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4cb98cd3-4506-49ed-a9c5-625eb9c3ace5 | glosh-global-local-spherical-harmonics-for | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_GLoSH_Global-Local_Spherical_Harmonics_for_Intrinsic_Image_Decomposition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_GLoSH_Global-Local_Spherical_Harmonics_for_Intrinsic_Image_Decomposition_ICCV_2019_paper.pdf | GLoSH: Global-Local Spherical Harmonics for Intrinsic Image Decomposition | Traditional intrinsic image decomposition focuses on decomposing images into reflectance and shading, leaving surfaces normals and lighting entangled in shading. In this work, we propose a Global-Local Spherical Harmonics (GLoSH) lighting model to improve the lighting component, and jointly predict reflectance and surf... | [' David W. Jacobs', ' Xiang Yu', 'Hao Zhou'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.06807095e-01 -5.00737131e-02 2.06520259e-01 -5.32748997e-01
-3.70626420e-01 -3.17206413e-01 4.89033937e-01 -5.19295573e-01
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1.54365122e-01 -8.14809978e-01 -7.36575365e-01 -8.69819045e-01
5.05784333e-01 1.88906174e-02 6.30236045e-02 -1.17227659... | [9.899665832519531, -2.9699509143829346] |
39e4ab8c-f5c3-4c89-8276-05462c02c14a | improving-paraphrase-detection-with-the | 2106.07691 | null | https://arxiv.org/abs/2106.07691v1 | https://arxiv.org/pdf/2106.07691v1.pdf | Improving Paraphrase Detection with the Adversarial Paraphrasing Task | If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instea... | ['John Licato', 'Animesh Nighojkar'] | 2021-06-14 | null | https://aclanthology.org/2021.acl-long.552 | https://aclanthology.org/2021.acl-long.552.pdf | acl-2021-5 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 5.83402157e-01 1.32239804e-01 1.17193803e-01 -5.46383977e-01
-7.00876296e-01 -1.25099635e+00 5.94248295e-01 5.47143519e-01
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5.26122808e-01 4.15024698e-01 -2.30323081e-03 -5.80554903... | [11.34171199798584, 9.22806453704834] |
33f1e652-8c5d-4d21-86cb-d2b846fbe9cd | language-modeling-for-formal-mathematics | 2006.04757 | null | https://arxiv.org/abs/2006.04757v3 | https://arxiv.org/pdf/2006.04757v3.pdf | Mathematical Reasoning via Self-supervised Skip-tree Training | We examine whether self-supervised language modeling applied to mathematical formulas enables logical reasoning. We suggest several logical reasoning tasks that can be used to evaluate language models trained on formal mathematical statements, such as type inference, suggesting missing assumptions and completing equali... | ['Dennis Lee', 'Kshitij Bansal', 'Christian Szegedy', 'Markus N. Rabe'] | 2020-06-08 | null | https://openreview.net/forum?id=YmqAnY0CMEy | https://openreview.net/pdf?id=YmqAnY0CMEy | iclr-2021-1 | ['mathematical-reasoning'] | ['natural-language-processing'] | [-1.03961341e-01 6.63028657e-01 -3.35189462e-01 -5.49546123e-01
-3.47795665e-01 -5.08674562e-01 5.37944019e-01 3.75031143e-01
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-4.28448290e-01 4.57965434e-01 -2.21785121e-02 -1.91363275... | [9.151759147644043, 7.1607232093811035] |
e86c5497-010c-4782-9eea-640917b77029 | prediction-of-brain-tumor-recurrence-location | 2304.13725 | null | https://arxiv.org/abs/2304.13725v1 | https://arxiv.org/pdf/2304.13725v1.pdf | Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning | Brain tumor is one of the leading causes of cancer death. The high-grade brain tumors are easier to recurrent even after standard treatment. Therefore, developing a method to predict brain tumor recurrence location plays an important role in the treatment planning and it can potentially prolong patient's survival time.... | ['Su Ruan', 'Maxime Fontanilles', 'Sébastien Thureau', 'Fethi Ghazouani', 'Romain Modzelewski', 'Alexandra Noeuveglise', 'Tongxue Zhou'] | 2023-04-11 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 1.77368056e-02 2.05370169e-02 -3.00768942e-01 -5.11219680e-01
-1.12428832e+00 1.55323476e-01 2.35535920e-01 -2.37386040e-02
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1.69099793e-01 3.08715671e-01 2.82840371e-01 1.75574437... | [14.51521110534668, -2.3234496116638184] |
89e57b27-4396-4fee-9d58-015dc5afb390 | hyperntf-a-hypergraph-regularized-nonnegative | 2101.06827 | null | https://arxiv.org/abs/2101.06827v3 | https://arxiv.org/pdf/2101.06827v3.pdf | HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction | Tensor decomposition is an effective tool for learning multi-way structures and heterogeneous features from high-dimensional data, such as the multi-view images and multichannel electroencephalography (EEG) signals, are often represented by tensors. However, most of tensor decomposition methods are the linear feature e... | ['Zhengming Ma', 'Youzhi Qu', 'Quanying Liu', 'Wanguang Yin'] | 2021-01-18 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-3.93070936e-01 -6.20883465e-01 5.98612949e-02 -1.13998845e-01
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-6.40994072e-01 -4.44676965e-01 -1.53278619e-01 -1.02897823e+00
-3.79564762e-01 7.13107884e-02 -1.99076504e-01 -1.82583764... | [8.132966041564941, 4.575297832489014] |
104b7d15-695a-402b-8a64-ba0fafeb8b07 | reference-based-image-composition-with-sketch | 2304.09748 | null | https://arxiv.org/abs/2304.09748v1 | https://arxiv.org/pdf/2304.09748v1.pdf | Reference-based Image Composition with Sketch via Structure-aware Diffusion Model | Recent remarkable improvements in large-scale text-to-image generative models have shown promising results in generating high-fidelity images. To further enhance editability and enable fine-grained generation, we introduce a multi-input-conditioned image composition model that incorporates a sketch as a novel modal, al... | ['Jaegul Choo', 'Junsoo Lee', 'Sunghyun Park', 'Kangyeol Kim'] | 2023-03-31 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 7.79644132e-01 2.18112320e-01 -5.66130038e-04 -4.68511954e-02
-4.82327759e-01 -7.61424839e-01 9.11805451e-01 -4.15549457e-01
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8.15967992e-02 -1.04764807e+00 -8.42922449e-01 -6.77917421e-01
4.82637703e-01 2.17203006e-01 -3.54987755e-02 -3.14458370... | [11.450334548950195, -0.4219026565551758] |
144b010f-cc12-4bd9-a98b-01c9b3204419 | incorporating-global-visual-features-into | 1701.06521 | null | http://arxiv.org/abs/1701.06521v1 | http://arxiv.org/pdf/1701.06521v1.pdf | Incorporating Global Visual Features into Attention-Based Neural Machine Translation | We introduce multi-modal, attention-based neural machine translation (NMT)
models which incorporate visual features into different parts of both the
encoder and the decoder. We utilise global image features extracted using a
pre-trained convolutional neural network and incorporate them (i) as words in
the source senten... | ['Qun Liu', 'Nick Campbell', 'Iacer Calixto'] | 2017-01-23 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.97702068e-01 2.88820595e-01 -4.37969677e-02 -1.37543947e-01
-1.17603433e+00 -3.83034557e-01 1.25368845e+00 -8.60237554e-02
-9.54467237e-01 8.54527593e-01 3.97129625e-01 -4.58468914e-01
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3.44828010e-01 8.09077144e-01 2.15039745e-01 -2.24764749... | [11.417673110961914, 1.5016207695007324] |
349204cc-8da4-490f-95d1-8e620a0f9703 | the-role-of-context-and-uncertainty-in | null | null | https://aclanthology.org/2022.coling-1.67 | https://aclanthology.org/2022.coling-1.67.pdf | The Role of Context and Uncertainty in Shallow Discourse Parsing | Discourse parsing has proven to be useful for a number of NLP tasks that require complex reasoning. However, over a decade since the advent of the Penn Discourse Treebank, predicting implicit discourse relations in text remains challenging. There are several possible reasons for this, and we hypothesize that models sho... | ['Malihe Alikhani', 'Junyi Jessy Li', 'Remi Choi', 'Katherine Atwell'] | null | null | null | null | coling-2022-10 | ['discourse-parsing'] | ['natural-language-processing'] | [-8.54789764e-02 7.58665144e-01 -4.07528758e-01 -5.53125441e-01
-9.02941048e-01 -7.08943427e-01 8.17732334e-01 7.11397707e-01
-7.31433809e-01 1.09501827e+00 7.89616346e-01 -6.01787269e-01
-3.51077095e-02 -5.35945475e-01 -5.05203605e-01 -2.00721785e-01
1.54306650e-01 4.82418656e-01 2.63828307e-01 -5.86316176... | [10.603748321533203, 8.739092826843262] |
a20438a7-94da-4bab-9647-6402fbd30122 | faster-gradient-free-algorithms-for-nonsmooth | 2301.06428 | null | https://arxiv.org/abs/2301.06428v2 | https://arxiv.org/pdf/2301.06428v2.pdf | Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization | We consider the optimization problem of the form $\min_{x \in \mathbb{R}^d} f(x) \triangleq \mathbb{E}_{\xi} [F(x; \xi)]$, where the component $F(x;\xi)$ is $L$-mean-squared Lipschitz but possibly nonconvex and nonsmooth. The recently proposed gradient-free method requires at most $\mathcal{O}( L^4 d^{3/2} \epsilon^{-4... | ['Luo Luo', 'Jing Xu', 'Lesi Chen'] | 2023-01-16 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-1.06752217e-01 2.60656208e-01 4.46288511e-02 -8.24109539e-02
-1.14142764e+00 -6.34708285e-01 -4.40045297e-01 -4.91499193e-02
-8.78541827e-01 1.20890284e+00 -5.55769801e-01 -6.13753319e-01
-8.92718315e-01 -8.06138813e-01 -6.79046690e-01 -9.95643616e-01
-8.34142625e-01 1.81376711e-01 -7.67648891e-02 -3.23466301... | [6.396448612213135, 4.549167156219482] |
92432293-d102-418e-a57d-4619694d36b3 | physically-interpretable-neural-networks-for | 1912.01752 | null | https://arxiv.org/abs/1912.01752v2 | https://arxiv.org/pdf/1912.01752v2.pdf | Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability | Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired o... | ['Imme Ebert-Uphoff', 'Benjamin A. Toms', 'Elizabeth A. Barnes'] | 2019-12-04 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 4.47834522e-01 1.66658282e-01 -6.61453903e-02 -4.24499303e-01
2.80720323e-01 -6.77479446e-01 7.35580564e-01 2.12896809e-01
-3.83509189e-01 5.66975176e-01 2.06194311e-01 -1.06829762e+00
-5.16675770e-01 -8.88278067e-01 -7.42916524e-01 -7.27251768e-01
-2.39300460e-01 2.49266192e-01 -2.51340896e-01 -3.27991128... | [6.8022260665893555, 2.9716856479644775] |
b0f2a81b-2492-4637-b1db-49609bda8c82 | the-cat-set-on-the-mat-cross-attention-for | 2111.00243 | null | https://arxiv.org/abs/2111.00243v1 | https://arxiv.org/pdf/2111.00243v1.pdf | The CAT SET on the MAT: Cross Attention for Set Matching in Bipartite Hypergraphs | Usual relations between entities could be captured using graphs; but those of a higher-order -- more so between two different types of entities (which we term "left" and "right") -- calls for a "bipartite hypergraph". For example, given a left set of symptoms and right set of diseases, the relation between a set subset... | ['M. Narasimha Murty', 'V. Susheela Devi', 'Swyam Prakash Singh', 'Govind Sharma'] | 2021-10-30 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 2.16989979e-01 7.78181851e-01 -3.19429070e-01 -4.08743948e-01
-1.44638792e-01 -3.88367474e-01 4.58858192e-01 5.56181669e-01
1.05579160e-01 6.02295220e-01 3.39780301e-01 -3.91396552e-01
-7.43385553e-01 -1.37445045e+00 -9.98469234e-01 -4.23089385e-01
-4.33118284e-01 7.82622635e-01 -2.20616870e-02 -4.53410000... | [7.355543613433838, 6.530724048614502] |
b63d89d4-b7e4-4492-bad6-447d5edba9f7 | deep-sparse-and-low-rank-prior-for | null | null | https://ieeexplore.ieee.org/document/9884071 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9884071 | Deep Sparse and Low-Rank Prior for Hyperspectral Image Denoising | Spectral and spatial correlation in hyperspectral images (HSIs) can be exploited in HSI processing because it directly induces a sparse and low-rank prior via linear transformations. Researchers have used the sparse and low-rank prior as an image prior for HSI restoration, such as denoising, deblurring, and super-resol... | ['Han V. Nguyen; Magnus O. Ulfarsson; Jakob Sigurdsson; Johannes R. Sveinsson'] | 2022-09-28 | null | null | null | ieee-international-geoscience-and-remote-1 | ['deblurring'] | ['computer-vision'] | [ 4.30657476e-01 -6.23446941e-01 1.73490852e-01 -1.99018940e-01
-7.09181905e-01 -3.04420710e-01 2.33211502e-01 -4.78878886e-01
-7.00522885e-02 6.26854181e-01 7.26939440e-01 2.69448459e-01
-3.90459985e-01 -9.05928195e-01 -4.49181587e-01 -1.20973575e+00
-2.73300931e-02 -3.46108109e-01 9.73107740e-02 -2.46645898... | [10.488300323486328, -2.1039810180664062] |
7b546a0c-ceab-444a-b36e-7b38c2bc83d0 | differentiable-programming-of-reaction | 2107.06862 | null | https://arxiv.org/abs/2107.06862v1 | https://arxiv.org/pdf/2107.06862v1.pdf | Differentiable Programming of Reaction-Diffusion Patterns | Reaction-Diffusion (RD) systems provide a computational framework that governs many pattern formation processes in nature. Current RD system design practices boil down to trial-and-error parameter search. We propose a differentiable optimization method for learning the RD system parameters to perform example-based text... | ['Eyvind Niklasson', 'Ettore Randazzo', 'Alexander Mordvintsev'] | 2021-06-22 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 2.34033331e-01 1.96068157e-02 6.29845336e-02 1.99327439e-01
-3.31823915e-01 -5.01054466e-01 1.17472458e+00 -3.11958879e-01
-1.32496819e-01 1.04955268e+00 -6.82491958e-02 -2.53737867e-01
-5.88642918e-02 -8.75431776e-01 -6.55553818e-01 -1.37825835e+00
-5.08094840e-02 4.47487235e-01 2.65836507e-01 -4.67917055... | [11.427066802978516, -0.35613998770713806] |
35d7622b-3421-42d1-9fa7-84e9d20107ec | an-adapter-based-multi-label-pre-training-for | 2211.06041 | null | https://arxiv.org/abs/2211.06041v1 | https://arxiv.org/pdf/2211.06041v1.pdf | An Adapter based Multi-label Pre-training for Speech Separation and Enhancement | In recent years, self-supervised learning (SSL) has achieved tremendous success in various speech tasks due to its power to extract representations from massive unlabeled data. However, compared with tasks such as speech recognition (ASR), the improvements from SSL representation in speech separation (SS) and enhanceme... | ['Weibin Zhu', 'Shu Yu', 'Zhuo Chen', 'Xie Chen', 'Tianrui Wang'] | 2022-11-11 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 5.13281524e-01 2.32337028e-01 -1.80181786e-01 -7.37089038e-01
-1.11786854e+00 -1.60337135e-01 6.02864385e-01 -8.40295926e-02
-3.82179618e-01 5.01298368e-01 3.80271763e-01 -4.54917878e-01
2.94166535e-01 -9.87993255e-02 -3.68078649e-01 -7.62792945e-01
1.68337479e-01 6.32497892e-02 3.49293023e-01 -2.34213978... | [14.567848205566406, 6.3883562088012695] |
dfc6ec59-011a-43e4-85c7-e24f49b413c3 | multi-level-adaptive-region-of-interest-and | 2102.12154 | null | https://arxiv.org/abs/2102.12154v1 | https://arxiv.org/pdf/2102.12154v1.pdf | Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition | In facial action unit (AU) recognition tasks, regional feature learning and AU relation modeling are two effective aspects which are worth exploring. However, the limited representation capacity of regional features makes it difficult for relation models to embed AU relationship knowledge. In this paper, we propose a n... | ['ShiLiang Pu', 'Chunmao Wang', 'Qiang Li', 'Jingjing Wang', 'Boyuan Jiang', 'Jingwei Yan'] | 2021-02-24 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 1.24522448e-01 3.04435492e-01 -4.22065228e-01 -4.01654720e-01
-2.85224259e-01 -7.31139258e-02 4.00414616e-01 -4.27290089e-02
-6.49545714e-03 3.57290208e-01 2.16388986e-01 2.15274602e-01
-1.95483699e-01 -1.01236701e+00 -4.96779412e-01 -6.63669884e-01
-1.23278268e-01 -1.96889699e-01 5.12540936e-01 -3.35629880... | [13.652281761169434, 1.5612372159957886] |
7186df4c-0175-43d5-8387-2eb08ce2aadc | gmote-gaussian-based-minority-oversampling | 2105.03855 | null | https://arxiv.org/abs/2105.03855v1 | https://arxiv.org/pdf/2105.03855v1.pdf | GMOTE: Gaussian based minority oversampling technique for imbalanced classification adapting tail probability of outliers | Classification of imbalanced data is one of the common problems in the recent field of data mining. Imbalanced data substantially affects the performance of standard classification models. Data-level approaches mainly use the oversampling methods to solve the problem, such as synthetic minority oversampling Technique (... | ['Kyung Joon Cha', 'Seung Jee Yang'] | 2021-05-09 | null | null | null | null | ['classification'] | ['methodology'] | [ 5.36693784e-04 -2.07454890e-01 -1.13960020e-01 -4.46465641e-01
-4.78516251e-01 1.36757299e-01 4.09778118e-01 5.25060892e-01
-1.64227948e-01 9.78684604e-01 -1.89494863e-01 -1.04767159e-01
-1.90300167e-01 -1.01878941e+00 -5.20739734e-01 -8.34338307e-01
1.58206090e-01 5.45660377e-01 1.30831376e-01 4.07673903... | [8.579293251037598, 4.184606075286865] |
040e2d80-cb0b-4915-a6d8-efe5d12ded78 | adaptive-depth-graph-attention-networks | 2301.06265 | null | https://arxiv.org/abs/2301.06265v1 | https://arxiv.org/pdf/2301.06265v1.pdf | Adaptive Depth Graph Attention Networks | As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely used in various graph mining tasks with impressive results. However, since GAT was proposed, none of the existing studies have provided syst... | ['Rui Zhang', 'Ruqiong Zhang', 'Yixuan Du', 'Jingbo Zhou'] | 2023-01-16 | null | null | null | null | ['graph-mining'] | ['graphs'] | [-1.18391484e-01 1.77737489e-01 -4.89292324e-01 3.03784329e-02
2.29624003e-01 -5.47600677e-03 1.79438323e-01 1.07329100e-01
-2.63500839e-01 3.96866828e-01 1.93472177e-01 -4.72192079e-01
-2.51404315e-01 -1.05668640e+00 -6.14493668e-01 -6.03808463e-01
-1.78033724e-01 2.56923381e-02 3.95280182e-01 -3.63021672... | [7.1935930252075195, 6.250176429748535] |
ed1561c3-c45f-4999-a17c-902713cbd9c9 | entity-level-text-guided-image-manipulation | 2302.11383 | null | https://arxiv.org/abs/2302.11383v1 | https://arxiv.org/pdf/2302.11383v1.pdf | Entity-Level Text-Guided Image Manipulation | Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical applications. In this work, we study a novel task on text-guided image manipulation on the entity level in the real world (eL-TGIM). The task im... | ['Yanwei Fu', 'Wei zhang', 'Zhenguo Li', 'Hang Xu', 'Guansong Lu', 'Jianan Wang', 'Yikai Wang'] | 2023-02-22 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 5.79981625e-01 5.23710288e-02 3.99698615e-01 -2.89276391e-01
-4.31801379e-01 -4.03379381e-01 7.30999768e-01 -3.36014062e-01
-2.65873581e-01 3.99407595e-01 -3.06057297e-02 1.05282880e-01
-2.57123590e-01 -9.43238437e-01 -7.85428941e-01 -8.29084754e-01
3.84230793e-01 4.63195980e-01 2.36382917e-01 -2.55483598... | [11.282054901123047, -0.9698629975318909] |
bd01d11b-7cbe-4c43-8218-f013aec1e34b | hole-filling-method-for-depth-image-based | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7836315 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7836315 | Hole Filling Method for Depth Image Based Rendering Based on Boundary Decision | In three-dimensional display systems, depth image based rendering is the most commonly used technique for generating images captured from a virtual viewpoint through reference views and depth maps. However, disoccluded hole filling remain a challenging issue as newly exposed area appears in the virtual view. Image inpa... | ['Taejeong Kim', 'Hyuk Choi', 'Wonseok Song', 'Jea-Hyung Cho'] | 2017-03-01 | null | null | null | signal-processing-letters-2017-3 | ['image-inpainting'] | ['computer-vision'] | [ 6.04645669e-01 3.25381868e-02 2.50828326e-01 6.15244098e-02
-1.88387513e-01 -2.44805753e-01 1.16670847e-01 5.84372655e-02
2.32766252e-02 7.64231086e-01 1.68815836e-01 -2.75444030e-03
2.27044374e-01 -9.96364772e-01 -3.57262492e-01 -8.44811082e-01
5.47094703e-01 -4.87018637e-02 7.63951242e-01 1.16059184... | [9.350672721862793, -2.448458194732666] |
b13395c8-fd48-4b63-b632-53e28842fd18 | leveraging-explanations-in-interactive | 2207.14526 | null | https://arxiv.org/abs/2207.14526v2 | https://arxiv.org/pdf/2207.14526v2.pdf | Leveraging Explanations in Interactive Machine Learning: An Overview | Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental model of a trained ML model. However, explanations can go beyond this one way communication as a mechanism to elicit user control, because once... | ['Elizabeth Daly', 'Wolfang Stammer', 'Öznur Alkan', 'Stefano Teso'] | 2022-07-29 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 3.47807735e-01 9.41005111e-01 -5.70092499e-01 -5.89857280e-01
-1.72043536e-02 -6.59847319e-01 8.58728349e-01 3.90609115e-01
1.72172815e-01 5.05483687e-01 2.96786606e-01 -7.29443431e-01
7.39964098e-02 -4.76958364e-01 -3.88497591e-01 -5.16758338e-02
4.93163802e-02 4.71371353e-01 -1.71680495e-01 -7.87783116... | [9.002471923828125, 6.221385478973389] |
00cd7b9c-8259-4849-a3cf-db19c018b908 | learning-delays-in-spiking-neural-networks | 2306.17670 | null | https://arxiv.org/abs/2306.17670v1 | https://arxiv.org/pdf/2306.17670v1.pdf | Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings | Spiking Neural Networks (SNNs) are a promising research direction for building power-efficient information processing systems, especially for temporal tasks such as speech recognition. In SNNs, delays refer to the time needed for one spike to travel from one neuron to another. These delays matter because they influence... | ['Timothée Masquelier', 'Ismail Khalfaoui-Hassani', 'Ilyass Hammouamri'] | 2023-06-30 | null | null | null | null | ['speech-recognition'] | ['speech'] | [ 2.25126222e-01 -5.86888790e-01 3.07027459e-01 -2.56242961e-01
-7.68436417e-02 -6.88761950e-01 4.96084601e-01 1.96171641e-01
-9.03859377e-01 6.09597564e-01 -2.47760937e-01 -2.86852598e-01
-9.68577266e-02 -6.88009977e-01 -9.54641879e-01 -1.06542134e+00
-5.54617345e-01 1.44036382e-01 8.87324750e-01 -3.02829564... | [8.158945083618164, 2.5601913928985596] |
6d78fc16-700f-44cb-9042-eb7b3c192a16 | attention-based-end-to-end-speech-recognition | 1707.07167 | null | http://arxiv.org/abs/1707.07167v3 | http://arxiv.org/pdf/1707.07167v3.pdf | Attention-Based End-to-End Speech Recognition on Voice Search | Recently, there has been a growing interest in end-to-end speech recognition
that directly transcribes speech to text without any predefined alignments. In
this paper, we explore the use of attention-based encoder-decoder model for
Mandarin speech recognition on a voice search task. Previous attempts have
shown that ap... | ['Junbo Zhang', 'Yujun Wang', 'Changhao Shan', 'Lei Xie'] | 2017-07-22 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 2.66000777e-01 9.19440091e-02 2.55337097e-02 -3.52320701e-01
-1.07800031e+00 -2.67311037e-01 3.92108649e-01 -2.45042577e-01
-6.56417370e-01 6.18213773e-01 4.93899196e-01 -7.35800207e-01
3.39157313e-01 -2.57779241e-01 -6.55778944e-01 -5.81829667e-01
4.90797430e-01 1.96386859e-01 1.04558103e-01 1.71408951... | [14.464195251464844, 6.900814533233643] |
e8a88af3-4987-4daa-bdfd-a8823b15a9f9 | predict-persian-reverse-dictionary | 2105.00309 | null | https://arxiv.org/abs/2105.00309v2 | https://arxiv.org/pdf/2105.00309v2.pdf | PREDICT: Persian Reverse Dictionary | Finding the appropriate words to convey concepts (i.e., lexical access) is essential for effective communication. Reverse dictionaries fulfill this need by helping individuals to find the word(s) which could relate to a specific concept or idea. To the best of our knowledge, this resource has not been available for the... | ['Ali Mohades', 'Amin Gheibi', 'Arman Malekzadeh'] | 2021-05-01 | null | null | null | null | ['reverse-dictionary'] | ['natural-language-processing'] | [-1.41663387e-01 4.67331745e-02 -1.56956211e-01 -1.36653945e-01
-3.31268072e-01 -5.69471598e-01 7.03364074e-01 8.60146165e-01
-8.55612874e-01 8.17916989e-01 3.62340868e-01 -3.47417116e-01
-1.93183333e-01 -8.17203879e-01 -2.78988868e-01 -3.16886574e-01
3.35189134e-01 8.58835042e-01 -1.79442242e-01 -6.36757493... | [10.498750686645508, 9.4298734664917] |
b18cb36f-6620-4bee-b7ad-6b7924b09f08 | brightness-restricted-adversarial-attack | 2307.00421 | null | https://arxiv.org/abs/2307.00421v1 | https://arxiv.org/pdf/2307.00421v1.pdf | Brightness-Restricted Adversarial Attack Patch | Adversarial attack patches have gained increasing attention due to their practical applicability in physical-world scenarios. However, the bright colors used in attack patches represent a significant drawback, as they can be easily identified by human observers. Moreover, even though these attacks have been highly succ... | ['Mingzhen Shao'] | 2023-07-01 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 2.13123053e-01 -2.35775933e-01 1.56804025e-01 2.00677574e-01
-1.93449900e-01 -1.10174131e+00 6.26975179e-01 -4.48629260e-02
-2.45018497e-01 5.59000671e-01 -1.93790615e-01 -4.80544746e-01
-1.17061339e-01 -8.35541129e-01 -5.20522892e-01 -9.41978574e-01
-3.40080798e-01 -6.34024739e-01 6.02251112e-01 -3.26477081... | [5.408068656921387, 7.916049957275391] |
70001a7e-3e9e-4afe-b16c-2f46d6705f0f | crossmodal-learning-for-audio-visual-speech | 2003.04358 | null | https://arxiv.org/abs/2003.04358v2 | https://arxiv.org/pdf/2003.04358v2.pdf | Cross modal video representations for weakly supervised active speaker localization | An objective understanding of media depictions, such as inclusive portrayals of how much someone is heard and seen on screen such as in film and television, requires the machines to discern automatically who, when, how, and where someone is talking, and not. Speaker activity can be automatically discerned from the rich... | ['Krishna Somandepalli', 'Rahul Sharma', 'Shrikanth Narayanan'] | 2020-03-09 | null | null | null | null | ['active-speaker-localization'] | ['audio'] | [ 1.50424257e-01 -1.61774491e-03 -2.41867602e-01 -3.66882771e-01
-9.54548419e-01 -8.59148145e-01 8.78719151e-01 2.45665357e-01
-5.45185864e-01 2.20626801e-01 6.37313068e-01 4.83700773e-03
2.39443511e-01 -3.54456693e-01 -6.64755046e-01 -7.36150980e-01
-7.76239485e-02 2.27125600e-01 4.49376404e-02 1.00474127... | [14.53526782989502, 5.036471366882324] |
749e6608-97df-4f4f-aa19-9dc9920b83b7 | neural-probabilistic-motor-primitives-for | 1811.11711 | null | http://arxiv.org/abs/1811.11711v2 | http://arxiv.org/pdf/1811.11711v2.pdf | Neural probabilistic motor primitives for humanoid control | We focus on the problem of learning a single motor module that can flexibly
express a range of behaviors for the control of high-dimensional physically
simulated humanoids. To do this, we propose a motor architecture that has the
general structure of an inverse model with a latent-variable bottleneck. We
show that it i... | ['Arun Ahuja', 'Nicolas Heess', 'Josh Merel', 'Alexandre Galashov', 'Yee Whye Teh', 'Vu Pham', 'Leonard Hasenclever', 'Greg Wayne'] | 2018-11-28 | neural-probabilistic-motor-primitives-for-1 | https://openreview.net/forum?id=BJl6TjRcY7 | https://openreview.net/pdf?id=BJl6TjRcY7 | iclr-2019-5 | ['humanoid-control'] | ['robots'] | [ 1.56483293e-01 5.75720608e-01 -2.07248271e-01 2.24508584e-01
-5.84616065e-01 -6.23077691e-01 5.48143148e-01 -6.30539417e-01
-4.92319375e-01 8.55696976e-01 2.48168528e-01 -2.17034474e-01
-7.02665448e-02 -2.77745157e-01 -1.24953234e+00 -7.45672047e-01
-2.54461169e-01 5.17844558e-01 1.42808229e-01 -8.10951516... | [4.519857406616211, 1.009150505065918] |
d5980ec6-4d94-40cc-9688-187daec97d32 | neural-intrinsic-embedding-for-non-rigid | 2303.01038 | null | https://arxiv.org/abs/2303.01038v1 | https://arxiv.org/pdf/2303.01038v1.pdf | Neural Intrinsic Embedding for Non-rigid Point Cloud Matching | As a primitive 3D data representation, point clouds are prevailing in 3D sensing, yet short of intrinsic structural information of the underlying objects. Such discrepancy poses great challenges on directly establishing correspondences between point clouds sampled from deformable shapes. In light of this, we propose Ne... | ['Ruqi Huang', 'Mingze Sun', 'Puhua Jiang'] | 2023-03-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_Neural_Intrinsic_Embedding_for_Non-Rigid_Point_Cloud_Matching_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Neural_Intrinsic_Embedding_for_Non-Rigid_Point_Cloud_Matching_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-registration'] | ['computer-vision'] | [ 1.77398175e-01 1.83174953e-01 -1.55124724e-01 -3.46109927e-01
-6.37233019e-01 -7.81552732e-01 7.88970470e-01 -3.19975503e-02
-1.75780728e-01 1.15945891e-01 8.32594484e-02 -1.44526765e-01
-1.39857844e-01 -7.56974876e-01 -9.44158494e-01 -5.79269767e-01
2.02186540e-01 8.03476214e-01 6.42528981e-02 -6.46880642... | [8.24924087524414, -3.2735655307769775] |
9ff37d9e-5f95-4820-a3d9-6e3aefd56e04 | benchmarks-for-automated-commonsense | 2302.04752 | null | https://arxiv.org/abs/2302.04752v2 | https://arxiv.org/pdf/2302.04752v2.pdf | Benchmarks for Automated Commonsense Reasoning: A Survey | More than one hundred benchmarks have been developed to test the commonsense knowledge and commonsense reasoning abilities of artificial intelligence (AI) systems. However, these benchmarks are often flawed and many aspects of common sense remain untested. Consequently, we do not currently have any reliable way of meas... | ['Ernest Davis'] | 2023-02-09 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 4.37077552e-01 1.12812571e-01 1.70796543e-01 -4.60508168e-01
-1.59142986e-01 -6.61975324e-01 8.69445145e-01 1.26183897e-01
-2.79417455e-01 9.16389704e-01 3.11942995e-01 -3.08769017e-01
-3.71088088e-01 -8.84603381e-01 -4.86520559e-01 -1.46054626e-01
3.88304919e-01 4.29240078e-01 2.86077708e-01 -8.64882648... | [9.954985618591309, 7.895907878875732] |
4d49111c-bbba-43bd-b62c-5b0f208ea375 | joint-edge-model-sparse-learning-is-provably | 2302.02922 | null | https://arxiv.org/abs/2302.02922v1 | https://arxiv.org/pdf/2302.02922v1.pdf | Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks | Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs. Examples include \textit{graph sparsification} that samples a subgraph to reduce the amount of data aggregation and \textit{mode... | ['Miao Liu', 'Songtao Lu', 'Sijia Liu', 'Pin-Yu Chen', 'Meng Wang', 'Shuai Zhang'] | 2023-02-06 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 5.44745445e-01 5.91826975e-01 -3.19493622e-01 -1.86518505e-01
-3.51469368e-01 -1.75259620e-01 8.81513506e-02 2.15670228e-01
-2.88100839e-01 8.72398973e-01 -2.72837192e-01 -3.68912965e-01
-5.40819466e-01 -8.92302334e-01 -9.89990950e-01 -8.08948219e-01
-2.34844297e-01 4.23064470e-01 -5.72971739e-02 2.58588493... | [8.388063430786133, 3.555940628051758] |
740a30c5-5b62-4068-ae92-ff8d979422fd | foveation-based-mechanisms-alleviate | 1511.06292 | null | http://arxiv.org/abs/1511.06292v3 | http://arxiv.org/pdf/1511.06292v3.pdf | Foveation-based Mechanisms Alleviate Adversarial Examples | We show that adversarial examples, i.e., the visually imperceptible
perturbations that result in Convolutional Neural Networks (CNNs) fail, can be
alleviated with a mechanism based on foveations---applying the CNN in different
image regions. To see this, first, we report results in ImageNet that lead to a
revision of t... | ['Xavier Boix', 'Gemma Roig', 'Yan Luo', 'Tomaso Poggio', 'Qi Zhao'] | 2015-11-19 | null | null | null | null | ['foveation'] | ['computer-vision'] | [ 3.64672482e-01 7.42277563e-01 6.47064745e-01 1.58707444e-02
4.34656590e-02 -1.10144615e+00 7.67353714e-01 -3.47090513e-01
-4.42107648e-01 4.39017385e-01 9.04799998e-02 -3.03385139e-01
3.37972730e-01 -8.55437994e-01 -1.43185925e+00 -8.91013265e-01
2.69719027e-02 -2.85558045e-01 4.21346962e-01 -5.47750890... | [5.647904396057129, 7.868929386138916] |
e36bdaf9-c8cf-4212-a8ec-912c83788e20 | mangngalapp-an-integrated-package-of | 2301.02893 | null | https://arxiv.org/abs/2301.02893v1 | https://arxiv.org/pdf/2301.02893v1.pdf | MangngalApp -- An integrated package of technology for COVID-19 response and rural development: Acceptability and usability using TAM | The COVID19 pandemic has challenged universities and organizations to devise mechanisms to uplift the well-being and welfare of people and communities. In response, the design and development of an integrated package of technologies, MangngalApp -- A web-based portal and mobile responsive application for rural developm... | ['Jesty S. Agoto', 'James Karl A. Agpalza', 'Leo P. Paliuanan', 'Billy S. Javier'] | 2023-01-07 | null | null | null | null | ['culture'] | ['speech'] | [-2.97214717e-01 2.36252517e-01 3.12521905e-02 1.33501306e-01
4.96059749e-03 -8.36056411e-01 6.18322566e-02 3.04995030e-01
-3.63095134e-01 3.37729335e-01 4.70194340e-01 -8.23410511e-01
-2.98528284e-01 -7.47939825e-01 -4.06605661e-01 -5.46285808e-01
-2.00085230e-02 -3.69254440e-01 5.58957122e-02 -6.29979670... | [9.085504531860352, 6.257633686065674] |
bd603f03-511d-491e-95b5-4f2f2fdf7874 | priberam-at-mesinesp-multi-label | 2105.05614 | null | https://arxiv.org/abs/2105.05614v1 | https://arxiv.org/pdf/2105.05614v1.pdf | Priberam at MESINESP Multi-label Classification of Medical Texts Task | Medical articles provide current state of the art treatments and diagnostics to many medical practitioners and professionals. Existing public databases such as MEDLINE contain over 27 million articles, making it difficult to extract relevant content without the use of efficient search engines. Information retrieval too... | ['Sebastião Miranda', 'Afonso Mendes', 'Zita Marinho', 'Ruben Cardoso'] | 2021-05-12 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 4.71662521e-01 4.69927937e-02 -6.72412932e-01 -2.28238896e-01
-1.19132376e+00 -6.34418726e-01 6.20309711e-01 9.34896171e-01
-6.72175527e-01 1.07140911e+00 3.59100066e-02 -3.79030049e-01
-5.78506827e-01 -4.51455832e-01 -1.50817588e-01 -6.71039522e-01
2.97129810e-01 1.14568675e+00 3.25134009e-01 -1.15959495... | [8.398963928222656, 8.588618278503418] |
54f30920-0cae-41b3-bfda-adabb5d7d522 | combining-hololens-with-instant-nerfs | 2304.14301 | null | https://arxiv.org/abs/2304.14301v2 | https://arxiv.org/pdf/2304.14301v2.pdf | Combining HoloLens with Instant-NeRFs: Advanced Real-Time 3D Mobile Mapping | This work represents a large step into modern ways of fast 3D reconstruction based on RGB camera images. Utilizing a Microsoft HoloLens 2 as a multisensor platform that includes an RGB camera and an inertial measurement unit for SLAM-based camera-pose determination, we train a Neural Radiance Field (NeRF) as a neural s... | ['Patrick Huebner', 'Miriam Jaeger', 'Markus Ulrich', 'Boris Jutzi', 'Dennis Haitz'] | 2023-04-27 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 2.96582848e-01 -1.71050832e-01 1.66654065e-01 -4.01831895e-01
-7.27761209e-01 -4.87815768e-01 4.72863883e-01 -1.34001940e-01
-9.12686348e-01 5.78123152e-01 -3.33927393e-01 -3.47566277e-01
-5.12365736e-02 -1.31144500e+00 -1.26209629e+00 -5.64669430e-01
-1.25115039e-02 1.03799832e+00 5.14902174e-02 -1.17449537... | [7.372773170471191, -2.2548844814300537] |
4fe5888c-356a-4985-94ad-a185f25eb57a | biked-a-dataset-and-machine-learning | 2103.05844 | null | https://arxiv.org/abs/2103.05844v3 | https://arxiv.org/pdf/2103.05844v3.pdf | BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks | In this paper, we present "BIKED," a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and support the development of data-driven design methods. The dataset is comprised of a variety of ... | ['Faez Ahmed', 'Brent Curry', 'Lyle Regenwetter'] | 2021-03-10 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [-1.12297215e-01 -2.53583580e-01 -4.12699282e-01 -1.13005817e-01
-3.99495572e-01 -8.26862514e-01 3.57965469e-01 -6.40705347e-01
3.06014836e-01 7.16170073e-01 5.95457852e-01 -6.10990882e-01
-6.47007883e-01 -7.22374976e-01 -7.08893359e-01 -6.10420823e-01
2.96143681e-01 5.34210920e-01 -3.93351436e-01 -4.30143178... | [5.8431267738342285, 3.254589557647705] |
aec077e4-fb96-453a-b627-deb9374e4886 | st-mvl-filling-missing-values-in-geo-sensory | null | null | https://www.microsoft.com/en-us/research/publication/st-mvl-filling-missing-values-in-geo-sensory-time-series-data/ | https://www.ijcai.org/Proceedings/16/Papers/384.pdf | ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data | Many sensors have been deployed in the physical world, generating massive geo-tagged time series data. In reality, readings of sensors are usually lost at various unexpected moments because of sensor or communication errors. Those missing readings do not only affect real-time monitoring but also compromise the performa... | ['Yu Zheng', 'Tianrui Li', 'Junbo Zhang', 'Xiuwen Yi'] | 2016-07-09 | null | null | null | ijcai-2016-2016-7 | ['multivariate-time-series-imputation'] | ['time-series'] | [ 8.85992795e-02 -5.02877235e-01 -8.45611989e-02 -5.12010276e-01
-1.05918562e+00 -5.05184948e-01 5.75215518e-01 7.00223565e-01
-2.92610228e-01 1.01335895e+00 6.10247612e-01 -1.46112755e-01
-6.96484506e-01 -1.13050795e+00 -6.92509294e-01 -7.74734855e-01
-3.26932877e-01 1.24788061e-01 2.42359579e-01 -1.97836310... | [6.778815746307373, 2.731411933898926] |
5e45668a-c3fd-47b0-9fcb-c74b1dec5b9d | atttrack-online-deep-attention-transfer-for | 2210.08648 | null | https://arxiv.org/abs/2210.08648v2 | https://arxiv.org/pdf/2210.08648v2.pdf | AttTrack: Online Deep Attention Transfer for Multi-object Tracking | Multi-object tracking (MOT) is a vital component of intelligent video analytics applications such as surveillance and autonomous driving. The time and storage complexity required to execute deep learning models for visual object tracking hinder their adoption on embedded devices with limited computing power. In this pa... | ['Rong Zheng', 'Keivan Nalaie'] | 2022-10-16 | null | null | null | null | ['deep-attention', 'visual-object-tracking', 'deep-attention'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.66661230e-02 -4.87570986e-02 -2.24495918e-01 -2.60942757e-01
-2.49033898e-01 -3.71389270e-01 4.16187465e-01 -6.22297935e-02
-5.25754333e-01 4.59431499e-01 -4.04160649e-01 -5.60135543e-01
1.55968890e-01 -5.77515602e-01 -9.21740890e-01 -5.77236414e-01
1.70034319e-01 5.87048471e-01 1.04428303e+00 2.84857243... | [6.324197292327881, -2.074770927429199] |
2768d521-b925-497f-8daf-18a061efacdd | lmpriors-pre-trained-language-models-as-task | 2210.12530 | null | https://arxiv.org/abs/2210.12530v1 | https://arxiv.org/pdf/2210.12530v1.pdf | LMPriors: Pre-Trained Language Models as Task-Specific Priors | Particularly in low-data regimes, an outstanding challenge in machine learning is developing principled techniques for augmenting our models with suitable priors. This is to encourage them to learn in ways that are compatible with our understanding of the world. But in contrast to generic priors such as shrinkage or sp... | ['Stefano Ermon', 'Sanjari Srivastava', 'Chris Cundy', 'Kristy Choi'] | 2022-10-22 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 2.54176855e-01 4.08652991e-01 -8.06613088e-01 -6.82596922e-01
-8.65095913e-01 -3.88625860e-01 1.08452928e+00 1.84916109e-01
-5.44389725e-01 9.12960351e-01 8.83898914e-01 -3.65063727e-01
-1.53120056e-01 -5.97786605e-01 -9.23438311e-01 -2.81050056e-01
-3.28349099e-02 4.60305184e-01 -2.79062539e-01 -5.44802062... | [10.456806182861328, 8.0504150390625] |
eb83ac8f-3218-4090-88fe-b05ca043ec3d | joint-metrics-matter-a-better-standard-for | 2305.06292 | null | https://arxiv.org/abs/2305.06292v1 | https://arxiv.org/pdf/2305.06292v1.pdf | Joint Metrics Matter: A Better Standard for Trajectory Forecasting | Multi-modal trajectory forecasting methods commonly evaluate using single-agent metrics (marginal metrics), such as minimum Average Displacement Error (ADE) and Final Displacement Error (FDE), which fail to capture joint performance of multiple interacting agents. Only focusing on marginal metrics can lead to unnatural... | ['Kris Kitani', 'Deva Ramanan', 'Hana Hoshino', 'Erica Weng'] | 2023-05-10 | null | null | null | null | ['trajectory-forecasting'] | ['computer-vision'] | [-3.50953281e-01 -2.13682115e-01 6.07452542e-02 -1.22126147e-01
-6.81867480e-01 -5.36058009e-01 1.16681767e+00 3.73337328e-01
-7.20817208e-01 1.05314910e+00 4.57558990e-01 -6.11396953e-02
-5.06273985e-01 -8.29141080e-01 -5.43521166e-01 -5.90052009e-01
-6.36449695e-01 6.02156460e-01 2.95792818e-01 -4.61328894... | [5.799829483032227, 0.9513121843338013] |
bae6a775-bab4-4962-9647-c2cc1977f7f2 | exploring-the-limits-of-a-base-bart-for-multi | null | null | https://aclanthology.org/2022.sdp-1.23 | https://aclanthology.org/2022.sdp-1.23.pdf | Exploring the limits of a base BART for multi-document summarization in the medical domain | This paper is a description of our participation in the Multi-document Summarization for Literature Review (MSLR) Shared Task, in which we explore summarization models to create an automatic review of scientific results. Rather than maximizing the metrics using expensive computational models, we placed ourselves in a s... | ['Horacio Saggion', 'Silvia Casola', 'Ishmael Obonyo'] | null | null | null | null | sdp-coling-2022-10 | ['document-summarization'] | ['natural-language-processing'] | [ 5.71268380e-01 6.66933954e-01 -5.96930087e-01 -9.91654322e-02
-1.33772755e+00 -6.83881044e-01 7.71252155e-01 4.57280487e-01
-4.95292127e-01 1.08737016e+00 8.16765726e-01 -6.84709430e-01
-1.13125429e-01 -1.77983910e-01 -4.88745630e-01 -2.59192288e-01
3.05277407e-01 3.25554401e-01 -2.64709797e-02 2.29198448... | [12.340157508850098, 9.589061737060547] |
a5497826-5621-4920-b6c5-ee2465b81313 | learning-scene-flow-with-skeleton-guidance | 2306.13285 | null | https://arxiv.org/abs/2306.13285v1 | https://arxiv.org/pdf/2306.13285v1.pdf | Learning Scene Flow With Skeleton Guidance For 3D Action Recognition | Among the existing modalities for 3D action recognition, 3D flow has been poorly examined, although conveying rich motion information cues for human actions. Presumably, its susceptibility to noise renders it intractable, thus challenging the learning process within deep models. This work demonstrates the use of 3D flo... | ['Athanasios Psaltis', 'Vasileios Magoulianitis'] | 2023-06-23 | null | null | null | null | ['action-recognition-in-videos', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.72776991e-01 -7.55441561e-02 -4.38934565e-01 -1.54851645e-01
-5.94705403e-01 -6.19604960e-02 7.32870877e-01 -2.17799976e-01
-3.46365780e-01 4.51262206e-01 6.66181445e-01 2.68553019e-01
-2.59818882e-01 -4.15531576e-01 -4.08832788e-01 -8.53686988e-01
-3.32173258e-01 1.41025394e-01 2.87590891e-01 1.74555760... | [7.882893085479736, 0.34209510684013367] |
d75fe57c-f5f5-46b3-9ee5-3d965f1d9752 | skeleton-aided-articulated-motion-generation | 1707.01058 | null | http://arxiv.org/abs/1707.01058v2 | http://arxiv.org/pdf/1707.01058v2.pdf | Skeleton-aided Articulated Motion Generation | This work make the first attempt to generate articulated human motion
sequence from a single image. On the one hand, we utilize paired inputs
including human skeleton information as motion embedding and a single human
image as appearance reference, to generate novel motion frames, based on the
conditional GAN infrastru... | ['Bingbing Ni', 'Yichao Yan', 'Jingwei Xu', 'Xiaokang Yang'] | 2017-07-04 | null | null | null | null | ['gesture-to-gesture-translation'] | ['computer-vision'] | [ 4.88836437e-01 2.30677366e-01 -4.79242206e-02 6.16364852e-02
-4.61961806e-01 -4.11344707e-01 9.45308685e-01 -1.00559628e+00
-4.18126553e-01 9.68032002e-01 1.87669858e-01 1.70102447e-01
3.81586283e-01 -5.46753645e-01 -6.64191246e-01 -8.56285691e-01
2.61315972e-01 9.21874419e-02 1.79420143e-01 1.33889094... | [10.86663818359375, -0.7326459884643555] |
d13765d6-0e7b-403c-be74-06f6c12fcef0 | breaking-on-device-training-memory-wall-a | 2306.10388 | null | https://arxiv.org/abs/2306.10388v1 | https://arxiv.org/pdf/2306.10388v1.pdf | Breaking On-device Training Memory Wall: A Systematic Survey | On-device training has become an increasingly popular approach to machine learning, enabling models to be trained directly on mobile and edge devices. However, a major challenge in this area is the limited memory available on these devices, which can severely restrict the size and complexity of the models that can be t... | ['Li Li', 'Rui Ma', 'Kahou Tam', 'Chunlin Tian', 'Shitian Li'] | 2023-06-17 | null | null | null | null | ['navigate'] | ['reasoning'] | [ 3.77762049e-01 6.76270872e-02 -1.12624240e+00 -1.01802073e-01
-5.62488973e-01 -3.93215179e-01 -1.63700193e-01 -1.72675103e-01
-2.46781990e-01 5.85559547e-01 -1.89059511e-01 -1.01250982e+00
-1.56171665e-01 -6.33765340e-01 -7.94778585e-01 -9.88487303e-02
2.09676608e-01 6.50015920e-02 -2.10226048e-02 3.26816291... | [8.595466613769531, 3.1079325675964355] |
d57457e9-9045-4ab8-afb6-a4e261cc9b87 | large-scale-study-of-curiosity-driven | 1808.04355 | null | http://arxiv.org/abs/1808.04355v1 | http://arxiv.org/pdf/1808.04355v1.pdf | Large-Scale Study of Curiosity-Driven Learning | Reinforcement learning algorithms rely on carefully engineering environment
rewards that are extrinsic to the agent. However, annotating each environment
with hand-designed, dense rewards is not scalable, motivating the need for
developing reward functions that are intrinsic to the agent. Curiosity is a
type of intrins... | ['Harri Edwards', 'Yuri Burda', 'Deepak Pathak', 'Amos Storkey', 'Alexei A. Efros', 'Trevor Darrell'] | 2018-08-13 | large-scale-study-of-curiosity-driven-1 | https://openreview.net/forum?id=rJNwDjAqYX | https://openreview.net/pdf?id=rJNwDjAqYX | iclr-2019-5 | ['snes-games'] | ['playing-games'] | [-2.25060329e-01 -1.51517153e-01 7.05160499e-02 -3.05006862e-01
-8.65423322e-01 -7.04992712e-01 5.64089000e-01 -3.07375193e-02
-9.05638337e-01 1.04427373e+00 2.17653781e-01 7.44801015e-02
-3.10215265e-01 -6.63703620e-01 -6.94975913e-01 -7.64982700e-01
-5.51504016e-01 3.41859996e-01 3.94598663e-01 -7.72160113... | [3.8789546489715576, 1.6108957529067993] |
e0f490ca-d290-4fc8-a2ce-4c9000244e33 | graph-neural-network-for-cell-tracking-in | 2202.04731 | null | https://arxiv.org/abs/2202.04731v2 | https://arxiv.org/pdf/2202.04731v2.pdf | Graph Neural Network for Cell Tracking in Microscopy Videos | We present a novel graph neural network (GNN) approach for cell tracking in high-throughput microscopy videos. By modeling the entire time-lapse sequence as a direct graph where cell instances are represented by its nodes and their associations by its edges, we extract the entire set of cell trajectories by looking for... | ['Tammy Riklin Raviv', 'Tal Ben-Haim'] | 2022-02-09 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-6.99426457e-02 -3.35122585e-01 3.86007093e-02 2.05658991e-02
-3.43832254e-01 -6.38889015e-01 6.75921977e-01 4.95642245e-01
-7.57003605e-01 9.69592750e-01 -1.79734334e-01 -3.01183797e-02
-6.11222163e-02 -8.28385174e-01 -7.48610079e-01 -1.25166881e+00
-4.48515445e-01 4.85432804e-01 3.07188213e-01 1.44551158... | [14.571443557739258, -3.1927428245544434] |
0574ab6a-e58c-4d93-b1e9-88c46daa4cbd | language-quantized-autoencoders-towards | 2302.00902 | null | https://arxiv.org/abs/2302.00902v2 | https://arxiv.org/pdf/2302.00902v2.pdf | Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment | Recent progress in scaling up large language models has shown impressive capabilities in performing few-shot learning across a wide range of text-based tasks. However, a key limitation is that these language models fundamentally lack visual perception - a crucial attribute needed to extend these models to be able to in... | ['Pieter Abbeel', 'Wilson Yan', 'Hao liu'] | 2023-02-02 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.63396859e-01 2.64185909e-02 -3.65645736e-02 -2.97404587e-01
-6.72737181e-01 -3.98031205e-01 8.31976771e-01 -8.57993066e-02
-6.26592457e-01 1.61627844e-01 1.33547947e-01 -2.07385674e-01
3.14336389e-01 -6.73767626e-01 -8.82961631e-01 -4.59095508e-01
4.12948459e-01 5.60333788e-01 2.36035213e-01 -2.68619806... | [10.496408462524414, 1.8802789449691772] |
805aa9a8-5bd2-4702-bab0-e6bd368a2e26 | learning-cross-lingual-ir-from-an-english-1 | null | null | https://openreview.net/forum?id=OXqk4rKn9LT | https://openreview.net/pdf?id=OXqk4rKn9LT | Learning Cross-Lingual IR from an English Retriever | We present a new cross-lingual information retrieval (CLIR) system trained using multi-stage knowledge distillation (KD). The teacher relies on a highly effective but expensive two-stage process consisting of query translation and monolingual IR, while the student executes a single CLIR step. We teach the student power... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-9.52550843e-02 -7.62368664e-02 -7.07552791e-01 -2.33021602e-01
-2.06815600e+00 -1.03928864e+00 6.58079028e-01 3.08295786e-01
-1.13008237e+00 8.11291814e-01 -3.29760052e-02 -5.61717868e-01
-1.28348231e-01 -4.17802334e-01 -7.17909694e-01 -3.67737204e-01
4.64387506e-01 1.26606560e+00 -7.58036552e-03 -6.76775157... | [11.352873802185059, 9.787575721740723] |
7375a625-8d17-467f-a781-ada04e0e8543 | an-overview-of-hierarchical-task-network | 1403.7426 | null | http://arxiv.org/abs/1403.7426v1 | http://arxiv.org/pdf/1403.7426v1.pdf | An Overview of Hierarchical Task Network Planning | Hierarchies are the most common structure used to understand the world
better. In galaxies, for instance, multiple-star systems are organised in a
hierarchical system. Then, governmental and company organisations are
structured using a hierarchy, while the Internet, which is used on a daily
basis, has a space of domain... | ['Ilche Georgievski', 'Marco Aiello'] | 2014-03-28 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 8.15126970e-02 7.06838965e-01 -2.23908007e-01 -1.90206707e-01
-1.13865912e-01 -7.50784814e-01 1.10906637e+00 4.38142605e-02
-6.58501610e-02 5.39372027e-01 6.37986004e-01 -3.58334810e-01
-8.81944478e-01 -1.07465398e+00 -2.87562166e-03 -3.87187928e-01
-1.04870059e-01 1.12761343e+00 7.23057151e-01 -6.47043467... | [8.655872344970703, 6.895969390869141] |
679648ae-5a4a-41a1-8c3d-7681ef206ca9 | universal-weak-coreset | 2305.16890 | null | https://arxiv.org/abs/2305.16890v1 | https://arxiv.org/pdf/2305.16890v1.pdf | Universal Weak Coreset | Coresets for $k$-means and $k$-median problems yield a small summary of the data, which preserve the clustering cost with respect to any set of $k$ centers. Recently coresets have also been constructed for constrained $k$-means and $k$-median problems. However, the notion of coresets has the drawback that (i) they can ... | ['Amit Kumar', 'Ragesh Jaiswal'] | 2023-05-26 | null | null | null | null | ['data-compression'] | ['time-series'] | [-2.73670461e-02 -1.16156720e-01 -2.66600370e-01 -1.53309703e-01
-5.28898001e-01 -6.47343278e-01 -2.20101506e-01 4.80963051e-01
-4.03809458e-01 3.69849026e-01 -2.02813536e-01 -2.17156969e-02
-9.92784142e-01 -1.14816046e+00 -7.12952256e-01 -7.69937336e-01
-5.02558231e-01 9.05662060e-01 3.22478682e-01 -8.23023543... | [6.790865898132324, 4.976040840148926] |
daee6f19-364d-4057-9bb0-56c44363271a | unicon-ictcas-ucas-submission-to-the-ava | 2206.10861 | null | https://arxiv.org/abs/2206.10861v1 | https://arxiv.org/pdf/2206.10861v1.pdf | UniCon+: ICTCAS-UCAS Submission to the AVA-ActiveSpeaker Task at ActivityNet Challenge 2022 | This report presents a brief description of our winning solution to the AVA Active Speaker Detection (ASD) task at ActivityNet Challenge 2022. Our underlying model UniCon+ continues to build on our previous work, the Unified Context Network (UniCon) and Extended UniCon which are designed for robust scene-level ASD. We ... | ['Shiguang Shan', 'Shuang Yang', 'Susan Liang', 'Yuanhang Zhang'] | 2022-06-22 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 8.31620842e-02 1.91329762e-01 -1.24359280e-01 -4.98402297e-01
-1.28792822e+00 -6.83100700e-01 1.10111225e+00 -3.24998915e-01
-3.10384959e-01 4.70467776e-01 7.51303852e-01 -2.63119996e-01
2.30095983e-01 -6.94512995e-03 -2.40812182e-01 -2.37280205e-01
-1.91629738e-01 4.39648300e-01 8.08997750e-01 -3.73908937... | [14.405085563659668, 5.80491828918457] |
0b54e3e5-528c-4ff2-af62-c050e2169dfb | team-enigma-at-argmining-emnlp-2021 | 2110.12370 | null | https://arxiv.org/abs/2110.12370v1 | https://arxiv.org/pdf/2110.12370v1.pdf | Team Enigma at ArgMining-EMNLP 2021: Leveraging Pre-trained Language Models for Key Point Matching | We present the system description for our submission towards the Key Point Analysis Shared Task at ArgMining 2021. Track 1 of the shared task requires participants to develop methods to predict the match score between each pair of arguments and keypoints, provided they belong to the same topic under the same stance. We... | ['Abhilash Nandy', 'Varun Madhavan', 'Siba Smarak Panigrahi', 'Sohan Patnaik', 'Manav Nitin Kapadnis'] | 2021-10-24 | null | https://aclanthology.org/2021.argmining-1.21 | https://aclanthology.org/2021.argmining-1.21.pdf | emnlp-argmining-2021-11 | ['key-point-matching'] | ['natural-language-processing'] | [-2.35893950e-01 4.61719632e-01 -3.62756491e-01 -5.54198146e-01
-1.44051254e+00 -1.11550987e+00 1.26022530e+00 7.30756640e-01
-6.46651447e-01 5.39829910e-01 4.49548185e-01 -7.26513416e-02
-2.62474447e-01 -5.09547412e-01 -8.75281811e-01 2.29834821e-02
-1.96940467e-01 6.77970052e-01 4.40845758e-01 -2.99884319... | [10.179322242736816, 9.152390480041504] |
6fc66565-df8a-4deb-8035-d8226bab4165 | flowtransformer-a-transformer-framework-for | 2304.14746 | null | https://arxiv.org/abs/2304.14746v1 | https://arxiv.org/pdf/2304.14746v1.pdf | FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems | This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer leverages the strengths of transformer models in identifying the long-term behaviour and characteristics of networks, which are often overlooked by most exi... | ['Marius Portmann', 'Mohanad Sarhan', 'Gayan K. Kulatilleke', 'Wai Weng Lo', 'Siamak Layeghy', 'Liam Daly Manocchio'] | 2023-04-28 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 1.73152417e-01 -5.47084391e-01 -1.53672665e-01 -2.42500275e-01
-1.15196981e-01 -9.38712180e-01 8.29263985e-01 1.38335079e-01
-3.24791789e-01 4.03239071e-01 -2.35559896e-01 -1.07020926e+00
-5.38953304e-01 -9.51865673e-01 -1.04635537e-01 -2.94075072e-01
-2.33323723e-01 6.20475829e-01 6.72822833e-01 -3.71739686... | [5.325231552124023, 7.3015947341918945] |
03a4a99e-6fe3-4857-84e7-998bf3768792 | a-method-for-studying-semantic-construal-in | 2305.18598 | null | https://arxiv.org/abs/2305.18598v1 | https://arxiv.org/pdf/2305.18598v1.pdf | A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces | We study semantic construal in grammatical constructions using large language models. First, we project contextual word embeddings into three interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. We validate these interpretable spaces and then use them to automatically derive... | ['Katrin Erk', 'Kyle Mahowald', 'Gabriella Chronis'] | 2023-05-29 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [-1.82646289e-01 3.57402295e-01 -3.63415759e-03 -8.40937376e-01
7.08924159e-02 -9.73742783e-01 5.94463646e-01 7.01560080e-01
-7.16035306e-01 4.95138735e-01 9.92066503e-01 -5.68671107e-01
-6.43810630e-02 -1.00230539e+00 -5.47827721e-01 -7.25041747e-01
3.57475691e-02 3.55083615e-01 -1.91261157e-01 -6.32010102... | [10.362661361694336, 8.978385925292969] |
5197da2a-e0e6-44e6-b544-bdfc49eb6185 | causality-between-sentiment-and | 2306.05803 | null | https://arxiv.org/abs/2306.05803v1 | https://arxiv.org/pdf/2306.05803v1.pdf | Causality between Sentiment and Cryptocurrency Prices | This study investigates the relationship between narratives conveyed through microblogging platforms, namely Twitter, and the value of crypto assets. Our study provides a unique technique to build narratives about cryptocurrency by combining topic modelling of short texts with sentiment analysis. First, we used an unsu... | ['Abhijeet Chandra', 'Sarwesh P', 'Began Gowsik S', 'Abinandhan S', 'Udeshya Raj', 'Lubdhak Mondal'] | 2023-06-09 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-4.90949690e-01 1.18554339e-01 -4.68724191e-01 2.22577363e-01
-6.30842030e-01 -9.88895416e-01 1.39586663e+00 6.22371376e-01
-1.72263622e-01 4.64186251e-01 1.14965475e+00 -4.19031411e-01
1.54729575e-01 -1.05395150e+00 -3.51654947e-01 -6.74903393e-01
-3.66735101e-01 8.89173821e-02 -1.81644619e-01 -4.87774581... | [4.525018215179443, 4.425189018249512] |
7d0fd543-ec3c-4dc2-812c-15328c406c39 | audio-visual-recognition-of-overlapped-speech | 2001.01656 | null | https://arxiv.org/abs/2001.01656v1 | https://arxiv.org/pdf/2001.01656v1.pdf | Audio-visual Recognition of Overlapped speech for the LRS2 dataset | Automatic recognition of overlapped speech remains a highly challenging task to date. Motivated by the bimodal nature of human speech perception, this paper investigates the use of audio-visual technologies for overlapped speech recognition. Three issues associated with the construction of audio-visual speech recogniti... | ['Shansong Liu', 'Shi-Xiong Zhang', 'Shahram Ghorbani', 'Jian Wu', 'Helen Meng', 'Xunying Liu', 'Bo Wu', 'Shiyin Kang', 'Jianwei Yu', 'Dong Yu'] | 2020-01-06 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 4.39545661e-01 -4.95711342e-02 2.43525013e-01 -4.71780807e-01
-1.29010582e+00 -3.56719911e-01 6.38287425e-01 -2.16184050e-01
-3.79531264e-01 3.57898414e-01 2.69126296e-01 -5.13415515e-01
8.38385746e-02 1.12989284e-01 -3.75327498e-01 -8.28050017e-01
2.36143976e-01 2.95232832e-02 2.36204028e-01 -1.94412097... | [14.481708526611328, 5.332518577575684] |
0408110f-08f3-48c4-a90a-b9c6e280799b | noisy-label-detection-for-speaker-recognition | 2212.00239 | null | https://arxiv.org/abs/2212.00239v2 | https://arxiv.org/pdf/2212.00239v2.pdf | Inconsistency Ranking-based Noisy Label Detection for High-quality Data | The success of deep learning requires high-quality annotated and massive data. However, the size and the quality of a dataset are usually a trade-off in practice, as data collection and cleaning are expensive and time-consuming. In real-world applications, especially those using crowdsourcing datasets, it is important ... | ['Zhizheng Wu', 'Lei Zhang', 'Yushi Ye', 'Yifan He', 'Yi Wang', 'Hanzhi Yin', 'Ruibin Yuan'] | 2022-12-01 | null | null | null | null | ['speaker-recognition', 'speaker-verification'] | ['speech', 'speech'] | [-7.21651837e-02 -2.38329187e-01 2.75796443e-01 -8.78225088e-01
-1.26183784e+00 -5.59875786e-01 2.23925561e-01 3.53589386e-01
-6.73055768e-01 8.09573770e-01 5.75008169e-02 -2.80470289e-02
-2.29670331e-01 -4.59638983e-01 -5.70068240e-01 -8.37197185e-01
1.91482827e-01 3.49637151e-01 -1.84454001e-03 1.38227776... | [9.428751945495605, 3.850180149078369] |
eb82c20f-8573-4ae0-8b99-fb9027c5fc35 | attack-agnostic-adversarial-detection-on | 2105.01959 | null | https://arxiv.org/abs/2105.01959v1 | https://arxiv.org/pdf/2105.01959v1.pdf | Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning | Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks has shown the ease of designing samples to mislead a model into making ... | ['Noura Al Moubayed', 'Matthew Watson'] | 2021-05-05 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 4.12201971e-01 6.70703232e-01 2.20411703e-01 -4.43658829e-01
-9.76574600e-01 -8.66669595e-01 4.99632210e-01 3.18939209e-01
-1.67770311e-01 5.84203839e-01 -5.40784022e-05 -8.69271815e-01
-1.37588888e-01 -4.72141981e-01 -8.29462230e-01 -3.34747672e-01
-2.98293084e-01 6.77477479e-01 -3.39508057e-01 9.65606794... | [5.844677448272705, 7.605072975158691] |
0419e254-f78c-4d04-8c74-291a24ba19e2 | span-identification-of-epistemic-stance | 2306.02038 | null | https://arxiv.org/abs/2306.02038v1 | https://arxiv.org/pdf/2306.02038v1.pdf | Span Identification of Epistemic Stance-Taking in Academic Written English | Responding to the increasing need for automated writing evaluation (AWE) systems to assess language use beyond lexis and grammar (Burstein et al., 2016), we introduce a new approach to identify rhetorical features of stance in academic English writing. Drawing on the discourse-analytic framework of engagement in the Ap... | ['Kristopher Kyle', 'Masaki Eguchi'] | 2023-06-03 | null | null | null | null | ['automated-writing-evaluation'] | ['natural-language-processing'] | [ 9.19898227e-02 5.98314524e-01 -5.44675291e-01 -3.40560526e-01
-1.09415925e+00 -9.60896671e-01 9.94324744e-01 7.51080036e-01
-6.13514960e-01 8.62158239e-01 9.39932883e-01 -9.79426861e-01
-1.02670407e-02 -3.07161152e-01 -2.81147033e-01 -4.38482836e-02
4.65778857e-01 -3.12393475e-02 -3.71639132e-01 -1.43548876... | [11.204071998596191, 9.424884796142578] |
add9c0bb-ae4d-45f1-a6dd-53d0c031cd88 | building-fast-and-compact-convolutional | 1702.07975 | null | http://arxiv.org/abs/1702.07975v1 | http://arxiv.org/pdf/1702.07975v1.pdf | Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition | Like other problems in computer vision, offline handwritten Chinese character
recognition (HCCR) has achieved impressive results using convolutional neural
network (CNN)-based methods. However, larger and deeper networks are needed to
deliver state-of-the-art results in this domain. Such networks intuitively
appear to ... | ['Yafeng Yang', 'Xuefeng Xiao', 'Weixin Yang', 'Lianwen Jin', 'Jun Sun', 'Tianhai Chang'] | 2017-02-26 | null | null | null | null | ['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character'] | ['computer-vision', 'natural-language-processing'] | [ 2.12891027e-01 -3.58140469e-01 -5.84958168e-03 -3.00818145e-01
-5.22936404e-01 -2.68572956e-01 1.93675473e-01 -6.02785647e-02
-9.46231723e-01 4.28847075e-01 -3.11469883e-01 -7.11282313e-01
5.49447276e-02 -7.06203401e-01 -7.07507551e-01 -6.37799919e-01
-5.55859804e-02 2.50645459e-01 2.66167104e-01 -9.45634991... | [11.673197746276855, 2.612318515777588] |
129bd941-d5c6-4548-95e4-e10eb8a10e69 | counterexample-guided-abstraction-refinement-1 | 2301.08687 | null | https://arxiv.org/abs/2301.08687v1 | https://arxiv.org/pdf/2301.08687v1.pdf | Counterexample Guided Abstraction Refinement with Non-Refined Abstractions for Multi-Agent Path Finding | Counterexample guided abstraction refinement (CEGAR) represents a powerful symbolic technique for various tasks such as model checking and reachability analysis. Recently, CEGAR combined with Boolean satisfiability (SAT) has been applied for multi-agent path finding (MAPF), a problem where the task is to navigate agent... | ['Pavel Surynek'] | 2023-01-20 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.67584324e-01 6.11525178e-01 8.31535012e-02 5.87220900e-02
-6.82554543e-01 -6.80651486e-01 5.91491938e-01 6.95371687e-01
-2.86256611e-01 1.36572039e+00 -1.72817513e-01 -4.79447991e-01
-3.82742971e-01 -1.17345643e+00 -6.70500100e-01 -4.31528658e-01
-5.73862553e-01 1.19668770e+00 9.88019764e-01 -5.39134026... | [4.981617450714111, 1.9072625637054443] |
fa0bd870-0c45-4d0a-a6b1-05628fd12c45 | structural-restricted-boltzmann-machine-for | 2306.09628 | null | https://arxiv.org/abs/2306.09628v1 | https://arxiv.org/pdf/2306.09628v1.pdf | Structural Restricted Boltzmann Machine for image denoising and classification | Restricted Boltzmann Machines are generative models that consist of a layer of hidden variables connected to another layer of visible units, and they are used to model the distribution over visible variables. In order to gain a higher representability power, many hidden units are commonly used, which, in combination wi... | ['Santana Roberto', 'Pérez Aritz', 'Bidaurrazaga Arkaitz'] | 2023-06-16 | null | null | null | null | ['image-denoising', 'classification-1'] | ['computer-vision', 'methodology'] | [ 2.94122785e-01 2.21470118e-01 1.26508489e-01 -2.14898840e-01
-9.80813876e-02 -2.09222108e-01 7.79661834e-01 -3.07295114e-01
-5.75034261e-01 7.45696902e-01 -2.03036871e-02 -1.42297432e-01
8.39430094e-02 -1.04232299e+00 -7.11290777e-01 -1.36810756e+00
8.86428207e-02 4.64774966e-01 9.05659720e-02 3.87151004... | [9.148859024047852, 2.6410422325134277] |
3a419869-f3d0-4a22-9af8-4a3eca63d40a | constant-memory-attention-block | 2306.12599 | null | https://arxiv.org/abs/2306.12599v1 | https://arxiv.org/pdf/2306.12599v1.pdf | Constant Memory Attention Block | Modern foundation model architectures rely on attention mechanisms to effectively capture context. However, these methods require linear or quadratic memory in terms of the number of inputs/datapoints, limiting their applicability in low-compute domains. In this work, we propose Constant Memory Attention Block (CMAB), ... | ['Mohamed Osama Ahmed', 'Yoshua Bengio', 'Hossein Hajimirsadeghi', 'Frederick Tung', 'Leo Feng'] | 2023-06-21 | null | null | null | null | ['point-processes'] | ['methodology'] | [-8.70702490e-02 -3.10752720e-01 -1.80549577e-01 -4.22964208e-02
-5.85817158e-01 -4.48722452e-01 8.58828127e-01 4.56638545e-01
-6.49732113e-01 4.22189295e-01 1.65198520e-01 -3.31785768e-01
9.31868628e-02 -9.31605577e-01 -9.52542305e-01 -4.03964579e-01
2.33898908e-02 4.59039092e-01 4.68457788e-01 1.13917485... | [10.748778343200684, 6.762882709503174] |
e1eeffa8-afa6-400d-a493-073452acc77c | personalized-keyphrase-detection-using | 2104.13970 | null | https://arxiv.org/abs/2104.13970v2 | https://arxiv.org/pdf/2104.13970v2.pdf | Personalized Keyphrase Detection using Speaker and Environment Information | In this paper, we introduce a streaming keyphrase detection system that can be easily customized to accurately detect any phrase composed of words from a large vocabulary. The system is implemented with an end-to-end trained automatic speech recognition (ASR) model and a text-independent speaker verification model. To ... | ['Ian McGraw', 'Arun Narayanan', 'Huang', 'Yiteng', 'Ding Zhao', 'Yanzhang He', 'Qiao Liang', 'Quan Wang', 'Rajeev Rikhye'] | 2021-04-28 | null | null | null | null | ['text-independent-speaker-verification', 'speaker-separation'] | ['speech', 'speech'] | [ 3.58377963e-01 -3.58414024e-01 2.31966406e-01 -2.60501355e-01
-1.39277232e+00 -7.99229205e-01 6.80182397e-01 3.02545011e-01
-4.91782367e-01 1.05140634e-01 5.17378449e-01 -4.07237202e-01
2.70519704e-01 -2.32339531e-01 -4.76129442e-01 -6.26297712e-01
4.90835123e-02 -1.19047128e-01 2.99091637e-01 -1.97367057... | [14.513395309448242, 6.225064754486084] |
938a94ad-9c1f-4b50-8276-42d97026a9a7 | leveraging-sparsity-for-efficient-submodular | 1703.02690 | null | http://arxiv.org/abs/1703.02690v1 | http://arxiv.org/pdf/1703.02690v1.pdf | Leveraging Sparsity for Efficient Submodular Data Summarization | The facility location problem is widely used for summarizing large datasets
and has additional applications in sensor placement, image retrieval, and
clustering. One difficulty of this problem is that submodular optimization
algorithms require the calculation of pairwise benefits for all items in the
dataset. This is i... | ['Alexandros G. Dimakis', 'Erik M. Lindgren', 'Shanshan Wu'] | 2017-03-08 | leveraging-sparsity-for-efficient-submodular-1 | http://papers.nips.cc/paper/6382-leveraging-sparsity-for-efficient-submodular-data-summarization | http://papers.nips.cc/paper/6382-leveraging-sparsity-for-efficient-submodular-data-summarization.pdf | neurips-2016-12 | ['data-summarization'] | ['miscellaneous'] | [ 3.52685928e-01 2.05433726e-01 -5.70104182e-01 -4.02637422e-01
-8.53258371e-01 -8.10829103e-01 2.35707611e-02 8.82352114e-01
-2.33265877e-01 9.42367792e-01 5.31275570e-01 -2.66798526e-01
-7.39622951e-01 -9.47105110e-01 -9.33779776e-01 -6.80330575e-01
-4.72191334e-01 6.05942667e-01 1.77277625e-01 -1.64450899... | [6.632683753967285, 4.945424556732178] |
b734ce50-0b8a-4958-8316-253949834742 | quantum-recurrent-neural-networks-for | 2302.03244 | null | https://arxiv.org/abs/2302.03244v1 | https://arxiv.org/pdf/2302.03244v1.pdf | Quantum Recurrent Neural Networks for Sequential Learning | Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but up to now there is still a lack o... | ['Yongjian Gu', 'Guoqiang Zhong', 'Haiyong Zheng', 'Ruimin Shang', 'Jiaxin Li', 'Shangshang Shi', 'Rongbing Han', 'Zhimin Wang', 'Yanan Li'] | 2023-02-07 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 2.63282135e-02 -4.38031077e-01 -1.01715796e-01 -1.19711962e-02
-2.82578856e-01 -9.79082361e-02 4.28483874e-01 -1.26020744e-01
-6.02588117e-01 6.40462101e-01 -2.04738468e-01 -5.67027450e-01
-2.28803381e-01 -1.19661522e+00 -6.76902413e-01 -1.25819242e+00
3.67579252e-01 -1.84812307e-01 4.67303813e-01 -8.72393012... | [5.564509391784668, 4.971056938171387] |
f0ba841d-0906-408c-912a-ff7f3321d5a5 | cross-lingual-low-resource-set-to-description | 2005.08188 | null | https://arxiv.org/abs/2005.08188v1 | https://arxiv.org/pdf/2005.08188v1.pdf | Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce | With the prosperous of cross-border e-commerce, there is an urgent demand for designing intelligent approaches for assisting e-commerce sellers to offer local products for consumers from all over the world. In this paper, we explore a new task of cross-lingual information retrieval, i.e., cross-lingual set-to-descripti... | ['Xiaozhong Liu', 'Jian Wang', 'Hongsong Li', 'Chang Liu', 'Rui Yan', 'Dongyan Zhao', 'Lidong Bing', 'Juntao Li'] | 2020-05-17 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.40382031e-01 -6.25503123e-01 -7.05801904e-01 -7.85579562e-01
-1.55499232e+00 -9.72452164e-01 6.58428311e-01 2.07301244e-01
-4.53772128e-01 3.81432742e-01 2.50003710e-02 -2.03555465e-01
-3.34981173e-01 -7.21240580e-01 -6.92236423e-01 -3.52175057e-01
8.94019380e-02 1.16965330e+00 -2.99351960e-01 -8.71756792... | [11.495587348937988, 9.922776222229004] |
153c4613-858c-47c1-ba32-c561e8357842 | towards-deep-observation-a-systematic-survey | 2201.07935 | null | https://arxiv.org/abs/2201.07935v2 | https://arxiv.org/pdf/2201.07935v2.pdf | Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images | Developing innovative informatics approaches aimed to enhance fetal monitoring is a burgeoning field of study in reproductive medicine. Several reviews have been conducted regarding Artificial intelligence (AI) techniques to improve pregnancy outcomes. They are limited by focusing on specific data such as mother's care... | ['Michel Makhlouf', 'Alaa Abd-Alrazaq', 'Mowafa Househ', 'Mohammed Anbar', 'Uzair Shah', 'Khaled A Althelaya', 'Khalid Alyafei', 'Marco Agus', 'Mahmood Alzubaidi'] | 2022-01-17 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 3.53961438e-01 7.59309947e-01 -8.67825627e-01 -1.30787060e-01
-2.07116812e-01 -5.77919543e-01 1.10084489e-01 6.47539318e-01
-3.13112408e-01 5.18347681e-01 6.39582798e-02 -8.70126188e-01
-4.10481900e-01 -7.81754434e-01 -7.00808585e-01 -4.17250037e-01
-2.67356694e-01 6.54560566e-01 -1.95810467e-01 3.93274099... | [14.05460262298584, -2.2413952350616455] |
9fa77937-c9a1-4938-9d0f-edcf0b0ec771 | discovery-of-2d-materials-using-transformer | 2301.05824 | null | https://arxiv.org/abs/2301.05824v1 | https://arxiv.org/pdf/2301.05824v1.pdf | Discovery of 2D materials using Transformer Network based Generative Design | Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently less than 6,000 experimentally synthesized 2D materials have been reported. Recently, deep learning, data-mining, and density functional the... | ['Jianjun Hu', 'Edirisuriya M. D. Siriwardane', 'Yuqi Song', 'Rongzhi Dong'] | 2023-01-14 | null | null | null | null | ['formation-energy', 'self-learning'] | ['miscellaneous', 'natural-language-processing'] | [-1.35053933e-01 -1.54717907e-01 -2.05414444e-02 -2.05436647e-01
-8.42230976e-01 -3.38732332e-01 6.45085037e-01 7.47372508e-02
2.07351536e-01 1.09027863e+00 1.49193734e-01 -4.87395704e-01
4.79607694e-02 -1.09499192e+00 -8.09605539e-01 -1.07110214e+00
-2.51420975e-01 8.33987832e-01 2.68572718e-01 -4.42317247... | [5.1774492263793945, 5.371654033660889] |
1b2309cc-870e-40fb-adf3-f6895bb3040c | unbiased-scene-graph-generation-via-rich-and | 2002.00176 | null | https://arxiv.org/abs/2002.00176v1 | https://arxiv.org/pdf/2002.00176v1.pdf | Unbiased Scene Graph Generation via Rich and Fair Semantic Extraction | Extracting graph representation of visual scenes in image is a challenging task in computer vision. Although there has been encouraging progress of scene graph generation in the past decade, we surprisingly find that the performance of existing approaches is largely limited by the strong biases, which mainly stem from ... | ['Xianglong Liu', 'Lei Huang', 'Jie Luo', 'Bin Wen'] | 2020-02-01 | null | null | null | null | ['unbiased-scene-graph-generation'] | ['computer-vision'] | [ 2.67557800e-01 2.98297256e-01 -4.33985323e-01 -4.69328940e-01
-1.41612411e-01 -3.47755790e-01 7.06549227e-01 1.43009856e-01
-9.98898596e-02 3.93249959e-01 4.54707026e-01 5.46932966e-03
-1.68635964e-01 -8.24836969e-01 -6.40334904e-01 -5.30066073e-01
1.07130744e-01 4.01148468e-01 4.82911944e-01 -3.17585170... | [10.308211326599121, 1.6656508445739746] |
7580196f-e1f8-44bd-a873-85d7b902ef1d | spatio-temporal-outdoor-lighting-aggregation | 2202.09206 | null | https://arxiv.org/abs/2202.09206v1 | https://arxiv.org/pdf/2202.09206v1.pdf | Spatio-Temporal Outdoor Lighting Aggregation on Image Sequences using Transformer Networks | In this work, we focus on outdoor lighting estimation by aggregating individual noisy estimates from images, exploiting the rich image information from wide-angle cameras and/or temporal image sequences. Photographs inherently encode information about the scene's lighting in the form of shading and shadows. Recovering ... | ['Carsten Rother', 'Jan Rexilius', 'Robert Herzog', 'Christian Homeyer', 'Haebom Lee'] | 2022-02-18 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 4.64551270e-01 -3.23231488e-01 3.48991215e-01 -7.44864881e-01
-6.01488471e-01 -5.89750350e-01 7.38581181e-01 -3.66394222e-01
-5.22937894e-01 4.51011866e-01 3.04948777e-01 6.57315850e-02
3.25134128e-01 -5.21639228e-01 -1.01822877e+00 -7.03908324e-01
4.51217830e-01 1.81584775e-01 1.16511136e-01 3.29114683... | [9.775296211242676, -2.885542392730713] |
25a17fbb-95ae-4a45-9b79-36ece6262929 | cnn-bilstm-model-for-english-handwriting | 2307.00664 | null | https://arxiv.org/abs/2307.00664v1 | https://arxiv.org/pdf/2307.00664v1.pdf | CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Dataset | We present a CNN-BiLSTM system for the problem of offline English handwriting recognition, with extensive evaluations on the public IAM dataset, including the effects of model size, data augmentation and the lexicon. Our best model achieves 3.59\% CER and 9.44\% WER using CNN-BiLSTM network with CTC layer. Test time au... | ['Berrin Yanikoglu', 'Firat Kizilirmak'] | 2023-07-02 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 3.18578094e-01 -3.55067812e-02 -2.15751261e-01 -4.47243810e-01
-1.04843223e+00 -6.67148113e-01 3.46125960e-01 -4.98459250e-01
-7.45243728e-01 5.43701649e-01 -1.71724670e-02 -7.91324198e-01
2.92184293e-01 -4.15522069e-01 -7.73253143e-01 -3.60931098e-01
2.91712105e-01 3.27562183e-01 3.51306796e-02 -1.82446931... | [11.877700805664062, 2.4863877296447754] |
03f6cbe5-b82c-4388-8dc9-c01d99f8809d | a-novel-approach-for-dimensionality-reduction | 2210.13901 | null | https://arxiv.org/abs/2210.13901v1 | https://arxiv.org/pdf/2210.13901v1.pdf | A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy | During the last decade, hyperspectral images have attracted increasing interest from researchers worldwide. They provide more detailed information about an observed area and allow an accurate target detection and precise discrimination of objects compared to classical RGB and multispectral images. Despite the great pot... | ['Nacir Chafik', 'Ahmed Hammouch', 'Elkebir Sarhrouni', 'Hasna Nhaila', 'Asma Elmaizi'] | 2022-10-25 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 8.66098225e-01 -6.59113169e-01 -9.54044312e-02 -4.38338146e-02
-4.13558275e-01 -6.84076846e-01 4.13670391e-01 2.82776088e-01
-3.71966094e-01 1.01970863e+00 -2.07629219e-01 -1.34117668e-02
-1.08537221e+00 -9.41421926e-01 -1.30253628e-01 -1.25873029e+00
-1.27431661e-01 6.83040693e-02 1.54719232e-02 -1.95577055... | [9.74095630645752, -1.8030469417572021] |
2f24ba0f-e1ea-46df-852d-54e40098be12 | fcc-fusing-conversation-history-and-candidate | 2304.00180 | null | https://arxiv.org/abs/2304.00180v1 | https://arxiv.org/pdf/2304.00180v1.pdf | FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems | Response ranking in dialogues plays a crucial role in retrieval-based conversational systems. In a multi-turn dialogue, to capture the gist of a conversation, contextual information serves as essential knowledge to achieve this goal. In this paper, we present a flexible neural framework that can integrate contextual in... | ['Jinho Choi', 'Eugene Agichtein', 'ZiHao Wang'] | 2023-03-31 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 2.47497588e-01 4.69724089e-02 -2.24487141e-01 -6.95762992e-01
-1.16906488e+00 -5.72650254e-01 1.03959942e+00 1.17275789e-01
-6.13824487e-01 8.18483353e-01 8.67226541e-01 -7.93209746e-02
-8.74381065e-02 -5.95269799e-01 -8.82773474e-02 -3.20339441e-01
1.55951008e-01 6.26577854e-01 4.18156564e-01 -9.18868303... | [12.426114082336426, 7.887401580810547] |
ef5f5c75-a10f-4541-aca2-0907876e27e8 | fast-3d-registration-with-accurate | 2112.03053 | null | https://arxiv.org/abs/2112.03053v1 | https://arxiv.org/pdf/2112.03053v1.pdf | Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021 | Current approaches for deformable medical image registration often struggle to fulfill all of the following criteria: versatile applicability, small computation or training times, and the being able to estimate large deformations. Furthermore, end-to-end networks for supervised training of registration often become ove... | ['Mattias P. Heinrich', 'Lasse Hansen', 'Hanna Siebert'] | 2021-12-06 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 3.67603362e-01 2.17313379e-01 2.53976941e-01 -4.15892810e-01
-1.31777537e+00 -4.51210618e-01 7.45941579e-01 4.44095105e-01
-7.92939246e-01 6.47736132e-01 1.07194282e-01 1.42052621e-01
-3.55189115e-01 -4.14896965e-01 -6.17019594e-01 -6.19525850e-01
-1.34216011e-01 8.12422812e-01 4.08705413e-01 -4.82330054... | [14.099178314208984, -2.542720317840576] |
a0963169-6975-437f-8cce-3f46d03b6d02 | generating-lead-sheets-with-affect-a-novel | 2104.13056 | null | https://arxiv.org/abs/2104.13056v1 | https://arxiv.org/pdf/2104.13056v1.pdf | Generating Lead Sheets with Affect: A Novel Conditional seq2seq Framework | The field of automatic music composition has seen great progress in the last few years, much of which can be attributed to advances in deep neural networks. There are numerous studies that present different strategies for generating sheet music from scratch. The inclusion of high-level musical characteristics (e.g., pe... | ['Dorien Herremans', 'Kat R. Agres', 'Dimos Makris'] | 2021-04-27 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.30661738e-01 -1.12356201e-01 7.31708556e-02 -2.33944014e-01
-5.72761655e-01 -9.09429789e-01 6.63627267e-01 -1.94352582e-01
-1.81913108e-01 5.38567305e-01 2.00254962e-01 1.02837101e-01
-6.20059073e-02 -9.91160274e-01 -6.54643238e-01 -6.67080045e-01
1.69602513e-01 3.34401250e-01 -1.28637984e-01 -5.93669891... | [15.990113258361816, 5.539989948272705] |
6fcf8b09-f47b-4ad3-942b-a5e9e7611812 | bleurt-has-universal-translations-an-analysis | 2307.03131 | null | https://arxiv.org/abs/2307.03131v2 | https://arxiv.org/pdf/2307.03131v2.pdf | BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training | Automatic metrics play a crucial role in machine translation. Despite the widespread use of n-gram-based metrics, there has been a recent surge in the development of pre-trained model-based metrics that focus on measuring sentence semantics. However, these neural metrics, while achieving higher correlations with human ... | ['Mingxuan Wang', 'Jiajun Chen', 'ShuJian Huang', 'Chengqi Zhao', 'Tao Wang', 'Yiming Yan'] | 2023-07-06 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 2.13626876e-01 -2.08402276e-01 -3.48780513e-01 -5.88038266e-01
-1.09180224e+00 -6.53398097e-01 7.42926776e-01 3.91461849e-01
-4.72662002e-01 7.11915374e-01 5.03840268e-01 -6.54191196e-01
-1.25015993e-02 -5.70809364e-01 -5.40358961e-01 -4.25100476e-01
4.31856215e-01 2.52951831e-01 -1.43643141e-01 -5.57809174... | [11.522455215454102, 10.178117752075195] |
ac378d77-5e41-4c43-a745-02ccc1c59875 | look-into-object-self-supervised-structure | 2003.14142 | null | https://arxiv.org/abs/2003.14142v1 | https://arxiv.org/pdf/2003.14142v1.pdf | Look-into-Object: Self-supervised Structure Modeling for Object Recognition | Most object recognition approaches predominantly focus on learning discriminative visual patterns while overlooking the holistic object structure. Though important, structure modeling usually requires significant manual annotations and therefore is labor-intensive. In this paper, we propose to "look into object" (expli... | ['Wei zhang', 'Yalong Bai', 'Mohan Zhou', 'Tiejun Zhao', 'Tao Mei'] | 2020-03-31 | look-into-object-self-supervised-structure-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.pdf | cvpr-2020-6 | ['image-recognition'] | ['computer-vision'] | [ 1.05302021e-01 4.28707339e-02 -4.35397983e-01 -6.67362094e-01
-5.16643941e-01 -7.77321637e-01 4.41540092e-01 1.12675771e-01
-9.61026996e-02 7.44896084e-02 -1.32615298e-01 -2.11480573e-01
-1.10082023e-01 -6.43117309e-01 -1.05389166e+00 -5.20234883e-01
3.97438705e-02 5.90449393e-01 6.44720256e-01 2.94107288... | [9.500125885009766, 1.1366032361984253] |
6c9e0c91-178b-4b2d-a6b3-a337f97a76cc | a-robust-semantic-frame-parsing-pipeline-on-a | 2212.08987 | null | https://arxiv.org/abs/2212.08987v1 | https://arxiv.org/pdf/2212.08987v1.pdf | A Robust Semantic Frame Parsing Pipeline on a New Complex Twitter Dataset | Most recent semantic frame parsing systems for spoken language understanding (SLU) are designed based on recurrent neural networks. These systems display decent performance on benchmark SLU datasets such as ATIS or SNIPS, which contain short utterances with relatively simple patterns. However, the current semantic fram... | ['Hongxia Jin', 'Yu Wang'] | 2022-12-18 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [-4.78625484e-02 3.55715960e-01 -2.04530403e-01 -6.06455564e-01
-9.28623915e-01 -7.68123984e-01 6.42581522e-01 2.10976806e-02
-3.56777996e-01 7.15745509e-01 6.21369123e-01 -8.01025987e-01
1.07290626e-01 -8.36839557e-01 -7.02449322e-01 -2.66372859e-01
3.55408698e-01 5.63106298e-01 3.59071136e-01 -7.88678050... | [13.951425552368164, 7.066309452056885] |
e5e208b1-13ef-4307-9967-2e8ef3f85572 | binocular-tone-mapping-with-improved-overall | 1809.06036 | null | http://arxiv.org/abs/1809.06036v1 | http://arxiv.org/pdf/1809.06036v1.pdf | Binocular Tone Mapping with Improved Overall Contrast and Local Details | Tone mapping is a commonly used technique that maps the set of colors in
high-dynamic-range (HDR) images to another set of colors in low-dynamic-range
(LDR) images, to fit the need for print-outs, LCD monitors and projectors.
Unfortunately, during the compression of dynamic range, the overall contrast
and local details... | ['Tien-Tsin Wong', 'Xinghong Hu', 'Zhuming Zhang', 'Xueting Liu'] | 2018-09-17 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.33794409e-01 -6.06489241e-01 -1.32945143e-02 -1.25474976e-02
8.08580592e-02 -5.59139788e-01 3.03818524e-01 -4.26794231e-01
-4.24776524e-02 6.39492810e-01 -3.65647450e-02 -3.36951256e-01
-9.34605896e-02 -9.86964345e-01 -4.20132309e-01 -4.58297461e-01
4.51963991e-01 -3.86385232e-01 5.39158165e-01 -3.83609354... | [10.867596626281738, -2.4523656368255615] |
eedaabd1-5c80-4bc6-a404-5112ccc544e9 | end-to-end-context-aided-unicity-matching-for | 2210.12008 | null | https://arxiv.org/abs/2210.12008v2 | https://arxiv.org/pdf/2210.12008v2.pdf | End-to-End Context-Aided Unicity Matching for Person Re-identification | Most existing person re-identification methods compute the matching relations between person images across camera views based on the ranking of the pairwise similarities. This matching strategy with the lack of the global viewpoint and the context's consideration inevitably leads to ambiguous matching results and sub-o... | ['Qi Tian', 'Junchi Yan', 'Chen Chen', 'Cong Ding', 'Min Cao'] | 2022-10-20 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 1.52123436e-01 -4.24756795e-01 -1.16675282e-02 -4.86640066e-01
-2.38223016e-01 -3.64265710e-01 5.68184376e-01 -6.72406470e-03
-5.64824700e-01 2.93276489e-01 4.51210946e-01 5.10453999e-01
-3.43228132e-01 -7.47610271e-01 -2.72865057e-01 -4.67410654e-01
2.51902461e-01 4.42671925e-01 7.60134310e-02 -1.72490537... | [14.73765754699707, 0.9596536159515381] |
d6a7ace9-8f3b-4b0b-bb6f-3c4ddb1634dd | smart-data-collection-system-for-brownfield | null | null | https://doi.org/10.1016/j.procir.2022.04.022 | https://doi.org/10.1016/j.procir.2022.04.022 | Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring | Manufacturing processes have undergone tremendous technological progress in recent decades. To meet the agile philosophy in industry, data-driven algorithms need to handle growing complexity, particularly in Computer Numerical Control machining. To enhance the scalability of machine learning in real-world applications,... | ['Klaus Diepold', 'Michael Feil', 'Mohamed-Ali Tnani'] | 2022-06-29 | null | null | null | procedia-cirp-2022-6 | ['time-series-clustering'] | ['time-series'] | [ 8.78402442e-02 -4.53566819e-01 1.43784538e-01 -2.68769950e-01
2.29305878e-01 -1.76129401e-01 1.82647794e-01 5.04543364e-01
2.48461053e-01 4.10848528e-01 -5.99106729e-01 -1.07927531e-01
-5.58601797e-01 -8.56359303e-01 -3.33681107e-01 -6.62734985e-01
-1.93768561e-01 1.00338626e+00 -2.33708903e-01 -3.19660813... | [6.985742568969727, 2.2975776195526123] |
4679655f-3037-4d78-ada4-2d4a3515046a | body-part-based-representation-learning-for | 2211.03679 | null | https://arxiv.org/abs/2211.03679v1 | https://arxiv.org/pdf/2211.03679v1.pdf | Body Part-Based Representation Learning for Occluded Person Re-Identification | Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. For addressing occluded ReID, part-based methods have been shown beneficial as they offer fine-grained information and are well suited to represent partially visible human bodies. Howeve... | ['Alexandre Alahi', 'Christophe De Vleeschouwer', 'Vladimir Somers'] | 2022-11-07 | null | null | null | null | ['person-retrieval', 'human-parsing', 'part-based-representation-learning'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.60892043e-02 5.59075586e-02 -2.01010913e-01 -3.89596403e-01
-5.44174552e-01 -3.67480546e-01 5.97894609e-01 -1.02637433e-01
-1.68852925e-01 6.33356392e-01 3.68832797e-01 3.98775667e-01
3.86307910e-02 -5.88931203e-01 -8.65206540e-01 -6.20687485e-01
7.87682980e-02 7.41489232e-01 6.57860637e-02 -8.91661420... | [14.6673583984375, 0.913096010684967] |
731818e0-8501-4c62-bfcc-c58752de2d86 | enhanced-blind-face-restoration-with-multi | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Enhanced_Blind_Face_Restoration_With_Multi-Exemplar_Images_and_Adaptive_Spatial_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Enhanced_Blind_Face_Restoration_With_Multi-Exemplar_Images_and_Adaptive_Spatial_CVPR_2020_paper.pdf | Enhanced Blind Face Restoration With Multi-Exemplar Images and Adaptive Spatial Feature Fusion | In many real-world face restoration applications, e.g., smartphone photo albums and old films, multiple high-quality (HQ) images of the same person usually are available for a given degraded low-quality (LQ) observation. However, most existing guided face restoration methods are based on single HQ exemplar image, and a... | [' Wangmeng Zuo', ' Meng Wang', ' Hongzhi Zhang', ' Dongwei Ren', ' Wenyu Li', 'Xiaoming Li'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['blind-face-restoration'] | ['computer-vision'] | [ 2.51064986e-01 -4.14150953e-01 1.14101224e-01 -4.55748975e-01
-8.48268747e-01 -4.28768918e-02 5.15943289e-01 -4.38333869e-01
4.55956021e-03 5.07085443e-01 6.65235579e-01 1.45757973e-01
-4.21402127e-01 -4.44695830e-01 -5.58269083e-01 -9.68537331e-01
1.51847899e-01 -1.59299895e-01 -3.12398493e-01 -1.67513013... | [12.846181869506836, -0.05700518563389778] |
d776cf4e-89a1-469e-8392-9782f0b9156d | dense-interspecies-face-embedding | null | null | https://openreview.net/forum?id=m67FNFdgLO9 | https://openreview.net/pdf?id=m67FNFdgLO9 | Dense Interspecies Face Embedding | Dense Interspecies Face Embedding (DIFE) is a new direction for understanding faces of various animals by extracting common features among animal faces including human face. There are three main obstacles for interspecies face understanding: (1) lack of animal data compared to human, (2) ambiguous connection between fa... | ['Seon Joo Kim', 'Seonghyeon Nam', 'Subin Jeon', 'Sejong Yang'] | 2022-11-28 | null | null | null | neruips-2022-11 | ['interspecies-facial-keypoint-transfer', 'keypoint-detection', 'image-manipulation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.39265656e-01 -1.14456378e-01 1.66784927e-01 -5.56481242e-01
-2.92933106e-01 -6.91273570e-01 4.39230651e-01 -6.15817010e-01
2.24140957e-02 5.38176596e-01 1.01588473e-01 2.06753090e-01
-3.97521369e-02 -5.66734731e-01 -7.08584607e-01 -5.17161071e-01
-1.38239861e-02 1.99557111e-01 -2.37960309e-01 -2.55183250... | [12.865028381347656, 0.02118346467614174] |
61d18c2f-e4f8-487b-80d0-d0830647cf7c | incorporating-expert-opinion-on-observable | 2302.06391 | null | https://arxiv.org/abs/2302.06391v1 | https://arxiv.org/pdf/2302.06391v1.pdf | Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework | This article describes an approach to incorporate expert opinion on observable quantities through the use of a loss function which updates a prior belief as opposed to specifying parameters on the priors. Eliciting information on observable quantities allows experts to provide meaningful information on a quantity famil... | ['Arthur White', 'Philip Cooney'] | 2023-02-10 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 2.81134665e-01 6.24806583e-01 -1.35469884e-01 -6.43873334e-01
-1.08143282e+00 -7.40765750e-01 5.22493899e-01 6.65954828e-01
-6.61946118e-01 1.06789780e+00 1.08257741e-01 -2.38046736e-01
-4.05168682e-01 -7.94224262e-01 -4.77480620e-01 -8.02179694e-01
1.30933106e-01 6.69739842e-01 2.32504427e-01 1.35748178... | [6.497837543487549, 3.96376633644104] |
26b74409-548d-447b-bbca-9db2c947ebcd | simvp-simpler-yet-better-video-prediction-1 | 2206.05099 | null | https://arxiv.org/abs/2206.05099v1 | https://arxiv.org/pdf/2206.05099v1.pdf | SimVP: Simpler yet Better Video Prediction | From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This pa... | ['Stan Z. Li', 'Lirong Wu', 'Cheng Tan', 'Zhangyang Gao'] | 2022-06-09 | simvp-simpler-yet-better-video-prediction | http://openaccess.thecvf.com//content/CVPR2022/html/Gao_SimVP_Simpler_Yet_Better_Video_Prediction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gao_SimVP_Simpler_Yet_Better_Video_Prediction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-prediction'] | ['computer-vision'] | [-6.89903274e-03 -2.09399927e-02 -4.72273618e-01 -4.74834859e-01
-6.36760414e-01 -1.82692498e-01 2.39120841e-01 -5.10633588e-01
-1.33305922e-01 5.84803283e-01 2.15872779e-01 -5.14601052e-01
2.90838242e-01 -5.99391639e-01 -8.59749913e-01 -6.81225061e-01
-6.38643727e-02 -3.91975529e-02 2.27601066e-01 -2.83370107... | [9.084601402282715, 0.43628621101379395] |
184fe19b-a748-4ac0-870d-d9ef518ec92a | scenegenie-scene-graph-guided-diffusion | 2304.14573 | null | https://arxiv.org/abs/2304.14573v1 | https://arxiv.org/pdf/2304.14573v1.pdf | SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis | Text-conditioned image generation has made significant progress in recent years with generative adversarial networks and more recently, diffusion models. While diffusion models conditioned on text prompts have produced impressive and high-quality images, accurately representing complex text prompts such as the number o... | ['Nassir Navab', 'Björn Ommer', 'Chengzhi Shen', 'Yu Chi', 'Yousef Yeganeh', 'Azade Farshad'] | 2023-04-28 | null | null | null | null | ['image-generation-from-scene-graphs'] | ['computer-vision'] | [ 5.92629015e-01 2.07535610e-01 -1.28678503e-02 -5.51212072e-01
-6.77987456e-01 -6.38457358e-01 1.07746017e+00 6.97813109e-02
-2.21302658e-01 4.29268479e-01 4.12841082e-01 -8.96344483e-02
1.62144601e-01 -1.29275560e+00 -1.04759765e+00 -4.83491331e-01
3.17607164e-01 6.85284972e-01 2.68975019e-01 -1.66715264... | [11.30673885345459, -0.2583547532558441] |
c3ad034b-26e8-4511-88a6-acfbfde7bb3e | moving-avatars-and-agents-in-social-extended | 2306.14484 | null | https://arxiv.org/abs/2306.14484v1 | https://arxiv.org/pdf/2306.14484v1.pdf | Moving Avatars and Agents in Social Extended Reality Environments | Natural interaction between multiple users within a shared virtual environment (VE) relies on each other's awareness of the current position of the interaction partners. This, however, cannot be warranted when users employ noncontinuous locomotion techniques, such as teleportation, which may cause confusion among bysta... | ['Frank Steinicke', 'Bernhard E. Riecke', 'Susanne Schmidt', 'Jann Philipp Freiwald'] | 2023-06-26 | null | null | null | null | ['navigate'] | ['reasoning'] | [-2.63080150e-01 3.78009439e-01 7.27637634e-02 8.79876986e-02
-3.01565558e-01 -7.05962360e-01 3.48167479e-01 1.05770670e-01
-5.31992197e-01 5.61320543e-01 2.49318123e-01 -1.73115190e-02
2.25423016e-02 -7.30420947e-01 -2.25731179e-01 -3.73281837e-01
-3.09657574e-01 4.34654802e-02 1.53716356e-01 -7.52562106... | [12.455071449279785, -0.11397939175367355] |
a89b9c52-6195-4b73-a010-50ce17eb8c26 | deep-learning-based-f0-synthesis-for-speaker | 2306.16860 | null | https://arxiv.org/abs/2306.16860v1 | https://arxiv.org/pdf/2306.16860v1.pdf | Deep Learning-based F0 Synthesis for Speaker Anonymization | Voice conversion for speaker anonymization is an emerging concept for privacy protection. In a deep learning setting, this is achieved by extracting multiple features from speech, altering the speaker identity, and waveform synthesis. However, many existing systems do not modify fundamental frequency (F0) trajectories,... | ['Nils Peters', 'Ünal Ege Gaznepoglu'] | 2023-06-29 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 1.32483348e-01 3.02793920e-01 1.04980670e-01 -4.34120566e-01
-8.40515316e-01 -1.05381274e+00 5.37160516e-01 8.78057554e-02
-3.93125802e-01 5.58612287e-01 7.58812547e-01 -1.50177702e-01
2.28519499e-01 -5.96046686e-01 -5.86784244e-01 -6.82096720e-01
6.07267767e-02 -3.59902412e-01 -4.06733334e-01 -1.37221292... | [14.01090145111084, 5.872395992279053] |
b696a128-6fe0-48ce-89f9-632008bdbf0d | bodega-benchmark-for-adversarial-example | 2303.08032 | null | https://arxiv.org/abs/2303.08032v1 | https://arxiv.org/pdf/2303.08032v1.pdf | BODEGA: Benchmark for Adversarial Example Generation in Credibility Assessment | Text classification methods have been widely investigated as a way to detect content of low credibility: fake news, social media bots, propaganda, etc. Quite accurate models (likely based on deep neural networks) help in moderating public electronic platforms and often cause content creators to face rejection of their ... | ['Horacio Saggion', 'Alexander Shvets', 'Piotr Przybyła'] | 2023-03-14 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 9.06054154e-02 4.10442621e-01 -9.90890637e-02 -1.20589145e-01
-4.44330633e-01 -1.00830233e+00 1.20471179e+00 5.56897223e-01
-2.68025070e-01 6.77689612e-01 1.37826189e-01 -4.91915047e-01
3.66477698e-01 -8.44351470e-01 -7.86453247e-01 -4.07555282e-01
3.25630372e-03 4.28596646e-01 3.00357193e-01 -5.40931761... | [8.129610061645508, 10.22708511352539] |
3e9f829c-2d51-48b0-aaca-91e1e51f3623 | mcl-3d-a-database-for-stereoscopic-image | 1405.1403 | null | http://arxiv.org/abs/1405.1403v1 | http://arxiv.org/pdf/1405.1403v1.pdf | MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source | A new stereoscopic image quality assessment database rendered using the
2D-image-plus-depth source, called MCL-3D, is described and the performance
benchmarking of several known 2D and 3D image quality metrics using the MCL-3D
database is presented in this work. Nine image-plus-depth sources are first
selected, and a d... | ['C. -C. Jay Kuo', 'Rui Song', 'Hyunsuk Ko'] | 2014-03-23 | null | null | null | null | ['stereoscopic-image-quality-assessment'] | ['computer-vision'] | [ 3.45414013e-01 -4.55882907e-01 2.77829021e-01 -2.71104336e-01
-1.01030898e+00 -4.45040137e-01 3.09549451e-01 -1.04997130e-02
-2.57415920e-01 5.91287076e-01 2.78126538e-01 -1.99188128e-01
-1.45712510e-01 -7.12827265e-01 -2.50301898e-01 -7.08585680e-01
-2.35982582e-01 -2.43525967e-01 5.64355135e-01 -3.14005464... | [11.75365924835205, -1.9155763387680054] |
538ed936-cd74-46a7-96c9-c0a178684658 | an-extension-to-hough-transform-based-on | 1510.04863 | null | http://arxiv.org/abs/1510.04863v1 | http://arxiv.org/pdf/1510.04863v1.pdf | An Extension to Hough Transform Based on Gradient Orientation | The Hough transform is one of the most common methods for line detection. In
this paper we propose a novel extension of the regular Hough transform. The
proposed extension combines the extension of the accumulator space and the
local gradient orientation resulting in clutter reduction and yielding more
prominent peaks,... | ['Sven Lončarić', 'Tomislav Petković'] | 2015-10-16 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [-9.38268676e-02 -4.33897167e-01 -8.66452511e-03 3.04524660e-01
-3.46932381e-01 -4.41858232e-01 2.23626062e-01 3.67442459e-01
-6.37768209e-01 7.46397138e-01 -2.91597575e-01 -2.97509909e-01
1.80550441e-02 -9.19138610e-01 -3.02161545e-01 -5.44687331e-01
-2.52575219e-01 -2.59082377e-01 9.05849159e-01 -2.67371505... | [8.480840682983398, -1.615005373954773] |
d83fef4c-eea0-49b0-bd7a-2229eb981da2 | background-click-supervision-for-temporal | 2111.12449 | null | https://arxiv.org/abs/2111.12449v1 | https://arxiv.org/pdf/2111.12449v1.pdf | Background-Click Supervision for Temporal Action Localization | Weakly supervised temporal action localization aims at learning the instance-level action pattern from the video-level labels, where a significant challenge is action-context confusion. To overcome this challenge, one recent work builds an action-click supervision framework. It requires similar annotation costs but can... | ['Jianxin Chen', 'Dingwen Zhang', 'Tianwei Lin', 'Tao Zhao', 'Junwei Han', 'Le Yang'] | 2021-11-24 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 3.79270494e-01 -3.42973620e-01 -4.46538180e-01 -4.59380209e-01
-7.85836041e-01 -2.49397680e-01 4.21453059e-01 -2.74536639e-01
-4.65954870e-01 5.78445256e-01 3.06536704e-01 8.97695497e-02
2.90879220e-01 -2.45392695e-01 -6.89663827e-01 -1.02686608e+00
6.68707564e-02 -1.25002757e-01 9.14985418e-01 2.28718311... | [8.494546890258789, 0.5865764617919922] |
e6054796-62f8-441c-b271-ff39b4eee51a | adversarial-audio-synthesis-with-complex | 2206.06811 | null | https://arxiv.org/abs/2206.06811v2 | https://arxiv.org/pdf/2206.06811v2.pdf | Adversarial Audio Synthesis with Complex-valued Polynomial Networks | Time-frequency (TF) representations in audio synthesis have been increasingly modeled with real-valued networks. However, overlooking the complex-valued nature of TF representations can result in suboptimal performance and require additional modules (e.g., for modeling the phase). To this end, we introduce complex-valu... | ['Volkan Cevher', 'Grigorios G Chrysos', 'Yongtao Wu'] | 2022-06-14 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 1.30870761e-02 2.59106010e-01 5.34771942e-02 -2.09158778e-01
-6.36687636e-01 -7.07510889e-01 5.92559338e-01 -3.20723593e-01
7.98582956e-02 3.82003576e-01 5.37867010e-01 -2.64299333e-01
-1.98718712e-01 -8.67468655e-01 -7.09771991e-01 -5.97886622e-01
-6.15168154e-01 1.65639490e-01 -1.06696360e-01 -6.28081977... | [15.602115631103516, 5.926140308380127] |
9bc3ec13-2225-4d22-be13-dca447cbf4a0 | face-hallucination-using-split-attention-in | 2010.11575 | null | https://arxiv.org/abs/2010.11575v3 | https://arxiv.org/pdf/2010.11575v3.pdf | Face Hallucination via Split-Attention in Split-Attention Network | Recently, convolutional neural networks (CNNs) have been widely employed to promote the face hallucination due to the ability to predict high-frequency details from a large number of samples. However, most of them fail to take into account the overall facial profile and fine texture details simultaneously, resulting in... | ['Junjun Jiang', 'Yanduo Zhang', 'Zhongyuan Wang', 'Wei Liu', 'Yu Wang', 'Tao Lu', 'Yuanzhi Wang'] | 2020-10-22 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 5.02828173e-02 6.32437617e-02 1.97940052e-01 -3.56533438e-01
-3.16882998e-01 3.00054485e-03 4.68419582e-01 -5.56499839e-01
1.45779833e-01 5.44325352e-01 4.05332714e-01 3.19340557e-01
-1.38157560e-02 -8.16380799e-01 -6.26520574e-01 -8.60320032e-01
4.95223761e-01 -2.09126115e-01 -2.97768325e-01 -1.37080148... | [12.840513229370117, -0.01770983263850212] |
051bc4d6-5e11-4766-b179-60b9d957e8ad | mathematical-modeling-in-human-evaluation-and | 2110.13909 | null | https://arxiv.org/abs/2110.13909v1 | https://arxiv.org/pdf/2110.13909v1.pdf | Mathematical Modeling, In-Human Evaluation and Analysis of Volume Kinetics and Kidney Function after Burn Injury and Resuscitation | Existing burn resuscitation protocols exhibit large variability in treatment efficacy. Hence, they must be further optimized based on comprehensive knowledge of burn pathophysiology. A physics-based mathematical model that can replicate physiological responses in diverse burn patients can serve as an attractive basis t... | ['Jose Salinas', 'Jin-OH Hahn', 'George C. Kramer', 'Chris Meador', 'Ali Tivay', 'Ghazal ArabiDarrehDor'] | 2021-10-24 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 2.26547867e-01 -7.98193693e-01 6.89976066e-02 1.45229383e-03
-2.16413617e-01 8.31846818e-02 -2.53269166e-01 9.21256304e-01
-7.06352293e-01 5.48770189e-01 5.14591098e-01 -5.62401652e-01
-3.79566282e-01 -5.85378528e-01 -4.00652230e-01 -8.20559978e-01
-4.05870110e-01 5.89987874e-01 -2.03443378e-01 2.54193209... | [8.06595230102539, 6.141534328460693] |
8894719e-512a-457e-bb64-8cb508f16786 | hyperspectral-unmixing-via-nonnegative-matrix | 2010.04611 | null | https://arxiv.org/abs/2010.04611v1 | https://arxiv.org/pdf/2010.04611v1.pdf | Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors | Nowadays, nonnegative matrix factorization (NMF) based methods have been widely applied to blind spectral unmixing. Introducing proper regularizers to NMF is crucial for mathematically constraining the solutions and physically exploiting spectral and spatial properties of images. Generally, properly handcrafting regula... | ['Wei Chen', 'Jie Chen', 'Tiande Gao', 'Min Zhao'] | 2020-10-09 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.50423837e-01 -5.67113757e-01 -2.26971194e-01 -3.25999819e-02
-4.45354491e-01 -4.55161750e-01 4.90006089e-01 -3.00322622e-01
-3.25191796e-01 7.00651228e-01 3.99243325e-01 8.85571390e-02
-4.58577782e-01 -4.76791412e-01 -5.38620234e-01 -1.02985322e+00
2.15252206e-01 2.71028966e-01 -4.33057368e-01 -9.21102464... | [10.074566841125488, -2.0694520473480225] |
afc2a55b-0598-4398-832a-101edfe24eae | cross-lingual-argument-mining-in-the-medical | 2301.10527 | null | https://arxiv.org/abs/2301.10527v1 | https://arxiv.org/pdf/2301.10527v1.pdf | Cross-lingual Argument Mining in the Medical Domain | Nowadays the medical domain is receiving more and more attention in applications involving Artificial Intelligence. Clinicians have to deal with an enormous amount of unstructured textual data to make a conclusion about patients' health in their everyday life. Argument mining helps to provide a structure to such data b... | ['Rodrigo Agerri', 'Anar Yeginbergenova'] | 2023-01-25 | null | null | null | null | ['argument-mining'] | ['natural-language-processing'] | [ 5.06326735e-01 8.09471130e-01 -4.88714337e-01 -2.36135498e-01
-8.94594133e-01 -3.96007389e-01 6.65016294e-01 1.05380630e+00
-7.74633527e-01 1.05164397e+00 6.79190278e-01 -9.10576582e-01
-1.83834314e-01 -6.87581122e-01 -3.80254000e-01 -3.87606949e-01
4.35212433e-01 8.42938244e-01 9.71129257e-03 -4.44536000... | [8.572619438171387, 8.77623462677002] |
7749b4d5-7f21-46e0-8d65-ed0f62d0982f | completing-partial-point-clouds-with-outliers | 2203.09772 | null | https://arxiv.org/abs/2203.09772v1 | https://arxiv.org/pdf/2203.09772v1.pdf | Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation | Most existing point cloud completion methods are only applicable to partial point clouds without any noises and outliers, which does not always hold in practice. We propose in this paper an end-to-end network, named CS-Net, to complete the point clouds contaminated by noises or containing outliers. In our CS-Net, the c... | ['Yanwen Guo', 'Chongjun Wang', 'Jie Guo', 'Yang Yang', 'Changfeng Ma'] | 2022-03-18 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 1.29610032e-01 -5.03371730e-02 3.72765034e-01 -3.61057341e-01
-9.02363777e-01 -4.97226119e-01 2.98611045e-01 4.79565822e-02
-1.20182067e-01 3.14452320e-01 -1.65305659e-01 5.15993536e-02
1.17524505e-01 -6.25701785e-01 -1.13934648e+00 -5.96357584e-01
1.30776972e-01 5.14406681e-01 3.11057031e-01 -5.92441596... | [8.275812149047852, -3.504476547241211] |
2ad75b3f-ba55-4243-bada-4438fb5984a5 | medvit-a-robust-vision-transformer-for | 2302.09462 | null | https://arxiv.org/abs/2302.09462v1 | https://arxiv.org/pdf/2302.09462v1.pdf | MedViT: A Robust Vision Transformer for Generalized Medical Image Classification | Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, there are still concerns about the reliability of deep medical diagnosis systems against the potential threats of adversarial attacks since inaccurate diagnosis could lead to disastrous consequences in ... | ['Ahmad Ayatollahi', 'Shahriar B. Shokouhi', 'Hossein Kashiani', 'Hamid Ahmadabadi', 'Omid Nejati Manzari'] | 2023-02-19 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 5.26333507e-03 2.40878224e-01 3.98601592e-01 -1.66815564e-01
-5.65953016e-01 -4.83876705e-01 2.85885960e-01 3.63859236e-02
-5.23964584e-01 3.61911893e-01 1.57891154e-01 -4.52783406e-01
-3.21883500e-01 -7.61280656e-01 -7.29743779e-01 -8.62043619e-01
-5.75089864e-02 1.05980419e-01 -4.48812656e-02 -1.22627035... | [5.6604461669921875, 7.810205936431885] |
f34fd0b7-fdb6-4717-ab35-4eacd7dd1b74 | fake-it-till-you-make-it-face-analysis-in-the | 2109.15102 | null | https://arxiv.org/abs/2109.15102v2 | https://arxiv.org/pdf/2109.15102v2.pdf | Fake It Till You Make It: Face analysis in the wild using synthetic data alone | We demonstrate that it is possible to perform face-related computer vision in the wild using synthetic data alone. The community has long enjoyed the benefits of synthesizing training data with graphics, but the domain gap between real and synthetic data has remained a problem, especially for human faces. Researchers h... | ['Jamie Shotton', 'Thomas J. Cashman', 'Virginia Estellers', 'Matthew Johnson', 'Sebastian Dziadzio', 'Charlie Hewitt', 'Tadas Baltrušaitis', 'Erroll Wood'] | 2021-09-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wood_Fake_It_Till_You_Make_It_Face_Analysis_in_the_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wood_Fake_It_Till_You_Make_It_Face_Analysis_in_the_ICCV_2021_paper.pdf | iccv-2021-1 | ['face-alignment', 'face-model', 'face-parsing'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.01232207e-01 7.59111404e-01 2.72762567e-01 -6.33420765e-01
-1.01749027e+00 -8.43019009e-01 7.74520576e-01 -5.08823812e-01
-4.33004014e-02 5.24524033e-01 -1.09421477e-01 -3.15691650e-01
3.14544111e-01 -6.97949708e-01 -7.60435224e-01 -3.43985438e-01
1.61680549e-01 8.90637875e-01 6.47844970e-02 -2.58621693... | [12.413986206054688, -0.3238179683685303] |
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