
bbf87cd3-aca6-4b5e-b7d3-5c549d4ca784 | tight-sample-complexity-of-large-margin | null | null | http://papers.nips.cc/paper/4032-tight-sample-complexity-of-large-margin-learning | http://papers.nips.cc/paper/4032-tight-sample-complexity-of-large-margin-learning.pdf | Tight Sample Complexity of Large-Margin Learning | We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 regularization: We introduce the gamma-adapted-dimension, which is a simple function of the spectrum of a distribution's covariance matrix, and show distribution-specific upper and lower bounds on th... | ['Sivan Sabato', 'Nathan Srebro', 'Naftali Tishby'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['l2-regularization'] | ['methodology'] | [-2.13351011e-01 2.35454977e-01 -5.76306283e-01 -3.32082272e-01
-1.42516053e+00 -6.45750642e-01 3.36158544e-01 3.63014370e-01
-4.73025620e-01 7.22360551e-01 -1.77212190e-02 -4.56784636e-01
-3.54986489e-01 -4.69607323e-01 -4.36193407e-01 -1.13860893e+00
-2.86776125e-01 3.87628019e-01 1.26459941e-01 1.68013513... | [7.597989082336426, 4.159363269805908] |
0338e5d6-d50d-44ee-afbe-8d6e72602ce3 | m3ae-multimodal-representation-learning-for | 2303.05302 | null | https://arxiv.org/abs/2303.05302v1 | https://arxiv.org/pdf/2303.05302v1.pdf | M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities | Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-region analysis of brain tumors. Plenty of methods have been proposed for automatic brain tumor segmentation using four common MRI modalities and achieved remarkable performance. In practice, however, it is common to have one or more... | ['Yefeng Zheng', 'Liansheng Wang', 'Jinghan Sun', 'Donghuan Lu', 'Dong Wei', 'Hong Liu'] | 2023-03-09 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 4.73221421e-01 1.69043481e-01 -2.87492573e-01 -2.41057634e-01
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2.06025183e-01 5.84692419e-01 1.62083060e-01 1.15990408... | [14.586515426635742, -2.2317092418670654] |
168d777f-2661-4f5e-98b4-d86864d883de | development-and-whole-body-validation-of | 2305.13918 | null | https://arxiv.org/abs/2305.13918v1 | https://arxiv.org/pdf/2305.13918v1.pdf | Development and Whole-Body Validation of Personalizable Female and Male Pedestrian SAFER Human Body Models | Vulnerable road users are overrepresented in the worldwide number of road-traffic injury victims. Developing biofidelic male and female pedestrian HBMs representing a range of anthropometries is imperative to follow through with the efforts to increase road safety and propose intervention strategies. In this study, a 5... | ['Xiaogai Li', 'Svein Kleiven', 'Bengt Pipkorn', 'Qiantailang Yuan', 'Natalia Lindgren'] | 2023-05-11 | null | null | null | null | ['image-registration'] | ['computer-vision'] | [-2.28680268e-01 2.49245703e-01 3.48867178e-02 -3.23613316e-01
-6.07660890e-01 -4.78412583e-02 4.07741278e-01 1.72125444e-01
-8.01035523e-01 8.58002961e-01 2.44253218e-01 -7.61375353e-02
-3.42636555e-01 -9.46302056e-01 -7.71148860e-01 -5.49950898e-01
-2.78776318e-01 9.16892350e-01 2.89457709e-01 -6.38819635... | [14.043680191040039, -1.8688111305236816] |
bd13b042-bc4e-42cd-8201-46eb29ffa7a5 | detecting-pulse-from-head-motions-in-video | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Balakrishnan_Detecting_Pulse_from_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Balakrishnan_Detecting_Pulse_from_2013_CVPR_paper.pdf | Detecting Pulse from Head Motions in Video | We extract heart rate and beat lengths from videos by measuring subtle head motion caused by the Newtonian reaction to the influx of blood at each beat. Our method tracks features on the head and performs principal component analysis (PCA) to decompose their trajectories into a set of component motions. It then chooses... | ['John Guttag', 'Guha Balakrishnan', 'Fredo Durand'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['heart-rate-variability'] | ['medical'] | [-8.95190239e-02 -2.17494741e-02 -1.76597461e-02 1.29163787e-01
-2.15562269e-01 -6.02597594e-01 2.17403129e-01 1.84541894e-03
-1.67384714e-01 4.78666723e-01 5.63709259e-01 1.27420649e-01
1.67219099e-02 -2.28186235e-01 -3.43939140e-02 -7.86224306e-01
-5.81120431e-01 3.33811074e-01 -8.50728825e-02 1.45566553... | [13.970006942749023, 2.971041679382324] |
fcc83f29-e303-40e4-a14f-c3b13ecb8c3f | kalmannet-a-learnable-kalman-filter-for | 2301.12363 | null | https://arxiv.org/abs/2301.12363v2 | https://arxiv.org/pdf/2301.12363v2.pdf | NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation | The Kalman filter is widely used for addressing acoustic echo cancellation (AEC) problems due to their robustness to double-talk and fast convergence. However, the inability to model nonlinearity and the need to tune control parameters cast limitations on such adaptive filtering algorithms. In this paper, we integrate ... | ['DeLiang Wang', 'Dong Yu', 'Hao Zhang', 'Meng Yu', 'Yixuan Zhang'] | 2023-01-29 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [-2.18250737e-01 -5.46922445e-01 3.82127583e-01 -3.51386726e-01
-9.78098810e-01 -4.10947472e-01 3.99882257e-01 -6.38775826e-01
-4.26098228e-01 3.81114483e-01 7.13609457e-01 -3.65482002e-01
-1.93932772e-01 -1.02965683e-01 -5.72928369e-01 -8.16911340e-01
-1.98527113e-01 -3.68404806e-01 -9.81659889e-02 -5.86288646... | [15.04650592803955, 5.933996677398682] |
fbedcf72-6c44-49cb-a4da-831e77432d9d | sketchanimar-sketch-based-3d-animal-fine | 2304.05731 | null | https://arxiv.org/abs/2304.05731v1 | https://arxiv.org/pdf/2304.05731v1.pdf | SketchANIMAR: Sketch-based 3D Animal Fine-Grained Retrieval | The retrieval of 3D objects has gained significant importance in recent years due to its broad range of applications in computer vision, computer graphics, virtual reality, and augmented reality. However, the retrieval of 3D objects presents significant challenges due to the intricate nature of 3D models, which can var... | ['Minh-Triet Tran', 'Akihiro Sugimoto', 'Hai-Dang Nguyen', 'Minh-Hoa Doan', 'Hoai-Danh Vo', 'Minh-Quang Nguyen', 'Nhu-Vinh Hoang', 'Kim-Phat Tran', 'Tuan-Anh Yang', 'Thien-Phuc Tran', 'Xuan-Hieu Nguyen', 'Khanh-Duy Ho', 'Tuong-Nghiem Diep', 'Truong Hoai Phong', 'Tuong-Vy Truong-Thuy', 'Ngoc-Linh Nguyen-Ha', 'Thanh-Danh... | 2023-04-12 | null | null | null | null | ['3d-object-retrieval'] | ['computer-vision'] | [-9.80079323e-02 -6.69358850e-01 -3.37445037e-03 -2.64150590e-01
-5.94859302e-01 -8.37999225e-01 7.08386898e-01 1.76351532e-01
-9.57416669e-02 8.67341310e-02 4.56751511e-02 -8.10139105e-02
-3.31759065e-01 -7.66753018e-01 -4.67426658e-01 -7.61248544e-02
-2.92247683e-01 7.02957809e-01 3.81620228e-01 -3.50555807... | [8.475739479064941, -3.5774261951446533] |
77f9e22a-5b45-4148-89f3-a590d0c7c4c6 | pressure-predictions-of-turbine-blades-with | 1806.06940 | null | http://arxiv.org/abs/1806.06940v1 | http://arxiv.org/pdf/1806.06940v1.pdf | Pressure Predictions of Turbine Blades with Deep Learning | Deep learning has been used in many areas, such as feature detections in
images and the game of go. This paper presents a study that attempts to use the
deep learning method to predict turbomachinery performance. Three different
deep neural networks are built and trained to predict the pressure
distributions of turbine... | ["Cheng'an Bai", 'Chao Zhou'] | 2018-06-12 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-8.87375951e-01 -7.15295747e-02 2.40759328e-01 -8.25226754e-02
3.58804375e-01 -1.87827468e-01 3.68719816e-01 -3.47522050e-01
-9.50903520e-02 4.08948600e-01 -2.60972202e-01 -4.84499365e-01
-6.31321147e-02 -8.43449414e-01 -5.81546247e-01 -7.92065501e-01
-4.65012789e-01 3.87671739e-01 4.35523480e-01 -5.55508494... | [6.830648422241211, 2.540900230407715] |
cc28da78-a6f6-4c73-8d1f-342f95f5ac45 | safe-and-sample-efficient-reinforcement | 2303.14265 | null | https://arxiv.org/abs/2303.14265v1 | https://arxiv.org/pdf/2303.14265v1.pdf | Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic Environments | This study proposes a safe and sample-efficient reinforcement learning (RL) framework to address two major challenges in developing applicable RL algorithms: satisfying safety constraints and efficiently learning with limited samples. To guarantee safety in real-world complex environments, we use the safe set algorithm... | ['Changliu Liu', 'Hongyi Chen'] | 2023-03-24 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-3.65049988e-02 1.59791127e-01 -5.44024110e-01 1.77263632e-01
-8.99289966e-01 -6.28725410e-01 2.79063970e-01 -2.76220351e-01
-5.05375206e-01 1.32297897e+00 -9.99727249e-02 -5.30395806e-01
-2.89894700e-01 -6.17963970e-01 -8.70635331e-01 -1.00884104e+00
-4.70772237e-01 3.10208291e-01 1.95324883e-01 -1.40248731... | [4.443434238433838, 2.1762375831604004] |
05b8bb58-66ef-45d2-8fed-69446b07b6f7 | real-face-foundation-representation-learning | 2303.08439 | null | https://arxiv.org/abs/2303.08439v1 | https://arxiv.org/pdf/2303.08439v1.pdf | Real Face Foundation Representation Learning for Generalized Deepfake Detection | The emergence of deepfake technologies has become a matter of social concern as they pose threats to individual privacy and public security. It is now of great significance to develop reliable deepfake detectors. However, with numerous face manipulation algorithms present, it is almost impossible to collect sufficient ... | ['Shiguang Shan', 'Jie Zhang', 'Liang Shi'] | 2023-03-15 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-7.42077306e-02 3.71997431e-02 -2.19069123e-02 -4.55869317e-01
-4.86813694e-01 -4.51593012e-01 5.06664991e-01 -4.81763601e-01
1.63626865e-01 6.01758182e-01 2.82750763e-02 1.33785546e-01
1.57560676e-01 -9.04459715e-01 -9.03006494e-01 -6.70718253e-01
-6.34554178e-02 1.17114656e-01 -7.30777606e-02 -3.12532902... | [12.72298526763916, 1.0308406352996826] |
42350be9-3119-4f4b-a9b0-a32350a62e7c | intention-aware-feature-propagation-network | 2203.05145 | null | https://arxiv.org/abs/2203.05145v2 | https://arxiv.org/pdf/2203.05145v2.pdf | Intention-aware Feature Propagation Network for Interactive Segmentation | We aim to tackle the problem of point-based interactive segmentation, in which two key challenges are to infer user's intention correctly and to propagate the user-provided annotations to unlabeled regions efficiently. To address those challenges, we propose a novel intention-aware feature propagation strategy that per... | ['Xuming He', 'Yongfei Liu', 'Chuanyang Hu', 'Chuyu Zhang'] | 2022-03-10 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 3.22751999e-01 -4.99657057e-02 -4.19605523e-01 -4.10017520e-01
-7.47437835e-01 -3.88661355e-01 2.16503456e-01 1.56120762e-01
-3.21119845e-01 3.16316128e-01 1.06338851e-01 -6.82716146e-02
-8.94849002e-02 -8.11308086e-01 -8.56330395e-01 -2.66313493e-01
-2.39223167e-01 5.42717159e-01 1.27552581e+00 1.07377544... | [9.31329345703125, -0.03652995079755783] |
5a1c96f5-72c6-447e-aedc-81c136e629fb | msstn-multi-scale-spatial-temporal-network | null | null | https://ieeexplore.ieee.org/document/9005574 | https://ieeexplore.ieee.org/document/9005574 | MSSTN: Multi-Scale Spatial Temporal Network for Air Pollution Prediction | Air pollution has become an important factor constraining city development and threatening public health in recent years. Air pollution prediction has been considered as the key part for the early warning of pollution event. Considering the multi-scale nature of geo-sensory data such as air pollution signal, in this pa... | ['Zhiyuan Wu ; Yue Wang ; Lin Zhang'] | 2019-09-12 | null | null | null | 2019-ieee-international-conference-on-big-3 | ['air-pollution-prediction'] | ['miscellaneous'] | [-1.29742920e-01 -6.53891087e-01 -3.92226223e-03 -9.91941690e-02
-4.91797447e-01 -4.85030375e-02 3.36197108e-01 3.28625411e-01
-1.82230279e-01 8.68225753e-01 3.66005301e-01 -7.07682788e-01
-7.94176042e-01 -1.66016281e+00 -5.66344559e-01 -7.75061607e-01
-3.36682737e-01 -9.57575366e-02 2.66295642e-01 -2.23460183... | [6.29055643081665, 2.5033836364746094] |
fde9d741-c379-470e-9dcc-7c032325160d | a-comparison-of-decision-analysis-and-expert | 1304.2362 | null | http://arxiv.org/abs/1304.2362v1 | http://arxiv.org/pdf/1304.2362v1.pdf | A Comparison of Decision Analysis and Expert Rules for Sequential Diagnosis | There has long been debate about the relative merits of decision theoretic
methods and heuristic rule-based approaches for reasoning under uncertainty. We
report an experimental comparison of the performance of the two approaches to
troubleshooting, specifically to test selection for fault diagnosis. We use as
experime... | ['Max Henrion', 'Jayant Kalagnanam'] | 2013-03-27 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [-3.47616039e-02 3.91365379e-01 -1.00022078e-01 -3.30809981e-01
-8.33273053e-01 -4.80864018e-01 2.90393680e-01 2.19937578e-01
-2.33577285e-02 1.05137217e+00 -5.70763409e-01 -1.18233681e+00
-1.01767027e+00 -6.45670176e-01 -5.12729347e-01 -4.35857236e-01
-7.10102692e-02 9.91765916e-01 4.27122802e-01 3.23063493... | [5.425131320953369, 2.700094699859619] |
c8b66165-936b-4c0b-b028-17bc4e181584 | 190412658 | 1904.12658 | null | http://arxiv.org/abs/1904.12658v2 | http://arxiv.org/pdf/1904.12658v2.pdf | MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching | Disparity prediction from stereo images is essential to computer vision
applications including autonomous driving, 3D model reconstruction, and object
detection. To predict accurate disparity map, we propose a novel deep learning
architecture for detectingthe disparity map from a rectified pair of stereo
images, called... | ['Zhibo Rao', 'Renjie He', 'Bo Li', 'Mingyi He', 'Zhidong Zhu', 'Yuchao Dai'] | 2019-04-25 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [-4.92133163e-02 -2.45026395e-01 3.80001664e-02 -6.08355224e-01
-4.65080738e-01 -1.49324432e-01 5.17587900e-01 -4.24440056e-01
-4.78051186e-01 3.87216896e-01 1.81041092e-01 -2.85761535e-01
3.24823350e-01 -1.10084164e+00 -7.90677309e-01 -4.18845564e-01
1.31728455e-01 1.60874069e-01 8.97025049e-01 -2.24562421... | [8.860562324523926, -2.294553518295288] |
0fed5793-d831-455c-b400-10f58df13983 | tricubenet-2d-kernel-based-object | 2104.11435 | null | https://arxiv.org/abs/2104.11435v2 | https://arxiv.org/pdf/2104.11435v2.pdf | TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection | We present a novel approach for oriented object detection, named TricubeNet, which localizes oriented objects using visual cues ($i.e.,$ heatmap) instead of oriented box offsets regression. We represent each object as a 2D Tricube kernel and extract bounding boxes using simple image-processing algorithms. Our approach ... | ['Junmo Kim', 'Doyeon Kim', 'Sihaeng Lee', 'Janghyeon Lee', 'Beomyoung Kim'] | 2021-04-23 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-3.79312970e-02 -2.36451164e-01 -9.97983366e-02 8.00323039e-02
-5.41666389e-01 -5.52618086e-01 -8.80797207e-02 -1.12403855e-01
-1.32238790e-01 4.00275826e-01 -2.83587039e-01 -1.29319474e-01
1.74856018e-02 -7.27698565e-01 -5.76406479e-01 -5.91938257e-01
-4.77198064e-01 1.96525395e-01 8.39010417e-01 4.70959768... | [8.700018882751465, -0.7997917532920837] |
443c3d95-3582-4467-9a21-b9bb0bf2e092 | policy-gradient-search-online-planning-and | 1904.03646 | null | http://arxiv.org/abs/1904.03646v1 | http://arxiv.org/pdf/1904.03646v1.pdf | Policy Gradient Search: Online Planning and Expert Iteration without Search Trees | Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to
improve policies online. During search, the simulation policy is adapted to
explore the most promising lines of play. MCTS has been used by
state-of-the-art programs for many problems, however a disadvantage to MCTS is
that it estimates the va... | ['Tim Salimans', 'John Schulman', 'Thomas Anthony', 'Robert Nishihara', 'Philipp Moritz'] | 2019-04-07 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [-5.06816208e-01 -1.78025827e-01 -6.07947171e-01 4.05822881e-02
-5.87841809e-01 -6.83608055e-01 5.52156985e-01 -1.50935864e-02
-1.02076149e+00 1.35719585e+00 -6.14679866e-02 -8.34127188e-01
1.66163482e-02 -9.38588738e-01 -7.80837595e-01 -5.74762344e-01
-1.75972730e-01 9.88471448e-01 7.41480410e-01 -3.34233940... | [3.6685805320739746, 1.6445536613464355] |
712cb9e0-c992-4324-970e-6e7ace816e48 | what-s-the-meaning-of-superhuman-performance | 2305.08414 | null | https://arxiv.org/abs/2305.08414v1 | https://arxiv.org/pdf/2305.08414v1.pdf | What's the Meaning of Superhuman Performance in Today's NLU? | In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension. These PLMs have achiev... | ['Roberto Navigli', 'Ekaterina Shutova', 'Rico Sennrich', 'Steven Schockaert', 'Simon Krek', 'Alexander Koller', 'Eduard H. Hovy', 'Daniel Hershcovich', 'Jan Hajic', 'Thierry Declerck', 'Johan Bos', 'Simone Tedeschi'] | 2023-05-15 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [-8.42747912e-02 6.77473366e-01 -1.47219989e-02 -4.01382983e-01
-2.97140688e-01 -5.92885673e-01 1.05435419e+00 6.42539859e-01
-7.18788028e-01 5.27556121e-01 6.37931585e-01 -6.67178154e-01
-8.48724470e-02 -6.30005538e-01 -5.06628036e-01 7.69322291e-02
-1.29722282e-02 6.33062959e-01 3.48800480e-01 -5.64700246... | [9.73334789276123, 7.507755756378174] |
20c89590-71ee-47cf-929d-f5564e53bea7 | multi-task-attentive-residual-networks-for | 2102.12227 | null | https://arxiv.org/abs/2102.12227v3 | https://arxiv.org/pdf/2102.12227v3.pdf | Multi-Task Attentive Residual Networks for Argument Mining | We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five d... | ['Paolo Torroni', 'Marco Lippi', 'Andrea Galassi'] | 2021-02-24 | null | null | null | null | ['component-classification'] | ['natural-language-processing'] | [ 1.66561007e-01 5.58297932e-01 -3.64023954e-01 -2.49210924e-01
-1.09323323e+00 -4.71794903e-01 9.86371219e-01 3.70822251e-01
-7.17095852e-01 9.72617447e-01 4.67672586e-01 -1.09862792e+00
-3.71761054e-01 -5.10010540e-01 -7.60452151e-01 -2.77527779e-01
2.77537972e-01 7.08974302e-01 3.68189104e-02 -3.68859917... | [9.633999824523926, 9.658268928527832] |
73952034-068c-462d-a1e3-2b7fa78c4b84 | independent-policy-gradient-for-large-scale | 2202.04129 | null | https://arxiv.org/abs/2202.04129v3 | https://arxiv.org/pdf/2202.04129v3.pdf | Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence | We examine global non-asymptotic convergence properties of policy gradient methods for multi-agent reinforcement learning (RL) problems in Markov potential games (MPG). To learn a Nash equilibrium of an MPG in which the size of state space and/or the number of players can be very large, we propose new independent polic... | ['Mihailo R. Jovanović', 'Kaiqing Zhang', 'Chen-Yu Wei', 'Dongsheng Ding'] | 2022-02-08 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-3.88744026e-01 1.46974102e-01 -2.70224214e-01 2.74377853e-01
-1.00978649e+00 -8.46085727e-01 3.82164717e-02 -2.52349563e-02
-1.14414716e+00 1.35964358e+00 -3.35964829e-01 -6.80051625e-01
-5.33531249e-01 -7.32259154e-01 -8.14289570e-01 -9.23706293e-01
-6.35247409e-01 7.59433746e-01 1.65069699e-01 -4.73538399... | [4.185699939727783, 2.6150784492492676] |
1060f9e3-7d4a-428f-8754-a5d285b42596 | olagpt-empowering-llms-with-human-like | 2305.16334 | null | https://arxiv.org/abs/2305.16334v1 | https://arxiv.org/pdf/2305.16334v1.pdf | OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities | In most current research, large language models (LLMs) are able to perform reasoning tasks by generating chains of thought through the guidance of specific prompts. However, there still exists a significant discrepancy between their capability in solving complex reasoning problems and that of humans. At present, most a... | ['Zang Li', 'Bo Hu', 'Chengxiang Zhuo', 'Lei Chen', 'Beibei Kong', 'Chenglin Li', 'WenTao Wei', 'Mingxiong Lin', 'Tao Xie', 'Yuanzhen Xie'] | 2023-05-23 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [-1.75986841e-01 2.69265532e-01 8.36367682e-02 -2.27325007e-01
3.05360034e-02 -4.44476664e-01 5.93219995e-01 3.63999665e-01
-2.66430616e-01 2.70858049e-01 1.03661992e-01 -5.52484930e-01
-6.27832472e-01 -1.03657985e+00 -2.12703183e-01 -2.37743214e-01
3.61175209e-01 7.01477706e-01 2.75303155e-01 -4.79300499... | [9.561595916748047, 7.399538516998291] |
b956c707-9330-4b8d-bb54-373f1f32d3f4 | marginal-contrastive-correspondence-for | 2204.00442 | null | https://arxiv.org/abs/2204.00442v1 | https://arxiv.org/pdf/2204.00442v1.pdf | Marginal Contrastive Correspondence for Guided Image Generation | Exemplar-based image translation establishes dense correspondences between a conditional input and an exemplar (from two different domains) for leveraging detailed exemplar styles to achieve realistic image translation. Existing work builds the cross-domain correspondences implicitly by minimizing feature-wise distance... | ['Changgong Zhang', 'Shijian Lu', 'Jiahui Zhang', 'Rongliang Wu', 'Yingchen Yu', 'Fangneng Zhan'] | 2022-04-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhan_Marginal_Contrastive_Correspondence_for_Guided_Image_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhan_Marginal_Contrastive_Correspondence_for_Guided_Image_Generation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['texture-synthesis'] | ['computer-vision'] | [ 3.81436080e-01 -1.11330986e-01 -1.48925036e-01 -3.54774833e-01
-8.98303151e-01 -3.39208871e-01 1.01749086e+00 -3.67106706e-01
-9.72453877e-02 8.14179182e-01 1.44425809e-01 6.74487501e-02
-1.29266888e-01 -7.89745629e-01 -1.15558314e+00 -7.27118969e-01
3.88132870e-01 2.82045513e-01 9.05474871e-02 -4.04208571... | [11.57646656036377, -0.5514593720436096] |
82df2a9c-c917-4ec4-8f85-a511263862d2 | unsupervised-pre-training-for-temporal-action | 2203.13609 | null | https://arxiv.org/abs/2203.13609v1 | https://arxiv.org/pdf/2203.13609v1.pdf | Unsupervised Pre-training for Temporal Action Localization Tasks | Unsupervised video representation learning has made remarkable achievements in recent years. However, most existing methods are designed and optimized for video classification. These pre-trained models can be sub-optimal for temporal localization tasks due to the inherent discrepancy between video-level classification ... | ['Yuexian Zou', 'Jue Wang', 'Meng Cao', 'Junwu Weng', 'Tianyu Yang', 'Can Zhang'] | 2022-03-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Unsupervised_Pre-Training_for_Temporal_Action_Localization_Tasks_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Unsupervised_Pre-Training_for_Temporal_Action_Localization_Tasks_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-localization', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 3.61669034e-01 -3.16289037e-01 -6.01799846e-01 -2.99313873e-01
-1.03168523e+00 -4.91796166e-01 3.65507454e-01 -2.85959810e-01
-1.92973763e-01 4.39793080e-01 4.55123842e-01 2.44319230e-01
1.44170761e-01 -2.92643577e-01 -8.14301789e-01 -6.77967370e-01
-2.30715841e-01 2.25124490e-02 3.38387012e-01 1.54545948... | [8.680459022521973, 0.7041987180709839] |
03b5360f-9620-4dc5-87e9-0a4bfb35aa64 | pamir-parametric-model-conditioned-implicit | 2007.03858 | null | https://arxiv.org/abs/2007.03858v2 | https://arxiv.org/pdf/2007.03858v2.pdf | PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction | Modeling 3D humans accurately and robustly from a single image is very challenging, and the key for such an ill-posed problem is the 3D representation of the human models. To overcome the limitations of regular 3D representations, we propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines t... | ['Zerong Zheng', 'Yebin Liu', 'Tao Yu', 'Qionghai Dai'] | 2020-07-08 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [ 7.71193802e-02 -5.38809448e-02 -8.43464304e-03 -3.67855340e-01
-4.81555015e-01 -9.47376192e-02 1.22097902e-01 -3.31882238e-01
-1.75988138e-01 2.25433126e-01 2.32669085e-01 4.62015510e-01
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4.46740299e-01 7.19838142e-01 1.45315677e-01 -4.56273675... | [7.1237335205078125, -1.1445029973983765] |
d77defaf-6210-43ca-9a43-dd5a77a5d9fd | a-bert-based-universal-model-for-both-within | null | null | https://aclanthology.org/W19-1908 | https://aclanthology.org/W19-1908.pdf | A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction | Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to... | ['Guergana Savova', 'Dmitriy Dligach', 'Chen Lin', 'Timothy Miller', 'Steven Bethard'] | 2019-06-01 | null | null | null | ws-2019-6 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 3.56538355e-01 6.36538625e-01 -6.26941442e-01 -5.27611613e-01
-1.24732816e+00 -4.68786657e-01 5.27528524e-01 6.66634798e-01
-4.68047976e-01 9.75026846e-01 6.11220896e-01 -6.00682139e-01
-4.92378920e-01 -8.56680572e-01 -7.02318847e-01 -2.39504009e-01
-5.65495193e-01 5.76707959e-01 2.08050758e-01 -5.52231133... | [8.50844955444336, 8.947549819946289] |
166efdc2-86c7-4e69-80b0-5933a093a4d8 | multi-dimensional-nuanced-and-subjective | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bryant_Multi-Dimensional_Nuanced_and_Subjective_-_Measuring_the_Perception_of_Facial_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bryant_Multi-Dimensional_Nuanced_and_Subjective_-_Measuring_the_Perception_of_Facial_CVPR_2022_paper.pdf | Multi-Dimensional, Nuanced and Subjective - Measuring the Perception of Facial Expressions | Humans can perceive multiple expressions, each one with varying intensity, in the picture of a face. We propose a methodology for collecting and modeling multidimensional modulated expression annotations from human annotators. Our data reveals that the perception of some expressions can be quite different across ob... | ['Pietro Perona', 'Wei Xia', 'Nashlie Sephus', 'Siqi Deng', "De'Aira Bryant"] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.26291344e-01 1.99708909e-01 -1.82310924e-01 -9.81970489e-01
-4.59770828e-01 -7.42878079e-01 5.82959533e-01 -3.40696365e-01
-3.44258368e-01 4.18862015e-01 4.82893050e-01 4.42964464e-01
3.32905464e-02 -5.50489612e-02 -2.08513424e-01 -7.12670624e-01
-2.38540042e-02 2.29774579e-01 -5.17079830e-01 -2.67655253... | [13.549432754516602, 1.8563284873962402] |
f2c92006-10ed-490c-a6d2-39ebb0c30040 | the-multital-nlp-tool-infrastructure | null | null | https://aclanthology.org/W16-4021 | https://aclanthology.org/W16-4021.pdf | The MultiTal NLP tool infrastructure | This paper gives an overview of the MultiTal project, which aims to create a research infrastructure that ensures long-term distribution of NLP tools descriptions. The goal is to make NLP tools more accessible and usable to end-users of different disciplines. The infrastructure is built on a meta-data scheme modelling ... | ['Satenik Mkhitaryan', 'Driss Sadoun', 'Damien Nouvel', 'Mathieu Valette'] | 2016-12-01 | null | null | null | ws-2016-12 | ['multilingual-nlp'] | ['natural-language-processing'] | [-2.52872229e-01 6.75600290e-01 -4.28229064e-01 -3.99513990e-01
-3.07104915e-01 -9.97388303e-01 9.46800590e-01 3.15501511e-01
-2.70001411e-01 7.48902678e-01 4.54785496e-01 -3.52528840e-01
-6.02871895e-01 -7.11959183e-01 -1.15035936e-01 8.47740192e-03
2.79859990e-01 1.03417337e+00 4.28395540e-01 -2.24917576... | [9.417336463928223, 8.578184127807617] |
61284fa7-d69a-4876-b49f-b2762bcb57af | analysis-and-classification-of-heart-diseases | null | null | https://doi.org/10.1186/s40537-019-0244-x | https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-019-0244-x | Analysis and classification of heart diseases using heartbeat features and machine learning algorithms | This study proposed an ECG (Electrocardiogram) classification approach using machine learning based on several ECG features. An electrocardiogram (ECG) is a signal that measures the electric activity of the heart. The proposed approach is implemented using ML-libs and Scala language on Apache Spark framework; MLlib is ... | ['Fajr Ibrahem Alarsan', 'Mamoon Younes'] | 2019-08-31 | null | null | null | journal-of-big-data-2019-2019-8 | ['ecg-classification', 'heartbeat-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [-2.11466774e-01 -4.79699701e-01 3.09242994e-01 -4.02686656e-01
-3.41804922e-01 -5.71126819e-01 3.18920389e-02 6.58228695e-01
-2.53295720e-01 8.39163303e-01 -9.45340246e-02 -5.99720180e-01
-1.21013202e-01 -9.11860764e-01 2.32552998e-02 -6.91953480e-01
-2.66302943e-01 5.76478422e-01 -9.42693278e-02 -1.62618642... | [14.235665321350098, 3.2485077381134033] |
23cd23c1-e4a5-4cd0-9f14-fbee81d24e56 | from-cities-to-series-complex-networks-and | 2206.01176 | null | https://arxiv.org/abs/2206.01176v1 | https://arxiv.org/pdf/2206.01176v1.pdf | From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics* | Graphs have often been used to answer questions about the interaction between real-world entities by taking advantage of their capacity to represent complex topologies. Complex networks are known to be graphs that capture such non-trivial topologies; they are able to represent human phenomena such as epidemic processes... | ['Jose F. Rodrigues-Jr', 'Gabriel Spadon'] | 2022-06-01 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 8.50984827e-03 3.53267223e-01 -1.16636448e-01 2.66431451e-01
5.64100742e-01 -2.97815740e-01 9.54937100e-01 7.15598941e-01
-2.79611021e-01 7.07483470e-01 1.03335515e-01 -8.95879030e-01
-8.06567132e-01 -1.30595124e+00 -2.32235700e-01 -5.37677944e-01
-8.90811265e-01 1.00234902e+00 2.97058046e-01 -7.79054105... | [6.8355631828308105, 5.2600579261779785] |
dd6ca596-4984-4b6c-9bcf-2fe823f4f51b | iiit-at-semeval-2016-task-11-complex-word | null | null | https://aclanthology.org/S16-1158 | https://aclanthology.org/S16-1158.pdf | IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification | null | ['Radhika Mamidi', 'Ashish Palakurthi'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.347353458404541, 3.6187050342559814] |
66fdbc20-a872-440a-831a-16fb1a9e9b42 | vpe-variational-policy-embedding-for-transfer | 1809.03548 | null | http://arxiv.org/abs/1809.03548v2 | http://arxiv.org/pdf/1809.03548v2.pdf | VPE: Variational Policy Embedding for Transfer Reinforcement Learning | Reinforcement Learning methods are capable of solving complex problems, but
resulting policies might perform poorly in environments that are even slightly
different. In robotics especially, training and deployment conditions often
vary and data collection is expensive, making retraining undesirable.
Simulation training... | ['Johannes A. Stork', 'Isac Arnekvist', 'Danica Kragic'] | 2018-09-10 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 1.00441992e-01 1.74515613e-03 -1.60749868e-01 -3.46986651e-02
-5.51887155e-01 -7.80607045e-01 5.77090323e-01 -1.15982860e-01
-7.69407690e-01 1.13914120e+00 -1.36608839e-01 -3.13213229e-01
-4.03712183e-01 -5.36854982e-01 -9.99570608e-01 -9.59127665e-01
-4.05493706e-01 9.33537841e-01 1.66581646e-01 -4.11177576... | [4.291670799255371, 1.6867369413375854] |
92e13da0-0768-4e80-8505-02e3e96370f9 | evaluating-text-segmentation-using-boundary | null | null | https://aclanthology.org/P13-1167 | https://aclanthology.org/P13-1167.pdf | Evaluating Text Segmentation using Boundary Edit Distance | null | ['Chris Fournier'] | 2013-08-01 | null | null | null | acl-2013-8 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.320622444152832, 3.7316415309906006] |
49f348fe-43ee-415b-ad2b-988f8acdd37e | white-box-testing-of-nlp-models-with-mask | 2205.05050 | null | https://arxiv.org/abs/2205.05050v1 | https://arxiv.org/pdf/2205.05050v1.pdf | White-box Testing of NLP models with Mask Neuron Coverage | Recent literature has seen growing interest in using black-box strategies like CheckList for testing the behavior of NLP models. Research on white-box testing has developed a number of methods for evaluating how thoroughly the internal behavior of deep models is tested, but they are not applicable to NLP models. We pro... | ['Yanjun Qi', 'Matthew B. Dwyer', 'Yangfeng Ji', 'Arshdeep Sekhon'] | 2022-05-10 | null | https://aclanthology.org/2022.findings-naacl.116 | https://aclanthology.org/2022.findings-naacl.116.pdf | findings-naacl-2022-7 | ['fault-detection'] | ['miscellaneous'] | [ 3.37874919e-01 4.95927155e-01 -2.60338902e-01 -4.22121435e-01
-7.29811788e-01 -8.13545465e-01 -4.83566225e-02 -2.37251278e-02
2.12254703e-01 8.54271531e-01 -2.08464921e-01 -9.39838648e-01
-4.04865384e-01 -9.42788422e-01 -9.61030066e-01 2.28021927e-02
4.36648540e-02 4.89617199e-01 3.79518777e-01 2.52143472... | [7.0875959396362305, 7.608401775360107] |
6e7ded37-288b-481c-b19b-d1811f9413da | sampling-free-inference-for-ab-initio | 2205.14962 | null | https://arxiv.org/abs/2205.14962v3 | https://arxiv.org/pdf/2205.14962v3.pdf | Sampling-free Inference for Ab-Initio Potential Energy Surface Networks | Recently, it has been shown that neural networks not only approximate the ground-state wave functions of a single molecular system well but can also generalize to multiple geometries. While such generalization significantly speeds up training, each energy evaluation still requires Monte Carlo integration which limits t... | ['Stephan Günnemann', 'Nicholas Gao'] | 2022-05-30 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 1.55865744e-01 6.54970035e-02 -2.17608824e-01 -4.82072711e-01
-1.05338633e+00 -3.86820108e-01 3.74115199e-01 4.08391207e-01
-5.51726103e-01 1.43954933e+00 -1.25357687e-01 -5.81585228e-01
1.66667029e-01 -1.19587076e+00 -1.39660060e+00 -7.34980941e-01
-1.44190282e-01 5.80647707e-01 -5.98947629e-02 -1.18459433... | [5.318996429443359, 5.298526763916016] |
0b07afcc-7636-43bc-9344-457fdb2f1078 | diverse-and-consistent-multi-view-networks | null | null | https://openreview.net/forum?id=J9_7t9m8xRj | https://openreview.net/pdf?id=J9_7t9m8xRj | Diverse and Consistent Multi-view Networks for Semi-supervised Regression | Label collection is costly in many applications, which poses the need for label-efficient learning. In this work, we present Diverse and Consistent Multi-view Networks (DiCoM) — a novel semi-supervised regression technique based on a multi-view learning framework. DiCoM combines diversity with consistency — two seeming... | ['Chuan-Sheng Foo', 'Kangkang Lu', 'Balagopal Unnikrishnan', 'Xun Xu', 'Arun Raja', 'Le Zhang', 'Cuong Manh Nguyen'] | 2021-09-29 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.47912160e-01 1.56826258e-01 -7.59264112e-01 -8.88273537e-01
-7.97657132e-01 -9.72415507e-01 5.16781390e-01 -1.86048046e-01
8.89210701e-02 6.49218976e-01 5.29530287e-01 3.07731349e-02
-1.42499462e-01 -2.75313407e-01 -2.56289780e-01 -5.48321486e-01
3.20995122e-01 7.56067634e-01 -1.22512348e-01 3.47430229... | [8.576848030090332, 4.400577545166016] |
cb74fa46-62f5-4554-9344-bf9800dbbf3c | relation-aware-compositional-zero-shot | 2108.04603 | null | https://arxiv.org/abs/2108.04603v1 | https://arxiv.org/pdf/2108.04603v1.pdf | Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition | This paper proposes a novel model for recognizing images with composite attribute-object concepts, notably for composite concepts that are unseen during model training. We aim to explore the three key properties required by the task --- relation-aware, consistent, and decoupled --- to learn rich and robust features for... | ['Mohan Kankanhalli', 'Yongkang Wong', 'Guangzhi Wang', 'Ziwei Xu'] | 2021-08-10 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.98781687e-01 3.36450130e-01 -4.42086440e-03 -6.46169603e-01
-4.57887203e-01 -3.40296715e-01 8.91802609e-01 5.45277953e-01
-3.53192091e-01 1.76910952e-01 -1.78978011e-01 6.38501048e-02
3.12905349e-02 -8.06864619e-01 -6.97423100e-01 -7.95967579e-01
-2.83039331e-01 3.20222467e-01 1.33182049e-01 4.12581339... | [10.036674499511719, 2.002230167388916] |
85fcaf3b-0541-4a18-8c8e-cc0689e86b41 | embedding-decomposition-for-artifacts-removal | 2112.00989 | null | https://arxiv.org/abs/2112.00989v2 | https://arxiv.org/pdf/2112.00989v2.pdf | Embedding Decomposition for Artifacts Removal in EEG Signals | Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of them rely on prior experience for analysis. Here, we propose an deep learning framework to separate neural signal and artifacts in the embe... | ['Quanying Liu', 'Chen Wei', 'Kexin Lou', 'Chenyi Li', 'Junjie Yu'] | 2021-12-02 | null | null | null | null | ['eeg-denoising'] | ['methodology'] | [ 2.14842800e-02 -3.18363100e-01 6.13327086e-01 -4.00854737e-01
-6.28849745e-01 -2.91342676e-01 9.47724208e-02 -2.55826592e-01
-2.79647499e-01 7.39981115e-01 3.57979357e-01 1.99867770e-01
-2.61679977e-01 -2.88281053e-01 -5.86595654e-01 -8.63193452e-01
-6.36612996e-02 -5.63765228e-01 -2.71898717e-01 6.52870610... | [13.14979362487793, 3.4107329845428467] |
528230c5-ba39-43df-ab7d-1765cfcf6d6b | 2d-image-relighting-with-image-to-image | 2006.07816 | null | https://arxiv.org/abs/2006.07816v2 | https://arxiv.org/pdf/2006.07816v2.pdf | 2D Image Relighting with Image-to-Image Translation | With the advent of Generative Adversarial Networks (GANs), a finer level of control in manipulating various features of an image has become possible. One example of such fine manipulation is changing the position of the light source in a scene. This is fundamentally an ill-posed problem, since it requires understanding... | ['Paul Gafton', 'Erick Maraz'] | 2020-06-14 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 3.64278376e-01 -2.00605690e-01 5.31171679e-01 -1.98847011e-01
-1.06422476e-01 -9.40022290e-01 7.48216927e-01 -4.33129191e-01
-3.49258780e-01 8.57605040e-01 -1.74709648e-01 -2.70373315e-01
3.27746756e-02 -1.16221464e+00 -1.01590669e+00 -1.11809671e+00
4.39973205e-01 3.80091906e-01 1.91324174e-01 -4.12602305... | [11.615243911743164, -0.5233241319656372] |
3fef50d8-974e-46e8-940f-b4d9d34c5a98 | neuro-symbolic-zero-shot-code-cloning-with | 2304.13350 | null | https://arxiv.org/abs/2304.13350v1 | https://arxiv.org/pdf/2304.13350v1.pdf | Neuro-symbolic Zero-Shot Code Cloning with Cross-Language Intermediate Representation | In this paper, we define a neuro-symbolic approach to address the task of finding semantically similar clones for the codes of the legacy programming language COBOL, without training data. We define a meta-model that is instantiated to have an Intermediate Representation (IR) in the form of Abstract Syntax Trees (ASTs)... | ['Ravindra Naik', 'Lovekesh Vig', 'Raveendra Kumar Medicherla', 'Manasi Patwardhan', 'Shrishti Pradhan', 'Krishnam Hasija'] | 2023-04-26 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 6.03643712e-03 -8.02698359e-02 -5.08799076e-01 -2.65714705e-01
-5.99245965e-01 -6.19960785e-01 4.02681887e-01 1.96091775e-02
-1.24802217e-02 -1.05373189e-01 -2.27487143e-02 -8.54073763e-01
8.99251997e-02 -7.50671744e-01 -1.00070846e+00 2.41305940e-02
-3.37262601e-01 2.55503953e-02 2.53538668e-01 -3.71916562... | [7.667928695678711, 7.843573093414307] |
7133c50e-f502-45d0-8353-6a270160e40e | shape-and-spatially-varying-reflectance | 1512.05278 | null | http://arxiv.org/abs/1512.05278v3 | http://arxiv.org/pdf/1512.05278v3.pdf | Shape and Spatially-Varying Reflectance Estimation From Virtual Exemplars | This paper addresses the problem of estimating the shape of objects that
exhibit spatially-varying reflectance. We assume that multiple images of the
object are obtained under a fixed view-point and varying illumination, i.e.,
the setting of photometric stereo. At the core of our techniques is the
assumption that the B... | ['Aswin C. Sankaranarayanan', 'Zhuo Hui'] | 2015-12-16 | null | null | null | null | ['brdf-estimation'] | ['computer-vision'] | [ 5.93820512e-01 -8.87791812e-02 3.97866994e-01 -2.85735011e-01
-6.37106657e-01 -6.73992336e-01 4.07829791e-01 -3.31752181e-01
-9.83572081e-02 5.89456499e-01 -1.59578755e-01 -1.29107879e-02
-6.77998364e-02 -6.03217542e-01 -7.31430411e-01 -9.99307454e-01
5.73151052e-01 3.57463777e-01 3.28792840e-01 -9.95857827... | [9.793997764587402, -2.962496042251587] |
5141a9fe-3195-4b86-925f-2a668bd297b3 | unsupervised-partial-sentence-matching-for | null | null | https://aclanthology.org/2022.sdp-1.11 | https://aclanthology.org/2022.sdp-1.11.pdf | Unsupervised Partial Sentence Matching for Cited Text Identification | Given a citation in the body of a research paper, cited text identification aims to find the sentences in the cited paper that are most relevant to the citing sentence. The task is fundamentally one of sentence matching, where affinity is often assessed by a cosine similarity between sentence embeddings. However, (a) s... | ['Andrew McCallum', 'Purujit Goyal', 'Haw-Shiuan Chang', 'Kathryn Ricci'] | null | null | null | null | sdp-coling-2022-10 | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 2.06841305e-01 5.59054315e-03 -3.90723765e-01 -4.54526767e-02
-1.07762897e+00 -9.43607807e-01 9.38187003e-01 9.68197703e-01
-6.42273784e-01 6.95370853e-01 7.76662230e-01 -3.99755836e-01
-6.55711412e-01 -5.08696139e-01 -4.43468541e-01 -3.38443190e-01
4.22928452e-01 4.95940983e-01 2.33578846e-01 -2.30133403... | [11.092133522033691, 8.892005920410156] |
dfe8cef9-dc91-4190-8ffc-258529722815 | learning-knowledge-rich-sequential-model-for | 2304.02715 | null | https://arxiv.org/abs/2304.02715v1 | https://arxiv.org/pdf/2304.02715v1.pdf | Learning Knowledge-Rich Sequential Model for Planar Homography Estimation in Aerial Video | This paper presents an unsupervised approach that leverages raw aerial videos to learn to estimate planar homographic transformation between consecutive video frames. Previous learning-based estimators work on pairs of images to estimate their planar homographic transformations but suffer from severe over-fitting issue... | ['Xiaobai Liu', 'Pu Li'] | 2023-04-05 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 2.71358073e-01 -2.26156101e-01 -1.74400687e-01 -4.35804188e-01
-9.75850582e-01 -8.80692363e-01 5.60759068e-01 -4.01039898e-01
-1.63616970e-01 3.60801965e-01 2.78686315e-01 1.33236852e-02
5.09049110e-02 -3.21792424e-01 -1.03932047e+00 -5.08991718e-01
-1.54138550e-01 -5.97946695e-04 2.50851184e-01 1.34131491... | [8.398228645324707, -1.9868508577346802] |
d46773b9-bd1c-4abe-b1b9-4dc418693d14 | fairer-fairness-as-decision-rationale | 2306.15299 | null | https://arxiv.org/abs/2306.15299v1 | https://arxiv.org/pdf/2306.15299v1.pdf | FAIRER: Fairness as Decision Rationale Alignment | Deep neural networks (DNNs) have made significant progress, but often suffer from fairness issues, as deep models typically show distinct accuracy differences among certain subgroups (e.g., males and females). Existing research addresses this critical issue by employing fairness-aware loss functions to constrain the la... | ['Yang Liu', 'Zhiming Li', 'Mengnan Du', 'Aishan Liu', 'Qing Guo', 'Tianlin Li'] | 2023-06-27 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [-5.41114584e-02 1.38308465e-01 -5.07634819e-01 -1.04986072e+00
7.04048872e-02 -2.18193337e-01 5.51712453e-01 -5.10246307e-02
-7.15810120e-01 7.96982765e-01 3.45833689e-01 -1.53477758e-01
-9.27954540e-02 -6.72771990e-01 -4.49336559e-01 -6.91116989e-01
4.13310230e-01 9.28376168e-02 -2.70255297e-01 9.00140479... | [8.968282699584961, 5.173184871673584] |
cd32dd78-56aa-4be7-a936-c25c75943620 | an-empirical-approach-for-modeling-fuzzy | 1703.10429 | null | http://arxiv.org/abs/1703.10429v1 | http://arxiv.org/pdf/1703.10429v1.pdf | An Empirical Approach for Modeling Fuzzy Geographical Descriptors | We present a novel heuristic approach that defines fuzzy geographical
descriptors using data gathered from a survey with human subjects. The
participants were asked to provide graphical interpretations of the descriptors
`north' and `south' for the Galician region (Spain). Based on these
interpretations, our approach b... | ['Kees Van Deemter', 'Alejandro Ramos-Soto', 'Jose M. Alonso', 'Ehud Reiter', 'Albert Gatt'] | 2017-03-30 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [-1.08413264e-01 3.22590411e-01 -6.14146106e-02 -7.44780123e-01
-4.52016145e-01 -6.64006889e-01 1.14729142e+00 5.21823466e-01
-4.03971821e-01 9.96355593e-01 3.11564952e-01 -4.19670194e-01
-5.62441707e-01 -1.17441213e+00 -8.72919187e-02 -1.53550833e-01
1.54940218e-01 6.30403638e-01 -1.69732884e-01 -6.51512563... | [9.976624488830566, 9.159798622131348] |
761abf5e-dae1-40b6-a9f2-2b97f44b844f | masked-spatial-spectral-autoencoders-are | 2207.07803 | null | https://arxiv.org/abs/2207.07803v1 | https://arxiv.org/pdf/2207.07803v1.pdf | Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders | Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised learning theory, for enh... | ['Ping Zhong', 'Yu Zhang', 'Wei Xue', 'YongQian Li', 'Chen Chen', 'Kangcheng Bin', 'Xingyue Liu', 'Zhiqiang Gong', 'Jiahao Qi'] | 2022-07-16 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 6.67608559e-01 -1.60882935e-01 2.71135509e-01 -6.14827052e-02
-2.28833601e-01 -7.55462945e-01 2.98036188e-01 -8.66526663e-02
1.57113940e-01 4.82477486e-01 5.84452553e-03 -3.54695559e-01
-3.75477672e-01 -1.16013014e+00 -6.01872027e-01 -1.25881362e+00
-8.09543803e-02 -4.18425977e-01 4.95688878e-02 -4.99621540... | [5.467585563659668, 7.998121738433838] |
dab44789-3f64-454e-ad2a-88ffe498f303 | exploring-stylegan-latent-space-for-face | null | null | https://hal.archives-ouvertes.fr/INRIA/hal-03778322v1 | https://hal.archives-ouvertes.fr/hal-03778322/document | Exploring StyleGAN Latent Space for Face Alignment with Limited Training Data | With deep learning models growing in size over the years, sometimes exceeding a billion parameters now, the need for large, annotated training datasets grows too. To alleviate this problem, the interest in self-supervised learning is also increasing. In this domain, with the rise of Generative Adversarial Networks (GAN... | ['Bertrand Coüasnon', 'Yann Ricquebourg', 'Christian Raymond', 'Philippe-Henri Gosselin', 'Martin Dornier'] | 2022-09-16 | null | null | null | hal-2022-9 | ['face-alignment'] | ['computer-vision'] | [ 2.73200452e-01 4.10744309e-01 3.83010954e-02 -4.89530176e-01
-5.22973180e-01 -2.67459005e-01 8.47299397e-01 -8.20403278e-01
-1.46748558e-01 6.96436942e-01 2.82894045e-01 3.06665599e-01
3.81567955e-01 -8.26334715e-01 -7.04633474e-01 -7.08051741e-01
5.33275843e-01 6.29761100e-01 -5.82248151e-01 -2.35436291... | [12.456259727478027, -0.22090555727481842] |
99e21f1c-89fd-4f63-824f-c4b1efdd2b0f | modelling-neuronal-behaviour-with-time-series-1 | null | null | https://openreview.net/forum?id=k-sNDIPY-1T | https://openreview.net/pdf?id=k-sNDIPY-1T | Modelling neuronal behaviour with time series regression: Recurrent Neural Networks on synthetic C. elegans data | Given the inner complexity of the human nervous system, insight into the dynamics of brain activity can be gained from understanding smaller and simpler organisms, such as the nematode C. elegans. The behavioural and structural biology of these organisms is well-known, making them prime candidates for benchmarking mode... | ['L. Miguel Silveira', 'Arlindo L. Oliveira', 'Ruxandra Barbulescu', 'Gonçalo Leote Cardoso Mestre'] | 2021-09-29 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 3.83986056e-01 -1.77528039e-01 5.86408734e-01 7.15965114e-04
2.39165753e-01 -4.15501356e-01 7.50004351e-01 -6.94225803e-02
-7.23253727e-01 7.01604187e-01 -3.22526276e-01 -3.37977558e-01
-7.50229508e-02 -4.72219914e-01 -7.25114048e-01 -8.74225318e-01
-4.62245494e-01 3.41331184e-01 4.84649539e-01 -4.70963776... | [8.052016258239746, 2.928699493408203] |
448c9777-5307-4a34-8b36-6fd2aca846c3 | nio-lightweight-neural-operator-based | 2211.10791 | null | https://arxiv.org/abs/2211.10791v2 | https://arxiv.org/pdf/2211.10791v2.pdf | AdaFNIO: Adaptive Fourier Neural Interpolation Operator for video frame interpolation | We present, AdaFNIO - Adaptive Fourier Neural Interpolation Operator, a neural operator-based architecture to perform video frame interpolation. Current deep learning based methods rely on local convolutions for feature learning and suffer from not being scale-invariant, thus requiring training data to be augmented thr... | ['Aniket Bera', 'Rashmi Bhaskara', 'Md Ashiqur Rahman', 'Hrishikesh Viswanath'] | 2022-11-19 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 1.80098981e-01 -4.79445964e-01 1.08742341e-01 -4.68711615e-01
-5.54082155e-01 -1.84272423e-01 4.73000377e-01 -2.28485763e-01
-5.25601029e-01 7.34986484e-01 2.41694257e-01 -9.33477283e-02
1.53040607e-02 -6.72763824e-01 -1.10304344e+00 -6.25703275e-01
-6.18970394e-01 -4.23963457e-01 1.31712943e-01 -1.04187481... | [10.901784896850586, -1.4709690809249878] |
53972540-5fc9-46a5-8784-c393a2e00dcf | 3d-point-capsule-networks | 1812.10775 | null | https://arxiv.org/abs/1812.10775v2 | https://arxiv.org/pdf/1812.10775v2.pdf | 3D Point Capsule Networks | In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D la... | ['Federico Tombari', 'Tolga Birdal', 'Haowen Deng', 'Yongheng Zhao'] | 2018-12-27 | 3d-point-capsule-networks-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_3D_Point_Capsule_Networks_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_3D_Point_Capsule_Networks_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-feature-matching', '3d-object-classification', '3d-object-reconstruction', '3d-shape-generation', '3d-point-cloud-matching', '3d-shape-representation', '3d-geometry-perception', '3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.98684886e-01 3.79625261e-01 -1.87422812e-01 -9.34320390e-02
-3.62309694e-01 -6.21780515e-01 6.55048549e-01 8.65023434e-02
6.29322752e-02 3.31817120e-01 2.31980041e-01 -8.25888813e-02
-2.96041191e-01 -6.92551196e-01 -1.21862292e+00 -4.06123072e-01
-1.99934840e-01 6.50695622e-01 -1.43123120e-01 -2.04376020... | [8.245460510253906, -3.558516502380371] |
ebbb5e45-6275-41a4-b193-b80d44209c2b | dh-fbk-at-semeval-2022-task-4-leveraging | null | null | https://aclanthology.org/2022.semeval-1.42 | https://aclanthology.org/2022.semeval-1.42.pdf | DH-FBK at SemEval-2022 Task 4: Leveraging Annotators’ Disagreement and Multiple Data Views for Patronizing Language Detection | The subtle and typically unconscious use of patronizing and condescending language (PCL) in large-audience media outlets undesirably feeds stereotypes and strengthens power-knowledge relationships, perpetuating discrimination towards vulnerable communities. Due to its subjective and subtle nature, PCL detection is an o... | ['Elisa Leonardelli', 'Alan Ramponi'] | null | null | null | null | semeval-naacl-2022-7 | ['multi-view-learning', 'semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-2-multi-label-pcl', 'semeval-2022-task-4-1-binary-pcl-detection'] | ['computer-vision', 'miscellaneous', 'music', 'natural-language-processing', 'natural-language-processing'] | [ 2.61547510e-02 1.67224392e-01 -4.25672024e-01 -3.56378779e-02
-9.41997588e-01 -9.89780426e-01 9.23725367e-01 5.45320272e-01
-4.68941212e-01 4.24251407e-01 7.36569583e-01 -3.75192940e-01
2.42787004e-01 -2.98568487e-01 -2.71418095e-01 -4.83377546e-01
2.30876833e-01 6.19784236e-01 1.39489576e-01 -2.52346277... | [8.773964881896973, 10.500272750854492] |
e2b62981-e49c-4c7d-bf5c-f67401ced05d | the-tembusu-treebank-an-english-learner | null | null | https://aclanthology.org/2022.lrec-1.515 | https://aclanthology.org/2022.lrec-1.515.pdf | The Tembusu Treebank: An English Learner Treebank | This paper reports on the creation and development of the Tembusu Learner Treebank — an open treebank created from the NTU Corpus of Learner English, unique for incorporating mal-rules in the annotation of ungrammatical sentences. It describes the motivation and development of the treebank, as well as its exploitation ... | ['Roger V. P. Winder', 'Francis Bond', 'Luís Morgado da Costa'] | null | null | null | null | lrec-2022-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-1.73860744e-01 8.34192038e-01 2.97758728e-01 -6.60578907e-01
-7.84516990e-01 -4.01520520e-01 -1.50856063e-01 5.58937013e-01
-6.06671572e-01 1.02634811e+00 6.25732005e-01 -6.69305801e-01
-1.66984871e-02 -6.28042936e-01 -3.43611240e-01 1.42203078e-01
1.22948103e-01 7.29089379e-01 2.29173511e-01 -5.54877460... | [10.880488395690918, 10.487277030944824] |
4eeb4bfa-a3b2-47c1-b173-d902da5769fc | instant-neural-radiance-fields-stylization | 2303.16884 | null | https://arxiv.org/abs/2303.16884v1 | https://arxiv.org/pdf/2303.16884v1.pdf | Instant Neural Radiance Fields Stylization | We present Instant Neural Radiance Fields Stylization, a novel approach for multi-view image stylization for the 3D scene. Our approach models a neural radiance field based on neural graphics primitives, which use a hash table-based position encoder for position embedding. We split the position encoder into two parts, ... | ['Ye Pan', 'Shaoxu Li'] | 2023-03-29 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 3.84738654e-01 -9.84052289e-03 2.92095214e-01 -4.56513703e-01
-3.67467105e-01 -7.63492525e-01 6.63007736e-01 -5.16923487e-01
-2.13258564e-02 4.09653872e-01 5.70098422e-02 -1.97402626e-01
6.78610563e-01 -1.15578711e+00 -1.05792153e+00 -6.75259233e-01
4.90246862e-01 3.36911052e-01 1.53720826e-01 -1.97041988... | [9.26524829864502, -3.212921142578125] |
22c93f37-3271-445a-badd-198804ed3092 | enhancing-dialogue-generation-via-dynamic | 2306.16195 | null | https://arxiv.org/abs/2306.16195v1 | https://arxiv.org/pdf/2306.16195v1.pdf | Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation | Incorporating external graph knowledge into neural chatbot models has been proven effective for enhancing dialogue generation. However, in conventional graph neural networks (GNNs), message passing on a graph is independent from text, resulting in the graph representation hidden space differing from that of the text. T... | ['Frank Guerin', 'Chenghua Lin', 'Tyler Loakman', 'Hongbo Zhang', 'Chen Tang'] | 2023-06-28 | null | null | null | null | ['graph-attention', 'chatbot', 'dialogue-generation', 'chatbot', 'dialogue-generation'] | ['graphs', 'methodology', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.64603516e-01 1.08084261e+00 -2.35877246e-01 -8.56399685e-02
-4.94484961e-01 -5.04036844e-01 8.21653306e-01 1.12247743e-01
-8.31188112e-02 9.31434572e-01 5.57192504e-01 -2.60808468e-01
1.06411673e-01 -1.46102166e+00 -6.09820783e-01 -4.73147541e-01
1.30972669e-01 7.02445149e-01 1.54275045e-01 -8.62933099... | [12.297744750976562, 8.126145362854004] |
d7baaf8a-0d14-46ad-a4b5-9e5567c26f2a | q-yolop-quantization-aware-you-only-look-once | 2307.04537 | null | https://arxiv.org/abs/2307.04537v1 | https://arxiv.org/pdf/2307.04537v1.pdf | Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception | In this work, we present an efficient and quantization-aware panoptic driving perception model (Q- YOLOP) for object detection, drivable area segmentation, and lane line segmentation, in the context of autonomous driving. Our model employs the Efficient Layer Aggregation Network (ELAN) as its backbone and task-specific... | ['Kai-Chiang Wu', 'Kuan-Cheng Lin', 'Yu-Chen Lu', 'Sheng-Feng Yu', 'Pei-Shuo Wang', 'Wei-Cheng Lin', 'Chi-Chih Chang'] | 2023-07-10 | null | null | null | null | ['object-detection', 'autonomous-driving', 'data-augmentation', 'quantization'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 3.63639463e-03 6.20216914e-02 -1.95926785e-01 -6.85714602e-01
-6.30411386e-01 -3.90801787e-01 3.80186439e-01 1.29435226e-01
-5.62166810e-01 2.60782599e-01 -4.25549150e-01 -6.99694693e-01
9.85195786e-02 -9.48764384e-01 -6.84750795e-01 -5.96826375e-01
5.87021038e-02 4.68943596e-01 9.36155140e-01 -2.62503147... | [8.082719802856445, -1.4309128522872925] |
f577c175-a7dc-43f1-9576-adfbb0b8e5d2 | enhancing-unsupervised-generative-dependency | null | null | https://aclanthology.org/P19-1526 | https://aclanthology.org/P19-1526.pdf | Enhancing Unsupervised Generative Dependency Parser with Contextual Information | Most of the unsupervised dependency parsers are based on probabilistic generative models that learn the joint distribution of the given sentence and its parse. Probabilistic generative models usually explicit decompose the desired dependency tree into factorized grammar rules, which lack the global features of the enti... | ['Kewei Tu', 'Yong Jiang', 'Wenjuan Han'] | 2019-07-01 | null | null | null | acl-2019-7 | ['constituency-grammar-induction', 'dependency-grammar-induction', 'unsupervised-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.68163121e-01 4.06445235e-01 -6.16722219e-02 -8.54372621e-01
-8.63119364e-01 -5.98405600e-01 5.53567767e-01 -9.92041156e-02
5.99645916e-03 7.15938210e-01 7.09050655e-01 -2.30010122e-01
1.12397879e-01 -1.01395273e+00 -6.99965477e-01 -1.05403924e+00
2.83064872e-01 7.83553958e-01 1.13501199e-01 -7.78049324... | [10.36905574798584, 9.661114692687988] |
c89d8a3f-ff9d-4047-95ab-eff4922806fc | generating-a-temporally-coherent-visual-story | null | null | https://openreview.net/forum?id=L99I9HrEtEm | https://openreview.net/pdf?id=L99I9HrEtEm | Generating a Temporally Coherent Visual Story by Multimodal Recurrent Transformers | Story visualization is a challenging text-to-image generation task for the difficulty of rendering visual details from abstract text descriptions. Besides the difficulty of image generation, the generator also needs to conform to the narrative of a multi-sentence story input. While prior arts in this domain have focuse... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['story-visualization'] | ['computer-vision'] | [ 5.38222790e-01 1.97204337e-01 2.20475987e-01 -2.38933519e-01
-7.22965717e-01 -6.33332551e-01 1.17097402e+00 -1.72084391e-01
2.83726841e-01 7.34430850e-01 5.55691659e-01 -1.04637310e-01
3.03227812e-01 -4.86942500e-01 -8.75037849e-01 -4.88554090e-01
4.52772588e-01 2.69307464e-01 7.57238790e-02 -1.92162856... | [11.1683931350708, 0.5861569046974182] |
41298e64-d503-42d5-a255-9a00eea163f5 | nu-wave-2-a-general-neural-audio-upsampling | 2206.08545 | null | https://arxiv.org/abs/2206.08545v2 | https://arxiv.org/pdf/2206.08545v2.pdf | NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates | Conventionally, audio super-resolution models fixed the initial and the target sampling rates, which necessitate the model to be trained for each pair of sampling rates. We introduce NU-Wave 2, a diffusion model for neural audio upsampling that enables the generation of 48 kHz audio signals from inputs of various sampl... | ['Junhyeok Lee', 'Seungu Han'] | 2022-06-17 | null | null | null | null | ['audio-super-resolution', 'audio-super-resolution'] | ['audio', 'music'] | [ 8.81135464e-02 -1.15981296e-01 -1.23172037e-01 2.76732929e-02
-1.05092919e+00 -4.11169469e-01 2.62384892e-01 -4.82954681e-01
-5.11751324e-02 5.63486397e-01 4.92929071e-01 -2.17825159e-01
6.22720011e-02 -8.08505774e-01 -5.75820565e-01 -3.48527163e-01
-2.20466405e-01 -1.68853343e-01 3.05271447e-01 -1.92109764... | [15.469454765319824, 5.852564334869385] |
80e42d96-e345-484e-ad9c-52ee3853e262 | t-rain-robust-generalization-under-weather | 2305.08302 | null | https://arxiv.org/abs/2305.08302v1 | https://arxiv.org/pdf/2305.08302v1.pdf | t-RAIN: Robust generalization under weather-aliasing label shift attacks | In the classical supervised learning settings, classifiers are fit with the assumption of balanced label distributions and produce remarkable results on the same. In the real world, however, these assumptions often bend and in turn adversely impact model performance. Identifying bad learners in skewed target distributi... | ['Sanjana Prabhu', 'Aboli Marathe'] | 2023-05-15 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 1.66874930e-01 -2.92075306e-01 5.84046543e-01 -6.26338243e-01
-4.35262233e-01 -6.97787285e-01 6.57169402e-01 2.90369511e-01
-6.13584816e-01 1.09789121e+00 -3.23802531e-01 -2.48308688e-01
3.64450723e-01 -9.29442286e-01 -8.84868503e-01 -1.04165781e+00
9.27325115e-02 6.48418427e-01 6.53610945e-01 -4.30920959... | [8.184553146362305, -1.2690984010696411] |
d940720f-6d7e-4356-8bf5-0b418384e23c | github-typo-corpus-a-large-scale-multilingual | 1911.12893 | null | https://arxiv.org/abs/1911.12893v1 | https://arxiv.org/pdf/1911.12893v1.pdf | GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors | The lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GEC). As a complementary new resource for these tasks, we present the GitHub Typo Corpus, a large-scale, multilingual dataset of misspellings and grammatical errors a... | ['Masato Mita', 'Masato Hagiwara'] | 2019-11-28 | github-typo-corpus-a-large-scale-multilingual-1 | https://aclanthology.org/2020.lrec-1.835 | https://aclanthology.org/2020.lrec-1.835.pdf | lrec-2020-5 | ['spelling-correction'] | ['natural-language-processing'] | [ 1.05748005e-01 3.39317024e-02 2.06139241e-03 -3.37422729e-01
-1.20238900e+00 -8.33071113e-01 4.45546150e-01 8.90825272e-01
-6.40134990e-01 9.58265185e-01 2.30702206e-01 -2.15302035e-01
1.49469882e-01 -3.28936696e-01 -1.05407667e+00 -7.24784061e-02
3.83442432e-01 7.07588613e-01 3.06868643e-01 -3.41913879... | [11.05034065246582, 10.68716812133789] |
6e674cb8-3be2-46ad-a0cc-9c79d5a71c92 | ernie-enhanced-language-representation-with | 1905.07129 | null | https://arxiv.org/abs/1905.07129v3 | https://arxiv.org/pdf/1905.07129v3.pdf | ERNIE: Enhanced Language Representation with Informative Entities | Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks. However, the existing pre-trained language models rarely consider incorporating knowledge graphs ... | ['Qun Liu', 'Zhengyan Zhang', 'Maosong Sun', 'Zhiyuan Liu', 'Xu Han', 'Xin Jiang'] | 2019-05-17 | ernie-enhanced-language-representation-with-1 | https://aclanthology.org/P19-1139 | https://aclanthology.org/P19-1139.pdf | acl-2019-7 | ['linguistic-acceptability'] | ['natural-language-processing'] | [-4.78977799e-01 1.75077558e-01 -8.07577908e-01 -2.15516970e-01
-6.33237600e-01 -4.50459510e-01 4.88299817e-01 1.72952235e-01
-5.39236188e-01 1.00473297e+00 5.32521069e-01 -3.04720938e-01
-8.79184008e-02 -1.20982492e+00 -7.39055216e-01 -1.39822051e-01
1.71812892e-01 4.91953969e-01 3.32093209e-01 -3.34222078... | [9.300619125366211, 8.303519248962402] |
c2ba281b-d649-4133-bf19-8b60f7d55c30 | exploring-the-limits-of-natural-language | 2112.07434 | null | https://arxiv.org/abs/2112.07434v1 | https://arxiv.org/pdf/2112.07434v1.pdf | Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection | One of the core components of goal-oriented dialog systems is the task of Intent Detection. Few-shot Learning upon Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works making use of metric-based and optimization-based methods have been proposed, the task is still ... | ['Jithendra Veppa', 'Ayush Kumar', 'Vijit Malik'] | 2021-12-14 | null | null | null | null | ['generalized-few-shot-learning', 'goal-oriented-dialog'] | ['methodology', 'natural-language-processing'] | [ 1.70236066e-01 3.71606126e-02 -9.17859972e-02 -5.27217090e-01
-8.46430957e-01 -2.52854258e-01 7.22794771e-01 4.73376401e-02
-5.79670668e-01 7.48946846e-01 6.28428996e-01 -3.35932784e-02
4.64477055e-02 -5.42244494e-01 -4.17581806e-03 -4.64213371e-01
6.71078935e-02 6.56935930e-01 4.25029278e-01 -6.20424211... | [12.19976806640625, 7.572532653808594] |
e2c500ac-e751-4c51-ae04-1266400410a9 | comparing-exploratory-graphical-analyses-and | 2210.13230 | null | https://arxiv.org/abs/2210.13230v3 | https://arxiv.org/pdf/2210.13230v3.pdf | An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics | Developing interpretable machine learning models has become an increasingly important issue. One way in which data scientists have been able to develop interpretable models has been to use dimension reduction techniques. In this paper, we examine several dimension reduction techniques including two recent approaches de... | ['Alexander P. Christensen', 'Sean H. Merritt'] | 2022-10-19 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 6.47524446e-02 3.92961800e-01 -8.26018527e-02 -2.03825489e-01
1.64604709e-01 -6.76237941e-01 4.85498548e-01 3.09813350e-01
-1.75796941e-01 7.02838898e-01 3.20388079e-01 -8.63477588e-01
-9.49760616e-01 -9.12544906e-01 -9.11210179e-02 -4.67056453e-01
-1.68846771e-01 7.12333083e-01 -3.60526919e-01 -1.04479872... | [7.995397567749023, 4.872899532318115] |
9cb4d11c-e138-40ef-ae00-ce87958af878 | over-the-air-federated-learning-in-satellite | 2306.02996 | null | https://arxiv.org/abs/2306.02996v1 | https://arxiv.org/pdf/2306.02996v1.pdf | Over-the-Air Federated Learning in Satellite systems | Federated learning in satellites offers several advantages. Firstly, it ensures data privacy and security, as sensitive data remains on the satellites and is not transmitted to a central location. This is particularly important when dealing with sensitive or classified information. Secondly, federated learning allows s... | ['Mitra Hassani', 'Raphael Pinard', 'Edward Akito Carlos'] | 2023-06-05 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-3.67049515e-01 2.32800022e-02 -2.58429021e-01 -4.75224555e-01
-3.83432984e-01 -1.05543435e+00 4.48071778e-01 2.43891835e-01
-3.63987833e-01 1.09667969e+00 4.09347611e-03 -2.16709897e-01
-5.53662539e-01 -1.10748732e+00 -3.87257129e-01 -1.18312252e+00
-4.80890781e-01 3.34598482e-01 1.81986839e-01 -2.42798999... | [5.893773555755615, 6.232236862182617] |
1f51e5d8-3608-453a-bf43-044a65ac1e17 | mitigating-the-accuracy-robustness-trade-off | 2306.16170 | null | https://arxiv.org/abs/2306.16170v2 | https://arxiv.org/pdf/2306.16170v2.pdf | Mitigating the Accuracy-Robustness Trade-off via Multi-Teacher Adversarial Distillation | Adversarial training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performance toward clean examples is negatively affected after adversarial training, which means a trade-off exists between accuracy and robustness. R... | ['Xingxing Wei', 'Xizhe Wang', 'Shiji Zhao'] | 2023-06-28 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [-2.22019896e-01 -2.04445445e-03 2.39015426e-02 -2.37504706e-01
-5.23566663e-01 -8.02470684e-01 2.86531717e-01 -5.51014692e-02
-5.08531988e-01 7.42569983e-01 -9.13184136e-02 -3.24227244e-01
-1.32007688e-01 -1.04811978e+00 -8.27323675e-01 -1.24429631e+00
3.01969767e-01 -1.06145248e-01 4.35170323e-01 -3.52894425... | [5.565924167633057, 7.920808792114258] |
640bdde3-ff7b-48bb-9682-73f4c08276f7 | multimodal-image-text-matching-improves | 2303.17579 | null | https://arxiv.org/abs/2303.17579v2 | https://arxiv.org/pdf/2303.17579v2.pdf | Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation | Automated generation of clinically accurate radiology reports can improve patient care. Previous report generation methods that rely on image captioning models often generate incoherent and incorrect text due to their lack of relevant domain knowledge, while retrieval-based attempts frequently retrieve reports that are... | ['Pranav Rajpurkar', 'Subathra Adithan', 'Michael Pohlen', 'David Osayande', 'Juan Calle', 'Fardad Behzadi', 'Sina Hartung', 'Andrew Li', 'Katherine Tian', 'Jaehwan Jeong'] | 2023-03-29 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 4.35423940e-01 2.45298207e-01 5.64199723e-02 -3.76557350e-01
-1.87929404e+00 -4.67479050e-01 5.85598588e-01 8.68690431e-01
-1.36482462e-01 6.29666150e-01 9.11076367e-01 -4.37674880e-01
-3.07849228e-01 -5.70093811e-01 -5.21761000e-01 -1.35734901e-01
1.48118496e-01 5.79788923e-01 -1.17750548e-01 -8.30583423... | [15.064642906188965, -1.412023663520813] |
bfdf10ad-95ba-46f8-956d-3961368475c2 | a-semi-supervised-geometric-driven | 2207.05514 | null | https://arxiv.org/abs/2207.05514v2 | https://arxiv.org/pdf/2207.05514v2.pdf | A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels | Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data plays a significant role in tracking vessel activity and mapping mobility patterns such as those found in fishing. Accordingly, this paper proposes a geo... | ['Stan Matwin', 'Amilcar Soares', 'Gabriel Spadon', 'Martha Dais Ferreira'] | 2022-07-12 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.96233299e-02 -2.60063678e-01 3.95834707e-02 -3.97149503e-01
-5.16374588e-01 -1.07827103e+00 6.61365688e-01 2.23709241e-01
-6.55148745e-01 1.24066778e-01 4.01354969e-01 -2.58511931e-01
-6.31574810e-01 -7.85189748e-01 -4.76353437e-01 -8.92899454e-01
-7.71891117e-01 4.19835784e-02 -3.55979353e-02 -3.37920576... | [7.091564655303955, 2.5166943073272705] |
9904040e-f1b1-46b8-a9aa-6c7e4fe356dd | graphcast-learning-skillful-medium-range | 2212.12794 | null | https://arxiv.org/abs/2212.12794v1 | https://arxiv.org/pdf/2212.12794v1.pdf | GraphCast: Learning skillful medium-range global weather forecasting | We introduce a machine-learning (ML)-based weather simulator--called "GraphCast"--which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel hig... | ['Peter Battaglia', 'Shakir Mohamed', 'Oriol Vinyals', 'Jacklynn Stott', 'George Holland', 'Stephan Hoyer', 'Alexander Merose', 'Weihua Hu', 'Zach Eaton-Rosen', 'Ferran Alet', 'Timo Ewalds', 'Suman Ravuri', 'Alexander Pritzel', 'Meire Fortunato', 'Peter Wirnsberger', 'Matthew Willson', 'Alvaro Sanchez-Gonzalez', 'Remi ... | 2022-12-24 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-6.17573678e-01 -1.40440211e-01 -1.17587065e-02 -2.69695520e-01
-4.10796940e-01 -7.18820512e-01 7.51185894e-01 1.04251780e-01
7.16261491e-02 1.15307724e+00 3.21274221e-01 -1.21352649e+00
6.71510547e-02 -1.49983513e+00 -3.84264439e-01 -8.06893885e-01
-9.26696956e-01 6.59938931e-01 -2.25425921e-02 -8.29813659... | [6.566751480102539, 2.901853322982788] |
2d13a438-20a6-4f0a-97b0-8d5dce2824f0 | using-auxiliary-tasks-in-multimodal-fusion-of | 2302.13661 | null | https://arxiv.org/abs/2302.13661v1 | https://arxiv.org/pdf/2302.13661v1.pdf | Using Auxiliary Tasks In Multimodal Fusion Of Wav2vec 2.0 And BERT For Multimodal Emotion Recognition | The lack of data and the difficulty of multimodal fusion have always been challenges for multimodal emotion recognition (MER). In this paper, we propose to use pretrained models as upstream network, wav2vec 2.0 for audio modality and BERT for text modality, and finetune them in downstream task of MER to cope with the l... | ['Jiqing Han', 'Yancheng He', 'Dekai Sun'] | 2023-02-27 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-8.14637020e-02 -1.52198300e-01 1.40299141e-01 -5.00596583e-01
-9.44575131e-01 -2.48997465e-01 4.19574559e-01 -2.08795741e-01
-8.18199337e-01 5.69485247e-01 5.28726876e-01 8.51793513e-02
2.45443687e-01 -2.06793979e-01 -3.55237156e-01 -5.87303579e-01
4.44355518e-01 7.76827633e-02 -2.47647583e-01 -3.76949131... | [13.23205852508545, 5.1861395835876465] |
2ce0df34-9942-462b-b6e9-83667ecf733e | few-shot-viewpoint-estimation | 1905.04957 | null | https://arxiv.org/abs/1905.04957v2 | https://arxiv.org/pdf/1905.04957v2.pdf | Few-Shot Viewpoint Estimation | Viewpoint estimation for known categories of objects has been improved significantly thanks to deep networks and large datasets, but generalization to unknown categories is still very challenging. With an aim towards improving performance on unknown categories, we introduce the problem of category-level few-shot viewpo... | ['Ming-Hsuan Yang', 'Hung-Yu Tseng', 'Jonathan Tremblay', 'Sifei Liu', 'Jan Kautz', 'Stan Birchfield', 'Shalini De Mello'] | 2019-05-13 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 4.08210419e-02 3.96893546e-02 -1.72137380e-01 -5.88975668e-01
-8.39474559e-01 -5.30957222e-01 7.20356286e-01 -1.80693731e-01
-2.78008103e-01 2.21229866e-01 1.35597169e-01 2.58808851e-01
-9.25578475e-02 -6.35846615e-01 -8.40394318e-01 -3.01871181e-01
-2.71513890e-02 6.69869959e-01 6.02140903e-01 -7.11857006... | [7.7911529541015625, -2.8516247272491455] |
060695cb-2dc1-4d38-a126-51d42f0a3882 | policy-contrastive-imitation-learning | 2307.02829 | null | https://arxiv.org/abs/2307.02829v1 | https://arxiv.org/pdf/2307.02829v1.pdf | Policy Contrastive Imitation Learning | Adversarial imitation learning (AIL) is a popular method that has recently achieved much success. However, the performance of AIL is still unsatisfactory on the more challenging tasks. We find that one of the major reasons is due to the low quality of AIL discriminator representation. Since the AIL discriminator is tra... | ['Yang Gao', 'Yingdong Hu', 'ZhaoHeng Yin', 'Jialei Huang'] | 2023-07-06 | null | null | null | null | ['representation-learning', 'imitation-learning'] | ['methodology', 'methodology'] | [-1.12679899e-02 7.26440270e-03 -7.45072007e-01 -3.91980255e-04
-7.50992715e-01 -5.76215982e-01 1.01202476e+00 -9.27887112e-02
-6.98006988e-01 9.37172830e-01 1.85242936e-01 -3.49836975e-01
-2.57525861e-01 -4.39078599e-01 -8.64782810e-01 -8.31444979e-01
-9.80931371e-02 3.46517682e-01 6.25365525e-02 -3.40658903... | [4.182637691497803, 2.0246500968933105] |
9b767f8e-e3b1-4bf5-9406-a7f7809ba2d1 | weakly-supervised-information-extraction-from | 2306.06823 | null | https://arxiv.org/abs/2306.06823v1 | https://arxiv.org/pdf/2306.06823v1.pdf | Weakly supervised information extraction from inscrutable handwritten document images | State-of-the-art information extraction methods are limited by OCR errors. They work well for printed text in form-like documents, but unstructured, handwritten documents still remain a challenge. Adapting existing models to domain-specific training data is quite expensive, because of two factors, 1) limited availabili... | ['Gaurav Aggarwal', 'Pradeep Kumar', 'Narayan Hegde', 'Akankshya Mishra', 'Gagan Madan', 'Sujoy Paul'] | 2023-06-12 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 3.89821470e-01 6.59323782e-02 -4.28372592e-01 -2.57456452e-01
-9.94189918e-01 -1.06507373e+00 4.26560193e-01 3.94568175e-01
-3.64265084e-01 9.29753602e-01 1.01793915e-01 -3.54164183e-01
1.75455622e-02 -6.70568228e-01 -7.57008851e-01 -4.17635322e-01
5.25689185e-01 9.15393949e-01 5.00733890e-02 2.35878155... | [11.720795631408691, 2.5102880001068115] |
fc29facd-7e9f-4565-a09c-d8136b76f073 | thoops-a-multi-aspect-analytical-framework | 1712.01199 | null | http://arxiv.org/abs/1712.01199v5 | http://arxiv.org/pdf/1712.01199v5.pdf | tHoops: A Multi-Aspect Analytical Framework Spatio-Temporal Basketball Data | During the past few years advancements in sports information systems and
technology has allowed us to collect a number of detailed spatio-temporal data
capturing various aspects of basketball. For example, shot charts, that is,
maps capturing locations of (made or missed) shots, and spatio-temporal
trajectories for all... | ['Konstantinos Pelechrinis', 'Evangelos Papalexakis'] | 2017-12-04 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-1.10313490e-01 -6.94865108e-01 -5.51236644e-02 3.44010405e-02
-1.20746411e-01 -7.70015776e-01 6.11250162e-01 6.03018224e-01
-4.38654453e-01 2.33524501e-01 5.95074475e-01 6.10391870e-02
-1.01142633e+00 -9.32058930e-01 -1.58649966e-01 -6.81411505e-01
-2.55468100e-01 4.09489036e-01 2.90910512e-01 -5.58061421... | [7.024385452270508, 0.3955935835838318] |
77c70655-dfac-4ed3-9896-7f820592f2d5 | statistical-dialogue-management-using | null | null | https://aclanthology.org/I13-1127 | https://aclanthology.org/I13-1127.pdf | Statistical Dialogue Management using Intention Dependency Graph | null | ['Koichiro Yoshino', 'John R. Hershey', 'Shinji Watanabe', 'Jonathan Le Roux'] | 2013-10-01 | statistical-dialogue-management-using-1 | https://aclanthology.org/I13-1127 | https://aclanthology.org/I13-1127.pdf | ijcnlp-2013-10 | ['dialogue-management'] | ['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... | [-7.274928569793701, 3.6527109146118164] |
554bd489-6a7f-42b6-8e6e-be856b1152fa | a-new-cross-domain-strategy-based-xai-models | 2302.02122 | null | https://arxiv.org/abs/2302.02122v1 | https://arxiv.org/pdf/2302.02122v1.pdf | A New cross-domain strategy based XAI models for fake news detection | In this study, we presented a four-level cross-domain strategy for fake news detection on pre-trained models. Cross-domain text classification is a task of a model adopting a target domain by using the knowledge of the source domain. Explainability is crucial in understanding the behaviour of these complex models. A fi... | ['Deepak Kanneganti'] | 2023-02-04 | null | null | null | null | ['cross-domain-text-classification'] | ['natural-language-processing'] | [-1.87410221e-01 4.05074090e-01 -6.92632556e-01 -4.42085654e-01
-6.96914077e-01 -6.81112647e-01 1.24098969e+00 1.88745081e-01
3.24038386e-01 1.03232574e+00 3.00217360e-01 -3.29514205e-01
-4.40298349e-01 -5.70513010e-01 -5.89252412e-01 1.47925578e-02
1.40723839e-01 1.14557171e+00 3.85896564e-01 -6.88582480... | [8.154829978942871, 10.271937370300293] |
56d15853-19bd-4475-b5cf-6a863d627878 | matrix-completion-from-general-deterministic | 2306.02283 | null | https://arxiv.org/abs/2306.02283v1 | https://arxiv.org/pdf/2306.02283v1.pdf | Matrix Completion from General Deterministic Sampling Patterns | Most of the existing works on provable guarantees for low-rank matrix completion algorithms rely on some unrealistic assumptions such that matrix entries are sampled randomly or the sampling pattern has a specific structure. In this work, we establish theoretical guarantee for the exact and approximate low-rank matrix ... | ['Jean Honorio', 'Qifan Song', 'Rahul Mazumder', 'Hanbyul Lee'] | 2023-06-04 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 2.98645705e-01 4.39291388e-01 -3.48452777e-01 -3.06367618e-03
-6.18583620e-01 -7.15682507e-01 4.08043623e-01 -8.98986030e-03
-4.94815223e-02 6.40110970e-01 3.82586747e-01 -2.90881068e-01
-4.80181545e-01 -7.18291223e-01 -8.93349707e-01 -8.35204422e-01
-6.67535841e-01 6.74753487e-01 6.86868280e-03 -2.16207147... | [6.909853935241699, 4.899941921234131] |
7d641d48-09a0-4e44-93c7-55f24ab216d3 | c-vton-context-driven-image-based-virtual-try | 2212.04437 | null | https://arxiv.org/abs/2212.04437v1 | https://arxiv.org/pdf/2212.04437v1.pdf | C-VTON: Context-Driven Image-Based Virtual Try-On Network | Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing techniques are currently still limited in the quality of the try-on results they are able to produce from input images of diver... | ['Vitomir Štruc', 'Peter Peer', 'Ajda Lampe', 'Benjamin Fele'] | 2022-12-08 | null | null | null | null | ['geometric-matching', 'virtual-try-on'] | ['computer-vision', 'computer-vision'] | [ 1.29439503e-01 -2.26549625e-01 2.96041906e-01 -5.26570022e-01
-6.26034796e-01 -6.22095644e-01 4.75847572e-01 -2.18746550e-02
-3.27256247e-02 2.54342109e-01 -2.46272665e-02 -9.68015417e-02
1.55901462e-01 -6.61876619e-01 -8.79070818e-01 -2.54222780e-01
2.49409765e-01 5.17808735e-01 3.14591527e-01 -5.65986097... | [11.899432182312012, -0.8353299498558044] |
babff6cb-0088-4a32-8052-ac95d98c8d1e | bop-challenge-2020-on-6d-object-localization | 2009.07378 | null | https://arxiv.org/abs/2009.07378v2 | https://arxiv.org/pdf/2009.07378v2.pdf | BOP Challenge 2020 on 6D Object Localization | This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image. In 2020, to reduce the domain gap between synthetic training and ... | ['Martin Sundermeyer', 'Jiri Matas', 'Yann Labbe', 'Tomas Hodan', 'Frank Michel', 'Bertram Drost', 'Eric Brachmann', 'Carsten Rother'] | 2020-09-15 | null | null | null | null | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-1.18169941e-01 3.19602579e-01 4.00072068e-01 -1.16763048e-01
-8.84434700e-01 -4.98017550e-01 7.05067933e-01 -1.34151340e-01
-5.13069332e-01 4.21457022e-01 -1.39787003e-01 -1.18775316e-01
-9.32170972e-02 -8.30892801e-01 -1.10469377e+00 -4.33694690e-01
9.42693204e-02 6.90654397e-01 1.45346716e-01 -5.42116284... | [7.666784763336182, -2.637888193130493] |
3b20acb2-efee-469c-8453-a033a699c742 | introspective-action-advising-for | 2306.12314 | null | https://arxiv.org/abs/2306.12314v1 | https://arxiv.org/pdf/2306.12314v1.pdf | Introspective Action Advising for Interpretable Transfer Learning | Transfer learning can be applied in deep reinforcement learning to accelerate the training of a policy in a target task by transferring knowledge from a policy learned in a related source task. This is commonly achieved by copying pretrained weights from the source policy to the target policy prior to training, under t... | ['Katia Sycara', 'Simon Stepputtis', 'Fiona Xie', 'Yue Guo', 'Joseph Campbell'] | 2023-06-21 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 4.23403621e-01 3.14447463e-01 -3.85785162e-01 -2.84282297e-01
-3.94157529e-01 -8.87684762e-01 7.25680947e-01 3.02157491e-01
-7.72054970e-01 1.03609204e+00 1.35763749e-01 -4.76353496e-01
-1.23944618e-01 -8.90975535e-01 -1.00435305e+00 -8.70298684e-01
7.18696266e-02 7.52939939e-01 3.43475431e-01 -3.89309317... | [4.090144634246826, 1.5378797054290771] |
6f9291ab-5c2a-4078-8e08-726a4fff2bf0 | periodic-graph-transformers-for-crystal | 2209.11807 | null | https://arxiv.org/abs/2209.11807v1 | https://arxiv.org/pdf/2209.11807v1.pdf | Periodic Graph Transformers for Crystal Material Property Prediction | We consider representation learning on periodic graphs encoding crystal materials. Different from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on a regular lattice in 3D space. How to effectively encode these periodic structures poses unique challenges not present in regular graph rep... | ['Shuiwang Ji', 'Yuchao Lin', 'Yi Liu', 'Keqiang Yan'] | 2022-09-23 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 3.89021546e-01 1.97544873e-01 -2.85149783e-01 -1.04158729e-01
-1.05299808e-01 -7.04943001e-01 3.60412836e-01 2.77470779e-02
4.56365317e-01 6.14380956e-01 5.20255327e-01 3.45794670e-02
-8.20417106e-02 -1.16671622e+00 -1.18285382e+00 -1.07645130e+00
-3.01127613e-01 5.67547679e-01 9.33989212e-02 -2.49876201... | [5.318416118621826, 5.776086330413818] |
69fe4b7c-127e-4edc-9dc0-a3613395f338 | trajectory-prediction-with-observations-of | 2306.05478 | null | https://arxiv.org/abs/2306.05478v1 | https://arxiv.org/pdf/2306.05478v1.pdf | Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios | Accurate trajectory prediction of nearby vehicles is crucial for the safe motion planning of automated vehicles in dynamic driving scenarios such as highway merging. Existing methods cannot initiate prediction for a vehicle unless observed for a fixed duration of two or more seconds. This prevents a fast reaction by th... | ['Mehrdad Dianati', 'Graham Lee', 'Konstantinos Koufos', 'Mreza Alipour Sormoli', 'Sajjad Mozaffari'] | 2023-06-08 | null | null | null | null | ['trajectory-prediction', 'motion-planning'] | ['computer-vision', 'robots'] | [-3.02825391e-01 1.40968338e-01 -5.50217509e-01 -3.61862242e-01
-4.71190155e-01 -2.83702582e-01 6.45651460e-01 1.29300728e-01
-4.60802138e-01 6.35430992e-01 -1.55701816e-01 -7.47080207e-01
-8.60671997e-02 -9.13257539e-01 -7.48608768e-01 -6.46651685e-01
-2.61993557e-01 3.54236633e-01 7.12961018e-01 -2.85198271... | [5.784133434295654, 1.0897554159164429] |
a8d4863f-9fc6-4573-a986-407fa659f4a7 | propbank-comes-of-age-larger-smarter-and-more | null | null | https://aclanthology.org/2022.starsem-1.24 | https://aclanthology.org/2022.starsem-1.24.pdf | PropBank Comes of Age—Larger, Smarter, and more Diverse | This paper describes the evolution of the PropBank approach to semantic role labeling over the last two decades. During this time the PropBank frame files have been expanded to include non-verbal predicates such as adjectives, prepositions and multi-word expressions. The number of domains, genres and languages that hav... | ['Martha Palmer', 'Kristin Wright-Bettner', 'James Gung', 'Tim O’Gorman', 'Kathryn Conger', 'Skatje Myers', 'Julia Bonn', 'Sameer Pradhan'] | null | null | null | null | sem-naacl-2022-7 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 3.19465250e-02 6.04116797e-01 -6.01970077e-01 -7.59452343e-01
-4.75266337e-01 -9.89721298e-01 8.69588971e-01 7.72675991e-01
-6.91228628e-01 1.48813069e+00 8.42825413e-01 -1.01642318e-01
-2.08620399e-01 -7.05416203e-01 -4.89929840e-02 -2.44965672e-01
-1.09016426e-01 7.45166421e-01 6.22333765e-01 -7.62158990... | [10.189464569091797, 9.34164810180664] |
d1140c31-02e0-434c-8b5e-a529dbacb746 | uncrtaints-uncertainty-quantification-for | 2304.05464 | null | https://arxiv.org/abs/2304.05464v1 | https://arxiv.org/pdf/2304.05464v1.pdf | UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series | Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface. Although modern deep learning methods can implicitly learn to ignore such occlusions, explicit cloud removal as pre-processing enables manual interpretation and allows training models when only few ann... | ['Xiao Xiang Zhu', 'Jan Dirk Wegner', 'Michael Schmitt', 'Vivien Sainte Fare Garnot', 'Patrick Ebel'] | 2023-04-11 | null | null | null | null | ['cloud-removal', 'image-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.69780403e-02 -3.07819515e-01 1.26548231e-01 -4.88384396e-01
-1.01245201e+00 -7.16947079e-01 5.81816673e-01 -6.86963424e-02
-1.09081574e-01 8.37954164e-01 -5.35804108e-02 -3.06025654e-01
-2.23083451e-01 -8.01654160e-01 -7.96988606e-01 -8.52805257e-01
-1.90868586e-01 7.44999170e-01 2.21887305e-02 -4.57479209... | [9.847743034362793, -1.798410415649414] |
f0994b1e-6b8f-44af-a99a-9bafc3df990a | pre-training-on-dynamic-graph-neural-networks | 2102.12380 | null | https://arxiv.org/abs/2102.12380v2 | https://arxiv.org/pdf/2102.12380v2.pdf | Pre-Training on Dynamic Graph Neural Networks | The pre-training on the graph neural network model can learn the general features of large-scale networks or networks of the same type by self-supervised methods, which allows the model to work even when node labels are missing. However, the existing pre-training methods do not take network evolution into consideration... | ['Yuxuan Dai', 'Yunyun Wang', 'Linpu Jiang', 'Jiajun Zhang', 'Ke-Jia Chen'] | 2021-02-24 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 1.69644848e-01 4.80603218e-01 -5.62904775e-01 -4.04444963e-01
4.47044522e-01 -2.45410174e-01 5.19980252e-01 -3.69316712e-03
8.51639360e-03 7.88621247e-01 -2.70102888e-01 -3.93250644e-01
-2.91254550e-01 -1.42974675e+00 -6.47468626e-01 -5.53891182e-01
-5.88535070e-01 7.29523301e-01 7.08560526e-01 -2.42816642... | [7.301405906677246, 6.275106430053711] |
5651ba39-5fab-407a-a07f-e40dcf543702 | kernel-cf-collaborative-filtering-done-right | 2303.04561 | null | https://arxiv.org/abs/2303.04561v1 | https://arxiv.org/pdf/2303.04561v1.pdf | Kernel-CF: Collaborative filtering done right with social network analysis and kernel smoothing | Collaborative filtering is the simplest but oldest machine learning algorithm in the field of recommender systems. In spite of its long history, it remains a discussion topic in research venues. Usually people use users/items whose similarity scores with the target customer greater than 0 to compute the algorithms. How... | ['Hao Wang'] | 2023-03-08 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-3.05856407e-01 -3.56848836e-01 -2.19524279e-01 -5.23738563e-01
-1.00227192e-01 -5.91560245e-01 2.68159628e-01 4.18355130e-02
-4.01870579e-01 4.96856868e-01 1.24821156e-01 -5.23912311e-01
-7.20510781e-01 -8.96834671e-01 3.05441767e-02 -7.52450645e-01
-2.56902516e-01 4.31347251e-01 2.48679414e-01 -4.18442249... | [9.994680404663086, 5.732145309448242] |
e922b8ce-f175-4b0d-a9ca-90517aedceca | learning-embeddings-for-image-clustering-an | 2007.03123 | null | https://arxiv.org/abs/2007.03123v1 | https://arxiv.org/pdf/2007.03123v1.pdf | Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches | In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Lo... | ['Franz-Josef Pfreundt', 'Margret Keuper', 'Janis Keuper', 'Kalun Ho'] | 2020-07-06 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-7.13160038e-02 -3.60461652e-01 -7.35800192e-02 -7.88830876e-01
-1.00018120e+00 -3.52530777e-01 5.81412435e-01 3.83247972e-01
-6.50403261e-01 4.72554564e-01 -6.33751974e-02 8.56356174e-02
-8.66652906e-01 -2.79173225e-01 -4.90392536e-01 -1.04512763e+00
-3.01198900e-01 5.11865020e-01 -3.33399087e-01 4.57253069... | [9.277107238769531, 3.185239553451538] |
58faeb94-f5ec-4d6c-89a6-791ca9528596 | spell-correction-for-azerbaijani-language | 2102.03218 | null | https://arxiv.org/abs/2102.03218v1 | https://arxiv.org/pdf/2102.03218v1.pdf | Spell Correction for Azerbaijani Language using Deep Neural Networks | Spell correction is used to detect and correct orthographic mistakes in texts. Most of the time, traditional dictionary lookup with string similarity methods is suitable for the languages that have a less complex structure such as the English language. However, the Azerbaijani language has a more complex structure and ... | ['Saber Malekzadeh', 'Ahmad Ahmadzade'] | 2021-02-05 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 5.43653518e-02 -6.45981967e-01 1.00798249e-01 -1.45544499e-01
-1.48530900e-01 -6.41854703e-01 3.23955566e-01 6.91183567e-01
-7.62540400e-01 8.52171779e-01 3.82303894e-01 -5.45112252e-01
3.93247716e-02 -8.51384819e-01 -4.66999590e-01 -4.15485471e-01
6.30098999e-01 4.63610888e-01 1.65767953e-01 -5.97214043... | [10.905909538269043, 10.67260456085205] |
ae570a66-1d31-4acc-9d73-50fcaec2f495 | cross-lktcn-modern-convolution-utilizing | 2306.02326 | null | https://arxiv.org/abs/2306.02326v1 | https://arxiv.org/pdf/2306.02326v1.pdf | Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting | The past few years have witnessed the rapid development in multivariate time series forecasting. The key to accurate forecasting results is capturing the long-term dependency between each time step (cross-time dependency) and modeling the complex dependency between each variable (cross-variable dependency) in multivari... | ['Xue Wang', 'Donghao Luo'] | 2023-06-04 | null | null | null | null | ['multivariate-time-series-forecasting'] | ['time-series'] | [-3.60082239e-01 -1.04177392e+00 -1.50022998e-01 -6.50914013e-01
1.22496523e-02 -4.80785191e-01 5.09742260e-01 -3.20468813e-01
-2.71824598e-02 5.38318455e-01 3.79854366e-02 -6.79065466e-01
-3.01540524e-01 -6.94607556e-01 -6.02864683e-01 -7.25254774e-01
-5.92781782e-01 -1.00343689e-01 -1.62700117e-01 -4.95007485... | [6.904794692993164, 2.859097957611084] |
0700bd60-52a1-4bc7-ada2-fc768cad8198 | concrete-problems-in-ai-safety | 1606.06565 | null | http://arxiv.org/abs/1606.06565v2 | http://arxiv.org/pdf/1606.06565v2.pdf | Concrete Problems in AI Safety | Rapid progress in machine learning and artificial intelligence (AI) has
brought increasing attention to the potential impacts of AI technologies on
society. In this paper we discuss one such potential impact: the problem of
accidents in machine learning systems, defined as unintended and harmful
behavior that may emerg... | ['Dan Mané', 'Paul Christiano', 'John Schulman', 'Jacob Steinhardt', 'Dario Amodei', 'Chris Olah'] | 2016-06-21 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 3.44256401e-01 7.62404621e-01 -1.84717730e-01 -5.16049564e-01
-6.16599321e-01 -5.82509153e-02 5.28403938e-01 1.72547668e-01
-7.10878968e-01 8.50986063e-01 -2.73412671e-02 -6.86626792e-01
-3.32868636e-01 -5.83175600e-01 -8.31049860e-01 -7.28812039e-01
-1.51831254e-01 4.70044583e-01 -2.71360606e-01 -2.44568884... | [8.910962104797363, 6.0224609375] |
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