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0a1e6660-42a8-4563-b1f4-9deae33fa8db | clip-forge-towards-zero-shot-text-to-shape | 2110.02624 | null | https://arxiv.org/abs/2110.02624v2 | https://arxiv.org/pdf/2110.02624v2.pdf | CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation | Generating shapes using natural language can enable new ways of imagining and creating the things around us. While significant recent progress has been made in text-to-image generation, text-to-shape generation remains a challenging problem due to the unavailability of paired text and shape data at a large scale. We pr... | ['Kamal Rahimi Malekshan', 'Marco Fumero', 'Chin-Yi Cheng', 'Ye Wang', 'Joseph G. Lambourne', 'Hang Chu', 'Aditya Sanghi'] | 2021-10-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Sanghi_CLIP-Forge_Towards_Zero-Shot_Text-To-Shape_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sanghi_CLIP-Forge_Towards_Zero-Shot_Text-To-Shape_Generation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['text-to-shape-generation'] | ['computer-vision'] | [ 6.26729131e-01 1.44330427e-01 2.80935287e-01 -2.44778648e-01
-1.00912488e+00 -7.59210646e-01 9.80732441e-01 -1.63747072e-01
6.08126745e-02 7.31242418e-01 1.51592836e-01 -2.56935477e-01
1.88163489e-01 -9.83549654e-01 -7.02833593e-01 -6.28688574e-01
5.75719237e-01 6.23299241e-01 5.03058545e-02 -3.45800996... | [11.346632957458496, -0.2715771794319153] |
780c0c08-4b3d-4de4-be8c-32fecfd01bd7 | jpeg-xt-image-compression-with-hue | 1904.11315 | null | http://arxiv.org/abs/1904.11315v1 | http://arxiv.org/pdf/1904.11315v1.pdf | JPEG XT Image Compression with Hue Compensation for Two-Layer HDR Coding | We propose a novel JPEG XT image compression with hue compensation for
two-layer HDR coding. LDR images produced from JPEG XT bitstreams have some
distortion in hue due to tone mapping operations. In order to suppress the
color distortion, we apply a novel hue compensation method based on the
maximally saturated colors... | ['Hitoshi Kiya', 'Hiroyuki Kobayashi'] | 2019-04-25 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.31878716e-01 -4.37837631e-01 -1.05117261e-01 -1.24291539e-01
-1.36328146e-01 -2.22306296e-01 1.43057734e-01 -2.41918996e-01
-3.67739439e-01 8.64864528e-01 -9.01165083e-02 -1.33556142e-01
2.10401058e-01 -9.54065859e-01 -2.43296176e-01 -7.28527129e-01
1.41148552e-01 -3.23321909e-01 3.77888501e-01 -3.07668477... | [10.92265796661377, -2.421475410461426] |
ff509f8c-1a71-45c7-afbc-61767f32181e | wr-one2set-towards-well-calibrated-keyphrase | 2211.06862 | null | https://arxiv.org/abs/2211.06862v2 | https://arxiv.org/pdf/2211.06862v2.pdf | WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation | Keyphrase generation aims to automatically generate short phrases summarizing an input document. The recently emerged ONE2SET paradigm (Ye et al., 2021) generates keyphrases as a set and has achieved competitive performance. Nevertheless, we observe serious calibration errors outputted by ONE2SET, especially in the ove... | ['Jinsong Su', 'Min Zhang', 'Xiaoli Wang', 'Jun Xie', 'Huan Lin', 'Baosong Yang', 'Xiangpeng Wei', 'Binbin Xie'] | 2022-11-13 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.79698336e-01 3.08716059e-01 -4.50946122e-01 1.78247038e-02
-1.13736796e+00 -6.13777578e-01 6.47263288e-01 3.45623136e-01
-4.41436529e-01 1.03731966e+00 1.76200435e-01 -1.13104329e-01
-2.70237803e-01 -7.78103709e-01 -7.30375290e-01 -7.37622917e-01
1.22866817e-01 5.46600103e-01 3.05510014e-01 -1.66785896... | [12.1439208984375, 8.883356094360352] |
1be5c804-fdb7-4559-83c4-a4a0c7f1b463 | neural-activation-constellations-unsupervised | 1504.08289 | null | http://arxiv.org/abs/1504.08289v3 | http://arxiv.org/pdf/1504.08289v3.pdf | Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks | Part models of object categories are essential for challenging recognition
tasks, where differences in categories are subtle and only reflected in
appearances of small parts of the object. We present an approach that is able
to learn part models in a completely unsupervised manner, without part
annotations and even wit... | ['Marcel Simon', 'Erik Rodner'] | 2015-04-30 | neural-activation-constellations-unsupervised-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Simon_Neural_Activation_Constellations_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Simon_Neural_Activation_Constellations_ICCV_2015_paper.pdf | iccv-2015-12 | ['model-discovery'] | ['miscellaneous'] | [ 2.05572292e-01 6.84753284e-02 -1.46623537e-01 -6.45958185e-01
-3.04181010e-01 -9.69920099e-01 7.18993485e-01 8.88269171e-02
-1.63565889e-01 4.74052370e-01 -8.05652812e-02 2.76364125e-02
-3.51953268e-01 -8.11189055e-01 -1.05720258e+00 -4.57009822e-01
-4.84116264e-02 7.05182731e-01 2.81891733e-01 -2.21356243... | [9.627416610717773, 1.94867742061615] |
a73bff52-58b8-4e63-8e60-b4a8428177dd | pcr4all-a-comprehensive-evaluation-benchmark | null | null | https://aclanthology.org/2022.lrec-1.641 | https://aclanthology.org/2022.lrec-1.641.pdf | PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English | Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to. The correct resolution of pronouns typically involves the complex inference over both linguistic knowledge and general world knowledge. Recently, with the help of pre-trained language representation model... | ['Yangqiu Song', 'Hongming Zhang', 'Xinran Zhao'] | null | null | null | null | lrec-2022-6 | ['coreference-resolution'] | ['natural-language-processing'] | [-5.02692424e-02 1.10715926e-01 -6.85129225e-01 -2.74373680e-01
-1.19756508e+00 -8.16910207e-01 7.28268266e-01 3.67200106e-01
-4.51236159e-01 8.64553928e-01 6.09025657e-01 -1.20969810e-01
-9.16954800e-02 -5.66060305e-01 -4.26439732e-01 -4.32785630e-01
1.73385724e-01 7.04458177e-01 1.90196589e-01 -4.88158107... | [9.353658676147461, 9.480691909790039] |
a4be58b1-5d45-4286-a1cc-2d3b5e48031b | selfpromer-self-prompt-dehazing-transformers | 2303.07033 | null | https://arxiv.org/abs/2303.07033v2 | https://arxiv.org/pdf/2303.07033v2.pdf | SelfPromer: Self-Prompt Dehazing Transformers with Depth-Consistency | This work presents an effective depth-consistency self-prompt Transformer for image dehazing. It is motivated by an observation that the estimated depths of an image with haze residuals and its clear counterpart vary. Enforcing the depth consistency of dehazed images with clear ones, therefore, is essential for dehazin... | ['Xiao-Ming Wu', 'Jiangxin Dong', 'WanYu Lin', 'Jinshan Pan', 'Cong Wang'] | 2023-03-13 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 2.32131436e-01 -6.18852489e-02 4.62357342e-01 -4.62589741e-01
-3.25603127e-01 -1.49059430e-01 4.87993300e-01 -3.45019400e-01
-1.35891110e-04 4.68850642e-01 3.79463971e-01 -7.12379143e-02
-1.95477530e-02 -1.15911829e+00 -8.56410027e-01 -1.08820915e+00
1.70294330e-01 -3.89276356e-01 4.12647218e-01 -5.73988318... | [10.977899551391602, -3.231940746307373] |
164f6988-2a74-43d0-ae9b-a36462e7bc71 | flexibly-mining-better-subgroups | 1510.08382 | null | http://arxiv.org/abs/1510.08382v1 | http://arxiv.org/pdf/1510.08382v1.pdf | Flexibly Mining Better Subgroups | In subgroup discovery, also known as supervised pattern mining, discovering
high quality one-dimensional subgroups and refinements of these is a crucial
task. For nominal attributes, this is relatively straightforward, as we can
consider individual attribute values as binary features. For numerical
attributes, the task... | ['Hoang-Vu Nguyen', 'Jilles Vreeken'] | 2015-10-28 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 1.98651284e-01 2.06020679e-02 -5.88549018e-01 -5.38606167e-01
-4.98306155e-01 -5.80766976e-01 1.08071193e-01 5.91347933e-01
-3.73168468e-01 1.03962493e+00 1.00222766e-01 -3.45612019e-01
-8.03213716e-01 -1.07769418e+00 -4.44212645e-01 -5.95679760e-01
-4.06552941e-01 9.01304364e-01 1.91141486e-01 1.99850708... | [7.779691219329834, 4.870410442352295] |
f1bb5c48-89f2-4d86-b1fe-0f3f8d56c277 | a-probabilistic-framework-for-dynamic-object | 2201.11608 | null | https://arxiv.org/abs/2201.11608v1 | https://arxiv.org/pdf/2201.11608v1.pdf | A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method | In this thesis a probabilistic framework is developed and proposed for Dynamic Object Recognition in 3D Environments. A software package is developed using C++ and Python in ROS that performs the detection and tracking task. Furthermore, a novel Gaussian Process Regression (GPR) based method is developed to detect grou... | ['Pouria Mehrabi'] | 2022-01-27 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.05570853e-01 7.93961659e-02 5.24489939e-01 -3.94650698e-01
-6.97834909e-01 -2.07693353e-01 2.69241124e-01 3.25491935e-01
-2.17107907e-01 7.26355433e-01 -4.78633940e-01 -2.51862761e-02
-1.62794679e-01 -1.08735001e+00 -4.44142669e-01 -9.46289718e-01
-2.08205864e-01 8.04746747e-01 8.10711205e-01 1.47920310... | [7.391127109527588, -2.1387243270874023] |
2d372063-3776-4386-b8c8-2af210d2ea31 | diffusion-model-based-low-light-image | 2306.14227 | null | https://arxiv.org/abs/2306.14227v1 | https://arxiv.org/pdf/2306.14227v1.pdf | Diffusion Model Based Low-Light Image Enhancement for Space Satellite | Space-based visible camera is an important sensor for space situation awareness during proximity operations. However, visible camera can be easily affected by the low illumination in the space environment. Recently, deep learning approaches have achieved remarkable success in image enhancement of natural images dataset... | ['Yu Guo', 'Jingyi Yuan', 'Lu Wang', 'Yiman Zhu'] | 2023-06-25 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 2.11165071e-01 -5.01200736e-01 2.59528965e-01 -5.66944927e-02
-1.59770682e-01 -3.00119281e-01 3.79754692e-01 -5.14330208e-01
-6.16385281e-01 7.21343040e-01 -1.15802921e-01 -1.52604848e-01
-1.46511048e-01 -7.54174829e-01 -4.62586075e-01 -1.20340395e+00
1.27431363e-01 -1.24928966e-01 2.74627894e-01 -5.28634012... | [10.575260162353516, -2.4342379570007324] |
bdb68339-d125-446d-87ff-1190ae8fe68b | master-multi-task-pre-trained-bottlenecked | 2212.07841 | null | https://arxiv.org/abs/2212.07841v2 | https://arxiv.org/pdf/2212.07841v2.pdf | MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers | Pre-trained Transformers (\eg BERT) have been commonly used in existing dense retrieval methods for parameter initialization, and recent studies are exploring more effective pre-training tasks for further improving the quality of dense vectors. Although various novel and effective tasks have been proposed, their differ... | ['Ji-Rong Wen', 'Nan Duan', 'Daxin Jiang', 'Wayne Xin Zhao', 'Yeyun Gong', 'Xiao Liu', 'Kun Zhou'] | 2022-12-15 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-1.02396213e-01 -4.32722420e-01 -1.51459754e-01 -2.14697614e-01
-1.26514030e+00 -2.71817237e-01 6.58623457e-01 3.52847837e-02
-4.87941891e-01 6.07582510e-01 7.06971765e-01 9.25252810e-02
-1.43434823e-01 -6.58631027e-01 -7.82858551e-01 -5.77362180e-01
1.83455214e-01 7.00270712e-01 1.52194083e-01 -4.04365510... | [11.392664909362793, 7.705741882324219] |
1fe30a37-22a5-46ba-875d-ae802744deca | leandojo-theorem-proving-with-retrieval | 2306.15626 | null | https://arxiv.org/abs/2306.15626v1 | https://arxiv.org/pdf/2306.15626v1.pdf | LeanDojo: Theorem Proving with Retrieval-Augmented Language Models | Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean. However, existing methods are difficult to reproduce or build on, due to private code, data, and large compute requirements. This has created substantial barriers to research on machine learning methods for t... | ['Anima Anandkumar', 'Ryan Prenger', 'Saad Godil', 'Shixing Yu', 'Peiyang Song', 'Rahul Chalamala', 'Alex Gu', 'Aidan M. Swope', 'Kaiyu Yang'] | 2023-06-27 | null | null | null | null | ['retrieval', 'automated-theorem-proving', 'automated-theorem-proving'] | ['methodology', 'miscellaneous', 'reasoning'] | [-1.16045117e-01 2.89202537e-02 -5.29852867e-01 -8.39410126e-02
-1.23951030e+00 -1.09520733e+00 5.17725587e-01 3.46607745e-01
6.95791468e-02 7.72678256e-01 -2.55226225e-01 -1.43765092e+00
-2.81568229e-01 -9.51837540e-01 -1.31308997e+00 7.25355148e-02
-3.77363145e-01 4.23162013e-01 2.28195325e-01 -5.81749231... | [8.937458038330078, 7.052908897399902] |
5e66eba3-08d2-42f9-bcad-f65a3ba1c62d | on-the-eigenvalues-of-global-covariance | 2205.13282 | null | https://arxiv.org/abs/2205.13282v1 | https://arxiv.org/pdf/2205.13282v1.pdf | On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition | The Fine-Grained Visual Categorization (FGVC) is challenging because the subtle inter-class variations are difficult to be captured. One notable research line uses the Global Covariance Pooling (GCP) layer to learn powerful representations with second-order statistics, which can effectively model inter-class difference... | ['Wei Wang', 'Nicu Sebe', 'Yue Song'] | 2022-05-26 | null | null | null | null | ['fine-grained-visual-recognition', 'fine-grained-image-classification', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.12214017e-01 -2.55648553e-01 -2.26949498e-01 -3.01379651e-01
-4.39915299e-01 -5.56997001e-01 6.01254284e-01 8.51390511e-02
-2.84556389e-01 6.05106831e-01 1.92031235e-01 -2.46907189e-01
-3.07352424e-01 -6.88914239e-01 -5.51954031e-01 -9.40126717e-01
-1.77841634e-01 -1.68467090e-01 3.49046677e-01 -2.25394100... | [9.597843170166016, 2.1108856201171875] |
f1078a22-c41b-4878-a08a-165553afe03e | cross-camera-deep-colorization | 2209.01211 | null | https://arxiv.org/abs/2209.01211v2 | https://arxiv.org/pdf/2209.01211v2.pdf | Cross-Camera Deep Colorization | In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align and fuse images from it in an efficient and cost-effective way. Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilit... | ['Ruqi Huang', 'Mengqi Ji', 'Haitian Zheng', 'Yaping Zhao'] | 2022-08-26 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 1.37175545e-01 -5.73919356e-01 -3.42185274e-02 -1.15390122e-01
-9.48278189e-01 -9.01642561e-01 2.52990991e-01 -6.59122169e-01
-3.02621454e-01 3.23851585e-01 -7.17487261e-02 -3.90913606e-01
2.12483585e-01 -5.34983873e-01 -6.75979078e-01 -5.93090296e-01
4.53938663e-01 -3.36892933e-01 1.41454548e-01 -1.68374449... | [10.740056991577148, -2.0167064666748047] |
80e939ed-c33d-404a-9db3-89c66baae4a5 | cs-um6p-at-semeval-2021-task-7-deep-multi | null | null | https://aclanthology.org/2021.semeval-1.159 | https://aclanthology.org/2021.semeval-1.159.pdf | CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense | Humor detection has become a topic of interest for several research teams, especially those involved in socio-psychological studies, with the aim to detect the humor and the temper of a targeted population (e.g. a community, a city, a country, the employees of a given company). Most of the existing studies have formula... | ['Ismail Berrada', 'Nabil El Mamoun', 'Abdelkader El Mahdaouy', 'Abdellah El Mekki', 'Kabil Essefar'] | 2021-08-01 | null | null | null | semeval-2021 | ['humor-detection'] | ['natural-language-processing'] | [-2.57793009e-01 3.19892466e-02 9.75932106e-02 -2.50272751e-01
-4.09065098e-01 1.64699391e-01 6.65288329e-01 2.80734122e-01
-3.04708451e-01 6.59230053e-01 5.88123143e-01 2.91609764e-02
4.32832539e-01 -6.49638474e-01 -3.33118886e-01 -3.85941595e-01
2.55177766e-01 5.07496715e-01 -9.04947817e-02 -3.71049404... | [8.873779296875, 11.04246997833252] |
314dfe7d-3e64-4666-8bc4-f148d6964560 | hologen-an-open-source-toolbox-for-high-speed | 2008.12214 | null | https://arxiv.org/abs/2008.12214v2 | https://arxiv.org/pdf/2008.12214v2.pdf | HoloGen: An open source toolbox for high-speed hologram generation | The rise of mixed reality systems such as Microsoft HoloLens has prompted an increase in interest in the fields of 2D and 3D holography. Already applied in fields including telecommunications, imaging, projection, lithography, beam shaping and optical tweezing, Computer Generated Holography (CGH) offers an exciting app... | ['Timothy D. Wilkinson', 'George S. D. Gordon', 'Andrew Kadis', 'Peter J. Christopher'] | 2020-08-24 | null | null | null | null | ['3d-holography'] | ['computer-vision'] | [ 3.71115297e-01 -1.47298992e-01 7.57095218e-01 -2.50375301e-01
-4.73811239e-01 -3.61331999e-01 7.70539701e-01 -2.89088398e-01
-4.46918011e-01 9.80142236e-01 2.90285498e-01 -2.81989247e-01
-4.61066812e-01 -1.21360338e+00 -3.17601025e-01 -7.89946020e-01
2.04737470e-01 9.61543798e-01 6.10571921e-01 -2.35006511... | [9.614103317260742, -2.629831075668335] |
c9ac32d3-b599-4942-923e-03b2d965737e | meta-embedding-sentence-representation-for | null | null | https://aclanthology.org/R19-1055 | https://aclanthology.org/R19-1055.pdf | Meta-Embedding Sentence Representation for Textual Similarity | Word embedding models are now widely used in most NLP applications. Despite their effectiveness, there is no clear evidence about the choice of the most appropriate model. It often depends on the nature of the task and on the quality and size of the used data sets. This remains true for bottom-up sentence embedding mod... | ['Hern', 'Amir Hazem', 'Nicolas ez'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['question-similarity'] | ['natural-language-processing'] | [ 1.25403047e-01 1.73937287e-02 -2.87633836e-01 -2.63341784e-01
-7.46819377e-01 -4.47401613e-01 8.13836992e-01 9.00836229e-01
-8.39513421e-01 4.03472543e-01 7.31167138e-01 -5.01595199e-01
-2.72229254e-01 -6.48563921e-01 -3.01040858e-01 -3.29179555e-01
2.54117250e-01 3.19651246e-01 3.33723962e-01 -5.36879778... | [10.692692756652832, 8.737533569335938] |
53878d26-6ee4-4215-bbbd-11b0ef151377 | distinguishing-rule-and-exemplar-based-1 | 2110.04328 | null | https://arxiv.org/abs/2110.04328v2 | https://arxiv.org/pdf/2110.04328v2.pdf | Distinguishing rule- and exemplar-based generalization in learning systems | Machine learning systems often do not share the same inductive biases as humans and, as a result, extrapolate or generalize in ways that are inconsistent with our expectations. The trade-off between exemplar- and rule-based generalization has been studied extensively in cognitive psychology; in this work, we present a ... | ['Thomas L. Griffiths', 'Erin Grant', 'Ishita Dasgupta'] | 2021-10-08 | distinguishing-rule-and-exemplar-based | https://openreview.net/forum?id=ljCoTzUsdS | https://openreview.net/pdf?id=ljCoTzUsdS | null | ['systematic-generalization'] | ['reasoning'] | [ 3.83990407e-01 -2.19142474e-02 -4.69475210e-01 -9.44618642e-01
3.72975498e-01 -6.53523862e-01 8.17755282e-01 6.22639179e-01
-9.04677510e-01 7.99933195e-01 1.79374903e-01 -4.90425438e-01
-4.89156485e-01 -9.14660811e-01 -5.47716141e-01 -4.13464099e-01
8.29871371e-02 3.26737970e-01 -3.64088535e-01 -2.21353516... | [9.415956497192383, 6.26626443862915] |
d504ab34-59df-491a-8f3a-f90aa2ba4f80 | tokenflow-rethinking-fine-grained-cross-modal | 2209.13822 | null | https://arxiv.org/abs/2209.13822v2 | https://arxiv.org/pdf/2209.13822v2.pdf | TokenFlow: Rethinking Fine-grained Cross-modal Alignment in Vision-Language Retrieval | Most existing methods in vision-language retrieval match two modalities by either comparing their global feature vectors which misses sufficient information and lacks interpretability, detecting objects in images or videos and aligning the text with fine-grained features which relies on complicated model designs, or mo... | ['Zhongyuan Wang', 'Lele Cheng', 'Changqiao Wu', 'Xiaohan Zou'] | 2022-09-28 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-6.13662452e-02 -5.15951037e-01 -4.08841610e-01 -3.08545500e-01
-5.51558971e-01 -6.64247930e-01 1.15813649e+00 3.14895868e-01
-3.75062138e-01 1.55834034e-01 5.38353741e-01 -6.11709803e-02
-5.03651083e-01 -5.57855666e-01 -5.07324815e-01 -5.41429877e-01
1.26646489e-01 1.51873291e-01 3.74336869e-01 -1.26890332... | [10.512856483459473, 1.098780870437622] |
586805db-257b-4d01-a2f0-4a77c1e00141 | spam-detection-using-bert | 2206.02443 | null | https://arxiv.org/abs/2206.02443v2 | https://arxiv.org/pdf/2206.02443v2.pdf | Spam Detection Using BERT | Emails and SMSs are the most popular tools in today communications, and as the increase of emails and SMSs users are increase, the number of spams is also increases. Spam is any kind of unwanted, unsolicited digital communication that gets sent out in bulk, spam emails and SMSs are causing major resource wastage by unn... | ['Dr. Mohammad Mikki', 'Thaer Sahmoud'] | 2022-06-06 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 2.79617961e-02 -1.16740309e-01 -3.97399738e-02 -3.07003975e-01
3.45233679e-02 -7.08851695e-01 1.09713566e+00 1.50205027e-02
-2.64072299e-01 8.48475456e-01 1.81061029e-01 -7.22682953e-01
2.26483867e-01 -1.13389719e+00 -8.56050104e-02 -1.54453471e-01
1.95499152e-01 6.17864430e-01 9.05463219e-01 -6.29042327... | [7.853854179382324, 10.03609561920166] |
1838ea6e-d8ac-4e1f-b9cc-6feefe355709 | 3d-mpa-multi-proposal-aggregation-for-3d | 2003.13867 | null | https://arxiv.org/abs/2003.13867v1 | https://arxiv.org/pdf/2003.13867v1.pdf | 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation | We present 3D-MPA, a method for instance segmentation on 3D point clouds. Given an input point cloud, we propose an object-centric approach where each point votes for its object center. We sample object proposals from the predicted object centers. Then, we learn proposal features from grouped point features that voted ... | ['Matthias Nießner', 'Martin Bokeloh', 'Francis Engelmann', 'Bastian Leibe', 'Alireza Fathi'] | 2020-03-30 | null | null | null | null | ['3d-instance-segmentation-1', '3d-semantic-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.88857272e-01 4.07940894e-01 -2.60827363e-01 -7.25606322e-01
-7.75549412e-01 -4.26545978e-01 5.97003222e-01 6.68813407e-01
-2.37756789e-01 -7.85462931e-03 -4.63886738e-01 -8.02155361e-02
4.68856618e-02 -8.94256115e-01 -1.11287546e+00 -4.95372295e-01
-3.11474085e-01 1.22415757e+00 1.27179015e+00 3.69877875... | [7.966889381408691, -3.1372084617614746] |
3e530dc1-419a-41e6-a781-dd838ae86762 | a-non-asymptotic-analysis-of-oversmoothing-in | 2212.10701 | null | https://arxiv.org/abs/2212.10701v2 | https://arxiv.org/pdf/2212.10701v2.pdf | A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks | Oversmoothing is a central challenge of building more powerful Graph Neural Networks (GNNs). While previous works have only demonstrated that oversmoothing is inevitable when the number of graph convolutions tends to infinity, in this paper, we precisely characterize the mechanism behind the phenomenon via a non-asympt... | ['Ali Jadbabaie', 'William Wang', 'Zhengdao Chen', 'Xinyi Wu'] | 2022-12-21 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.76804745e-01 4.61215019e-01 1.76257581e-01 -9.30642709e-02
-2.46948272e-01 -3.82369220e-01 4.64240670e-01 3.12508643e-01
-4.12849545e-01 3.51518214e-01 2.29468092e-01 -5.04636407e-01
-1.11686312e-01 -9.38246191e-01 -1.02929556e+00 -7.35848248e-01
-5.72304845e-01 4.46791053e-02 2.02352583e-01 -3.14631790... | [6.856550693511963, 6.054480075836182] |
8770f1d7-049c-4592-864f-14a4f0f7e005 | face-morphing-attacks-and-face-image-quality | 2208.05864 | null | https://arxiv.org/abs/2208.05864v2 | https://arxiv.org/pdf/2208.05864v2.pdf | Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality | Morphing attacks are a form of presentation attacks that gathered increasing attention in recent years. A morphed image can be successfully verified to multiple identities. This operation, therefore, poses serious security issues related to the ability of a travel or identity document to be verified to belong to multip... | ['Naser Damer', 'Biying Fu'] | 2022-08-11 | null | null | null | null | ['face-image-quality'] | ['computer-vision'] | [ 2.12053835e-01 5.27739525e-02 9.69896689e-02 -2.95801371e-01
-5.73470771e-01 -7.46415734e-01 8.14473867e-01 1.23070359e-01
-3.32933426e-01 3.26300442e-01 -2.23364294e-01 -3.22842687e-01
-3.15939397e-01 -9.03960049e-01 -5.57822108e-01 -6.18567348e-01
-3.03452045e-01 2.64165998e-01 -6.50826618e-02 -2.33079404... | [12.954313278198242, 0.994962215423584] |
491c5bc6-d56a-4e9a-8939-88a0aae740e4 | momentum-contrastive-pre-training-for | 2212.05762 | null | https://arxiv.org/abs/2212.05762v2 | https://arxiv.org/pdf/2212.05762v2.pdf | Momentum Contrastive Pre-training for Question Answering | Existing pre-training methods for extractive Question Answering (QA) generate cloze-like queries different from natural questions in syntax structure, which could overfit pre-trained models to simple keyword matching. In order to address this problem, we propose a novel Momentum Contrastive pRe-training fOr queStion an... | ['Irwin King', 'Yasheng Wang', 'Muzhi Li', 'Minda Hu'] | 2022-12-12 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [ 2.19623119e-01 1.79814875e-01 6.10003136e-02 -4.47784960e-01
-1.85261440e+00 -5.54851174e-01 5.05030394e-01 1.67484775e-01
-5.19551814e-01 4.63044405e-01 5.25748730e-01 -3.74448627e-01
4.03904803e-02 -8.50770354e-01 -7.20539987e-01 9.64694917e-02
4.60987806e-01 7.82745481e-01 5.43248594e-01 -5.42153358... | [11.37887954711914, 8.033238410949707] |
2a4ba4ab-f13c-47b3-8eb2-2cc17f4e520e | nilc-at-webnlg-pretrained-sequence-to | null | null | https://aclanthology.org/2020.webnlg-1.14 | https://aclanthology.org/2020.webnlg-1.14.pdf | NILC at WebNLG+: Pretrained Sequence-to-Sequence Models on RDF-to-Text Generation | This paper describes the submission by the NILC Computational Linguistics research group of the University of São Paulo/Brazil to the RDF-to-Text task for English at the WebNLG+ challenge. The success of the current pretrained models like BERT or GPT-2 in text-to-text generation tasks is well-known, however, its applic... | ['Thiago A. S. Pardo', 'Marco Antonio Sobrevilla Cabezudo'] | null | null | null | null | acl-webnlg-inlg-2020-12 | ['data-to-text-generation'] | ['natural-language-processing'] | [-1.55516453e-02 7.49715567e-01 -1.15776531e-01 -1.65938690e-01
-8.98865700e-01 -3.86327356e-01 1.21674204e+00 1.00754023e-01
-6.42515481e-01 1.30510473e+00 7.50609100e-01 -3.53154629e-01
1.83641478e-01 -7.31471241e-01 -6.85322583e-01 -3.51561964e-01
4.53385800e-01 1.21508098e+00 1.45118739e-02 -7.62273550... | [11.422876358032227, 9.267518997192383] |
e8c2c32d-e66f-46ec-8785-70b0da18af8d | evil-from-within-machine-learning-backdoors | 2304.08411 | null | https://arxiv.org/abs/2304.08411v2 | https://arxiv.org/pdf/2304.08411v2.pdf | Evil from Within: Machine Learning Backdoors through Hardware Trojans | Backdoors pose a serious threat to machine learning, as they can compromise the integrity of security-critical systems, such as self-driving cars. While different defenses have been proposed to address this threat, they all rely on the assumption that the hardware on which the learning models are executed during infere... | ['Christof Paar', 'Konrad Rieck', 'Jan-Niklas Möller', 'Julian Speith', 'Alexander Warnecke'] | 2023-04-17 | null | null | null | null | ['backdoor-attack', 'traffic-sign-recognition', 'self-driving-cars'] | ['adversarial', 'computer-vision', 'computer-vision'] | [ 2.59156823e-01 8.86259973e-02 -6.49231791e-01 -1.63949206e-01
-4.31451678e-01 -1.00397158e+00 4.53464866e-01 9.28109810e-02
-4.22663569e-01 1.45164251e-01 -6.90127671e-01 -1.44285631e+00
5.37726700e-01 -8.07566822e-01 -1.10880148e+00 -4.21800137e-01
-3.99347171e-02 -7.36963972e-02 7.50365198e-01 -1.51888564... | [5.658563137054443, 7.450559139251709] |
7b51745a-2e43-474a-b48d-d193305a8def | rethinking-pseudo-labeled-sample-mining-for | null | null | https://openreview.net/forum?id=60GDNLY-m3M | https://openreview.net/pdf?id=60GDNLY-m3M | Rethinking Pseudo-labeled Sample Mining for Semi-Supervised Object Detection | Consistency-based method has been proved effective for semi-supervised learning (SSL). However, the impact of the pseudo-labeled samples' quality as well as the mining strategies for high quality training sample have rarely been studied in SSL. An intuitive idea is to select pseudo-labeled training samples by threshold... | ['Xiaokang Yang', 'Fei Wu', 'Wenming Tan', 'Yi Niu', 'ShiLiang Pu', 'Zhanzhan Cheng', 'Sanli Tang', 'Duo Li'] | 2021-01-01 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 7.18358159e-02 -1.62460700e-01 -4.75415558e-01 -7.94756353e-01
-4.74912018e-01 -2.91177630e-01 3.92677367e-01 1.62537754e-01
-4.98754829e-01 8.12869072e-01 -3.26185852e-01 1.58690482e-01
-3.64665687e-01 -5.87028503e-01 -4.31766242e-01 -8.20802689e-01
1.78681081e-03 4.86028641e-01 6.50570452e-01 1.31008580... | [9.204261779785156, 3.9933085441589355] |
22ed904b-e5a5-4d34-beef-6b4d4d4f3b39 | applicability-and-interpretation-of-the | 1803.03104 | null | http://arxiv.org/abs/1803.03104v1 | http://arxiv.org/pdf/1803.03104v1.pdf | Applicability and interpretation of the deterministic weighted cepstral distance | Quantifying similarity between data objects is an important part of modern
data science. Deciding what similarity measure to use is very application
dependent. In this paper, we combine insights from systems theory and machine
learning, and investigate the weighted cepstral distance, which was previously
defined for si... | ['Oliver Lauwers', 'Bart De Moor'] | 2018-03-08 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 3.38049978e-01 -1.05683059e-01 2.06121117e-01 -2.81389579e-02
-1.72834173e-01 -9.68515575e-01 6.35119677e-01 2.15237439e-01
-2.55675465e-01 3.65357429e-01 -1.23671949e-01 -4.36516166e-01
-7.80317724e-01 -5.09272933e-01 -1.90072909e-01 -1.09432507e+00
-3.00461113e-01 1.62410870e-01 1.34612257e-02 -4.45713848... | [7.439803600311279, 3.496417284011841] |
26c3a415-27e8-4161-86ed-35e75001cb16 | distance-preserving-machine-learning-for | 2307.02367 | null | https://arxiv.org/abs/2307.02367v1 | https://arxiv.org/pdf/2307.02367v1.pdf | Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions | Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard method for this task, but they can struggle with large, high-dimensional dat... | ['Sudarshan Harave', 'Sarah Cousineau', 'Majdi I. Radaideh', 'Jared Walden', 'Dan Lu', 'Chris Pappas', 'Thomas Britton', 'Kishansingh Rajput', 'Malachi Schram', 'Steven Goldenberg'] | 2023-07-05 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-1.17559157e-01 5.04515171e-02 3.63842219e-01 -4.75293308e-01
-7.35119939e-01 -1.41985729e-01 5.73657990e-01 6.17300510e-01
-5.36102355e-01 5.95177174e-01 7.36668259e-02 -6.33622766e-01
-4.53388810e-01 -7.88291395e-01 -4.24230844e-01 -1.05566657e+00
-1.81953721e-02 1.26416469e+00 2.72245735e-01 2.64543384... | [7.225008964538574, 3.771913528442383] |
347395b5-c01b-402d-bb38-ac8217d4eaff | identification-of-binary-neutron-star-mergers | 2207.00591 | null | https://arxiv.org/abs/2207.00591v1 | https://arxiv.org/pdf/2207.00591v1.pdf | Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection | We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave data of current generation interferometer detectors. We also present a thorough explanation of the syn... | ['José A. Font', 'Gonçalo Gonçalves', 'Constança Providência', 'Antonio Onofre', 'Márcio Ferreira', 'Felipe F. Freitas', 'João Aveiro'] | 2022-07-01 | null | null | null | null | ['one-shot-object-detection'] | ['computer-vision'] | [-2.42822379e-01 1.11067288e-01 6.47306919e-01 -2.61629000e-02
-7.43046522e-01 -3.64312202e-01 1.05714238e+00 2.62657762e-01
-7.54868150e-01 4.38768446e-01 -4.28688258e-01 -5.02874553e-01
-9.27942544e-02 -6.89287126e-01 -3.57909918e-01 -9.95588899e-01
-4.10233065e-02 9.40516949e-01 7.32559025e-01 1.10243849... | [7.547070503234863, 3.137256622314453] |
cccc332e-64d5-41db-ac14-7ef74f53e4ba | fetsmcs-feature-based-ets-model-component | 2206.12882 | null | https://arxiv.org/abs/2206.12882v1 | https://arxiv.org/pdf/2206.12882v1.pdf | fETSmcs: Feature-based ETS model component selection | The well-developed ETS (ExponenTial Smoothing or Error, Trend, Seasonality) method incorporating a family of exponential smoothing models in state space representation has been widely used for automatic forecasting. The existing ETS method uses information criteria for model selection by choosing an optimal model with ... | ['Suling Jia', 'Qiang Wang', 'Xixi Li', 'Lingzhi Qi'] | 2022-06-26 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 1.27681091e-01 -9.72468928e-02 -5.28852195e-02 -3.32628399e-01
-7.05075264e-01 -2.42585495e-01 5.92775941e-01 3.97581428e-01
-4.18373078e-01 7.64620125e-01 4.80906181e-02 -6.70420706e-01
-4.88391012e-01 -5.36114931e-01 -7.78012797e-02 -6.51690304e-01
-5.03376424e-01 4.46592957e-01 1.44299731e-01 -4.25227433... | [6.850902557373047, 3.1843698024749756] |
e6df1c15-2c0f-408d-947a-e4a3aa5c17b1 | detection-of-paroxysmal-atrial-fibrillation | 1805.09133 | null | http://arxiv.org/abs/1805.09133v1 | http://arxiv.org/pdf/1805.09133v1.pdf | Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks | Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is
difficult since many cases of AF are usually clinically silent and undiagnosed.
In particular paroxysmal AF is a form of AF that occurs occasionally, and has a
higher probability of being undetected. In this work, we present an attention
based deep... | ['Gari. D. Clifford', 'Amit J. Shah', 'Supreeth P. Shashikumar', 'Shamim Nemati'] | 2018-05-07 | null | null | null | null | ['atrial-fibrillation-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 3.92864585e-01 -4.65936869e-01 1.39904797e-01 -2.55730867e-01
-9.73057210e-01 -7.35355437e-01 -7.59847388e-02 6.61214218e-02
-2.21470445e-01 8.94819438e-01 1.28422752e-01 -7.02888310e-01
-1.67293176e-01 -5.06520987e-01 -3.80948484e-01 -6.33274078e-01
-7.34680057e-01 1.02139108e-01 -5.75806916e-01 4.34048057... | [14.269021034240723, 3.2455484867095947] |
20c82f36-2b31-4c76-acd4-7705d821765c | a-deep-learning-approach-for-multimodal | 1803.00344 | null | http://arxiv.org/abs/1803.00344v1 | http://arxiv.org/pdf/1803.00344v1.pdf | A Deep Learning Approach for Multimodal Deception Detection | Automatic deception detection is an important task that has gained momentum
in computational linguistics due to its potential applications. In this paper,
we propose a simple yet tough to beat multi-modal neural model for deception
detection. By combining features from different modalities such as video,
audio, and tex... | ['Erik Cambria', 'Soujanya Poria', 'Gangeshwar Krishnamurthy', 'Navonil Majumder'] | 2018-03-01 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-1.65367246e-01 -3.68523508e-01 -2.11027399e-01 -6.11511230e-01
-1.02379394e+00 -5.15704930e-01 6.29315794e-01 -1.08558767e-01
-4.45479035e-01 4.91428673e-01 3.26933384e-01 5.73454462e-02
2.93536067e-01 -3.80364470e-02 -1.74730837e-01 -4.12190288e-01
7.83058256e-02 -4.02124822e-01 -2.27149472e-01 -1.31720811... | [13.266925811767578, 2.029111623764038] |
7872640e-20a2-4eaf-a9f0-ecf6bb39b6c5 | power-efficient-analog-features-for-audio | 2110.03715 | null | https://arxiv.org/abs/2110.03715v2 | https://arxiv.org/pdf/2110.03715v2.pdf | PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition | At the end of Moore's law, new computing paradigms are required to prolong the battery life of wearable and IoT smart audio devices. Theoretical analysis and physical validation have shown that analog signal processing (ASP) can be more power-efficient than its digital counterpart in the realm of low-to-medium signal-t... | ['Milos Cernak', 'Minhao Yang', 'Boris Bergsma'] | 2021-10-07 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 5.15369594e-01 -3.84405345e-01 2.10694019e-02 -3.61450136e-01
-9.80861843e-01 -5.87178767e-01 -6.06960505e-02 4.88520533e-01
-3.76856834e-01 4.57702935e-01 -1.46022916e-01 -3.83740008e-01
-3.67888927e-01 -6.20415926e-01 -2.89937139e-01 -5.53229332e-01
-1.82486758e-01 1.71970751e-04 5.14051080e-01 5.80552965... | [14.523079872131348, 5.574769496917725] |
cb6fb17c-860e-4e1b-986d-9369c412a81f | you-can-generate-it-again-data-to-text | 2306.15933 | null | https://arxiv.org/abs/2306.15933v1 | https://arxiv.org/pdf/2306.15933v1.pdf | You Can Generate It Again: Data-to-text Generation with Verification and Correction Prompting | Despite significant advancements in existing models, generating text descriptions from structured data input, known as data-to-text generation, remains a challenging task. In this paper, we propose a novel approach that goes beyond traditional one-shot generation methods by introducing a multi-step process consisting o... | ['Lingqiao Liu', 'Xuan Ren'] | 2023-06-28 | null | null | null | null | ['text-generation', 'data-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.63211226e-01 3.96442860e-01 -7.90432915e-02 -2.88315922e-01
-1.06082499e+00 -5.64268589e-01 6.27403617e-01 5.53586125e-01
3.31403613e-02 8.74171257e-01 3.38210374e-01 -3.66703242e-01
2.64804482e-01 -7.55474746e-01 -5.47010243e-01 3.79695115e-03
6.36988401e-01 5.99258900e-01 1.59866408e-01 -1.90168902... | [11.777220726013184, 8.894551277160645] |
83d55c29-39e7-44b7-a2b0-a9ad2a7c5cfc | illumination-aware-image-quality-assessment | null | null | https://link.springer.com/chapter/10.1007/978-3-030-88010-1_19 | https://link.springer.com/chapter/10.1007/978-3-030-88010-1_19 | Illumination-Aware Image Quality Assessment for Enhanced Low-light Image | Images captured in a dark environment may suffer from low visibility, which may degrade the visual aesthetics of images and the performance of vision-based systems. Extensive studies have focused on the low-light image enhancement (LIE) problem. However, we observe that even though the state-of-the-art LIE methods may ... | ['Sigan Yao，Yiqin Zhu，Lingyu Liang，Tao Wang'] | 2021-10-22 | null | null | null | prcv-2021-2021-10 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 3.12473089e-01 -4.45112139e-01 3.56823057e-01 -4.63564068e-01
-6.81597888e-01 -2.64501154e-01 3.12352210e-01 -3.55946720e-01
-1.51048735e-01 4.54329997e-01 5.05940728e-02 2.01892197e-01
-2.27754444e-01 -8.77041399e-01 -6.13414049e-01 -1.04213989e+00
3.91168326e-01 -6.82201207e-01 3.63399796e-02 -2.48508260... | [10.727858543395996, -2.5181689262390137] |
ae8bc451-bd7a-4936-ac7e-d28bf0e3b273 | learning-an-efficient-multimodal-depth | 2208.10771 | null | https://arxiv.org/abs/2208.10771v1 | https://arxiv.org/pdf/2208.10771v1.pdf | Learning an Efficient Multimodal Depth Completion Model | With the wide application of sparse ToF sensors in mobile devices, RGB image-guided sparse depth completion has attracted extensive attention recently, but still faces some problems. First, the fusion of multimodal information requires more network modules to process different modalities. But the application scenarios ... | ['Yang Zhao', 'Kai Zhao', 'Yuanyuan Du', 'Dewang Hou'] | 2022-08-23 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 6.73478067e-01 1.96465105e-01 -4.43213806e-02 -5.42509675e-01
-8.98851156e-01 1.54628515e-01 9.37766358e-02 -2.86015451e-01
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3.12670618e-01 1.01969294e-01 4.26835179e-01 -6.93389326... | [8.927861213684082, -2.55668306350708] |
ba49e594-e032-47aa-a78f-2813f897dc14 | discrete-time-nonlinear-feedback | 2303.08884 | null | https://arxiv.org/abs/2303.08884v1 | https://arxiv.org/pdf/2303.08884v1.pdf | Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning | We present a physics-informed machine learning (PIML) scheme for the feedback linearization of nonlinear discrete-time dynamical systems. The PIML finds the nonlinear transformation law, thus ensuring stability via pole placement, in one step. In order to facilitate convergence in the presence of steep gradients in the... | ['Ioannis G. Kevrekidis', 'Constantinos Siettos', 'Nikolaos Kazantzis', 'Gianluca Fabiani', 'Hector Vargas Alvarez'] | 2023-03-15 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-8.64476413e-02 2.61619002e-01 1.77718893e-01 3.90088081e-01
-4.64426041e-01 -6.84894800e-01 7.94932485e-01 5.83692253e-01
-1.43792018e-01 6.96285665e-01 -3.38105768e-01 -7.15459704e-01
-3.59646082e-01 -5.53250015e-01 -1.15096068e+00 -6.86779797e-01
-2.11665183e-01 6.24309123e-01 9.78350863e-02 -6.87508285... | [6.511131286621094, 3.471754312515259] |
a9fe2031-f636-4528-942c-5ec9315f0268 | domain-adaptation-in-multi-channel | 1907.04048 | null | https://arxiv.org/abs/1907.04048v1 | https://arxiv.org/pdf/1907.04048v1.pdf | Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing | While the performance of face recognition systems has improved significantly in the last decade, they are proved to be highly vulnerable to presentation attacks (spoofing). Most of the research in the field of face presentation attack detection (PAD), was focused on boosting the performance of the systems within a sing... | ['Olegs Nikisins', 'Anjith George', 'Sebastien Marcel'] | 2019-07-09 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 1.92186147e-01 -1.83968306e-01 1.72216073e-01 -1.99156970e-01
-5.36747754e-01 -7.23832846e-01 5.75774252e-01 -3.54166240e-01
-2.79810011e-01 3.52209359e-01 -6.29483238e-02 -3.58021520e-02
2.24612262e-02 -7.43811965e-01 -7.15532720e-01 -9.88378346e-01
-1.59024298e-01 1.29245192e-01 7.17377290e-02 -3.14221352... | [13.11751651763916, 1.1109609603881836] |
6dbe5b31-09ca-45d4-b7b4-bace6fd7de95 | 2305-14826 | 2305.14826 | null | https://arxiv.org/abs/2305.14826v1 | https://arxiv.org/pdf/2305.14826v1.pdf | Building Transportation Foundation Model via Generative Graph Transformer | Efficient traffic management is crucial for maintaining urban mobility, especially in densely populated areas where congestion, accidents, and delays can lead to frustrating and expensive commutes. However, existing prediction methods face challenges in terms of optimizing a single objective and understanding the compl... | ['Yilun Lin', 'Liang Chen', 'Ding Wang', 'Xuhong Wang'] | 2023-05-24 | null | null | null | null | ['traffic-prediction'] | ['time-series'] | [-2.16050386e-01 1.58762038e-02 -2.62992412e-01 2.41442118e-02
-1.89400434e-01 -1.70098394e-01 5.15014589e-01 3.05528581e-01
6.21551834e-02 9.80593503e-01 1.20701678e-01 -8.58132482e-01
-5.36606610e-01 -1.35796583e+00 -3.81205618e-01 -3.16657633e-01
-1.81489214e-01 7.07080483e-01 4.36757714e-01 -6.97955728... | [6.265746593475342, 1.8514360189437866] |
923e0699-75cf-41e7-889c-1bcecd84a45e | github-copilot-ai-pair-programmer-asset-or | 2206.15331 | null | https://arxiv.org/abs/2206.15331v2 | https://arxiv.org/pdf/2206.15331v2.pdf | GitHub Copilot AI pair programmer: Asset or Liability? | Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empiri... | ['Jiang', 'Zhen Ming', 'Michel C. Desmarais', 'Foutse khomh', 'Amin Nikanjam', 'Vahid Majdinasab', 'Arghavan Moradi Dakhel'] | 2022-06-30 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-5.72146416e-01 -1.48384282e-02 2.24952828e-02 -2.05540717e-01
-3.34010273e-01 -5.22767007e-01 1.83565229e-01 3.98476183e-01
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-1.33047163e-01 -9.76243317e-01 -7.38687515e-01 -3.64070892e-01
1.59788221e-01 3.29405338e-01 1.67288378e-01 -2.94332415... | [7.927910804748535, 7.6548848152160645] |
fa7404fa-e854-4701-a3d0-89d06d47a402 | fairness-in-multi-task-learning-via | 2306.10155 | null | https://arxiv.org/abs/2306.10155v2 | https://arxiv.org/pdf/2306.10155v2.pdf | Fairness in Multi-Task Learning via Wasserstein Barycenters | Algorithmic Fairness is an established field in machine learning that aims to reduce biases in data. Recent advances have proposed various methods to ensure fairness in a univariate environment, where the goal is to de-bias a single task. However, extending fairness to a multi-task setting, where more than one objectiv... | ['Arthur Charpentier', 'Philipp Ratz', 'François Hu'] | 2023-06-16 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 2.43197367e-01 1.33104801e-01 -4.56597894e-01 -8.83712053e-01
-8.08529615e-01 -1.41670451e-01 4.99073952e-01 3.78593177e-01
-8.16328824e-01 1.19448233e+00 1.70173004e-01 -4.53138292e-01
-3.84064555e-01 -5.17749906e-01 -4.26896662e-01 -8.18193495e-01
1.38956693e-03 3.83142799e-01 -4.59369242e-01 -6.91837296... | [8.871633529663086, 5.277050971984863] |
95beb2d7-c12a-4397-aca0-ea6a8c5ef7de | what-do-language-models-know-about-word | 2302.03353 | null | https://arxiv.org/abs/2302.03353v1 | https://arxiv.org/pdf/2302.03353v1.pdf | What do Language Models know about word senses? Zero-Shot WSD with Language Models and Domain Inventories | Language Models are the core for almost any Natural Language Processing system nowadays. One of their particularities is their contextualized representations, a game changer feature when a disambiguation between word senses is necessary. In this paper we aim to explore to what extent language models are capable of disc... | ['German Rigau', 'Eneko Agirre', 'Oier Lopez de Lacalle', 'Oscar Sainz'] | 2023-02-07 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.09680936e-01 1.66544795e-01 -3.13183427e-01 -9.65767130e-02
-2.05843702e-01 -1.03940237e+00 1.16637254e+00 6.66856289e-01
-7.83553481e-01 6.58262491e-01 5.60413182e-01 -8.55323970e-01
-2.82700688e-01 -1.01862121e+00 -2.11353466e-01 -1.20521754e-01
1.34066314e-01 4.04556692e-01 5.11818349e-01 -1.00343442... | [10.222016334533691, 8.962084770202637] |
d207de9b-4b1b-4b6e-b925-eacb1411a817 | you-only-segment-once-towards-real-time | 2303.14651 | null | https://arxiv.org/abs/2303.14651v1 | https://arxiv.org/pdf/2303.14651v1.pdf | You Only Segment Once: Towards Real-Time Panoptic Segmentation | In this paper, we propose YOSO, a real-time panoptic segmentation framework. YOSO predicts masks via dynamic convolutions between panoptic kernels and image feature maps, in which you only need to segment once for both instance and semantic segmentation tasks. To reduce the computational overhead, we design a feature p... | ['Liujuan Cao', 'Rongrong Ji', 'Shengchuan Zhang', 'Tianhe Ren', 'Linyan Huang', 'Jie Hu'] | 2023-03-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hu_You_Only_Segment_Once_Towards_Real-Time_Panoptic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hu_You_Only_Segment_Once_Towards_Real-Time_Panoptic_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [-2.51129150e-01 -3.01523566e-01 2.41968706e-01 -3.54332089e-01
-7.21205533e-01 -6.90072894e-01 2.97521710e-01 -1.49548769e-01
-3.88004661e-01 -2.07306221e-02 -2.71122307e-01 -5.08728862e-01
2.25532472e-01 -1.11191654e+00 -7.57626295e-01 -5.85256994e-01
-4.86119874e-02 1.94422573e-01 5.23391843e-01 6.16139024... | [9.366968154907227, -0.024011297151446342] |
116b88e0-db68-462f-9a5e-844d29fbe029 | visual-keyword-spotting-with-attention | 2110.15957 | null | https://arxiv.org/abs/2110.15957v1 | https://arxiv.org/pdf/2110.15957v1.pdf | Visual Keyword Spotting with Attention | In this paper, we consider the task of spotting spoken keywords in silent video sequences -- also known as visual keyword spotting. To this end, we investigate Transformer-based models that ingest two streams, a visual encoding of the video and a phonetic encoding of the keyword, and output the temporal location of the... | ['Andrew Zisserman', 'Triantafyllos Afouras', 'Liliane Momeni', 'K R Prajwal'] | 2021-10-29 | null | null | null | null | ['visual-keyword-spotting'] | ['computer-vision'] | [ 4.16880369e-01 -2.81953011e-02 -3.77191693e-01 -3.00333172e-01
-1.16587937e+00 -5.56148291e-01 7.65661359e-01 -6.05817139e-01
-4.14499402e-01 2.62989461e-01 5.59022665e-01 -3.43130618e-01
2.67676443e-01 2.16811836e-01 -1.16389024e+00 -5.39904416e-01
1.18603343e-02 2.63676107e-01 3.98355514e-01 1.31682411... | [14.300654411315918, 5.008805751800537] |
6fcb0cec-0fb2-4dfb-84b8-c80366ec9dbc | optimised-convolutional-neural-networks-for | 2004.00505 | null | https://arxiv.org/abs/2004.00505v1 | https://arxiv.org/pdf/2004.00505v1.pdf | Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications | Wrist-worn smart devices are providing increased insights into human health, behaviour and performance through sophisticated analytics. However, battery life, device cost and sensor performance in the face of movement-related artefact present challenges which must be further addressed to see effective applications and ... | ['Alan F. Smeaton', 'Eoin Brophy', 'Willie Muehlhausen', 'Tomas E. Ward'] | 2020-03-30 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-estimation'] | ['medical', 'medical'] | [ 4.76349920e-01 7.25229830e-02 -1.14168683e-02 2.72482466e-02
-5.21053314e-01 -3.89115125e-01 2.63992310e-01 5.02113327e-02
-4.50947791e-01 6.96182370e-01 2.45332241e-01 -1.55888587e-01
1.96268454e-01 -4.24092412e-01 -2.70405591e-01 -4.61218566e-01
-3.23309571e-01 -3.47151428e-01 -4.26995188e-01 2.64854014... | [13.82914924621582, 3.0124168395996094] |
e17910f0-5f97-4cb0-88e4-1e25beaca298 | clustering-induced-generative-incomplete | 2209.13763 | null | https://arxiv.org/abs/2209.13763v2 | https://arxiv.org/pdf/2209.13763v2.pdf | Clustering-Induced Generative Incomplete Image-Text Clustering (CIGIT-C) | The target of image-text clustering (ITC) is to find correct clusters by integrating complementary and consistent information of multi-modalities for these heterogeneous samples. However, the majority of current studies analyse ITC on the ideal premise that the samples in every modality are complete. This presumption, ... | ['Zhiwei Xu', 'Zhiyong Pei', 'Limin Liu', 'Jiatai Wang', 'Xiaoming Su', 'Dongjin Guo'] | 2022-09-28 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [ 4.42057908e-01 -1.78192914e-01 -2.57132977e-01 -2.94161052e-01
-9.50121224e-01 -4.49380070e-01 6.30051672e-01 -4.60463077e-01
-1.18292674e-01 6.34992898e-01 6.76114857e-02 7.63334706e-02
-2.00106531e-01 -3.24303508e-01 -6.68013573e-01 -1.11332417e+00
4.62847352e-01 3.50036919e-01 -1.22540183e-01 2.28903040... | [8.627264976501465, 4.35468864440918] |
e682fa3c-8fc9-4e13-b321-ae2a1fe24224 | hgcn4mesh-hybrid-graph-convolution-network | null | null | https://aclanthology.org/2020.acl-srw.4 | https://aclanthology.org/2020.acl-srw.4.pdf | HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing | Recently deep learning has been used in Medical subject headings (MeSH) indexing to reduce the time and monetary cost by manual annotation, including DeepMeSH, TextCNN, etc. However, these models still suffer from failing to capture the complex correlations between MeSH terms. To this end, we introduce Graph Convolutio... | ['Yujiu Yang', 'Chenhui Li', 'Miaomiao Yu'] | 2020-07-01 | null | null | null | acl-2020-6 | ['extreme-multi-label-classification'] | ['methodology'] | [ 1.60346217e-02 2.26550445e-01 -2.02634841e-01 -1.23814449e-01
-3.26643258e-01 3.44772302e-02 2.74816960e-01 4.69850659e-01
-2.13754773e-01 4.66253191e-01 3.08999807e-01 -1.45250708e-01
-4.54152972e-01 -1.06028914e+00 -5.22458494e-01 -6.61725402e-01
-1.33985028e-01 6.17859244e-01 -1.99321479e-01 -3.21168303... | [7.454965591430664, 6.32596492767334] |
f741bc00-2d55-4080-ad60-d15e7c98c9e1 | rubq-a-russian-dataset-for-question-answering | 2005.10659 | null | https://arxiv.org/abs/2005.10659v1 | https://arxiv.org/pdf/2005.10659v1.pdf | RuBQ: A Russian Dataset for Question Answering over Wikidata | The paper presents RuBQ, the first Russian knowledge base question answering (KBQA) dataset. The high-quality dataset consists of 1,500 Russian questions of varying complexity, their English machine translations, SPARQL queries to Wikidata, reference answers, as well as a Wikidata sample of triples containing entities ... | ['Pavel Braslavski', 'Vladislav Korablinov'] | 2020-05-21 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-7.89701223e-01 6.79575861e-01 8.73650685e-02 -1.74057394e-01
-1.52208602e+00 -9.66129005e-01 3.72396618e-01 6.46866083e-01
-5.30210495e-01 1.45648468e+00 3.83966804e-01 -3.84525269e-01
-5.64657211e-01 -1.14925039e+00 -8.44625711e-01 5.11484087e-01
5.68156123e-01 1.43021798e+00 8.97148490e-01 -1.04767597... | [10.718945503234863, 7.940372943878174] |
e240c425-fba4-4d10-a399-d57805185500 | multi-domain-learning-for-motion | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Singh_Multi_Domain_Learning_for_Motion_Magnification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Singh_Multi_Domain_Learning_for_Motion_Magnification_CVPR_2023_paper.pdf | Multi Domain Learning for Motion Magnification | Video motion magnification makes subtle invisible motions visible, such as small chest movements while breathing, subtle vibrations in the moving objects etc. But small motions are prone to noise, illumination changes, large motions, etc. making the task difficult. Most state-of-the-art methods use hand-crafted con... | ['G. Sankara Raju Kosuru', 'Subrahmanyam Murala', 'Jasdeep Singh'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['motion-magnification'] | ['computer-vision'] | [ 2.25711420e-01 -1.08439758e-01 6.13423921e-02 3.34390849e-01
-3.01434174e-02 -4.55503374e-01 3.41037095e-01 -4.55160528e-01
-3.35241228e-01 5.92953742e-01 2.25647390e-01 -2.81732492e-02
-1.23927146e-01 -5.37880301e-01 -6.06918514e-01 -7.68953204e-01
-3.77618611e-01 -5.01264513e-01 6.52612984e-01 -1.13009445... | [10.876426696777344, -1.5235447883605957] |
d40d6b32-dfe6-4a5e-8ce7-01e78eda535e | synthetic-expressions-are-better-than-real | 2010.10979 | null | https://arxiv.org/abs/2010.10979v1 | https://arxiv.org/pdf/2010.10979v1.pdf | Synthetic Expressions are Better Than Real for Learning to Detect Facial Actions | Critical obstacles in training classifiers to detect facial actions are the limited sizes of annotated video databases and the relatively low frequencies of occurrence of many actions. To address these problems, we propose an approach that makes use of facial expression generation. Our approach reconstructs the 3D shap... | ['László A Jeni', 'Jeffrey F Cohn', 'Itir Onal Ertugrul', 'Koichiro Niinuma'] | 2020-10-21 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [ 4.46000487e-01 4.54523057e-01 7.39578679e-02 -4.78987902e-01
-3.77015889e-01 -3.22551459e-01 5.32018900e-01 -9.67063963e-01
-3.56453717e-01 7.80210972e-01 -7.59593546e-02 2.26934016e-01
3.59391391e-01 -5.95162094e-01 -1.02313387e+00 -8.25118482e-01
-1.30558074e-01 4.00101304e-01 2.05645729e-02 -7.02502728... | [13.480672836303711, 1.5098791122436523] |
c2a81486-1e0b-4048-bdca-eaf0258ab0fd | biologically-inspired-continual-learning-of | 2211.05231 | null | https://arxiv.org/abs/2211.05231v2 | https://arxiv.org/pdf/2211.05231v2.pdf | Biologically-Inspired Continual Learning of Human Motion Sequences | This work proposes a model for continual learning on tasks involving temporal sequences, specifically, human motions. It improves on a recently proposed brain-inspired replay model (BI-R) by building a biologically-inspired conditional temporal variational autoencoder (BI-CTVAE), which instantiates a latent mixture-of-... | ['Shih-Chii Liu', 'Joachim Ott'] | 2022-11-02 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 1.51650667e-01 3.34032893e-01 -6.88203499e-02 2.06244081e-01
-8.50289941e-01 -3.91776472e-01 1.26298404e+00 -7.35119939e-01
-3.71731669e-01 8.21160436e-01 2.77045369e-01 -6.38406426e-02
2.83608973e-01 -6.45235777e-01 -1.18184543e+00 -1.02261710e+00
-4.12389077e-02 5.58784068e-01 4.77633625e-01 6.97630867... | [7.325576305389404, -0.11761481314897537] |
aa7f8dff-dd34-4503-9256-afd94dd6e14a | isea-image-steganalysis-using-evolutionary | 1907.12914 | null | https://arxiv.org/abs/1907.12914v3 | https://arxiv.org/pdf/1907.12914v3.pdf | Evolutionary Algorithms and Efficient Data Analytics for Image Processing | Steganography algorithms facilitate communication between a source and a destination in a secret manner. This is done by embedding messages/text/data into images without impacting the appearance of the resultant images/videos. Steganalysis is the science of determining if an image has secret messages embedded/hidden in... | ['M. Hadi Amini', 'Farzan Shenavarmasouleh', 'Hamid R. Arabnia', 'Farid Ghareh Mohammadi'] | 2019-07-23 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 9.14293051e-01 -1.77279845e-01 3.17532569e-01 2.55759358e-01
-1.85295910e-01 -4.70601350e-01 4.97854441e-01 -1.75702900e-01
-2.21271902e-01 4.43855613e-01 -6.68072462e-01 -6.12846375e-01
2.05091715e-01 -1.10327613e+00 -7.12264836e-01 -1.02062023e+00
-2.82073557e-01 2.93243736e-01 7.03813955e-02 -5.21984994... | [4.301121711730957, 8.059426307678223] |
644ae718-0759-4b3d-8366-c3c6051653b6 | volumetric-medical-image-segmentation-a-3d | 2010.16074 | null | https://arxiv.org/abs/2010.16074v1 | https://arxiv.org/pdf/2010.16074v1.pdf | Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples | Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources. In this chapter, by rethinking the strategy to apply 3D Convoluti... | ['Alan L. Yuille', 'Elliot K. Fishman', 'Wei Shen', 'Yingda Xia', 'Yuyin Zhou', 'Zhuotun Zhu', 'Yingwei Li'] | 2020-10-29 | null | null | null | null | ['volumetric-medical-image-segmentation', 'pancreas-segmentation'] | ['medical', 'medical'] | [-6.65471405e-02 2.77586877e-01 1.88525319e-02 -7.77837262e-02
-6.35876775e-01 -5.62932193e-01 1.67711660e-01 8.66368636e-02
-5.02848089e-01 3.54749888e-01 -1.16642630e-02 -7.34542608e-01
-1.17357805e-01 -6.09522998e-01 -7.85944700e-01 -8.76067758e-01
-3.99621636e-01 1.67869061e-01 2.59209663e-01 5.50742410... | [14.470877647399902, -2.470282793045044] |
ba5489e1-46f9-4c23-a1f3-67e4f7fcc605 | universal-multi-source-domain-adaptation | 2011.02594 | null | https://arxiv.org/abs/2011.02594v1 | https://arxiv.org/pdf/2011.02594v1.pdf | Universal Multi-Source Domain Adaptation | Unsupervised domain adaptation enables intelligent models to transfer knowledge from a labeled source domain to a similar but unlabeled target domain. Recent study reveals that knowledge can be transferred from one source domain to another unknown target domain, called Universal Domain Adaptation (UDA). However, in the... | ['Xiaofu Wu', 'Haifeng Hu', 'Zhen Yang', 'Yueming Yin'] | 2020-11-05 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 1.95597574e-01 -1.40976787e-01 -4.26375568e-01 -2.75451660e-01
-7.97488749e-01 -8.63889515e-01 9.74055976e-02 -3.09995323e-01
-7.09145293e-02 1.00922608e+00 -3.50576550e-01 -9.26228985e-02
3.67092267e-02 -7.22640038e-01 -6.19920194e-01 -8.55313778e-01
4.62922007e-01 6.60908997e-01 4.33784187e-01 -1.72729701... | [10.411643028259277, 3.1505088806152344] |
58a48d15-4770-4d6a-9845-57d2fe2e7515 | semantic-segmentation-of-rgbd-images-with | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Deng_Semantic_Segmentation_of_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Deng_Semantic_Segmentation_of_ICCV_2015_paper.pdf | Semantic Segmentation of RGBD Images With Mutex Constraints | In this paper, we address the problem of semantic scene segmentation of RGB-D images of indoor scenes. We propose a novel image region labeling method which augments CRF formulation with hard mutual exclusion (mutex) constraints. This way our approach can make use of rich and accurate 3D geometric structure coming from... | ['Longin Jan Latecki', 'Sinisa Todorovic', 'Zhuo Deng'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['scene-labeling'] | ['computer-vision'] | [ 3.77621919e-01 1.05392687e-01 2.24820096e-02 -9.10721660e-01
-2.47497737e-01 -6.09030008e-01 4.30942714e-01 -1.70727037e-02
-6.37975335e-01 7.12055743e-01 -2.53608495e-01 -4.23563451e-01
8.21563825e-02 -8.96770120e-01 -7.58661509e-01 -4.04494166e-01
9.41469297e-02 5.66075325e-01 5.74312210e-01 -6.65164664... | [8.455305099487305, -2.6004371643066406] |
4b6fd568-a9fb-46f8-ad84-5380f37f6491 | on-the-origins-of-the-omicron-variant-of-the | 2201.07879 | null | https://arxiv.org/abs/2201.07879v1 | https://arxiv.org/pdf/2201.07879v1.pdf | On the origins of the Omicron variant of the SARS-CoV-2 virus | A possible explanation based on first principles for the appearance of the Omicron variant of the SARS-CoV-2 virus is proposed involving coinfection with HIV. The gist is that the resultant HIV-induced immunocompromise allows SARS-CoV-2 greater latitude to explore its own mutational space. This latitude is not withoutr... | ['Minus van Baalen', 'Robert Penner'] | 2021-12-08 | null | null | null | null | ['virology'] | ['miscellaneous'] | [ 4.56818998e-01 1.15790367e-01 -9.26967710e-02 -3.42921168e-01
-2.04939932e-01 -4.75965470e-01 3.76924634e-01 2.73504585e-01
-5.04682183e-01 1.26199555e+00 3.50810289e-02 -8.26663435e-01
8.96780267e-02 -4.46741134e-01 -6.21331215e-01 -1.00101125e+00
-1.94223404e-01 5.97365499e-01 5.88618740e-02 -2.96370953... | [4.739315986633301, 5.139683246612549] |
c39e41be-95c3-412e-89f7-c90dd9c8af83 | generating-diversified-comments-via-reader | 2102.06856 | null | https://arxiv.org/abs/2102.06856v1 | https://arxiv.org/pdf/2102.06856v1.pdf | Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection | Automatic comment generation is a special and challenging task to verify the model ability on news content comprehension and language generation. Comments not only convey salient and interesting information in news articles, but also imply various and different reader characteristics which we treat as the essential clu... | ['Hai-Tao Zheng', 'Piji Li', 'Wei Wang'] | 2021-02-13 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 2.22492754e-01 2.72470415e-01 -3.64425182e-01 -5.27418971e-01
-1.03909218e+00 -2.57684141e-01 7.39754975e-01 3.44425231e-01
-2.12626263e-01 6.67653739e-01 1.10404456e+00 1.22114815e-01
1.56072438e-01 -5.43344676e-01 -5.53507924e-01 -6.75522923e-01
5.21894932e-01 2.69081324e-01 4.69217032e-01 -2.69009411... | [12.398802757263184, 9.297928810119629] |
42ff0a51-9c36-4cbe-8cfc-8745e0523098 | strategic-features-for-general-games | 2101.00843 | null | https://arxiv.org/abs/2101.00843v1 | https://arxiv.org/pdf/2101.00843v1.pdf | Strategic Features for General Games | This short paper describes an ongoing research project that requires the automated self-play learning and evaluation of a large number of board games in digital form. We describe the approach we are taking to determine relevant features, for biasing MCTS playouts for arbitrary games played on arbitrary geometries. Bene... | ['Eric Piette', 'Dennis J. N. J. Soemers', 'Cameron Browne'] | 2021-01-04 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.72213649e-02 2.20004752e-01 5.96283861e-02 -2.06195936e-01
-7.29118526e-01 -6.65835261e-01 3.86880845e-01 -1.64070934e-01
-4.15849239e-01 1.09445536e+00 4.21032868e-02 -3.65764856e-01
-6.75352514e-01 -1.03451014e+00 -7.05371618e-01 -3.68661344e-01
-5.34711242e-01 5.87810934e-01 8.09200048e-01 -8.07859480... | [3.5009605884552, 1.4293394088745117] |
74769767-9618-4f68-83a3-00c7a1329e32 | make-the-blind-translator-see-the-world-a | null | null | https://aclanthology.org/2021.mtsummit-research.12 | https://aclanthology.org/2021.mtsummit-research.12.pdf | Make the Blind Translator See The World: A Novel Transfer Learning Solution for Multimodal Machine Translation | Based on large-scale pretrained networks and the liability to be easily overfitting with limited labelled training data of multimodal translation (MMT) is a critical issue in MMT. To this end and we propose a transfer learning solution. Specifically and 1) A vanilla Transformer is pre-trained on massive bilingual text-... | ['Hao Yang', 'Shimin Tao', 'Min Zhang', 'Chang Su', 'Yimeng Chen', 'Jiaxin Guo', 'Minghan Wang'] | null | null | null | null | mtsummit-2021-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.22445679e-01 3.66897732e-01 -1.69635400e-01 -2.90789604e-01
-1.37695408e+00 -6.34150863e-01 7.75513291e-01 -6.33386254e-01
-6.62913740e-01 7.21835554e-01 2.61564463e-01 -6.48179233e-01
3.71233672e-01 -2.60385215e-01 -1.22858071e+00 -7.01021850e-01
3.45040023e-01 9.23271835e-01 -2.65228212e-01 -2.55175084... | [11.45297622680664, 1.5367321968078613] |
06f663dc-0a4a-4873-9f9b-057c61e7ba13 | declipping-of-speech-signals-using-frequency | 2204.04068 | null | https://arxiv.org/abs/2204.04068v1 | https://arxiv.org/pdf/2204.04068v1.pdf | Declipping of Speech Signals Using Frequency Selective Extrapolation | The reconstruction of clipped speech signals is an important task in audio signal processing to achieve an enhanced audio quality for further processing. In this paper, Frequency Selective Extrapolation (FSE), which is commonly used for error concealment or the reconstruction of incomplete image data, is adapted to be ... | ['André Kaup', 'Jürgen Seiler', 'Markus Jonscher'] | 2022-04-07 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 7.39244461e-01 -4.04849984e-02 3.10831577e-01 1.30265881e-03
-8.34527314e-01 -7.81669468e-02 2.72913963e-01 1.25494286e-01
-3.83638829e-01 9.83162284e-01 2.52744019e-01 -6.41388744e-02
-1.81113213e-01 -3.46817166e-01 -4.69936311e-01 -8.73693705e-01
-2.16154516e-01 -2.08439767e-01 4.02714640e-01 2.18752295... | [14.919153213500977, 5.6867218017578125] |
868ef3b2-059f-4d69-aede-8f72623358b4 | generative-adversarial-reward-learning-for | 2105.00822 | null | https://arxiv.org/abs/2105.00822v2 | https://arxiv.org/pdf/2105.00822v2.pdf | Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference | Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e.g., in reinforcement learning based recommender systems. Reward function is crucial for most of reinforcement learning applications as it can provide the guideline about the o... | ['Quan Z. Sheng', 'Wenjie Zhang', 'Aixin Sun', 'Xianzhi Wang', 'Lina Yao', 'Xiaocong Chen'] | 2021-05-03 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [-1.63134903e-01 -7.39969462e-02 -4.50514257e-01 -4.72043246e-01
-4.82239902e-01 -2.18772456e-01 2.21801803e-01 -1.48782104e-01
-3.59588474e-01 8.45684171e-01 5.45786917e-02 -4.12356257e-01
-4.13100332e-01 -9.55831707e-01 -4.96177882e-01 -6.46455884e-01
-6.18464276e-02 6.85549617e-01 3.60834002e-01 -6.64028525... | [4.143524646759033, 2.256166934967041] |
3ebb8649-ea32-4302-bd8f-b1600cd577d6 | joint-super-resolution-and-synthesis-of-1-mm | 2012.13340 | null | https://arxiv.org/abs/2012.13340v1 | https://arxiv.org/pdf/2012.13340v1.pdf | Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast | Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e.g., MP-RAGE). This limitation prevents the analysis of millions of MRI scan... | ['Bruce Fischl', 'Brian L. Edlow', 'Polina Golland', 'Daniel C. Alexander', 'John Conklin', 'Azadeh Tabari', 'Yael Balbastre', 'Benjamin Billot', 'Juan Eugenio Iglesias'] | 2020-12-24 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 3.69531572e-01 5.08610718e-02 1.26822650e-01 -5.06667852e-01
-6.83644056e-01 -4.52025145e-01 3.24676096e-01 2.45333359e-01
-7.68546343e-01 6.37482166e-01 -8.14137980e-02 -4.10075694e-01
-1.11800410e-01 -7.92918861e-01 -6.40072346e-01 -7.22280383e-01
-3.97956848e-01 7.26848781e-01 5.09990275e-01 6.64829761... | [14.081799507141113, -2.314619541168213] |
245461cb-d548-4b2f-a05d-868d09023311 | coupling-top-down-and-bottom-up-methods-for | 1410.0117 | null | http://arxiv.org/abs/1410.0117v2 | http://arxiv.org/pdf/1410.0117v2.pdf | Coupling Top-down and Bottom-up Methods for 3D Human Pose and Shape Estimation from Monocular Image Sequences | Until recently Intelligence, Surveillance, and Reconnaissance (ISR) focused
on acquiring behavioral information of the targets and their activities.
Continuous evolution of intelligence being gathered of the human centric
activities has put increased focus on the humans, especially inferring their
innate characteristic... | ['Atul Kanaujia'] | 2014-10-01 | null | null | null | null | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [ 4.73156840e-01 -1.46265507e-01 3.97998579e-02 -2.78232276e-01
-3.36070150e-01 -6.18225992e-01 5.65327227e-01 -1.00588202e-01
-6.01961851e-01 9.86181021e-01 1.32063344e-01 4.35353905e-01
-2.45653003e-01 -2.69606471e-01 -7.27882087e-01 -7.44605660e-01
-2.66595066e-01 7.64481604e-01 2.22388133e-01 1.84983194... | [7.051532745361328, -0.9775857925415039] |
f6c76600-b09d-42f9-aa60-5ac2f1789334 | on-the-uncertainty-analysis-of-the-data | 2303.08455 | null | https://arxiv.org/abs/2303.08455v3 | https://arxiv.org/pdf/2303.08455v3.pdf | On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem | In practical engineering experiments, the data obtained through detectors are inevitably noisy. For the already proposed data-enabled physics-informed neural network (DEPINN) \citep{DEPINN}, we investigate the performance of DEPINN in calculating the neutron diffusion eigenvalue problem from several perspectives when t... | ['Shiquan Zhang', 'Qiaolin He', 'Yangtao Deng', 'Qihong Yang', 'Helin Gong', 'Yu Yang'] | 2023-03-15 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 1.26696736e-01 6.14068024e-02 6.57499731e-02 -3.36287290e-01
-5.34656286e-01 -3.83563899e-02 2.61715800e-01 9.51581597e-02
-8.37524116e-01 1.23529792e+00 -4.11596857e-02 -2.69254714e-01
-1.00845122e+00 -7.18900204e-01 -6.51363373e-01 -1.22339320e+00
-8.48682690e-03 3.33047181e-01 -5.11700884e-02 4.26855031... | [7.222363471984863, 3.576080560684204] |
9abdd5ba-45e1-4a56-92d8-7a0c6653b478 | complex-valued-frequency-selective | 2204.14193 | null | https://arxiv.org/abs/2204.14193v1 | https://arxiv.org/pdf/2204.14193v1.pdf | Complex-Valued Frequency Selective Extrapolation for Fast Image and Video Signal Extrapolation | Signal extrapolation tasks arise in miscellaneous manners in the field of image and video signal processing. But, due to the widespread use of low-power and mobile devices, the computational complexity of an algorithm plays a crucial role in selecting an algorithm for a given problem. Within the scope of this contribut... | ['André Kaup', 'Jürgen Seiler'] | 2022-04-27 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 7.68956065e-01 -1.65045455e-01 1.04197696e-01 5.05444652e-04
-6.90488815e-01 -2.91527569e-01 4.06363487e-01 -1.50719121e-01
-4.46819186e-01 7.61642873e-01 -2.09401220e-01 -2.60793120e-01
-2.51372129e-01 -4.81826812e-01 -6.14549994e-01 -7.91035533e-01
-3.16237509e-01 -1.89812094e-01 1.81301653e-01 -2.76161402... | [6.839897155761719, 1.6017234325408936] |
2fcbab23-271f-4682-8390-87548aa76071 | labr-a-large-scale-arabic-sentiment-analysis | 1411.6718 | null | http://arxiv.org/abs/1411.6718v2 | http://arxiv.org/pdf/1411.6718v2.pdf | LABR: A Large Scale Arabic Sentiment Analysis Benchmark | We introduce LABR, the largest sentiment analysis dataset to-date for the
Arabic language. It consists of over 63,000 book reviews, each rated on a scale
of 1 to 5 stars. We investigate the properties of the dataset, and present its
statistics. We explore using the dataset for two tasks: (1) sentiment polarity
classifi... | ['Amir Atiya', 'Mohamed Aly', 'Mahmoud Nabil'] | 2014-11-25 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-3.85178477e-02 -1.17893614e-01 -7.44258344e-01 -9.22151983e-01
-7.16062248e-01 -1.27942705e+00 7.16696858e-01 5.70820034e-01
-1.92366660e-01 6.98985219e-01 5.22520781e-01 -3.17374974e-01
3.35715026e-01 -5.86910069e-01 -2.23527044e-01 -4.31711763e-01
1.85553938e-01 4.41691190e-01 -1.61266446e-01 -8.13194573... | [11.236339569091797, 6.893028736114502] |
9c3e1d6a-00b0-46e9-bdc6-29de1987419c | spectrum-inspired-low-light-image-translation | 2303.10145 | null | https://arxiv.org/abs/2303.10145v1 | https://arxiv.org/pdf/2303.10145v1.pdf | Spectrum-inspired Low-light Image Translation for Saliency Detection | Saliency detection methods are central to several real-world applications such as robot navigation and satellite imagery. However, the performance of existing methods deteriorate under low-light conditions because training datasets mostly comprise of well-lit images. One possible solution is to collect a new dataset fo... | ['Kaushik Mitra', 'Mohit Lamba', 'Sudarshan Rajagopalan', 'Kitty Varghese'] | 2023-03-17 | null | null | null | null | ['saliency-detection', 'robot-navigation'] | ['computer-vision', 'robots'] | [ 5.27642727e-01 -2.33216897e-01 3.36360708e-02 -4.21438277e-01
-5.49272358e-01 -1.95257440e-01 4.31594491e-01 -2.14046553e-01
-7.31792986e-01 9.78079379e-01 -3.97283643e-01 -1.85501575e-01
2.94331759e-01 -9.91862118e-01 -8.97497356e-01 -8.89433920e-01
3.81426156e-01 2.05356792e-01 8.52938712e-01 -2.24302724... | [9.837296485900879, -2.3125617504119873] |
443cd7a5-7565-4964-aa11-f081796e9659 | dr-spider-a-diagnostic-evaluation-benchmark | 2301.08881 | null | https://arxiv.org/abs/2301.08881v2 | https://arxiv.org/pdf/2301.08881v2.pdf | Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness | Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries. However, recent studies reveal that text-to-SQL models are vulnerable to task-specific perturbations. Previous curated robustness test sets usually focus on individual phenomena. In this paper, we ... | ['Bing Xiang', 'Patrick Ng', 'Vittorio Castelli', 'Zhiguo Wang', 'William Yang Wang', 'Steve Ash', 'Joseph Lilien', 'Jiarong Jiang', 'Sheng Zhang', 'Wuwei Lan', 'Alexander Hanbo Li', 'Henghui Zhu', 'Lin Pan', 'Mingwen Dong', 'Jun Wang', 'Shuaichen Chang'] | 2023-01-21 | null | null | null | null | ['text-to-sql', 'natural-questions'] | ['computer-code', 'miscellaneous'] | [-1.29272386e-01 -2.21315339e-01 1.21623904e-01 -4.69274610e-01
-1.31401765e+00 -9.92068410e-01 5.19232929e-01 6.17396124e-02
-1.97252259e-01 2.19306618e-01 3.83955181e-01 -5.65789104e-01
-6.57979324e-02 -6.97404742e-01 -1.11634386e+00 -3.21291275e-02
1.94427788e-01 2.82843411e-01 4.32364523e-01 -6.87907040... | [6.293640613555908, 8.16315746307373] |
4e066522-be53-4d9a-9b29-b198ab5ff310 | smart-director-an-event-driven-directing | 2201.04024 | null | https://arxiv.org/abs/2201.04024v1 | https://arxiv.org/pdf/2201.04024v1.pdf | Smart Director: An Event-Driven Directing System for Live Broadcasting | Live video broadcasting normally requires a multitude of skills and expertise with domain knowledge to enable multi-camera productions. As the number of cameras keep increasing, directing a live sports broadcast has now become more complicated and challenging than ever before. The broadcast directors need to be much mo... | ['Tao Mei', 'Jingen Liu', 'Ting Yao', 'Ning Zhang', 'Qian Bao', 'Yue Chen', 'Yingwei Pan'] | 2022-01-11 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [-3.31443455e-03 -2.48207048e-01 1.00942282e-02 -3.89357805e-01
-9.77108300e-01 -8.25049281e-01 3.80939275e-01 -3.16560455e-02
-1.23875521e-01 2.81938072e-02 2.94580370e-01 1.49544120e-01
-2.62894388e-02 -6.89736545e-01 -9.41982388e-01 -3.66711974e-01
2.08894536e-02 7.08918452e-01 8.78358424e-01 -4.09142196... | [7.94981575012207, 0.14656655490398407] |
6a7bc931-8c3c-4827-bc0c-ea5fe0b53b4c | lwposr-lightweight-efficient-fine-grained | 2202.03544 | null | https://arxiv.org/abs/2202.03544v1 | https://arxiv.org/pdf/2202.03544v1.pdf | LwPosr: Lightweight Efficient Fine-Grained Head Pose Estimation | This paper presents a lightweight network for head pose estimation (HPE) task. While previous approaches rely on convolutional neural networks, the proposed network \textit{LwPosr} uses mixture of depthwise separable convolutional (DSC) and transformer encoder layers which are structured in two streams and three stages... | ['Naina Dhingra'] | 2022-02-07 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-2.97368348e-01 4.83851105e-01 5.71189858e-02 -5.87336361e-01
-9.64538157e-01 -3.52787599e-02 5.01542747e-01 -2.17987970e-01
-7.85079062e-01 6.80747807e-01 4.59222525e-01 -1.14426069e-01
-1.10168040e-01 -5.13303995e-01 -9.50662732e-01 -4.99149472e-01
-4.95941669e-01 5.36895335e-01 2.80237377e-01 -4.64643508... | [13.658293724060059, 0.2882228493690491] |
b177d1fc-f130-4877-b6d8-09fe06080221 | beit-bert-pre-training-of-image-transformers | 2106.08254 | null | https://arxiv.org/abs/2106.08254v2 | https://arxiv.org/pdf/2106.08254v2.pdf | BEiT: BERT Pre-Training of Image Transformers | We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image modeling task to pretrain vision Transformers. Specifically, each image has two view... | ['Furu Wei', 'Songhao Piao', 'Li Dong', 'Hangbo Bao'] | 2021-06-15 | beit-bert-pre-training-of-image-transformers-1 | https://openreview.net/forum?id=p-BhZSz59o4 | https://openreview.net/pdf?id=p-BhZSz59o4 | iclr-2022-4 | ['self-supervised-image-classification', 'document-image-classification', 'document-layout-analysis'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.84448850e-01 4.23196554e-01 -1.96380928e-01 -4.13997769e-01
-8.59788060e-01 -4.97652203e-01 2.12069571e-01 -4.95762467e-01
-6.09219074e-01 2.88046002e-01 -1.96052209e-01 -4.25853342e-01
5.91813266e-01 -7.38360941e-01 -1.41117430e+00 -5.06131232e-01
3.23809206e-01 2.91813254e-01 1.75527081e-01 1.94996700... | [9.606143951416016, 0.8276678323745728] |
1c2e5d71-f393-404d-8189-b5270ed0e05d | show-and-write-entity-aware-news-generation | 2112.05917 | null | https://arxiv.org/abs/2112.05917v2 | https://arxiv.org/pdf/2112.05917v2.pdf | Show and Write: Entity-aware Article Generation with Image Information | Many vision-language applications contain long articles of text paired with images (e.g., news or Wikipedia articles). Prior work learning to encode and/or generate these articles has primarily focused on understanding the article itself and some related metadata like the title or date it was written. However, the imag... | ['Bryan A. Plummer', 'Yiwen Gu', 'Zhongping Zhang'] | 2021-12-11 | null | null | null | null | ['news-generation'] | ['natural-language-processing'] | [-3.96145172e-02 2.69289047e-01 -3.07072401e-01 -2.98933715e-01
-9.50222194e-01 -7.07635045e-01 1.18990290e+00 2.09746882e-01
-6.88249946e-01 8.53001118e-01 7.06637919e-01 -8.29686690e-03
5.37470102e-01 -7.16453910e-01 -1.28165925e+00 -4.37045470e-02
4.03280616e-01 5.41839242e-01 1.71189785e-01 6.59131408... | [10.974937438964844, 1.0738558769226074] |
d4131f11-0ebe-4ede-b58a-938bc3d96cf9 | an-attribute-enhanced-domain-adaptive-model | null | null | https://aclanthology.org/C18-1160 | https://aclanthology.org/C18-1160.pdf | An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection | Spam detection has long been a research topic in both academic and industry due to its wide applications. Previous studies are mainly focused on extracting linguistic or behavior features to distinguish the spam and legitimate reviews. Such features are either ineffective or take long time to collect and thus are hard ... | ['Tieyun Qian', 'Zhenni You', 'Bing Liu'] | 2018-08-01 | an-attribute-enhanced-domain-adaptive-model-1 | https://aclanthology.org/C18-1160 | https://aclanthology.org/C18-1160.pdf | coling-2018-8 | ['spam-detection'] | ['natural-language-processing'] | [-3.98035258e-01 -5.96909702e-01 -3.26343834e-01 -7.83359587e-01
-4.58535224e-01 -4.43861574e-01 6.18300974e-01 1.33200198e-01
-4.10634816e-01 8.62391293e-01 6.85554296e-02 -1.45924345e-01
3.83616090e-02 -8.02926958e-01 -3.96032572e-01 -5.69589257e-01
4.68900114e-01 3.15587550e-01 3.46684247e-01 -2.45617285... | [7.904204368591309, 9.938251495361328] |
9b71fdf6-c1c9-4618-bffd-c15bd48bdb0a | motion-adjustable-neural-implicit-video | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Mai_Motion-Adjustable_Neural_Implicit_Video_Representation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mai_Motion-Adjustable_Neural_Implicit_Video_Representation_CVPR_2022_paper.pdf | Motion-Adjustable Neural Implicit Video Representation | Implicit neural representation (INR) has been successful in representing static images. Contemporary image-based INR, with the use of Fourier-based positional encoding, can be viewed as a mapping from sinusoidal patterns with different frequencies to image content. Inspired by that view, we hypothesize that it is p... | ['Feng Liu', 'Long Mai'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['motion-magnification'] | ['computer-vision'] | [ 6.11470640e-01 1.02083497e-01 -5.77216893e-02 -2.21853834e-02
-2.77197987e-01 -6.99648261e-01 8.40754151e-01 -1.71777889e-01
-1.26549050e-01 3.63425881e-01 4.65601742e-01 -3.03298645e-02
-4.82134223e-02 -7.31048226e-01 -1.09120500e+00 -8.52724910e-01
-3.55080873e-01 -2.74283469e-01 1.96921110e-01 -2.42809966... | [10.814191818237305, -0.7737213969230652] |
9d4f67fa-f375-4298-9943-db2b31f815a8 | the-art-of-embedding-fusion-optimizing-hate | 2306.14939 | null | https://arxiv.org/abs/2306.14939v1 | https://arxiv.org/pdf/2306.14939v1.pdf | The Art of Embedding Fusion: Optimizing Hate Speech Detection | Hate speech detection is a challenging natural language processing task that requires capturing linguistic and contextual nuances. Pre-trained language models (PLMs) offer rich semantic representations of text that can improve this task. However there is still limited knowledge about ways to effectively combine represe... | ['Sanyam Goyal', 'Mohit Jain', 'Neemesh Yadav', 'Mohammad Aflah Khan'] | 2023-06-26 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.92226142e-01 -1.84215769e-01 -2.04721585e-01 -9.36813578e-02
-9.51074958e-01 -5.29391706e-01 8.06997895e-01 4.19095427e-01
-5.30293584e-01 2.40139246e-01 7.34487712e-01 -3.21322143e-01
2.89360493e-01 -4.71685410e-01 -2.64094442e-01 -4.25399095e-01
2.45287046e-01 -2.52026528e-01 9.55836996e-02 -2.03637332... | [8.819266319274902, 10.55526351928711] |
f34dd132-6600-492a-a398-4d9a2aac2546 | augmented-message-passing-stein-variational | 2305.10636 | null | https://arxiv.org/abs/2305.10636v1 | https://arxiv.org/pdf/2305.10636v1.pdf | Augmented Message Passing Stein Variational Gradient Descent | Stein Variational Gradient Descent (SVGD) is a popular particle-based method for Bayesian inference. However, its convergence suffers from the variance collapse, which reduces the accuracy and diversity of the estimation. In this paper, we study the isotropy property of finite particles during the convergence process a... | ['Yue Qiu', 'Jiankui Zhou'] | 2023-05-18 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-4.43681389e-01 -4.64443505e-01 6.78014830e-02 1.41348457e-02
-7.33917415e-01 -4.71258201e-02 5.98133802e-01 -6.44130707e-02
-4.05872494e-01 1.06653368e+00 6.89276308e-02 -1.61623225e-01
-2.71567762e-01 -8.22748780e-01 -6.49692893e-01 -1.09626687e+00
1.67843238e-01 6.76613390e-01 5.08180320e-01 1.68987334... | [6.871944427490234, 4.049733638763428] |
83604973-a0cc-458c-b7d7-44c74d843fd2 | ev-tta-test-time-adaptation-for-event-based | 2203.12247 | null | https://arxiv.org/abs/2203.12247v2 | https://arxiv.org/pdf/2203.12247v2.pdf | Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition | We introduce Ev-TTA, a simple, effective test-time adaptation algorithm for event-based object recognition. While event cameras are proposed to provide measurements of scenes with fast motions or drastic illumination changes, many existing event-based recognition algorithms suffer from performance deterioration under e... | ['Young Min Kim', 'Inwoo Hwang', 'Junho Kim'] | 2022-03-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kim_Ev-TTA_Test-Time_Adaptation_for_Event-Based_Object_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kim_Ev-TTA_Test-Time_Adaptation_for_Event-Based_Object_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['event-based-vision'] | ['computer-vision'] | [ 4.48079199e-01 -8.44595969e-01 3.54250637e-03 -6.19037390e-01
-7.08248734e-01 -5.18673301e-01 7.06869781e-01 1.58986479e-01
-4.01992351e-01 4.62273806e-01 -2.08003018e-02 8.04090966e-03
-3.07416677e-01 -6.43031836e-01 -8.64693642e-01 -8.19513202e-01
-2.59046406e-01 3.23654383e-01 5.39747417e-01 5.67392148... | [8.476905822753906, -0.9137821793556213] |
fa6d08ae-4eed-4ef6-bdcd-7c8d093cb1d6 | introducing-model-inversion-attacks-on | 2301.03206 | null | https://arxiv.org/abs/2301.03206v1 | https://arxiv.org/pdf/2301.03206v1.pdf | Introducing Model Inversion Attacks on Automatic Speaker Recognition | Model inversion (MI) attacks allow to reconstruct average per-class representations of a machine learning (ML) model's training data. It has been shown that in scenarios where each class corresponds to a different individual, such as face classifiers, this represents a severe privacy risk. In this work, we explore a ne... | ['Konstantin Böttinger', 'Ugur Sahin', 'Franziska Boenisch', 'Karla Pizzi'] | 2023-01-09 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 1.11009920e+00 3.53711843e-01 7.20955804e-02 -2.71496415e-01
-1.01657939e+00 -9.08332288e-01 3.86074007e-01 -1.84865400e-01
-1.25739127e-01 3.96457464e-01 -7.20246062e-02 -4.39632565e-01
3.32158282e-02 -4.91077214e-01 -7.97393620e-01 -7.99559653e-01
-1.85136393e-01 1.84149653e-01 -1.34754643e-01 1.91391557... | [13.988228797912598, 5.842278957366943] |
94f8a0dc-bb64-47b0-92cc-59b45ad671a9 | from-the-detection-of-toxic-spans-in-online-1 | null | null | https://aclanthology.org/2022.acl-long.259 | https://aclanthology.org/2022.acl-long.259.pdf | From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer | We study the task of toxic spans detection, which concerns the detection of the spans that make a text toxic, when detecting such spans is possible. We introduce a dataset for this task, ToxicSpans, which we release publicly. By experimenting with several methods, we show that sequence labeling models perform best, but... | ['Ion Androutsopoulos', 'Jeffrey Sorensen', 'Alexandros Xenos', 'Leo Laugier', 'John Pavlopoulos'] | null | null | null | null | acl-2022-5 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 5.01375854e-01 -1.00666150e-01 -9.70639661e-02 2.61684597e-01
-1.26515377e+00 -1.09673822e+00 6.59580410e-01 6.04148984e-01
-3.40303153e-01 9.98862922e-01 6.38816178e-01 -4.33224142e-01
-1.99782886e-02 -6.55829430e-01 -8.72187376e-01 -5.10463595e-01
-4.27874029e-02 2.78171122e-01 5.01584709e-01 -1.56366169... | [8.968360900878906, 10.620347023010254] |
f6ec61bd-c446-4607-8954-7b858f965de2 | no-regret-constrained-bayesian-optimization | 2305.03824 | null | https://arxiv.org/abs/2305.03824v1 | https://arxiv.org/pdf/2305.03824v1.pdf | No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximations | This paper investigates the problem of efficient constrained global optimization of composite functions (hybrid models) whose input is an expensive black-box function with vector-valued outputs and noisy observations, which often arises in real-world science, engineering, manufacturing, and control applications. We pro... | ['Joel A. Paulson', 'Congwen Lu'] | 2023-05-05 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [ 2.42216155e-01 8.33609328e-02 -2.57460088e-01 -1.06134355e-01
-1.16829407e+00 -6.29310906e-01 1.31133124e-01 2.42773309e-01
-2.81321377e-01 1.19648850e+00 -3.32687467e-01 -5.18333614e-01
-8.36985230e-01 -6.75392151e-01 -9.97070014e-01 -1.02010858e+00
-9.05569941e-02 3.87022108e-01 -2.52194136e-01 3.72723630... | [5.660253047943115, 3.585454225540161] |
64aba5b3-41de-40c8-a756-3e4e5d2578cd | sequential-skip-prediction-with-few-shot-in | 1901.08203 | null | https://arxiv.org/abs/1901.08203v2 | https://arxiv.org/pdf/1901.08203v2.pdf | Sequential Skip Prediction with Few-shot in Streamed Music Contents | This paper provides an outline of the algorithms submitted for the WSDM Cup 2019 Spotify Sequential Skip Prediction Challenge (team name: mimbres). In the challenge, complete information including acoustic features and user interaction logs for the first half of a listening session is provided. Our goal is to predict w... | ['Sungkyun Chang', 'Kyogu Lee', 'Seungjin Lee'] | 2019-01-24 | null | null | null | null | ['sequential-skip-prediction'] | ['time-series'] | [ 3.08855057e-01 -2.05248430e-01 -1.19307160e-01 -7.09639907e-01
-1.16078758e+00 -4.46568727e-01 2.68016458e-01 -1.66795492e-01
-4.90478635e-01 6.85104787e-01 3.09754610e-01 -2.47542962e-01
-2.05967933e-01 -1.03285201e-01 -4.53571677e-01 -4.79944319e-01
-4.50971484e-01 1.72019541e-01 6.64971709e-01 -1.91961288... | [15.590384483337402, 5.135326385498047] |
8e9ead2a-56d1-4f68-b650-7907965fb725 | romnet-renovate-the-old-memories | 2202.02606 | null | https://arxiv.org/abs/2202.02606v2 | https://arxiv.org/pdf/2202.02606v2.pdf | ROMNet: Renovate the Old Memories | Renovating the memories in old photos is an intriguing research topic in computer vision fields. These legacy images often suffer from severe and commingled degradations such as cracks, noise, and color-fading, while lack of large-scale paired old photo datasets makes this restoration task very challenging. In this wor... | ['Hongkai Yu', 'Jiaqi Ma', 'Zibo Meng', 'Jinlong Li', 'Xiaoyu Dong', 'Yuanqi Du', 'Zhengzhong Tu', 'Runsheng Xu'] | 2022-02-05 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 3.15357059e-01 -2.96447098e-01 3.32489252e-01 -1.33843899e-01
-6.71758235e-01 -3.22024524e-01 5.52599013e-01 -1.74304679e-01
-3.38649005e-01 9.00188506e-01 2.16146365e-01 7.14284703e-02
3.51979494e-01 -7.90442586e-01 -1.00285101e+00 -9.00401771e-01
2.70726949e-01 -1.21622257e-01 4.26757693e-01 -1.99583143... | [11.098012924194336, -2.1017887592315674] |
5bd0c38c-bc21-4b5f-ad0f-52ce6b5c1579 | reconstructing-undersampled-photoacoustic | 2006.00251 | null | https://arxiv.org/abs/2006.00251v1 | https://arxiv.org/pdf/2006.00251v1.pdf | Reconstructing undersampled photoacoustic microscopy images using deep learning | One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed. In this study, we propose a novel application of deep learning principles to reconstruct undersampled PAM images and transcend the trade-off between spatial resolution and imaging ... | ['Junjie Yao', 'Maomao Chen', 'Daiwei Li', 'Jianwen Luo', 'Dong Zhang', 'Anthony DiSpirito III', 'Tri Vu', 'Roarke Horstmeyer'] | 2020-05-30 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 5.50579309e-01 -2.29256123e-01 4.39119041e-01 1.40163690e-01
-7.56587148e-01 -3.85892332e-01 2.98683941e-01 -3.28283668e-01
-8.25650454e-01 8.64986002e-01 -1.13274328e-01 -4.45494503e-01
7.09475996e-03 -6.49106801e-01 -7.17978716e-01 -1.08842599e+00
1.91382170e-02 -1.92259118e-01 3.93868059e-01 3.99484396... | [12.91319465637207, -2.675550699234009] |
29dece8c-3848-4af5-9888-2dc94a9d9dfa | automatic-graph-partitioning-for-very-large | 2103.16063 | null | https://arxiv.org/abs/2103.16063v1 | https://arxiv.org/pdf/2103.16063v1.pdf | Automatic Graph Partitioning for Very Large-scale Deep Learning | This work proposes RaNNC (Rapid Neural Network Connector) as middleware for automatic hybrid parallelism. In recent deep learning research, as exemplified by T5 and GPT-3, the size of neural network models continues to grow. Since such models do not fit into the memory of accelerator devices, they need to be partitione... | ['Kentaro Torisawa', 'Toshihiro Hanawa', 'Kenjiro Taura', 'Masahiro Tanaka'] | 2021-03-30 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-3.50261331e-01 8.12723301e-03 -1.08765624e-01 -4.01175112e-01
-9.91391912e-02 -4.20380622e-01 4.16539967e-01 -1.83795229e-01
-8.46285999e-01 1.93240792e-01 -3.77727956e-01 -7.61190832e-01
-4.24414054e-02 -1.02765119e+00 -6.85259104e-01 -4.24014300e-01
-6.44748136e-02 9.51504171e-01 8.05345714e-01 -2.57187635... | [8.527721405029297, 3.1388299465179443] |
abfe1110-db8b-4a30-b795-3d7d62553ec4 | conception-multilingually-enhanced-human | null | null | https://aclanthology.org/2020.coling-main.291 | https://aclanthology.org/2020.coling-main.291.pdf | Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations | To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space. Most current approaches, however, suffer from a monolingual bias, with their s... | ['Roberto Navigli', 'Simone Conia'] | 2020-12-01 | null | null | null | coling-2020-8 | ['word-similarity'] | ['natural-language-processing'] | [-2.74343520e-01 -2.94336230e-01 -5.17252028e-01 -2.32053652e-01
-8.62931192e-01 -8.57733846e-01 8.83236408e-01 7.47854054e-01
-6.27562225e-01 6.00000143e-01 6.17030084e-01 -3.29741180e-01
-1.27180800e-01 -7.03565180e-01 -1.79857612e-01 -1.94221616e-01
2.29296014e-01 6.77591205e-01 -7.65706673e-02 -7.83749461... | [10.722278594970703, 9.761646270751953] |
ff1b5434-b2d0-429d-96a6-788aef9e7a00 | a-knowledge-graph-embeddings-based-approach | 2201.09555 | null | https://arxiv.org/abs/2201.09555v3 | https://arxiv.org/pdf/2201.09555v3.pdf | A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals | Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available as Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to ... | ['Mehwish Alam', 'Harald Sack', 'Aldo Gangemi', 'Silvio Peroni', 'Genet Asefa Gesese', 'Cristian Santini'] | 2022-01-24 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-6.70652926e-01 -1.07267149e-01 -3.40286821e-01 5.30819148e-02
-7.32719183e-01 -6.08234286e-01 7.57381618e-01 3.48669142e-01
-4.17014837e-01 8.82823467e-01 4.82173324e-01 -1.56871945e-01
-5.25452971e-01 -9.00424421e-01 -6.46834075e-01 -5.90463042e-01
1.07244939e-01 8.09541702e-01 -1.05806246e-01 3.01490799... | [9.332148551940918, 8.132402420043945] |
543f3696-d9f4-481c-b2fb-c264a5e7e743 | comparing-humans-and-models-on-a-similar | 2305.15389 | null | https://arxiv.org/abs/2305.15389v1 | https://arxiv.org/pdf/2305.15389v1.pdf | Comparing Humans and Models on a Similar Scale: Towards Cognitive Gender Bias Evaluation in Coreference Resolution | Spurious correlations were found to be an important factor explaining model performance in various NLP tasks (e.g., gender or racial artifacts), often considered to be ''shortcuts'' to the actual task. However, humans tend to similarly make quick (and sometimes wrong) predictions based on societal and cognitive presupp... | ['Gabriel Stanovsky', 'Gili Lior'] | 2023-05-24 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 3.47412407e-01 6.75283015e-01 -1.42101824e-01 -5.29088140e-01
-2.18368292e-01 -4.57147866e-01 1.04496241e+00 5.36790013e-01
-9.13496077e-01 7.02185810e-01 2.55573452e-01 -5.30435622e-01
1.60552785e-01 -6.91755831e-01 -4.88649040e-01 -4.95924354e-01
3.88132393e-01 8.52189720e-01 1.38737457e-02 -5.60045779... | [9.962124824523926, 7.927724361419678] |
7405ffcf-a59d-4f3e-8a62-37f6727979a8 | moving-window-regression-a-novel-approach-to | 2203.13122 | null | https://arxiv.org/abs/2203.13122v1 | https://arxiv.org/pdf/2203.13122v1.pdf | Moving Window Regression: A Novel Approach to Ordinal Regression | A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank ($\rho$-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors ($\rho$-regressors) t... | ['Chang-Su Kim', 'Seon-Ho Lee', 'Nyeong-Ho Shin'] | 2022-03-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Shin_Moving_Window_Regression_A_Novel_Approach_to_Ordinal_Regression_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shin_Moving_Window_Regression_A_Novel_Approach_to_Ordinal_Regression_CVPR_2022_paper.pdf | cvpr-2022-1 | ['age-and-gender-classification', 'age-estimation', 'age-estimation'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 9.57804248e-02 -2.94274420e-01 -6.29442036e-01 -6.38795495e-01
-1.19783342e+00 9.28555205e-02 3.99251282e-01 -2.44959332e-02
-2.47702524e-01 6.33533239e-01 4.98425029e-02 -3.52273621e-02
-5.71166217e-01 -5.98633349e-01 -2.98541874e-01 -5.84897399e-01
-4.10676330e-01 1.81693628e-01 -2.15628631e-02 -3.10128722... | [13.687438011169434, 0.8507369160652161] |
20423c1b-92db-4f38-8765-aaf83dbad5a7 | progressive-point-cloud-deconvolution | 2007.05361 | null | https://arxiv.org/abs/2007.05361v1 | https://arxiv.org/pdf/2007.05361v1.pdf | Progressive Point Cloud Deconvolution Generation Network | In this paper, we propose an effective point cloud generation method, which can generate multi-resolution point clouds of the same shape from a latent vector. Specifically, we develop a novel progressive deconvolution network with the learning-based bilateral interpolation. The learning-based bilateral interpolation is... | ['Jian Yang', 'Rui Xu', 'Le Hui', 'Jianjun Qian', 'Jin Xie'] | 2020-07-10 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2354_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600392.pdf | eccv-2020-8 | ['point-cloud-generation'] | ['computer-vision'] | [-1.08969674e-01 -2.33530417e-01 3.14304829e-01 -1.84853643e-01
-7.95303047e-01 -4.35593456e-01 4.63355601e-01 -4.71423388e-01
-2.44943589e-01 6.25112832e-01 -4.66358736e-02 1.56137258e-01
-5.55157773e-02 -1.18465006e+00 -1.19240177e+00 -7.41833031e-01
2.00581640e-01 2.51970619e-01 1.28141701e-01 8.92759040... | [8.396364212036133, -3.599618434906006] |
daa71a4e-99a7-4f97-8f63-406195214872 | perspective-taking-and-pragmatics-for | 2109.08828 | null | https://arxiv.org/abs/2109.08828v2 | https://arxiv.org/pdf/2109.08828v2.pdf | Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes | Empathy is a complex cognitive ability based on the reasoning of others' affective states. In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance ... | ['Gunhee Kim', 'Byeongchang Kim', 'Hyunwoo Kim'] | 2021-09-18 | null | https://aclanthology.org/2021.emnlp-main.170 | https://aclanthology.org/2021.emnlp-main.170.pdf | emnlp-2021-11 | ['empathetic-response-generation', 'recognizing-emotion-cause-in-conversations'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.74744442e-01 7.63551533e-01 1.28265381e-01 -6.42122507e-01
-5.89070141e-01 -4.58031088e-01 6.09902620e-01 4.54704575e-02
-3.94611567e-01 9.01900470e-01 8.61362338e-01 1.88702390e-01
1.95045233e-01 -5.63625336e-01 7.67767131e-02 -4.43393350e-01
5.85871518e-01 6.85076416e-01 -3.97719622e-01 -7.65983403... | [13.073188781738281, 7.701498031616211] |
8ca77a1c-5bef-42f2-b8ce-2b51ff635848 | dualconv-dual-mesh-convolutional-networks-for | 2103.12459 | null | https://arxiv.org/abs/2103.12459v2 | https://arxiv.org/pdf/2103.12459v2.pdf | Dual Mesh Convolutional Networks for Human Shape Correspondence | Convolutional networks have been extremely successful for regular data structures such as 2D images and 3D voxel grids. The transposition to meshes is, however, not straight-forward due to their irregular structure. We explore how the dual, face-based representation of triangular meshes can be leveraged as a data struc... | ['Edmond Boyer', 'Jakob Verbeek', 'Adnane Boukhayma', 'Nitika Verma'] | 2021-03-23 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-8.09323639e-02 3.64804059e-01 -7.79789016e-02 -2.79731095e-01
-1.59333125e-02 -6.92249417e-01 7.94198513e-01 3.07103932e-01
-1.47600815e-01 4.66568708e-01 2.07964882e-01 -2.19626904e-01
2.58681495e-02 -1.31915295e+00 -8.24107468e-01 -2.64887542e-01
-4.35599416e-01 6.86042666e-01 1.95904478e-01 -3.84386957... | [8.409745216369629, -3.7405660152435303] |
c8cd18bb-27ed-4ef1-83ae-cb13e675cce5 | mined-semantic-analysis-a-new-concept-space | 1512.03465 | null | http://arxiv.org/abs/1512.03465v3 | http://arxiv.org/pdf/1512.03465v3.pdf | Mined Semantic Analysis: A New Concept Space Model for Semantic Representation of Textual Data | Mined Semantic Analysis (MSA) is a novel concept space model which employs
unsupervised learning to generate semantic representations of text. MSA
represents textual structures (terms, phrases, documents) as a Bag of Concepts
(BoC) where concepts are derived from concept rich encyclopedic corpora.
Traditional concept s... | ['Wlodek Zadrozny', 'Walid Shalaby'] | 2015-12-10 | null | null | null | null | ['implicit-relations'] | ['natural-language-processing'] | [ 0.250764 0.5082999 -0.09519564 -0.23893288 -0.35648647 -0.5564849
1.0415523 1.0312507 -0.4573531 0.56449413 0.7891666 -0.32968047
-0.65847415 -0.9479786 -0.21348159 -0.35172358 -0.06764056 0.3813442
0.14891885 -0.74797046 1.1033715 -0.19489565 -1.9598472 0.44145548
0.9111608 0.5095898 0.32... | [10.252057075500488, 8.732568740844727] |
08b7a618-92a4-475a-a700-def4eb6d8172 | point-level-temporal-action-localization | 2012.08236 | null | https://arxiv.org/abs/2012.08236v1 | https://arxiv.org/pdf/2012.08236v1.pdf | Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses | Point-Level temporal action localization (PTAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the frame-level prediction paradigm to learn from the sparse single-frame labels. However, such a framework inevitably suffers from a large sol... | ['Qi Tian', 'Yanfeng Wang', 'Ya zhang', 'Peisen Zhao', 'Chen Ju'] | 2020-12-15 | null | null | null | null | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 2.27570921e-01 -1.44302929e-02 -7.56191552e-01 -2.48012125e-01
-9.86901104e-01 -1.82458207e-01 5.54164231e-01 -2.94800643e-02
-3.03592652e-01 4.97082680e-01 1.59234837e-01 1.21027626e-01
1.03695981e-01 -1.42745152e-01 -8.13625872e-01 -6.73293114e-01
-1.56608313e-01 4.26371917e-02 9.13469493e-01 1.97854325... | [8.401805877685547, 0.5583764314651489] |
f88b40d9-3169-4ed9-94fa-d6fd7af5bfc8 | notes-on-the-interpretation-of-dependence | 2004.07649 | null | https://arxiv.org/abs/2004.07649v1 | https://arxiv.org/pdf/2004.07649v1.pdf | Notes on the interpretation of dependence measures | Besides the classical distinction of correlation and dependence, many dependence measures bear further pitfalls in their application and interpretation. The aim of this paper is to raise and recall awareness of some of these limitations by explicitly discussing Pearson's correlation and the multivariate dependence meas... | ['Björn Böttcher'] | 2020-04-16 | null | null | null | null | ['detection-of-higher-order-dependencies', 'detection-of-dependencies'] | ['methodology', 'methodology'] | [-1.90941721e-01 -1.08992003e-01 -2.75296092e-01 -4.12822008e-01
-2.65323430e-01 -6.30608261e-01 6.46843672e-01 5.14827430e-01
-7.35664785e-01 1.22284973e+00 -6.61584884e-02 -6.63349509e-01
-9.17134941e-01 -7.64532447e-01 -1.27255693e-01 -8.30394745e-01
-4.20855224e-01 4.40464586e-01 1.00383036e-01 -2.21041769... | [7.544607639312744, 4.550337314605713] |
6928bbe7-8700-40ea-b8b4-3ee083f219f2 | evolutionary-computation-in-action | 2303.00943 | null | https://arxiv.org/abs/2303.00943v2 | https://arxiv.org/pdf/2303.00943v2.pdf | Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images | One of the main obstacles of adopting digital pathology is the challenge of efficient processing of hyperdimensional digitized biopsy samples, called whole slide images (WSIs). Exploiting deep learning and introducing compact WSI representations are urgently needed to accelerate image analysis and facilitate the visual... | ['H. R. Tizhoosh', 'Abtin Riasatian', 'Taher Dehkharghanian', 'Shahryar Rahnamayan', 'Azam Asilian Bidgoli'] | 2023-03-02 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 3.13365430e-01 -1.07651040e-01 -4.39484380e-02 -4.91870455e-02
-9.26742673e-01 -2.58545816e-01 2.04090849e-01 6.49969876e-01
-5.75203955e-01 5.65483272e-01 -1.95106976e-02 -1.57940984e-01
-8.94374192e-01 -7.59376049e-01 -2.92490065e-01 -1.31562459e+00
-4.04844791e-01 3.96935582e-01 -1.22360438e-01 -1.24778099... | [15.123404502868652, -2.7586817741394043] |
fb0bd82d-93e4-4919-9128-d449f2923d5e | wavemix-lite-a-resource-efficient-neural | 2205.14375 | null | https://arxiv.org/abs/2205.14375v4 | https://arxiv.org/pdf/2205.14375v4.pdf | WaveMix: A Resource-efficient Neural Network for Image Analysis | We propose WaveMix -- a novel neural architecture for computer vision that is resource-efficient yet generalizable and scalable. WaveMix networks achieve comparable or better accuracy than the state-of-the-art convolutional neural networks, vision transformers, and token mixers for several tasks, establishing new bench... | ['Amit Sethi', 'Anandu A S', 'Kavitha Viswanathan', 'Pranav Jeevan'] | 2022-05-28 | null | null | null | null | ['spatial-token-mixer'] | ['computer-vision'] | [-5.44499494e-02 -2.55832821e-01 -1.30702332e-01 -1.78809881e-01
-4.78759348e-01 -4.21552122e-01 4.89557087e-01 -5.56450672e-02
-7.34792352e-01 2.19652116e-01 9.20371935e-02 -2.84557700e-01
-8.38244110e-02 -1.14489651e+00 -6.93673611e-01 -6.91305578e-01
-5.42819351e-02 1.42131478e-01 8.07558715e-01 1.23756481... | [9.227431297302246, 1.0667768716812134] |
5087b6c3-f0c6-47a1-934d-2f4f2f5f7c57 | gcn-sem-at-semeval-2019-task-1-semantic | null | null | https://aclanthology.org/S19-2014 | https://aclanthology.org/S19-2014.pdf | GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks | This paper describes the system submitted to the SemEval 2019 shared task 1 {`}Cross-lingual Semantic Parsing with UCCA{'}. We rely on the semantic dependency parse trees provided in the shared task which are converted from the original UCCA files and model the task as tagging. The aim is to predict the graph structure... | ['Sara Mo{\\v{z}}e', 'Shiva Taslimipoor', 'Omid Rohanian'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 4.69196402e-02 7.51337767e-01 -3.33005823e-02 -7.92398036e-01
-3.72637540e-01 -6.74496293e-01 2.56977439e-01 3.72145414e-01
-6.49909556e-01 5.53871810e-01 1.87104478e-01 -5.93928039e-01
3.04218024e-01 -1.01058912e+00 -7.16721237e-01 -3.48893464e-01
-1.30979747e-01 8.10534120e-01 2.52181232e-01 -2.72610962... | [10.379762649536133, 9.582059860229492] |
2fbca6ce-cd39-4398-8e0d-042b17302339 | self-promoted-prototype-refinement-for-few-1 | 2107.08918 | null | https://arxiv.org/abs/2107.08918v1 | https://arxiv.org/pdf/2107.08918v1.pdf | Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning | Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and prototype reorganization can only be achieved under little supervision. To address this problem, we propose a novel incremental prototype l... | ['Zheng-Jun Zha', 'Jie Cheng', 'Wei Zhai', 'Yang Cao', 'Kai Zhu'] | 2021-07-19 | self-promoted-prototype-refinement-for-few | http://openaccess.thecvf.com//content/CVPR2021/html/Zhu_Self-Promoted_Prototype_Refinement_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhu_Self-Promoted_Prototype_Refinement_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.22253099e-01 1.16848700e-01 -5.12740910e-01 -4.62605685e-01
-3.16829622e-01 -2.00462878e-01 5.76764047e-01 2.68556893e-01
-3.12640220e-01 7.57377684e-01 5.71398772e-02 1.11131713e-01
-2.65370667e-01 -8.83799672e-01 -3.95782232e-01 -8.39391649e-01
-5.57998642e-02 5.43671668e-01 5.38991511e-01 -1.28700301... | [9.8102388381958, 3.4112679958343506] |
f2f2c58f-a9c9-44df-9510-abe2a892c678 | 3dpct-3d-point-cloud-transformer-with-dual | 2209.11255 | null | https://arxiv.org/abs/2209.11255v2 | https://arxiv.org/pdf/2209.11255v2.pdf | 3DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation | Although the application of Transformers in 3D point cloud processing has achieved significant progress and success, it is still challenging for existing 3D Transformer methods to efficiently and accurately learn both valuable global features and valuable local features for improved applications. This paper presents a ... | ['Jonathan Li', 'Linlin Xu', 'Qian Xie', 'Kyle Gao', 'Dening Lu'] | 2022-09-21 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-1.01373680e-02 -2.79449791e-01 1.04239091e-01 -4.44103956e-01
-9.81716633e-01 -1.01778872e-01 4.73955572e-01 2.58528113e-01
1.80758238e-01 1.12983912e-01 -4.08381931e-02 -1.03670105e-01
-1.86945543e-01 -1.07697988e+00 -8.58513832e-01 -8.02553594e-01
-1.87106058e-01 2.97224343e-01 3.65806192e-01 -1.37088716... | [7.946136951446533, -3.463895082473755] |
82b2296f-49aa-433a-9c4a-e8e22ae6c7ec | simple-and-effective-few-shot-named-entity | 2010.02405 | null | https://arxiv.org/abs/2010.02405v1 | https://arxiv.org/pdf/2010.02405v1.pdf | Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning | We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference. Our system uses a supervised NER model trained on the source domain, as a feature extractor. Across several test domains, we show that a nearest neighbor classifier in this feature-space is fa... | ['Arzoo Katiyar', 'Yi Yang'] | 2020-10-06 | null | https://aclanthology.org/2020.emnlp-main.516 | https://aclanthology.org/2020.emnlp-main.516.pdf | emnlp-2020-11 | ['few-shot-ner'] | ['natural-language-processing'] | [ 5.21685667e-02 2.32723564e-01 -3.99962455e-01 -7.66678512e-01
-1.32244575e+00 -3.78805667e-01 6.08927429e-01 4.01389420e-01
-8.79191101e-01 7.68053174e-01 4.41637456e-01 1.18509561e-01
6.28437102e-02 -9.94967818e-01 -4.57981229e-01 -1.77146211e-01
-1.05083026e-01 5.99011481e-01 2.94143766e-01 -2.40720659... | [9.709789276123047, 9.40416145324707] |
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