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4b5e8f67-75e0-4e4d-a9cc-c0451110a9ed | learn2augment-learning-to-composite-videos | 2206.04790 | null | https://arxiv.org/abs/2206.04790v2 | https://arxiv.org/pdf/2206.04790v2.pdf | Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition | We address the problem of data augmentation for video action recognition. Standard augmentation strategies in video are hand-designed and sample the space of possible augmented data points either at random, without knowing which augmented points will be better, or through heuristics. We propose to learn what makes a go... | ['Laura Sevilla-Lara', 'Frank Keller', 'Marcus Rohrbach', 'Shreyank N Gowda'] | 2022-06-09 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 5.4149556e-01 -1.7467637e-01 -6.8537390e-01 -1.7081904e-01
-9.8809123e-01 -4.3996689e-01 6.0459429e-01 -2.4880260e-01
-6.7478567e-01 7.3457146e-01 4.7351927e-01 -1.8838020e-02
2.7290317e-01 -4.1934505e-01 -9.4000077e-01 -7.3894757e-01
-6.3031107e-02 6.6914946e-01 3.0385110e-01 5.9861176e-02
-5.2533474e-02... | [8.50503158569336, 0.6600521802902222] |
330ca883-297d-42fc-9f99-20c69050cea6 | hand-based-person-identification-using-global | 2101.05260 | null | https://arxiv.org/abs/2101.05260v8 | https://arxiv.org/pdf/2101.05260v8.pdf | Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning | In cases of serious crime, including sexual abuse, often the only available information with demonstrated potential for identification is images of the hands. Since this evidence is captured in uncontrolled situations, it is difficult to analyse. As global approaches to feature comparison are limited in this case, it i... | ['Plamen Angelov', 'Bryan Williams', 'Sue Black', 'Hossein Rahmani', 'Nathanael L. Baisa'] | 2021-01-13 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.17952287e-01 -2.69183904e-01 -2.37822562e-01 -6.32453799e-01
-8.81235242e-01 -6.87765121e-01 4.55617785e-01 1.88512295e-01
-6.60069466e-01 7.09126830e-01 2.38244295e-01 1.50629389e-03
-4.29436624e-01 -6.23253047e-01 -3.74729842e-01 -7.86154449e-01
-8.76884833e-02 7.31479347e-01 -1.71021327e-01 2.15918094... | [13.672229766845703, 0.9077821969985962] |
6619372a-3d0a-4ef9-8cdc-56dfcf39e077 | rapid-object-detection-using-a-boosted | null | null | https://ieeexplore.ieee.org/document/990517 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=990517 | Rapid Object Detection using a Boosted Cascade of Simple Features | This paper describes a machine learning approach for visual object detection which is capable of processing images
extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first
is the introduction of a new image representation called the “Integral linage” which... | ['Michael Jones', 'Paul Viola'] | 2003-04-15 | null | null | null | cvpr-2003-4 | ['face-detection'] | ['computer-vision'] | [ 1.00006707e-01 -1.04056425e-01 -1.26519218e-01 -2.88962811e-01
-5.48354030e-01 -3.75260651e-01 4.38984394e-01 2.60067642e-01
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-1.31831273e-01 3.26388240e-01 9.29377556e-01 -1.31834626... | [8.685957908630371, -0.3323928117752075] |
796647b7-444c-49f0-aa63-3c6ed0478c6a | image-captioners-are-scalable-vision-learners | 2306.07915 | null | https://arxiv.org/abs/2306.07915v1 | https://arxiv.org/pdf/2306.07915v1.pdf | Image Captioners Are Scalable Vision Learners Too | Contrastive pretraining on image-text pairs from the web is one of the most popular large-scale pretraining strategies for vision backbones, especially in the context of large multimodal models. At the same time, image captioning on this type of data is commonly considered an inferior pretraining strategy. In this pape... | ['Lucas Beyer', 'Neil Houlsby', 'Xiaohua Zhai', 'Andreas Steiner', 'Manoj Kumar', 'Michael Tschannen'] | 2023-06-13 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 4.23735738e-01 3.01005810e-01 -1.60543516e-01 -3.43875200e-01
-1.03892624e+00 -6.10094130e-01 9.76510167e-01 1.07305348e-01
-7.37719834e-01 5.50012529e-01 4.01464611e-01 -2.89536983e-01
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3.60623717e-01 6.65353596e-01 5.71133532e-02 -3.21633428... | [10.907065391540527, 1.520050287246704] |
7ba851a3-09ff-491d-b7f8-6012e0fdb86f | 190503692 | 1905.03692 | null | https://arxiv.org/abs/1905.03692v2 | https://arxiv.org/pdf/1905.03692v2.pdf | Improving Image-Based Localization with Deep Learning: The Impact of the Loss Function | This work investigates the impact of the loss function on the performance of Neural Networks, in the context of a monocular, RGB-only, image localization task. A common technique used when regressing a camera's pose from an image is to formulate the loss as a linear combination of positional and rotational mean squared... | ['Mohammed Bennamoun', 'M. A. Asim K. Jalwana', 'Isaac Ronald Ward'] | 2019-04-28 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [ 2.73716658e-01 -2.01695524e-02 2.03539282e-01 -6.51418686e-01
-6.74706876e-01 -5.71941257e-01 6.05932474e-01 -2.46008276e-03
-9.78566885e-01 6.42772615e-01 -4.81569879e-02 -6.59411326e-02
-3.15715335e-02 -3.71827275e-01 -1.15708542e+00 -7.33820438e-01
5.42278700e-02 -3.84675525e-02 1.50128573e-01 7.86982104... | [7.67065954208374, -2.1096909046173096] |
d677efd0-7a76-472a-8540-7cdcc86a8e9c | unsupervised-speech-enhancement-with-deep | 2306.07820 | null | https://arxiv.org/abs/2306.07820v1 | https://arxiv.org/pdf/2306.07820v1.pdf | Unsupervised speech enhancement with deep dynamical generative speech and noise models | This work builds on a previous work on unsupervised speech enhancement using a dynamical variational autoencoder (DVAE) as the clean speech model and non-negative matrix factorization (NMF) as the noise model. We propose to replace the NMF noise model with a deep dynamical generative model (DDGM) depending either on th... | ['Xavier Alameda-Pineda', 'Laurent Girin', 'Simon Leglaive', 'Xiaoyu Lin'] | 2023-06-13 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 3.13412882e-02 -7.03780726e-02 4.08224583e-01 -4.63440530e-02
-4.97655690e-01 -1.49254426e-01 8.49974155e-01 -4.92018342e-01
-5.70777357e-01 4.91101980e-01 6.56056404e-01 -3.52360427e-01
-7.90691748e-02 -7.37431169e-01 -4.47883755e-01 -1.22027647e+00
5.18975496e-01 2.17589572e-01 1.15618326e-01 -4.85149592... | [15.028271675109863, 6.041990756988525] |
613569a3-3278-415f-9ad4-07f2d8073b5f | inductive-visual-localisation-factorised | 1807.08179 | null | http://arxiv.org/abs/1807.08179v1 | http://arxiv.org/pdf/1807.08179v1.pdf | Inductive Visual Localisation: Factorised Training for Superior Generalisation | End-to-end trained Recurrent Neural Networks (RNNs) have been successfully
applied to numerous problems that require processing sequences, such as image
captioning, machine translation, and text recognition. However, RNNs often
struggle to generalise to sequences longer than the ones encountered during
training. In thi... | ['Andrew Zisserman', 'Andrea Vedaldi', 'Ankush Gupta'] | 2018-07-21 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 1.27010489e+00 4.55187410e-02 1.11769356e-01 -2.24709988e-01
-2.36216113e-01 -4.99288917e-01 1.02063465e+00 -1.49175361e-01
-6.37036085e-01 6.17817521e-01 -8.27796757e-02 -4.93250817e-01
2.68623978e-01 -4.57450837e-01 -1.12014043e+00 -8.97911072e-01
2.22672537e-01 5.18736959e-01 1.13319837e-01 4.50139027... | [10.862950325012207, 0.8693172335624695] |
322b9b71-34a8-4b45-89c3-d8e19c837c73 | facelet-bank-for-fast-portrait-manipulation | 1803.05576 | null | http://arxiv.org/abs/1803.05576v3 | http://arxiv.org/pdf/1803.05576v3.pdf | Facelet-Bank for Fast Portrait Manipulation | Digital face manipulation has become a popular and fascinating way to touch
images with the prevalence of smartphones and social networks. With a wide
variety of user preferences, facial expressions, and accessories, a general and
flexible model is necessary to accommodate different types of facial editing.
In this pap... | ['Ying-Cong Chen', 'Ruiyu Li', 'Xin Tao', 'Xiaoyong Shen', 'Jiaya Jia', 'Huaijia Lin', 'Yangang Ye', 'Michelle Shu'] | 2018-03-15 | facelet-bank-for-fast-portrait-manipulation-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Facelet-Bank_for_Fast_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Facelet-Bank_for_Fast_CVPR_2018_paper.pdf | cvpr-2018-6 | ['facial-editing'] | ['computer-vision'] | [ 5.31332418e-02 -3.63003939e-01 -2.46370941e-01 -7.68910110e-01
-1.88458398e-01 -4.16350275e-01 4.17611331e-01 -7.86202550e-01
-2.62005329e-01 4.53135341e-01 -7.09760860e-02 1.99140221e-01
2.05134809e-01 -6.60627902e-01 -5.61164856e-01 -1.95826024e-01
4.07366723e-01 2.50117004e-01 -1.59570143e-01 -2.01406211... | [12.672525405883789, -0.17738859355449677] |
4b71eeec-2243-46fc-afcf-08bd7b32670e | smart-contract-vulnerability-detection-from | 2106.09282 | null | https://arxiv.org/abs/2106.09282v1 | https://arxiv.org/pdf/2106.09282v1.pdf | Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion | Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches all... | ['Shouling Ji', 'Qinming He', 'Lei Zhu', 'Xiang Wang', 'Peng Qian', 'Zhenguang Liu'] | 2021-06-17 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-2.92762429e-01 4.31516528e-01 -5.63777685e-01 -3.42896402e-01
-5.90153396e-01 -8.69558632e-01 3.11065197e-01 9.57041010e-02
2.05917776e-01 2.85807073e-01 2.91926533e-01 -7.79727578e-01
-2.02279627e-01 -8.12271595e-01 -4.23156112e-01 -1.73726946e-01
-1.35276109e-01 3.19847077e-01 1.40826270e-01 -9.07802507... | [6.867950916290283, 7.361932277679443] |
99043dd2-e92b-4393-9811-be37a0f4d22a | image-declipping-with-deep-networks | 1811.06277 | null | http://arxiv.org/abs/1811.06277v1 | http://arxiv.org/pdf/1811.06277v1.pdf | Image declipping with deep networks | We present a deep network to recover pixel values lost to clipping. The
clipped area of the image is typically a uniform area of minimum or maximum
brightness, losing image detail and color fidelity. The degree to which the
clipping is visually noticeable depends on the amount by which values were
clipped, and the exte... | ['Shachar Honig', 'Michael Werman'] | 2018-11-15 | null | null | null | null | ['image-declipping'] | ['computer-vision'] | [ 5.65794826e-01 2.03431360e-02 1.62753835e-01 -2.40016207e-01
-4.89795953e-01 -8.29206467e-01 1.35599747e-01 -1.68443024e-01
-3.77361476e-01 7.53879726e-01 4.15793024e-02 -2.30261460e-01
4.03235018e-01 -8.21021736e-01 -1.11952174e+00 -4.44592744e-01
-2.67546028e-02 -1.30643412e-01 4.61599290e-01 -2.49490783... | [10.997556686401367, -2.170144557952881] |
c8e20e84-4fc6-45a2-9aa3-f0637ab9b015 | 3d-video-object-detection-with-learnable | 2303.15416 | null | https://arxiv.org/abs/2303.15416v1 | https://arxiv.org/pdf/2303.15416v1.pdf | 3D Video Object Detection with Learnable Object-Centric Global Optimization | We explore long-term temporal visual correspondence-based optimization for 3D video object detection in this work. Visual correspondence refers to one-to-one mappings for pixels across multiple images. Correspondence-based optimization is the cornerstone for 3D scene reconstruction but is less studied in 3D video objec... | ['Zhaoxiang Zhang', 'Naiyan Wang', 'Yuntao Chen', 'JiaWei He'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/He_3D_Video_Object_Detection_With_Learnable_Object-Centric_Global_Optimization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/He_3D_Video_Object_Detection_With_Learnable_Object-Centric_Global_Optimization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-scene-reconstruction', 'video-object-detection'] | ['computer-vision', 'computer-vision'] | [-3.19679439e-01 -4.96699303e-01 -2.51177549e-01 -2.34984696e-01
-7.94096172e-01 -6.06299758e-01 3.31162810e-01 -1.11241296e-01
-4.12733108e-01 -1.07908532e-01 1.48077458e-01 -4.94210934e-03
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5.80556057e-02 5.74592471e-01 6.06925845e-01 1.28642648... | [7.390590190887451, -2.4141974449157715] |
f9665cb1-6c50-41bf-99eb-a88c1ba17e40 | track-targets-by-dense-spatio-temporal | 2210.09455 | null | https://arxiv.org/abs/2210.09455v1 | https://arxiv.org/pdf/2210.09455v1.pdf | Track Targets by Dense Spatio-Temporal Position Encoding | In this work, we propose a novel paradigm to encode the position of targets for target tracking in videos using transformers. The proposed paradigm, Dense Spatio-Temporal (DST) position encoding, encodes spatio-temporal position information in a pixel-wise dense fashion. The provided position encoding provides location... | ['Kris Kitani', 'Hao Wu', 'Jinkun Cao'] | 2022-10-17 | null | null | null | null | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 9.84794721e-02 -3.26577127e-01 -2.68553168e-01 -3.71521056e-01
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-4.39898431e-01 3.26692522e-01 -4.40337099e-02 1.50032505e-01
-3.22744608e-01 -6.97590590e-01 -1.13109601e+00 -7.70803750e-01
-1.63768351e-01 4.22606438e-01 1.04880071e+00 3.43243510... | [6.342245101928711, -2.1083855628967285] |
e6b4614d-c528-4396-be66-1f0674941210 | fine-tuning-offline-reinforcement-learning | null | null | https://openreview.net/forum?id=wiSgdeJ29ee | https://openreview.net/pdf?id=wiSgdeJ29ee | Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization | In offline reinforcement learning (RL), we attempt to learn a control policy from a fixed dataset of environment interactions. This setting has the potential benefit of allowing us to learn effective policies without needing to collect additional interactive data, which can be expensive or dangerous in real-world syste... | ['Jeff Schneider', 'John Dolan', 'Adam Villaflor'] | 2021-01-01 | null | null | null | null | ['d4rl'] | ['robots'] | [-4.27460410e-02 4.50494848e-02 -4.37926769e-01 -1.97359815e-01
-7.10565090e-01 -6.26187027e-01 6.00818455e-01 2.46383145e-01
-8.19492996e-01 1.21132112e+00 -1.94102317e-01 -3.74499142e-01
-2.30913401e-01 -6.18210435e-01 -9.17949140e-01 -8.33424211e-01
-3.90716136e-01 7.22695053e-01 2.63928860e-01 -3.24427187... | [4.195271015167236, 2.26228666305542] |
fc57f7bb-b11d-436a-b678-a4a5a12af87d | deep-transfer-learning-for-intelligent | 2306.15110 | null | https://arxiv.org/abs/2306.15110v1 | https://arxiv.org/pdf/2306.15110v1.pdf | Deep Transfer Learning for Intelligent Vehicle Perception: a Survey | Deep learning-based intelligent vehicle perception has been developing prominently in recent years to provide a reliable source for motion planning and decision making in autonomous driving. A large number of powerful deep learning-based methods can achieve excellent performance in solving various perception problems o... | ['Hongkai Yu', 'Tianyun Zhang', 'Zhigang Xu', 'Huiming Sun', 'Jin Ma', 'Jinlong Li', 'Xinyu Liu'] | 2023-06-26 | null | null | null | null | ['transfer-learning', 'decision-making', 'motion-planning'] | ['miscellaneous', 'reasoning', 'robots'] | [-6.67849630e-02 -1.16511479e-01 -1.95861638e-01 -6.53145611e-01
-5.95612347e-01 -7.02959672e-02 5.60666323e-01 -2.56511420e-01
-4.36210126e-01 7.62874246e-01 -3.59904975e-01 -4.78630334e-01
3.90808471e-02 -1.10788727e+00 -8.97178590e-01 -7.64856398e-01
2.02238321e-01 5.05294323e-01 5.68358660e-01 -6.81122363... | [5.540101528167725, 0.9093061089515686] |
b5368b8c-bf7d-428d-bf57-8810d3342393 | fvp-fourier-visual-prompting-for-source-free | 2304.13672 | null | https://arxiv.org/abs/2304.13672v1 | https://arxiv.org/pdf/2304.13672v1.pdf | FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation | Medical image segmentation methods normally perform poorly when there is a domain shift between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the domain shift problem by training the model using both labeled data from the source domain and unlabeled data from the target domain. Source-Free U... | ['Haogang Zhu', 'Tao Liu', 'Zhenzhou Wu', 'Lanyun Zhu', 'Shuai Shao', 'Yixin Chen', 'Jian Cheng', 'Yan Wang'] | 2023-04-26 | null | null | null | null | ['visual-prompting'] | ['computer-vision'] | [ 6.32511735e-01 5.33506930e-01 -4.87821877e-01 -5.47038317e-01
-8.24131191e-01 -4.71489698e-01 1.67944446e-01 1.79823786e-01
-4.73545402e-01 3.68291527e-01 -2.79377755e-02 -2.59052813e-01
1.14672661e-01 -7.19815135e-01 -7.92839408e-01 -7.06617475e-01
1.63794458e-01 7.15244114e-01 3.44875067e-01 5.36358356... | [14.5526704788208, -1.9747159481048584] |
c70952b6-95a3-4d63-9a7e-a1b746553d38 | layerdiffusion-layered-controlled-image | 2305.18676 | null | https://arxiv.org/abs/2305.18676v1 | https://arxiv.org/pdf/2305.18676v1.pdf | LayerDiffusion: Layered Controlled Image Editing with Diffusion Models | Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining consistency between the subject and the background remains challenging. In this pa... | ['Zhiheng Li', 'Yikang Ding', 'QInxuan Huang', 'Pengzhi Li'] | 2023-05-30 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 8.39438438e-01 -3.82713750e-02 9.70236212e-02 -3.49647552e-01
-5.17968237e-01 -6.19476497e-01 7.02804804e-01 4.80911061e-02
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5.21401882e-01 2.58929819e-01 4.05554563e-01 -9.05366987... | [11.38927936553955, -0.5487061142921448] |
1ab77d1a-a738-4ee0-a8da-d823abd2526e | transfer-learning-without-knowing | 2007.08714 | null | https://arxiv.org/abs/2007.08714v2 | https://arxiv.org/pdf/2007.08714v2.pdf | Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources | Current transfer learning methods are mainly based on finetuning a pretrained model with target-domain data. Motivated by the techniques from adversarial machine learning (ML) that are capable of manipulating the model prediction via data perturbations, in this paper we propose a novel approach, black-box adversarial r... | ['Tsung-Yi Ho', 'Pin-Yu Chen', 'Yun-Yun Tsai'] | 2020-07-17 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/3642-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/3642-Paper.pdf | icml-2020-1 | ['diabetic-retinopathy-detection'] | ['medical'] | [ 7.02683568e-01 6.19208515e-01 -1.94075018e-01 -6.15660325e-02
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2.77272463e-01 5.49851835e-01 -3.20667624e-02 -3.28950375... | [10.325899124145508, 2.8568108081817627] |
78466608-9367-4081-82df-4d48b0125f09 | proving-equivalence-between-complex | 2106.02452 | null | https://arxiv.org/abs/2106.02452v2 | https://arxiv.org/pdf/2106.02452v2.pdf | Proving Equivalence Between Complex Expressions Using Graph-to-Sequence Neural Models | We target the problem of provably computing the equivalence between two complex expression trees. To this end, we formalize the problem of equivalence between two such programs as finding a set of semantics-preserving rewrite rules from one into the other, such that after the rewrite the two programs are structurally i... | ['Louis-Noël Pouchet', 'Théo Barollet', 'Steve Kommrusch'] | 2021-06-01 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 5.55952847e-01 5.13815165e-01 -2.22125188e-01 -3.75958085e-01
-9.55244362e-01 -1.05737543e+00 2.22087339e-01 3.38468522e-01
1.42180458e-01 6.34590507e-01 -2.04246283e-01 -1.45134401e+00
2.62091875e-01 -1.41070414e+00 -1.40627015e+00 -1.11934235e-02
-3.40600222e-01 4.90655631e-01 2.23724514e-01 -3.28077734... | [8.783308029174805, 7.186458587646484] |
3ba6823f-2237-492d-a98f-d029684cfec0 | learning-spatial-features-from-audio-visual | 2307.04760 | null | https://arxiv.org/abs/2307.04760v1 | https://arxiv.org/pdf/2307.04760v1.pdf | Learning Spatial Features from Audio-Visual Correspondence in Egocentric Videos | We propose a self-supervised method for learning representations based on spatial audio-visual correspondences in egocentric videos. In particular, our method leverages a masked auto-encoding framework to synthesize masked binaural audio through the synergy of audio and vision, thereby learning useful spatial relations... | ['Kristen Grauman', 'Ziad Al-Halah', 'Sagnik Majumder'] | 2023-07-10 | null | null | null | null | ['audio-denoising', 'denoising'] | ['audio', 'computer-vision'] | [-8.12261477e-02 9.51213948e-03 7.21901879e-02 -4.29595858e-01
-1.27351582e+00 -6.32439673e-01 8.62491012e-01 -4.77460951e-01
-9.58159119e-02 3.54295075e-01 1.14888251e+00 3.80529910e-01
5.30419089e-02 -3.04379910e-01 -1.13352203e+00 -4.72656101e-01
-3.13403219e-01 -2.14818999e-01 -1.47936448e-01 2.68563535... | [8.478352546691895, 0.6400660276412964] |
6cd0aac3-c685-4d21-a836-f520263eaea8 | wind-turbine-gearbox-fault-detection-based-on | 2303.03496 | null | https://arxiv.org/abs/2303.03496v1 | https://arxiv.org/pdf/2303.03496v1.pdf | Wind Turbine Gearbox Fault Detection Based on Sparse Filtering and Graph Neural Networks | The wind energy industry has been experiencing tremendous growth and confronting the failures of wind turbine components. Wind turbine gearbox malfunctions are particularly prevalent and lead to the most prolonged downtime and highest cost. This paper presents a data-driven gearbox fault detection algorithm base on hig... | ['Kenneth A. Loparo', 'Jinsong Wang'] | 2023-03-06 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-2.43943855e-02 -3.46029341e-01 2.44206280e-01 1.42963082e-01
4.88856941e-01 9.99351442e-02 -1.58941045e-01 -4.32805717e-01
1.34861350e-01 6.35537565e-01 4.36064936e-02 2.39422098e-02
-7.88384199e-01 -1.19888914e+00 -1.95575744e-01 -6.66023254e-01
-4.09949213e-01 3.03694665e-01 2.07427174e-01 -4.52355295... | [6.666131019592285, 2.370184898376465] |
e35b1cf7-04ac-462d-8f92-0b656abf940d | task-driven-semantic-coding-via-reinforcement | 2106.03511 | null | https://arxiv.org/abs/2106.03511v1 | https://arxiv.org/pdf/2106.03511v1.pdf | Task-driven Semantic Coding via Reinforcement Learning | Task-driven semantic video/image coding has drawn considerable attention with the development of intelligent media applications, such as license plate detection, face detection, and medical diagnosis, which focuses on maintaining the semantic information of videos/images. Deep neural network (DNN)-based codecs have bee... | ['Zhibo Chen', 'Jun Shi', 'Xin Li'] | 2021-06-07 | null | null | null | null | ['license-plate-detection'] | ['computer-vision'] | [ 4.36590493e-01 -2.71042198e-01 -2.10555702e-01 -3.98448765e-01
-6.34197831e-01 -2.76574045e-02 2.53899336e-01 -2.82693923e-01
-4.71334636e-01 5.20600200e-01 -9.49713122e-03 -1.52075201e-01
-1.20389774e-01 -6.63540006e-01 -5.20138741e-01 -8.45731735e-01
1.93796784e-01 6.60678893e-02 3.17016900e-01 5.51558891... | [11.285341262817383, -1.6432334184646606] |
863e69e5-f7dc-45be-aabe-4dc8fa3ca07b | commonsense-knowledge-augmented-pretrained-1 | 2112.08615 | null | https://arxiv.org/abs/2112.08615v3 | https://arxiv.org/pdf/2112.08615v3.pdf | Knowledge-Augmented Language Models for Cause-Effect Relation Classification | Previous studies have shown the efficacy of knowledge augmentation methods in pretrained language models. However, these methods behave differently across domains and downstream tasks. In this work, we investigate the augmentation of pretrained language models with commonsense knowledge in the cause-effect relation cla... | ['Mona Diab', 'David A. Broniatowski', 'Pedram Hosseini'] | 2021-12-16 | null | https://aclanthology.org/2022.csrr-1.6 | https://aclanthology.org/2022.csrr-1.6.pdf | csrr-acl-2022-5 | ['commonsense-causal-reasoning', 'relation-classification', 'cause-effect-relation-classification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.97917211e-01 4.55814153e-01 -4.65072334e-01 -3.94819200e-01
-4.30676341e-01 -5.58538914e-01 1.06694555e+00 3.43828171e-01
-3.77695054e-01 9.93607461e-01 7.13912964e-01 -4.96533126e-01
-2.94237763e-01 -1.03375435e+00 -7.57033050e-01 6.78849593e-02
1.43155515e-01 8.22311997e-01 2.44435012e-01 -6.54171169... | [10.000161170959473, 8.081756591796875] |
38bfc511-f46e-4488-aa05-331a013e25dc | simpler-but-more-accurate-semantic-dependency | 1807.01396 | null | http://arxiv.org/abs/1807.01396v1 | http://arxiv.org/pdf/1807.01396v1.pdf | Simpler but More Accurate Semantic Dependency Parsing | While syntactic dependency annotations concentrate on the surface or
functional structure of a sentence, semantic dependency annotations aim to
capture between-word relationships that are more closely related to the meaning
of a sentence, using graph-structured representations. We extend the LSTM-based
syntactic parser... | ['Timothy Dozat', 'Christopher D. Manning'] | 2018-07-03 | simpler-but-more-accurate-semantic-dependency-1 | https://aclanthology.org/P18-2077 | https://aclanthology.org/P18-2077.pdf | acl-2018-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 2.01649934e-01 9.92075264e-01 -3.21805000e-01 -6.32369995e-01
-5.80264270e-01 -6.42052770e-01 6.96445465e-01 6.62921011e-01
-4.33414102e-01 7.14534760e-01 6.07913196e-01 -5.66763699e-01
1.27625726e-02 -9.62272942e-01 -6.94061279e-01 -1.41927063e-01
-2.33494610e-01 2.35301465e-01 2.72791386e-01 -3.33960950... | [10.404014587402344, 9.335216522216797] |
827d6973-cc72-4039-8e93-ddc2ab82574d | prod-prompting-to-disentangle-domain | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ma_ProD_Prompting-To-Disentangle_Domain_Knowledge_for_Cross-Domain_Few-Shot_Image_Classification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ma_ProD_Prompting-To-Disentangle_Domain_Knowledge_for_Cross-Domain_Few-Shot_Image_Classification_CVPR_2023_paper.pdf | ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification | This paper considers few-shot image classification under the cross-domain scenario, where the train-to-test domain gap compromises classification accuracy. To mitigate the domain gap, we propose a prompting-to-disentangle (ProD) method through a novel exploration with the prompting mechanism. ProD adopts the popula... | ['Yi Yang', 'Zongxin Yang', 'Yifan Sun', 'Tianyi Ma'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['cross-domain-few-shot', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 2.48299956e-01 5.96344098e-02 -5.25419056e-01 -3.70046288e-01
-9.25836325e-01 -8.33214939e-01 7.13329732e-01 2.56294049e-02
-3.23783994e-01 7.72025287e-01 1.82396155e-02 -2.29218051e-01
-1.29352540e-01 -8.73720646e-01 -7.35459507e-01 -7.82926500e-01
1.95288181e-01 2.70107180e-01 4.91799474e-01 -2.00482145... | [10.139840126037598, 3.0991392135620117] |
d5a94e3d-20f2-4ad6-8f75-cd4d5384d078 | deep-representations-for-iris-face-and | 1410.1980 | null | https://arxiv.org/abs/1410.1980v3 | https://arxiv.org/pdf/1410.1980v3.pdf | Deep Representations for Iris, Face, and Fingerprint Spoofing Detection | Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or "spoofed") and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge,... | ['Alexandre Xavier Falcao', 'Helio Pedrini', 'Giovani Chiachia', 'Anderson Rocha', 'Allan Pinto', 'William Robson Schwartz', 'David Menotti'] | 2014-10-08 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 5.75397611e-01 -1.24538675e-01 -6.90312684e-03 -3.87633115e-01
1.95961725e-02 -6.45637453e-01 7.52309799e-01 -7.98092186e-02
-3.61352175e-01 5.13044775e-01 -1.80139810e-01 -5.35044789e-01
-7.90304840e-02 -8.12545180e-01 -5.22651911e-01 -6.89081967e-01
2.97256783e-02 1.80160433e-01 -1.00718871e-01 -4.38539445... | [12.909743309020996, 1.1804579496383667] |
e47439b5-37de-4d6d-b015-14a1e8b2832c | cesi-canonicalizing-open-knowledge-bases | 1902.00172 | null | http://arxiv.org/abs/1902.00172v1 | http://arxiv.org/pdf/1902.00172v1.pdf | CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information | Open Information Extraction (OpenIE) methods extract (noun phrase, relation
phrase, noun phrase) triples from text, resulting in the construction of large
Open Knowledge Bases (Open KBs). The noun phrases (NPs) and relation phrases in
such Open KBs are not canonicalized, leading to the storage of redundant and
ambiguou... | ['Shikhar Vashishth', 'Prince Jain', 'Partha Talukdar'] | 2019-02-01 | null | null | null | null | ['open-knowledge-graph-canonicalization'] | ['knowledge-base'] | [-3.20082307e-01 5.23896575e-01 -4.26712424e-01 -8.64050537e-02
-7.66366482e-01 -9.17375982e-01 2.46106133e-01 4.00610089e-01
-5.33473849e-01 1.23144293e+00 6.43466771e-01 -2.10485578e-01
-2.86916256e-01 -9.45615232e-01 -6.79328799e-01 -3.99428457e-01
-1.29598841e-01 6.55336142e-01 1.12009756e-01 -2.05265179... | [9.112207412719727, 8.273355484008789] |
37660397-63ad-44f7-a174-ce83d93d4ac6 | readability-controllable-biomedical-document | 2210.04705 | null | https://arxiv.org/abs/2210.04705v3 | https://arxiv.org/pdf/2210.04705v3.pdf | Readability Controllable Biomedical Document Summarization | Different from general documents, it is recognised that the ease with which people can understand a biomedical text is eminently varied, owing to the highly technical nature of biomedical documents and the variance of readers' domain knowledge. However, existing biomedical document summarization systems have paid littl... | ['Sophia Ananiadou', 'Qianqian Xie', 'Zheheng Luo'] | 2022-10-10 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 7.07032502e-01 4.49264556e-01 -2.00396314e-01 -2.69056380e-01
-1.16276085e+00 -4.34867352e-01 7.65320241e-01 8.43111038e-01
-3.34876627e-01 9.95235920e-01 9.13614333e-01 -6.58731163e-02
-3.24930757e-01 -3.52117568e-01 -6.86154515e-02 -3.75354618e-01
4.22502428e-01 7.32333243e-01 -7.44118020e-02 -2.43064836... | [12.32364273071289, 9.502964973449707] |
e7fa2245-a3f1-4429-ae90-5c8823ec26fd | controllable-image-to-video-translation-a | 1808.02992 | null | http://arxiv.org/abs/1808.02992v1 | http://arxiv.org/pdf/1808.02992v1.pdf | Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation | The recent advances in deep learning have made it possible to generate
photo-realistic images by using neural networks and even to extrapolate video
frames from an input video clip. In this paper, for the sake of both furthering
this exploration and our own interest in a realistic application, we study
image-to-video t... | ['Boqing Gong', 'Chuang Gan', 'Lijie Fan', 'Junzhou Huang', 'Wenbing Huang'] | 2018-08-09 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [ 3.54136586e-01 2.78360397e-01 6.37779608e-02 -3.87056589e-01
-4.51775074e-01 -5.41601300e-01 4.98020142e-01 -8.31457555e-01
-3.01118702e-01 7.60317981e-01 -4.10972945e-02 -1.32978648e-01
4.57788289e-01 -6.19495749e-01 -1.06421947e+00 -5.69346070e-01
1.47552684e-01 1.88591313e-02 -1.77230626e-01 -3.28103632... | [12.886663436889648, -0.19506454467773438] |
b1ae6f9a-3ae9-4162-88c9-fe7b46d93ac5 | document-level-relation-extraction-with-3 | 2204.12679 | null | https://arxiv.org/abs/2204.12679v1 | https://arxiv.org/pdf/2204.12679v1.pdf | Document-Level Relation Extraction with Sentences Importance Estimation and Focusing | Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences. Recent studies typically represent the entire document by sequence- or graph-based models to predict the relations of all entity pairs. However, we find that such a model is not robust a... | ['Tiejun Zhao', 'Lili Mou', 'Kehai Chen', 'Wang Xu'] | 2022-04-27 | null | https://aclanthology.org/2022.naacl-main.212 | https://aclanthology.org/2022.naacl-main.212.pdf | naacl-2022-7 | ['dialog-relation-extraction', 'document-level-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.85705775e-01 3.89938235e-01 -3.13373297e-01 -3.87011200e-01
-7.17912555e-01 -5.34242034e-01 7.99199939e-01 6.00940406e-01
-3.08997929e-01 9.62290943e-01 4.12681907e-01 -3.29893649e-01
-4.07715917e-01 -8.10661077e-01 -6.11514449e-01 -2.11065318e-02
-1.57546848e-01 6.36034906e-01 5.95233381e-01 -4.57362264... | [9.362035751342773, 8.674942970275879] |
b78188ca-365b-4323-b31f-0fcc49f9cd96 | biosentvec-creating-sentence-embeddings-for | 1810.09302 | null | https://arxiv.org/abs/1810.09302v6 | https://arxiv.org/pdf/1810.09302v6.pdf | BioSentVec: creating sentence embeddings for biomedical texts | Sentence embeddings have become an essential part of today's natural language processing (NLP) systems, especially together advanced deep learning methods. Although pre-trained sentence encoders are available in the general domain, none exists for biomedical texts to date. In this work, we introduce BioSentVec: the fir... | ['Qingyu Chen', 'Yifan Peng', 'Zhiyong Lu'] | 2018-10-22 | null | null | null | null | ['sentence-embeddings-for-biomedical-texts', 'sentence-embeddings-for-biomedical-texts'] | ['methodology', 'natural-language-processing'] | [-4.97211665e-02 -7.13084713e-02 -3.36423904e-01 -2.16288447e-01
-7.71999836e-01 -6.15740642e-02 3.91480833e-01 1.05493450e+00
-8.54712129e-01 7.67540574e-01 9.30781543e-01 -3.30575645e-01
2.43285578e-02 -4.64216471e-01 -2.59936094e-01 -4.21006292e-01
-4.29591164e-02 5.40503919e-01 -1.57440200e-01 -3.15398574... | [8.521490097045898, 8.647064208984375] |
30bf47ec-49a8-4bc9-9c5a-fc7eee3e239f | an-energy-efficient-reconfigurable | 2301.07050 | null | https://arxiv.org/abs/2301.07050v1 | https://arxiv.org/pdf/2301.07050v1.pdf | An Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA | Autoencoders are unsupervised neural networks that are used to process and compress input data and then reconstruct the data back to the original data size. This allows autoencoders to be used for different processing applications such as data compression, image classification, image noise reduction, and image coloring... | ['Lifeng Zhou', 'Matthew Oldland', 'Murat Isik'] | 2023-01-17 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 5.30057028e-02 -2.93680549e-01 2.28922352e-01 -3.91754448e-01
5.35347819e-01 -5.32640442e-02 2.77967930e-01 2.34834298e-01
-7.71154165e-01 2.88860470e-01 1.65051952e-01 -5.47060251e-01
-4.59004147e-03 -1.10114741e+00 -5.44539690e-01 -7.70046771e-01
-8.50535780e-02 1.84057355e-02 3.24950784e-01 -2.49028578... | [8.359750747680664, 2.7493269443511963] |
6346fb68-4858-4248-9ae7-f3586c8f2421 | font-representation-learning-via-paired-glyph | 2211.10967 | null | https://arxiv.org/abs/2211.10967v1 | https://arxiv.org/pdf/2211.10967v1.pdf | Font Representation Learning via Paired-glyph Matching | Fonts can convey profound meanings of words in various forms of glyphs. Without typography knowledge, manually selecting an appropriate font or designing a new font is a tedious and painful task. To allow users to explore vast font styles and create new font styles, font retrieval and font style transfer methods have b... | ['Jin Young Choi', 'Kyuewang Lee', 'Junho Cho'] | 2022-11-20 | null | null | null | null | ['font-style-transfer'] | ['computer-vision'] | [ 2.40489483e-01 -2.82255381e-01 -8.51254463e-02 -5.10414004e-01
-5.84353387e-01 -9.72062528e-01 5.00532150e-01 -5.35651334e-02
2.46354192e-02 4.01699424e-01 4.51918900e-01 -3.08771282e-01
2.42030159e-01 -7.15643346e-01 -5.19520462e-01 -6.81345880e-01
5.45501649e-01 2.06928462e-01 -1.57639638e-01 -5.29203117... | [11.731823921203613, -0.25214868783950806] |
6ce10019-42dd-403e-858f-0bda197b2ba4 | robust-multi-agent-pickup-and-delivery-with | 2303.17422 | null | https://arxiv.org/abs/2303.17422v1 | https://arxiv.org/pdf/2303.17422v1.pdf | Robust Multi-Agent Pickup and Delivery with Delays | Multi-Agent Pickup and Delivery (MAPD) is the problem of computing collision-free paths for a group of agents such that they can safely reach delivery locations from pickup ones. These locations are provided at runtime, making MAPD a combination between classical Multi-Agent Path Finding (MAPF) and online task assignme... | ['Francesco Amigoni', 'Nicola Basilico', 'Giacomo Lodigiani'] | 2023-03-30 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-1.63956329e-01 2.41383910e-01 -2.39650644e-02 -2.56455261e-02
-6.83356583e-01 -7.71629453e-01 5.21515965e-01 9.08807814e-01
-6.61621988e-01 1.13364565e+00 -2.27434993e-01 -7.10822120e-02
-9.31721449e-01 -9.17902350e-01 -6.84554458e-01 -5.96764028e-01
-1.01096761e+00 1.33088362e+00 1.02751207e+00 -3.65489185... | [4.963886260986328, 1.7145761251449585] |
ee3a2a2c-6517-4b9b-99d2-30694df7007c | does-speech-enhancement-of-publicly-available | 1910.13488 | null | https://arxiv.org/abs/1910.13488v2 | https://arxiv.org/pdf/1910.13488v2.pdf | Does Speech enhancement of publicly available data help build robust Speech Recognition Systems? | Automatic speech recognition (ASR) systems play a key role in many commercial products including voice assistants. Typically, they require large amounts of clean speech data for training which gives an undue advantage to large organizations which have tons of private data. In this paper, we have first curated a fairly ... | ['Klaus Mueller', 'Buvana Ramanan', 'Bhavya Ghai'] | 2019-10-29 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.68780923e-01 -7.82055501e-03 3.58981520e-01 -4.67840821e-01
-1.34485614e+00 -4.32817012e-01 5.24439692e-01 -1.29454061e-01
-7.20134795e-01 7.56683171e-01 4.69975144e-01 -6.60599828e-01
1.05258077e-01 -3.43625277e-01 -3.55751306e-01 -8.09651554e-01
2.10191742e-01 3.28269303e-01 -1.63013637e-02 -6.26015306... | [14.50525951385498, 6.628256320953369] |
718196a7-d494-4aea-a62b-fc898e068591 | level-set-binocular-stereo-with-occlusions | 2109.03464 | null | https://arxiv.org/abs/2109.03464v1 | https://arxiv.org/pdf/2109.03464v1.pdf | Level Set Binocular Stereo with Occlusions | Localizing stereo boundaries and predicting nearby disparities are difficult because stereo boundaries induce occluded regions where matching cues are absent. Most modern computer vision algorithms treat occlusions secondarily (e.g., via left-right consistency checks after matching) or rely on high-level cues to improv... | ['Todd Zickler', 'Jialiang Wang'] | 2021-09-08 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 3.06751668e-01 2.19913498e-01 -1.41948551e-01 -7.22150981e-01
-6.03168309e-01 -5.65884650e-01 3.27127963e-01 3.00572813e-01
-3.02590787e-01 6.17565989e-01 6.00332022e-01 -1.76025942e-01
1.85854882e-01 -9.42017317e-01 -9.40919459e-01 -5.07675707e-01
-2.67343689e-02 3.21125835e-01 6.84217751e-01 -4.19726849... | [8.896578788757324, -2.442319393157959] |
9e2e9dee-4568-4690-b467-82fd1f459ee5 | sicknl-a-dataset-for-dutch-natural-language | 2101.05716 | null | https://arxiv.org/abs/2101.05716v1 | https://arxiv.org/pdf/2101.05716v1.pdf | SICKNL: A Dataset for Dutch Natural Language Inference | We present SICK-NL (read: signal), a dataset targeting Natural Language Inference in Dutch. SICK-NL is obtained by translating the SICK dataset of Marelli et al. (2014)from English into Dutch. Having a parallel inference dataset allows us to compare both monolingual and multilingual NLP models for English and Dutch on ... | ['Michael Moortgat', 'Gijs Wijnholds'] | 2021-01-14 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [ 9.36996564e-02 3.48730087e-01 -3.59924167e-01 -5.48575997e-01
-7.15179145e-01 -1.04002535e+00 7.11361349e-01 2.24399880e-01
-8.31243873e-01 8.75828445e-01 1.02838516e+00 -4.73612189e-01
2.11861387e-01 -5.68050504e-01 -7.09556758e-01 -1.82454720e-01
4.86169726e-01 8.40390265e-01 -6.23674169e-02 -4.72536594... | [10.859232902526855, 9.804659843444824] |
a83c38e5-4546-458b-a12e-9ee17483d0cc | do-sentence-interactions-matter-leveraging | 1910.12203 | null | https://arxiv.org/abs/1910.12203v1 | https://arxiv.org/pdf/1910.12203v1.pdf | Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification | The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles. Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a d... | ['Vaibhav Vaibhav', 'Raghuram Mandyam Annasamy', 'Eduard Hovy'] | 2019-10-27 | do-sentence-interactions-matter-leveraging-1 | https://aclanthology.org/D19-5316 | https://aclanthology.org/D19-5316.pdf | ws-2019-11 | ['news-classification'] | ['natural-language-processing'] | [-3.34522218e-01 1.76510826e-01 -5.78704298e-01 -3.18600029e-01
-5.26929080e-01 -6.62763834e-01 1.15537429e+00 3.62997502e-01
7.61757642e-02 5.68875372e-01 8.51336002e-01 -5.52281141e-01
2.61146814e-01 -8.75493050e-01 -1.04774570e+00 -9.25463885e-02
1.20556377e-01 4.28256899e-01 4.22344476e-01 -9.06029105... | [8.161855697631836, 10.258249282836914] |
2764bfdb-e8aa-49b8-8402-4ef60804386b | visual-aware-hierarchy-based-food-recognition | 2012.03368 | null | https://arxiv.org/abs/2012.03368v1 | https://arxiv.org/pdf/2012.03368v1.pdf | Visual Aware Hierarchy Based Food Recognition | Food recognition is one of the most important components in image-based dietary assessment. However, due to the different complexity level of food images and inter-class similarity of food categories, it is challenging for an image-based food recognition system to achieve high accuracy for a variety of publicly availab... | ['Fengqing Zhu', 'Sri Kalyan Yarlagadda', 'Zeman Shao', 'Jiangpeng He', 'Runyu Mao'] | 2020-12-06 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 1.03906095e-02 -3.83557677e-01 -4.03635293e-01 -5.99718273e-01
-4.21312183e-01 -7.02625215e-01 4.48556319e-02 8.98951769e-01
-2.47501791e-01 9.50241014e-02 4.52256441e-01 1.01658210e-01
1.57953069e-01 -1.15253901e+00 -9.11738157e-01 -5.50905943e-01
-2.21042588e-01 8.19125473e-02 8.00973326e-02 -1.02719143... | [11.55958080291748, 4.390114784240723] |
a0b2d197-2791-48d1-a79d-3781b46de906 | link-prediction-for-egocentrically-sampled | 1803.04084 | null | http://arxiv.org/abs/1803.04084v1 | http://arxiv.org/pdf/1803.04084v1.pdf | Link prediction for egocentrically sampled networks | Link prediction in networks is typically accomplished by estimating or
ranking the probabilities of edges for all pairs of nodes. In practice,
especially for social networks, the data are often collected by egocentric
sampling, which means selecting a subset of nodes and recording all of their
edges. This sampling mech... | ['Yun-Jhong Wu', 'Ji Zhu', 'Elizaveta Levina'] | 2018-03-12 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [-1.50851220e-01 6.33082211e-01 -7.17499435e-01 -1.76139921e-01
8.21297467e-02 -4.13980097e-01 5.47818542e-01 3.12292099e-01
-3.42715271e-02 1.06885839e+00 2.57366061e-01 -1.82197630e-01
-6.29936099e-01 -1.38276851e+00 -5.50935149e-01 -2.60232538e-01
-8.06375623e-01 1.16649926e+00 4.35424477e-01 1.81140453... | [7.003965854644775, 5.481724739074707] |
26a4f94f-0fe5-4f42-a151-90f67a365ee7 | playing-to-learn-better-repeated-games-for | 2002.03924 | null | https://arxiv.org/abs/2002.03924v1 | https://arxiv.org/pdf/2002.03924v1.pdf | Playing to Learn Better: Repeated Games for Adversarial Learning with Multiple Classifiers | We consider the problem of prediction by a machine learning algorithm, called learner, within an adversarial learning setting. The learner's task is to correctly predict the class of data passed to it as a query. However, along with queries containing clean data, the learner could also receive malicious or adversarial ... | ['Michael McCarrick', 'Joseph B. Collins', 'Prithviraj Dasgupta'] | 2020-02-10 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 2.56361574e-01 6.42435133e-01 3.39851230e-01 -2.54804313e-01
-1.23399901e+00 -1.08274603e+00 4.54618663e-01 1.74941495e-01
-5.84438622e-01 4.86029536e-01 -1.27758101e-01 -3.42810363e-01
-8.41508582e-02 -1.30023813e+00 -9.18900132e-01 -8.42187405e-01
1.09653577e-01 8.54582429e-01 2.77737170e-01 -3.43783975... | [5.862133979797363, 7.706694602966309] |
0ba108dd-14fb-4d0e-b650-baee819cfc5e | preference-or-intent-double-disentangled | 2305.11084 | null | https://arxiv.org/abs/2305.11084v1 | https://arxiv.org/pdf/2305.11084v1.pdf | Preference or Intent? Double Disentangled Collaborative Filtering | People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both intent and preference factors are usually entangled in the modeling process, which significantly limits the robustness and interpretability ... | ['Hui Xiong', 'Wei Wu', 'Dazhong Shen', 'HengShu Zhu', 'Chao Wang'] | 2023-05-18 | null | null | null | null | ['disentanglement', 'collaborative-filtering', 'intent-recognition'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-2.38474205e-01 -3.92824084e-01 -4.67809498e-01 -6.30543768e-01
-5.96182235e-02 -4.45691615e-01 2.65415817e-01 -4.22453396e-02
-1.77187011e-01 3.89687270e-01 7.91271389e-01 -1.97214052e-01
-3.79758894e-01 -7.79743731e-01 -2.10597515e-01 -8.87101352e-01
2.85857409e-01 3.30631793e-01 -3.96546930e-01 -1.90605074... | [10.105376243591309, 5.5485758781433105] |
26fe9e3e-e2bf-413a-bc3c-f4341e568db9 | sim-suction-learning-a-suction-grasp-policy | 2305.16378 | null | https://arxiv.org/abs/2305.16378v1 | https://arxiv.org/pdf/2305.16378v1.pdf | Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark | This paper presents Sim-Suction, a robust object-aware suction grasp policy for mobile manipulation platforms with dynamic camera viewpoints, designed to pick up unknown objects from cluttered environments. Suction grasp policies typically employ data-driven approaches, necessitating large-scale, accurately-annotated s... | ['David J. Cappelleri', 'Juncheng Li'] | 2023-05-25 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-6.01845570e-02 -2.17612714e-01 -7.06764087e-02 -1.24588490e-01
-6.62010193e-01 -8.49857450e-01 -1.70743272e-01 -1.64186001e-01
-1.68950841e-01 4.10058886e-01 -3.25417340e-01 -1.06648043e-01
-2.90763229e-01 -3.84717435e-01 -1.13860309e+00 -6.48997247e-01
-4.68448460e-01 9.12543058e-01 6.14387989e-01 -3.46507579... | [5.788741111755371, -0.8714131116867065] |
1c92aa60-33df-4776-b65e-f8c7560469e2 | multiqg-ti-towards-question-generation-from | 2307.04643 | null | https://arxiv.org/abs/2307.04643v1 | https://arxiv.org/pdf/2307.04643v1.pdf | MultiQG-TI: Towards Question Generation from Multi-modal Sources | We study the new problem of automatic question generation (QG) from multi-modal sources containing images and texts, significantly expanding the scope of most of the existing work that focuses exclusively on QG from only textual sources. We propose a simple solution for our new problem, called MultiQG-TI, which enables... | ['Richard Baraniuk', 'Zichao Wang'] | 2023-07-07 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 4.15501058e-01 2.31433421e-01 4.65409428e-01 -5.97974248e-02
-1.55745447e+00 -1.09791028e+00 8.65009844e-01 -9.73652676e-02
-4.25980866e-01 4.06503439e-01 2.05020830e-01 -5.34869671e-01
2.67990589e-01 -6.24308467e-01 -9.30215120e-01 -5.17284095e-01
7.15019882e-01 5.45792282e-01 3.15388292e-01 -2.10583031... | [10.994093894958496, 1.442002534866333] |
0b19f65c-d369-47cd-a521-3731844aafd7 | a-new-approach-to-descriptors-generation-for | 2007.06624 | null | https://arxiv.org/abs/2007.06624v1 | https://arxiv.org/pdf/2007.06624v1.pdf | A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers | In this paper, we consider the problem of descriptors construction for the task of content-based image retrieval using deep neural networks. The idea of neural codes, based on fully connected layers activations, is extended by incorporating the information contained in convolutional layers. It is known that the total n... | ['Paweł Staszewski', 'Maciej Jaworski', 'Jinde Cao', 'Leszek Rutkowski'] | 2020-07-13 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 6.08043857e-02 -2.91469783e-01 -1.99390665e-01 -3.65879029e-01
-1.38308123e-01 -2.83881456e-01 7.16102183e-01 4.69076633e-01
-7.35367715e-01 4.69916493e-01 -1.92454234e-02 3.27725291e-01
-4.16602463e-01 -1.10366929e+00 -6.13045752e-01 -7.06749260e-01
-9.51503739e-02 2.98980493e-02 3.50502253e-01 -3.45983595... | [10.699698448181152, 0.3593747615814209] |
098c90c7-03c2-4f66-abcb-226d98fb7994 | the-finsim-2020-shared-task-learning-semantic | null | null | https://aclanthology.org/2020.finnlp-1.13 | https://aclanthology.org/2020.finnlp-1.13.pdf | The FinSim 2020 Shared Task: Learning Semantic Representations for the Financial Domain | null | ['Dialekti Valsamou-Stanislawski', 'Virginie Mouilleron', 'Youness Mansar', 'Ismail El Maarouf'] | null | null | null | null | finnlp-coling-2020-1 | ['learning-semantic-representations'] | ['methodology'] | [-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... | [-7.452961444854736, 3.6884958744049072] |
c1f96225-fae3-4e66-93bd-b499f37ad450 | privacy-attacks-against-biometric-models-with | 2209.11020 | null | https://arxiv.org/abs/2209.11020v1 | https://arxiv.org/pdf/2209.11020v1.pdf | Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models | Authentication systems are vulnerable to model inversion attacks where an adversary is able to approximate the inverse of a target machine learning model. Biometric models are a prime candidate for this type of attack. This is because inverting a biometric model allows the attacker to produce a realistic biometric inpu... | ['Kaleel Mahmood', 'Benjamin Fuller', 'Sohaib Ahmad'] | 2022-09-22 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 8.00842822e-01 1.74921945e-01 -1.08006135e-01 -1.22891091e-01
-1.87320039e-01 -8.24914336e-01 4.11819279e-01 -1.91883907e-01
-5.71369171e-01 4.53222305e-01 -6.20279491e-01 -6.43415570e-01
-7.81160593e-02 -8.25636089e-01 -7.99548507e-01 -6.22549236e-01
3.04167233e-02 3.38691622e-01 -1.99343458e-01 -8.78044143... | [12.952555656433105, 1.0963226556777954] |
402c742f-c7e9-4386-83dd-1b3142e70a03 | a-pose-only-solution-to-visual-reconstruction | 2103.01530 | null | https://arxiv.org/abs/2103.01530v3 | https://arxiv.org/pdf/2103.01530v3.pdf | A Pose-only Solution to Visual Reconstruction and Navigation | Visual navigation and three-dimensional (3D) scene reconstruction are essential for robotics to interact with the surrounding environment. Large-scale scenes and critical camera motions are great challenges facing the research community to achieve this goal. We raised a pose-only imaging geometry framework and algorith... | ['Dewen Hu', 'Wenxian Yu', 'Yuanxin Wu', 'Lilian Zhang', 'Qi Cai'] | 2021-03-02 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [-1.19919600e-02 -3.53226870e-01 -1.61512401e-02 -7.96060637e-02
-3.24670434e-01 -8.88200879e-01 4.47517812e-01 -4.82576191e-01
-5.05718470e-01 3.68461192e-01 -3.23052257e-01 -3.43366504e-01
2.42433734e-02 -3.80614042e-01 -5.83692014e-01 -6.79547906e-01
2.61697233e-01 3.93046826e-01 4.53766376e-01 -1.33528590... | [7.758023738861084, -2.2960288524627686] |
40ed8f31-a5a9-4045-8483-47b0c86b22f5 | choquet-integral-and-coalition-game-based | null | null | https://ieeexplore.ieee.org/document/9534669 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9534669 | Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray Images | Under the present circumstances, when we are still under the threat of different strains of coronavirus, and since the most widely used method for COVID-19 detection, RT-PCR is a tedious and time-consuming manual procedure with poor precision, the application of Artificial Intelligence (AI) and Computer-Aided Diagnosis... | ['Ram Sarkar', 'Jin Hee Yoon Zong Woo Geem', 'Subhankar Sen', 'Pratik Bhowal'] | 2021-09-09 | null | null | null | ieee-journal-of-biomedical-and-health-1 | ['covid-19-detection'] | ['medical'] | [ 2.26877164e-02 -4.33354616e-01 1.24217525e-01 -2.72124618e-01
-4.69597131e-01 -6.35668874e-01 2.68698603e-01 4.04915810e-01
-5.55492520e-01 7.66551852e-01 -3.42269987e-01 -4.06104624e-01
-7.63832092e-01 -9.75396216e-01 -2.30395868e-01 -7.73174047e-01
-2.06621036e-01 7.11191714e-01 -1.80753946e-01 -3.88798773... | [15.553349494934082, -1.7275340557098389] |
ea61d6dd-cf1b-4f87-8fbd-34561896b89f | bayesian-aggregation-improves-traditional | 2004.03468 | null | https://arxiv.org/abs/2004.03468v1 | https://arxiv.org/pdf/2004.03468v1.pdf | Bayesian aggregation improves traditional single image crop classification approaches | Machine learning (ML) methods and neural networks (NN) are widely implemented for crop types recognition and classification based on satellite images. However, most of these studies use several multi-temporal images which could be inapplicable for cloudy regions. We present a comparison between the classical ML approac... | ['Maria Pukalchik', 'Anna Petrovskaia', 'Mikhail Gasanov', 'Raghavendra Belur Jana', 'Ivan Oseledets', 'Ivan Matvienko'] | 2020-04-07 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 2.65088737e-01 -2.31183872e-01 -4.00983661e-01 -4.18070495e-01
-6.21557951e-01 -5.99497855e-01 5.37488580e-01 6.07557833e-01
-4.09323007e-01 1.09547472e+00 -4.55448419e-01 -7.94248819e-01
-1.41117647e-01 -1.34482455e+00 -7.45360136e-01 -1.05250919e+00
-3.22020113e-01 -3.54823731e-02 1.72054961e-01 -7.30915442... | [9.406697273254395, -1.579268217086792] |
c1eb630f-a1ec-47cf-8a19-b52fce260916 | adaptive-knowledge-distillation-between-text | 2303.03600 | null | https://arxiv.org/abs/2303.03600v1 | https://arxiv.org/pdf/2303.03600v1.pdf | Adaptive Knowledge Distillation between Text and Speech Pre-trained Models | Learning on a massive amount of speech corpus leads to the recent success of many self-supervised speech models. With knowledge distillation, these models may also benefit from the knowledge encoded by language models that are pre-trained on rich sources of texts. The distillation process, however, is challenging due t... | ['Erik Cambria', 'Bin Ma', 'Chong Zhang', 'Trung Hieu Nguyen', 'Han Lei', 'Dianwen Ng', 'Qian Chen', 'Wen Wang', 'Yukun Ma', 'Jinjie Ni'] | 2023-03-07 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 1.74919903e-01 6.42563403e-01 -1.34237483e-01 -7.06554174e-01
-7.37057686e-01 -5.59678972e-01 9.11262095e-01 2.41913825e-01
-6.58500016e-01 5.64205468e-01 8.86917889e-01 -4.12202984e-01
9.46138576e-02 -6.03359878e-01 -6.22008920e-01 -2.94489264e-01
1.82045132e-01 8.18887413e-01 9.46998820e-02 -3.19614321... | [14.032645225524902, 6.975203514099121] |
76de74aa-a8f3-4de9-b3b5-03aa37d77a16 | s3e-gnn-sparse-spatial-scene-embedding-with | 2205.05861 | null | https://arxiv.org/abs/2205.05861v1 | https://arxiv.org/pdf/2205.05861v1.pdf | S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization | Camera relocalization is the key component of simultaneous localization and mapping (SLAM) systems. This paper proposes a learning-based approach, named Sparse Spatial Scene Embedding with Graph Neural Networks (S3E-GNN), as an end-to-end framework for efficient and robust camera relocalization. S3E-GNN consists of two... | ['Tao Sun', 'Lige Liu', 'Yuan Chen', 'Xinyu Jiang', 'Ran Cheng'] | 2022-05-12 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 5.49056269e-02 -1.26007468e-01 -2.08463416e-01 -4.97126758e-01
-5.99563956e-01 -4.32620823e-01 5.34184635e-01 -5.98508678e-02
-3.46006304e-01 2.57097930e-01 4.20999348e-01 -9.39158127e-02
-2.35122830e-01 -7.68683136e-01 -9.35408771e-01 -3.55630606e-01
8.68798643e-02 3.44816715e-01 1.27148315e-01 -2.63265725... | [7.552641868591309, -2.166398286819458] |
6436d44d-eea3-4b65-b53f-a489bc12ed51 | benchmarking-large-language-model | 2306.16793 | null | https://arxiv.org/abs/2306.16793v1 | https://arxiv.org/pdf/2306.16793v1.pdf | Benchmarking Large Language Model Capabilities for Conditional Generation | Pre-trained large language models (PLMs) underlie most new developments in natural language processing. They have shifted the field from application-specific model pipelines to a single model that is adapted to a wide range of tasks. Autoregressive PLMs like GPT-3 or PaLM, alongside techniques like few-shot learning, h... | ['Sebastian Gehrmann', 'Priyanka Agrawal', 'Joshua Maynez'] | 2023-06-29 | null | null | null | null | ['few-shot-learning', 'benchmarking', 'text-generation', 'benchmarking'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'robots'] | [ 3.17677617e-01 7.85486922e-02 -1.40651092e-02 -2.39447743e-01
-8.82344127e-01 -6.51967406e-01 1.22858453e+00 1.65257931e-01
-3.22922468e-01 4.84681278e-01 3.66435677e-01 -3.48502994e-01
8.98054168e-02 -9.33774650e-01 -2.97230124e-01 -2.15641961e-01
2.66628832e-01 8.06542337e-01 2.41898149e-02 -6.52862549... | [11.527281761169434, 8.972618103027344] |
99620f25-8d39-4cad-9d26-c73f58d49acf | phrase-based-unsupervised-machine-translation | null | null | https://aclanthology.org/W18-6407 | https://aclanthology.org/W18-6407.pdf | Phrase-based Unsupervised Machine Translation with Compositional Phrase Embeddings | This paper describes the University of Tartu{'}s submission to the unsupervised machine translation track of WMT18 news translation shared task. We build several baseline translation systems for both directions of the English-Estonian language pair using monolingual data only; the systems belong to the phrase-based uns... | ['Andre T{\\"a}ttar', 'Maksym Del', 'Mark Fishel'] | 2018-10-01 | null | null | null | ws-2018-10 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.52085233e-01 3.30861062e-02 -5.93825579e-01 -2.74181634e-01
-1.18959367e+00 -8.79691303e-01 1.03272903e+00 -1.91920670e-03
-7.88019538e-01 1.16459882e+00 6.22249186e-01 -1.05738962e+00
2.41375104e-01 -2.19268754e-01 -4.97202814e-01 -5.48237145e-01
3.00659567e-01 1.17335153e+00 1.57938693e-02 -6.46776319... | [11.509159088134766, 10.339803695678711] |
b162552c-85ca-4709-bea6-251761fed970 | soliton-crystal-kerr-microcombs-for-high | 2101.12356 | null | https://arxiv.org/abs/2101.12356v1 | https://arxiv.org/pdf/2101.12356v1.pdf | Soliton crystal Kerr microcombs for high-speed, scalable optical neural networks at 10 GigaOPs/s | Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds, demonstrating the building block of the ONN, a... | ['David J. Moss', 'Mengxi Tan', 'Xingyuan Xu'] | 2021-01-29 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.68386358e-01 6.67106882e-02 4.05198820e-02 2.79645860e-01
3.37989181e-01 -2.26080000e-01 1.39431641e-01 -2.44558603e-01
-9.95871723e-01 7.29091167e-01 -7.64286101e-01 -6.24620318e-01
-1.00003324e-01 -8.68419707e-01 -3.81656080e-01 -9.48447406e-01
-5.66836447e-02 1.68838859e-01 3.02065551e-01 1.81768704... | [8.252399444580078, 2.579082489013672] |
05ef5169-cf94-4820-9f09-2a6c338d01ec | a-simple-riemannian-manifold-network-for | 1805.10628 | null | http://arxiv.org/abs/1805.10628v2 | http://arxiv.org/pdf/1805.10628v2.pdf | A Simple Riemannian Manifold Network for Image Set Classification | In the domain of image-set based classification, a considerable advance has
been made by representing original image sets as covariance matrices which
typical lie in a Riemannian manifold. Specifically, it is a Symmetric Positive
Definite (SPD) manifold. Traditional manifold learning methods inevitably have
the propert... | ['Xiao-Jun Wu', 'Rui Wang', 'Josef Kittler'] | 2018-05-27 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 8.34086090e-02 -2.19738513e-01 2.22112820e-01 -4.34051722e-01
-2.26100072e-01 -1.64805219e-01 3.89288574e-01 -5.97241402e-01
-5.03709733e-01 3.18671525e-01 -2.80519962e-01 -1.43790975e-01
-3.97394359e-01 -7.07935214e-01 -6.32525444e-01 -1.02421916e+00
-1.63276970e-01 -5.40339239e-02 8.83266795e-04 -8.48293826... | [7.9622697830200195, 3.9462456703186035] |
60f1742f-d8a8-4ca3-bff1-85b6478fc075 | llcaps-learning-to-illuminate-low-light | 2307.02452 | null | https://arxiv.org/abs/2307.02452v1 | https://arxiv.org/pdf/2307.02452v1.pdf | LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion | Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufacturing limitations, WCE vision signals may suffer from insufficient illumination, leading to a complicated screening and examination procedure... | ['Hongliang Ren', 'Mobarakol Islam', 'An Wang', 'Yanan Wu', 'Tong Chen', 'Long Bai'] | 2023-07-05 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 4.83299233e-02 -8.20122883e-02 -3.04509103e-02 2.70021390e-02
-6.08484089e-01 -1.94458216e-01 -1.87419821e-02 9.98932123e-03
-5.11236787e-01 2.37213075e-01 2.51951545e-01 -7.63583034e-02
-2.27374107e-01 -6.63498223e-01 -5.99481940e-01 -1.10794401e+00
-2.41201490e-01 -3.61497432e-01 2.48050615e-01 3.56964730... | [13.770379066467285, -2.6080355644226074] |
e80d22bf-e446-4e85-be43-afe973f0682a | action-recognition-in-untrimmed-videos-with | 1908.04353 | null | https://arxiv.org/abs/1908.04353v2 | https://arxiv.org/pdf/1908.04353v2.pdf | Action Recognition in Untrimmed Videos with Composite Self-Attention Two-Stream Framework | With the rapid development of deep learning algorithms, action recognition in video has achieved many important research results. One issue in action recognition, Zero-Shot Action Recognition (ZSAR), has recently attracted considerable attention, which classify new categories without any positive examples. Another diff... | ['Dong Cao', 'HaiBo Chen', 'Lisha Xu'] | 2019-08-04 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 4.83784407e-01 -3.18933874e-01 -3.85438740e-01 -2.22993214e-02
-3.39961022e-01 2.30987325e-01 2.01227382e-01 -3.89261931e-01
-2.04799861e-01 1.38501629e-01 4.94671643e-01 2.98359036e-01
-4.35251482e-02 -9.22987461e-01 -2.36617148e-01 -7.73395896e-01
1.44877017e-01 -1.94385827e-01 6.17848396e-01 1.34157734... | [8.415014266967773, 0.7645870447158813] |
f932fc96-9254-4cbc-896a-a403850a2617 | changing-data-sources-in-the-age-of-machine | 2306.04338 | null | https://arxiv.org/abs/2306.04338v1 | https://arxiv.org/pdf/2306.04338v1.pdf | Changing Data Sources in the Age of Machine Learning for Official Statistics | Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and in... | ['Michael Reusens', 'Cedric De Boom'] | 2023-06-07 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-6.33525178e-02 -1.23685226e-03 -5.41201949e-01 -3.81072044e-01
-7.38729417e-01 -8.37052107e-01 6.38395905e-01 9.80329752e-01
-7.66209900e-01 7.97563851e-01 8.10115755e-01 -7.48571396e-01
-3.17844540e-01 -6.39378726e-01 -8.10914278e-01 -4.67686236e-01
2.98714280e-01 1.82375088e-02 -3.71869415e-01 7.05759302... | [8.300558090209961, 5.969244003295898] |
88aadef2-c319-413b-b330-d458bbcc6e93 | who-is-mistaken | 1612.01175 | null | http://arxiv.org/abs/1612.01175v2 | http://arxiv.org/pdf/1612.01175v2.pdf | Who is Mistaken? | Recognizing when people have false beliefs is crucial for understanding their
actions. We introduce the novel problem of identifying when people in abstract
scenes have incorrect beliefs. We present a dataset of scenes, each visually
depicting an 8-frame story in which a character has a mistaken belief. We then
create ... | ['Carl Vondrick', 'Antonio Torralba', 'Benjamin Eysenbach'] | 2016-12-04 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 4.11318183e-01 5.20174325e-01 -1.78046346e-01 -7.34863579e-01
-3.52801144e-01 -3.41811568e-01 6.99477732e-01 3.41448039e-01
-3.65805745e-01 6.13993227e-01 8.01416278e-01 -1.29081517e-01
5.44814050e-01 -2.24019855e-01 -8.11428368e-01 -4.46070313e-01
4.21350032e-01 4.51172978e-01 1.41000614e-01 2.25444585... | [10.608596801757812, 1.4939615726470947] |
2a762487-c27d-443a-8485-024643d53f44 | detection-of-audio-video-synchronization | 2104.10116 | null | https://arxiv.org/abs/2104.10116v1 | https://arxiv.org/pdf/2104.10116v1.pdf | Detection of Audio-Video Synchronization Errors Via Event Detection | We present a new method and a large-scale database to detect audio-video synchronization(A/V sync) errors in tennis videos. A deep network is trained to detect the visual signature of the tennis ball being hit by the racquet in the video stream. Another deep network is trained to detect the auditory signature of the sa... | ['Zongyi Liu', 'Sriram Sethuraman', 'Hai Wei', 'Yongjun Wu', 'Joshua P. Ebenezer'] | 2021-04-20 | null | null | null | null | ['video-synchronization'] | ['computer-vision'] | [ 7.17375726e-02 -5.81751108e-01 7.72101283e-02 1.89801186e-01
-6.66652799e-01 -4.76805240e-01 -8.50803405e-02 3.50595504e-01
-5.67459941e-01 3.26685816e-01 1.35096848e-01 2.33804360e-01
4.22380157e-02 -5.16376078e-01 -1.07101548e+00 -4.53512460e-01
-5.04739106e-01 1.77410528e-01 6.96529925e-01 5.53542785... | [7.9580078125, 0.16271667182445526] |
dc5cdccc-9435-4ff9-abd6-33655f0f8da5 | coordinate-translator-for-learning-deformable | 2203.03626 | null | https://arxiv.org/abs/2203.03626v2 | https://arxiv.org/pdf/2203.03626v2.pdf | Coordinate Translator for Learning Deformable Medical Image Registration | The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to not only extract intensity features from the inputs but also unde... | ['Yuan Xue', 'Aaron Carass', 'Jerry L. Prince', 'Shuo Han', 'Lianrui Zuo', 'Yihao Liu'] | 2022-03-05 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 1.60606936e-01 1.15734532e-01 3.18428352e-02 -5.44294953e-01
-9.04766917e-01 -5.00163496e-01 4.24405217e-01 -6.04123697e-02
-6.87277734e-01 3.91791403e-01 8.70910883e-02 1.30155593e-01
-7.64114112e-02 -7.55604386e-01 -8.06321561e-01 -8.80199611e-01
-3.95823903e-02 5.19806147e-01 3.81476730e-01 -1.73733205... | [13.973954200744629, -2.6074180603027344] |
c96c3a7e-64b4-46f3-94a0-d50fd7159f12 | deep-attention-based-alignment-network-for | 2301.10015 | null | https://arxiv.org/abs/2301.10015v1 | https://arxiv.org/pdf/2301.10015v1.pdf | Deep Attention-Based Alignment Network for Melody Generation from Incomplete Lyrics | We propose a deep attention-based alignment network, which aims to automatically predict lyrics and melody with given incomplete lyrics as input in a way similar to the music creation of humans. Most importantly, a deep neural lyrics-to-melody net is trained in an encoder-decoder way to predict possible pairs of lyrics... | ['Suhua Tang', 'Simon Canales', 'Florian Harscoet', 'Yi Yu', 'Zhe Zhang', 'Gurunath Reddy M'] | 2023-01-23 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.88985318e-01 3.81620787e-03 -6.77451864e-02 -2.68955648e-01
-9.42609549e-01 -7.75787532e-01 8.08318973e-01 -2.66834080e-01
1.14972956e-01 8.16401601e-01 8.31439972e-01 2.94020116e-01
1.18017025e-01 -6.11591756e-01 -8.32773507e-01 -4.73601460e-01
4.54460204e-01 6.79774702e-01 -5.91423571e-01 -3.40266287... | [15.997493743896484, 5.6206865310668945] |
1a73b735-6940-4845-bcb8-162cc086bf53 | neural-supersampling-for-real-time-rendering | null | null | https://dl.acm.org/doi/10.1145/3386569.3392376 | https://dl.acm.org/doi/pdf/10.1145/3386569.3392376 | Neural supersampling for real-time rendering | Due to higher resolutions and refresh rates, as well as more photorealistic effects, real-time rendering has become increasingly challenging for video games and emerging virtual reality headsets. To meet this demand, modern graphics hardware and game engines often reduce the computational cost by rendering at a lower r... | ['Anton Kaplanyan', 'Douglas Lanman', 'Alexander Fix', 'Matt Chapman', 'Salah Nouri', 'Lei Xiao'] | 2020-08-01 | null | null | null | acm-transactions-on-graphics-2020-8 | ['video-super-resolution'] | ['computer-vision'] | [ 6.24765635e-01 -3.30761194e-01 -5.64564625e-03 1.69108659e-01
-5.85164487e-01 -4.05491620e-01 6.04125381e-01 -3.73556852e-01
-2.02285409e-01 6.40317619e-01 2.65039980e-01 -2.08328199e-03
6.93776645e-03 -8.60247314e-01 -6.44103110e-01 -5.49343944e-01
-1.60552412e-01 5.93043789e-02 4.75108653e-01 -4.97415662... | [10.438942909240723, -2.1420319080352783] |
9bea086a-67e1-4119-bf19-8083aa3374b2 | learning-motion-appearance-co-attention-for | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Learning_Motion-Appearance_Co-Attention_for_Zero-Shot_Video_Object_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Learning_Motion-Appearance_Co-Attention_for_Zero-Shot_Video_Object_Segmentation_ICCV_2021_paper.pdf | Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation | How to make the appearance and motion information interact effectively to accommodate complex scenarios is a fundamental issue in flow-based zero-shot video object segmentation. In this paper, we propose an Attentive Multi-Modality Collaboration Network (AMC-Net) to utilize appearance and motion information uniform... | ['Xiaoxing Zhang', 'Shuo Wang', 'Huchuan Lu', 'Jinqing Qi', 'Lu Zhang', 'Shu Yang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 1.74095601e-01 -4.13281351e-01 -2.41965219e-01 -3.09997082e-01
-4.30993021e-01 -2.09053129e-01 4.75914687e-01 -4.36205357e-01
-3.62355471e-01 4.13310617e-01 4.57282126e-01 -5.58437891e-02
4.92395498e-02 -5.09022653e-01 -5.79337120e-01 -7.23745346e-01
3.77672732e-01 -1.96995765e-01 7.60455906e-01 -1.53654128... | [9.412443161010742, -0.1749071180820465] |
67af1a01-e715-41b4-875b-30078b28d059 | spatiotemporal-modeling-of-seismic-images-for | 2006.15472 | null | https://arxiv.org/abs/2006.15472v1 | https://arxiv.org/pdf/2006.15472v1.pdf | Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation | Seismic inversion refers to the process of estimating reservoir rock properties from seismic reflection data. Conventional and machine learning-based inversion workflows usually work in a trace-by-trace fashion on seismic data, utilizing little to no information from the spatial structure of seismic images. We propose ... | ['Motaz Alfarraj', 'Ahmad Mustafa', 'Ghassan AlRegib'] | 2020-06-28 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 1.39145538e-01 -2.19533503e-01 2.66315162e-01 -1.48879170e-01
-9.37493920e-01 -1.61098912e-01 5.81329346e-01 -3.68162766e-02
-7.16680110e-01 5.15823185e-01 1.31748199e-01 -4.25133675e-01
-3.46371353e-01 -1.14653158e+00 -8.98760974e-01 -8.03544819e-01
-5.90259790e-01 8.23986530e-01 5.02565801e-01 -3.14464509... | [6.872437000274658, 2.522125482559204] |
cb7353b6-4d28-4524-ab52-a6743c8f1da2 | cross-lingual-joint-entity-and-word-embedding | null | null | https://aclanthology.org/D19-6107 | https://aclanthology.org/D19-6107.pdf | Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining | Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words... | ['Thamme Gowda', 'Scott Miller', 'Heng Ji', 'Xiaoman Pan', 'Jonathan May'] | 2019-11-01 | null | null | null | ws-2019-11 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-4.47631836e-01 1.19893149e-01 -5.87141335e-01 -2.80741692e-01
-1.02659547e+00 -9.44721162e-01 6.44785523e-01 5.47051430e-01
-7.60091782e-01 9.88049686e-01 6.58308983e-01 -8.49754438e-02
9.15845260e-02 -9.67424393e-01 -8.80529225e-01 -1.61891803e-01
1.37194261e-01 6.48200333e-01 1.46892995e-01 -6.08517051... | [9.66537094116211, 9.04476547241211] |
329005c7-384c-46b4-8be8-49ce748997e4 | brain-age-prediction-using-deep-learning | null | null | https://www.nature.com/articles/s41467-019-13163-9 | https://www.nature.com/articles/s41467-019-13163-9.pdf | Brain age prediction using deep learning uncovers associated sequence variants | Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a ... | ['M. O. Ulfarsson', 'K. Stefansson', 'H. Stefansson', 'D. F. Gudbjartsson', 'G. Bragi Walters', 'L. M. Ellingsen', 'T. E. Thorgeirsson', 'G. Bjornsdottir', 'B. A. Jonsson'] | 2019-11-27 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 1.63958862e-01 4.21552539e-01 -3.80672067e-02 -5.80214202e-01
-5.84398866e-01 -3.43849286e-02 4.23541844e-01 1.93544671e-01
-9.66276824e-01 1.19659078e+00 5.07834256e-01 -3.01031142e-01
-1.27782643e-01 -5.60576916e-01 -6.56180918e-01 -4.35214102e-01
-8.65291536e-01 5.11375904e-01 1.65124834e-02 2.18575150... | [14.093168258666992, -1.5523468255996704] |
d974bdc4-c4ff-4b82-9882-e86f5363ddda | side-information-for-face-completion-a-robust | 1801.07580 | null | http://arxiv.org/abs/1801.07580v1 | http://arxiv.org/pdf/1801.07580v1.pdf | Side Information for Face Completion: a Robust PCA Approach | Robust principal component analysis (RPCA) is a powerful method for learning
low-rank feature representation of various visual data. However, for certain
types as well as significant amount of error corruption, it fails to yield
satisfactory results; a drawback that can be alleviated by exploiting
domain-dependent prio... | ['Shiyang Cheng', 'Niannan Xue', 'Yannis Panagakis', 'Stefanos Zafeiriou', 'Jiankang Deng'] | 2018-01-20 | null | null | null | null | ['robust-face-recognition', 'facial-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.33406651e-01 -3.76593143e-01 1.77621022e-01 -1.42406508e-01
-8.50057244e-01 -7.05648899e-01 6.69403374e-01 -4.52176988e-01
-1.24159336e-01 7.55747795e-01 3.88070554e-01 6.41870201e-02
-2.88116366e-01 -5.34023464e-01 -6.07646465e-01 -9.68328238e-01
3.02717656e-01 2.07281947e-01 -3.43239456e-01 -1.06566250... | [12.742056846618652, 0.2941459119319916] |
1121a6c5-207e-4a4d-b5e0-ff975c8f3b97 | sndcnn-self-normalizing-deep-cnns-with-scaled | 1910.01992 | null | https://arxiv.org/abs/1910.01992v3 | https://arxiv.org/pdf/1910.01992v3.pdf | SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition | Very deep CNNs achieve state-of-the-art results in both computer vision and speech recognition, but are difficult to train. The most popular way to train very deep CNNs is to use shortcut connections (SC) together with batch normalization (BN). Inspired by Self- Normalizing Neural Networks, we propose the self-normaliz... | ['Tim Ng', 'Leo Liu', 'Zhen Huang', 'Xiaodan Zhuang', 'Henry Mason', 'Daben Liu'] | 2019-10-04 | null | null | null | null | ['inference-optimization'] | ['audio'] | [ 2.19630189e-02 2.06165567e-01 4.91914637e-02 -7.77882278e-01
-3.62802356e-01 -3.46332282e-01 6.13467157e-01 8.02432224e-02
-1.11490965e+00 3.73117685e-01 1.55994818e-01 -5.95840275e-01
4.97135967e-01 -8.81275654e-01 -8.03431511e-01 -5.29100537e-01
4.71856803e-01 1.96167231e-01 3.23657691e-01 -2.94935614... | [14.215165138244629, 6.477771759033203] |
4f7e0ed7-8b1c-49a2-b822-32022aeebfd4 | massively-multilingual-word-embeddings | 1602.01925 | null | http://arxiv.org/abs/1602.01925v2 | http://arxiv.org/pdf/1602.01925v2.pdf | Massively Multilingual Word Embeddings | We introduce new methods for estimating and evaluating embeddings of words in
more than fifty languages in a single shared embedding space. Our estimation
methods, multiCluster and multiCCA, use dictionaries and monolingual data; they
do not require parallel data. Our new evaluation method, multiQVEC-CCA, is
shown to c... | ['Waleed Ammar', 'Noah A. Smith', 'Chris Dyer', 'Yulia Tsvetkov', 'George Mulcaire', 'Guillaume Lample'] | 2016-02-05 | null | null | null | null | ['multilingual-word-embeddings'] | ['methodology'] | [-4.77467954e-01 -4.01582837e-01 -4.02322888e-01 -6.26310706e-01
-1.20506108e+00 -1.01554644e+00 7.44142234e-01 3.31360340e-01
-8.96449029e-01 4.47796196e-01 6.31446779e-01 -7.50681460e-01
3.87602985e-01 -3.90167892e-01 -1.28993183e-01 -3.67675841e-01
6.35245536e-03 5.95992744e-01 1.97176170e-02 -3.15471649... | [10.85145092010498, 9.876100540161133] |
08da9d3c-29e5-4e8d-ab73-311c3f8c7529 | query-based-single-document-summarization | null | null | https://aclanthology.org/U15-1001 | https://aclanthology.org/U15-1001.pdf | Query-Based Single Document Summarization Using an Ensemble Noisy Auto-Encoder | null | ["Diego Moll{\\'a} Aliod", 'Len Hamey', 'Mahmood Yousefi Azar', 'Kairit Sirts'] | 2015-12-01 | query-based-single-document-summarization-1 | https://aclanthology.org/U15-1001 | https://aclanthology.org/U15-1001.pdf | alta-2015-12 | ['query-based-extractive-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.178446292877197, 3.8445239067077637] |
a44f71cc-ded4-4f76-afba-5624641694ea | learning-an-invariant-speech-representation | 1406.3884 | null | http://arxiv.org/abs/1406.3884v1 | http://arxiv.org/pdf/1406.3884v1.pdf | Learning An Invariant Speech Representation | Recognition of speech, and in particular the ability to generalize and learn
from small sets of labelled examples like humans do, depends on an appropriate
representation of the acoustic input. We formulate the problem of finding
robust speech features for supervised learning with small sample complexity as
a problem o... | ['Stephen Voinea', 'Lorenzo Rosasco', 'Tomaso Poggio', 'Georgios Evangelopoulos', 'Chiyuan Zhang'] | 2014-06-16 | null | null | null | null | ['vowel-classification', 'sound-classification'] | ['audio', 'audio'] | [ 6.35896087e-01 -7.26993009e-02 2.69390047e-01 -5.88857889e-01
-5.54944396e-01 -5.61715245e-01 5.15173197e-01 7.18478411e-02
-5.00043154e-01 3.80372047e-01 2.26126283e-01 -1.68561384e-01
-1.13984048e-01 -6.47366524e-01 -4.68999982e-01 -8.54777813e-01
-2.13563159e-01 1.95654407e-01 4.22376692e-01 1.27064660... | [14.798969268798828, 6.2397661209106445] |
c5ceca73-77ce-4426-9128-4503c1ac0cc3 | we-built-a-fake-news-click-bait-filter-what-1 | null | null | https://aclanthology.org/R17-1045 | https://aclanthology.org/R17-1045.pdf | We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind! | It is completely amazing! Fake news and {``}click baits{''} have totally invaded the cyberspace. Let us face it: everybody hates them for three simple reasons. Reason {\#}2 will absolutely amaze you. What these can achieve at the time of election will completely blow your mind! Now, we all agree, this cannot go on, you... | ['Preslav Nakov', 'Pepa Gencheva', 'Ivan Koychev', 'Georgi Karadzhov'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['clickbait-detection'] | ['natural-language-processing'] | [-6.25626802e-01 -7.38656670e-02 -3.40813845e-01 -3.41040075e-01
-3.52800161e-01 -7.93797255e-01 2.51561195e-01 -1.39712282e-02
-4.64216411e-01 1.10692549e+00 3.41311067e-01 -8.52593362e-01
-1.83841392e-01 -6.56765103e-01 -9.07928228e-01 -3.59664977e-01
7.64467537e-01 4.25630152e-01 2.95987576e-01 -1.35998118... | [8.153901100158691, 10.180121421813965] |
98c5ca77-07db-4b2b-9645-27f83a56c293 | interpretable-not-just-posthoc-explainable-1 | 2304.09981 | null | https://arxiv.org/abs/2304.09981v1 | https://arxiv.org/pdf/2304.09981v1.pdf | Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions | We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death. Our approach avoids a specific pitfall of applying machine learning to this problem, which is an inflated estimate of the effect of interventions, due to survivors bias... | ['Carson C. Chow', 'Ted L. Chang', 'Rohit Mahajan', 'Sonya Mahajan', 'Sarah Nowak', 'Joshua C. Chang', 'Hongjing Xia'] | 2023-04-19 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 3.62563372e-01 3.27489108e-01 -5.58462679e-01 -2.48959064e-01
-1.10090661e+00 -2.49030024e-01 2.24435404e-01 6.51623905e-01
-9.22096133e-01 9.86076772e-01 1.04407132e+00 -9.73639846e-01
-6.36695743e-01 -7.05677211e-01 -6.01363420e-01 -7.04119384e-01
-2.70765424e-01 6.31052613e-01 -4.87296909e-01 4.61954683... | [7.959614276885986, 5.4397406578063965] |
8bef3edf-ece9-4224-8f69-4449c3d79f45 | population-mapping-in-informal-settlements | 2009.08410 | null | https://arxiv.org/abs/2009.08410v1 | https://arxiv.org/pdf/2009.08410v1.pdf | Population Mapping in Informal Settlements with High-Resolution Satellite Imagery and Equitable Ground-Truth | We propose a generalizable framework for the population estimation of dense, informal settlements in low-income urban areas--so called 'slums'--using high-resolution satellite imagery. Precise population estimates are a crucial factor for efficient resource allocations by government authorities and NGO's, for instance ... | ['João Porto de Albuquerque', 'Godwin Yeboah', 'Stephen A Jarvis', 'Konstantin Klemmer'] | 2020-09-17 | null | null | null | null | ['population-mapping'] | ['computer-vision'] | [ 2.18451738e-01 2.23802447e-01 -2.17901021e-01 -2.10948601e-01
-8.77912760e-01 -2.07727998e-01 3.93989980e-01 2.72973120e-01
-7.94140756e-01 1.40458560e+00 7.51323640e-01 -4.38236088e-01
-1.52480170e-01 -1.31905007e+00 -5.07755041e-01 -7.21282423e-01
-4.35451090e-01 7.56003022e-01 -2.85053909e-01 -3.81937504... | [9.374614715576172, -1.2332053184509277] |
b5245ea8-ec9c-4837-8b99-d400c9c810dc | unos-unified-unsupervised-optical-flow-and | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_UnOS_Unified_Unsupervised_Optical-Flow_and_Stereo-Depth_Estimation_by_Watching_Videos_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_UnOS_Unified_Unsupervised_Optical-Flow_and_Stereo-Depth_Estimation_by_Watching_Videos_CVPR_2019_paper.pdf | UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos | In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo depth estimation using convolutional neural network (CNN) by taking advantages of their inherent geometrical consistency based on the rigid-scene assumption. UnOS significantly outperforms other state-of-the-art (SOTA) unsupervis... | [' Wei Xu', ' Yi Yang', ' Chenxu Luo', ' Zhenheng Yang', ' Peng Wang', 'Yang Wang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.81554405e-02 -9.55889747e-02 -3.89478326e-01 -3.01129103e-01
-1.07169881e-01 -5.92700779e-01 5.48933268e-01 -4.39509898e-01
-5.37825286e-01 6.98813438e-01 2.99698770e-01 9.86410975e-02
2.61852086e-01 -7.51911819e-01 -8.70699406e-01 -4.93363380e-01
2.89113492e-01 5.08650839e-01 3.79560947e-01 2.45904386... | [8.614084243774414, -1.9754356145858765] |
606400ef-4d82-42d7-84d7-3ed7704c8fd6 | short-text-clustering-with-transformers | 2102.00541 | null | https://arxiv.org/abs/2102.00541v1 | https://arxiv.org/pdf/2102.00541v1.pdf | Short Text Clustering with Transformers | Recent techniques for the task of short text clustering often rely on word embeddings as a transfer learning component. This paper shows that sentence vector representations from Transformers in conjunction with different clustering methods can be successfully applied to address the task. Furthermore, we demonstrate th... | ['Mikhail Burtsev', 'Leonid Pugachev'] | 2021-01-31 | null | null | null | null | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.56466141e-01 -2.01457009e-01 -3.04459512e-01 -4.75476623e-01
-9.09254968e-01 -5.35666943e-01 6.02810502e-01 3.25852722e-01
-3.38006794e-01 2.72525996e-01 5.05036712e-01 -6.62343264e-01
-1.09795658e-02 -5.78428030e-01 -5.72459921e-02 -7.01942444e-01
1.10858858e-01 8.26222897e-01 2.76309729e-01 -4.04507309... | [10.46451473236084, 7.106919288635254] |
e04d2d0e-4801-41dd-9355-bd7290b8843b | raw-x-vector-multi-scale-time-domain-speaker | 2010.12951 | null | https://arxiv.org/abs/2010.12951v3 | https://arxiv.org/pdf/2010.12951v3.pdf | Y-Vector: Multiscale Waveform Encoder for Speaker Embedding | State-of-the-art text-independent speaker verification systems typically use cepstral features or filter bank energies as speech features. Recent studies attempted to extract speaker embeddings directly from raw waveforms and have shown competitive results. In this paper, we propose a novel multi-scale waveform encoder... | ['Zhiyao Duan', 'Fei Jiang', 'Ge Zhu'] | 2020-10-24 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 6.95308298e-02 -3.21309924e-01 6.11130781e-02 -7.06350863e-01
-1.12787914e+00 -6.45992756e-01 5.74906409e-01 2.25104019e-02
-3.51925433e-01 3.70655417e-01 5.14944375e-01 -3.42145145e-01
-3.91022563e-02 -3.55776757e-01 -3.29089701e-01 -7.93400109e-01
-4.60427940e-01 -2.13869601e-01 -2.68006586e-02 -2.08125934... | [14.3972806930542, 6.052150249481201] |
386a2095-7263-44bc-95f5-c998efdf632f | mface-multilingual-summarization-with-factual | 2212.10622 | null | https://arxiv.org/abs/2212.10622v1 | https://arxiv.org/pdf/2212.10622v1.pdf | mFACE: Multilingual Summarization with Factual Consistency Evaluation | Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries, reducing their utility for real-world application. Several ... | ['Mirella Lapata', 'Elizabeth Clark', 'Jonathan Herzig', 'Joshua Maynez', 'Shashi Narayan', 'Roee Aharoni'] | 2022-12-20 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 2.76008546e-01 2.68457085e-01 -4.67168510e-01 -2.32994273e-01
-1.71331561e+00 -5.67179859e-01 1.00627792e+00 5.71119606e-01
-3.09528381e-01 1.20134616e+00 1.21487975e+00 6.71356097e-02
2.94323742e-01 -4.73689169e-01 -6.04326308e-01 1.65733732e-02
1.35413229e-01 3.81788492e-01 -1.68864682e-01 -3.73980016... | [12.262245178222656, 9.367559432983398] |
d8bbb965-66bc-4cd1-9401-4d04a9c694d9 | mitigating-the-position-bias-of-transformer | 2101.06980 | null | https://arxiv.org/abs/2101.06980v1 | https://arxiv.org/pdf/2101.06980v1.pdf | Mitigating the Position Bias of Transformer Models in Passage Re-Ranking | Supervised machine learning models and their evaluation strongly depends on the quality of the underlying dataset. When we search for a relevant piece of information it may appear anywhere in a given passage. However, we observe a bias in the position of the correct answer in the text in two popular Question Answering ... | ['Allan Hanbury', 'Markus Zlabinger', 'Sophia Althammer', 'Aldo Lipani', 'Sebastian Hofstätter'] | 2021-01-18 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [ 1.20403029e-01 -1.62382945e-01 -1.93003818e-01 -2.61189491e-01
-1.47268343e+00 -8.79128277e-01 6.91184640e-01 5.87354422e-01
-6.15322649e-01 9.54995751e-01 5.30857205e-01 -1.30554438e-01
-4.45970416e-01 -8.22372198e-01 -8.99156332e-01 -4.91124511e-01
5.16926408e-01 7.15347052e-01 5.48459888e-01 -5.08908510... | [11.434341430664062, 7.6784892082214355] |
bb7a3026-1fe6-431f-95c3-af7bb9143204 | the-treachery-of-images-bayesian-scene | 2305.04718 | null | https://arxiv.org/abs/2305.04718v1 | https://arxiv.org/pdf/2305.04718v1.pdf | The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation | In policy learning for robotic manipulation, sample efficiency is of paramount importance. Thus, learning and extracting more compact representations from camera observations is a promising avenue. However, current methods often assume full observability of the scene and struggle with scale invariance. In many tasks an... | ['Abhinav Valada', 'Joschka Boedecker', 'Wolfram Burgard', 'Tim Welschehold', 'Eugenio Chisari', 'Jan Ole von Hartz'] | 2023-05-08 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.87176630e-01 -2.55426347e-01 -3.04995418e-01 4.60647121e-02
-6.15420818e-01 -9.43888247e-01 4.17937726e-01 1.17468737e-01
-5.09417474e-01 7.90026128e-01 -1.34292126e-01 9.35114697e-02
-4.13472027e-01 -1.53514296e-01 -8.70647132e-01 -8.60624492e-01
2.96697579e-03 5.51528037e-01 3.90765220e-01 1.96184710... | [4.772202968597412, 0.5573484301567078] |
b1ab7f27-9b42-4fcb-8790-b41b2904de6e | fast-parallel-exact-inference-on-bayesian | 2212.04241 | null | https://arxiv.org/abs/2212.04241v1 | https://arxiv.org/pdf/2212.04241v1.pdf | Fast Parallel Exact Inference on Bayesian Networks: Poster | Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for complex problems. To improve the efficiency, we propose a fast BN exact inference solution named Fast-BNI on multi-core CPUs. Fast-BNI enhances ... | ['Ajmal Mian', 'Atif Mansoor', 'Zeyi Wen', 'Jiantong Jiang'] | 2022-12-08 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-3.43055874e-01 2.21822821e-02 -4.12761062e-01 -8.23367059e-01
-6.30863011e-01 -2.73113072e-01 2.82438129e-01 -1.63409308e-01
-1.45741001e-01 1.08960795e+00 4.06967923e-02 -8.10591817e-01
-3.24455917e-01 -1.17714095e+00 -6.67434037e-01 -3.58592987e-01
-5.63821010e-03 9.51509058e-01 1.81033134e-01 5.07445991... | [7.338015556335449, 4.276655197143555] |
8dc30247-bca8-49c0-acf5-ba53206209a5 | graph-mixup-with-soft-alignments | 2306.06788 | null | https://arxiv.org/abs/2306.06788v1 | https://arxiv.org/pdf/2306.06788v1.pdf | Graph Mixup with Soft Alignments | We study graph data augmentation by mixup, which has been used successfully on images. A key operation of mixup is to compute a convex combination of a pair of inputs. This operation is straightforward for grid-like data, such as images, but challenging for graph data. The key difficulty lies in the fact that different... | ['Na Zou', 'Shuiwang Ji', 'Meng Liu', 'Zhimeng Jiang', 'Hongyi Ling'] | 2023-06-11 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 4.30029243e-01 2.33165264e-01 -1.10969879e-02 -3.38164061e-01
-2.34469146e-01 -6.59706116e-01 4.10128206e-01 4.03198779e-01
-9.18569118e-02 2.27741241e-01 -1.50448829e-01 -2.48301759e-01
5.50794750e-02 -1.07939863e+00 -8.07764411e-01 -7.97574401e-01
7.24967942e-02 2.88208842e-01 2.99350228e-02 -2.34568685... | [7.086554527282715, 6.307321071624756] |
a4903040-bf35-4261-8f51-6b0e908be92c | time-series-regression | 2006.12672 | null | https://arxiv.org/abs/2006.12672v3 | https://arxiv.org/pdf/2006.12672v3.pdf | Time Series Extrinsic Regression | This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical cla... | ['Geoffrey I. Webb', 'Francois Petitjean', 'Christoph Bergmeir', 'Chang Wei Tan'] | 2020-06-23 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 3.48474622e-01 -3.86319995e-01 -6.28393054e-01 -6.90764725e-01
-7.64835238e-01 -6.63859844e-01 9.47002947e-01 3.84606153e-01
-2.92132258e-01 7.70638824e-01 1.36339232e-01 -8.00415337e-01
-4.53012258e-01 -5.12495100e-01 -7.25133657e-01 -7.12338567e-01
-7.13476598e-01 4.83874679e-01 -8.03727955e-02 -4.37036008... | [7.161880970001221, 3.0901639461517334] |
ee384bbf-152d-48d0-a3af-6b2efe38ef38 | multi-layer-pruning-framework-for-compressing | 1811.08342 | null | http://arxiv.org/abs/1811.08342v1 | http://arxiv.org/pdf/1811.08342v1.pdf | Multi-layer Pruning Framework for Compressing Single Shot MultiBox Detector | We propose a framework for compressing state-of-the-art Single Shot MultiBox
Detector (SSD). The framework addresses compression in the following stages:
Sparsity Induction, Filter Selection, and Filter Pruning. In the Sparsity
Induction stage, the object detector model is sparsified via an improved global
threshold. I... | ['Manikandan. R', 'Vinay P. Namboodiri', 'Neeraj Matiyali', 'Pravendra Singh'] | 2018-11-20 | null | null | null | null | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [ 3.33412290e-01 -2.11801350e-01 -8.10964406e-02 -3.16190839e-01
-4.85872924e-01 -2.32162029e-01 4.85066652e-01 -3.97718623e-02
-8.33077431e-01 4.12415504e-01 1.29638493e-01 -3.80819499e-01
-8.30591470e-02 -6.28277183e-01 -7.56382823e-01 -4.04387355e-01
1.66474432e-01 6.44481555e-02 1.03591669e+00 2.77862828... | [8.641136169433594, -0.3222777843475342] |
848de19d-3fd4-41b5-86d3-03c0c95444b4 | dasgil-domain-adaptation-for-semantic-and | 2010.00573 | null | https://arxiv.org/abs/2010.00573v2 | https://arxiv.org/pdf/2010.00573v2.pdf | DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization | Long-Term visual localization under changing environments is a challenging problem in autonomous driving and mobile robotics due to season, illumination variance, etc. Image retrieval for localization is an efficient and effective solution to the problem. In this paper, we propose a novel multi-task architecture to fus... | ['Zhijian Qiao', 'Ming Cheng', 'Hesheng Wang', 'Hanjiang Hu', 'Zhe Liu'] | 2020-10-01 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-8.48290175e-02 -5.53050816e-01 -2.18897551e-01 -4.34940845e-01
-1.16940951e+00 -7.43507683e-01 7.05932975e-01 -2.92802572e-01
-8.73247206e-01 7.25040734e-01 -2.87794024e-01 9.91205312e-03
-6.12587407e-02 -4.75362450e-01 -1.05247819e+00 -7.56389916e-01
2.43502986e-02 4.61383462e-01 3.99681568e-01 -4.00253892... | [7.530377388000488, -2.0114707946777344] |
7dd75949-125a-4b8f-9677-5b823773f46f | contextual-graph-reasoning-networks | null | null | https://openreview.net/forum?id=Efiwpsy0ZE_ | https://openreview.net/pdf?id=Efiwpsy0ZE_ | Contextual Graph Reasoning Networks | Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks. However, the graph representation learned by existing methods is not effective enough as the relation between feature and graph is under-explored. In this work, we propose a novel... | ['Ming Wu', 'Ming Lu', 'Chuang Zhang', 'Mingming Gong', 'Yangyuxuan Kang', 'Jiaming Liu', 'Zhaoqing Wang'] | 2021-01-01 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 7.24130571e-02 2.92015940e-01 -4.99146953e-02 -4.80628222e-01
-2.45775104e-01 -3.53384465e-01 5.89367211e-01 1.21094562e-01
-8.26462582e-02 3.33748341e-01 6.39613047e-02 6.18630163e-02
-8.50070640e-02 -7.17709482e-01 -6.85701072e-01 -5.66087484e-01
1.78792849e-01 5.57445943e-01 4.27429169e-01 -2.84704566... | [9.607000350952148, 0.8933359384536743] |
837d4d67-dd3e-4df9-852f-92f25b7ce853 | coarse-grained-and-emergent-distributed | 2011.08138 | null | https://arxiv.org/abs/2011.08138v2 | https://arxiv.org/pdf/2011.08138v2.pdf | Coarse-grained and emergent distributed parameter systems from data | We explore the derivation of distributed parameter system evolution laws (and in particular, partial differential operators and associated partial differential equations, PDEs) from spatiotemporal data. This is, of course, a classical identification problem; our focus here is on the use of manifold learning techniques ... | ['Ioannis Kevrekidis', 'Tom Bertalan', 'Felix P. Kemeth', 'Hassan Arbabi'] | 2020-11-16 | null | null | null | null | ['variable-detection'] | ['natural-language-processing'] | [-7.24778175e-02 -4.39784527e-01 5.21179378e-01 3.86575967e-01
-2.38430083e-01 -7.89269328e-01 7.61002123e-01 2.84915745e-01
-4.84117597e-01 1.05663157e+00 -2.84158617e-01 3.32706445e-03
-5.48960328e-01 -7.82760680e-01 -1.10243946e-01 -1.23935986e+00
-5.85444927e-01 7.46372402e-01 2.11648792e-01 -4.92363274... | [6.546254634857178, 3.756570816040039] |
818c1684-cd00-491e-952e-8a257b231edc | classification-of-breast-cancer-histology-1 | 1802.09424 | null | http://arxiv.org/abs/1802.09424v1 | http://arxiv.org/pdf/1802.09424v1.pdf | Classification of breast cancer histology images using transfer learning | Breast cancer is one of the leading causes of mortality in women. Early
detection and treatment are imperative for improving survival rates, which have
steadily increased in recent years as a result of more sophisticated
computer-aided-diagnosis (CAD) systems. A critical component of breast cancer
diagnosis relies on h... | ['Amirabbas Davari', 'Sulaiman Vesal', 'Stephan Ellmann', 'Nishant Ravikumar', 'Andreas Maier'] | 2018-02-26 | null | null | null | null | ['classification-of-breast-cancer-histology'] | ['medical'] | [ 3.55380833e-01 6.32628724e-02 -1.11739390e-01 -3.08347851e-01
-1.09726501e+00 -5.18139839e-01 2.92041510e-01 5.12526453e-01
-5.71545541e-01 5.93880832e-01 -2.14925632e-01 -5.74584484e-01
6.11704923e-02 -8.42793345e-01 -3.51050645e-01 -9.25276875e-01
6.58400962e-03 4.18713212e-01 3.65339667e-02 6.20769970... | [15.137736320495605, -2.938610315322876] |
81b52682-78f7-42c9-8fb7-ef12d3d3be79 | syngec-syntax-enhanced-grammatical-error | 2210.12484 | null | https://arxiv.org/abs/2210.12484v1 | https://arxiv.org/pdf/2210.12484v1.pdf | SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser | This work proposes a syntax-enhanced grammatical error correction (GEC) approach named SynGEC that effectively incorporates dependency syntactic information into the encoder part of GEC models. The key challenge for this idea is that off-the-shelf parsers are unreliable when processing ungrammatical sentences. To confr... | ['Min Zhang', 'Chen Li', 'Zuyi Bao', 'Zhenghua Li', 'Bo Zhang', 'Yue Zhang'] | 2022-10-22 | null | null | null | null | ['syntax-representation', 'grammatical-error-correction'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.42256796e-01 3.97181064e-01 4.13207442e-01 -7.11234510e-01
-9.96414185e-01 -3.72025579e-01 -6.72428310e-02 1.11033723e-01
-2.60851204e-01 4.05176640e-01 3.96607637e-01 -7.16570318e-01
5.39900184e-01 -9.18988466e-01 -9.15773273e-01 -2.20501170e-01
2.37359419e-01 2.42835194e-01 3.64081608e-03 -3.32740366... | [11.058685302734375, 10.638772964477539] |
25fe8d96-6ac0-4255-a4bc-0015827c1ce6 | quantum-heavy-tailed-bandits | 2301.09680 | null | https://arxiv.org/abs/2301.09680v1 | https://arxiv.org/pdf/2301.09680v1.pdf | Quantum Heavy-tailed Bandits | In this paper, we study multi-armed bandits (MAB) and stochastic linear bandits (SLB) with heavy-tailed rewards and quantum reward oracle. Unlike the previous work on quantum bandits that assumes bounded/sub-Gaussian distributions for rewards, here we investigate the quantum bandits problem under a weaker assumption th... | ['Di Wang', 'Vaneet Aggarwal', 'Chaowen Guan', 'Yulian Wu'] | 2023-01-23 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 6.18323237e-02 2.34899580e-01 -4.37594861e-01 -3.16272497e-01
-1.23658955e+00 -7.17990756e-01 -1.04383327e-01 2.60345340e-01
-8.38867605e-01 1.36969864e+00 -3.85141909e-01 -7.28241146e-01
-7.27573335e-01 -1.20820749e+00 -1.07054770e+00 -9.87685621e-01
-2.50650018e-01 6.94753110e-01 -5.26912883e-02 -2.40220025... | [4.584300994873047, 3.3725996017456055] |
dea55566-9932-4a0f-9fc2-e3d21ce8bbbc | synthehicle-multi-vehicle-multi-camera | 2208.14167 | null | https://arxiv.org/abs/2208.14167v1 | https://arxiv.org/pdf/2208.14167v1.pdf | Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities | Smart City applications such as intelligent traffic routing or accident prevention rely on computer vision methods for exact vehicle localization and tracking. Due to the scarcity of accurately labeled data, detecting and tracking vehicles in 3D from multiple cameras proves challenging to explore. We present a massive ... | ['Gerhard Rigoll', 'Stefan Hörmann', 'Johannes Gilg', 'Torben Teepe', 'Junpeng Chen', 'Fabian Herzog'] | 2022-08-30 | null | null | null | null | ['panoptic-segmentation', 'vehicle-re-identification'] | ['computer-vision', 'computer-vision'] | [-8.69275630e-02 -7.11460710e-01 -3.04818422e-01 -3.18326414e-01
-9.85372663e-01 -1.22034717e+00 7.18332529e-01 -1.17721327e-01
-4.41032857e-01 5.27067065e-01 -4.20756668e-01 -3.21621120e-01
4.47173685e-01 -5.11983395e-01 -7.04266667e-01 -8.53575468e-01
1.11773692e-01 8.05206120e-01 7.64478207e-01 2.24266648... | [6.730383396148682, -2.1414706707000732] |
f442b268-8e36-453c-a206-8e169850db89 | video2stylegan-disentangling-local-and-global | 2205.13996 | null | https://arxiv.org/abs/2205.13996v2 | https://arxiv.org/pdf/2205.13996v2.pdf | Video2StyleGAN: Disentangling Local and Global Variations in a Video | Image editing using a pretrained StyleGAN generator has emerged as a powerful paradigm for facial editing, providing disentangled controls over age, expression, illumination, etc. However, the approach cannot be directly adopted for video manipulations. We hypothesize that the main missing ingredient is the lack of fin... | ['Peter Wonka', 'Niloy J. Mitra', 'Peihao Zhu', 'Rameen Abdal'] | 2022-05-27 | null | null | null | null | ['facial-editing'] | ['computer-vision'] | [ 4.45978045e-01 2.49016374e-01 1.10039629e-01 -3.09243262e-01
-4.09759969e-01 -8.81191730e-01 8.41105163e-01 -6.69823170e-01
-1.82186216e-01 6.51312351e-01 4.14185107e-01 2.30170771e-01
1.39135450e-01 -3.54147643e-01 -7.40718842e-01 -7.26454377e-01
1.73252746e-01 -5.69828860e-02 -4.56798732e-01 -2.58065820... | [12.628251075744629, -0.247219979763031] |
eef5a157-516b-4db7-a049-98f3ddb1698c | learning-word-embeddings-from-intrinsic-and | 1608.05852 | null | http://arxiv.org/abs/1608.05852v1 | http://arxiv.org/pdf/1608.05852v1.pdf | Learning Word Embeddings from Intrinsic and Extrinsic Views | While word embeddings are currently predominant for natural language
processing, most of existing models learn them solely from their contexts.
However, these context-based word embeddings are limited since not all words'
meaning can be learned based on only context. Moreover, it is also difficult to
learn the represen... | ['Zheng Zhang', 'Jifan Chen', 'Xuanjing Huang', 'Xipeng Qiu', 'Qi Zhang', 'Kan Chen'] | 2016-08-20 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [-1.56066179e-01 -3.64845812e-01 -7.25643754e-01 -4.11807179e-01
-2.23807573e-01 -3.60447168e-01 7.23752022e-01 7.14318573e-01
-6.71324909e-01 4.79137808e-01 5.61188936e-01 -3.60712886e-01
-1.06655255e-01 -8.63727152e-01 -2.22346172e-01 -3.59850854e-01
2.01249465e-01 2.00004131e-01 6.55495003e-02 -3.78335059... | [10.494051933288574, 8.628334045410156] |
5438fbe1-6fb8-4213-adc6-6e381c13864f | robust-offline-reinforcement-learning-with | 2210.10469 | null | https://arxiv.org/abs/2210.10469v1 | https://arxiv.org/pdf/2210.10469v1.pdf | Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation | A promising paradigm for offline reinforcement learning (RL) is to constrain the learned policy to stay close to the dataset behaviors, known as policy constraint offline RL. However, existing works heavily rely on the purity of the data, exhibiting performance degradation or even catastrophic failure when learning fro... | ['Zhiqiang Xu', 'Peilin Zhao', 'Deheng Ye', 'Liu Liu', 'Ke Xu', 'Chengqian Gao'] | 2022-10-19 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.80983037e-01 -1.16803579e-01 -4.43313330e-01 1.50649086e-01
-9.57647800e-01 -1.02183199e+00 4.83969122e-01 1.59676418e-01
-6.66060805e-01 1.34455478e+00 -3.91551182e-02 -3.51738751e-01
-4.18918908e-01 -5.29457033e-01 -1.01982892e+00 -9.07258153e-01
-4.23527569e-01 3.96526635e-01 8.28762949e-02 1.01002585... | [4.110424518585205, 2.217465877532959] |
7e2e2f30-57b0-4a5e-98e8-461991e4fff7 | bayesian-recurrent-neural-network-models-for | 1711.00636 | null | http://arxiv.org/abs/1711.00636v2 | http://arxiv.org/pdf/1711.00636v2.pdf | Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data | Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used
in the machine learning and dynamical systems literature to represent complex
dynamical or sequential relationships between variables. More recently, as deep
learning models have become more common, RNNs have been used to forecast
increasingl... | ['Christopher K. Wikle', 'Patrick L. McDermott'] | 2017-11-02 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-2.96811104e-01 -3.06006849e-01 1.64140880e-01 -3.18136871e-01
-1.23616964e-01 -4.56328899e-01 1.04420912e+00 -2.51339942e-01
-1.57533839e-01 1.01592088e+00 1.92832157e-01 -5.57369232e-01
-6.35376334e-01 -7.37089038e-01 -2.77838379e-01 -8.69259357e-01
-4.46548223e-01 2.18550995e-01 -1.43656377e-02 -2.51820505... | [6.663160800933838, 3.3277149200439453] |
5b3d05fc-3e11-4832-a045-adafe6dcf888 | signet-intrinsic-image-decomposition-by-a | 2208.14369 | null | https://arxiv.org/abs/2208.14369v1 | https://arxiv.org/pdf/2208.14369v1.pdf | SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes | Intrinsic image decomposition (IID) is an under-constrained problem. Therefore, traditional approaches use hand crafted priors to constrain the problem. However, these constraints are limited when coping with complex scenes. Deep learning-based approaches learn these constraints implicitly through the data, but they of... | ['Theo Gevers', 'Arjan Gijsenij', 'Sezer Karaoglu', 'Partha Das'] | 2022-08-30 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 5.37667274e-01 -3.47194336e-02 3.04971263e-02 -4.97167647e-01
-3.86266679e-01 -2.55101532e-01 5.29769301e-01 -4.34907407e-01
-2.89619565e-01 5.81201911e-01 2.98571169e-01 2.03267243e-02
-8.25298205e-02 -5.68421483e-01 -6.06627643e-01 -7.15097129e-01
2.82836825e-01 1.23395883e-01 2.68934369e-01 -1.16473466... | [10.05207347869873, -2.7303037643432617] |
0e6b05dd-510a-4649-8edd-dda39814ee42 | are-my-deep-learning-systems-fair-an | null | null | http://proceedings.neurips.cc/paper/2021/hash/fdda6e957f1e5ee2f3b311fe4f145ae1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/fdda6e957f1e5ee2f3b311fe4f145ae1-Paper.pdf | Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training | Deep learning (DL) systems have been gaining popularity in critical tasks such as credit evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL software implementations introduce variance: identical DL training runs (i.e., identical network, data, configuration, software, and hardware)... | ['Sameena Shah', 'Jiahao Chen', 'YaoLiang Yu', 'Lin Tan', 'Jungwon Kim', 'Zeou Hu', 'Thibaud Lutellier', 'Hung Pham', 'Shangshu Qian'] | 2021-12-01 | null | https://openreview.net/forum?id=kLWGdQYsmC5 | https://openreview.net/pdf?id=kLWGdQYsmC5 | neurips-2021-12 | ['crime-prediction'] | ['miscellaneous'] | [-4.21354383e-01 -4.62966040e-02 -3.11708331e-01 -7.27519631e-01
-1.95109826e-02 -5.63765049e-01 5.22964537e-01 1.38392031e-01
-8.05242419e-01 6.70087457e-01 -4.44814004e-02 -7.91443348e-01
1.89114198e-01 -7.51952827e-01 -6.09086990e-01 -2.12510064e-01
2.67386041e-03 2.21752867e-01 2.51583429e-03 5.99017404... | [8.921021461486816, 5.310534954071045] |
91800dde-236a-442f-92cf-0816727f30b7 | play-parametrically-conditioned-layout | 2301.11529 | null | https://arxiv.org/abs/2301.11529v2 | https://arxiv.org/pdf/2301.11529v2.pdf | PLay: Parametrically Conditioned Layout Generation using Latent Diffusion | Layout design is an important task in various design fields, including user interface, document, and graphic design. As this task requires tedious manual effort by designers, prior works have attempted to automate this process using generative models, but commonly fell short of providing intuitive user controls and ach... | ['Yang Li', 'Gang Li', 'Forrest Huang', 'Chin-Yi Cheng'] | 2023-01-27 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-3.41323726e-02 -3.74559194e-01 -1.96543962e-01 -3.96234065e-01
-1.08602196e-01 -8.40957701e-01 5.04037559e-01 -2.45168746e-01
1.48108333e-01 3.45065087e-01 7.69786239e-01 -7.17103422e-01
-4.49272722e-01 -7.94791460e-01 -1.93048909e-01 -3.52813303e-01
4.14323628e-01 3.25552255e-01 -2.05572218e-01 -3.40846628... | [11.290684700012207, -0.15675215423107147] |
db23f98d-e5bb-439a-8177-9d818270d5f4 | accidental-light-probes | 2301.05211 | null | https://arxiv.org/abs/2301.05211v3 | https://arxiv.org/pdf/2301.05211v3.pdf | Accidental Light Probes | Recovering lighting in a scene from a single image is a fundamental problem in computer vision. While a mirror ball light probe can capture omnidirectional lighting, light probes are generally unavailable in everyday images. In this work, we study recovering lighting from accidental light probes (ALPs) -- common, shiny... | ['Deqing Sun', 'Jiajun Wu', 'Noah Snavely', 'Richard Szeliski', 'Charles Herrmann', 'Samir Agarwala', 'Hong-Xing Yu'] | 2023-01-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_Accidental_Light_Probes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_Accidental_Light_Probes_CVPR_2023_paper.pdf | cvpr-2023-1 | ['lighting-estimation'] | ['computer-vision'] | [ 7.87578940e-01 -2.38263443e-01 5.71212888e-01 -3.26238453e-01
-3.21069986e-01 -6.75022781e-01 4.27208364e-01 -7.04099774e-01
6.59753159e-02 4.35634077e-01 -2.27878354e-02 -4.12459999e-01
2.17076346e-01 -6.85915709e-01 -1.08483577e+00 -6.24094427e-01
7.94649303e-01 1.94663793e-01 4.79153953e-02 -1.38467439... | [9.77939224243164, -3.0067975521087646] |
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