paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3a438cae-ce5d-4857-a4ab-cf4608d2c2c8 | deep-aesthetic-quality-assessment-with | 1604.04970 | null | http://arxiv.org/abs/1604.04970v3 | http://arxiv.org/pdf/1604.04970v3.pdf | Deep Aesthetic Quality Assessment with Semantic Information | Human beings often assess the aesthetic quality of an image coupled with the
identification of the image's semantic content. This paper addresses the
correlation issue between automatic aesthetic quality assessment and semantic
recognition. We cast the assessment problem as the main task among a multi-task
deep model, ... | ['Yueying Kao', 'Kaiqi Huang', 'Ran He'] | 2016-04-18 | null | null | null | null | ['aesthetics-quality-assessment'] | ['computer-vision'] | [ 7.27781877e-02 -1.64252564e-01 3.89591753e-02 -5.73764861e-01
-8.10649514e-01 -2.23111033e-01 4.31336433e-01 2.28701085e-02
-3.44992131e-01 1.86102316e-02 1.97687194e-01 2.85998315e-01
-3.54396731e-01 -7.63848901e-01 -3.18983823e-01 -6.59906566e-01
4.08461541e-01 1.65265173e-01 -1.56876564e-01 -2.25145310... | [11.526801109313965, -1.0289933681488037] |
b564fab6-fd0f-488f-ac9f-8219c73b2e41 | enhanced-center-coding-for-cell-detection | 1904.08864 | null | http://arxiv.org/abs/1904.08864v1 | http://arxiv.org/pdf/1904.08864v1.pdf | Enhanced Center Coding for Cell Detection with Convolutional Neural Networks | Cell imaging and analysis are fundamental to biomedical research because
cells are the basic functional units of life. Among different cell-related
analysis, cell counting and detection are widely used. In this paper, we focus
on one common step of learning-based cell counting approaches: coding the raw
dot labels into... | ['Jaideep Kapur', 'Cedric L. Williams', 'Daniel S. Weller', 'Aijaz Naik', 'Haoyi Liang'] | 2019-04-18 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-2.70980094e-02 -2.50649571e-01 1.20585091e-01 -8.69232491e-02
-3.93233538e-01 -2.77688622e-01 5.47116816e-01 5.71963072e-01
-8.54006886e-01 1.00611782e+00 -1.86737522e-01 -1.35543838e-01
1.98362157e-01 -7.58971155e-01 -2.64464676e-01 -1.13026035e+00
-8.03760961e-02 4.82507944e-01 5.21806896e-01 2.32326269... | [14.690690040588379, -3.2046170234680176] |
f66b7e43-fdef-4106-8e76-8bb516635b59 | recent-advances-in-the-applications-of | 1708.07281 | null | http://arxiv.org/abs/1708.07281v1 | http://arxiv.org/pdf/1708.07281v1.pdf | Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection | The fast growing deep learning technologies have become the main solution of
many machine learning problems for medical image analysis. Deep convolution
neural networks (CNNs), as one of the most important branch of the deep
learning family, have been widely investigated for various computer-aided
diagnosis tasks inclu... | ['Lin Yang', 'Fuyong Xing', 'Zizhao Zhang', 'Xiaoshuang Shi', 'Hai Su'] | 2017-08-24 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 3.88208270e-01 1.36845931e-01 -2.71054417e-01 -3.34516734e-01
-2.36095637e-01 -2.77230322e-01 1.61398295e-02 3.56305242e-01
-6.19732320e-01 4.72075045e-01 -2.72589177e-01 -5.68182886e-01
2.92931367e-02 -8.28375399e-01 -2.56626248e-01 -8.54232013e-01
-4.34316695e-01 8.36816132e-02 3.48258406e-01 -3.77573937... | [14.59304141998291, -2.572633743286133] |
5539a168-c7df-4c35-b952-fdf5d794fadd | physics-informed-deep-neural-networks-for | null | null | https://ieeexplore.ieee.org/document/9158400 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9158400 | Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis | In this paper, we propose a deep neural network based model to predict the time evolution of field values in transient electrodynamics. The key component of our model is a recurrent neural network, which learns representations of long-term spatial-temporal dependencies in the sequence of its input data. We develop an e... | ['Zhen Peng', 'Christos Christodoulou', 'Shu Wang', 'Oameed Noakoasteen'] | 2020-08-04 | null | null | null | ieee-open-journal-of-antennas-and-propagation | ['physics-informed-machine-learning'] | ['graphs'] | [-1.62456468e-01 -3.25923204e-01 7.49365509e-01 -1.10210799e-01
-7.19406247e-01 -1.82487607e-01 2.52855122e-01 -6.21945262e-01
-1.73576280e-01 8.32695127e-01 9.69931930e-02 -5.68068862e-01
-4.35477406e-01 -8.08740437e-01 -8.22353244e-01 -7.51047671e-01
-5.79881132e-01 4.50534761e-01 -2.82992005e-01 -2.86209792... | [6.781737804412842, 3.430706739425659] |
aa00f0d2-e512-4ba5-8019-5b607e7d1624 | efficiency-aware-answering-of-compositional | null | null | https://aclanthology.org/I17-2038 | https://aclanthology.org/I17-2038.pdf | Efficiency-aware Answering of Compositional Questions using Answer Type Prediction | This paper investigates the problem of answering compositional factoid questions over knowledge bases (KB) under efficiency constraints. The method, called TIPI, (i) decomposes compositional questions, (ii) predicts answer types for individual sub-questions, (iii) reasons over the compatibility of joint types, and fina... | ['Abdalghani Abujabal', 'Rishiraj Saha Roy', 'Gerhard Weikum', 'David Ziegler'] | 2017-11-01 | efficiency-aware-answering-of-compositional-1 | https://aclanthology.org/I17-2038 | https://aclanthology.org/I17-2038.pdf | ijcnlp-2017-11 | ['type-prediction'] | ['computer-code'] | [-1.68173343e-01 4.43769157e-01 -5.07927895e-01 -4.84316885e-01
-1.16070783e+00 -8.08201909e-01 4.33144182e-01 4.43088889e-01
-3.67481261e-01 8.57504725e-01 4.14257586e-01 -6.50854230e-01
-3.37338030e-01 -1.25360763e+00 -7.75260150e-01 1.52180232e-02
2.11487874e-01 1.09540594e+00 9.56449270e-01 -4.19620156... | [10.350957870483398, 7.841146469116211] |
ff687aac-24d8-41e1-ba55-ea6ac8899d6d | a-fast-partial-video-copy-detection-using-knn | 2105.01713 | null | https://arxiv.org/abs/2105.01713v2 | https://arxiv.org/pdf/2105.01713v2.pdf | A Fast Partial Video Copy Detection Using KNN and Global Feature Database | We propose a fast partial video copy detection framework in this paper. In this framework all frame features of the reference videos are organized in a KNN searchable database. Instead of scanning all reference videos, the query video segment does a fast KNN search in the global feature database. The returned results a... | ['Rushuai Liu', 'Hongwei Guo', 'Weijun Tan'] | 2021-05-04 | null | null | null | null | ['partial-video-copy-detection'] | ['computer-vision'] | [ 3.17086428e-02 -5.15044272e-01 -7.75070727e-01 -9.19348150e-02
-9.62750435e-01 -6.92142010e-01 2.74738520e-01 -7.89743736e-02
-6.41046882e-01 5.53340733e-01 1.68904543e-01 3.60275596e-01
-6.84079006e-02 -4.70965564e-01 -1.06510675e+00 -6.17060006e-01
-1.18634239e-01 9.74506661e-02 9.14822221e-01 2.95931220... | [9.199127197265625, 0.2959247827529907] |
6bbcc435-722b-42b3-85b8-0767301c1747 | real-time-scalable-dense-surfel-mapping | 1909.04250 | null | https://arxiv.org/abs/1909.04250v1 | https://arxiv.org/pdf/1909.04250v1.pdf | Real-time Scalable Dense Surfel Mapping | In this paper, we propose a novel dense surfel mapping system that scales well in different environments with only CPU computation. Using a sparse SLAM system to estimate camera poses, the proposed mapping system can fuse intensity images and depth images into a globally consistent model. The system is carefully design... | ['Shaojie Shen', 'Fei Gao', 'Kaixuan Wang'] | 2019-09-10 | null | null | null | null | ['3d-point-cloud-reconstruction'] | ['computer-vision'] | [ 9.12180692e-02 -1.83583856e-01 5.40220253e-02 -5.05922496e-01
-6.24526560e-01 -6.19344711e-01 2.34834552e-01 -1.68961123e-01
-5.27272761e-01 5.90830922e-01 -4.31012630e-01 2.39114746e-01
2.27159169e-02 -1.00980544e+00 -9.36885476e-01 -2.30456918e-01
3.09645385e-01 8.58546674e-01 7.48427391e-01 -3.20301831... | [7.370563507080078, -2.240346670150757] |
7de36431-d445-4362-a49d-6b7ceb88879e | how-machine-deep-learning-helps-us-understand | 1903.03408 | null | http://arxiv.org/abs/1903.03408v2 | http://arxiv.org/pdf/1903.03408v2.pdf | How Machine (Deep) Learning Helps Us Understand Human Learning: the Value of Big Ideas | I use simulation of two multilayer neural networks to gain intuition into the
determinants of human learning. The first network, the teacher, is trained to
achieve a high accuracy in handwritten digit recognition. The second network,
the student, learns to reproduce the output of the first network. I show that
learning... | ['Marc Maliar'] | 2019-02-16 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-2.56569535e-01 4.16932911e-01 -3.20349574e-01 -3.57698262e-01
1.29087225e-01 -2.29985222e-01 7.34015480e-02 6.89511672e-02
-5.71582317e-01 5.82330525e-01 2.34740204e-03 -6.48704171e-01
-2.89398819e-01 -6.81949973e-01 -6.76780701e-01 -5.67207456e-01
3.66390407e-01 1.04135245e-01 -2.74284512e-01 -1.42237858... | [10.197188377380371, 7.800768852233887] |
8c8cd65a-bbe7-4566-8f93-5cf216c6e4d4 | event-based-human-pose-tracking-by-spiking | 2303.09681 | null | https://arxiv.org/abs/2303.09681v3 | https://arxiv.org/pdf/2303.09681v3.pdf | Event-based Human Pose Tracking by Spiking Spatiotemporal Transformer | Event camera, as an emerging biologically-inspired vision sensor for capturing motion dynamics, presents new potential for 3D human pose tracking, or video-based 3D human pose estimation. However, existing works in pose tracking either require the presence of additional gray-scale images to establish a solid starting p... | ['Li Cheng', 'Sen Wang', 'Xinxin Zuo', 'Yuxuan Mu', 'Shihao Zou'] | 2023-03-16 | null | null | null | null | ['pose-tracking', '3d-human-pose-tracking', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.91701874e-01 -3.55238765e-01 2.15890035e-01 -1.26057984e-02
-5.12513340e-01 -2.38040075e-01 4.45532650e-01 -1.00920960e-01
-7.17061639e-01 7.58202493e-01 1.61640614e-01 3.90835851e-01
3.69464867e-02 -6.38701499e-01 -9.55918074e-01 -6.38831317e-01
-3.04183334e-01 4.79735941e-01 5.21953344e-01 -1.48931354... | [7.522775650024414, -0.7750266194343567] |
bf259bab-d189-4ffb-a580-28b510a5543a | learning-low-dimensional-temporal | null | null | https://icml.cc/Conferences/2018/Schedule?showEvent=1910 | http://proceedings.mlr.press/v80/su18a/su18a.pdf | Learning Low-Dimensional Temporal Representations |
Low-dimensional discriminative representations enhance machine learning methods in both performance and complexity, motivating supervised dimensionality reduction (DR) that transforms high-dimensional data to a discriminative subspace. Most DR methods require data to be i.i.d., however, in some domains, data natur... | ['Bing Su', 'Ying Wu'] | 2018-07-01 | null | null | null | icml-2018-7 | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 2.40952253e-01 -4.28609163e-01 -5.18632829e-01 -2.52046704e-01
-6.18900061e-01 -8.70429397e-01 7.77996778e-01 -2.88018107e-01
-2.52555192e-01 5.11683941e-01 5.46542346e-01 -4.56151515e-02
-3.89251441e-01 -2.76903898e-01 -4.97227639e-01 -1.22782624e+00
-5.15198350e-01 4.17225242e-01 -2.47913405e-01 1.98344454... | [7.825881481170654, 3.988450288772583] |
f9910a3e-d6e3-4061-bdd2-92bf4a285ad6 | fcgec-fine-grained-corpus-for-chinese | 2210.12364 | null | https://arxiv.org/abs/2210.12364v1 | https://arxiv.org/pdf/2210.12364v1.pdf | FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction | Grammatical Error Correction (GEC) has been broadly applied in automatic correction and proofreading system recently. However, it is still immature in Chinese GEC due to limited high-quality data from native speakers in terms of category and scale. In this paper, we present FCGEC, a fine-grained corpus to detect, ident... | ['Ming Cai', 'Jiayu Fu', 'Jiawei Peng', 'Jianwang Wu', 'Lvxiaowei Xu'] | 2022-10-22 | null | null | null | null | ['grammatical-error-detection', 'grammatical-error-correction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.22081959e-01 2.95957088e-01 1.68548256e-01 -4.02470738e-01
-1.26602292e+00 -3.06019545e-01 2.57910013e-01 4.50153023e-01
-4.40075636e-01 8.99165034e-01 4.46678191e-01 -5.76939702e-01
2.59287894e-01 -4.90298241e-01 -8.69350135e-01 -1.30608408e-02
5.85479915e-01 2.52726883e-01 3.84338409e-01 -5.19980073... | [11.060613632202148, 10.78318977355957] |
119aedc1-00fe-49fd-8042-97639e350ab2 | category-anchor-guided-unsupervised-domain | 1910.13049 | null | https://arxiv.org/abs/1910.13049v2 | https://arxiv.org/pdf/1910.13049v2.pdf | Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation | Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a certain model from a source domain to a target domain. UDA is of particular significance since no extra effort is devoted to annotating target domain samples. However, the different data distributions in the two domains, or \emph{do... | ['DaCheng Tao', 'Jing Zhang', 'Wei Liu', 'Qiming Zhang'] | 2019-10-29 | category-anchor-guided-unsupervised-domain-1 | http://papers.nips.cc/paper/8335-category-anchor-guided-unsupervised-domain-adaptation-for-semantic-segmentation | http://papers.nips.cc/paper/8335-category-anchor-guided-unsupervised-domain-adaptation-for-semantic-segmentation.pdf | neurips-2019-12 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.41118246e-01 1.01611987e-02 -3.04479957e-01 -7.63582051e-01
-1.00675428e+00 -5.82048714e-01 3.28086585e-01 4.01157737e-02
-3.58191013e-01 5.34974933e-01 -1.90018266e-01 -3.15919220e-02
-2.69892722e-01 -6.64565206e-01 -5.85741580e-01 -9.66280639e-01
2.57066220e-01 3.82997483e-01 5.10026276e-01 1.71641499... | [9.66067886352539, 1.3996021747589111] |
30d6b4cf-14e8-4d27-b689-03d30a212926 | why-artificial-intelligence-needs-a-task | 1604.04660 | null | http://arxiv.org/abs/1604.04660v2 | http://arxiv.org/pdf/1604.04660v2.pdf | Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like | The concept of "task" is at the core of artificial intelligence (AI): Tasks
are used for training and evaluating AI systems, which are built in order to
perform and automatize tasks we deem useful. In other fields of engineering
theoretical foundations allow thorough evaluation of designs by methodical
manipulation of ... | ['Jóna S. Sigurðardóttir', 'Thröstur Thorarensen', 'Kristinn R. Thórisson', 'Jordi Bieger', 'Bas R. Steunebrink'] | 2016-04-15 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 3.67323607e-01 -8.11533555e-02 3.44364345e-02 -1.46663919e-01
8.08126554e-02 -7.21217930e-01 5.82032859e-01 6.50058985e-02
-5.34781218e-01 6.63588405e-01 -2.98612207e-01 -6.94622755e-01
-9.38571811e-01 -7.41337657e-01 -3.16948712e-01 -7.73695588e-01
-2.05811650e-01 4.02563900e-01 2.60203272e-01 -6.62782848... | [5.615322113037109, 3.6655032634735107] |
fbb25e94-870c-46f2-806b-a92912dffb33 | learning-with-difference-attention-for | 2306.14603 | null | https://arxiv.org/abs/2306.14603v1 | https://arxiv.org/pdf/2306.14603v1.pdf | Learning with Difference Attention for Visually Grounded Self-supervised Representations | Recent works in self-supervised learning have shown impressive results on single-object images, but they struggle to perform well on complex multi-object images as evidenced by their poor visual grounding. To demonstrate this concretely, we propose visual difference attention (VDA) to compute visual attention maps in a... | ['Balaji Vasan Srinivasan', 'Srikrishna Karanam', 'Aishwarya Agarwal'] | 2023-06-26 | null | null | null | null | ['visual-grounding', 'self-supervised-learning'] | ['computer-vision', 'computer-vision'] | [ 5.45844972e-01 3.25837523e-01 -1.16665483e-01 -3.97499561e-01
-7.41855443e-01 -4.23820317e-01 7.56812334e-01 2.94389755e-01
-2.88535297e-01 4.96188998e-01 1.65836200e-01 -1.96823642e-01
1.92519948e-02 -4.02396679e-01 -1.06797874e+00 -3.77536684e-01
8.70109871e-02 7.44753033e-02 5.42285740e-01 -2.06301838... | [9.820395469665527, 1.3911646604537964] |
ece75529-9a81-4185-bde5-8c018f777aca | 2d-and-3d-cnn-based-fusion-approach-for-covid | 2303.08740 | null | https://arxiv.org/abs/2303.08740v1 | https://arxiv.org/pdf/2303.08740v1.pdf | 2D and 3D CNN-Based Fusion Approach for COVID-19 Severity Prediction from 3D CT-Scans | Since the appearance of Covid-19 in late 2019, Covid-19 has become an active research topic for the artificial intelligence (AI) community. One of the most interesting AI topics is Covid-19 analysis of medical imaging. CT-scan imaging is the most informative tool about this disease. This work is part of the 3nd COV19D ... | ['Abdelmalik Taleb-Ahmed', 'Cosimo Distante', 'Amir Nakib', 'Fadi Dornaika', 'Fares Bougourzi'] | 2023-03-15 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 8.19766670e-02 1.12969242e-01 -8.64278004e-02 -2.48645142e-01
-5.20670235e-01 -2.74636269e-01 3.56593937e-01 1.48627445e-01
-5.81719041e-01 5.75113297e-01 3.14120471e-01 -4.18867320e-01
-1.45605773e-01 -6.81702614e-01 -3.78230184e-01 -5.33447146e-01
-3.23182233e-02 8.68246317e-01 5.90586126e-01 -1.24008432... | [15.370205879211426, -1.863943099975586] |
cabbdf36-8323-43de-bbe9-cf974c1547a0 | s3c-self-supervised-stochastic-classifiers | 2307.02246 | null | https://arxiv.org/abs/2307.02246v1 | https://arxiv.org/pdf/2307.02246v1.pdf | S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL) aims to learn progressively about new classes with very few labeled samples, without forgetting the knowledge of already learnt classes. FSCIL suffers from two major challenges: (i) over-fitting on the new classes due to limited amount of data, (ii) catastrophically forgettin... | ['Soma Biswas', 'Jayateja Kalla'] | 2023-07-05 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology', 'methodology'] | [ 3.61027420e-01 8.51486772e-02 -2.46619266e-02 -2.33136043e-01
-4.16668743e-01 -3.34518224e-01 4.00473982e-01 4.65915918e-01
-4.31479543e-01 1.15593159e+00 -4.92486432e-02 2.14718208e-01
-1.24144800e-01 -8.23122561e-01 -6.12839997e-01 -8.51261079e-01
-1.36687204e-01 6.77864730e-01 8.72818708e-01 -1.13385297... | [9.902300834655762, 3.255535364151001] |
7527bc7e-0ee8-4473-8dba-667d40dac39f | supervised-discriminative-sparse-pca-with | 2001.03103 | null | https://arxiv.org/abs/2001.03103v2 | https://arxiv.org/pdf/2001.03103v2.pdf | Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction | Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approache... | ['Chin-Teng Lin', 'Yu-Kai Wang', 'Zhenhua Shi', 'Jian Huang', 'Dongrui Wu'] | 2020-01-09 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-9.33689699e-02 -5.63356578e-01 -8.16082209e-03 -4.09248710e-01
-3.14934582e-01 -5.87895632e-01 3.59919399e-01 3.07989240e-01
-1.76788028e-02 4.34916228e-01 6.08864188e-01 2.35532224e-01
-7.55602121e-01 -6.34895384e-01 -1.10566184e-01 -1.18300188e+00
-7.78407231e-02 2.12862700e-01 2.29725704e-01 2.13105202... | [7.860804557800293, 4.3155951499938965] |
13165b0b-a87a-4f42-829a-86d8770394b2 | pedestrian-attribute-recognition-a-survey | 1901.07474 | null | http://arxiv.org/abs/1901.07474v1 | http://arxiv.org/pdf/1901.07474v1.pdf | Pedestrian Attribute Recognition: A Survey | Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we... | ['Shaofei Zheng', 'Rui Yang', 'Jin Tang', 'Bin Luo', 'Xiao Wang'] | 2019-01-22 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-1.67146996e-01 -4.64813292e-01 -2.39152506e-01 -8.31267715e-01
-3.92413557e-01 -2.33625665e-01 6.59134150e-01 3.74806494e-01
-4.65268016e-01 8.79826784e-01 2.41613820e-01 5.18293232e-02
-1.61076263e-02 -9.86934245e-01 -5.42504847e-01 -9.21063423e-01
-2.76008751e-02 4.79660571e-01 4.29977886e-02 -1.89871460... | [14.480571746826172, 0.9678635597229004] |
adf313cc-7b67-4085-a0f5-21a7aa53497e | adversarial-attacks-on-deep-learning-based-3 | 2203.10183 | null | https://arxiv.org/abs/2203.10183v3 | https://arxiv.org/pdf/2203.10183v3.pdf | RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression | Video compression plays a crucial role in video streaming and classification systems by maximizing the end-user quality of experience (QoE) at a given bandwidth budget. In this paper, we conduct the first systematic study for adversarial attacks on deep learning-based video compression and downstream classification sys... | ['Farinaz Koushanfar', 'Seira Hidano', 'Mojan Javaheripi', 'Jung-Woo Chang'] | 2022-03-18 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 3.43563527e-01 -1.40477836e-01 -1.31064311e-01 -1.29384905e-01
-9.69309628e-01 -6.81503534e-01 1.92765165e-02 -9.40972492e-02
-2.40479618e-01 2.84813225e-01 -7.06542432e-02 -8.51029813e-01
4.08485867e-02 -8.26248348e-01 -9.31915581e-01 -8.80070508e-01
-7.91527987e-01 -5.85040331e-01 1.40609488e-01 -2.64258534... | [5.406497955322266, 7.89202356338501] |
490d0734-e60a-48ef-a009-98c8e2e97cda | attention-mixtures-for-time-aware-sequential | 2304.08158 | null | https://arxiv.org/abs/2304.08158v2 | https://arxiv.org/pdf/2304.08158v2.pdf | Attention Mixtures for Time-Aware Sequential Recommendation | Transformers emerged as powerful methods for sequential recommendation. However, existing architectures often overlook the complex dependencies between user preferences and the temporal context. In this short paper, we introduce MOJITO, an improved Transformer sequential recommender system that addresses this limitatio... | ['Romain Hennequin', 'Bruno Sguerra', 'Guillaume Salha-Galvan', 'Viet-Anh Tran'] | 2023-04-17 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-2.69781679e-01 -5.98010063e-01 -6.70565903e-01 -3.36876273e-01
-4.79649194e-02 -5.71283638e-01 7.17748821e-01 2.60079931e-02
-2.90032804e-01 4.02534425e-01 7.31488466e-01 -4.92840499e-01
-4.97661918e-01 -7.21425056e-01 -2.70636380e-01 -3.52668345e-01
-2.78364629e-01 5.29506326e-01 3.80035043e-01 -3.66964459... | [10.161585807800293, 5.678708076477051] |
2cff65cc-aa1a-4ffb-a62c-55d5ed0fde7c | analysis-of-constant-q-filterbank-based | 2211.16363 | null | https://arxiv.org/abs/2211.16363v1 | https://arxiv.org/pdf/2211.16363v1.pdf | Analysis of constant-Q filterbank based representations for speech emotion recognition | This work analyzes the constant-Q filterbank-based time-frequency representations for speech emotion recognition (SER). Constant-Q filterbank provides non-linear spectro-temporal representation with higher frequency resolution at low frequencies. Our investigation reveals how the increased low-frequency resolution bene... | ['Goutam Saha', 'Md Sahidullah', 'Shefali Waldekar', 'Premjeet Singh'] | 2022-11-29 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-2.67345756e-01 -2.25581706e-01 -9.12167951e-02 -3.65499288e-01
-9.18933511e-01 -4.24530834e-01 1.98101774e-01 4.67753708e-01
-5.34114480e-01 6.74132526e-01 5.26227593e-01 -3.53893377e-02
-2.89873481e-01 -4.39591080e-01 -4.63653244e-02 -6.07565761e-01
-7.14688659e-01 -7.24348187e-01 -5.46299741e-02 -7.34311163... | [15.119505882263184, 5.529289722442627] |
fb39e43d-c38d-4579-bb51-21703243743a | detecting-adversarial-text-attacks-via | null | null | https://openreview.net/forum?id=DZ_7nGQgZgk | https://openreview.net/pdf?id=DZ_7nGQgZgk | Detecting Adversarial Text Attacks via SHapley Additive exPlanations | State-of-the-art machine learning models are prone to adversarial attacks: maliciously crafted inputs to fool the model into making a wrong prediction, often with high confidence. While defense strategies have been extensively explored in the computer vision domain, research in natural language processing still lacks t... | ['Anonymous'] | 2021-05-16 | null | null | null | acl-arr-may-2021-5 | ['adversarial-text'] | ['adversarial'] | [ 3.28808308e-01 5.65717280e-01 -1.77520096e-01 -3.81125093e-01
-8.16748381e-01 -1.08606946e+00 8.55673730e-01 8.37304592e-02
-4.48419094e-01 6.52982712e-01 -3.05142164e-01 -7.93678105e-01
4.73606557e-01 -7.17016160e-01 -1.14594257e+00 -3.70433390e-01
1.19089238e-01 6.30796790e-01 4.72937554e-01 -5.83540574... | [5.862773895263672, 7.986826419830322] |
1140521e-2acc-40a9-b678-cdf6f6aab7a5 | lfkqg-a-controlled-generation-framework-with | null | null | https://aclanthology.org/2022.coling-1.572 | https://aclanthology.org/2022.coling-1.572.pdf | LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases | Question generation over knowledge bases (KBQG) aims at generating natural questions about a subgraph, which can be answered by a given answer entity. Existing KBQG models still face two main challenges: (1) Most models often focus on the most relevant part of the answer entity, while neglecting the rest of the subgrap... | ['Xuanjing Huang', 'Qi Zhang', 'Tao Gui', 'Xin Zhou', 'Zichu Fei'] | null | null | null | null | coling-2022-10 | ['natural-questions', 'question-generation'] | ['miscellaneous', 'natural-language-processing'] | [-3.72904688e-02 7.14952826e-01 -1.29881546e-01 -1.16524354e-01
-9.04105902e-01 -7.83342302e-01 4.82056379e-01 2.06886783e-01
2.64406595e-02 1.13652456e+00 5.53353846e-01 -3.47241610e-01
-1.44130290e-01 -1.47304749e+00 -9.84815776e-01 -1.33222446e-01
4.87559974e-01 7.12021530e-01 8.66033256e-01 -8.62366140... | [10.95822525024414, 7.931543350219727] |
53107625-6950-4679-a63b-48ee7c47f761 | a-generative-adversarial-approach-with | 2009.10663 | null | https://arxiv.org/abs/2009.10663v1 | https://arxiv.org/pdf/2009.10663v1.pdf | A Generative Adversarial Approach with Residual Learning for Dust and Scratches Artifacts Removal | Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to operate in a professional manner. One particularly challenging task for old photo retouching remains the removal of dust and scratches artifacts. Traditionally, this task has been completed manually wit... | ['Ionuţ Mironică'] | 2020-09-22 | null | null | null | null | ['photo-retouching'] | ['computer-vision'] | [ 7.45045364e-01 -1.15756921e-01 5.77755213e-01 -2.95878261e-01
-7.43365049e-01 -4.32078600e-01 3.67171168e-01 -2.56602585e-01
-2.57410944e-01 6.26477480e-01 -4.97716963e-02 1.45652238e-03
1.51880354e-01 -6.58612728e-01 -9.76155281e-01 -8.05064201e-01
5.45063496e-01 -9.23077911e-02 2.67740369e-01 -4.75108802... | [11.113311767578125, -2.051558017730713] |
9a326b1e-4635-4045-b95b-28da6bc36625 | a-deep-learning-technique-using-low-sampling | 2111.05120 | null | https://arxiv.org/abs/2111.05120v1 | https://arxiv.org/pdf/2111.05120v1.pdf | A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring | Individual device loads and energy consumption feedback is one of the important approaches for pursuing users to save energy in residences. This can help in identifying faulty devices and wasted energy by devices when left On unused. The main challenge is to identity and estimate the energy consumption of individual de... | ['Raghunath Reddy', 'Vishal Garg', 'Sahil Chilana', 'Ronak Aghera'] | 2021-11-07 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-3.91629199e-03 -1.70106683e-02 -1.12721100e-01 -2.87153363e-01
-8.30599010e-01 -5.74531913e-01 3.79701734e-01 -2.64439225e-01
1.42835900e-01 8.16119492e-01 4.01053846e-01 -3.15881312e-01
1.15112402e-02 -1.23508763e+00 -5.80749154e-01 -1.01079929e+00
5.51311933e-02 2.28527024e-01 -8.29614222e-01 2.92080015... | [16.0660400390625, 7.58033561706543] |
4bcce555-49f2-4dba-936b-ce652afd10f9 | neural-audio-fingerprint-for-high-specific | 2010.11910 | null | https://arxiv.org/abs/2010.11910v4 | https://arxiv.org/pdf/2010.11910v4.pdf | Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning | Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive... | ['Yoonchang Han', 'Karam Ko', 'Kyogu Lee', 'Hyungui Lim', 'Jeongsoo Park', 'Donmoon Lee', 'Sungkyun Chang'] | 2020-10-22 | null | null | null | null | ['audio-fingerprint'] | ['audio'] | [ 4.90474761e-01 -3.00410807e-01 -2.93859784e-02 -3.43324542e-01
-1.36956942e+00 -6.71781719e-01 2.73561299e-01 -2.86313206e-01
-2.14462012e-01 6.12637401e-01 6.89487383e-02 6.02190010e-02
-5.32847084e-02 -3.80131513e-01 -7.75247335e-01 -6.30006552e-01
-5.71524680e-01 2.57088035e-01 2.50522166e-01 3.23376685... | [15.37159252166748, 5.554564952850342] |
e113187c-7688-437b-92ed-dc7289992768 | self-organization-and-artificial-life-a | 1804.01144 | null | http://arxiv.org/abs/1804.01144v1 | http://arxiv.org/pdf/1804.01144v1.pdf | Self-Organization and Artificial Life: A Review | Self-organization has been an important concept within a number of
disciplines, which Artificial Life (ALife) also has heavily utilized since its
inception. The term and its implications, however, are often confusing or
misinterpreted. In this work, we provide a mini-review of self-organization and
its relationship wit... | ['Justin Werfel', 'Carlos Gershenson', 'Vito Trianni', 'Hiroki Sayama'] | 2018-04-03 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-7.76560679e-02 1.85630426e-01 -1.46873906e-01 1.72165126e-01
6.86883926e-01 -8.59675884e-01 4.05256569e-01 5.18829405e-01
4.86902148e-02 1.02318585e+00 1.11105636e-01 -1.11199073e-01
-4.64142829e-01 -6.99791133e-01 -2.79532552e-01 -8.44478369e-01
-2.11793184e-01 3.38245958e-01 -4.06955369e-02 -6.00761235... | [5.627943992614746, 4.1948347091674805] |
9d350385-f313-47fa-bcff-8b996becda02 | long-tail-learning-with-attributes | 2004.02235 | null | https://arxiv.org/abs/2004.02235v4 | https://arxiv.org/pdf/2004.02235v4.pdf | From Generalized zero-shot learning to long-tail with class descriptors | Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes. Often, classes can be accompanied by side information like textual descriptions, but it is not fully clear how to use them for learning with unbalanced long-tail data. Suc... | ['Dvir Samuel', 'Gal Chechik', 'Yuval Atzmon'] | 2020-04-05 | null | null | null | null | ['generalized-few-shot-learning', 'long-tail-learning-with-class-descriptors'] | ['methodology', 'methodology'] | [-6.41478822e-02 -2.25480214e-01 -6.48421109e-01 -6.98842049e-01
-1.09871221e+00 -3.39706540e-01 9.42072392e-01 2.51282096e-01
-4.73284960e-01 7.27082253e-01 4.30078506e-01 -5.30148670e-02
-1.68860152e-01 -7.85229921e-01 -6.74843371e-01 -7.50034690e-01
3.77471074e-02 9.05346334e-01 5.56524158e-01 -1.92357346... | [9.872037887573242, 2.6637041568756104] |
fa1628b3-0c7e-4f83-b481-2b742710959e | compilable-neural-code-generation-with | 2203.05132 | null | https://arxiv.org/abs/2203.05132v1 | https://arxiv.org/pdf/2203.05132v1.pdf | Compilable Neural Code Generation with Compiler Feedback | Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering. Existing deep-learning approaches model code generation as text generation, either constrained by grammar structures in deco... | ['Qun Liu', 'Xin Jiang', 'Hao Wu', 'Jin Liu', 'Pingyi Zhou', 'Yitong Li', 'Fei Mi', 'Yao Wan', 'Yasheng Wang', 'Xin Wang'] | 2022-03-10 | null | https://aclanthology.org/2022.findings-acl.2 | https://aclanthology.org/2022.findings-acl.2.pdf | findings-acl-2022-5 | ['text-to-code-generation'] | ['computer-code'] | [ 4.70910855e-02 2.89087027e-01 -2.18706846e-01 -2.69969285e-01
-1.10676873e+00 -6.14024639e-01 3.06870013e-01 1.55088007e-01
1.90557450e-01 5.81627131e-01 1.10612549e-01 -6.77038491e-01
5.09352863e-01 -1.01347816e+00 -9.63779211e-01 3.37320752e-02
1.79959044e-01 4.36861217e-01 -2.13015899e-01 -2.89841145... | [7.7608256340026855, 7.8157219886779785] |
0bf0f3aa-f4b5-4f83-a99c-ea0b8209425d | beyond-the-hype-assessing-the-performance | 2306.15887 | null | https://arxiv.org/abs/2306.15887v1 | https://arxiv.org/pdf/2306.15887v1.pdf | Beyond the Hype: Assessing the Performance, Trustworthiness, and Clinical Suitability of GPT3.5 | The use of large language models (LLMs) in healthcare is gaining popularity, but their practicality and safety in clinical settings have not been thoroughly assessed. In high-stakes environments like medical settings, trust and safety are critical issues for LLMs. To address these concerns, we present an approach to ev... | ['Mohammad R. K. Mofrad', 'Elizabeth Tong', 'Salmonn Talebi'] | 2023-06-28 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 2.86006369e-02 6.25010788e-01 -3.99033368e-01 -4.74205077e-01
-1.30911553e+00 -5.55728376e-01 1.21014705e-03 8.89253676e-01
-6.06687427e-01 4.12531674e-01 5.61075628e-01 -9.41586196e-01
-3.61314505e-01 -1.04710914e-01 -7.24441051e-01 -2.01247811e-01
1.86008990e-01 6.98857069e-01 -8.65953863e-02 4.71464604... | [8.71349811553955, 8.37412166595459] |
fb494a6e-0278-4d9b-b084-6f99d6dd310d | a-unified-learning-approach-for-hand-gesture | 2101.02047 | null | https://arxiv.org/abs/2101.02047v3 | https://arxiv.org/pdf/2101.02047v3.pdf | Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection | Head-mounted device-based human-computer interaction often requires egocentric recognition of hand gestures and fingertips detection. In this paper, a unified approach of egocentric hand gesture recognition and fingertip detection is introduced. The proposed algorithm uses a single convolutional neural network to predi... | ['S. M. Mahbubur Rahman', 'Mohammad Tariqul Islam', 'Mohammad Mahmudul Alam'] | 2021-01-06 | null | null | null | null | ['fingertip-detection'] | ['computer-vision'] | [ 1.05475806e-01 -1.60224661e-01 7.69929355e-03 -3.40881437e-01
-3.49532992e-01 -6.03025079e-01 4.96821135e-01 -5.42285025e-01
-7.98495412e-01 3.87675256e-01 -2.79046055e-02 -7.36161619e-02
-6.59466237e-02 -4.60772574e-01 -6.33301318e-01 -8.44400287e-01
8.93379003e-02 3.99876505e-01 3.26646984e-01 3.08691233... | [6.480550289154053, -0.42162081599235535] |
6d7f7557-957c-416d-a830-d22840717b2c | idioms-probing-and-dangerous-things-towards | 2304.14333 | null | https://arxiv.org/abs/2304.14333v1 | https://arxiv.org/pdf/2304.14333v1.pdf | Idioms, Probing and Dangerous Things: Towards Structural Probing for Idiomaticity in Vector Space | The goal of this paper is to learn more about how idiomatic information is structurally encoded in embeddings, using a structural probing method. We repurpose an existing English verbal multi-word expression (MWE) dataset to suit the probing framework and perform a comparative probing study of static (GloVe) and contex... | ['John D. Kelleher', 'Vasudevan Nedumpozhimana', 'Filip Klubička'] | 2023-04-27 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.63294250e-01 1.51322246e-01 -5.49132884e-01 -6.19088709e-01
-2.53395766e-01 -8.84805083e-01 9.30615425e-01 8.00990313e-03
-6.01180196e-01 5.11778116e-01 1.10030878e+00 -6.03367567e-01
-3.10372651e-01 -8.50221097e-01 -2.70405829e-01 -3.86210144e-01
-1.22086883e-01 5.70516646e-01 4.24925238e-02 -7.35899091... | [10.70042610168457, 9.384377479553223] |
fc1d60b0-5ae9-4109-a4d6-0ed1ed4fa751 | community-question-answering-entity-linking | 2205.11917 | null | https://arxiv.org/abs/2205.11917v1 | https://arxiv.org/pdf/2205.11917v1.pdf | Community Question Answering Entity Linking via Leveraging Auxiliary Data | Community Question Answering (CQA) platforms contain plenty of CQA texts (i.e., questions and answers corresponding to the question) where named entities appear ubiquitously. In this paper, we define a new task of CQA entity linking (CQAEL) as linking the textual entity mentions detected from CQA texts with their corre... | ['Yadong Wang', 'Jianbo Gao', 'Wei Shen', 'Yuhan Li'] | 2022-05-24 | null | null | null | null | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [-5.37149608e-01 1.76230073e-01 -2.70928480e-02 -1.89410131e-02
-1.21320105e+00 -8.26846659e-01 7.31174588e-01 8.47287953e-01
-5.73329449e-01 8.10463667e-01 6.43202126e-01 -1.58698574e-01
-3.40407014e-01 -1.08089435e+00 -5.97473562e-01 -2.29373917e-01
3.42467993e-01 6.16392195e-01 8.90268326e-01 -5.28080404... | [9.568812370300293, 8.710436820983887] |
60bfb095-b3c9-4c0c-a70c-b85509164fc4 | evaluating-graph-signal-processing-for | 1703.01842 | null | http://arxiv.org/abs/1703.01842v3 | http://arxiv.org/pdf/1703.01842v3.pdf | Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction | Graph Signal Processing (GSP) is a promising framework to analyze
multi-dimensional neuroimaging datasets, while taking into account both the
spatial and functional dependencies between brain signals. In the present work,
we apply dimensionality reduction techniques based on graph representations of
the brain to decode... | ['Vincent Gripon', 'Mathilde Ménoret', 'Nicolas Farrugia', 'Bastien Pasdeloup'] | 2017-03-06 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 3.50769520e-01 -4.16985787e-02 2.41801813e-01 -3.23931038e-01
1.16183020e-01 -6.51801705e-01 6.40289068e-01 1.94040984e-01
-3.26440454e-01 4.85515505e-01 4.36716527e-01 -1.11517482e-01
-7.72544026e-01 -8.47342432e-01 -2.97623008e-01 -7.18663216e-01
-8.28132868e-01 4.30570483e-01 -6.79936409e-02 -2.69777961... | [12.402898788452148, 3.415306568145752] |
1d367439-2dea-47c5-8bed-2c03ce89043f | ala-adversarial-lightness-attack-via | 2201.06070 | null | https://arxiv.org/abs/2201.06070v1 | https://arxiv.org/pdf/2201.06070v1.pdf | ALA: Adversarial Lightness Attack via Naturalness-aware Regularizations | Most researchers have tried to enhance the robustness of deep neural networks (DNNs) by revealing and repairing the vulnerability of DNNs with specialized adversarial examples. Parts of the attack examples have imperceptible perturbations restricted by Lp norm. However, due to their high-frequency property, the adversa... | ['Geguang Pu', 'Yang Liu', 'Jincao Feng', 'JiaYi Zhu', 'Qing Guo', 'Yihao Huang', 'Felix Juefei-Xu', 'Liangru Sun'] | 2022-01-16 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 2.03613728e-01 -3.29509974e-02 5.36566138e-01 -2.51247019e-01
-2.57576525e-01 -1.01818621e+00 4.90216583e-01 -5.42270541e-01
-3.85927886e-01 6.95778131e-01 -8.25473592e-02 -2.67010808e-01
1.72624648e-01 -9.90699589e-01 -1.16037989e+00 -1.07851911e+00
1.29744858e-01 -5.77942848e-01 1.77771360e-01 -4.86410230... | [5.489735126495361, 7.9291486740112305] |
505ca667-3c4a-4ae4-9dda-bbc17d5b5076 | leveraging-graph-based-cross-modal | 2211.00526 | null | https://arxiv.org/abs/2211.00526v1 | https://arxiv.org/pdf/2211.00526v1.pdf | Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation | Sign Language (SL), as the mother tongue of the deaf community, is a special visual language that most hearing people cannot understand. In recent years, neural Sign Language Translation (SLT), as a possible way for bridging communication gap between the deaf and the hearing people, has attracted widespread academic at... | ['Stan Z. Li', 'Yidong Chen', 'Chong Wu', 'Cheng Tan', 'Siyuan Li', 'Jiangbin Zheng'] | 2022-11-01 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 1.86842859e-01 1.20319828e-01 -3.01475406e-01 -2.22239390e-01
-7.19758570e-01 -7.39749074e-02 4.39461559e-01 -4.98712391e-01
-5.73624194e-01 4.07397240e-01 7.73191214e-01 -3.96215826e-01
8.53055567e-02 -7.47889280e-01 -6.26445234e-01 -5.57094455e-01
3.34432602e-01 3.17750335e-01 2.03874260e-01 -3.02374244... | [9.211767196655273, -6.520484447479248] |
9295c4ee-b5e6-4632-9273-c2e2d493f2f0 | 4d-temporally-coherent-light-field-video | 1804.11276 | null | http://arxiv.org/abs/1804.11276v1 | http://arxiv.org/pdf/1804.11276v1.pdf | 4D Temporally Coherent Light-field Video | Light-field video has recently been used in virtual and augmented reality
applications to increase realism and immersion. However, existing light-field
methods are generally limited to static scenes due to the requirement to
acquire a dense scene representation. The large amount of data and the absence
of methods to in... | ['Jean-yves Guillemaut', 'Marco Volino', 'Armin Mustafa', 'Adrian Hilton'] | 2018-04-30 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 5.23811042e-01 -8.86093795e-01 3.38975132e-01 -2.43910387e-01
-3.60007912e-01 -5.77775896e-01 5.28614879e-01 -2.91203469e-01
-1.66534185e-01 8.95872772e-01 2.33802542e-01 1.22699082e-01
-2.15807259e-01 -6.40897989e-01 -4.60942745e-01 -4.89876270e-01
2.89543241e-01 8.23634267e-02 5.13830125e-01 -1.64727226... | [9.558503150939941, -2.5265188217163086] |
ddcfbf17-a382-4f94-93d6-446557a9de18 | domain-adaptation-using-class-similarity-for | 2011.02782 | null | https://arxiv.org/abs/2011.02782v1 | https://arxiv.org/pdf/2011.02782v1.pdf | Domain Adaptation Using Class Similarity for Robust Speech Recognition | When only limited target domain data is available, domain adaptation could be used to promote performance of deep neural network (DNN) acoustic model by leveraging well-trained source model and target domain data. However, suffering from domain mismatch and data sparsity, domain adaptation is very challenging. This pap... | ['Pengyuan Zhang', 'Li Wang', 'Yuling Ren', 'Jiangjiang Zhao', 'Han Zhu'] | 2020-11-05 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 4.71066177e-01 -1.35663480e-01 -1.39484748e-01 -7.35305488e-01
-8.50982368e-01 -5.11175692e-01 4.18467253e-01 -1.16381474e-01
-6.15762651e-01 7.47155309e-01 3.40166211e-01 9.07945111e-02
3.69465172e-01 -6.41143680e-01 -7.44356573e-01 -8.24561059e-01
5.82972169e-01 3.59008133e-01 4.01906669e-01 -6.92059025... | [14.394051551818848, 6.583807945251465] |
d56b29c4-224b-4978-8e8c-b08cfc0f6ba4 | twisty-a-multilingual-twitter-stylometry | null | null | https://aclanthology.org/L16-1258 | https://aclanthology.org/L16-1258.pdf | TwiSty: A Multilingual Twitter Stylometry Corpus for Gender and Personality Profiling | Personality profiling is the task of detecting personality traits of authors based on writing style. Several personality typologies exist, however, the Briggs-Myer Type Indicator (MBTI) is particularly popular in the non-scientific community, and many people use it to analyse their own personality and talk about the re... | ['Walter Daelemans', 'Ben Verhoeven', 'Barbara Plank'] | 2016-05-01 | twisty-a-multilingual-twitter-stylometry-1 | https://aclanthology.org/L16-1258 | https://aclanthology.org/L16-1258.pdf | lrec-2016-5 | ['gender-prediction'] | ['computer-vision'] | [-7.15394735e-01 1.56944484e-01 -2.93727338e-01 -4.08234954e-01
-1.42803520e-01 -7.21051872e-01 9.15154636e-01 6.00014091e-01
-6.79849386e-01 7.79706895e-01 4.06476378e-01 1.44584496e-02
-3.00202668e-02 -6.73021913e-01 3.52017879e-01 -5.06244540e-01
-1.55673444e-01 8.57452750e-01 -2.66290158e-01 -1.40031278... | [9.388717651367188, 10.326961517333984] |
076b3164-7d49-47dc-b011-c4072a6509dc | a-safe-semi-supervised-graph-convolution | 2207.01960 | null | https://arxiv.org/abs/2207.01960v1 | https://arxiv.org/pdf/2207.01960v1.pdf | A Safe Semi-supervised Graph Convolution Network | In the semi-supervised learning field, Graph Convolution Network (GCN), as a variant model of GNN, has achieved promising results for non-Euclidean data by introducing convolution into GNN. However, GCN and its variant models fail to safely use the information of risk unlabeled data, which will degrade the performance ... | ['Zhiwei Ye', 'Jing Zhao', 'Haitao Gan', 'Yadong Yan', 'Zhi Yang'] | 2022-07-05 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.20746061e-01 3.47014934e-01 -3.31302106e-01 -5.23137450e-01
-2.47350469e-01 -3.00872684e-01 4.82588291e-01 7.24872798e-02
-3.81126285e-01 7.81021893e-01 -6.48742020e-02 -4.66856480e-01
-3.56864423e-01 -1.06004441e+00 -5.04923820e-01 -7.51278996e-01
-1.11137360e-01 5.15913665e-01 2.19688445e-01 2.90888697... | [7.363381862640381, 6.1165595054626465] |
c8a99a03-1497-4cbd-b4df-aa495a29c254 | tempadacos-learning-temporally-structured | 2305.10816 | null | https://arxiv.org/abs/2305.10816v1 | https://arxiv.org/pdf/2305.10816v1.pdf | TempAdaCos: Learning Temporally Structured Embeddings for Few-Shot Keyword Spotting with Dynamic Time Warping | Few-shot keyword spotting (KWS) systems often utilize a sliding window of fixed size. Because of the varying lengths of different keywords or their spoken instances, choosing the right window size is a problem: A window should be long enough to contain all necessary information needed to recognize a keyword but a longe... | ['Alessia Cornaggia-Urrigshardt', 'Kevin Wilkinghoff'] | 2023-05-18 | null | null | null | null | ['keyword-spotting', 'dynamic-time-warping'] | ['speech', 'time-series'] | [ 2.71042772e-02 -4.44340169e-01 -4.85459179e-01 -3.17774802e-01
-9.76153731e-01 -6.00118160e-01 6.77668035e-01 4.36264455e-01
-6.20206416e-01 3.26550066e-01 1.58117190e-01 -3.09128165e-01
-1.32441103e-01 -1.65841475e-01 -3.02410871e-01 -6.99967325e-01
-4.11701292e-01 5.04879132e-02 5.81834853e-01 -2.47169703... | [14.254700660705566, 6.370201110839844] |
64e51f74-3a90-4d37-980c-6539205d727d | plan-execution-for-multi-agent-path-finding | 2207.01752 | null | https://arxiv.org/abs/2207.01752v2 | https://arxiv.org/pdf/2207.01752v2.pdf | Plan Execution for Multi-Agent Path Finding with Indoor Quadcopters | We study the planning and acting phase for the problem of multi-agent path finding (MAPF) in this paper. MAPF is a problem of navigating agents from their start positions to specified individual goal positions so that agents do not collide with each other. Specifically we focus on executing MAPF plans with a group of C... | ['Pavel Surynek', 'Matouš Kulhan'] | 2022-07-05 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.57230273e-01 4.29966420e-01 6.76149577e-02 1.16504580e-02
-3.45222473e-01 -8.72666299e-01 6.43765450e-01 4.36617374e-01
-4.42010760e-01 1.14282477e+00 -2.09167525e-01 -4.09204125e-01
-8.98254693e-01 -1.26598358e+00 -4.44910794e-01 -6.59061372e-01
-7.81749249e-01 1.21522915e+00 8.54631245e-01 -6.95132494... | [4.955561637878418, 1.67836594581604] |
70521a9e-1be5-44cb-88e9-753a293149b5 | a-quasi-bayesian-perspective-to-online | 1602.00522 | null | http://arxiv.org/abs/1602.00522v3 | http://arxiv.org/pdf/1602.00522v3.pdf | A Quasi-Bayesian Perspective to Online Clustering | When faced with high frequency streams of data, clustering raises theoretical
and algorithmic pitfalls. We introduce a new and adaptive online clustering
algorithm relying on a quasi-Bayesian approach, with a dynamic (i.e.,
time-dependent) estimation of the (unknown and changing) number of clusters. We
prove that our a... | ['Sébastien Loustau', 'Le Li', 'Benjamin Guedj'] | 2016-02-01 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-3.36029142e-01 -9.11044255e-02 -2.21131906e-01 -4.38292116e-01
-1.05415523e+00 -7.90959060e-01 8.42539296e-02 1.13906145e-01
-5.20314574e-01 5.71391404e-01 6.95157424e-02 -2.83391267e-01
-3.69946837e-01 -5.66251040e-01 -9.44255412e-01 -9.08288062e-01
-8.21345508e-01 7.15427637e-01 2.23352730e-01 3.90307248... | [4.620736122131348, 3.426583766937256] |
16d9321b-b9f6-4d93-886d-1090a1b30c35 | keep-your-distance-determining-sampling-and | 2207.05078 | null | https://arxiv.org/abs/2207.05078v1 | https://arxiv.org/pdf/2207.05078v1.pdf | Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring | Machine Learning~(ML) has provided promising results in recent years across different applications and domains. However, in many cases, qualities such as reliability or even safety need to be ensured. To this end, one important aspect is to determine whether or not ML components are deployed in situations that are appr... | ['Daniel Schneider', 'Koorosh Aslansefat', 'Mohammed Naveed Akram', 'Andreas Schmidt', 'Ioannis Sorokos', 'Al-Harith Farhad'] | 2022-07-11 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 9.50687379e-02 -1.95459634e-01 -2.28368178e-01 -4.99594808e-01
-5.39477229e-01 -4.63648856e-01 4.96456832e-01 3.20186108e-01
-5.45098841e-01 6.06929421e-01 -6.88289106e-01 -8.78153741e-01
-2.37440199e-01 -5.45253038e-01 -6.02326095e-01 -8.08270812e-01
-2.46335492e-02 7.20545173e-01 6.98395669e-01 -5.53712361... | [5.904052734375, 1.1769529581069946] |
33bf3a5c-49ad-41a6-a7d1-1a94f432589c | clip-surgery-for-better-explainability-with | 2304.05653 | null | https://arxiv.org/abs/2304.05653v1 | https://arxiv.org/pdf/2304.05653v1.pdf | CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks | Contrastive Language-Image Pre-training (CLIP) is a powerful multimodal large vision model that has demonstrated significant benefits for downstream tasks, including many zero-shot learning and text-guided vision tasks. However, we notice some severe problems regarding the model's explainability, which undermines its c... | ['Xiaomeng Li', 'Yiqun Duan', 'Hualiang Wang', 'Yi Li'] | 2023-04-12 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 2.69396394e-01 3.46055627e-01 -1.81263342e-01 -3.02453429e-01
-8.96639168e-01 -5.36108315e-01 5.37826061e-01 -4.54567485e-02
-3.83419156e-01 4.60338622e-01 1.80281788e-01 -4.20297176e-01
-1.91139337e-02 -3.26733857e-01 -8.02214086e-01 -6.62157834e-01
5.84598422e-01 4.23471779e-01 2.40485862e-01 -1.86786443... | [9.893745422363281, 0.7048428058624268] |
69b02b48-6794-4602-b673-a33d5320568e | off-policy-evaluation-in-embedded-spaces | 2203.02807 | null | https://arxiv.org/abs/2203.02807v2 | https://arxiv.org/pdf/2203.02807v2.pdf | Off-Policy Evaluation in Embedded Spaces | Off-policy evaluation methods are important in recommendation systems and search engines, where data collected under an existing logging policy is used to estimate the performance of a new proposed policy. A common approach to this problem is weighting, where data is weighted by a density ratio between the probability ... | ['Georgios Theocharous', 'David Arbour', 'Jaron J. R. Lee'] | 2022-03-05 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 2.14254707e-01 -3.55005205e-01 -7.93497384e-01 -1.74937859e-01
-5.81914485e-01 -7.87344098e-01 6.99202001e-01 2.90647596e-01
-6.33199453e-01 8.53429019e-01 6.41122580e-01 -7.51041591e-01
-4.30490136e-01 -9.00810421e-01 -5.06046832e-01 -5.15981138e-01
-2.66099632e-01 3.77474070e-01 8.31267014e-02 3.79646391... | [4.121892929077148, 2.525270938873291] |
601d6c50-d783-4cf2-baea-1ba151501df3 | improving-retinanet-for-ct-lesion-detection | 1906.02283 | null | https://arxiv.org/abs/1906.02283v1 | https://arxiv.org/pdf/1906.02283v1.pdf | Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels | Accurate, automated lesion detection in Computed Tomography (CT) is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances. Recent work on CT lesion detection employs two-stage region proposal based methods trained with centroid or bounding-box annotations. We pro... | ['Qi Dou', 'Martin Zlocha', 'Ben Glocker'] | 2019-06-05 | null | null | null | null | ['medical-object-detection', 'skin-lesion-identification'] | ['computer-vision', 'medical'] | [ 3.79217148e-01 -2.91133523e-02 -5.31515598e-01 -1.83287978e-01
-1.35068882e+00 -5.65662503e-01 3.99242759e-01 3.94199252e-01
-7.75099635e-01 4.25502121e-01 2.48873755e-01 -2.84570843e-01
1.99288040e-01 -2.69596636e-01 -3.54448438e-01 -8.72341514e-01
-1.26271218e-01 7.21272528e-01 5.41438639e-01 3.00948828... | [15.05505084991455, -2.3024353981018066] |
2a54610c-9cc1-46f7-b73b-1f85d6b70656 | harrisz-harris-corner-selection-for-next-gen | 2109.12925 | null | https://arxiv.org/abs/2109.12925v6 | https://arxiv.org/pdf/2109.12925v6.pdf | HarrisZ$^+$: Harris Corner Selection for Next-Gen Image Matching Pipelines | Due to its role in many computer vision tasks, image matching has been subjected to an active investigation by researchers, which has lead to better and more discriminant feature descriptors and to more robust matching strategies, also thanks to the advent of the deep learning and the increased computational power of t... | ['Dmytro Mishkin', 'Fabio Bellavia'] | 2021-09-27 | null | null | null | null | ['image-matching'] | ['computer-vision'] | [-1.03435919e-01 -2.61931479e-01 -7.03183189e-02 -3.61542672e-01
-8.13767731e-01 -2.27838814e-01 7.35963464e-01 4.37903225e-01
-6.02070987e-01 1.78534552e-01 -1.89586002e-02 4.01317552e-02
-3.44931692e-01 -6.30153656e-01 -5.44375658e-01 -6.98977113e-01
-2.33195037e-01 7.21818268e-01 5.43076694e-01 -5.63099921... | [10.482585906982422, 0.2142300009727478] |
e1effbe3-00d0-4407-9386-19bb3966b3e1 | a-conditional-generative-adversarial-network-1 | null | null | https://ieeexplore.ieee.org/document/8519215 | https://ieeexplore.ieee.org/document/8519215 | A Conditional Generative Adversarial Network to Fuse Sar And Multispectral Optical Data For Cloud Removal From Sentinel-2 Images | In this paper, we present the first conditional generative adversarial network (cGAN) architecture that is specifically designed to fuse synthetic aperture radar (SAR) and optical multi-spectral (MS) image data to generate cloud- and haze-free MS optical data from a cloud-corrupted MS input and an auxiliary SAR image. ... | ['Xiaoxiang Zhu', 'Michael Schmitt', 'Claas Grohnfeldt'] | 2018-11-04 | null | null | null | igarss-2018-11 | ['cloud-removal'] | ['computer-vision'] | [ 6.80831909e-01 -4.68098819e-02 5.70086837e-01 -2.96028972e-01
-1.03960121e+00 -8.28000247e-01 5.86325109e-01 -8.00480187e-01
-3.36989552e-01 1.05327129e+00 -2.30366603e-01 -6.51964366e-01
3.73146348e-02 -9.30072129e-01 -5.94967365e-01 -1.10179448e+00
2.36396879e-01 1.94510520e-01 -2.55200937e-02 -3.43068689... | [10.084554672241211, -2.0867834091186523] |
c95cba0f-eae8-421e-bbe1-07b76290a71a | gflownets-for-ai-driven-scientific-discovery | 2302.00615 | null | https://arxiv.org/abs/2302.00615v2 | https://arxiv.org/pdf/2302.00615v2.pdf | GFlowNets for AI-Driven Scientific Discovery | Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the pace of scientific discovery. While science has traditionally relied on trial and error and even serendipity to a large extent, the last few decades have seen a surge of data-driven... | ['Yoshua Bengio', 'Alex Hernandez-Garcia', 'Cheng-Hao Liu', 'Jason Hartford', 'Tristan Deleu', 'Moksh Jain'] | 2023-02-01 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 3.48889738e-01 8.54683593e-02 -4.25899476e-01 -1.40656680e-01
-8.21892619e-01 -6.05259299e-01 7.63883948e-01 4.69558775e-01
-5.98493993e-01 1.23759782e+00 -5.77545725e-02 -6.53405428e-01
-4.66221571e-01 -7.82264531e-01 -8.78609896e-01 -8.84350300e-01
-1.17442384e-01 9.70667541e-01 -7.23256767e-02 2.54653752... | [5.983887672424316, 4.57805061340332] |
d5df277c-4e0d-403e-8df1-51a3e7e462b7 | part-aware-contrastive-learning-for-self | 2305.00666 | null | https://arxiv.org/abs/2305.00666v2 | https://arxiv.org/pdf/2305.00666v2.pdf | Part Aware Contrastive Learning for Self-Supervised Action Recognition | In recent years, remarkable results have been achieved in self-supervised action recognition using skeleton sequences with contrastive learning. It has been observed that the semantic distinction of human action features is often represented by local body parts, such as legs or hands, which are advantageous for skeleto... | ['Shiqian Wu', 'Chen Chen', 'Mengyuan Liu', 'Aidong Lu', 'Ce Zheng', 'Wenhan Wu', 'Yilei Hua'] | 2023-05-01 | null | null | null | null | ['skeleton-based-action-recognition', 'action-recognition-in-videos', 'self-supervised-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.86718833e-01 1.63264945e-02 -6.63109958e-01 -3.57287049e-01
-4.40704972e-01 4.31758225e-01 5.49049973e-01 -3.34912091e-01
-3.72597992e-01 5.00812352e-01 9.58989799e-01 6.81044877e-01
-1.62777096e-01 -5.26006401e-01 -4.88077611e-01 -8.92287850e-01
5.63478321e-02 2.28395671e-01 3.79729837e-01 -4.34895426... | [8.060609817504883, 0.6095632314682007] |
f98998a0-e20e-4cfe-b05c-76ddffa3f973 | graph-signal-sampling-for-inductive-one-bit | 2302.03933 | null | https://arxiv.org/abs/2302.03933v1 | https://arxiv.org/pdf/2302.03933v1.pdf | Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution | Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros. We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing. T... | ['Junchi Yan', 'Xiaokang Yang', 'Hua Chai', 'Zhaobing Han', 'Gang Zeng', 'Haoyu Geng', 'Chao Chen'] | 2023-02-08 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.07239115e-01 5.63089885e-02 -7.04401433e-02 -2.79316902e-01
-8.68160367e-01 -4.82166141e-01 1.05858840e-01 -1.47157116e-02
7.92451203e-02 4.09055471e-01 3.23997080e-01 -1.62641019e-01
-2.98365623e-01 -7.37295151e-01 -1.06716454e+00 -7.04192162e-01
-3.68979007e-01 2.28139862e-01 -1.08422183e-01 -2.16792017... | [6.982749938964844, 4.780142784118652] |
0efc69d8-0f12-469f-aafb-78c2e5aa817c | two-stage-mr-image-segmentation-method-for | 2304.08072 | null | https://arxiv.org/abs/2304.08072v1 | https://arxiv.org/pdf/2304.08072v1.pdf | Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism | Multimodal magnetic resonance imaging (MRI) can reveal different patterns of human tissue and is crucial for clinical diagnosis. However, limited by cost, noise and manual labeling, obtaining diverse and reliable multimodal MR images remains a challenge. For the same lesion, different MRI manifestations have great diff... | ['Jin Li', 'Lin Lu', 'Jiawei Jiang', 'Li Zhu'] | 2023-04-17 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 3.24153990e-01 1.13790385e-01 1.52075663e-01 -4.02090698e-02
-9.55474555e-01 -3.05951267e-01 1.29107103e-01 -6.09902024e-01
-2.36427084e-01 7.24613249e-01 2.53979951e-01 -1.62412792e-01
1.11815862e-01 -8.80157590e-01 -5.09584069e-01 -1.28393090e+00
3.21775228e-01 2.62713104e-01 3.64014745e-01 -2.01830998... | [14.142081260681152, -2.123483419418335] |
efb4fc7c-7e23-4416-8e4b-5b443e75bd6b | depth-completion-using-geometry-aware | 2203.10912 | null | https://arxiv.org/abs/2203.10912v2 | https://arxiv.org/pdf/2203.10912v2.pdf | Depth Completion using Geometry-Aware Embedding | Exploiting internal spatial geometric constraints of sparse LiDARs is beneficial to depth completion, however, has been not explored well. This paper proposes an efficient method to learn geometry-aware embedding, which encodes the local and global geometric structure information from 3D points, e.g., scene layout, obj... | ['Yi Zhang', 'Hongyu Yang', 'Hu Chen', 'Wenchao Du'] | 2022-03-21 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [-8.29827115e-02 7.82276019e-02 -2.57357266e-02 -3.77933174e-01
-3.73895228e-01 -4.57415372e-01 2.41362751e-01 3.40922624e-02
2.72182319e-02 3.66214871e-01 5.19721694e-02 -8.76498297e-02
-1.88286006e-01 -1.11516094e+00 -6.12456858e-01 -6.14741445e-01
-3.61779854e-02 3.46960098e-01 2.41903514e-01 5.41052371... | [8.626337051391602, -2.9159913063049316] |
206789a2-85f1-4841-b49f-f0ef7044638b | unsupervised-melody-to-lyric-generation | 2305.19228 | null | https://arxiv.org/abs/2305.19228v1 | https://arxiv.org/pdf/2305.19228v1.pdf | Unsupervised Melody-to-Lyric Generation | Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody. It is of significant practical interest and more challenging than unconstrained lyric generation as the music imposes additional constraints onto the lyrics. The training data is limited as most songs are copyri... | ['Nanyun Peng', 'Jing Huang', 'Tagyoung Chung', 'Wenbo Zhao', 'Chenyang Tao', 'Gunnar Sigurdsson', 'Alessandra Cervone', 'Shereen Oraby', 'Anjali Narayan-Chen', 'Yufei Tian'] | 2023-05-30 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 2.12916389e-01 4.39634621e-02 -1.24057382e-01 -1.07709251e-01
-1.11881959e+00 -1.19464099e+00 6.32966101e-01 -6.64255679e-01
2.66611695e-01 6.80141509e-01 8.57463002e-01 2.13769563e-02
1.57224596e-01 -5.84627748e-01 -6.05111063e-01 -6.32075310e-01
4.17581201e-01 6.21884465e-01 -4.10695016e-01 -3.75390232... | [15.890233993530273, 5.64668083190918] |
a0bc599f-803b-4494-8579-e490c45263b2 | lpf-defense-3d-adversarial-defense-based-on | 2202.11287 | null | https://arxiv.org/abs/2202.11287v2 | https://arxiv.org/pdf/2202.11287v2.pdf | LPF-Defense: 3D Adversarial Defense based on Frequency Analysis | Although 3D point cloud classification has recently been widely deployed in different application scenarios, it is still very vulnerable to adversarial attacks. This increases the importance of robust training of 3D models in the face of adversarial attacks. Based on our analysis on the performance of existing adversar... | ['Kimia Noorbakhsh', 'Shohreh Kasaei', 'Arian Etemadi', 'Hanieh Naderi'] | 2022-02-23 | null | null | null | null | ['adversarial-defense', 'point-cloud-classification'] | ['adversarial', 'computer-vision'] | [-1.45600051e-01 -1.40492976e-01 2.20206678e-01 9.11772400e-02
-4.81234998e-01 -1.08095753e+00 7.75172234e-01 7.78883472e-02
-1.67721570e-01 4.66944188e-01 -3.89930695e-01 -4.57546920e-01
-9.87168029e-03 -9.79888618e-01 -8.61836076e-01 -8.09369624e-01
-3.06871533e-01 3.33680451e-01 5.35991430e-01 -4.19861406... | [7.696038246154785, -4.481686592102051] |
334e33e0-0f20-47af-9041-f3f9fc468bd7 | answering-ambiguous-questions-through | 2011.13137 | null | https://arxiv.org/abs/2011.13137v2 | https://arxiv.org/pdf/2011.13137v2.pdf | Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction | In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system needs to find possible interpretations of the question, and predict one or multiple plausible answers. When multiple plausible answers are fou... | ['Bing Xiang', 'Andrew O. Arnold', 'Ramesh Nallapati', 'Dejiao Zhang', 'Feng Nan', 'Zhiguo Wang', 'Cicero Nogueira dos santos', 'Patrick Ng', 'Henghui Zhu', 'Yifan Gao'] | 2020-11-26 | null | https://aclanthology.org/2021.acl-long.253 | https://aclanthology.org/2021.acl-long.253.pdf | acl-2021-5 | ['triviaqa'] | ['miscellaneous'] | [ 1.14138322e-02 4.71691072e-01 2.42953405e-01 -5.77078581e-01
-1.75275433e+00 -1.15250349e+00 2.10051641e-01 2.11994097e-01
-1.94497809e-01 9.63697851e-01 3.38968933e-01 -7.57345676e-01
-1.66225910e-01 -8.91511559e-01 -6.52617812e-01 -7.15158507e-02
6.89922154e-01 1.25314510e+00 8.28272045e-01 -7.82735407... | [11.431597709655762, 8.03747844696045] |
f9d4248b-d28c-41ef-b855-819f1160ba14 | learning-localized-generative-models-for-3d | null | null | https://openreview.net/forum?id=SJeXSo09FQ | https://openreview.net/pdf?id=SJeXSo09FQ | Learning Localized Generative Models for 3D Point Clouds via Graph Convolution | Point clouds are an important type of geometric data and have widespread use in computer graphics and vision. However, learning representations for point clouds is particularly challenging due to their nature as being an unordered collection of points irregularly distributed in 3D space. Graph convolution, a generaliza... | ['Giulia Fracastoro', 'Enrico Magli', 'Diego Valsesia'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['point-cloud-generation'] | ['computer-vision'] | [ 2.64542013e-01 4.98963982e-01 1.68333456e-01 -4.27164555e-01
-3.59703898e-01 -5.72348297e-01 7.59535730e-01 2.06905529e-01
-8.33402015e-03 2.92911977e-01 -1.10607229e-01 -2.43470311e-01
2.39413530e-01 -1.40830672e+00 -1.09673488e+00 -7.28572190e-01
-1.50462151e-01 9.90440965e-01 -1.87813044e-01 5.46802506... | [8.760618209838867, -3.7198987007141113] |
3a253734-e6f6-441e-b2df-17628707c8d5 | deepquarantine-for-suspicious-mail | 2001.04168 | null | https://arxiv.org/abs/2001.04168v1 | https://arxiv.org/pdf/2001.04168v1.pdf | DeepQuarantine for Suspicious Mail | In this paper, we introduce DeepQuarantine (DQ), a cloud technology to detect and quarantine potential spam messages. Spam attacks are becoming more diverse and can potentially be harmful to email users. Despite the high quality and performance of spam filtering systems, detection of a spam campaign can take some time.... | ['Nikita Benkovich', 'Dmitry Golubev', 'Roman Dedenok'] | 2020-01-13 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-2.58740336e-01 -7.61298180e-01 2.73486108e-01 -2.19879270e-01
-4.10287321e-01 -8.86385322e-01 6.57508492e-01 4.25059617e-01
-4.53239560e-01 4.46029007e-01 -1.63519934e-01 -6.75977170e-01
8.68327990e-02 -1.31751871e+00 -3.37873876e-01 -4.71572548e-01
3.72780822e-02 4.24244732e-01 7.70389557e-01 -4.64559227... | [7.815227031707764, 10.001577377319336] |
4780458f-f5e6-4bd0-9445-af0a49deb8a0 | filtering-distillation-and-hard-negatives-for | 2301.02280 | null | https://arxiv.org/abs/2301.02280v2 | https://arxiv.org/pdf/2301.02280v2.pdf | Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training | Vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems. In this paper we improve the following three aspects of the contrastive pre-training pipeline: dataset noise, model initialization and the training objective. First, w... | ['Dhruv Mahajan', 'Vignesh Ramanathan', 'Yi Wen', 'Yash Patel', 'Simon Vandenhende', 'Todor Mihaylov', 'Abhishek Kadian', 'Abhimanyu Dubey', 'Filip Radenovic'] | 2023-01-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Radenovic_Filtering_Distillation_and_Hard_Negatives_for_Vision-Language_Pre-Training_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Radenovic_Filtering_Distillation_and_Hard_Negatives_for_Vision-Language_Pre-Training_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-spotting'] | ['computer-vision'] | [ 4.74511206e-01 -1.92522302e-01 -2.37052575e-01 -4.82732534e-01
-1.49566305e+00 -2.37107366e-01 9.84284759e-01 6.17650896e-02
-8.16589177e-01 3.64585996e-01 3.80388170e-01 -2.75027156e-01
3.06921929e-01 -2.99890995e-01 -6.19843721e-01 -5.23447990e-01
4.39108491e-01 4.38562602e-01 3.14744771e-01 -2.22869843... | [10.041793823242188, 2.2284281253814697] |
fd06f8e1-0d5f-4b3d-a071-3f9b897949c2 | learning-a-sensor-invariant-embedding-of | 2107.09092 | null | https://arxiv.org/abs/2107.09092v2 | https://arxiv.org/pdf/2107.09092v2.pdf | Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring | Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and Synthetic Aperture Radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learn... | ['Konrad Schindler', 'Emmanuel Baltsavias', 'Yuchang Jiang', 'Manu Tom'] | 2021-07-19 | null | null | null | null | ['lake-ice-detection', 'lake-ice-detection'] | ['computer-vision', 'miscellaneous'] | [ 5.34108758e-01 -1.87802002e-01 7.06559196e-02 -5.97912848e-01
-9.92256224e-01 -7.46641517e-01 6.96575344e-01 1.31508663e-01
-6.95706904e-01 7.27615058e-01 1.76847473e-01 -3.18996876e-01
-2.17498273e-01 -1.02202725e+00 -6.55601859e-01 -1.07585132e+00
-4.28846657e-01 1.74597487e-01 -1.75878808e-01 -4.84587371... | [9.68061351776123, -1.6738847494125366] |
9c2de8b8-ddb9-43dd-804e-52e7f7fb6216 | explore-faster-localization-learning-for | 2207.01342 | null | https://arxiv.org/abs/2207.01342v1 | https://arxiv.org/pdf/2207.01342v1.pdf | Explore Faster Localization Learning For Scene Text Detection | Generally pre-training and long-time training computation are necessary for obtaining a good-performance text detector based on deep networks. In this paper, we present a new scene text detection network (called FANet) with a Fast convergence speed and Accurate text localization. The proposed FANet is an end-to-end tex... | ['Weiqiang Wang', 'Weijia Wu', 'Yuanqiang Cai', 'Yuzhong Zhao'] | 2022-07-04 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [-7.11600259e-02 -6.65627122e-01 -8.11074525e-02 -4.72047448e-01
-5.39007008e-01 -2.01370474e-02 7.49621630e-01 -1.13994770e-01
-5.83433151e-01 4.76372009e-03 7.35122040e-02 -9.23015103e-02
2.02195123e-01 -5.36235332e-01 -5.31440556e-01 -3.66586864e-01
4.80294406e-01 4.68234360e-01 5.71178734e-01 7.08307773... | [12.007072448730469, 2.2172110080718994] |
d797d544-d032-407e-ba19-a1ebb8767ae3 | malunet-a-multi-attention-and-light-weight | 2211.01784 | null | https://arxiv.org/abs/2211.01784v1 | https://arxiv.org/pdf/2211.01784v1.pdf | MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation | Recently, some pioneering works have preferred applying more complex modules to improve segmentation performances. However, it is not friendly for actual clinical environments due to limited computing resources. To address this challenge, we propose a light-weight model to achieve competitive performances for skin lesi... | ['Yuzhuo Fu', 'Ting Liu', 'Mingye Xie', 'Suncheng Xiang', 'Jiacheng Ruan'] | 2022-11-03 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 4.09237921e-01 1.02997579e-01 -6.74412027e-02 -2.59761035e-01
-8.68735731e-01 -1.31897658e-01 2.09322169e-01 1.92553326e-01
-6.41119242e-01 4.01831865e-01 -6.60646409e-02 -4.16149497e-01
-6.94946796e-02 -8.50037575e-01 -5.05626976e-01 -8.24372649e-01
1.33040309e-01 3.76285501e-02 5.13726056e-01 2.65960936... | [14.699980735778809, -2.620511054992676] |
e0edb632-9675-4c26-b81b-49a77b0deae0 | textray-contour-based-geometric-modeling-for | 2008.04851 | null | https://arxiv.org/abs/2008.04851v2 | https://arxiv.org/pdf/2008.04851v2.pdf | TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene Text Detection | Arbitrary-shaped text detection is a challenging task due to the complex geometric layouts of texts such as large aspect ratios, various scales, random rotations and curve shapes. Most state-of-the-art methods solve this problem from bottom-up perspectives, seeking to model a text instance of complex geometric layouts ... | ['Yifeng Chen', 'Xi Li', 'Fangfang Wang', 'Fei Wu'] | 2020-08-11 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.16915751e-01 -1.69020575e-02 1.24605224e-01 -2.32682511e-01
-7.28893816e-01 -7.04596519e-01 7.03373790e-01 4.62946206e-01
-7.17878565e-02 1.06471470e-02 -1.41935095e-01 -2.61849225e-01
2.09172964e-01 -9.60785627e-01 -7.77169645e-01 -5.71922839e-01
1.67305961e-01 5.95968723e-01 4.80379730e-01 -2.26644084... | [12.070260047912598, 2.271317958831787] |
e806ff1c-29c7-4dc2-8438-09691df7b1a4 | c-pack-of-ipas-a-c90-program-benchmark-of | 2206.08768 | null | https://arxiv.org/abs/2206.08768v1 | https://arxiv.org/pdf/2206.08768v1.pdf | C-Pack of IPAs: A C90 Program Benchmark of Introductory Programming Assignments | Due to the vast number of students enrolled in Massive Open Online Courses (MOOCs), there has been an increasing number of automated program repair techniques focused on introductory programming assignments (IPAs). Such techniques take advantage of previous correct student implementations in order to provide automated,... | ['Vasco Manquinho', 'Mikoláš Janota', 'Pedro Orvalho'] | 2022-06-17 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-2.29648337e-01 -1.41975850e-01 3.43647599e-01 -4.04037207e-01
-4.31806087e-01 -1.09226036e+00 -1.74550638e-01 9.92578208e-01
6.25079870e-02 1.50313243e-01 -3.20691705e-01 -7.88926780e-01
-1.03155375e-01 -1.11119974e+00 -7.83595324e-01 9.14902538e-02
4.17460352e-02 -3.58866714e-02 7.31976032e-01 -5.28884292... | [9.449725151062012, 7.4172749519348145] |
3dc8f50e-e593-4ad1-8a65-45641bfa0aa3 | unsupervised-neural-machine-translation-with | 1901.04112 | null | http://arxiv.org/abs/1901.04112v1 | http://arxiv.org/pdf/1901.04112v1.pdf | Unsupervised Neural Machine Translation with SMT as Posterior Regularization | Without real bilingual corpus available, unsupervised Neural Machine
Translation (NMT) typically requires pseudo parallel data generated with the
back-translation method for the model training. However, due to weak
supervision, the pseudo data inevitably contain noises and errors that will be
accumulated and reinforced... | ['Zhirui Zhang', 'Shuai Ma', 'Ming Zhou', 'Shuo Ren', 'Shujie Liu'] | 2019-01-14 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 2.18776792e-01 -9.59554538e-02 -4.19401854e-01 -4.00812209e-01
-1.09824765e+00 -3.55474412e-01 5.81073523e-01 -2.33073562e-01
-3.84612173e-01 9.12512422e-01 4.53592569e-01 -6.08257651e-01
5.21410048e-01 -6.19033098e-01 -1.11794341e+00 -5.97307742e-01
7.38024175e-01 9.45142686e-01 -4.73307997e-01 -3.04845572... | [11.637060165405273, 10.217557907104492] |
3a776ca3-71f3-4ae7-826f-8f4774b639cb | identity-construction-in-a-misogynist-incels | 2306.15745 | null | https://arxiv.org/abs/2306.15745v2 | https://arxiv.org/pdf/2306.15745v2.pdf | Identity Construction in a Misogynist Incels Forum | Online communities of involuntary celibates (incels) are a prominent source of misogynist hate speech. In this paper, we use quantitative text and network analysis approaches to examine how identity groups are discussed on <incels.is>, the largest black-pilled incels forum. We find that this community produces a wide r... | ['Meredith Pruden', 'Kathleen M. Carley', 'David West Brown', 'Chloe Perry', 'Michael Miller Yoder'] | 2023-06-27 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.13707691e-01 2.42041916e-01 -4.49552089e-01 2.02105224e-01
3.08692664e-01 -8.80213022e-01 1.04638541e+00 5.99387884e-01
-1.72944412e-01 4.75334018e-01 1.00361598e+00 -5.03313184e-01
1.09394722e-01 -6.90004945e-01 -3.03203445e-02 -3.88558567e-01
3.42667639e-01 2.20972046e-01 -1.64084271e-01 -7.20249176... | [8.778169631958008, 10.441375732421875] |
58e3b342-0351-492f-93b6-c5f9cd47fb5a | an-evolutionary-computing-enriched-rs-attack | 1709.08362 | null | http://arxiv.org/abs/1709.08362v2 | http://arxiv.org/pdf/1709.08362v2.pdf | An Evolutionary Computing Enriched RS Attack Resilient Medical Image Steganography Model for Telemedicine Applications | The recent advancement in computing technologies and resulting vision based
applications have gives rise to a novel practice called telemedicine that
requires patient diagnosis images or allied information to recommend or even
perform diagnosis practices being located remotely. However, to ensure accurate
and optimal t... | ['Elsaid MD. Abdelrahim', 'Romany F. Mansour'] | 2017-09-25 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 1.03109729e+00 1.09171286e-01 3.48110467e-01 -2.91677788e-02
-7.51118883e-02 -6.06346190e-01 4.42087471e-01 2.48970419e-01
-2.60083288e-01 6.99557900e-01 1.14366829e-01 -5.16191185e-01
-3.63742828e-01 -9.77490187e-01 -3.22483748e-01 -8.25922310e-01
-2.27845553e-02 -2.24359576e-02 1.76170737e-01 -4.46899891... | [4.298358917236328, 8.051968574523926] |
3e43c0ed-21a1-42d2-9dfe-ccca710a7df0 | rethinking-zero-shot-video-classification-end | 2003.01455 | null | https://arxiv.org/abs/2003.01455v4 | https://arxiv.org/pdf/2003.01455v4.pdf | Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications | Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL) proposes one solution to this problem. ZSL trains a model once, and generalizes to new tasks whose classes are not present in the training... | ['Biagio Brattoli', 'Krzysztof Chalupka', 'Joseph Tighe', 'Fedor Zhdanov', 'Pietro Perona'] | 2020-03-03 | rethinking-zero-shot-video-classification-end-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Brattoli_Rethinking_Zero-Shot_Video_Classification_End-to-End_Training_for_Realistic_Applications_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Brattoli_Rethinking_Zero-Shot_Video_Classification_End-to-End_Training_for_Realistic_Applications_CVPR_2020_paper.pdf | cvpr-2020-6 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 1.82553485e-01 -1.67620361e-01 -5.79211891e-01 -4.31231916e-01
-8.73025298e-01 -9.46947932e-01 6.53008401e-01 -2.19719097e-01
-2.80019611e-01 6.18386745e-01 1.23660505e-01 -2.19070867e-01
1.01585269e-01 -5.84464610e-01 -1.02680302e+00 -3.10711443e-01
-5.25726862e-02 4.32531655e-01 4.62341577e-01 1.21066116... | [8.998577117919922, 1.1414693593978882] |
07c29d15-c33c-4b9b-981a-7915cca21e72 | improving-multi-hop-knowledge-base-question | 2101.03737 | null | https://arxiv.org/abs/2101.03737v2 | https://arxiv.org/pdf/2101.03737v2.pdf | Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals | Multi-hop Knowledge Base Question Answering (KBQA) aims to find the answer entities that are multiple hops away in the Knowledge Base (KB) from the entities in the question. A major challenge is the lack of supervision signals at intermediate steps. Therefore, multi-hop KBQA algorithms can only receive the feedback fro... | ['Ji-Rong Wen', 'Wayne Xin Zhao', 'Jing Jiang', 'Yunshi Lan', 'Gaole He'] | 2021-01-11 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.32995614e-01 4.73638445e-01 -2.82330066e-01 -3.41388673e-01
-9.60613489e-01 -5.21147370e-01 8.90959576e-02 3.16738427e-01
-2.35904828e-01 1.07497013e+00 5.25453649e-02 -4.60007548e-01
-4.24446225e-01 -1.12509704e+00 -9.21446383e-01 -5.75350821e-01
4.57927436e-01 5.36267340e-01 8.35964322e-01 -5.27118683... | [10.787293434143066, 7.922609806060791] |
055f4f49-dd4b-4c04-924b-5a27f64f0cc9 | the-offense-defense-balance-of-scientific | 2001.00463 | null | https://arxiv.org/abs/2001.00463v2 | https://arxiv.org/pdf/2001.00463v2.pdf | The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse? | There is growing concern over the potential misuse of artificial intelligence (AI) research. Publishing scientific research can facilitate misuse of the technology, but the research can also contribute to protections against misuse. This paper addresses the balance between these two effects. Our theoretical framework e... | ['Toby Shevlane', 'Allan Dafoe'] | 2019-12-27 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 3.77511799e-01 2.58121818e-01 -3.20118219e-01 2.93877963e-02
-2.17638507e-01 -1.04734302e+00 7.40087688e-01 3.21515769e-01
-6.17701054e-01 4.63075221e-01 2.24029377e-01 -1.42539418e+00
-1.64210543e-01 -7.06356049e-01 -5.81787884e-01 -6.77803278e-01
4.59666461e-01 -1.42344594e-01 1.02114722e-01 1.80294681... | [8.945463180541992, 6.536774158477783] |
6eacc117-d362-4a0e-99d5-11f83397f2ae | diffusion-model-augmented-behavioral-cloning | 2302.13335 | null | https://arxiv.org/abs/2302.13335v2 | https://arxiv.org/pdf/2302.13335v2.pdf | Diffusion Model-Augmented Behavioral Cloning | Imitation learning addresses the challenge of learning by observing an expert's demonstrations without access to reward signals from environments. Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e.g., ... | ['Chun-Mao Lai', 'Ming-Hao Hsu', 'Shao-Hua Sun', 'Shang-Fu Chen', 'Hsiang-Chun Wang'] | 2023-02-26 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 4.19136584e-02 1.07976101e-01 -2.23889694e-01 -1.24077894e-01
-3.74938488e-01 -4.70090389e-01 7.13277459e-01 -2.59708971e-01
-6.36808455e-01 7.91496813e-01 -1.13522455e-01 -3.21244299e-01
-6.51299953e-03 -5.64039826e-01 -1.23903346e+00 -8.27258408e-01
-2.17712224e-01 4.00384814e-01 1.81244299e-01 4.17991653... | [4.359593868255615, 1.2818074226379395] |
4e8dd7ec-6f62-4cac-8331-d61358b64895 | identifying-cover-songs-using-information | 1407.2433 | null | http://arxiv.org/abs/1407.2433v3 | http://arxiv.org/pdf/1407.2433v3.pdf | Identifying Cover Songs Using Information-Theoretic Measures of Similarity | This paper investigates methods for quantifying similarity between audio
signals, specifically for the task of of cover song detection. We consider an
information-theoretic approach, where we compute pairwise measures of
predictability between time series. We compare discrete-valued approaches
operating on quantised au... | ['Simon Dixon', 'Peter Foster', 'Anssi Klapuri'] | 2014-07-09 | null | null | null | null | ['cover-song-identification'] | ['music'] | [ 7.70446360e-01 -4.35659170e-01 1.23501025e-01 -2.34992504e-01
-1.52560842e+00 -9.02545333e-01 5.95334172e-01 6.22610986e-01
-4.79622036e-01 2.31966510e-01 3.55006248e-01 1.11404369e-02
-6.97695315e-01 -6.15090191e-01 -4.38332379e-01 -5.37200332e-01
-8.87180686e-01 1.10525765e-01 1.65058270e-01 -5.20264320... | [15.69459342956543, 5.323807239532471] |
bba1f87b-72e1-4857-86cb-34c15fb137d6 | evaluation-metrics-for-headline-generation | null | null | https://aclanthology.org/2020.lrec-1.222 | https://aclanthology.org/2020.lrec-1.222.pdf | Evaluation Metrics for Headline Generation Using Deep Pre-Trained Embeddings | With the explosive growth in textual data, it is becoming increasingly important to summarize text automatically. Recently, generative language models have shown promise in abstractive text summarization tasks. Since these models rephrase text and thus use similar but different words as found in the summarized text, ex... | ['Georg Groh', 'Gerhard Hagerer', 'Yang An', 'Abdul Moeed'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['headline-generation'] | ['natural-language-processing'] | [-1.49477075e-03 1.77772045e-01 -1.92057490e-01 -6.33173212e-02
-8.86213720e-01 -5.66057205e-01 8.77407551e-01 8.06149662e-01
-4.82398510e-01 9.47131455e-01 9.63034749e-01 -1.54474348e-01
-1.54715791e-01 -5.52002370e-01 -1.79488435e-01 -2.28958875e-01
-2.26552468e-02 3.94515216e-01 2.60750085e-01 -3.27451319... | [12.297891616821289, 9.475226402282715] |
0a4ba403-51b0-45cb-84de-a2b8bd2363be | on-batching-variable-size-inputs-for-training | 2301.10587 | null | https://arxiv.org/abs/2301.10587v2 | https://arxiv.org/pdf/2301.10587v2.pdf | On Batching Variable Size Inputs for Training End-to-End Speech Enhancement Systems | The performance of neural network-based speech enhancement systems is primarily influenced by the model architecture, whereas training times and computational resource utilization are primarily affected by training parameters such as the batch size. Since noisy and reverberant speech mixtures can have different duratio... | ['Tobias May', 'Tommy Sonne Alstrøm', 'Philippe Gonzalez'] | 2023-01-25 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [-1.22052833e-01 -5.63851476e-01 2.17666060e-01 -4.46715653e-01
-2.96098948e-01 -2.45582610e-01 4.00642455e-01 7.28250667e-02
-9.38943923e-01 2.73614973e-01 9.06765908e-02 -5.84795713e-01
3.50791123e-03 -4.52857196e-01 -3.34760576e-01 -9.90761459e-01
-5.18684983e-02 3.32294963e-02 4.81102854e-01 -1.48527473... | [14.879294395446777, 5.9369707107543945] |
030ff9cc-958c-4e59-97de-e2a19eeddeba | au-aware-graph-convolutional-network-for | 2303.09114 | null | https://arxiv.org/abs/2303.09114v1 | https://arxiv.org/pdf/2303.09114v1.pdf | AU-aware graph convolutional network for Macro- and Micro-expression spotting | Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs. When solving this problem, previous works generally lack in considering the structures of human faces and the correspondence between expressions and re... | ['Enhong Chen', 'Sirui Zhao', 'Shifeng Liu', 'Tong Xu', 'Shiwei Wu', 'Shukang Yin'] | 2023-03-16 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 2.16403097e-01 8.65687504e-02 -3.19526464e-01 -5.13108909e-01
-3.32923889e-01 -1.85307384e-01 3.25640291e-01 -4.41254020e-01
-9.94447246e-02 4.32679236e-01 2.81681061e-01 2.36724257e-01
-5.02503589e-02 -6.23821437e-01 -4.98585105e-01 -5.88983834e-01
-2.23931447e-01 -9.55917761e-02 -3.97700705e-02 -2.93024868... | [13.642552375793457, 1.6052922010421753] |
405fb63a-043b-4d4d-bef3-f986d30bbdc4 | for-the-sake-of-privacy-skeleton-based | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9897358 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9897358 | FOR THE SAKE OF PRIVACY: SKELETON-BASED SALIENT BEHAVIOR RECOGNITION | Authorities as well as emergency and rescue services have an increasing interest in smart support systems to ensure public safety which includes in particular behavioral analysis of pedestrians by using video surveillance systems. In order to accommodate concerns of citizens regarding their personal rights, the demand ... | ['Jurgen Beyerer', 'Mickael Cormier', 'Johanna Thiemich', 'Thomas Golda'] | 2022-10-18 | null | null | null | 2022-ieee-international-conference-on-image | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 1.63643584e-01 4.24397886e-02 -1.18899317e-02 -6.63231611e-01
-5.83828092e-01 -4.43774849e-01 7.16289401e-01 2.34332472e-01
-8.51712048e-01 7.22017348e-01 3.11439842e-01 -3.00562203e-01
2.79777646e-01 -5.54828942e-01 -2.41023153e-01 -5.97001493e-01
3.09658479e-02 1.00825354e-01 4.91816580e-01 -2.17222482... | [7.85410737991333, 1.1266200542449951] |
b6a51459-c12e-4d3d-a9f0-636ba006e6a2 | bring-your-own-data-self-supervised | 2306.13651 | null | https://arxiv.org/abs/2306.13651v2 | https://arxiv.org/pdf/2306.13651v2.pdf | Bring Your Own Data! Self-Supervised Evaluation for Large Language Models | With the rise of Large Language Models (LLMs) and their ubiquitous deployment in diverse domains, measuring language model behavior on realistic data is imperative. For example, a company deploying a client-facing chatbot must ensure that the model will not respond to client requests with profanity. Current evaluations... | ['Tom Goldstein', 'Jonas Geiping', 'Micah Goldblum', 'Aniruddha Saha', 'Manli Shu', 'John Kirchenbauer', 'Yuxin Wen', 'Khalid Saifullah', 'Neel Jain'] | 2023-06-23 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-1.51274085e-01 -1.38967363e-02 -3.09904367e-01 -7.29678333e-01
-1.12000787e+00 -1.01056552e+00 6.12079144e-01 2.94570237e-01
-6.09632194e-01 6.88079715e-01 8.26501325e-02 -4.29130733e-01
1.50830030e-01 -5.41312456e-01 -3.84213835e-01 -1.21418945e-01
1.92123890e-01 9.58783567e-01 1.11507669e-01 -2.76065379... | [12.588116645812988, 7.917078495025635] |
18a8dc4b-4708-482e-b017-07ab0f777ae7 | triplenet-triple-attention-network-for-multi | 1909.10666 | null | https://arxiv.org/abs/1909.10666v2 | https://arxiv.org/pdf/1909.10666v2.pdf | TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots | We consider the importance of different utterances in the context for selecting the response usually depends on the current query. In this paper, we propose the model TripleNet to fully model the task with the triple <context, query, response> instead of <context, response> in previous works. The heart of TripleNet is ... | ['Wei-Nan Zhang', 'Shijin Wang', 'Yiming Cui', 'Ting Liu', 'Su He', 'Nan Shao', 'Guoping Hu', 'Wentao Ma'] | 2019-09-24 | triplenet-triple-attention-network-for-multi-1 | https://aclanthology.org/K19-1069 | https://aclanthology.org/K19-1069.pdf | conll-2019-11 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 6.91642538e-02 -2.29807898e-01 -3.12925935e-01 -7.38672733e-01
-1.04841626e+00 -2.41420791e-01 2.88050503e-01 1.62632734e-01
-4.53435898e-01 4.37811464e-01 6.26758039e-01 -2.01409563e-01
6.72533214e-02 -4.16938603e-01 -5.31117558e-01 -3.62972379e-01
5.33017278e-01 5.85028291e-01 5.26112497e-01 -5.35468757... | [12.362241744995117, 7.837774753570557] |
9f0beeb1-2ff1-4520-93bc-b13829d00d7a | ssl4eo-s12-a-large-scale-multi-modal-multi | 2211.07044 | null | https://arxiv.org/abs/2211.07044v2 | https://arxiv.org/pdf/2211.07044v2.pdf | SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation | Self-supervised pre-training bears potential to generate expressive representations without human annotation. Most pre-training in Earth observation (EO) are based on ImageNet or medium-size, labeled remote sensing (RS) datasets. We share an unlabeled RS dataset SSL4EO-S12 (Self-Supervised Learning for Earth Observatio... | ['Xiao Xiang Zhu', 'Conrad M Albrecht', 'Chenying Liu', 'Zhitong Xiong', 'Nassim Ait Ali Braham', 'Yi Wang'] | 2022-11-13 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.66418725e-01 1.77144215e-01 -1.16898753e-01 -7.77256846e-01
-9.49807286e-01 -6.19562745e-01 6.76648200e-01 1.96575105e-01
-4.39093918e-01 7.16591418e-01 4.02387708e-01 -7.26831555e-01
-5.78224175e-02 -9.55272079e-01 -6.05706871e-01 -4.85036612e-01
-7.61730134e-01 2.77684689e-01 -4.63328987e-01 -6.42569304... | [9.576154708862305, -1.4506242275238037] |
6107ed92-b2a1-42e3-aef4-fa25ed25c176 | fine-robotic-manipulation-without-force | 2301.13413 | null | https://arxiv.org/abs/2301.13413v1 | https://arxiv.org/pdf/2301.13413v1.pdf | Fine Robotic Manipulation without Force/Torque Sensor | Force Sensing and Force Control are essential to many industrial applications. Typically, a 6-axis Force/Torque (F/T) sensor is mounted between the robot's wrist and the end-effector in order to measure the forces and torques exerted by the environment onto the robot (the external wrench). Although a typical 6-axis F/T... | ['Quang-Cuong Pham', 'Shilin Shan'] | 2023-01-31 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 3.08161825e-01 1.06791377e-01 -1.86093360e-01 1.35689810e-01
-1.88212365e-01 -5.78068793e-01 1.90374210e-01 -1.04923882e-01
-4.52224642e-01 5.73704779e-01 -4.51215595e-01 -1.59756511e-01
-3.82273853e-01 -4.95100200e-01 -8.56212199e-01 -4.55686331e-01
8.17867462e-03 4.55772728e-01 2.54722327e-01 -3.58790070... | [4.880010604858398, 1.24251127243042] |
4e658c96-cf09-402f-99c1-471031b66f7a | towards-high-order-complementary | 2212.04966 | null | https://arxiv.org/abs/2212.04966v1 | https://arxiv.org/pdf/2212.04966v1.pdf | Towards High-Order Complementary Recommendation via Logical Reasoning Network | Complementary recommendation gains increasing attention in e-commerce since it expedites the process of finding frequently-bought-with products for users in their shopping journey. Therefore, learning the product representation that can reflect this complementary relationship plays a central role in modern recommender ... | ['Dawei Zhou', 'Yao Zhou', 'Longfeng Wu'] | 2022-12-09 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-1.05883954e-02 -2.84064919e-01 -5.83868921e-01 -9.02229965e-01
-1.75525129e-01 -9.33298349e-01 6.10400558e-01 4.20686930e-01
-6.22117408e-02 -4.73072156e-02 4.68202353e-01 -4.11952406e-01
-9.05273914e-01 -1.15111279e+00 -5.97009838e-01 -2.48995319e-01
-1.99986950e-01 4.27161783e-01 -1.99886575e-01 -6.49785638... | [10.108487129211426, 5.747125625610352] |
8d3af08d-ee04-44a1-939e-c7d82ada250a | double-and-single-descent-in-causal-inference | 2305.00700 | null | https://arxiv.org/abs/2305.00700v1 | https://arxiv.org/pdf/2305.00700v1.pdf | Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control | Motivated by a recent literature on the double-descent phenomenon in machine learning, we consider highly over-parametrized models in causal inference, including synthetic control with many control units. In such models, there may be so many free parameters that the model fits the training data perfectly. As a motivati... | ['Amar Venugopal', 'Guido Imbens', 'Jann Spiess'] | 2023-05-01 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 2.47415692e-01 4.28787947e-01 -8.22806418e-01 -4.40705717e-01
-1.04244065e+00 -2.38734707e-01 7.64272809e-01 8.78003240e-03
-4.53051746e-01 1.30615389e+00 7.38170743e-01 -2.71708876e-01
-5.17076552e-01 -8.62274289e-01 -1.02958751e+00 -7.61100173e-01
-4.79193516e-02 5.26027203e-01 -5.67962945e-01 1.89359169... | [7.965087890625, 5.245583534240723] |
7daf0f3e-5772-4d01-88df-95f954767be2 | tracking-public-attitudes-toward-chatgpt-on | 2306.12951 | null | https://arxiv.org/abs/2306.12951v1 | https://arxiv.org/pdf/2306.12951v1.pdf | Tracking public attitudes toward ChatGPT on Twitter using sentiment analysis and topic modeling | ChatGPT sets a new record with the fastest-growing user base, as a chatbot powered by a large language model (LLM). While it demonstrates state-of-the-art capabilities in a variety of language-generating tasks, it also raises widespread public concerns regarding its societal impact. In this paper, we utilize natural la... | ['Hyeju Jang', 'Yanling Pan', 'Ratanond Koonchanok'] | 2023-06-22 | null | null | null | null | ['chatbot', 'question-answering', 'sentiment-analysis', 'chatbot'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-3.46801192e-01 4.44283277e-01 -5.52756727e-01 -1.79551467e-01
-9.70618606e-01 -2.75893450e-01 9.81209159e-01 4.85025406e-01
-3.21799219e-01 8.54981184e-01 7.24161506e-01 -5.21665692e-01
1.68958575e-01 -8.24685693e-01 5.53221069e-02 -2.87677884e-01
4.52783465e-01 4.19648230e-01 1.96851000e-01 -8.54830325... | [10.758415222167969, 7.309314250946045] |
a24faf5e-9330-448a-8d0b-ec5d7243d7aa | a-survey-of-some-density-based-clustering | 2306.09256 | null | https://arxiv.org/abs/2306.09256v1 | https://arxiv.org/pdf/2306.09256v1.pdf | A Survey of Some Density Based Clustering Techniques | Density Based Clustering are a type of Clustering methods using in data mining for extracting previously unknown patterns from data sets. There are a number of density based clustering methods such as DBSCAN, OPTICS, DENCLUE, VDBSCAN, DVBSCAN, DBCLASD and ST-DBSCAN. In this paper, a study of these methods is done along... | ['Samarjeet Borah', 'Rupanka Bhuyan'] | 2023-06-15 | null | null | null | null | ['clustering'] | ['methodology'] | [-7.44591117e-01 -5.13089597e-01 -7.96090364e-02 -5.71107745e-01
1.35572508e-01 -5.10049462e-01 4.98859674e-01 3.98231745e-01
-1.95222512e-01 1.05272460e+00 2.76352078e-01 -2.73427069e-01
-1.07689941e+00 -9.89642203e-01 3.32379565e-02 -8.47866416e-01
-5.08010745e-01 1.16996503e+00 8.16627145e-01 2.44217053... | [7.478668689727783, 4.542597770690918] |
59218a8e-d616-45be-a15a-f0db0a49d3db | multimodal-pathophysiological-dataset-of | 2002.09154 | null | https://arxiv.org/abs/2002.09154v2 | https://arxiv.org/pdf/2002.09154v2.pdf | Multimodal pathophysiological dataset of gradual cerebral ischemia in a cohort of juvenile pigs | Ischemic brain injuries are frequent and difficult to detect reliably or early. We present the multi-modal data set containing cardiovascular (blood pressure, blood flow, electrocardiogram) and brain electrical activities to derive electroencephalogram (EEG) biomarkers of corticothalamic communication under normal, sed... | ['Reinhard Bauer', 'Christophe L. Herry', 'Bernd Walter', 'Martin G. Frasch'] | 2020-02-21 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 6.81339353e-02 -4.81847554e-01 5.84474444e-01 -1.81745395e-01
-2.88569123e-01 -3.84915441e-01 1.55549601e-01 2.91340351e-01
-4.66242880e-01 9.29117322e-01 3.22094440e-01 3.45375948e-02
-5.67887604e-01 -4.27044719e-01 -2.88097233e-01 -9.36054230e-01
-9.45887566e-01 -1.73308089e-01 -3.55043143e-01 4.44369838... | [13.216248512268066, 3.3816192150115967] |
4a6997dc-bc46-49da-a2b0-706c8aad5b02 | deuteros-2-0-peptide-level-significance | 2005.08380 | null | http://arxiv.org/abs/2005.08380v1 | http://arxiv.org/pdf/2005.08380v1.pdf | Deuteros 2.0: Peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometry | Summary: Hydrogen deuterium exchange mass spectrometry (HDX-MS) is becoming
increasing routine for monitoring changes in the structural dynamics of
proteins. Differential HDX-MS allows comparison of individual protein states,
such as in the absence or presence of a ligand. This can be used to attribute
changes in confo... | [] | 2020-05-17 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 9.58487913e-02 -3.85117978e-01 -1.84242904e-01 -5.11796236e-01
-4.37887698e-01 -7.11583614e-01 3.59078467e-01 7.74907172e-01
-3.80636722e-01 8.32538307e-01 -1.81487277e-01 -6.55672789e-01
2.57211309e-02 -3.98036420e-01 -4.61313516e-01 -6.24677837e-01
-2.55955070e-01 7.88002372e-01 3.12965035e-01 -1.59173116... | [4.781728267669678, 5.385626316070557] |
bc1adf68-b64b-4fb9-a42b-c577b66c3729 | animc-a-soft-framework-for-auto-weighted | 2011.10331 | null | https://arxiv.org/abs/2011.10331v3 | https://arxiv.org/pdf/2011.10331v3.pdf | ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering | Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods. However, missing instances may make these methods difficult to use directly and noises will lead to ... | ['Dapeng Oliver Wu', 'Pan Zhou', 'Yuchong Hu', 'Xiang Fang'] | 2020-11-20 | null | null | null | null | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [ 9.64818057e-03 -5.87616086e-01 6.85917512e-02 -3.87223572e-01
-5.19061923e-01 -2.66143769e-01 9.83311459e-02 -3.28796417e-01
-2.48846307e-01 1.93607897e-01 4.05867100e-01 2.29663193e-01
-3.83724719e-01 -5.26480913e-01 -2.86361635e-01 -1.18553865e+00
4.69255030e-01 2.80960143e-01 1.76351666e-01 -7.38133267... | [8.328559875488281, 4.613122463226318] |
e8f7ebb7-62a1-4ddb-9996-6a9be75f13b0 | data-driven-mitigation-of-adversarial-text | 2202.09483 | null | https://arxiv.org/abs/2202.09483v1 | https://arxiv.org/pdf/2202.09483v1.pdf | Data-Driven Mitigation of Adversarial Text Perturbation | Social networks have become an indispensable part of our lives, with billions of people producing ever-increasing amounts of text. At such scales, content policies and their enforcement become paramount. To automate moderation, questionable content is detected by Natural Language Processing (NLP) classifiers. However, ... | ['Igor Markov', 'Anand Bhaskar', 'Mohammad Al-Rubaie', 'Rasika Bhalerao'] | 2022-02-19 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 2.12680608e-01 2.31373250e-01 -4.19997811e-01 2.11973675e-02
-5.60156763e-01 -1.08642650e+00 1.16939628e+00 7.55165100e-01
-5.44034481e-01 5.92979491e-01 8.03019762e-01 -5.91990829e-01
2.98509032e-01 -1.01149869e+00 -4.60328996e-01 -1.94522396e-01
1.51451007e-01 1.34478882e-01 1.49466112e-01 -4.85500515... | [8.3579683303833, 10.25439167022705] |
ddfd3d56-c22d-4efa-8c01-2e42199edcf4 | real-time-patient-specific-ecg-classification | 2110.02215 | null | https://arxiv.org/abs/2110.02215v1 | https://arxiv.org/pdf/2110.02215v1.pdf | Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks | Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge t... | ['Moncef Gabbouj', 'Turker Ince', 'Serkan Kiranyaz', 'Ozer Can Devecioglu', 'Junaid Malik'] | 2021-09-30 | null | null | null | null | ['arrhythmia-detection', 'ecg-classification'] | ['medical', 'medical'] | [ 2.34792084e-02 3.25957723e-02 -1.34634480e-01 -2.32672691e-01
-4.30082351e-01 -3.11145961e-01 -1.92567304e-01 2.48973817e-01
-3.81543845e-01 7.61442304e-01 -3.94321084e-01 -3.68502468e-01
-5.06447196e-01 -4.26638186e-01 -3.44376087e-01 -5.43087959e-01
-4.45950925e-01 4.08335954e-01 -1.44993901e-01 4.63158078... | [14.263278007507324, 3.2765533924102783] |
f41be975-06c4-448b-8682-3068c6d48a81 | gligen-open-set-grounded-text-to-image | 2301.07093 | null | https://arxiv.org/abs/2301.07093v2 | https://arxiv.org/pdf/2301.07093v2.pdf | GLIGEN: Open-Set Grounded Text-to-Image Generation | Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained tex... | ['Yong Jae Lee', 'Chunyuan Li', 'Jianfeng Gao', 'Jianwei Yang', 'Fangzhou Mu', 'Qingyang Wu', 'Haotian Liu', 'Yuheng Li'] | 2023-01-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_GLIGEN_Open-Set_Grounded_Text-to-Image_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_GLIGEN_Open-Set_Grounded_Text-to-Image_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-inpainting'] | ['computer-vision'] | [ 4.06593800e-01 3.15668672e-01 -2.37913623e-01 -1.67943642e-01
-7.67160118e-01 -9.18789089e-01 1.08658206e+00 -5.76251335e-02
-1.54212549e-01 7.68222749e-01 5.25944173e-01 -4.09074068e-01
2.83467084e-01 -9.17453766e-01 -8.63683760e-01 -5.00849426e-01
9.60658193e-02 5.72363973e-01 4.17115569e-01 -5.40144145... | [11.270124435424805, -0.07982894778251648] |
b1bda8d5-af23-42d1-a6f2-1a4213a5e5a6 | a-review-on-generative-adversarial-networks-1 | 2302.09119 | null | https://arxiv.org/abs/2302.09119v3 | https://arxiv.org/pdf/2302.09119v3.pdf | A Review on Generative Adversarial Networks for Data Augmentation in Person Re-Identification Systems | Interest in automatic people re-identification systems has significantly grown in recent years, mainly for developing surveillance and smart shops software. Due to the variability in person posture, different lighting conditions, and occluded scenarios, together with the poor quality of the images obtained by different... | ['Laura Alvarez-Gonzalez', 'Anabel Martin-Gonzalez', 'Victor Uc-Cetina'] | 2023-02-17 | null | null | null | null | ['person-re-identification', 'pose-transfer'] | ['computer-vision', 'computer-vision'] | [ 4.98896599e-01 -4.49839979e-03 2.03035265e-01 -2.46621490e-01
-6.73952922e-02 -6.74026906e-01 8.61599684e-01 -1.05171010e-01
-5.51811576e-01 8.88544321e-01 1.41692653e-01 3.54205340e-01
2.21092343e-01 -7.36191154e-01 -6.20865464e-01 -5.99576175e-01
4.34146762e-01 9.38244402e-01 -1.55935779e-01 -2.69981027... | [14.665138244628906, 1.004502296447754] |
f2f23280-9030-4a3e-8c5a-dc2af6105bc5 | doc2edag-an-end-to-end-document-level | 1904.07535 | null | https://arxiv.org/abs/1904.07535v2 | https://arxiv.org/pdf/1904.07535v2.pdf | Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction | Most existing event extraction (EE) methods merely extract event arguments within the sentence scope. However, such sentence-level EE methods struggle to handle soaring amounts of documents from emerging applications, such as finance, legislation, health, etc., where event arguments always scatter across different sent... | ['Wei Xu', 'Shun Zheng', 'Wei Cao', 'Jiang Bian'] | 2019-04-16 | doc2edag-an-end-to-end-document-level-1 | https://aclanthology.org/D19-1032 | https://aclanthology.org/D19-1032.pdf | ijcnlp-2019-11 | ['document-level-event-extraction'] | ['natural-language-processing'] | [-1.97070524e-01 8.75634514e-03 -1.49450243e-01 -4.70192045e-01
-1.00879121e+00 -7.62860000e-01 6.90556884e-01 5.72615862e-01
-3.34887654e-01 7.17870891e-01 7.85350025e-01 -5.75132191e-01
-1.27657354e-01 -1.00147092e+00 -5.05596161e-01 -1.75607473e-01
-1.53612047e-02 1.40748858e-01 4.66795057e-01 -2.10464731... | [9.066614151000977, 9.174013137817383] |
167a5efb-f515-4fec-aa46-ac89430fc56e | audio-source-separation-using-a-deep | 1412.7193 | null | http://arxiv.org/abs/1412.7193v1 | http://arxiv.org/pdf/1412.7193v1.pdf | Audio Source Separation Using a Deep Autoencoder | This paper proposes a novel framework for unsupervised audio source
separation using a deep autoencoder. The characteristics of unknown source
signals mixed in the mixed input is automatically by properly configured
autoencoders implemented by a network with many layers, and separated by
clustering the coefficient vect... | ['Yung-Hwan Oh', 'Han-Gyu Kim', 'Giljin Jang'] | 2014-12-22 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 6.66038021e-02 -1.00549959e-01 2.80607671e-01 -5.41860685e-02
-3.90298009e-01 -4.13406223e-01 1.21845797e-01 -3.70892650e-03
1.41584137e-02 4.23123896e-01 3.69591922e-01 4.26958352e-01
-5.36171198e-01 -6.52978957e-01 -2.69546568e-01 -1.22707808e+00
-5.19391179e-01 1.68099165e-01 1.80475637e-02 -1.23144269... | [15.388568878173828, 5.678995609283447] |
62869af5-ac49-417a-a8df-b8c2f6eb1bf3 | spartans-lt-edi-eacl2021-inclusive-speech | null | null | https://aclanthology.org/2021.ltedi-1.28 | https://aclanthology.org/2021.ltedi-1.28.pdf | Spartans@LT-EDI-EACL2021: Inclusive Speech Detection using Pretrained Language Models | We describe our system that ranked first in Hope Speech Detection (HSD) shared task and fourth in Offensive Language Identification (OLI) shared task, both in Tamil language. The goal of HSD and OLI is to identify if a code-mixed comment or post contains hope speech or offensive content respectively. We pre-train a tra... | ['Gaurav Arora', 'Megha Sharma'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-1.2813293e-02 1.3988262e-01 -1.9950454e-01 -3.4774326e-02
-1.5886772e+00 -8.3222026e-01 1.0682065e+00 1.9955160e-01
-1.3524994e-01 2.3953508e-01 8.3203930e-01 -1.0409194e+00
4.5472875e-01 -1.2889838e-01 -3.5260081e-01 -1.5267566e-01
-9.1940634e-02 5.6297588e-01 -1.5571526e-01 -6.0603738e-01
4.4517061e-01... | [9.137288093566895, 10.616043090820312] |
3db80d62-beef-4bd0-b3e6-05eb6c8bef1e | plasticity-and-evolvability-under | 2202.08834 | null | https://arxiv.org/abs/2202.08834v3 | https://arxiv.org/pdf/2202.08834v3.pdf | Plasticity and evolvability under environmental variability: the joint role of fitness-based selection and niche-limited competition | The diversity and quality of natural systems have been a puzzle and inspiration for communities studying artificial life. It is now widely admitted that the adaptation mechanisms enabling these properties are largely influenced by the environments they inhabit. Organisms facing environmental variability have two altern... | ['Clément Moulin-Frier', 'Eleni Nisioti'] | 2022-02-17 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.12093940e-01 -3.08519661e-01 1.76685467e-01 1.26819208e-01
8.55746269e-01 -6.97744250e-01 6.38900936e-01 3.28045398e-01
-6.30224109e-01 1.05091989e+00 -1.05286427e-01 -3.06465507e-01
-3.56656134e-01 -9.65450227e-01 -6.60855114e-01 -1.10113418e+00
-4.21878248e-01 2.26056799e-01 4.49358076e-01 -5.40222168... | [5.6186089515686035, 4.160006999969482] |
1a1af09a-b73b-49ae-9376-a7aeed9215e5 | mutux-at-semeval-2018-task-1-exploring | null | null | https://aclanthology.org/S18-1052 | https://aclanthology.org/S18-1052.pdf | Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection | This paper describes MuTuX, our system that is designed for task 1-5a, emotion classification analysis of tweets on SemEval2018. The system aims at exploring the potential of context information of terms for emotion analysis. A Recurrent Neural Network is adopted to capture the context information of terms in tweets. O... | ['Jian-Yun Nie', 'Pan Du'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['product-recommendation'] | ['miscellaneous'] | [-5.76555468e-02 -7.72909373e-02 -1.50346771e-01 -6.70789480e-01
-2.58617282e-01 -2.19297424e-01 6.88464105e-01 4.97725427e-01
-7.50823915e-01 3.47458005e-01 4.59107518e-01 -2.94885159e-01
2.26718441e-01 -4.53001916e-01 -5.61741106e-02 -3.32344055e-01
-5.43316543e-01 -2.27136821e-01 -6.06771588e-01 -8.23773026... | [12.913999557495117, 6.203357696533203] |
66077df1-de3a-4247-bcee-2812681278fa | extract-select-and-rewrite-a-new-modular | null | null | https://openreview.net/forum?id=fg1zmL4XRu2 | https://openreview.net/pdf?id=fg1zmL4XRu2 | Extract, Select and Rewrite: A New Modular Summarization Method | Prior works on supervised summarization are mainly based on end-to-end models, leading to low modularity, unfaithfulness and low interpretability. To address this, we propose a new three-phase modular abstractive sentence summarization method.We split up the summarization problem explicitly into three stages, namely kn... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 4.01276022e-01 7.52877355e-01 -4.86974925e-01 -2.57446259e-01
-1.04843366e+00 -7.48102307e-01 6.17623627e-01 4.35635716e-01
-3.12087119e-01 8.41389179e-01 9.50493097e-01 5.06016314e-02
2.27343515e-01 -5.28675795e-01 -7.51601636e-01 8.84319395e-02
2.27993056e-01 5.46121001e-01 2.10156471e-01 -3.45926523... | [12.44015121459961, 9.44245433807373] |
9d43878a-cc2b-4c36-b53e-60c3336f1bf2 | chatbot-with-a-discourse-structure-driven | null | null | https://aclanthology.org/E17-3022 | https://aclanthology.org/E17-3022.pdf | Chatbot with a Discourse Structure-Driven Dialogue Management | We build a chat bot with iterative content exploration that leads a user through a personalized knowledge acquisition session. The chat bot is designed as an automated customer support or product recommendation agent assisting a user in learning product features, product usability, suitability, troubleshooting and othe... | ['Boris Galitsky', 'Dmitry Ilvovsky'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['product-recommendation'] | ['miscellaneous'] | [ 6.93946257e-02 8.00031900e-01 -3.32720220e-01 -3.45673919e-01
-5.27184486e-01 -1.07912278e+00 6.00748062e-01 5.20111382e-01
1.49705768e-01 3.58472615e-01 5.66258907e-01 -6.46454632e-01
-1.14581995e-01 -7.99375713e-01 -1.13579467e-01 -3.12429398e-01
2.64467776e-01 6.52487397e-01 4.05983955e-01 -3.21743876... | [12.791146278381348, 7.985222339630127] |
ee91854c-254d-4f3e-9497-10cafca501fa | a-causal-framework-to-quantify-the-robustness | 2210.12023 | null | https://arxiv.org/abs/2210.12023v3 | https://arxiv.org/pdf/2210.12023v3.pdf | A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models | We have recently witnessed a number of impressive results on hard mathematical reasoning problems with language models. At the same time, the robustness of these models has also been called into question; recent works have shown that models can rely on shallow patterns in the problem description when generating a solut... | ['Mrinmaya Sachan', 'Bernhard Schölkopf', 'Kumar Shridhar', 'Zhijing Jin', 'Alessandro Stolfo'] | 2022-10-21 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.21334471e-01 3.38776141e-01 1.25367597e-01 -9.24917310e-02
-2.28635043e-01 -9.41342652e-01 7.05277026e-01 5.15677392e-01
1.18456557e-01 3.45537722e-01 1.34264916e-01 -8.16779196e-01
-4.18868840e-01 -1.32074404e+00 -1.06261456e+00 -1.07185163e-01
-1.43257171e-01 3.14805686e-01 4.36929226e-01 -5.12230814... | [9.154844284057617, 7.233509540557861] |
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