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178a31b6-8df8-494e-9c56-be13052a4a0a | teaser-towards-efficient-aspect-based | null | null | https://aclanthology.org/2021.ranlp-main.13 | https://aclanthology.org/2021.ranlp-main.13.pdf | TEASER: Towards Efficient Aspect-based SEntiment Analysis and Recognition | Sentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspect-based sentiment analysis aims to extract the aspects of the given target entities and their respective sentiments. Prior works formulat... | ['Radhika Mamidi', 'Srinath Nair', 'Ishan Upadhyay', 'Kartikey Pant', 'Vaibhav Bajaj'] | null | null | https://aclanthology.org/2021.ranlp-1.13 | https://aclanthology.org/2021.ranlp-1.13.pdf | ranlp-2021-9 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 3.13783944e-01 -3.80063243e-02 -5.00291526e-01 -6.33007407e-01
-8.14219713e-01 -1.01958048e+00 5.47994852e-01 5.58097720e-01
-4.73852426e-01 8.68423939e-01 4.46421266e-01 -2.06460461e-01
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3.38172555e-01 1.12610646e-01 2.51741081e-01 -4.90295351... | [11.468026161193848, 6.637418270111084] |
c872439d-526c-4cc4-af14-816f73880102 | tensors-learning-and-kolmogorov-extension-for | 1712.00205 | null | http://arxiv.org/abs/1712.00205v2 | http://arxiv.org/pdf/1712.00205v2.pdf | Tensors, Learning, and 'Kolmogorov Extension' for Finite-alphabet Random Vectors | Estimating the joint probability mass function (PMF) of a set of random
variables lies at the heart of statistical learning and signal processing.
Without structural assumptions, such as modeling the variables as a Markov
chain, tree, or other graphical model, joint PMF estimation is often considered
mission impossible... | ['Nikos Kargas', 'Xiao Fu', 'Nicholas D. Sidiropoulos'] | 2017-12-01 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 3.49347889e-01 1.84952512e-01 1.53926492e-03 -2.51076072e-02
-7.92197406e-01 -7.52377629e-01 3.55654627e-01 -2.63893837e-03
-3.20865899e-01 7.76501477e-01 -8.47875327e-02 -4.57754165e-01
-7.64868617e-01 -4.42225188e-01 -6.96694970e-01 -1.11875165e+00
-3.83503884e-01 7.88468838e-01 -2.79337615e-01 2.19869092... | [7.021578788757324, 4.432036876678467] |
7838c60e-8658-4b8c-8d09-ecd9453fca4a | towards-universal-vision-language-omni | 2303.06547 | null | https://arxiv.org/abs/2303.06547v1 | https://arxiv.org/pdf/2303.06547v1.pdf | Towards Universal Vision-language Omni-supervised Segmentation | Existing open-world universal segmentation approaches usually leverage CLIP and pre-computed proposal masks to treat open-world segmentation tasks as proposal classification. However, 1) these works cannot handle universal segmentation in an end-to-end manner, and 2) the limited scale of panoptic datasets restricts the... | ['WangMeng Zuo', 'Hang Xu', 'Jianhua Han', 'Jiaxi Gu', 'Bowen Dong'] | 2023-03-12 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 9.86623764e-02 -1.17498092e-01 -7.06077576e-01 -4.65962172e-01
-6.78537786e-01 -7.61290789e-01 1.92876205e-01 -3.53940785e-01
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-1.13519104e-02 6.21437788e-01 6.93099916e-01 1.76491722... | [9.527199745178223, 0.29990172386169434] |
0f6d98cc-5139-4590-8541-077465f50767 | pairwise-learning-for-neural-link-prediction | 2112.02936 | null | https://arxiv.org/abs/2112.02936v6 | https://arxiv.org/pdf/2112.02936v6.pdf | Pairwise Learning for Neural Link Prediction | In this paper, we aim at providing an effective Pairwise Learning Neural Link Prediction (PLNLP) framework. The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.e., neighborhood encoder, link predictor, negative sampler and objective function. The framework... | ['Shouzhi Chen', 'Hanjing Su', 'Yuanhang Zou', 'Litao Hong', 'Yong Zhou', 'Zhitao Wang'] | 2021-12-06 | null | null | null | null | ['link-property-prediction'] | ['graphs'] | [-2.85192043e-01 1.98462293e-01 -9.64093983e-01 -5.10958016e-01
-6.57308519e-01 -3.62413287e-01 3.77082944e-01 2.50574708e-01
4.26218696e-02 1.28885961e+00 1.16469003e-01 -4.50523913e-01
-7.12224245e-01 -1.22582996e+00 -9.84503210e-01 -3.79378945e-01
-6.57198370e-01 8.66011262e-01 5.21690965e-01 -1.28069520... | [7.306596279144287, 6.334572792053223] |
258016df-94df-4e7a-924a-898dac6f0b1e | probabilistic-bilevel-coreset-selection | 2301.0988 | null | https://arxiv.org/abs/2301.09880v1 | https://arxiv.org/pdf/2301.09880v1.pdf | Probabilistic Bilevel Coreset Selection | The goal of coreset selection in supervised learning is to produce a weighted subset of data, so that training only on the subset achieves similar performance as training on the entire dataset. Existing methods achieved promising results in resource-constrained scenarios such as continual learning and streaming. Howeve... | ['Tong Zhang', 'Yong Lin', 'Weizhong Zhang', 'Renjie Pi', 'Xiao Zhou'] | 2023-01-24 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [ 2.91006386e-01 -2.06477582e-01 -6.07819259e-01 -4.50010300e-01
-9.47844923e-01 -7.03374222e-02 8.28585327e-02 2.85077631e-01
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-3.90445679e-01 -6.11786902e-01 -5.63232303e-01 -9.07146096e-01
1.66769460e-01 9.57680821e-01 1.34629318e-02 1.96965396... | [8.452035903930664, 4.138492107391357] |
7a9ba174-637b-47cd-9d22-195464de271f | building-a-tocfl-learner-corpus-for-chinese | null | null | https://aclanthology.org/L18-1363 | https://aclanthology.org/L18-1363.pdf | Building a TOCFL Learner Corpus for Chinese Grammatical Error Diagnosis | null | ['Li-Ping Chang', 'Yuen-Hsien Tseng', 'Lung-Hao Lee'] | 2018-05-01 | building-a-tocfl-learner-corpus-for-chinese-1 | https://aclanthology.org/L18-1363 | https://aclanthology.org/L18-1363.pdf | lrec-2018-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.229593276977539, 3.7639360427856445] |
68ff7c56-6f04-4de3-b70e-1ed138e1d9a7 | plug-and-play-priors-for-bright-field | 1512.07331 | null | http://arxiv.org/abs/1512.07331v1 | http://arxiv.org/pdf/1512.07331v1.pdf | Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation | Many material and biological samples in scientific imaging are characterized
by non-local repeating structures. These are studied using scanning electron
microscopy and electron tomography. Sparse sampling of individual pixels in a
2D image acquisition geometry, or sparse sampling of projection images with
large tilt i... | ['Charles A. Bouman', 'Lawrence F. Drummy', 'S. V. Venkatakrishnan', 'Suhas Sreehari', 'Jeffrey P. Simmons', 'Brendt Wohlberg'] | 2015-12-23 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 6.27263963e-01 -2.83237875e-01 2.93363333e-01 -3.10544729e-01
-6.94567978e-01 -4.40573618e-02 3.83092076e-01 -3.62606436e-01
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-2.70088375e-01 -4.41421837e-01 -5.84948242e-01 -1.10993564e+00
2.13636994e-01 6.35184050e-01 2.34324172e-01 2.23794907... | [12.857656478881836, -2.7907793521881104] |
faad786a-8596-43cc-b16c-4e05e2c4ef64 | finegym-a-hierarchical-video-dataset-for-fine | 2004.06704 | null | https://arxiv.org/abs/2004.06704v1 | https://arxiv.org/pdf/2004.06704v1.pdf | FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding | On public benchmarks, current action recognition techniques have achieved great success. However, when used in real-world applications, e.g. sport analysis, which requires the capability of parsing an activity into phases and differentiating between subtly different actions, their performances remain far from being sat... | ['Dahua Lin', 'Dian Shao', 'Bo Dai', 'Yue Zhao'] | 2020-04-14 | finegym-a-hierarchical-video-dataset-for-fine-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.pdf | cvpr-2020-6 | ['action-understanding'] | ['computer-vision'] | [ 4.04777497e-01 -8.60670954e-02 -5.35291135e-01 -4.94553834e-01
-6.13329291e-01 -7.17036843e-01 7.32006669e-01 2.01276109e-01
-1.87670380e-01 6.93833709e-01 6.78606033e-01 2.00605039e-02
-3.06565106e-01 -7.27443814e-01 -3.30100387e-01 -6.02388024e-01
-1.72306567e-01 3.82178336e-01 5.24869978e-01 -4.49601710... | [8.212789535522461, 0.562946081161499] |
537a8738-1fa3-414c-b14f-ce24b02b09cf | counterfactual-reasoning-testing-language | 2305.16572 | null | https://arxiv.org/abs/2305.16572v1 | https://arxiv.org/pdf/2305.16572v1.pdf | Counterfactual reasoning: Testing language models' understanding of hypothetical scenarios | Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on the understanding of real world. We tease these factors apart by leveraging counterfactual condition... | ['Allyson Ettinger', 'Lang Yu', 'Jiaxuan Li'] | 2023-05-26 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 8.32511634e-02 5.91213107e-01 -2.22424686e-01 -3.38560551e-01
-6.22247517e-01 -6.76489234e-01 1.21795559e+00 3.09852839e-01
-5.83106399e-01 1.28257859e+00 9.90870297e-01 -9.30694282e-01
-2.75936782e-01 -1.09932673e+00 -1.04302597e+00 -1.59902334e-01
-3.54809880e-01 2.36255527e-01 1.27470434e-01 -4.27086860... | [9.908065795898438, 7.8842973709106445] |
7a50ba8c-0eb9-4fe3-ad32-216fc323596d | finsbd-2020-the-2nd-shared-task-on-sentence | null | null | https://aclanthology.org/2020.finnlp-1.8 | https://aclanthology.org/2020.finnlp-1.8.pdf | FinSBD-2020: The 2nd Shared Task on Sentence Boundary Detection in Unstructured Text in the Financial Domain | null | ['Dialekti Valsamou-Stanislawski', 'Abderrahim Ait Azzi', 'Bianca Chong', 'Willy Au'] | null | null | null | null | finnlp-coling-2020-1 | ['boundary-detection'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.475850582122803, 3.6605305671691895] |
613bde6b-68e8-41f8-8958-9c8f269dde24 | celldefectnet-a-machine-designed-attention | 2204.11766 | null | https://arxiv.org/abs/2204.11766v1 | https://arxiv.org/pdf/2204.11766v1.pdf | CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection | Photovoltaic cells are electronic devices that convert light energy to electricity, forming the backbone of solar energy harvesting systems. An essential step in the manufacturing process for photovoltaic cells is visual quality inspection using electroluminescence imaging to identify defects such as cracks, finger int... | ['Alexander Wong', 'Mohammad Javad Shafiee', 'Saeejith Nair', 'Gautam Bathla', 'Mahmoud Famouri', 'Carol Xu'] | 2022-04-25 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.42448235e-01 -3.05338204e-01 3.60559851e-01 3.21813405e-01
-2.91690588e-01 -6.33860171e-01 -9.44436714e-02 1.74733941e-02
-2.81641781e-01 8.86410832e-01 -6.10002816e-01 -5.20510733e-01
1.56962201e-01 -8.98180604e-01 -7.64663279e-01 -1.04091394e+00
4.41121608e-01 1.84328616e-01 -2.47627825e-01 3.60242315... | [7.316662788391113, 1.9077208042144775] |
ac98383b-1d08-4d7d-9496-062674cccc8b | e-vfia-event-based-video-frame-interpolation | 2209.09359 | null | https://arxiv.org/abs/2209.09359v3 | https://arxiv.org/pdf/2209.09359v3.pdf | E-VFIA : Event-Based Video Frame Interpolation with Attention | Video frame interpolation (VFI) is a fundamental vision task that aims to synthesize several frames between two consecutive original video images. Most algorithms aim to accomplish VFI by using only keyframes, which is an ill-posed problem since the keyframes usually do not yield any accurate precision about the trajec... | ['A. Aydin Alatan', 'Ahmet Akman', 'Onur Selim Kılıç'] | 2022-09-19 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 2.04215217e-02 -4.49096590e-01 1.65565044e-01 -8.00337270e-02
-3.64698112e-01 -3.79505269e-02 6.12473428e-01 -6.89844266e-02
-4.44975734e-01 9.59831834e-01 9.50061753e-02 2.92913616e-02
3.79592702e-02 -7.63413668e-01 -9.05633688e-01 -6.99046612e-01
-3.38681787e-02 -1.95893079e-01 6.06837273e-01 7.05187842... | [10.765256881713867, -1.533995270729065] |
554ee5bb-ca5e-4bc6-850d-85985289930d | brouhaha-multi-task-training-for-voice | 2210.13248 | null | https://arxiv.org/abs/2210.13248v3 | https://arxiv.org/pdf/2210.13248v3.pdf | Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation | Most automatic speech processing systems register degraded performance when applied to noisy or reverberant speech. But how can one tell whether speech is noisy or reverberant? We propose Brouhaha, a neural network jointly trained to extract speech/non-speech segments, speech-to-noise ratios, and C50room acoustics from... | ['Hervé Bredin', 'Emmanuel Dupoux', 'Alejandrina Cristia', 'Elika Bergelson', 'Morgane Rivière', 'Jade Copet', 'Alodie Boissonnet', 'Hadrien Titeux', 'Marianne Métais', 'Marvin Lavechin'] | 2022-10-24 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 3.00376415e-01 -1.04300067e-01 7.97796428e-01 -4.54064846e-01
-1.62273347e+00 -5.48927307e-01 4.21333045e-01 -1.55694753e-01
-3.63685876e-01 3.77949744e-01 5.75404346e-01 -5.63516855e-01
2.12825209e-01 -1.15014814e-01 -6.20469928e-01 -8.39450836e-01
7.88646713e-02 1.99847668e-01 4.84664738e-02 -8.83045420... | [14.837136268615723, 6.092375755310059] |
133586ac-b660-405c-9d12-a8181c06a7b9 | high-resolution-cloud-removal-with-multi | 2301.03432 | null | https://arxiv.org/abs/2301.03432v1 | https://arxiv.org/pdf/2301.03432v1.pdf | High-Resolution Cloud Removal with Multi-Modal and Multi-Resolution Data Fusion: A New Baseline and Benchmark | In this paper, we introduce Planet-CR, a benchmark dataset for high-resolution cloud removal with multi-modal and multi-resolution data fusion. Planet-CR is the first public dataset for cloud removal to feature globally sampled high resolution optical observations, in combination with paired radar measurements as well ... | ['Xiao Xiang Zhu', 'Wen Yang', 'Patrick Ebel', 'Yilei Shi', 'Fang Xu'] | 2023-01-09 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 4.34545487e-01 -8.57589662e-01 9.25383121e-02 -1.44989714e-01
-1.38365507e+00 -7.15019882e-01 4.88627166e-01 -1.20396987e-01
4.48415689e-02 6.64960921e-01 -1.37266787e-02 -1.80534005e-01
-2.49005765e-01 -1.18520749e+00 -3.45822513e-01 -9.14406300e-01
1.24957316e-01 1.48813546e-01 -3.78638902e-03 -3.41006428... | [9.895493507385254, -1.8516641855239868] |
08253bbc-fb10-457c-b5e8-f956ae46fbb8 | graph-community-detection-from-coarse | 2102.13135 | null | https://arxiv.org/abs/2102.13135v1 | https://arxiv.org/pdf/2102.13135v1.pdf | Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model | We study the problem of community recovery from coarse measurements of a graph. In contrast to the problem of community recovery of a fully observed graph, one often encounters situations when measurements of a graph are made at low-resolution, each measurement integrating across multiple graph nodes. Such low-resoluti... | ['Stark C. Draper', 'Gautam Dasarathy', 'Nafiseh Ghoroghchian'] | 2021-02-25 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 4.05496001e-01 3.52161348e-01 1.49921298e-01 2.32365847e-01
-5.79354942e-01 -8.03743005e-01 4.00065601e-01 4.84784126e-01
6.28985241e-02 7.20341325e-01 1.98372364e-01 1.49497427e-02
-3.71617317e-01 -1.08464646e+00 -7.32975066e-01 -8.19448411e-01
-4.42519337e-01 6.55420840e-01 1.18359715e-01 1.34890079... | [6.863042831420898, 5.117288589477539] |
b4c44a79-9dd9-4af4-a17b-538034095ebd | high-order-joint-embedding-for-multi-level | 2111.05265 | null | https://arxiv.org/abs/2111.05265v1 | https://arxiv.org/pdf/2111.05265v1.pdf | High-order joint embedding for multi-level link prediction | Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses. In contrast to traditional graph representation modeling which only predicts two-way pairwise relations, we propose a novel tensor-based joint network embedding approach on simultaneously encoding pa... | ['Annie Qu', 'Yubai Yuan'] | 2021-11-07 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-1.26428977e-01 5.01093566e-01 -7.35821128e-01 -2.10506558e-01
1.04886949e-01 -5.26798964e-01 4.80661690e-01 4.09318566e-01
2.17407271e-01 7.72783935e-01 4.70059365e-01 -2.45855615e-01
-9.06544149e-01 -1.25965941e+00 -2.64976859e-01 -4.08958942e-01
-7.51957417e-01 7.23807752e-01 4.04330909e-01 7.60059757... | [7.235590934753418, 6.016635417938232] |
a4b99d93-79f1-4c8c-9499-fe56a55b52a2 | zaebuc-an-annotated-arabic-english-bilingual | null | null | https://aclanthology.org/2022.lrec-1.9 | https://aclanthology.org/2022.lrec-1.9.pdf | ZAEBUC: An Annotated Arabic-English Bilingual Writer Corpus | We present ZAEBUC, an annotated Arabic-English bilingual writer corpus comprising short essays by first-year university students at Zayed University in the United Arab Emirates. We describe and discuss the various guidelines and pipeline processes we followed to create the annotations and quality check them. The annota... | ['David Palfreyman', 'Nizar Habash'] | null | null | null | null | lrec-2022-6 | ['lemmatization'] | ['natural-language-processing'] | [-1.65046826e-01 3.88331935e-02 -1.68223634e-01 -3.50138903e-01
-1.05446529e+00 -1.35399282e+00 6.12103164e-01 5.38056910e-01
-5.33469200e-01 8.78618240e-01 6.10686481e-01 -5.11892259e-01
3.06589250e-02 -3.47341418e-01 -2.10017357e-02 -2.71704942e-01
5.06638288e-01 6.65649116e-01 2.53408346e-02 -6.21278346... | [10.38989543914795, 10.285167694091797] |
c9cf7d36-b33e-4d79-bf5f-d3b6d1b2384b | high-fidelity-and-low-latency-universal | 2105.09856 | null | https://arxiv.org/abs/2105.09856v2 | https://arxiv.org/pdf/2105.09856v2.pdf | High-Fidelity and Low-Latency Universal Neural Vocoder based on Multiband WaveRNN with Data-Driven Linear Prediction for Discrete Waveform Modeling | This paper presents a novel high-fidelity and low-latency universal neural vocoder framework based on multiband WaveRNN with data-driven linear prediction for discrete waveform modeling (MWDLP). MWDLP employs a coarse-fine bit WaveRNN architecture for 10-bit mu-law waveform modeling. A sparse gated recurrent unit with ... | ['Tomoki Toda', 'Patrick Lumban Tobing'] | 2021-05-20 | null | null | null | null | ['low-latency-processing'] | ['robots'] | [-0.04011761 -0.3480215 0.21987627 -0.42548573 -1.249539 -0.164571
0.0638475 -0.15842183 -0.29150656 0.61534995 0.15199703 -0.39354962
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-0.04006032 -0.06412769 -0.4290126 0.38129145 -1.7463744 0.30054596
0.45907134 1.243617 0.42... | [14.990265846252441, 6.093010902404785] |
c5def424-737e-4b55-88ce-b4a5f90f2e78 | a-data-augmentation-method-for-fully | 2202.06344 | null | https://arxiv.org/abs/2202.06344v2 | https://arxiv.org/pdf/2202.06344v2.pdf | A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation | Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimensional U-Net architecture for brain tumor segmentation. The main ideas included that firs... | ['Hongbing Xiao', 'Yarong Ji', 'Yu Wang'] | 2022-02-13 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 1.44849062e-01 1.91459246e-02 -4.76670787e-02 -2.60868371e-01
-6.35316610e-01 -1.08083703e-01 1.44991621e-01 -3.98638211e-02
-5.89142025e-01 5.24579406e-01 2.90814400e-01 -1.29011273e-01
-1.08489081e-01 -6.83233857e-01 -1.34391293e-01 -1.29665732e+00
1.58739135e-01 4.79586542e-01 2.12970793e-01 1.62075773... | [14.480636596679688, -2.430408000946045] |
0c1979c3-019c-45c0-956f-10fb3d39fc88 | improving-auto-encoders-self-supervised-image | 2012.03322 | null | https://arxiv.org/abs/2012.03322v2 | https://arxiv.org/pdf/2012.03322v2.pdf | A Pseudo-labelling Auto-Encoder for unsupervised image classification | In this paper, we introduce a unique variant of the denoising Auto-Encoder and combine it with the perceptual loss to classify images in an unsupervised manner. The proposed method, called Pseudo Labelling, consists of first applying a randomly sampled set of data augmentation transformations to each training image. As... | ['Abdelhakim Saim', 'Rachid Deriche', 'Karim Atif', 'Aymene Mohammed Bouayed'] | 2020-12-06 | null | null | null | null | ['self-supervised-image-classification', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision'] | [ 6.76602721e-01 2.61128485e-01 -3.36715346e-03 -6.68519557e-01
-4.71471697e-01 -2.20528767e-01 5.77839971e-01 3.03096265e-01
-6.83368027e-01 8.22570384e-01 -1.37959063e-01 1.83413640e-01
3.08068879e-02 -8.52626264e-01 -8.20641398e-01 -1.05900860e+00
2.20985889e-01 2.86127806e-01 6.44070506e-02 1.50388628... | [9.320601463317871, 2.9949095249176025] |
24cd634e-6d5c-4f0d-85e9-b60b9f8e8242 | prottrans-towards-cracking-the-language-of | 2007.06225 | null | https://arxiv.org/abs/2007.06225v3 | https://arxiv.org/pdf/2007.06225v3.pdf | ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing | Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models taken from NLP. These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Ele... | ['Tom Gibbs', 'Tamas Feher', 'Llion Jones', 'Ahmed Elnaggar', 'Yu Wang', 'Michael Heinzinger', 'Martin Steinegger', 'Ghalia Rihawi', 'Debsindhu Bhowmik', 'Christian Dallago', 'Burkhard Rost', 'Christoph Angerer'] | 2020-07-13 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 9.69559327e-02 1.93302780e-01 -7.31314123e-02 -2.43327826e-01
-6.20832026e-01 -6.04579687e-01 4.82293576e-01 3.69912833e-01
-6.04687274e-01 1.22648740e+00 1.30865470e-01 -7.73962557e-01
1.09202951e-01 -4.61140335e-01 -1.14649212e+00 -9.92220163e-01
-9.44289938e-02 7.19111264e-01 -1.74727831e-02 -1.73208460... | [4.71624755859375, 5.643178939819336] |
ee722a42-99ae-49c9-9f12-2550b3ddcf1a | learning-bloch-simulations-for-mr | 2008.04139 | null | https://arxiv.org/abs/2008.04139v2 | https://arxiv.org/pdf/2008.04139v2.pdf | Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks | Magnetic resonance fingerprinting (MRF) enables fast and multiparametric MR imaging. Despite fast acquisition, the state-of-the-art reconstruction of MRF based on dictionary matching is slow and lacks scalability. To overcome these limitations, neural network (NN) approaches estimating MR parameters from fingerprints h... | ['Benjamin Marty', 'Olivier Scheidegger', 'Fabian Balsiger', 'Mauricio Reyes', 'Alain Jungo'] | 2020-08-10 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 5.76626539e-01 -5.01335859e-02 -3.59092891e-01 -4.82022494e-01
-7.99477637e-01 -2.30209395e-01 3.63172859e-01 1.12004648e-03
-5.51863849e-01 6.84116840e-01 1.99413046e-01 -4.67401505e-01
-4.44585979e-01 -5.37470579e-01 -9.78705764e-01 -8.65913212e-01
-3.73652250e-01 4.92959321e-01 -7.42832478e-03 8.19495693... | [13.521500587463379, -2.403188705444336] |
d38978b8-9884-4794-9cde-33ad14292803 | continual-semantic-segmentation-with | 2304.05015 | null | https://arxiv.org/abs/2304.05015v1 | https://arxiv.org/pdf/2304.05015v1.pdf | Continual Semantic Segmentation with Automatic Memory Sample Selection | Continual Semantic Segmentation (CSS) extends static semantic segmentation by incrementally introducing new classes for training. To alleviate the catastrophic forgetting issue in CSS, a memory buffer that stores a small number of samples from the previous classes is constructed for replay. However, existing methods se... | ['Jun Liu', 'Simon See', 'Jianxiong Yin', 'Tianrun Chen', 'Lanyun Zhu'] | 2023-04-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_Continual_Semantic_Segmentation_With_Automatic_Memory_Sample_Selection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_Continual_Semantic_Segmentation_With_Automatic_Memory_Sample_Selection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['continual-semantic-segmentation'] | ['computer-vision'] | [ 4.45506990e-01 -3.44580449e-02 -5.77183068e-01 -6.38914168e-01
-9.37157452e-01 -4.21572208e-01 4.14674044e-01 -1.08669242e-02
-8.73313963e-01 8.82181466e-01 -2.02962682e-01 -2.39923924e-01
2.16665372e-01 -8.00888121e-01 -9.34960961e-01 -7.29073882e-01
3.04777116e-01 5.79247594e-01 7.57878423e-01 1.59565076... | [9.476090431213379, 2.2301483154296875] |
c5d67d23-5067-4bba-b743-657f692b2393 | metadata-based-raw-reconstruction-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Metadata-Based_RAW_Reconstruction_via_Implicit_Neural_Functions_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Metadata-Based_RAW_Reconstruction_via_Implicit_Neural_Functions_CVPR_2023_paper.pdf | Metadata-Based RAW Reconstruction via Implicit Neural Functions | Many low-level computer vision tasks are desirable to utilize the unprocessed RAW image as input, which remains the linear relationship between pixel values and scene radiance. Recent works advocate to embed the RAW image samples into sRGB images at capture time, and reconstruct the RAW from sRGB by these metadata ... | ['Qinmin Yang', 'Qi Ye', 'Huijie Qiao', 'Leyi Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['raw-reconstruction'] | ['computer-vision'] | [ 5.72202742e-01 -1.01441421e-01 3.04227434e-02 -4.80762869e-01
-7.97878742e-01 -1.65117905e-01 3.61608744e-01 -5.19943774e-01
-4.64444876e-01 7.40596950e-01 2.15715513e-01 -4.27132919e-02
-1.53563228e-02 -1.00615573e+00 -9.41868007e-01 -9.05910432e-01
2.67026603e-01 -1.70274958e-01 2.20176190e-01 -2.87098140... | [10.584247589111328, -2.348267078399658] |
68a79e1f-b54e-42d3-909a-9e1ec8bb80ec | mri-multi-modal-3d-human-pose-estimation | 2210.08394 | null | https://arxiv.org/abs/2210.08394v1 | https://arxiv.org/pdf/2210.08394v1.pdf | mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors | The ability to estimate 3D human body pose and movement, also known as human pose estimation (HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) ... | ['Umit Ogras', 'Yin Li', 'Sizhe An'] | 2022-10-15 | null | null | null | null | ['action-understanding', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.56119168e-01 4.90693115e-02 -2.69281268e-01 -9.24786478e-02
-8.85933042e-01 1.32272132e-02 -2.26748064e-01 -3.16538244e-01
-5.09915531e-01 5.21649539e-01 9.71893072e-01 2.90504068e-01
-7.92128891e-02 -5.86685300e-01 -3.58367920e-01 -4.83277172e-01
-5.33365250e-01 4.00657713e-01 5.18129161e-03 -3.86732012... | [7.154171943664551, 0.11492877453565598] |
ea7b8333-a799-434c-8659-f547eeb7411f | inesc-id-at-semeval-2016-task-4-a-reducing | null | null | https://aclanthology.org/S16-1036 | https://aclanthology.org/S16-1036.pdf | INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words | null | ["M{\\'a}rio J. Silva", 'Ramon Astudillo', 'Wang Ling', 'Silvio Amir', 'Isabel Trancoso'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.301971912384033, 3.620332717895508] |
ea7505c6-44f8-44ef-adb9-19a3ba1be534 | on-the-role-of-bidirectionality-in-language | 2205.11726 | null | https://arxiv.org/abs/2205.11726v2 | https://arxiv.org/pdf/2205.11726v2.pdf | On the Role of Bidirectionality in Language Model Pre-Training | Prior work on language model pre-training has explored different architectures and learning objectives, but differences in data, hyperparameters and evaluation make a principled comparison difficult. In this work, we focus on bidirectionality as a key factor that differentiates existing approaches, and present a compre... | ['Ves Stoyanov', 'Luke Zettlemoyer', 'Naman Goyal', 'Jingfei Du', 'Mikel Artetxe'] | 2022-05-24 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 3.12541574e-02 -2.19535977e-02 -5.39150774e-01 -2.10616082e-01
-6.68060541e-01 -8.63044858e-01 1.09060550e+00 5.77851338e-03
-5.86628914e-01 5.13999939e-01 6.52723789e-01 -9.78370368e-01
2.27600727e-02 -6.15325689e-01 -6.05444729e-01 -4.40677166e-01
-2.83956602e-02 4.13012803e-01 3.87122601e-01 -5.93294084... | [10.818471908569336, 8.48800277709961] |
3fe3bea7-0c89-4f4d-8922-134bd0b6b0fe | 1d-convolutional-neural-network-models-for | 1903.01552 | null | http://arxiv.org/abs/1903.01552v1 | http://arxiv.org/pdf/1903.01552v1.pdf | 1D Convolutional Neural Network Models for Sleep Arousal Detection | Sleep arousals transition the depth of sleep to a more superficial stage. The
occurrence of such events is often considered as a protective mechanism to
alert the body of harmful stimuli. Thus, accurate sleep arousal detection can
lead to an enhanced understanding of the underlying causes and influencing the
assessment... | ['Simo Särkkä', 'Ali Bahrami Rad', 'Serkan Kiranyaz', 'Moncef Gabbouj', 'Morteza Zabihi'] | 2019-03-01 | null | null | null | null | ['sleep-quality-prediction', 'sleep-arousal-detection'] | ['medical', 'medical'] | [ 1.84711143e-01 -1.46552473e-01 -1.21120207e-01 -3.57097924e-01
-2.05804422e-01 -1.17302142e-01 3.97996694e-01 7.23103702e-01
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-1.29415274e-01 -2.82283604e-01 -1.21244989e-01 -7.73314357e-01
-3.14515412e-01 2.99126823e-02 -1.41025502e-02 -1.52952433... | [13.51624584197998, 3.513427495956421] |
da257d69-2bbd-4931-8ced-3de610bd41bf | ao14eae3ec-aa1i-cc2ii12a14c3-the-duplex-model | null | null | https://aclanthology.org/O17-1017 | https://aclanthology.org/O17-1017.pdf | 基於雙工音高感知模型之神經網路旋律抽取演算法 (The duplex model of pitch perception inspired neural network for melody extraction) [In Chinese] | null | ['Tai-Shih Chi', 'Hsin Chou'] | 2017-11-01 | the-duplex-model-of-pitch-perception-inspired | https://aclanthology.org/O17-1017 | https://aclanthology.org/O17-1017.pdf | roclingijclclp-2017-11 | ['melody-extraction'] | ['music'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3553595542907715, 3.7211642265319824] |
28e7fa42-9546-4f0c-a1c7-29dd6501ddaf | automatic-language-identification-using-deep | null | null | https://ieeexplore.ieee.org/document/6854622 | https://static.googleusercontent.com/media/research.google.com/ru//pubs/archive/42538.pdf | AUTOMATIC LANGUAGE IDENTIFICATION USING DEEP NEURAL NETWORKS | This work studies the use of deep neural networks (DNNs) to address automatic language identification (LID). Motivated by their recent success in acoustic modelling, we adapt DNNs to the problem of identifying the language of a given spoken utterance from short-term acoustic features. The proposed approach is compared ... | ['Pedro Moreno', 'Joaquin Gonzalez-Rodriguez', 'David Martinez', 'Oldrich Plchot', 'Javier Gonzalez-Dominguez', 'Ignacio Lopez-Moreno'] | 2014-05-04 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [-2.19788522e-01 -2.13151380e-01 4.76819165e-02 -5.16715884e-01
-1.21175432e+00 -5.39495826e-01 7.14897096e-01 -4.41576779e-01
-9.49070275e-01 2.97820985e-01 5.66505492e-01 -4.52132493e-01
3.66608471e-01 -4.15224470e-02 -5.47134876e-01 -4.49609190e-01
1.19335152e-01 6.07031524e-01 -3.00990760e-01 -2.07187291... | [14.207420349121094, 6.6167168617248535] |
7938e6c1-f550-42b5-9968-1695c90b548b | siammask-a-framework-for-fast-online-object | 2207.02088 | null | https://arxiv.org/abs/2207.02088v1 | https://arxiv.org/pdf/2207.02088v1.pdf | SiamMask: A Framework for Fast Online Object Tracking and Segmentation | In this paper we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method. We improve the offline training procedure of popular fully-convolutional Siamese approaches by augmenting their losses with a binary segmentation task. Once t... | ['Philip H. S. Torr', 'Luca Bertinetto', 'Li Zhang', 'Qiang Wang', 'Weiming Hu'] | 2022-07-05 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [ 1.52179867e-01 -2.96301991e-01 -2.50636578e-01 -1.91456363e-01
-9.71541047e-01 -8.30020130e-01 3.87024999e-01 8.34202915e-02
-8.71536970e-01 2.79297709e-01 -6.80499434e-01 -2.15578586e-01
4.10991371e-01 -3.73227298e-01 -1.14834261e+00 -5.97580492e-01
2.66316589e-02 7.79344201e-01 9.66312051e-01 3.37973177... | [8.981701850891113, -0.22268594801425934] |
55076973-4bdd-4bdc-8afe-066442d8227d | class-incremental-novel-class-discovery | 2207.08605 | null | https://arxiv.org/abs/2207.08605v1 | https://arxiv.org/pdf/2207.08605v1.pdf | Class-incremental Novel Class Discovery | We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained on a labelled data set containing disjoint yet related categories. Apart from discovering novel cla... | ['Elisa Ricci', 'Nicu Sebe', 'Zhun Zhong', 'Mingxuan Liu', 'Subhankar Roy'] | 2022-07-18 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 4.26705718e-01 3.27677131e-01 -2.01222152e-01 -4.95770097e-01
-6.75264657e-01 -6.11922503e-01 8.10418785e-01 5.14915407e-01
-3.78654420e-01 7.15239167e-01 -2.14302972e-01 -2.21542180e-01
-2.22101703e-01 -6.36367202e-01 -7.82039046e-01 -6.75486088e-01
-3.05106014e-01 6.89750314e-01 5.01991272e-01 3.01212102... | [9.86412239074707, 3.2219293117523193] |
d082d03c-1c85-4041-b39b-ef80a5368f40 | quantifying-character-similarity-with-vision | 2305.14672 | null | https://arxiv.org/abs/2305.14672v1 | https://arxiv.org/pdf/2305.14672v1.pdf | Quantifying Character Similarity with Vision Transformers | Record linkage is a bedrock of quantitative social science, as analyses often require linking data from multiple, noisy sources. Off-the-shelf string matching methods are widely used, as they are straightforward and cheap to implement and scale. Not all character substitutions are equally probable, and for some setting... | ['Melissa Dell', 'Shao-Yu Jheng', 'Abhishek Arora', 'Xinmei Yang'] | 2023-05-24 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 3.80733907e-01 -3.78549844e-01 -9.82649848e-02 -4.65855241e-01
-4.72155184e-01 -9.14532304e-01 4.80800003e-01 6.08829141e-01
-7.03133106e-01 6.42497897e-01 5.04646122e-01 -8.91302675e-02
-2.14594185e-01 -1.02816927e+00 -7.77417600e-01 -2.82850236e-01
-2.28930898e-02 4.98535246e-01 -1.94379255e-01 -3.97009760... | [10.04007339477539, 10.279000282287598] |
959808d2-db87-4305-a3ec-d7e07c475db5 | unsupervised-hebbian-learning-on-point-sets | 2207.12323 | null | https://arxiv.org/abs/2207.12323v1 | https://arxiv.org/pdf/2207.12323v1.pdf | Unsupervised Hebbian Learning on Point Sets in StarCraft II | Learning the evolution of real-time strategy (RTS) game is a challenging problem in artificial intelligent (AI) system. In this paper, we present a novel Hebbian learning method to extract the global feature of point sets in StarCraft II game units, and its application to predict the movement of the points. Our model i... | ['Saibal Mukhopadhyay', 'Saurabh Dash', 'Harshit Kumar', 'Beomseok Kang'] | 2022-07-13 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [ 4.88201529e-02 4.29424196e-02 -1.00945070e-01 -1.06581777e-01
-1.13026887e-01 -1.87597424e-01 4.25052285e-01 -1.68504938e-01
-1.03983188e+00 8.07144701e-01 -4.41379920e-02 1.25404358e-01
-2.54225194e-01 -9.26183283e-01 -8.48753452e-01 -1.00917530e+00
-2.98593521e-01 1.13135271e-01 7.62773097e-01 -6.58275247... | [4.0051445960998535, 1.7337857484817505] |
b7c335d9-b839-4421-a10f-0e09db0d55c5 | decipherment | null | null | https://aclanthology.org/P13-5003 | https://aclanthology.org/P13-5003.pdf | Decipherment | null | ['Kevin Knight'] | 2013-08-01 | null | null | null | acl-2013-8 | ['decipherment'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.395583152770996, 3.554941415786743] |
e5463ecf-5281-49cf-9044-ea3034832607 | sahaayak-2023-the-multi-domain-bilingual | 2307.00021 | null | https://arxiv.org/abs/2307.00021v1 | https://arxiv.org/pdf/2307.00021v1.pdf | SAHAAYAK 2023 -- the Multi Domain Bilingual Parallel Corpus of Sanskrit to Hindi for Machine Translation | The data article presents the large bilingual parallel corpus of low-resourced language pair Sanskrit-Hindi, named SAHAAYAK 2023. The corpus contains total of 1.5M sentence pairs between Sanskrit and Hindi. To make the universal usability of the corpus and to make it balanced, data from multiple domain has been incorpo... | ['Jitendra Nasariwala', 'Vishvajitsinh Bakrola'] | 2023-06-27 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 1.41454324e-01 -1.46702990e-01 1.80832040e-03 -4.28106815e-01
-1.16163957e+00 -9.68347311e-01 8.16527188e-01 1.20050497e-01
-5.63569546e-01 1.14313233e+00 6.67032719e-01 -4.39535052e-01
4.46924008e-02 -4.11939830e-01 -4.68834341e-01 -2.17245087e-01
4.03421819e-01 1.04509795e+00 3.39885317e-02 -7.24236250... | [11.258213996887207, 10.331754684448242] |
699861ef-fe95-447b-ac48-2dda2678fe26 | contrastive-label-disambiguation-for-partial | null | null | https://openreview.net/forum?id=EhYjZy6e1gJ | https://openreview.net/pdf?id=EhYjZy6e1gJ | Contrastive Label Disambiguation for Partial Label Learning | Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we b... | ['Junbo Zhao', 'Gang Chen', 'Gang Niu', 'Lei Feng', 'Sharon Li', 'Ruixuan Xiao', 'Haobo Wang'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['partial-label-learning', 'pico'] | ['methodology', 'natural-language-processing'] | [ 6.16738856e-01 3.47415924e-01 -7.27793992e-01 -4.27610666e-01
-1.00226986e+00 -5.65276086e-01 5.91238022e-01 4.68326002e-01
-3.49324852e-01 8.30488861e-01 -1.89440444e-01 -1.34066911e-02
-2.31262863e-01 -4.40333456e-01 -2.33305633e-01 -7.75173724e-01
2.04416201e-01 8.99109364e-01 -3.32134068e-02 1.12187825... | [9.53475284576416, 4.0557169914245605] |
0fe199b0-82c3-483a-a933-a8945ff7cd0a | a-data-centric-solution-to-nonhomogeneous | 2304.07874 | null | https://arxiv.org/abs/2304.07874v2 | https://arxiv.org/pdf/2304.07874v2.pdf | A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer | Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-h... | ['Jun Chen', 'Zijun Wu', 'Liangyan Li', 'Huan Liu', 'Yangyi Liu'] | 2023-04-16 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.30584764e-01 -7.70732835e-02 3.05903614e-01 -3.14973503e-01
-5.68442941e-01 -1.45334512e-01 4.58523482e-01 -2.66946882e-01
-4.50774729e-01 5.87615192e-01 2.49600355e-02 -1.51674673e-01
-3.48542184e-01 -1.10571063e+00 -7.99040854e-01 -1.30200219e+00
2.26613939e-01 5.54773919e-02 1.98996425e-01 -6.40198290... | [10.931629180908203, -3.147167921066284] |
c4074bc9-7ea8-4ea8-a92e-36c0a82c0b62 | explainable-fmri-based-brain-decoding-via | 2210.05713 | null | https://arxiv.org/abs/2210.05713v1 | https://arxiv.org/pdf/2210.05713v1.pdf | Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network | Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning methods for fMRI-based brain decoding either suffer from low classification performance or poor explainability. Here, we address this issue by pro... | ['Quanying Liu', 'Mo Wang', 'Zhichao Liang', 'Youzhi Qu', 'Ziyuan Ye'] | 2022-10-08 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 3.43532920e-01 2.56832123e-01 1.44093603e-01 -4.42697257e-01
1.18412107e-01 -3.12192708e-01 5.45760572e-01 -7.29580820e-02
-1.97102502e-02 5.59359312e-01 5.77546835e-01 -3.55701774e-01
-4.92481440e-01 -6.34539902e-01 -6.95481300e-01 -3.77334893e-01
-4.08314288e-01 1.08462483e-01 -2.35117972e-02 -1.25106052... | [12.49023723602295, 3.379387855529785] |
8a41afaa-b344-4c17-88e9-13bbb8d2e67f | heartbeat-heart-beat-estimation-through | 1810.08554 | null | http://arxiv.org/abs/1810.08554v2 | http://arxiv.org/pdf/1810.08554v2.pdf | HeartBEAT: Heart Beat Estimation through Adaptive Tracking | In this paper, we propose an algorithm denoted as HeartBEAT that tracks heart
rate from wrist-type photoplethysmography (PPG) signals and simultaneously
recorded three-axis acceleration data. HeartBEAT contains three major parts:
spectrum estimation of PPG signals and acceleration data, elimination of motion
artifacts ... | ['Ghassan AlRegib', 'Dogancan Temel', 'Huijie Pan'] | 2018-10-19 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.51584285e-01 -1.52877467e-02 -9.32956953e-03 -1.65248722e-01
-4.47399348e-01 -3.43538672e-01 -2.03481525e-01 -3.44350278e-01
-3.66326183e-01 7.75391459e-01 1.96928665e-01 -7.75590912e-02
-2.84276038e-01 -2.42250219e-01 -1.44605646e-02 -4.75318015e-01
-7.02950239e-01 -1.68596506e-02 -1.73501790e-01 1.00801565... | [13.960573196411133, 3.006913423538208] |
1407cc7c-36e5-4bd9-b175-3d560ee36df9 | adaptive-multi-stage-density-ratio-estimation | 2209.08739 | null | https://arxiv.org/abs/2209.08739v1 | https://arxiv.org/pdf/2209.08739v1.pdf | Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model | This paper studies the fundamental problem of learning energy-based model (EBM) in the latent space of the generator model. Learning such prior model typically requires running costly Markov Chain Monte Carlo (MCMC). Instead, we propose to use noise contrastive estimation (NCE) to discriminatively learn the EBM through... | ['Tian Han', 'Zhisheng Xiao'] | 2022-09-19 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 2.25720838e-01 -2.37699285e-01 -1.02943033e-01 -3.11900437e-01
-1.23995078e+00 -2.47427821e-01 9.15352106e-01 -2.44409487e-01
-4.11583990e-01 6.78075969e-01 2.27481022e-01 -1.12921976e-01
1.67051539e-01 -7.95304716e-01 -8.76739919e-01 -8.69180977e-01
1.55373439e-01 7.73149371e-01 2.49185175e-01 3.34699363... | [7.079527854919434, 3.8064730167388916] |
b4060cd3-a811-458f-afb0-270015c85451 | a-hybrid-statistical-machine-learning | 2212.02255 | null | https://arxiv.org/abs/2212.02255v1 | https://arxiv.org/pdf/2212.02255v1.pdf | A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study | We apply classical statistical methods in conjunction with the state-of-the-art machine learning techniques to develop a hybrid interpretable model to analyse 454,897 online customers' behavior for a particular product category at the largest online retailer in China, that is JD. While most mere machine learning method... | ['Foaad Iravani', 'Ali Eshragh', 'Kasun Bandara', 'Saed Alizami'] | 2022-12-01 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-3.15176815e-01 -6.68688938e-02 -8.15097213e-01 -7.73698926e-01
-4.00407195e-01 -6.21653259e-01 2.41786823e-01 6.16685569e-01
-2.96431720e-01 1.85985744e-01 3.25033724e-01 -6.49554968e-01
-4.60357338e-01 -8.70143414e-01 -4.47137594e-01 -7.17248440e-01
-5.70415109e-02 5.81156850e-01 -4.13336426e-01 -5.49183607... | [9.379195213317871, 5.804562568664551] |
fa1bbd51-d3fa-418e-b5dc-e87b0f651a8a | inductive-biases-for-deep-learning-of-higher | 2011.15091 | null | https://arxiv.org/abs/2011.15091v4 | https://arxiv.org/pdf/2011.15091v4.pdf | Inductive Biases for Deep Learning of Higher-Level Cognition | A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselve... | ['Yoshua Bengio', 'Anirudh Goyal'] | 2020-11-30 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 3.65642607e-02 4.10567284e-01 6.55806139e-02 -4.26009417e-01
6.42789841e-01 -5.42460442e-01 8.73284459e-01 3.44196916e-01
-5.63049376e-01 6.62570894e-01 -8.48670527e-02 -5.11341453e-01
-4.01789874e-01 -1.05773640e+00 -4.69908208e-01 -5.05497277e-01
-2.71000981e-01 6.16369724e-01 3.39351624e-01 -6.00743234... | [9.087322235107422, 6.4797844886779785] |
3d7b7d38-938b-4f45-88ac-aa2a8a642104 | new-sqrt-n-consistent-numerically-stable | 2302.08097 | null | https://arxiv.org/abs/2302.08097v1 | https://arxiv.org/pdf/2302.08097v1.pdf | New $\sqrt{n}$-consistent, numerically stable higher-order influence function estimators | Higher-Order Influence Functions (HOIFs) provide a unified theory for constructing rate-optimal estimators for a large class of low-dimensional (smooth) statistical functionals/parameters (and sometimes even infinite-dimensional functions) that arise in substantive fields including epidemiology, economics, and the soci... | ['Chang Li', 'Lin Liu'] | 2023-02-16 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.50856510e-01 1.77169248e-01 -5.14597178e-01 -1.42919749e-01
-8.38022113e-01 -4.20634955e-01 7.42313683e-01 2.46994883e-01
-5.51095188e-01 1.22524858e+00 1.80809513e-01 -3.01794589e-01
-4.15008426e-01 -5.80754161e-01 -6.63784266e-01 -9.71040964e-01
-5.47082841e-01 1.64523408e-01 1.46671310e-01 1.37008473... | [7.305613994598389, 4.241812229156494] |
87284432-fcd0-470d-aff7-b710588782d5 | deepir-a-deep-semantics-driven-framework-for | 1811.07793 | null | https://arxiv.org/abs/1811.07793v3 | https://arxiv.org/pdf/1811.07793v3.pdf | DeepIR: A Deep Semantics Driven Framework for Image Retargeting | We present \emph{Deep Image Retargeting} (\emph{DeepIR}), a coarse-to-fine framework for content-aware image retargeting. Our framework first constructs the semantic structure of input image with a deep convolutional neural network. Then a uniform re-sampling that suits for semantic structure preserving is devised to r... | ['Zhibo Chen', 'Jianxin Lin', 'Tiankuang Zhou'] | 2018-11-19 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 3.9855194e-01 2.4554588e-01 -2.0565777e-01 -5.1070559e-01
-8.7386918e-01 -7.8282112e-01 6.4252639e-01 3.7249871e-02
-5.5402535e-01 6.0099012e-01 7.8794557e-01 1.6252303e-01
-1.6197388e-01 -9.3626338e-01 -7.1352410e-01 -3.4156826e-01
6.2736863e-01 -1.4382219e-01 4.3176609e-01 -4.5399922e-01
5.7745838e-01... | [11.252364158630371, -1.028210163116455] |
8e3ef591-97e0-4719-8516-c73617b9eb7e | ra-unet-a-hybrid-deep-attention-aware-network | 1811.01328 | null | http://arxiv.org/abs/1811.01328v1 | http://arxiv.org/pdf/1811.01328v1.pdf | RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans | Automatic extraction of liver and tumor from CT volumes is a challenging task
due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep
convolutional neural networks have become popular in medical image segmentation
tasks because of the utilization of large labeled datasets to learn
hierarchical feature... | ['Qiangguo Jin', 'Zhaopeng Meng', 'Leyi Wei', 'Ran Su', 'Changming Sun'] | 2018-11-04 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-2.26472139e-01 3.52122545e-01 -3.26247305e-01 -3.68006796e-01
-6.50180042e-01 1.01583009e-03 3.72740328e-01 -3.60725485e-02
-3.95539194e-01 3.17841113e-01 3.54265541e-01 -1.98069438e-01
1.43689722e-01 -6.00628674e-01 -4.78518575e-01 -8.45156312e-01
-2.78120309e-01 7.67537892e-01 4.23976302e-01 5.98269068... | [14.599202156066895, -2.551504373550415] |
f53a637f-82ca-4ad9-8699-30666fb78115 | semantic-vad-low-latency-voice-activity | 2305.1245 | null | https://arxiv.org/abs/2305.12450v1 | https://arxiv.org/pdf/2305.12450v1.pdf | Semantic VAD: Low-Latency Voice Activity Detection for Speech Interaction | For speech interaction, voice activity detection (VAD) is often used as a front-end. However, traditional VAD algorithms usually need to wait for a continuous tail silence to reach a preset maximum duration before segmentation, resulting in a large latency that affects user experience. In this paper, we propose a novel... | ['Li-Rong Dai', 'Jie Zhang', 'Shiliang Zhang', 'Qian Chen', 'Lingyun Zuo', 'Yuchun Shu', 'Mohan Shi'] | 2023-05-21 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 3.57233942e-01 -1.59728050e-01 1.53453082e-01 -4.21391964e-01
-8.36001039e-01 -5.82547486e-01 2.73936801e-02 2.36026451e-01
-4.86892104e-01 3.85399193e-01 9.41592678e-02 -6.95888996e-01
4.53325689e-01 -3.31969708e-01 -3.02393138e-01 -5.36742985e-01
5.40247917e-01 1.95065483e-01 7.35681891e-01 1.47005513... | [14.687544822692871, 6.529976844787598] |
618d25b3-e5cb-4841-998e-562e8942fe0c | dynamic-mlp-for-mri-reconstruction | 2301.08868 | null | https://arxiv.org/abs/2301.08868v2 | https://arxiv.org/pdf/2301.08868v2.pdf | Computationally Efficient 3D MRI Reconstruction with Adaptive MLP | Compared with 2D MRI, 3D MRI provides superior volumetric spatial resolution and signal-to-noise ratio. However, it is more challenging to reconstruct 3D MRI images. Current methods are mainly based on convolutional neural networks (CNN) with small kernels, which are difficult to scale up to have sufficient fitting pow... | ['Chi Zhang', 'Eric Z. Chen', 'Shanhui Sun', 'Terrence Chen', 'Yikang Liu', 'Xiao Chen'] | 2023-01-21 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [-8.06633160e-02 -1.51467845e-01 -2.33887248e-02 -1.95688367e-01
-6.52002692e-01 1.11469679e-01 -2.66559273e-02 7.80044720e-02
-6.10558093e-01 3.21702272e-01 2.40760222e-01 -3.36988658e-01
-2.28173956e-01 -1.04578698e+00 -7.76947021e-01 -8.46034110e-01
-2.38758087e-01 1.61956683e-01 5.32945991e-01 1.60242036... | [13.591370582580566, -2.4545438289642334] |
16c96ab3-005b-424d-b0c7-08d2ab65de47 | synthetic-data-augmentation-using-gan-for-1 | 2212.09317 | null | https://arxiv.org/abs/2212.09317v1 | https://arxiv.org/pdf/2212.09317v1.pdf | Synthetic Data Augmentation Using GAN For Improved Automated Visual Inspection | Quality control is a crucial activity performed by manufacturing companies to ensure their products conform to the requirements and specifications. The introduction of artificial intelligence models enables to automate the visual quality inspection, speeding up the inspection process and ensuring all products are evalu... | ['Dunja Mladenić', 'Blaž Fortuna', 'Erik Koehorst', 'Spyros Theodoropoulos', 'Patrik Zajec', 'Jože M. Rožanec'] | 2022-12-19 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 4.44901735e-01 4.98480290e-01 1.99065119e-01 -4.29246247e-01
-2.71255255e-01 -3.96587312e-01 1.99898139e-01 5.67529678e-01
-9.97927114e-02 4.13448691e-01 -2.82483369e-01 1.88264437e-03
-2.10628688e-01 -9.36652362e-01 -5.64054608e-01 -6.00851238e-01
2.41336510e-01 4.44835454e-01 -6.35613501e-02 -3.60524608... | [7.355600833892822, 1.9559049606323242] |
9b8d4181-0eee-4ace-8b1b-547adb12a24e | greenkgc-a-lightweight-knowledge-graph | 2208.09137 | null | https://arxiv.org/abs/2208.09137v1 | https://arxiv.org/pdf/2208.09137v1.pdf | GreenKGC: A Lightweight Knowledge Graph Completion Method | Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs). Most prior KGC work focuses on learning representations for entities and relations. Yet, a higher-dimensional embedding space is usually required for a better reasoning capability, which leads to a larger... | ['C. -C. Jay Kuo', 'Bin Wang', 'Xiou Ge', 'Yun-Cheng Wang'] | 2022-08-19 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-1.72290921e-01 3.98731083e-01 -5.35611749e-01 -7.76917636e-02
-3.08329195e-01 -2.66894370e-01 3.65044564e-01 6.71417773e-01
-7.76639357e-02 4.91258889e-01 1.57290995e-01 -4.63865370e-01
-5.92762828e-01 -1.24495435e+00 -4.64810669e-01 -5.27642787e-01
-5.29555857e-01 4.49825257e-01 1.84507638e-01 -1.81448609... | [8.741541862487793, 7.857255935668945] |
bef75037-e1f1-492b-9a16-70d37b27d089 | wave-propagation-of-visual-stimuli-in-focus | 2006.11035 | null | https://arxiv.org/abs/2006.11035v1 | https://arxiv.org/pdf/2006.11035v1.pdf | Wave Propagation of Visual Stimuli in Focus of Attention | Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visual field. While current computational models keep improving their predictive ability thanks to the increasing availability of data, they stil... | ['Lapo Faggi', 'Marco Gori', 'Dario Zanca', 'Alessandro Betti', 'Stefano Melacci'] | 2020-06-19 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [ 2.17481971e-01 6.17871396e-02 1.27023518e-01 1.48814648e-01
2.92032301e-01 -3.62561345e-01 6.12030685e-01 3.20202410e-01
-5.49788237e-01 3.98112416e-01 3.42936993e-01 -3.53776366e-02
-3.97975534e-01 -8.16154957e-01 -7.12093949e-01 -7.10525036e-01
-2.95090288e-01 2.65666187e-01 7.18276978e-01 -3.79138857... | [9.995736122131348, 1.7233139276504517] |
544d65ab-214f-4a04-b35e-8d7c62cd3da9 | wsgat-weighted-and-signed-graph-attention | 2109.11519 | null | https://arxiv.org/abs/2109.11519v1 | https://arxiv.org/pdf/2109.11519v1.pdf | wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction | Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains. In this paper we propose wsGAT, an extension of the Graph Attention Network (GAT) layers, meant to address the lack of GNNs that can handle graphs with signed and weigh... | ['Giuseppe Mangioni', 'Marco Grassia'] | 2021-09-21 | null | null | null | null | ['link-sign-prediction'] | ['graphs'] | [-1.33093372e-01 5.30782104e-01 -3.39752585e-01 -2.51423836e-01
3.58032823e-01 -2.39118606e-01 7.97471642e-01 2.47660220e-01
-1.93279326e-01 6.98504806e-01 1.63104400e-01 -4.52019006e-01
-4.55787748e-01 -1.14338505e+00 -5.90326965e-01 -3.04293722e-01
-6.65138543e-01 6.13449872e-01 6.19389236e-01 -5.00129580... | [7.030376434326172, 6.206997871398926] |
e165915e-65aa-435b-a6ac-6f12a665f1ff | a-self-paced-regularization-framework-for-1 | 1804.07759 | null | http://arxiv.org/abs/1804.07759v2 | http://arxiv.org/pdf/1804.07759v2.pdf | A Self-paced Regularization Framework for Partial-Label Learning | Partial label learning (PLL) aims to solve the problem where each training
instance is associated with a set of candidate labels, one of which is the
correct label. Most PLL algorithms try to disambiguate the candidate label set,
by either simply treating each candidate label equally or iteratively
identifying the true... | ['Congyang Lang', 'Songhe Feng', 'Gengyu Lyu'] | 2018-04-20 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 3.90188456e-01 8.84344131e-02 -7.24605322e-01 -5.06151915e-01
-6.11524880e-01 -6.06207430e-01 6.22265577e-01 3.93046856e-01
-6.26912057e-01 8.08192134e-01 -1.15294665e-01 -8.11409876e-02
-3.08878213e-01 -6.50504231e-01 -2.02140138e-01 -9.10438895e-01
1.56169966e-01 7.66600668e-01 1.65656894e-01 3.91239554... | [9.435420989990234, 4.004659652709961] |
700f3962-d9f1-4373-9c8c-7c3d59f926cb | hdr-reconstruction-from-bracketed-exposures | 2203.14825 | null | https://arxiv.org/abs/2203.14825v1 | https://arxiv.org/pdf/2203.14825v1.pdf | HDR Reconstruction from Bracketed Exposures and Events | Reconstruction of high-quality HDR images is at the core of modern computational photography. Significant progress has been made with multi-frame HDR reconstruction methods, producing high-resolution, rich and accurate color reconstructions with high-frequency details. However, they are still prone to fail in dynamic o... | ['Eduardo Perez-Pellitero', 'Ales Leonardis', 'Sibi Catley-Chandar', 'Richard Shaw'] | 2022-03-28 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 4.36206430e-01 -5.00606060e-01 1.70333177e-01 -3.93017262e-01
-1.05079544e+00 -1.89700171e-01 5.00596225e-01 -1.13726981e-01
-3.75902385e-01 8.01550329e-01 2.99011499e-01 3.94166201e-01
-2.47105211e-02 -8.06306481e-01 -9.32760596e-01 -8.06828499e-01
1.40972942e-01 -1.41506821e-01 3.36580336e-01 -1.92806363... | [10.678972244262695, -2.1089367866516113] |
7c192e3c-104c-4505-a7b4-29f46e69dc8d | conditional-generative-adversarial-nets | 1411.1784 | null | https://arxiv.org/abs/1411.1784v1 | https://arxiv.org/pdf/1411.1784v1.pdf | Conditional Generative Adversarial Nets | Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this... | ['Mehdi Mirza', 'Simon Osindero'] | 2014-11-06 | null | null | null | null | ['human-action-generation'] | ['computer-vision'] | [ 6.33001089e-01 8.05798292e-01 1.82189375e-01 -6.17966712e-01
-8.40683937e-01 -1.05257285e+00 1.07076502e+00 -5.44104874e-01
-2.70775914e-01 1.13464224e+00 8.20564851e-02 -2.56044298e-01
2.46151671e-01 -1.15411949e+00 -8.63825500e-01 -7.77367294e-01
1.57222256e-01 9.31713998e-01 -1.02729686e-01 -1.66241273... | [11.552664756774902, -0.17095650732517242] |
ba967527-f9fe-4b56-812a-eb86a664658c | robust-automated-human-activity-recognition | 1607.04867 | null | http://arxiv.org/abs/1607.04867v2 | http://arxiv.org/pdf/1607.04867v2.pdf | Robust Automated Human Activity Recognition and its Application to Sleep Research | Human Activity Recognition (HAR) is a powerful tool for understanding human
behaviour. Applying HAR to wearable sensors can provide new insights by
enriching the feature set in health studies, and enhance the personalisation
and effectiveness of health, wellness, and fitness applications. Wearable
devices provide an un... | ['Jaideep Srivastava', 'Luis Fernandes-Luque', 'Ferda Ofli', 'Aarti Sathyanarayana', 'Ahmed Elmagarmid', 'Shahrad Taheri', 'Teresa Arora'] | 2016-07-17 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [ 3.11745703e-01 -2.09240645e-01 -5.62582016e-01 -9.13997293e-02
-2.06269830e-01 -2.03352839e-01 1.27821907e-01 4.04034197e-01
-4.52698588e-01 7.70518363e-01 3.67769361e-01 -1.55097455e-01
-2.27765247e-01 -6.95635319e-01 -4.76346351e-02 -8.27798605e-01
-7.62271211e-02 -1.48548678e-01 8.72821137e-02 5.25162332... | [13.559586524963379, 3.3891026973724365] |
bafb1f04-1efb-4440-9449-0c5e0c1d35b6 | breaking-trade-offs-in-speech-separation-with | 2211.06493 | null | https://arxiv.org/abs/2211.06493v2 | https://arxiv.org/pdf/2211.06493v2.pdf | Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts | Employing a monaural speech separation (SS) model as a front-end for automatic speech recognition (ASR) involves balancing two kinds of trade-offs. First, while a larger model improves the SS performance, it also requires a higher computational cost. Second, an SS model that is more optimized for handling overlapped sp... | ['Takuya Yoshioka', 'Naoyuki Kanda', 'Jian Wu', 'Yu Shi', 'Zhuo Chen', 'Xiaofei Wang'] | 2022-11-11 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 1.65283516e-01 -1.44711480e-01 3.24231148e-01 -3.88725728e-01
-1.25295889e+00 -1.70152932e-01 1.65200219e-01 -1.13327548e-01
-1.76076889e-01 3.91023904e-01 4.87824887e-01 -2.72565663e-01
7.41668642e-02 -6.11995794e-02 -4.62496608e-01 -6.37359142e-01
5.83905503e-02 -6.76687658e-02 3.05763930e-01 -1.26798138... | [14.956242561340332, 5.907796859741211] |
27c3ace4-8246-4607-af2f-9dc2b296ab22 | robust-contrastive-language-image-pretraining | 2303.06854 | null | https://arxiv.org/abs/2303.06854v1 | https://arxiv.org/pdf/2303.06854v1.pdf | Robust Contrastive Language-Image Pretraining against Adversarial Attacks | Contrastive vision-language representation learning has achieved state-of-the-art performance for zero-shot classification, by learning from millions of image-caption pairs crawled from the internet. However, the massive data that powers large multimodal models such as CLIP, makes them extremely vulnerable to various t... | ['Baharan Mirzasoleiman', 'Wenhan Yang'] | 2023-03-13 | null | null | null | null | ['data-poisoning', 'backdoor-attack'] | ['adversarial', 'adversarial'] | [ 3.11091896e-02 -1.54998749e-01 -1.91147611e-01 2.04643682e-01
-1.25540090e+00 -1.17710185e+00 6.69570446e-01 1.87383324e-03
-7.00412393e-01 4.92095381e-01 -1.22226000e-01 -3.48367423e-01
4.12501276e-01 -6.37369275e-01 -1.28401554e+00 -6.90542042e-01
5.16114384e-02 5.29512525e-01 4.19113487e-01 -3.27100277... | [5.860833644866943, 7.868347644805908] |
7abe72b4-3f19-4dfa-835c-93b22d723495 | rotateqvs-representing-temporal-information | 2203.07993 | null | https://arxiv.org/abs/2203.07993v2 | https://arxiv.org/pdf/2203.07993v2.pdf | RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion | Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention. In TKG, relation patterns inherent with temporality are required to be studied for repre... | ['Aiping Li', 'Yitong Li', 'Ye Wang', 'Kai Chen'] | 2022-03-15 | null | https://aclanthology.org/2022.acl-long.402 | https://aclanthology.org/2022.acl-long.402.pdf | acl-2022-5 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-4.33165550e-01 9.52180997e-02 -8.06420982e-01 -1.10748984e-01
5.29671371e-01 -6.08111620e-01 9.76600111e-01 4.89540577e-01
-2.67034527e-02 7.68462002e-01 3.60966831e-01 -6.47167027e-01
-6.95969403e-01 -1.04049885e+00 -6.48789585e-01 -3.55888039e-01
-7.08873451e-01 4.24468309e-01 4.48821157e-01 -7.86702216... | [8.53089714050293, 7.930069923400879] |
d99507bd-2dee-490b-a693-7c788721c18c | can-audio-captions-be-evaluated-with-image | 2110.04684 | null | https://arxiv.org/abs/2110.04684v2 | https://arxiv.org/pdf/2110.04684v2.pdf | Can Audio Captions Be Evaluated with Image Caption Metrics? | Automated audio captioning aims at generating textual descriptions for an audio clip. To evaluate the quality of generated audio captions, previous works directly adopt image captioning metrics like SPICE and CIDEr, without justifying their suitability in this new domain, which may mislead the development of advanced m... | ['Kenny Q. Zhu', 'Mengyue Wu', 'Zeyu Xie', 'Xuenan Xu', 'Zhiling Zhang', 'Zelin Zhou'] | 2021-10-10 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 2.41722777e-01 4.82344627e-03 6.67817593e-02 -3.28374207e-01
-1.24931276e+00 -5.90698242e-01 6.03640437e-01 3.49281251e-01
-3.49743336e-01 7.94442117e-01 6.14465356e-01 1.45453006e-01
7.76907578e-02 -3.19998056e-01 -5.11632204e-01 -2.41182223e-01
1.24335639e-01 1.04336858e-01 9.68787521e-02 -1.07326388... | [15.259384155273438, 4.872631072998047] |
1303246e-65e2-40ba-8225-9736a5b81046 | comparing-knowledge-based-reinforcement | 1901.04626 | null | https://arxiv.org/abs/1901.04626v2 | https://arxiv.org/pdf/1901.04626v2.pdf | Comparing Knowledge-based Reinforcement Learning to Neural Networks in a Strategy Game | The paper reports on an experiment, in which a Knowledge-Based Reinforcement Learning (KB-RL) method was compared to a Neural Network (NN) approach in solving a classical Artificial Intelligence (AI) task. In contrast to NNs, which require a substantial amount of data to learn a good policy, the KB-RL method seeks to e... | ['Liudmyla Nechepurenko', 'Viktor Voss', 'Vyacheslav Gritsenko'] | 2019-01-15 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 5.16687483e-02 8.48503768e-01 -2.71501839e-01 -1.69824034e-01
-2.45314792e-01 -3.19635600e-01 7.41002917e-01 1.89897329e-01
-6.80449188e-01 1.23074985e+00 -7.55383596e-02 -4.85668302e-01
-6.24239504e-01 -9.59977210e-01 -7.21026242e-01 -7.64404953e-01
-1.37263536e-03 6.80091918e-01 -7.56188557e-02 -4.11008209... | [4.227792263031006, 1.6675138473510742] |
97fb5984-b32e-4348-ad5a-86e2d6e54a75 | from-isolated-islands-to-pangea-unifying | 2304.00553 | null | https://arxiv.org/abs/2304.00553v2 | https://arxiv.org/pdf/2304.00553v2.pdf | From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding | Action understanding matters and attracts attention. It can be formed as the mapping from the action physical space to the semantic space. Typically, researchers built action datasets according to idiosyncratic choices to define classes and push the envelope of benchmarks respectively. Thus, datasets are incompatible w... | ['Cewu Lu', 'Xudong Lu', 'Jingru Tan', 'Yixing Li', 'Junyi Zhang', 'Yikun Ji', 'Yiming Dou', 'Xinpeng Liu', 'Xiaoqian Wu', 'Yong-Lu Li'] | 2023-04-02 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 5.32528125e-02 -5.96412830e-02 -5.31374514e-01 -3.76444906e-01
-3.85827214e-01 -5.39975762e-01 7.18081951e-01 -3.26659709e-01
-1.88121438e-01 5.78935802e-01 5.82599998e-01 7.10519031e-04
-4.98281896e-01 -1.04750144e+00 -5.94856620e-01 -6.87493563e-01
6.15232527e-01 1.22521199e-01 5.18509924e-01 -3.12392294... | [8.477058410644531, 0.7623854279518127] |
53f78a09-fe91-4c7c-bbde-4b42f8f3857c | deepfake-detection-with-deep-learning | 2304.03698 | null | https://arxiv.org/abs/2304.03698v1 | https://arxiv.org/pdf/2304.03698v1.pdf | Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers | The rapid evolvement of deepfake creation technologies is seriously threating media information trustworthiness. The consequences impacting targeted individuals and institutions can be dire. In this work, we study the evolutions of deep learning architectures, particularly CNNs and Transformers. We identified eight pro... | ['Vrizlynn L. L. Thing'] | 2023-04-07 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-9.46133256e-01 -3.98048788e-01 -1.25767305e-01 4.28160615e-02
-4.15843934e-01 -9.59476173e-01 1.12743926e+00 2.10295413e-02
-4.25367147e-01 6.04447067e-01 9.09355357e-02 -2.68464327e-01
-1.73610225e-01 -1.06959772e+00 -4.90195572e-01 -3.91130239e-01
-2.50687420e-01 4.22574341e-01 3.10721040e-01 8.46255850... | [12.38290786743164, 1.193517804145813] |
d331861f-6c76-435e-9ab2-85ad158ecf5f | lime-live-intrinsic-material-estimation | 1801.01075 | null | http://arxiv.org/abs/1801.01075v2 | http://arxiv.org/pdf/1801.01075v2.pdf | LIME: Live Intrinsic Material Estimation | We present the first end to end approach for real time material estimation
for general object shapes with uniform material that only requires a single
color image as input. In addition to Lambertian surface properties, our
approach fully automatically computes the specular albedo, material shininess,
and a foreground s... | ['Hans-Peter Seidel', 'Michael Zollhoefer', 'Maxim Maximov', 'Avishek Chatterjee', 'Abhimitra Meka', 'Christian Theobalt', 'Christian Richardt'] | 2018-01-03 | lime-live-intrinsic-material-estimation-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Meka_LIME_Live_Intrinsic_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Meka_LIME_Live_Intrinsic_CVPR_2018_paper.pdf | cvpr-2018-6 | ['foreground-segmentation'] | ['computer-vision'] | [ 7.47783363e-01 -1.99857205e-01 6.78177238e-01 -3.10446799e-01
-7.16480255e-01 -7.49375045e-01 6.74797297e-01 -3.97609055e-01
-8.65637138e-02 5.15418649e-01 -2.96044558e-01 -9.70095098e-02
3.17196518e-01 -8.97544742e-01 -1.10543442e+00 -5.72941184e-01
1.95926756e-01 6.96901262e-01 3.88272226e-01 -2.17216879... | [9.671793937683105, -3.1005594730377197] |
c7ae10ed-fb5e-4d85-b132-bb880a6eed5d | reconstructing-the-image-scanning-microscopy | 2211.1251 | null | https://arxiv.org/abs/2211.12510v1 | https://arxiv.org/pdf/2211.12510v1.pdf | Reconstructing the Image Scanning Microscopy Dataset: an Inverse Problem | Confocal laser-scanning microscopy (CLSM) is one of the most popular optical architectures for fluorescence imaging. In CLSM, a focused laser beam excites the fluorescence emission from a specific specimen position. Some actuators scan the probed region across the sample and a photodetector collects a single intensity ... | ['Giuseppe Vicidomini', 'Marco Castello', 'Alessandro Zunino'] | 2022-11-22 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 9.06160235e-01 -3.02281320e-01 1.83848023e-01 -1.75580420e-02
-7.08302379e-01 -5.45049906e-01 2.66212344e-01 -5.12864925e-02
-1.05866241e+00 8.13848555e-01 -5.82517326e-01 -7.34103844e-03
-8.62468854e-02 -6.45141304e-01 -6.55068934e-01 -1.40209961e+00
3.58082294e-01 5.64772010e-01 1.68370768e-01 6.87908173... | [12.837176322937012, -2.788691759109497] |
99122de3-2c7e-4c34-ab0b-446763a09276 | adaptive-experimental-design-and | 2210.14369 | null | https://arxiv.org/abs/2210.14369v1 | https://arxiv.org/pdf/2210.14369v1.pdf | Adaptive Experimental Design and Counterfactual Inference | Adaptive experimental design methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods. This paper shares lessons learned regarding the challenges and pitfalls of naively using adaptive experimentation systems in in... | ['Lalit Jain', 'Houssam Nassif', 'Arick Chen', 'Sergio Gamez', 'Tanner Fiez'] | 2022-10-25 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.58593792e-01 -1.62059635e-01 -5.74851573e-01 -2.93745905e-01
-2.46629730e-01 -6.86666369e-01 3.75126690e-01 -3.76483142e-01
-3.43269706e-01 1.20324564e+00 -2.97979087e-01 -1.24674666e+00
-5.96133411e-01 -4.54421967e-01 -6.69836819e-01 -3.59976172e-01
-3.40892136e-01 2.66500205e-01 -1.11641616e-01 1.64503455... | [4.767499923706055, 2.4916179180145264] |
c922b686-036d-40cf-8b1c-8f2dfb6b29fa | acne-severity-grading-on-face-images-via | null | null | https://ieeexplore.ieee.org/document/9995101 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9995101 | Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge | Acne Vulgaris seriously affects people’s daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowled... | ['Xue Cheng', 'Jing Yang', 'Haiyan You', 'Xiguang Liu', 'Yi Guan', 'Zhaoyang Ma', 'Dongxin Chen', 'Jingchi Jiang', 'Yi Lin'] | 2023-01-02 | null | null | null | ieee-international-conference-on-6 | ['acne-severity-grading'] | ['medical'] | [ 2.15460956e-01 4.65281084e-02 -6.25567019e-01 -4.11561400e-01
-3.50794554e-01 -1.81840375e-01 1.71271190e-01 -2.46006146e-01
-7.56512508e-02 6.08265340e-01 -1.63409352e-01 7.86439627e-02
-1.31521255e-01 -9.38684583e-01 -9.35265943e-02 -7.21027374e-01
3.71054858e-01 4.33211714e-01 5.38403690e-01 -1.99421644... | [15.69981861114502, -2.970245361328125] |
b3dded28-2437-4cc4-8d2f-1afc0422db01 | context-aware-video-reconstruction-for | 2205.12912 | null | https://arxiv.org/abs/2205.12912v1 | https://arxiv.org/pdf/2205.12912v1.pdf | Context-Aware Video Reconstruction for Rolling Shutter Cameras | With the ubiquity of rolling shutter (RS) cameras, it is becoming increasingly attractive to recover the latent global shutter (GS) video from two consecutive RS frames, which also places a higher demand on realism. Existing solutions, using deep neural networks or optimization, achieve promising performance. However, ... | ['Mingyi He', 'Qi Liu', 'Zhiyuan Zhang', 'Yuchao Dai', 'Bin Fan'] | 2022-05-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Fan_Context-Aware_Video_Reconstruction_for_Rolling_Shutter_Cameras_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Fan_Context-Aware_Video_Reconstruction_for_Rolling_Shutter_Cameras_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-reconstruction', 'motion-compensation'] | ['computer-vision', 'computer-vision'] | [ 8.51704627e-02 -5.23624241e-01 -2.00704873e-01 -2.02436849e-01
-4.68561590e-01 -2.80791432e-01 4.66901422e-01 -5.09172440e-01
-2.69411206e-01 7.06484675e-01 2.00634763e-01 -2.87592053e-01
2.21941620e-01 -5.64178109e-01 -6.75656796e-01 -7.91904211e-01
4.54420745e-01 -3.91812146e-01 2.22819820e-01 -3.76867913... | [10.70122241973877, -1.5325424671173096] |
4b57de34-1321-4649-b015-c76cde908c77 | towards-automatic-short-answer-assessment-for | null | null | https://aclanthology.org/2022.bea-1.30 | https://aclanthology.org/2022.bea-1.30.pdf | Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task | Automatic grouping of textual answers has the potential of allowing batch grading, but is challenging because the answers, especially longer essays, have many claims. To explore the feasibility of grouping together answers based on their semantic meaning, this paper investigates the grouping of short textual answers, p... | ['Filip Ginter', 'Jenna Kanerva', 'Li-Hsin Chang'] | null | null | null | null | naacl-bea-2022-7 | ['paraphrase-identification'] | ['natural-language-processing'] | [-6.50768876e-02 1.88717812e-01 -2.01246560e-01 -5.08291721e-01
-8.20967376e-01 -8.78515124e-01 5.56571186e-01 5.54519713e-01
-4.62452143e-01 4.92921770e-01 7.94120848e-01 -5.83985984e-01
-4.28575456e-01 -5.93379557e-01 -2.62351245e-01 -1.80108830e-01
7.36145616e-01 4.24630344e-01 1.26240224e-01 -8.70702565... | [11.30352783203125, 9.24051570892334] |
b357adcb-88f0-4307-907c-3be2e21faec3 | machine-learning-based-assessment-of-energy | 2111.08295 | null | https://arxiv.org/abs/2111.08295v1 | https://arxiv.org/pdf/2111.08295v1.pdf | Machine Learning-Based Assessment of Energy Behavior of RC Shear Walls | Current seismic design codes primarily rely on the strength and displacement capacity of structural members and do not account for the influence of the ground motion duration or the hysteretic behavior characteristics. The energy-based approach serves as a supplemental index to response quantities and includes the effe... | ['Zeynep Tuna Deger', 'Fatih Sutcu', 'Gulsen Taskin Kaya', 'Berkay Topaloglu'] | 2021-11-16 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-3.69268917e-02 -2.06003323e-01 4.97054867e-02 5.65789863e-02
-5.04952133e-01 -1.83435515e-01 3.38396490e-01 5.47630131e-01
-2.23649517e-01 5.38346648e-01 4.08214390e-01 -2.87884504e-01
-8.78490925e-01 -9.37717378e-01 -2.93311238e-01 -1.11083674e+00
-3.87570888e-01 4.91501987e-02 4.25641596e-01 -3.72641206... | [6.323398113250732, 3.0178449153900146] |
65944591-73f0-4765-ae5b-fbf09eabd793 | a-fault-localization-and-debugging-support | 2103.02386 | null | https://arxiv.org/abs/2103.02386v1 | https://arxiv.org/pdf/2103.02386v1.pdf | A Fault Localization and Debugging Support Framework driven by Bug Tracking Data | Fault localization has been determined as a major resource factor in the software development life cycle. Academic fault localization techniques are mostly unknown and unused in professional environments. Although manual debugging approaches can vary significantly depending on bug type (e.g. memory bugs or semantic bug... | ['Thomas Hirsch'] | 2021-03-03 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-4.02020603e-01 -3.86294186e-01 -2.69956082e-01 -2.71023899e-01
-3.52048934e-01 -6.11541808e-01 4.11518570e-03 5.75778782e-01
3.98824543e-01 5.94639361e-01 -2.54387796e-01 -4.03730810e-01
-5.75623035e-01 -6.37940526e-01 -3.75570863e-01 -6.33960404e-03
-5.03499694e-02 -8.43945611e-03 2.96857685e-01 -9.53378826... | [7.579617500305176, 7.70314884185791] |
0f2874c2-3515-4ab6-9158-17dcb0bcfcc8 | a-multi-purpose-audio-visual-corpus-for-multi | 2301.1018 | null | https://arxiv.org/abs/2301.10180v1 | https://arxiv.org/pdf/2301.10180v1.pdf | A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset | In recent years, significant progress has been made in automatic lip reading. But these methods require large-scale datasets that do not exist for many low-resource languages. In this paper, we have presented a new multipurpose audio-visual dataset for Persian. This dataset consists of almost 220 hours of videos with 1... | ['Nasser Mozayani', 'Mohammad Reza Mohammadi', 'Hossein Zeinali', 'Ali Lashini', 'Samin Heydarian', 'Javad Peymanfard'] | 2023-01-21 | null | null | null | null | ['speaker-recognition', 'audio-visual-speech-recognition'] | ['speech', 'speech'] | [ 1.40765309e-01 3.37662771e-02 -3.39439034e-01 -8.33312608e-03
-1.17746139e+00 -2.17424765e-01 6.78792894e-01 -5.16875237e-02
-4.25256670e-01 8.28141153e-01 5.57684183e-01 -1.19926833e-01
4.36710477e-01 -3.41377467e-01 -4.18969780e-01 -7.39988148e-01
4.60181355e-01 3.75733674e-01 3.16914320e-01 1.89556271... | [14.312867164611816, 5.010200023651123] |
b6d9d9bd-697b-4285-92a3-a444c2e98295 | vector-quantized-semantic-communication | 2209.11519 | null | https://arxiv.org/abs/2209.11519v2 | https://arxiv.org/pdf/2209.11519v2.pdf | Vector Quantized Semantic Communication System | Although analog semantic communication systems have received considerable attention in the literature, there is less work on digital semantic communication systems. In this paper, we develop a deep learning (DL)-enabled vector quantized (VQ) semantic communication system for image transmission, named VQ-DeepSC. Specifi... | ['Xiaoming Tao', 'Gregory Slabaugh', 'Zhijin Qin', 'Huiqiang Xie', 'Qifan Fu'] | 2022-09-23 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 4.95120555e-01 1.09259717e-01 -5.69332913e-02 -3.55968833e-01
-6.67302251e-01 -3.83335888e-01 5.03420889e-01 -2.81047523e-01
-1.93735257e-01 6.13309383e-01 2.50754923e-01 -3.79068345e-01
-8.27682465e-02 -1.13142049e+00 -6.29303932e-01 -6.42294228e-01
8.00478309e-02 -4.47226405e-01 7.13561326e-02 -4.94701922... | [11.285303115844727, -1.7512351274490356] |
1b9c82bb-4ad2-45e6-b434-08b1eceb6c6b | neural-semi-markov-crf-for-monolingual-word | 2106.02569 | null | https://arxiv.org/abs/2106.02569v2 | https://arxiv.org/pdf/2106.02569v2.pdf | Neural semi-Markov CRF for Monolingual Word Alignment | Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc. In this paper, we present a novel neural semi-Markov CRF alignment mo... | ['Wei Xu', 'Chao Jiang', 'Wuwei Lan'] | 2021-06-04 | null | https://aclanthology.org/2021.acl-long.531 | https://aclanthology.org/2021.acl-long.531.pdf | acl-2021-5 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 6.03534102e-01 3.77810746e-02 -2.51633108e-01 -4.65116888e-01
-1.22403324e+00 -5.96916676e-01 6.88650608e-01 1.50880232e-01
-5.92247367e-01 1.22006333e+00 5.75064540e-01 -6.71002030e-01
4.26032305e-01 -4.64553446e-01 -6.91348255e-01 -3.57700020e-01
7.45920539e-01 1.05901337e+00 -3.56108010e-01 -7.58400023... | [11.333330154418945, 10.261322021484375] |
40fc8c70-a5dd-46be-8fba-dc7f94a60315 | evaluation-of-dynamic-causal-modelling-and | 2306.15859 | null | https://arxiv.org/abs/2306.15859v1 | https://arxiv.org/pdf/2306.15859v1.pdf | Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neurons | Inferring the mechanisms underlying physiological and pathological processes in the brain from recorded electrical activity is challenging. Bayesian model selection and dynamic causal modelling aim to identify likely biophysical models to explain data and to fit the model parameters. Here, we use data generated by simu... | ['Matthew G. Thomas'] | 2023-06-28 | null | null | null | null | ['model-selection'] | ['methodology'] | [ 6.17134929e-01 -7.26686567e-02 2.09118709e-01 1.39794827e-01
4.48861942e-02 -3.33966136e-01 8.83858323e-01 2.59566102e-02
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-3.32415909e-01 4.19183314e-01 5.07016361e-01 2.16815948... | [7.873845100402832, 3.036752223968506] |
922fc2cb-f1f0-4067-a233-d0ab789c2b2e | 2305-14386 | 2305.14386 | null | https://arxiv.org/abs/2305.14386v1 | https://arxiv.org/pdf/2305.14386v1.pdf | Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation | In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models. Our approach is designed to consider the student model's weaknesses and foster a tailored learning experience by generating targeted exercises a... | ['Ashwin Kaylan', 'Xiangliang Zhang', 'Peter Clark', 'Tanmay Rajpurohit', 'Wenhao Yu', 'Zhenwen Liang'] | 2023-05-22 | null | null | null | null | ['math-word-problem-solving', 'knowledge-tracing', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'miscellaneous', 'reasoning', 'time-series'] | [ 1.95947126e-01 2.29217276e-01 -2.58670717e-01 -1.57395303e-01
-8.12785327e-01 -9.60997045e-01 3.83895129e-01 3.89855534e-01
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3.19320232e-01 4.04505372e-01 3.90027732e-01 -3.21170568... | [10.005475044250488, 7.333791255950928] |
9d323f76-b661-40df-993e-5c83ab051b89 | a-single-image-dehazing-technique-using-the | null | null | https://ieeexplore.ieee.org/document/9458242 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9458242 | A Single Image Dehazing Technique Using the Dual Transmission Maps Strategy and Gradient-Domain Guided Image Filtering | In this paper, a single image dehazing technique using dual transmission maps strategy and gradient-domain guided image filtering is presented. A new strategy is adopted to compute the dual transmission maps using the dark channel and atmospheric light. Further, the transmission maps are refined to remove any remaining... | ['A. Ullah and E. Elbasi', 'M. Imran', 'S. M. Ehsan'] | 2021-07-17 | null | null | null | journal-2021-7 | ['image-dehazing'] | ['computer-vision'] | [ 4.00193006e-01 -6.01149261e-01 7.96301007e-01 -3.14958543e-02
-2.20337614e-01 -7.98237398e-02 4.25179899e-01 -3.97268355e-01
-4.39967841e-01 8.98756981e-01 -4.03910391e-02 6.63505122e-03
-1.58257574e-01 -9.16657269e-01 -2.51373559e-01 -1.37866890e+00
-4.04264554e-02 -4.72215921e-01 7.80439317e-01 -4.90397364... | [10.846946716308594, -3.1593830585479736] |
0033596c-ac93-48ed-b71a-53208359c5c8 | enhance-enriching-health-data-by-annotations | 2107.12734 | null | https://arxiv.org/abs/2107.12734v2 | https://arxiv.org/pdf/2107.12734v2.pdf | ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification | We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visual ABC (asymmetry, border, colour) features from non-expert annotation sources: undergraduate students, crowd workers from Amazon MTurk and ... | ['Veronika Cheplygina', 'Josien P. W. Pluim', 'Max Joosten', 'Gerard Schouten', 'Ralf Raumanns'] | 2021-07-27 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 8.92293304e-02 1.75475180e-01 -2.64212549e-01 -2.39569157e-01
-9.62058127e-01 -1.03559470e+00 5.71181536e-01 5.32693565e-01
-6.00200891e-01 5.47780633e-01 1.29655540e-01 2.77462490e-02
-1.48021847e-01 -3.28106016e-01 -3.68848622e-01 -6.77240968e-01
2.42445111e-01 4.70255047e-01 7.60933936e-01 -5.37714846... | [15.663431167602539, -2.9090678691864014] |
4acb536c-2a26-4d6f-a139-6c42f615885d | eusdisparser-improving-an-under-resourced | null | null | https://aclanthology.org/W19-2709 | https://aclanthology.org/W19-2709.pdf | EusDisParser: improving an under-resourced discourse parser with cross-lingual data | Development of discourse parsers to annotate the relational discourse structure of a text is crucial for many downstream tasks. However, most of the existing work focuses on English, assuming a quite large dataset. Discourse data have been annotated for Basque, but training a system on these data is challenging since t... | ["Chlo{\\'e} Braud", 'Mikel Iruskieta'] | 2019-06-01 | null | null | null | ws-2019-6 | ['multilingual-word-embeddings'] | ['methodology'] | [-1.81237325e-01 7.28531837e-01 5.57918698e-02 -4.11790371e-01
-9.30762053e-01 -8.60814035e-01 9.82861817e-01 5.29202580e-01
-7.01155782e-01 1.11867988e+00 7.57769823e-01 -4.21433032e-01
2.08557293e-01 -7.29247153e-01 -5.53616822e-01 -4.30265903e-01
1.44028133e-02 8.97635996e-01 6.74888372e-01 -8.64172876... | [10.768061637878418, 9.513350486755371] |
a2c7aed5-adda-4cb9-b032-09b583372b06 | implicit-feedback-deep-collaborative | 2009.0895 | null | https://arxiv.org/abs/2009.08950v2 | https://arxiv.org/pdf/2009.08950v2.pdf | Implicit Feedback Deep Collaborative Filtering Product Recommendation System | In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing behaviours. The latent factors are used to generalize the purchasing pattern of the customers and to provide prod... | ['Yuri Lawryshyn', 'Deepa Kundur', 'Karthik Raja Kalaiselvi Bhaskar'] | 2020-09-08 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 4.70125377e-02 -1.63473248e-01 -6.90302610e-01 -9.80174303e-01
-2.28263170e-01 -3.42306942e-01 4.78928715e-01 1.80929840e-01
-2.43047073e-01 6.78891838e-01 4.61216241e-01 -5.22276759e-01
-7.30656683e-01 -7.58310080e-01 -1.04499944e-01 -5.81866682e-01
-3.29137176e-01 4.45619315e-01 -2.57996917e-01 -2.64510423... | [10.007655143737793, 5.7537150382995605] |
d8109521-21d5-4583-b53d-37b14ce6479f | real-word-error-correction-with-trigrams | 2302.04096 | null | https://arxiv.org/abs/2302.04096v1 | https://arxiv.org/pdf/2302.04096v1.pdf | Real-Word Error Correction with Trigrams: Correcting Multiple Errors in a Sentence | Spelling correction is a fundamental task in Text Mining. In this study, we assess the real-word error correction model proposed by Mays, Damerau and Mercer and describe several drawbacks of the model. We propose a new variation which focuses on detecting and correcting multiple real-word errors in a sentence, by manip... | ['Seyed MohammadSadegh Dashti'] | 2023-02-07 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 1.33783549e-01 -1.18352294e-01 -3.98343243e-02 -2.39406079e-01
-6.96848571e-01 -2.60478884e-01 3.93198401e-01 8.36310387e-01
-9.63231087e-01 1.05292201e+00 1.69906661e-01 -6.95884049e-01
-5.45513034e-01 -6.56329155e-01 -2.11975887e-01 -6.95759207e-02
1.28248528e-01 4.28015321e-01 6.08578980e-01 -3.68941724... | [10.878190994262695, 10.62899398803711] |
c86f7ce9-f99b-4777-9984-8c0f4b89ee9e | is-it-possible-not-to-cheat-on-the-turing | 2206.14672 | null | https://arxiv.org/abs/2206.14672v4 | https://arxiv.org/pdf/2206.14672v4.pdf | Not Cheating on the Turing Test: Towards Grounded Language Learning in Artificial Intelligence | Recent hype surrounding the increasing sophistication of language processing models has renewed optimism regarding machines achieving a human-like command of natural language. Research in the area of natural language understanding (NLU) in artificial intelligence claims to have been making great strides in this area, h... | ['Lize Alberts'] | 2022-06-29 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 3.54529798e-01 6.08104408e-01 -1.97544217e-01 -4.20493722e-01
1.02586458e-02 -6.38191879e-01 7.14903772e-01 6.38046086e-01
-3.46164912e-01 2.55951554e-01 3.98118049e-01 -1.01891756e+00
-1.75832957e-01 -6.90466285e-01 -2.64528215e-01 -1.83084402e-02
3.70335191e-01 2.74724036e-01 -1.85180813e-01 -6.25024319... | [10.18826961517334, 8.341496467590332] |
8deb673f-143b-473e-83b4-dc920cae584e | attribute-based-chinese-named-entity | null | null | https://aclanthology.org/W12-6324 | https://aclanthology.org/W12-6324.pdf | Attribute based Chinese Named Entity Recognition and Disambiguation | null | ['Zhenni Huang', 'Wei Han', 'Guang Liu', 'Yuzhao Mao'] | 2012-12-01 | attribute-based-chinese-named-entity-1 | https://aclanthology.org/W12-6324 | https://aclanthology.org/W12-6324.pdf | ws-2012-12 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.419615268707275, 3.7974092960357666] |
abfdc6d5-0f5f-4d3b-803f-855b7802f4cc | large-scale-learning-on-non-homophilous | 2110.14446 | null | https://arxiv.org/abs/2110.14446v1 | https://arxiv.org/pdf/2110.14446v1.pdf | Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods | Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networks (GNNs) have been developed that move beyond the homophily regime; however, their evaluation has often been conducted on small graphs with... | ['Ser-Nam Lim', 'Omkar Bhalerao', 'Vaishnavi Gupta', 'Sijia Linda Huang', 'Xiuyu Li', 'Felix Hohne', 'Derek Lim'] | 2021-10-27 | null | http://proceedings.neurips.cc/paper/2021/hash/ae816a80e4c1c56caa2eb4e1819cbb2f-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ae816a80e4c1c56caa2eb4e1819cbb2f-Paper.pdf | neurips-2021-12 | ['node-classification-on-non-homophilic'] | ['graphs'] | [-1.87386498e-01 3.34427267e-01 -6.08152628e-01 -2.35434428e-01
-9.71537679e-02 -4.79865402e-01 5.73491454e-01 3.32637519e-01
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-2.48579741e-01 -1.12538934e+00 -8.37506175e-01 -5.05868435e-01
-8.13753486e-01 8.13698411e-01 4.75601137e-01 -1.38273714... | [6.975368499755859, 6.186704158782959] |
b8de7f9f-3aab-4232-aa66-37d734bec43f | exploring-efficient-volumetric-medical-image | 2010.06163 | null | https://arxiv.org/abs/2010.06163v2 | https://arxiv.org/pdf/2010.06163v2.pdf | Bridging 2D and 3D Segmentation Networks for Computation Efficient Volumetric Medical Image Segmentation: An Empirical Study of 2.5D Solutions | Recently, deep convolutional neural networks have achieved great success for medical image segmentation. However, unlike segmentation of natural images, most medical images such as MRI and CT are volumetric data. In order to make full use of volumetric information, 3D CNNs are widely used. However, 3D CNNs suffer from ... | ['Jicong Zhang', 'Le Ding', 'Qingcheng Liao', 'Yichi Zhang'] | 2020-10-13 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 8.42434242e-02 1.97113752e-01 -3.02746445e-01 -4.24773872e-01
-6.44400656e-01 -2.62697786e-01 1.74036846e-01 3.45743269e-01
-5.73426008e-01 6.36735260e-01 -1.44224539e-01 -4.51176077e-01
5.96024059e-02 -8.59675467e-01 -4.31563199e-01 -6.12644672e-01
-3.23836386e-01 5.84212184e-01 5.52686334e-01 8.83216634... | [14.445869445800781, -2.4330620765686035] |
16dc90f8-d8d9-4d9a-b214-2eb544090b7b | variance-covariance-regularization-improves | 2306.13292 | null | https://arxiv.org/abs/2306.13292v1 | https://arxiv.org/pdf/2306.13292v1.pdf | Variance-Covariance Regularization Improves Representation Learning | Transfer learning has emerged as a key approach in the machine learning domain, enabling the application of knowledge derived from one domain to improve performance on subsequent tasks. Given the often limited information about these subsequent tasks, a strong transfer learning approach calls for the model to capture a... | ['Yann Lecun', 'Yubei Chen', 'Ravid Shwartz-Ziv', 'Jiachen Zhu'] | 2023-06-23 | null | null | null | null | ['self-supervised-learning', 'transfer-learning'] | ['computer-vision', 'miscellaneous'] | [ 4.12173390e-01 -4.40424532e-02 -2.83337474e-01 -5.05201697e-01
-5.18060565e-01 -3.14030528e-01 4.59888309e-01 1.98236987e-01
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-4.29727852e-01 -8.63969445e-01 -7.64349520e-01 -6.59429371e-01
6.26884922e-02 4.84656654e-02 7.67802522e-02 -2.50055939... | [9.511385917663574, 3.0520811080932617] |
91a8da5e-7081-438b-b3e3-c17d8315f5a5 | efficient-and-accurate-monitoring-of-the | 1706.08088 | null | http://arxiv.org/abs/1706.08088v1 | http://arxiv.org/pdf/1706.08088v1.pdf | Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance | Wireless Multimedia Sensor Network (WMSN) is a promising technology capturing
rich multimedia data like audio and video, which can be useful to monitor an
environment under surveillance. However, many scenarios in real time monitoring
requires 3D depth information. In this research work, we propose to use the
disparity... | ['Rony Darazi', 'Anthony Tannoury', 'Christophe Guyeux', 'Abdallah Makhoul'] | 2017-06-25 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 8.01336706e-01 5.72316535e-02 1.37308657e-01 -2.59481758e-01
-2.28726357e-01 -3.76769990e-01 3.79475683e-01 4.31508303e-01
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-1.11616261e-01 -1.53837800e+00 -3.36558998e-01 -9.73408163e-01
-4.38794255e-01 5.37358187e-02 1.04162180e+00 3.66592743... | [8.581315994262695, -1.271619200706482] |
9dae68fe-3b8c-441e-a6a9-a8ad12154bc7 | point-spread-function-estimation-for-blind | 2112.11004 | null | https://arxiv.org/abs/2112.11004v1 | https://arxiv.org/pdf/2112.11004v1.pdf | Point spread function estimation for blind image deblurring problems based on framelet transform | One of the most important issues in the image processing is the approximation of the image that has been lost due to the blurring process. These types of matters are divided into non-blind and blind problems. The second type of problem is more complex in terms of calculations than the first problems due to the unknown ... | ['Reza Parvaz'] | 2021-12-21 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [-6.13888167e-02 -3.17041993e-01 5.78326583e-01 -1.15505278e-01
1.49757624e-01 -1.54848188e-01 2.67733753e-01 -2.68465310e-01
-4.66781795e-01 1.05113220e+00 5.81822574e-01 1.06845617e-01
-4.47522223e-01 -3.54250342e-01 -2.77523488e-01 -5.24839818e-01
2.54777938e-01 2.41332818e-02 8.93476531e-02 -2.05316275... | [11.599422454833984, -2.660295009613037] |
cbf77879-58cc-4aac-a7db-76cc57fbcee7 | ordinal-depth-supervision-for-3d-human-pose | 1805.04095 | null | http://arxiv.org/abs/1805.04095v1 | http://arxiv.org/pdf/1805.04095v1.pdf | Ordinal Depth Supervision for 3D Human Pose Estimation | Our ability to train end-to-end systems for 3D human pose estimation from
single images is currently constrained by the limited availability of 3D
annotations for natural images. Most datasets are captured using Motion Capture
(MoCap) systems in a studio setting and it is difficult to reach the
variability of 2D human ... | ['Xiaowei Zhou', 'Georgios Pavlakos', 'Kostas Daniilidis'] | 2018-05-10 | ordinal-depth-supervision-for-3d-human-pose-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Pavlakos_Ordinal_Depth_Supervision_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Pavlakos_Ordinal_Depth_Supervision_CVPR_2018_paper.pdf | cvpr-2018-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-1.33768901e-01 1.58299685e-01 -1.51682273e-01 -2.80557513e-01
-7.44655430e-01 -6.36120737e-01 5.99699020e-01 -2.36923695e-01
-9.15813267e-01 5.29294550e-01 2.72035480e-01 3.69408101e-01
7.73254037e-02 -1.59771353e-01 -7.28153527e-01 -2.60674953e-01
-2.67700851e-01 7.96716154e-01 4.11368966e-01 -4.68499005... | [7.0401105880737305, -0.895715057849884] |
9d8ed2f7-3b3c-4b06-86e8-01c36f488b9a | an-open-source-part-of-speech-tagger-for | null | null | https://aclanthology.org/L14-1622 | https://aclanthology.org/L14-1622.pdf | An open source part-of-speech tagger for Norwegian: Building on existing language resources | This paper presents an open source part-of-speech tagger for the Norwegian language. It describes how an existing language processing library (FreeLing) was used to build a new part-of-speech tagger for this language. This part-of-speech tagger has been built on already available resources, in particular a Norwegian di... | ["Cristina S{\\'a}nchez Marco"] | 2014-05-01 | null | null | null | lrec-2014-5 | ['morphological-tagging'] | ['natural-language-processing'] | [-3.74535829e-01 4.07532632e-01 1.45624861e-01 -5.04455745e-01
-9.98105109e-01 -5.91588557e-01 7.79316008e-01 4.60913628e-01
-7.68449008e-01 5.41854620e-01 6.57770574e-01 -3.73046458e-01
1.59348100e-01 -5.90367734e-01 6.20527891e-04 -2.41653457e-01
-8.02238658e-02 8.95094454e-01 7.49418318e-01 -8.01028490... | [10.300787925720215, 10.089329719543457] |
55f7121c-5fe1-4516-bdcb-f6be2064e145 | a-random-forest-and-current-fault-texture | 2211.03789 | null | https://arxiv.org/abs/2211.03789v1 | https://arxiv.org/pdf/2211.03789v1.pdf | A Random Forest and Current Fault Texture Feature-Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR | Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random... | ['Ya-nan Dong', 'Quan-de Yuan', 'Yang Li', 'Xiu-hui Ni', 'Yi Zheng', 'Xiao-dong Gong', 'Lei Kou'] | 2022-11-08 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 2.79734910e-01 -7.51254082e-01 -3.06101471e-01 6.03907816e-02
-4.00448106e-02 -2.65065879e-01 2.15262443e-01 -3.49364251e-01
3.47844958e-01 6.29957020e-01 -2.92181104e-01 -3.73610586e-01
-7.29795933e-01 -7.46802032e-01 -2.12752298e-02 -1.17254925e+00
4.57484387e-02 9.37140882e-02 4.38403070e-01 -1.76828071... | [6.492737293243408, 2.373523473739624] |
f104b05b-3b84-434c-92df-b6b18b0a028e | a-comparative-genomic-analysis-of-coronavirus | 2107.06282 | null | https://arxiv.org/abs/2107.06282v1 | https://arxiv.org/pdf/2107.06282v1.pdf | A Comparative Genomic Analysis of Coronavirus Families Using Chaos Game Representation and Fisher-Shannon Complexity | From its first emergence in Wuhan, China in December, 2019 the COVID-19 pandemic has caused unprecedented health crisis throughout the world. The novel coronavirus disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which belongs to the coronaviridae family. In this paper, a comparative ge... | ['S. K. Laha'] | 2021-07-13 | null | null | null | null | ['information-plane'] | ['methodology'] | [ 6.23356886e-02 -6.23875856e-01 2.66037226e-01 2.06645995e-01
-2.71241311e-02 -9.46883976e-01 2.16208503e-01 3.25463444e-01
-2.85010010e-01 6.28340542e-01 1.06171697e-01 -6.82931483e-01
-3.71090055e-01 -4.77860183e-01 6.88507780e-02 -6.73668444e-01
-8.44038069e-01 6.18003368e-01 -2.65051335e-01 -2.22054005... | [4.981790065765381, 5.26345157623291] |
717b3b15-3c88-4343-b3eb-3e628dd3edef | a-learning-approach-for-joint-design-of-event | 2205.0707 | null | https://arxiv.org/abs/2205.07070v1 | https://arxiv.org/pdf/2205.07070v1.pdf | A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation | In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5G) wi... | ['Mehdi Rasti', 'Atefeh Termehchi'] | 2022-05-14 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 1.31855205e-01 5.45073688e-01 -5.33749223e-01 8.08867291e-02
-3.50599498e-01 -4.77896452e-01 -1.12072192e-02 -9.83887538e-02
-4.95749433e-03 1.13497996e+00 -4.97088194e-01 -5.00369787e-01
-5.66322029e-01 -8.43394518e-01 -3.78677905e-01 -1.19655335e+00
-3.49496335e-01 6.87420368e-02 -6.53766170e-02 2.07717597... | [5.8379130363464355, 1.6787192821502686] |
d43781b4-2025-4d84-8ae2-6265fe8a472e | range-gan-range-constrained-generative | 2103.0623 | null | https://arxiv.org/abs/2103.06230v1 | https://arxiv.org/pdf/2103.06230v1.pdf | Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis | Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the tr... | ['Faez Ahmed', 'Wei Chen', 'Amin Heyrani Nobari'] | 2021-03-10 | null | null | null | null | ['design-synthesis', '3d-shape-generation'] | ['adversarial', 'computer-vision'] | [ 5.24266958e-01 2.84478545e-01 -1.64905399e-01 -5.22679925e-01
-5.26740253e-01 -4.12060350e-01 1.01395920e-01 -4.03667718e-01
4.29121852e-01 6.30062282e-01 4.24826384e-01 -3.08574252e-02
-3.99439633e-01 -1.07562602e+00 -6.67241693e-01 -4.02378023e-01
3.86423528e-01 7.60383904e-01 -5.20743370e-01 -3.04678738... | [5.841895580291748, 3.3025553226470947] |
b1253f22-7e1f-42bd-b0f0-eb61e6e5c1c4 | dissecting-image-crops | 2011.11831 | null | https://arxiv.org/abs/2011.11831v4 | https://arxiv.org/pdf/2011.11831v4.pdf | Dissecting Image Crops | The elementary operation of cropping underpins nearly every computer vision system, ranging from data augmentation and translation invariance to computational photography and representation learning. This paper investigates the subtle traces introduced by this operation. For example, despite refinements to camera optic... | ['Carl Vondrick', 'Basile Van Hoorick'] | 2020-11-24 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Van_Hoorick_Dissecting_Image_Crops_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Van_Hoorick_Dissecting_Image_Crops_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-forensics', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [ 7.42894292e-01 -9.73633826e-02 7.73731768e-02 -3.51101339e-01
-2.14484632e-01 -8.07654917e-01 4.75230932e-01 2.64702737e-02
-1.51081234e-01 3.46880376e-01 2.57000476e-01 -5.63003480e-01
-9.17062908e-02 -5.32861352e-01 -1.13110399e+00 -7.17061341e-01
-2.46969834e-02 -3.93679857e-01 -9.07052904e-02 -9.01110768... | [11.495616912841797, 0.5610564351081848] |
be22e198-62b3-43b3-9014-783dd4403619 | se-gsl-a-general-and-effective-graph | 2303.09778 | null | https://arxiv.org/abs/2303.09778v1 | https://arxiv.org/pdf/2303.09778v1.pdf | SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization | Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather than an exception in real-world graphs. Existing graph structure learning (GSL) frameworks still lack robustness and interpretability. This pa... | ['Philip S. Yu', 'Chunyang Liu', 'Jia Wu', 'JianXin Li', 'Renyu Yang', 'Xiang Huang', 'Hao Peng', 'Dongcheng Zou'] | 2023-03-17 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 2.28911802e-01 7.86708653e-01 -1.81008235e-01 -1.76560953e-01
-3.49668823e-02 -3.78411382e-01 2.26913556e-01 6.88886464e-01
2.47850977e-02 8.60909820e-01 4.02874440e-01 -5.83121590e-02
-7.88935304e-01 -1.38671327e+00 -6.42803848e-01 -9.83661711e-01
-6.16708279e-01 3.57838333e-01 -2.38636676e-02 -2.99845099... | [6.974442958831787, 6.156213283538818] |
f529cef8-53dc-4e21-baf8-10624b241778 | speech-enhancement-for-virtual-meetings-on | 2302.00868 | null | https://arxiv.org/abs/2302.00868v2 | https://arxiv.org/pdf/2302.00868v2.pdf | Speech Enhancement for Virtual Meetings on Cellular Networks | We study speech enhancement using deep learning (DL) for virtual meetings on cellular devices, where transmitted speech has background noise and transmission loss that affects speech quality. Since the Deep Noise Suppression (DNS) Challenge dataset does not contain practical disturbance, we collect a transmitted DNS (t... | ['Ojas Bhargave', 'Joseph Konan', 'Minjeong Kim', 'Kawon Lee', 'Minseon Gwak', 'Hojeong Lee'] | 2023-02-02 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [-2.04048127e-01 1.23896427e-01 2.52868712e-01 -1.10744707e-01
-7.47005343e-01 -2.99480647e-01 4.79670852e-01 -5.99385500e-01
-4.04938072e-01 8.74896228e-01 8.52484584e-01 -6.51424289e-01
7.87430350e-03 -5.49345851e-01 -2.88072079e-01 -8.54864836e-01
-6.42782375e-02 -2.18966991e-01 9.13796201e-02 -6.09874070... | [14.949984550476074, 6.019769191741943] |
21fe4b73-f4af-4d3a-ae5b-a3555ef8b9f3 | functional-code-building-genetic-programming | 2206.04561 | null | https://arxiv.org/abs/2206.04561v1 | https://arxiv.org/pdf/2206.04561v1.pdf | Functional Code Building Genetic Programming | General program synthesis has become an important application area for genetic programming (GP), and for artificial intelligence more generally. Code Building Genetic Programming (CBGP) is a recently introduced GP method for general program synthesis that leverages reflection and first class specifications to support t... | ['Lee Spector', 'Thomas Helmuth', 'Edward Pantridge'] | 2022-06-09 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 2.28953287e-01 3.11006427e-01 -2.30985835e-01 -6.12369217e-02
-2.42604718e-01 -5.43929636e-01 4.34883207e-01 2.39362702e-01
1.07855454e-01 8.23817134e-01 -4.37503517e-01 -7.33544350e-01
-7.98465833e-02 -1.38352370e+00 -8.87519896e-01 -5.60107648e-01
-2.05132067e-01 1.93604037e-01 3.36208373e-01 -6.52586341... | [8.04471206665039, 7.298954486846924] |
4f5042be-f023-4657-8fb4-b1b7dd4c41c3 | one-shot-affordance-detection | 2106.14747 | null | https://arxiv.org/abs/2106.14747v1 | https://arxiv.org/pdf/2106.14747v1.pdf | One-Shot Affordance Detection | Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we consider the challenging one-shot affordance detection problem in this paper, i.e., given... | ['DaCheng Tao', 'Yang Cao', 'Jing Zhang', 'Wei Zhai', 'Hongchen Luo'] | 2021-06-28 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 1.75224438e-01 -1.37223706e-01 -1.73527479e-01 -3.37476283e-01
-1.56722471e-01 -2.74188668e-01 3.83947432e-01 -1.49886876e-01
-3.33601207e-01 1.96975738e-01 3.90270174e-01 1.84571952e-01
-3.77363175e-01 -2.75535703e-01 -7.72221804e-01 -5.21572113e-01
-2.08212450e-01 1.91731408e-01 4.12298471e-01 -3.40484768... | [5.147587776184082, -0.09687581658363342] |
6eb73d36-5dfa-4281-995f-b679568ab47c | drug-repurposing-for-cancer-an-nlp-approach | 1911.07819 | null | https://arxiv.org/abs/1911.07819v2 | https://arxiv.org/pdf/1911.07819v2.pdf | Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies | More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of generic drugs represents a major opportunity to rapidly improve outcomes f... | ['Laura B. Kleiman', 'Prasanna Sattigeri', 'Dmitriy A. Katz-Rogozhnikov', 'Shivashankar Subramanian', 'Sushma Ravichandran', 'Pradeep Mangalath', 'Kush R. Varshney', 'Karthikeyan Natesan Ramamurthy', 'Annmarie Wang', 'Ioana Baldini'] | 2019-11-18 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 3.03233296e-01 1.29012123e-01 -1.04612780e+00 -1.40249774e-01
-1.36215091e+00 -8.07061374e-01 6.17720068e-01 1.11373878e+00
-4.51055795e-01 1.10509312e+00 3.28244895e-01 -8.51630330e-01
-1.94608018e-01 -5.67985713e-01 -3.23746890e-01 -4.03447986e-01
2.40700379e-01 5.70912182e-01 9.83038452e-03 3.15537214... | [8.414144515991211, 8.656289100646973] |
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