paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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d5cd9b77-27a2-4289-bd66-935cd8e78cfc | an-algorithm-unrolling-approach-to-deep-blind | 1902.03493 | null | https://arxiv.org/abs/1902.03493v3 | https://arxiv.org/pdf/1902.03493v3.pdf | Deep Algorithm Unrolling for Blind Image Deblurring | Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed a... | ['Vishal Monga', 'Yuelong Li', 'Junyi Geng', 'Yonina C. Eldar', 'Mohammad Tofighi'] | 2019-02-09 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.91842958e-01 -3.95810813e-01 -2.18161836e-01 -1.53429717e-01
-3.56097519e-01 -2.32165501e-01 4.79767323e-01 -4.76011962e-01
-1.59824222e-01 4.50712085e-01 4.10828680e-01 -4.69420642e-01
-1.50683448e-01 -1.90814152e-01 -6.61868930e-01 -8.26498866e-01
6.99310601e-02 -8.12557042e-02 -2.08741695e-01 3.81548703... | [11.579588890075684, -2.6643214225769043] |
970c69d3-f926-4ca8-a913-7ed57d2429ec | computationally-enhanced-approach-for-chance | 2306.14527 | null | https://arxiv.org/abs/2306.14527v2 | https://arxiv.org/pdf/2306.14527v2.pdf | Computationally Enhanced Approach for Chance-Constrained OPF Considering Voltage Stability | The effective management of stochastic characteristics of renewable power generations is vital for ensuring the stable and secure operation of power systems. This paper addresses the formidable task of optimizing the chance-constrained voltage-stability-constrained optimal power flow (CC-VSC-OPF) problem, which is hind... | ['Jingtao Zhao', 'Shu Zheng', 'Wei Gu', 'Huan Long', 'Yijun Xu', 'Zhi Wu', 'Yuanxi Wu'] | 2023-06-26 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.72151580e-01 -4.63793159e-01 -2.64089614e-01 4.64547910e-02
-2.93529361e-01 -7.48744488e-01 2.08455592e-01 -1.52745157e-01
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-2.22929329e-01 -2.44463757e-01 -3.59636813e-01 -2.51018792... | [5.712527751922607, 2.6081104278564453] |
b24e284f-15f3-4360-9336-64eee0a466e5 | action-quality-assessment-with-temporal | 2207.0927 | null | https://arxiv.org/abs/2207.09270v1 | https://arxiv.org/pdf/2207.09270v1.pdf | Action Quality Assessment with Temporal Parsing Transformer | Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences. Existing state-of-the-art methods typically rely on the holistic video representations for score regression or ranking, which limits the generalization to capture fine-gr... | ['Jingdong Wang', 'Yang Long', 'Yu Guan', 'Errui Ding', 'Jian Wang', 'Songyang Zhang', 'Desen Zhou', 'Yang Bai'] | 2022-07-19 | null | null | null | null | ['action-quality-assessment', 'action-understanding'] | ['computer-vision', 'computer-vision'] | [ 4.22983736e-01 -4.58551437e-01 -6.41106963e-01 -6.36337817e-01
-1.27721000e+00 -2.74330795e-01 3.10634762e-01 -2.54033238e-01
-1.75788328e-01 4.10222709e-01 6.42019033e-01 1.55047327e-01
-2.17582345e-01 -5.39470792e-01 -6.45274639e-01 -6.10729396e-01
-3.40772793e-02 -1.62390113e-01 6.26470566e-01 -5.83348423... | [8.431574821472168, 0.7042214274406433] |
f88b4aae-923b-4da8-b9f4-a0f374797813 | grounded-keys-to-text-generation-towards | 2212.01956 | null | https://arxiv.org/abs/2212.01956v1 | https://arxiv.org/pdf/2212.01956v1.pdf | Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation | Large pre-trained language models have recently enabled open-ended generation frameworks (e.g., prompt-to-text NLG) to tackle a variety of tasks going beyond the traditional data-to-text generation. While this framework is more general, it is under-specified and often leads to a lack of controllability restricting thei... | ['Jianfeng Gao', 'Snigdha Chaturvedi', 'Bill Dolan', 'Sudha Rao', 'Michel Galley', 'Baolin Peng', 'Faeze Brahman'] | 2022-12-04 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 2.88827419e-02 6.26428902e-01 -6.83244541e-02 -3.83751035e-01
-1.39132488e+00 -8.02803993e-01 1.16033053e+00 1.99685663e-01
-3.72979343e-01 1.13180876e+00 5.61617076e-01 -1.16651915e-01
-4.00380380e-02 -9.78935540e-01 -4.34468061e-01 -1.86070710e-01
2.81295955e-01 6.30620718e-01 -2.44111288e-02 -6.05827451... | [11.815940856933594, 8.918071746826172] |
c74599f4-f1de-44e7-aa12-5998389a042f | group-shift-pointwise-convolution-for | 2109.12629 | null | https://arxiv.org/abs/2109.12629v1 | https://arxiv.org/pdf/2109.12629v1.pdf | Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation | Recent studies have witnessed the effectiveness of 3D convolutions on segmenting volumetric medical images. Compared with the 2D counterparts, 3D convolutions can capture the spatial context in three dimensions. Nevertheless, models employing 3D convolutions introduce more trainable parameters and are more computationa... | ['Yu Qiao', 'Lixu Gu', 'Shanshan Wang', 'Wanli Chen', 'Diping Song', 'Cheng Li', 'Jin Ye', 'Junjun He'] | 2021-09-26 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 1.52103305e-01 -7.46610686e-02 2.71774493e-02 -5.55537462e-01
-1.50498256e-01 -3.28095257e-01 4.21168774e-01 -3.09122000e-02
-8.40904117e-01 5.85443735e-01 -1.93061735e-02 -7.56555021e-01
-1.91318497e-01 -7.83752203e-01 -6.55398369e-01 -6.40851080e-01
-1.94748715e-01 -1.14599504e-01 3.91521335e-01 -2.28398219... | [14.512409210205078, -2.5908234119415283] |
0ea92955-23d2-4626-87ab-c4175828abbb | towards-a-structured-representation-of | null | null | https://aclanthology.org/R13-1009 | https://aclanthology.org/R13-1009.pdf | Towards a Structured Representation of Generic Concepts and Relations in Large Text Corpora | null | ['Archana Bhattarai', 'Vasile Rus'] | 2013-09-01 | towards-a-structured-representation-of-1 | https://aclanthology.org/R13-1009 | https://aclanthology.org/R13-1009.pdf | ranlp-2013-9 | ['open-information-extraction'] | ['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... | [-7.279925346374512, 3.847116470336914] |
9cd6bc87-84c8-490f-8a02-e9cacd60d69a | detecting-and-mitigating-hallucinations-in-1 | 2305.13632 | null | https://arxiv.org/abs/2305.13632v1 | https://arxiv.org/pdf/2305.13632v1.pdf | Detecting and Mitigating Hallucinations in Multilingual Summarisation | Hallucinations pose a significant challenge to the reliability of neural models for abstractive summarisation. While automatically generated summaries may be fluent, they often lack faithfulness to the original document. This issue becomes even more pronounced in low-resource settings, such as cross-lingual transfer. W... | ['Shay B. Cohen', 'Edoardo M. Ponti', 'Anna Korhonen', 'Yftah Ziser', 'Yifu Qiu'] | 2023-05-23 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-4.58165491e-03 5.11379093e-02 -1.37652770e-01 -3.36472601e-01
-1.39507210e+00 -5.85968375e-01 7.68658519e-01 3.23862433e-01
-4.03470963e-01 9.56155241e-01 9.20945585e-01 9.89009589e-02
2.11060718e-01 -4.78682339e-01 -6.46602690e-01 -3.17457587e-01
3.49486381e-01 2.92579353e-01 -2.89657056e-01 -3.20611507... | [12.130143165588379, 9.319857597351074] |
3db6a1be-de6d-4ad4-92c4-d39ca53b23f9 | phase-retrieval-exact-solution-based-on | 2005.05542 | null | http://arxiv.org/abs/2005.05542v1 | http://arxiv.org/pdf/2005.05542v1.pdf | Phase-retrieval exact solution based on window modulation | Quantitative phase imaging (QPI) is a promising tool for imaging complex
objects. By Combining self-reference interferometry and phase retrieval, this
paper proposes a general exact QPI for arbitrary complex-objects as well as a
one-shot exact QPI for transparent objects with small phase range (i.e., weak
scattering ob... | [] | 2020-05-12 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 4.54231322e-01 -2.71900296e-01 1.12978205e-01 -2.54507452e-01
-3.52886647e-01 -1.38892472e-01 6.11028560e-02 -4.31260586e-01
-6.21741533e-01 8.17899048e-01 -5.25080979e-01 1.57237664e-01
-4.35325205e-01 -8.61206472e-01 -3.63281518e-01 -1.37051809e+00
9.19513628e-02 7.05666840e-01 5.10988176e-01 -2.37861928... | [11.064508438110352, -2.595252513885498] |
699326b0-ad16-4c51-8714-5ca423b43c90 | clinicalgpt-large-language-models-finetuned | 2306.09968 | null | https://arxiv.org/abs/2306.09968v1 | https://arxiv.org/pdf/2306.09968v1.pdf | ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation | Large language models have exhibited exceptional performance on various Natural Language Processing (NLP) tasks, leveraging techniques such as the pre-training, and instruction fine-tuning. Despite these advances, their effectiveness in medical applications is limited, due to challenges such as factual inaccuracies, re... | ['Xiaohu Li', 'Longjun Fan', 'Zongxin Du', 'Guoxing Yang', 'Guangyu Wang'] | 2023-06-16 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [ 5.33592068e-02 6.09786630e-01 -3.87625337e-01 -5.18168747e-01
-1.08633697e+00 -3.81801456e-01 1.56202450e-01 7.85676301e-01
-5.30009627e-01 8.31699729e-01 5.89706481e-01 -6.84100628e-01
-3.64205569e-01 -6.17974818e-01 -2.51501709e-01 -1.29193529e-01
-2.88723456e-03 9.88956630e-01 -2.35616770e-02 -3.68620604... | [8.725122451782227, 8.598854064941406] |
660bc6a4-1298-4a91-8756-7394c2d3fdde | zero-shot-motor-health-monitoring-by-blind | 2212.06154 | null | https://arxiv.org/abs/2212.06154v1 | https://arxiv.org/pdf/2212.06154v1.pdf | Zero-Shot Motor Health Monitoring by Blind Domain Transition | Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data... | ['Moncef Gabbouj', 'Onur Avci', 'Osama Abdeljaber', 'Turker Ince', 'Sadok Sassi', 'Amir Alhams', 'Ozer Can Devecioglu', 'Serkan Kiranyaz'] | 2022-12-12 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 6.63472831e-01 -3.27729317e-03 -7.89449140e-02 2.47929484e-01
-7.63845503e-01 -2.47582808e-01 3.09577525e-01 -9.51081067e-02
2.79415458e-01 5.00633061e-01 -4.51528549e-01 -1.43606588e-01
-6.91989576e-03 -6.75251186e-01 -9.29755867e-01 -9.23973083e-01
-1.67840555e-01 3.45685154e-01 3.28627497e-01 -8.73946100... | [6.784180641174316, 2.3818764686584473] |
6f6b3371-5466-499a-ac7c-b6684c8a328e | energy-based-residual-latent-transport-for | 2211.0682 | null | https://arxiv.org/abs/2211.06820v1 | https://arxiv.org/pdf/2211.06820v1.pdf | Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion | Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence. Differing from existing deterministic approaches, we advocate generative modeling based unsupervised point cloud completion to explore the missing correspondence. Speci... | ['Nick Barnes', 'Jing Zhang', 'Saeed Anwar', 'Shi Qiu', 'Ruikai Cui'] | 2022-11-13 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 4.24263835e-01 5.38210809e-01 1.64340675e-01 -3.40440512e-01
-9.95751202e-01 -4.90862489e-01 8.70274484e-01 -1.01135045e-01
1.05472013e-01 2.51535237e-01 1.45004824e-01 -1.72835472e-03
2.29871389e-03 -1.04791915e+00 -1.18247294e+00 -5.58302760e-01
3.12513530e-01 9.99027908e-01 -1.64103974e-02 2.26723701... | [8.733274459838867, -3.439751148223877] |
f98767d8-a845-418d-b364-424c772018dd | csmcnet-scalable-video-compressive-sensing | 2108.01522 | null | https://arxiv.org/abs/2108.01522v1 | https://arxiv.org/pdf/2108.01522v1.pdf | CSMCNet: Scalable Video Compressive Sensing Reconstruction with Interpretable Motion Estimation | Most deep network methods for compressive sensing reconstruction suffer from the black-box characteristic of DNN. In this paper, a deep neural network with interpretable motion estimation named CSMCNet is proposed. The network is able to realize high-quality reconstruction of video compressive sensing by unfolding the ... | ['Yibo Fan', 'Jinjia Zhou', 'Xiao Yan', 'Bowen Huang'] | 2021-08-03 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 3.38834435e-01 -2.35604063e-01 -1.85987920e-01 2.56940466e-03
-2.85888255e-01 1.81313902e-01 1.98380858e-01 -4.71468747e-01
-3.52560610e-01 5.91961741e-01 4.31998104e-01 -2.78804421e-01
6.75464272e-02 -7.14044929e-01 -7.41559029e-01 -7.52637565e-01
-6.80258945e-02 -2.32115611e-01 1.01455897e-01 -1.59362257... | [11.157299995422363, -2.0167059898376465] |
1b25a0df-8f26-4fe3-9f3c-f45a146bde30 | learning-correspondency-in-frequency-domain | 2207.08602 | null | https://arxiv.org/abs/2207.08602v3 | https://arxiv.org/pdf/2207.08602v3.pdf | Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints | The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening. With the development of convolutional neural networks, the performance of pansharpening methods has been improved, however, the blur... | ['Yanning Zhang', 'Xiuwei Zhang', 'Houjun He', 'Yan Zhang', 'Yinghui Xing'] | 2022-07-18 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 7.15928257e-01 -5.62716126e-01 1.17107388e-02 -1.69118032e-01
-6.03305340e-01 -1.82994902e-01 4.47567463e-01 -3.49196225e-01
-3.35417271e-01 6.12459421e-01 1.72103986e-01 2.07613140e-01
-5.89937925e-01 -1.22056246e+00 -5.56116223e-01 -1.06911588e+00
1.35434076e-01 -6.15387797e-01 1.82691723e-01 -4.94543701... | [10.193730354309082, -1.9067124128341675] |
b5ae286c-1ff7-46d5-b286-1baa76bf00ed | structure-preserving-guided-retinal-image | 1805.06625 | null | http://arxiv.org/abs/1805.06625v2 | http://arxiv.org/pdf/1805.06625v2.pdf | Structure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis | Retinal fundus photographs have been used in the diagnosis of many ocular
diseases such as glaucoma, pathological myopia, age-related macular
degeneration and diabetic retinopathy. With the development of computer
science, computer aided diagnosis has been developed to process and analyse the
retinal images automatical... | ['Jiang Liu', 'Huazhu Fu', 'Zhengguo Li', 'Zaiwang Gu', 'Damon Wing Kee Wong', 'Jun Cheng'] | 2018-05-17 | null | null | null | null | ['optic-cup-segmentation'] | ['medical'] | [ 4.37645346e-01 -1.83017269e-01 5.60060978e-01 -2.15484217e-01
-1.18743181e-01 -7.67637640e-02 3.22432876e-01 -1.87638979e-02
-4.43574101e-01 6.76478744e-01 2.80433357e-01 -2.45605141e-01
-3.25088412e-01 -6.73738360e-01 -4.33734596e-01 -8.55920792e-01
6.56457022e-02 1.21987481e-02 4.75741625e-01 1.52861521... | [15.795419692993164, -3.982178211212158] |
f7a289ea-d67e-4a7e-96a6-e5623d41509a | quinoa-a-q-function-you-infer-normalized-over | 1911.01831 | null | https://arxiv.org/abs/1911.01831v1 | https://arxiv.org/pdf/1911.01831v1.pdf | Quinoa: a Q-function You Infer Normalized Over Actions | We present an algorithm for learning an approximate action-value soft Q-function in the relative entropy regularised reinforcement learning setting, for which an optimal improved policy can be recovered in closed form. We use recent advances in normalising flows for parametrising the policy together with a learned valu... | ['Abbas Abdolmaleki', 'Martin Riedmiller', 'Nicolas Heess', 'Jost Tobias Springenberg', 'Jonas Degrave'] | 2019-11-05 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [ 1.39322296e-01 2.86234409e-01 -7.42470086e-01 1.40766576e-02
-1.11982918e+00 -8.02764535e-01 8.72937322e-01 8.95090252e-02
-8.83548200e-01 1.31912959e+00 3.61025631e-01 -4.44133282e-01
-4.60055918e-01 -4.82883126e-01 -5.37089765e-01 -1.02911592e+00
-2.66390055e-01 4.40496445e-01 1.24412648e-01 -3.42465967... | [4.1274919509887695, 2.217689037322998] |
a1ba4d3a-f2bc-4f64-89a9-5ada00bd6d93 | 3d-pop-an-automated-annotation-approach-to | 2303.13174 | null | https://arxiv.org/abs/2303.13174v1 | https://arxiv.org/pdf/2303.13174v1.pdf | 3D-POP - An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture | Recent advances in machine learning and computer vision are revolutionizing the field of animal behavior by enabling researchers to track the poses and locations of freely moving animals without any marker attachment. However, large datasets of annotated images of animals for markerless pose tracking, especially high-r... | ['Máté Nagy', 'Fumihiro Kano', 'Iain D. Couzin', 'Mathilde Delacoux', 'Junran Yang', 'Alex Hoi Hang Chan', 'Hemal Naik'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Naik_3D-POP_-_An_Automated_Annotation_Approach_to_Facilitate_Markerless_2D-3D_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Naik_3D-POP_-_An_Automated_Annotation_Approach_to_Facilitate_Markerless_2D-3D_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pose-tracking', '3d-pose-estimation', 'multiple-object-tracking', 'animal-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.62769154e-01 -5.57909250e-01 1.04941335e-02 -2.97659278e-01
-1.83000848e-01 -1.12849796e+00 2.57920533e-01 2.11271778e-01
-9.55150425e-01 3.68632197e-01 -2.15510339e-01 4.16381776e-01
-7.03620389e-02 -9.11874995e-02 -8.81574512e-01 -3.55621308e-01
-5.84898889e-01 5.15646935e-01 7.02426553e-01 -2.99731418... | [7.6869354248046875, -0.9853897094726562] |
3905b76a-c901-4340-b903-e7cf7d6eb34c | 3d-object-reconstruction-and-6d-pose | 2203.01051 | null | https://arxiv.org/abs/2203.01051v1 | https://arxiv.org/pdf/2203.01051v1.pdf | 3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects | We propose a method for 3D object reconstruction and 6D-pose estimation from 2D images that uses knowledge about object shape as the primary key. In the proposed pipeline, recognition and labeling of objects in 2D images deliver 2D segment silhouettes that are compared with the 2D silhouettes of projections obtained fr... | ['Babette Dellen', 'Florentin Wörgötter', 'Tomas Kulvicius', 'Osman Kaya', 'Marcell Wolnitza'] | 2022-03-02 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction', 'robotic-grasping'] | ['computer-vision', 'computer-vision', 'robots'] | [ 3.67632359e-01 2.50212908e-01 1.41237438e-01 -3.77235532e-01
-4.57303941e-01 -7.59627342e-01 4.16713893e-01 1.49670750e-01
-5.39673865e-01 1.64825022e-01 -6.25467062e-01 -8.49226713e-02
-6.87195212e-02 -5.14271855e-01 -8.41322780e-01 -4.75531399e-01
3.01720612e-02 1.33571768e+00 5.92113853e-01 1.51565466... | [7.483335971832275, -2.6081371307373047] |
ffa27a36-8800-4802-a174-81cc06fe89b9 | caire-an-end-to-end-empathetic-chatbot | 1907.12108 | null | https://arxiv.org/abs/1907.12108v4 | https://arxiv.org/pdf/1907.12108v4.pdf | CAiRE: An Empathetic Neural Chatbot | In this paper, we present an end-to-end empathetic conversation agent CAiRE. Our system adapts TransferTransfo (Wolf et al., 2019) learning approach that fine-tunes a large-scale pre-trained language model with multi-task objectives: response language modeling, response prediction and dialogue emotion detection. We eva... | ['Farhad Bin Siddique', 'Genta Indra Winata', 'Pascale Fung', 'Zihan Liu', 'Peng Xu', 'Jamin Shin', 'Zhaojiang Lin'] | 2019-07-28 | null | null | null | null | ['empathetic-response-generation'] | ['natural-language-processing'] | [-3.19337964e-01 5.90225458e-01 1.16030410e-01 -6.94449604e-01
-1.08322132e+00 -3.98789465e-01 8.50999415e-01 -1.19433947e-01
-6.05236948e-01 1.08058369e+00 7.97655046e-01 2.36506373e-01
3.71018410e-01 -4.86017793e-01 3.40521872e-01 -1.63028151e-01
2.16785386e-01 1.02587914e+00 -4.49075013e-01 -1.22000253... | [13.140615463256836, 7.655147552490234] |
768bb3cf-af72-4b31-a35b-4e38e2bd9e1d | probabilistic-time-series-forecasting-with | 2010.07349 | null | https://arxiv.org/abs/2010.07349v3 | https://arxiv.org/pdf/2010.07349v3.pdf | Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity | Probabilistic forecasting consists in predicting a distribution of possible future outcomes. In this paper, we address this problem for non-stationary time series, which is very challenging yet crucially important. We introduce the STRIPE model for representing structured diversity based on shape and time features, ens... | ['Nicolas Thome', 'Vincent Le Guen'] | 2020-10-14 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-1.36525571e-01 -2.11264327e-01 -1.26272023e-01 -2.91549891e-01
-7.36917853e-01 -7.95220435e-01 1.21168876e+00 1.94245260e-02
2.04824001e-01 7.23661542e-01 3.68819475e-01 -2.86952287e-01
-5.74181378e-01 -7.64337778e-01 -5.47309577e-01 -1.00239444e+00
-4.32912111e-01 5.96247733e-01 2.11077943e-01 -1.53490618... | [7.035571575164795, 3.407447099685669] |
c3aca083-8a14-475b-a252-75896c791143 | collecting-high-quality-adversarial-data-for | 2206.14272 | null | https://arxiv.org/abs/2206.14272v1 | https://arxiv.org/pdf/2206.14272v1.pdf | Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop | We present our experience as annotators in the creation of high-quality, adversarial machine-reading-comprehension data for extractive QA for Task 1 of the First Workshop on Dynamic Adversarial Data Collection (DADC). DADC is an emergent data collection paradigm with both models and humans in the loop. We set up a quas... | ['John Culnan', 'Magdalena Anioł', 'Damian Y. Romero Diaz'] | 2022-06-28 | null | https://aclanthology.org/2022.dadc-1.6 | https://aclanthology.org/2022.dadc-1.6.pdf | naacl-dadc-2022-7 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 6.61866367e-02 6.85095191e-01 5.76660931e-01 -3.37290257e-01
-1.36876142e+00 -1.40827692e+00 7.05867827e-01 7.21728861e-01
-6.29450798e-01 4.74316061e-01 1.01293802e+00 -6.02121174e-01
-4.10753489e-02 -7.59816915e-02 -5.94878733e-01 -1.91813082e-01
1.25791162e-01 8.08654845e-01 1.96042880e-01 -5.32772362... | [6.34376335144043, 8.2019681930542] |
5bcb10d4-13e7-42b9-aac3-5842b285c624 | embodied-multimodal-multitask-learning | 1902.01385 | null | http://arxiv.org/abs/1902.01385v1 | http://arxiv.org/pdf/1902.01385v1.pdf | Embodied Multimodal Multitask Learning | Recent efforts on training visual navigation agents conditioned on language
using deep reinforcement learning have been successful in learning policies for
different multimodal tasks, such as semantic goal navigation and embodied
question answering. In this paper, we propose a multitask model capable of
jointly learnin... | ['Lisa Lee', 'Ruslan Salakhutdinov', 'Dhruv Batra', 'Devendra Singh Chaplot', 'Devi Parikh'] | 2019-02-04 | null | https://openreview.net/forum?id=r1lQQeHYPr | https://openreview.net/pdf?id=r1lQQeHYPr | null | ['embodied-question-answering'] | ['computer-vision'] | [-5.10364398e-02 1.81500882e-01 3.75547307e-03 -2.38841653e-01
-5.93636334e-01 -8.71547341e-01 9.76689398e-01 1.22537352e-01
-7.26816475e-01 5.86711168e-01 5.57420433e-01 -4.43142414e-01
7.33260512e-02 -5.50550461e-01 -1.04634202e+00 -5.57474494e-01
-1.86769500e-01 5.85563302e-01 1.81532986e-02 -4.34620142... | [4.536915302276611, 0.5586137771606445] |
41b31e7c-53eb-4534-bc09-3b725ac9dc75 | farewell-to-mutual-information-variational | 2104.02862 | null | https://arxiv.org/abs/2104.02862v2 | https://arxiv.org/pdf/2104.02862v2.pdf | Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification | The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which ... | ['Lizhuang Ma', 'Yuan Xie', 'Yanyun Qu', 'Shaohui Lin', 'Zhizhong Zhang', 'Xudong Tian'] | 2021-04-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Tian_Farewell_to_Mutual_Information_Variational_Distillation_for_Cross-Modal_Person_Re-Identification_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Tian_Farewell_to_Mutual_Information_Variational_Distillation_for_Cross-Modal_Person_Re-Identification_CVPR_2021_paper.pdf | cvpr-2021-1 | ['cross-view-person-re-identification', 'multi-view-learning'] | ['computer-vision', 'computer-vision'] | [ 2.95350820e-01 2.97536030e-02 -4.39898908e-01 -4.37573373e-01
-8.11421573e-01 -4.30386424e-01 6.16497338e-01 1.34170562e-01
-2.28193477e-01 6.14730656e-01 5.33215404e-01 2.15674210e-02
-4.23483491e-01 -3.74833673e-01 -4.13988233e-01 -7.10347652e-01
3.45561840e-02 3.89493942e-01 -1.43712178e-01 2.39599193... | [8.529748916625977, 4.360558032989502] |
fa3742cb-0f44-46db-9fe3-3e977ad1c8ed | social-and-scene-aware-trajectory-prediction | 1909.0884 | null | https://arxiv.org/abs/1909.08840v1 | https://arxiv.org/pdf/1909.08840v1.pdf | Social and Scene-Aware Trajectory Prediction in Crowded Spaces | Mimicking human ability to forecast future positions or interpret complex interactions in urban scenarios, such as streets, shopping malls or squares, is essential to develop socially compliant robots or self-driving cars. Autonomous systems may gain advantage on anticipating human motion to avoid collisions or to natu... | ['Lamberto Ballan', 'Matteo Lisotto', 'Pasquale Coscia'] | 2019-09-19 | null | null | null | null | ['social-navigation'] | ['robots'] | [-1.14618316e-02 4.72210407e-01 2.41767168e-02 -5.02632320e-01
2.56484091e-01 -1.50193796e-01 1.02259254e+00 8.39748308e-02
-7.46741891e-01 7.79119551e-01 5.69069266e-01 -3.52267981e-01
-4.52115536e-01 -1.03389299e+00 -6.72146916e-01 -3.19254398e-01
-7.41137624e-01 7.45211244e-01 3.13399017e-01 -6.33923531... | [5.9342756271362305, 0.7975581884384155] |
5e4939e5-b204-44f7-b6aa-e76b65fd0b2c | fine-grained-graph-learning-for-multi-view | 2201.04604 | null | https://arxiv.org/abs/2201.04604v3 | https://arxiv.org/pdf/2201.04604v3.pdf | Fine-grained Graph Learning for Multi-view Subspace Clustering | Multi-view subspace clustering (MSC) is a popular unsupervised method by integrating heterogeneous information to reveal the intrinsic clustering structure hidden across views. Usually, MSC methods use graphs (or affinity matrices) fusion to learn a common structure, and further apply graph-based approaches to clusteri... | ['Haoxi Zhan', 'Xiaobing Pei', 'Yidi Wang'] | 2022-01-12 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-1.73776448e-01 -3.47024441e-01 -3.42279583e-01 -1.77579731e-01
-5.67471206e-01 -4.92869973e-01 3.32989335e-01 6.25409856e-02
1.51261136e-01 2.40674302e-01 4.24004167e-01 1.15357332e-01
-3.90034676e-01 -6.15470290e-01 -2.85479128e-01 -1.13308167e+00
1.72448292e-01 2.04825863e-01 2.11455107e-01 6.87255263... | [8.06954288482666, 4.783566474914551] |
8d929850-b59d-4798-a1db-6318f8bb5cc2 | automatic-skin-lesion-segmentation-using-semi | 1703.04301 | null | http://arxiv.org/abs/1703.04301v1 | http://arxiv.org/pdf/1703.04301v1.pdf | Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique | Skin cancer is the most common of all cancers and each year million cases of
skin cancer are treated. Treating and curing skin cancer is easy, if it is
diagnosed and treated at an early stage. In this work we propose an automatic
technique for skin lesion segmentation in dermoscopic images which helps in
classifying th... | ['P. Mirunalini', 'Aravindan Chandrabose', 'S. M. Jaisakthi'] | 2017-03-13 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.64312124e-01 -5.40604554e-02 -2.73098916e-01 -2.41111308e-01
-5.09951353e-01 -7.03413367e-01 3.54588419e-01 4.62720066e-01
-7.62987137e-01 5.75242043e-01 -1.26360953e-01 -1.92489564e-01
-1.81954224e-02 -8.39343965e-01 -6.79585477e-03 -8.74130070e-01
2.21155435e-01 2.37058580e-01 5.71498036e-01 1.52844757... | [15.508115768432617, -3.0071685314178467] |
89a5f599-8cbb-4bea-8523-94e829f5a6d1 | automatic-vocabulary-and-graph-verification | 2107.14611 | null | https://arxiv.org/abs/2107.14611v1 | https://arxiv.org/pdf/2107.14611v1.pdf | Automatic Vocabulary and Graph Verification for Accurate Loop Closure Detection | Localizing pre-visited places during long-term simultaneous localization and mapping, i.e. loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies. As one of the most effective and efficient solutions, Bag-of-Words (BoW) builds a visual vocabulary to associate features and then detec... | ['Zhengguo Li', 'Fanghong Guo', 'Wei Wang', 'Weihai Chen', 'Jinyu Miao', 'Haosong Yue'] | 2021-07-30 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-2.10584939e-01 -3.78574163e-01 -3.16300057e-02 -1.44543424e-01
-4.36638027e-01 -6.71649337e-01 4.94792163e-01 8.13322365e-01
-2.75176018e-01 4.98841316e-01 -8.33644792e-02 -2.88743615e-01
-3.62392873e-01 -8.84511054e-01 -6.01873279e-01 -5.64509094e-01
-2.13569343e-01 2.19109952e-01 6.42616808e-01 -2.34242901... | [7.574217796325684, -2.1839425563812256] |
94c80b0a-6421-4b85-a551-c55a3fbf968e | fairdisco-fairer-ai-in-dermatology-via | 2208.10013 | null | https://arxiv.org/abs/2208.10013v1 | https://arxiv.org/pdf/2208.10013v1.pdf | FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning | Deep learning models have achieved great success in automating skin lesion diagnosis. However, the ethnic disparity in these models' predictions, where lesions on darker skin types are usually underrepresented and have lower diagnosis accuracy, receives little attention. In this paper, we propose FairDisCo, a disentang... | ['Rafeef Garbi', 'Ghassan Hamarneh', 'Nourhan Bayasi', 'Ben Hers', 'Siyi Du'] | 2022-08-22 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.13682997e-01 2.11030141e-01 -6.29166424e-01 -9.09953117e-01
-6.39360070e-01 -2.08601311e-01 5.23979068e-01 4.96560991e-01
-6.07891738e-01 1.00700092e+00 2.32293785e-01 -5.00895120e-02
-4.08772141e-01 -8.82130921e-01 -1.53253913e-01 -8.14043701e-01
1.51023284e-01 2.78946519e-01 -3.43006194e-01 4.76924330... | [15.434767723083496, -2.6467080116271973] |
ed7f978e-cb9a-43a4-8227-74cef709954c | adversarial-attack-and-defense-of-yolo | 2202.04781 | null | https://arxiv.org/abs/2202.04781v2 | https://arxiv.org/pdf/2202.04781v2.pdf | Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios | Visual detection is a key task in autonomous driving, and it serves as a crucial foundation for self-driving planning and control. Deep neural networks have achieved promising results in various visual tasks, but they are known to be vulnerable to adversarial attacks. A comprehensive understanding of deep visual detect... | ['Qing Tian', 'Jung Im Choi'] | 2022-02-10 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [-1.38416156e-01 -9.69476178e-02 -6.09146170e-02 -1.15005597e-01
-3.28367233e-01 -6.78532124e-01 6.75445318e-01 -1.06566086e-01
-5.01092613e-01 5.67807078e-01 -4.19369608e-01 -4.64923471e-01
2.83621967e-01 -1.00160038e+00 -9.01109636e-01 -8.49861503e-01
-5.25553040e-02 -9.48126689e-02 8.33026588e-01 -5.28656960... | [5.426267147064209, 7.8778767585754395] |
022e1f51-2603-4509-8985-8c7dff0baf9d | harms-a-hardware-acceleration-architecture | 2112.06772 | null | https://arxiv.org/abs/2112.06772v1 | https://arxiv.org/pdf/2112.06772v1.pdf | hARMS: A Hardware Acceleration Architecture for Real-Time Event-Based Optical Flow | Event-based vision sensors produce asynchronous event streams with high temporal resolution based on changes in the visual scene. The properties of these sensors allow for accurate and fast calculation of optical flow as events are generated. Existing solutions for calculating optical flow from event data either fail t... | ['Ryad B. Benosman', 'Alan D. George', 'Himanshu Akolkar', 'Daniel C. Stumpp'] | 2021-12-13 | null | null | null | null | ['event-based-optical-flow', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 3.70070308e-01 -4.78429407e-01 6.31546676e-01 -2.37679631e-01
-6.53923452e-02 -4.68510985e-01 5.54265797e-01 3.30698133e-01
-1.02155244e+00 7.23424792e-01 -1.42270610e-01 4.17845175e-02
-5.23488298e-02 -8.74440312e-01 -4.32049066e-01 -6.34881318e-01
-1.84550762e-01 1.32679284e-01 9.46873188e-01 2.42247939... | [8.68730354309082, -1.2646853923797607] |
0261e9de-183d-48a0-ae7a-d4ce78258928 | when-complexity-is-good-do-we-need-recurrent | null | null | https://openreview.net/forum?id=u6ybkty-bL | https://openreview.net/pdf?id=u6ybkty-bL | When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection? | Outlier detection is a critical part of understanding a dataset and extracting results. Outlier detection is used in different domains for various reasons; including detecting stolen credit cards, spikes of energy usage, web attacks, or in-home activity monitoring. Within this paper, we look at when it is appropriate t... | ['Payam M. Barnaghi', 'David J. Sharp', 'Mazdak Ghajari', 'Samaneh Kouchaki', 'Alexander Capstick'] | 2021-09-29 | null | null | null | null | ['home-activity-monitoring'] | ['miscellaneous'] | [-7.36257881e-02 -3.92667621e-01 -5.35980314e-02 -2.04837695e-01
-3.91354799e-01 -1.30962715e-01 4.82523829e-01 6.89745188e-01
-4.49563026e-01 5.77213943e-01 6.44550800e-01 -3.68710458e-01
-2.39265874e-01 -7.28081286e-01 -5.07938206e-01 -5.15464842e-01
-9.45239723e-01 2.62289822e-01 -3.98764051e-02 8.82481262... | [7.4303789138793945, 2.603548526763916] |
7d7b0322-9bd4-4bf8-9a40-b62c8e170320 | skoltechnlp-at-semeval-2020-task-11-exploring | null | null | https://aclanthology.org/2020.semeval-1.234 | https://aclanthology.org/2020.semeval-1.234.pdf | SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection | This paper presents a solution for the Span Identification (SI) task in the {``}Detection of Propaganda Techniques in News Articles{''} competition at SemEval-2020. The goal of the SI task is to identify specific fragments of each article which contain the use of at least one propaganda technique. This is a binary sequ... | ['Alexander Panchenko', 'Igor Markov', 'Daryna Dementieva'] | 2020-12-01 | null | null | null | semeval-2020 | ['text-augmentation', 'propaganda-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.52760607e-01 3.70785147e-02 -4.90034640e-01 -2.43221596e-01
-9.99429405e-01 -9.09884870e-01 1.18799663e+00 5.64091444e-01
-8.99273455e-01 7.31867731e-01 7.06231415e-01 -6.74416006e-01
-7.86853582e-03 -5.28000951e-01 -6.07095957e-01 -4.67993766e-01
3.15805860e-02 5.59346259e-01 4.65613484e-01 -4.79664952... | [8.504135131835938, 10.721508979797363] |
a554eb14-0147-4b2c-b471-744c35fad533 | break-the-ceiling-stronger-multi-scale-deep | 1906.02174 | null | https://arxiv.org/abs/1906.02174v3 | https://arxiv.org/pdf/1906.02174v3.pdf | Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks | Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems. However, their bottlenecks still need to be addressed, and the advantages of multi-scale information and deep architectures have not been sufficiently exploited. In this paper, we theor... | ['Doina Precup', 'Xiao-Wen Chang', 'Mingde Zhao', 'Sitao Luan'] | 2019-06-05 | break-the-ceiling-stronger-multi-scale-deep-1 | http://papers.nips.cc/paper/9276-break-the-ceiling-stronger-multi-scale-deep-graph-convolutional-networks | http://papers.nips.cc/paper/9276-break-the-ceiling-stronger-multi-scale-deep-graph-convolutional-networks.pdf | neurips-2019-12 | ['node-classification-on-non-homophilic'] | ['graphs'] | [ 9.09594744e-02 2.08229348e-01 -1.48605675e-01 -8.74840021e-02
-2.08378374e-03 -5.13219118e-01 4.61005688e-01 8.36677253e-02
-3.04495394e-01 5.60260057e-01 -4.24340228e-03 -6.04628980e-01
-3.06316167e-01 -9.05739844e-01 -5.57922959e-01 -5.91107726e-01
-4.78282958e-01 3.22777003e-01 2.00136781e-01 -3.49644244... | [6.878276824951172, 6.095010280609131] |
ed4db732-71ae-4cf0-8784-e9a718ba1118 | multiple-source-domain-adaptation-via | 2201.1187 | null | https://arxiv.org/abs/2201.11870v2 | https://arxiv.org/pdf/2201.11870v2.pdf | Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers | We present a novel multiple-source unsupervised model for text classification under domain shift. Our model exploits the update rates in document representations to dynamically integrate domain encoders. It also employs a probabilistic heuristic to infer the error rate in the target domain in order to pair source class... | ['Payam Karisani'] | 2022-01-28 | null | null | null | null | ['cross-domain-text-classification'] | ['natural-language-processing'] | [ 2.67235309e-01 4.21765357e-01 -4.95783061e-01 -6.21428013e-01
-9.54978466e-01 -7.16983020e-01 8.51762474e-01 2.93122053e-01
-6.67730987e-01 8.84932101e-01 1.37959346e-01 -2.54113138e-01
-7.75802732e-02 -6.11493409e-01 -7.86973894e-01 -3.77627134e-01
7.24648014e-02 9.64808643e-01 2.82509238e-01 -4.08498853... | [10.752206802368164, 7.976846694946289] |
430013a4-492a-4204-b14e-93bf4a555f55 | joint-adaptive-feature-smoothing-and-topology | 2006.07988 | null | https://arxiv.org/abs/2006.07988v6 | https://arxiv.org/pdf/2006.07988v6.pdf | Adaptive Universal Generalized PageRank Graph Neural Network | In many important graph data processing applications the acquired information includes both node features and observations of the graph topology. Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally trade-off their utility and integrate them in a manner that is also un... | ['Pan Li', 'Olgica Milenkovic', 'Jianhao Peng', 'Eli Chien'] | 2020-06-14 | null | https://openreview.net/forum?id=n6jl7fLxrP | https://openreview.net/pdf?id=n6jl7fLxrP | iclr-2021-1 | ['node-classification-on-non-homophilic'] | ['graphs'] | [ 1.75105155e-01 2.06145346e-01 -4.63181794e-01 -2.14848489e-01
-2.61792485e-02 -4.53475744e-01 6.93848193e-01 4.53466028e-01
-2.84853160e-01 6.00547135e-01 -1.01565346e-01 -3.83702695e-01
-5.97900331e-01 -1.18190479e+00 -5.77186406e-01 -9.48973954e-01
-6.71425998e-01 7.85484433e-01 3.05391490e-01 -2.87403017... | [6.946630954742432, 5.992105484008789] |
c0e131d2-0735-4ff6-8867-53a4a18038a9 | a-data-augmentation-method-and-the-embedding | 2303.12801 | null | https://arxiv.org/abs/2303.12801v1 | https://arxiv.org/pdf/2303.12801v1.pdf | A Data Augmentation Method and the Embedding Mechanism for Detection and Classification of Pulmonary Nodules on Small Samples | Detection of pulmonary nodules by CT is used for screening lung cancer in early stages.omputer aided diagnosis (CAD) based on deep-learning method can identify the suspected areas of pulmonary nodules in CT images, thus improving the accuracy and efficiency of CT diagnosis. The accuracy and robustness of deep learning ... | ['Chi-Chun Zhou', 'Bin Wang', 'Si-Jing Li', 'Xin-Hui Li', 'Chen-Xin Qin', 'Yue-Jie Hou', 'Yang Liu'] | 2023-03-02 | null | null | null | null | ['pulmonary-nodules-classification'] | ['medical'] | [ 1.81508482e-01 5.74228227e-01 -1.53347194e-01 3.04521263e-01
-2.54393369e-01 -1.53487056e-01 4.71549749e-01 -4.82414395e-01
-1.00908414e-01 4.51271057e-01 2.68696934e-01 -4.49507922e-01
1.10136732e-01 -1.28808987e+00 -4.19778556e-01 -1.09656107e+00
1.91384405e-01 5.15149474e-01 5.13045430e-01 1.52586818... | [15.221870422363281, -2.1408073902130127] |
03b02298-1624-40f6-93d4-bfc5f37c4a1e | out-of-distribution-detection-in-satellite | 2104.05442 | null | https://arxiv.org/abs/2104.05442v1 | https://arxiv.org/pdf/2104.05442v1.pdf | Out-of-distribution detection in satellite image classification | In satellite image analysis, distributional mismatch between the training and test data may arise due to several reasons, including unseen classes in the test data and differences in the geographic area. Deep learning based models may behave in unexpected manner when subjected to test data that has such distributional ... | ['Xiao Xiang Zhu', 'Anna Kruspe', 'Sudipan Saha', 'Jakob Gawlikowski'] | 2021-04-09 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 9.78204682e-02 1.29800200e-01 2.81855494e-01 -7.53712118e-01
-7.51933873e-01 -5.41510165e-01 7.75757849e-01 3.29491273e-02
-2.12797880e-01 9.37218368e-01 5.31458333e-02 -4.39145416e-01
-5.77435851e-01 -9.07523990e-01 -6.98983967e-01 -8.78246546e-01
-2.56837130e-01 9.38674927e-01 -1.64987132e-01 2.81023830... | [7.5638604164123535, 3.346796989440918] |
4bc2bb2c-1263-4fbd-b702-1c034e24a986 | learning-medical-image-denoising-with-deep | null | null | https://www.mdpi.com/2227-7390/8/12/2192 | https://www.mdpi.com/2227-7390/8/12/2192/pdf | Learning Medical Image Denoising with Deep Dynamic Residual Attention Network | Image denoising performs a prominent role in medical image analysis. In many cases, it can drastically accelerate the diagnostic process by enhancing the perceptual quality of noisy image samples. However, despite the extensive practicability of medical image denoising, the existing denoising methods illustrate deficie... | ['Mithun Biswas', 'Rizwan Ali Naqvi 2', 'S M A Sharif'] | 2020-12-09 | null | null | null | null | ['medical-image-denoising'] | ['computer-vision'] | [ 3.35452586e-01 -2.54411131e-01 3.62067431e-01 -2.35013977e-01
-6.54277563e-01 1.52204130e-02 3.13280761e-01 5.00045307e-02
-5.75654685e-01 6.43980026e-01 3.68538827e-01 5.45447730e-02
-4.11420435e-01 -7.16291964e-01 -2.47405753e-01 -1.26037061e+00
1.98042989e-01 -2.93133616e-01 -5.06084040e-02 -2.69809902... | [13.277234077453613, -2.4761669635772705] |
0b2b2b4b-750e-4e01-9270-e81e336e9478 | hanet-hierarchical-alignment-networks-for | 2107.12059 | null | https://arxiv.org/abs/2107.12059v2 | https://arxiv.org/pdf/2107.12059v2.pdf | HANet: Hierarchical Alignment Networks for Video-Text Retrieval | Video-text retrieval is an important yet challenging task in vision-language understanding, which aims to learn a joint embedding space where related video and text instances are close to each other. Most current works simply measure the video-text similarity based on video-level and text-level embeddings. However, the... | ['Jing Liu', 'Yiliang Lv', 'Mingqian Tang', 'Xiangteng He', 'Peng Wu'] | 2021-07-26 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 1.50267389e-02 -5.32153368e-01 -4.77963030e-01 -4.01862711e-01
-5.07553875e-01 -3.31429183e-01 7.95941949e-01 2.64095634e-01
-3.98467630e-01 6.02701679e-02 8.48501861e-01 2.55225450e-01
-2.21426953e-02 -5.10495484e-01 -4.65481162e-01 -5.94117820e-01
3.26249510e-01 6.60505220e-02 2.47935057e-01 7.61801973... | [10.465185165405273, 1.0933457612991333] |
1f7b6179-2c8e-4863-b9a2-f0ea8c3f0191 | evident-a-development-methodology-and-a | 2211.10291 | null | https://arxiv.org/abs/2211.10291v1 | https://arxiv.org/pdf/2211.10291v1.pdf | Evident: a Development Methodology and a Knowledge Base Topology for Data Mining, Machine Learning and General Knowledge Management | Software has been developed for knowledge discovery, prediction and management for over 30 years. However, there are still unresolved pain points when using existing project development and artifact management methodologies. Historically, there has been a lack of applicable methodologies. Further, methodologies that ha... | ['Samer Haidar', 'Gao', 'Mingwu'] | 2022-11-09 | null | null | null | null | ['general-knowledge', 'logical-reasoning'] | ['miscellaneous', 'reasoning'] | [-3.87506723e-01 2.26876453e-01 -2.35350758e-01 -2.35594109e-01
3.09033394e-01 -1.65413395e-01 2.06174284e-01 4.04788435e-01
1.98691472e-01 8.32414210e-01 -1.73994333e-01 -3.50065142e-01
-9.40107107e-01 -9.19606805e-01 -4.57910955e-01 -7.90505856e-02
-8.47749263e-02 1.37717620e-01 -1.21687494e-01 -3.58383685... | [8.911514282226562, 6.821671485900879] |
e948f0aa-f962-4c1e-9e31-da89b32f8df1 | 1-d-convolutional-graph-convolutional | 2211.0293 | null | https://arxiv.org/abs/2211.02930v1 | https://arxiv.org/pdf/2211.02930v1.pdf | 1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems | This paper presents a 1-D convolutional graph neural network for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme in... | ['Rob Hovsapian', 'Mayank Panwar', 'Thai-Thanh Nguyen', 'Tuyen Vu', 'Bang L. H. Nguyen'] | 2022-11-05 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-8.23622704e-01 -3.29922974e-01 3.64962161e-01 -6.08367585e-02
-2.03479722e-01 -3.08780283e-01 1.28004953e-01 3.40187699e-02
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-2.50499457e-01 -1.00213933e+00 -3.95787448e-01 -7.09240675e-01
-9.99289751e-01 2.70916015e-01 -1.58807144e-01 -3.28751385... | [6.277884006500244, 2.502114772796631] |
8da5cb8f-2f7f-4167-81da-083fb769cef6 | co-training-an-unsupervised-constituency | 2110.02283 | null | https://arxiv.org/abs/2110.02283v2 | https://arxiv.org/pdf/2110.02283v2.pdf | Co-training an Unsupervised Constituency Parser with Weak Supervision | We introduce a method for unsupervised parsing that relies on bootstrapping classifiers to identify if a node dominates a specific span in a sentence. There are two types of classifiers, an inside classifier that acts on a span, and an outside classifier that acts on everything outside of a given span. Through self-tra... | ['Shay B. Cohen', 'Nickil Maveli'] | 2021-10-05 | null | https://aclanthology.org/2022.findings-acl.101 | https://aclanthology.org/2022.findings-acl.101.pdf | findings-acl-2022-5 | ['constituency-grammar-induction'] | ['natural-language-processing'] | [ 1.65969551e-01 7.60899186e-01 -2.98364967e-01 -7.99621105e-01
-1.22258747e+00 -8.84383142e-01 2.43158564e-01 3.60209256e-01
-4.84882683e-01 8.83368850e-01 2.78364927e-01 -7.95499623e-01
4.19278473e-01 -7.63173044e-01 -9.34878469e-01 -3.47058624e-01
8.13295916e-02 5.32474279e-01 4.84080166e-01 -3.58844548... | [10.39371109008789, 9.615803718566895] |
68c4a696-3595-44d5-b0b1-8dc376b89575 | towards-multi-scale-style-control-for | 2104.03521 | null | https://arxiv.org/abs/2104.03521v1 | https://arxiv.org/pdf/2104.03521v1.pdf | Towards Multi-Scale Style Control for Expressive Speech Synthesis | This paper introduces a multi-scale speech style modeling method for end-to-end expressive speech synthesis. The proposed method employs a multi-scale reference encoder to extract both the global-scale utterance-level and the local-scale quasi-phoneme-level style features of the target speech, which are then fed into t... | ['Helen Meng', 'Jia Jia', 'Zhiyong Wu', 'Jingbei Li', 'Changhe Song', 'Xiang Li'] | 2021-04-08 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 3.89582366e-01 2.62040645e-01 -1.14616398e-02 -6.09539211e-01
-7.94735193e-01 -6.20557308e-01 4.80218709e-01 -6.47491693e-01
-6.56452924e-02 6.14190638e-01 5.53288579e-01 -1.62898511e-01
2.70324737e-01 -5.95142305e-01 -4.22725469e-01 -5.55632651e-01
6.16765440e-01 1.15222618e-01 -1.51945695e-01 -5.37943900... | [14.939289093017578, 6.550870895385742] |
e34c16a9-7eb3-4d7b-9c9b-857be11a0a21 | an-algorithm-and-heuristic-based-on | 2210.13456 | null | https://arxiv.org/abs/2210.13456v1 | https://arxiv.org/pdf/2210.13456v1.pdf | An Algorithm and Heuristic based on Normalized Mutual Information for Dimensionality Reduction and Classification of Hyperspectral images | In the feature classification domain, the choice of data affects widely the results. The Hyperspectral image (HSI), is a set of more than a hundred bidirectional measures (called bands), of the same region (called ground truth map: GT). The HSI is modelized at a set of N vectors. So we have N features (or attributes) e... | ['Driss Aboutajdine', 'Ahmed Hammouch', 'Elkebir Sarhrouni'] | 2022-10-22 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 8.85945916e-01 -4.95658010e-01 -3.57362151e-01 -4.68955010e-01
-3.36369723e-01 -6.75655305e-01 2.57482260e-01 -5.34044988e-02
-7.69237205e-02 9.05570686e-01 1.15655944e-01 9.19033065e-02
-1.03566206e+00 -1.22109437e+00 -1.84671767e-02 -9.55081522e-01
-3.02941591e-01 1.93516184e-02 -3.20482880e-01 -1.78699240... | [9.747203826904297, -1.8299728631973267] |
02025425-ed21-435c-8b78-71e13f9ee769 | textdragon-an-end-to-end-framework-for | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Feng_TextDragon_An_End-to-End_Framework_for_Arbitrary_Shaped_Text_Spotting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Feng_TextDragon_An_End-to-End_Framework_for_Arbitrary_Shaped_Text_Spotting_ICCV_2019_paper.pdf | TextDragon: An End-to-End Framework for Arbitrary Shaped Text Spotting | Most existing text spotting methods either focus on horizontal/oriented texts or perform arbitrary shaped text spotting with character-level annotations. In this paper, we propose a novel text spotting framework to detect and recognize text of arbitrary shapes in an end-to-end manner, using only word/line-level annotat... | [' Cheng-Lin Liu', ' Xu-Yao Zhang', ' Fei Yin', ' Wenhao He', 'Wei Feng'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['text-spotting'] | ['computer-vision'] | [ 0.3557838 -0.3645509 0.05119157 -0.24534364 -0.7214719 -0.7466567
0.7308 -0.10035124 -0.37480918 -0.12115435 0.09267446 -0.42694512
0.3718867 -0.54760784 -0.47344688 -0.65645045 0.9243107 0.6613372
0.32344556 0.0173509 0.5271839 0.46774352 -0.914036 0.5538726
0.7814977 0.989852 0.296... | [11.999177932739258, 2.266062021255493] |
bec562d2-6b3c-483b-ae86-da77b2744343 | application-of-adnn-for-background | 2301.00264 | null | https://arxiv.org/abs/2301.00264v1 | https://arxiv.org/pdf/2301.00264v1.pdf | Application Of ADNN For Background Subtraction In Smart Surveillance System | Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them ... | ['Neeraj Goyal', 'Gagan Raj Singh', 'Piyush Batra'] | 2022-12-31 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 4.32206362e-01 -4.11341488e-01 -3.45332436e-02 -2.65043676e-01
-1.37882277e-01 -1.94716185e-01 3.67621690e-01 -8.44803900e-02
-6.35454476e-01 6.29094124e-01 -3.72876167e-01 -3.63542497e-01
3.53529751e-01 -8.59552979e-01 -6.34294689e-01 -1.07218206e+00
7.82484636e-02 3.42402495e-02 8.56921852e-01 1.20392081... | [8.822426795959473, -0.7407134175300598] |
24de91cd-5d42-42a2-bf27-1fbf7dbe5283 | sequential-recommendation-with-bidirectional | 2112.0646 | null | https://arxiv.org/abs/2112.06460v4 | https://arxiv.org/pdf/2112.06460v4.pdf | Sequential Recommendation with Bidirectional Chronological Augmentation of Transformer | Sequential recommendation can capture user chronological preferences from their historical behaviors, yet the learning of short sequences (cold-start problem) in many benchmark datasets is still an open challenge. Recently, data augmentation with pseudo-prior items generated by Transformers has drawn considerable atten... | ['Jae Boum Kim', 'Yingtao Luo', 'Sunghun Kim', 'Kai Zhang', 'Juyong Jiang'] | 2021-12-13 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 3.29825461e-01 -3.83922756e-01 -6.58497512e-01 -2.87333548e-01
-4.22143877e-01 -5.47680736e-01 5.55547297e-01 5.59313186e-02
-4.60087240e-01 1.00004399e+00 5.74084640e-01 -2.08176836e-01
-2.49355566e-02 -7.49984443e-01 -7.80541241e-01 -6.93131447e-01
9.37998965e-02 2.78380573e-01 -1.22710718e-02 -3.82234544... | [10.186452865600586, 5.484277248382568] |
696e4658-cd23-49d9-8210-8660d9c438ba | unet-a-nested-u-net-architecture-for-medical | 1807.10165 | null | http://arxiv.org/abs/1807.10165v1 | http://arxiv.org/pdf/1807.10165v1.pdf | UNet++: A Nested U-Net Architecture for Medical Image Segmentation | Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet | ['Jianming Liang', 'Nima Tajbakhsh', 'Md Mahfuzur Rahman Siddiquee', 'Zongwei Zhou'] | 2018-07-18 | null | null | null | null | ['thermal-image-segmentation', 'video-polyp-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.14402346e-01 3.46072555e-01 5.28311551e-01 -5.40884793e-01
-1.24646239e-01 -6.87594116e-01 3.93433958e-01 -4.63457674e-01
-6.52572215e-01 4.52866465e-01 3.73579681e-01 -7.81422257e-01
-8.68354514e-02 -6.54622138e-01 -8.92265737e-01 -3.93539578e-01
-2.07485661e-01 1.15865517e+00 7.43402243e-01 -4.08142768... | [9.649759292602539, 0.3686968684196472] |
5daeb89e-bf98-43d4-9137-5b0ec53459f6 | multi-scale-cross-contrastive-learning-for | 2306.14293 | null | https://arxiv.org/abs/2306.14293v1 | https://arxiv.org/pdf/2306.14293v1.pdf | Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation | Semi-supervised learning has demonstrated great potential in medical image segmentation by utilizing knowledge from unlabeled data. However, most existing approaches do not explicitly capture high-level semantic relations between distant regions, which limits their performance. In this paper, we focus on representation... | ['Fani Deligianni', 'Paul Henderson', 'Xiao Gu', 'Qianying Liu'] | 2023-06-25 | null | null | null | null | ['contrastive-learning', 'semi-supervised-medical-image-segmentation', 'medical-image-segmentation', 'contrastive-learning'] | ['computer-vision', 'computer-vision', 'medical', 'methodology'] | [ 5.19393206e-01 2.84058213e-01 -5.08626103e-01 -6.70513451e-01
-1.10840333e+00 -4.35952991e-01 2.20525384e-01 4.37565297e-01
-3.61859471e-01 5.21413982e-01 2.33147606e-01 -5.70057742e-02
-1.96887463e-01 -7.17597067e-01 -4.91111428e-01 -6.20023310e-01
6.89398497e-02 4.76879478e-01 3.85119289e-01 3.48808169... | [14.744087219238281, -2.120853900909424] |
99567c90-5421-4625-b538-633b549310e3 | isles-2022-a-multi-center-magnetic-resonance | 2206.06694 | null | https://arxiv.org/abs/2206.06694v1 | https://arxiv.org/pdf/2206.06694v1.pdf | ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset | Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous thrombolysis or endovascular therapy. MRI is later used in the duration of hospital stay to predict outcome by visualizing infarct core size... | ['Jan S. Kirschke', 'Benedikt Wiestler', 'Benno Ikenberg', 'Maria Berndt', 'Tobias Boeckh-Behrens', 'Claus Zimmer', 'Lukas Meyer', 'Gabriel Broocks', 'Bjoern Menze', 'The Anh Baran', 'Johannes Bürkle', 'Teresa Zarth', 'Tassilo Friedrich', 'Alexander Hutton', 'David Robben', 'Ivan Ezhov', 'Florian Kofler', 'Sook-Lei Lie... | 2022-06-14 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [ 1.52246594e-01 -4.63682204e-01 -3.22378308e-01 -2.85544842e-01
-1.09044647e+00 -9.76312578e-01 5.13373554e-01 4.98684019e-01
-7.58110821e-01 6.44908369e-01 6.66562259e-01 -9.71045494e-01
-7.19212443e-02 -5.61416507e-01 2.15784591e-02 -4.51403439e-01
-3.90719861e-01 1.04120445e+00 4.40882683e-01 3.21610123... | [14.214051246643066, -2.0397300720214844] |
17507b0b-d8ec-40e7-8c13-0ee5d2d67f7d | earthquake-prediction-with-artificial-neural | 1907.02209 | null | https://arxiv.org/abs/1907.02209v1 | https://arxiv.org/pdf/1907.02209v1.pdf | Earthquake Prediction With Artificial Neural Network Method: The Application Of West Anatolian Fault In Turkey | A method that exactly knows the earthquakes beforehand and can generalize them cannot still been developed. However, earthquakes are tried to be predicted through numerous methods. One of these methods, artificial neural networks give appropriate outputs to different patterns by learning the relationship between the de... | ['Osman Duman', 'Handan Cam'] | 2019-05-26 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [-1.77713156e-01 3.17544267e-02 2.60589659e-01 -4.81544137e-01
3.49243656e-02 -1.89960033e-01 1.96555853e-01 8.99718329e-02
-4.25716758e-01 9.92537916e-01 4.81700040e-02 -4.29318607e-01
-3.20858657e-01 -1.15981257e+00 -5.81274629e-01 -6.79420769e-01
-2.11916342e-01 2.40111887e-01 4.07715499e-01 -4.96500462... | [6.329278469085693, 3.094984531402588] |
3fdd9498-e2bc-499b-987d-e111b6e77ad7 | m-3-video-masked-motion-modeling-for-self | 2210.06096 | null | https://arxiv.org/abs/2210.06096v2 | https://arxiv.org/pdf/2210.06096v2.pdf | Masked Motion Encoding for Self-Supervised Video Representation Learning | How to learn discriminative video representation from unlabeled videos is challenging but crucial for video analysis. The latest attempts seek to learn a representation model by predicting the appearance contents in the masked regions. However, simply masking and recovering appearance contents may not be sufficient to ... | ['Chuang Gan', 'Mingkui Tan', 'Changhao Li', 'Thomas H. Li', 'LiangWei Chen', 'Peihao Chen', 'Xinyu Sun'] | 2022-10-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_Masked_Motion_Encoding_for_Self-Supervised_Video_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_Masked_Motion_Encoding_for_Self-Supervised_Video_Representation_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 1.30309686e-01 -3.98287117e-01 -3.34612668e-01 -1.53242096e-01
-5.78722894e-01 -6.11769497e-01 4.49330240e-01 -4.16628629e-01
2.03073062e-02 5.94435871e-01 3.73461694e-01 2.05111474e-01
2.37814978e-01 -4.40741628e-01 -8.54135454e-01 -7.68502772e-01
-2.88644403e-01 -2.47908279e-01 3.05452824e-01 2.48067137... | [8.816534042358398, 0.48715218901634216] |
882d5cac-d396-4505-8ea3-cb4f70fe325c | neural-code-search-revisited-enhancing-code | 2008.12193 | null | https://arxiv.org/abs/2008.12193v1 | https://arxiv.org/pdf/2008.12193v1.pdf | Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent | In this work, we propose and study annotated code search: the retrieval of code snippets paired with brief descriptions of their intent using natural language queries. On three benchmark datasets, we investigate how code retrieval systems can be improved by leveraging descriptions to better capture the intents of code ... | ['Geert Heyman', 'Tom Van Cutsem'] | 2020-08-27 | null | null | null | null | ['code-search', 'annotated-code-search', 'code-search'] | ['computer-code', 'computer-code', 'computer-vision'] | [-3.47764641e-02 -2.77442753e-01 -6.49187803e-01 -2.30342597e-01
-1.38047707e+00 -9.24742877e-01 6.88940287e-01 6.45477474e-01
-4.17037398e-01 2.89976716e-01 4.15325135e-01 -6.07957125e-01
-1.36214063e-01 -4.91967559e-01 -5.44566274e-01 3.49264629e-02
-2.32376322e-01 3.99218053e-01 2.54069179e-01 -2.88737357... | [7.524329662322998, 8.074219703674316] |
d72aa180-9e7d-4ff1-959e-16ffa3741501 | data-integration-for-toxic-comment | null | null | https://aclanthology.org/2021.woah-1.17 | https://aclanthology.org/2021.woah-1.17.pdf | Data Integration for Toxic Comment Classification: Making More Than 40 Datasets Easily Accessible in One Unified Format | With the rise of research on toxic comment classification, more and more annotated datasets have been released. The wide variety of the task (different languages, different labeling processes and schemes) has led to a large amount of heterogeneous datasets that can be used for training and testing very specific setting... | ['Ralf Krestel', 'Philipp Schmidt', 'Julian Risch'] | null | null | null | null | acl-woah-2021-8 | ['data-integration', 'toxic-comment-classification'] | ['knowledge-base', 'natural-language-processing'] | [-5.28585017e-02 -2.90714025e-01 -4.36524123e-01 -5.70225716e-01
-6.27256930e-01 -1.03317821e+00 7.71615386e-01 8.71863544e-01
-4.88093197e-01 6.76645219e-01 2.49583274e-01 -3.62130404e-01
-1.19860105e-01 -6.21379733e-01 7.06683993e-02 -3.94303203e-01
5.17800391e-01 6.82458580e-01 5.86749494e-01 -7.56211020... | [9.861549377441406, 9.619660377502441] |
e265ff38-824f-400f-b23f-2178fe02a2d8 | panoptic-feature-pyramid-networks | 1901.02446 | null | http://arxiv.org/abs/1901.02446v2 | http://arxiv.org/pdf/1901.02446v2.pdf | Panoptic Feature Pyramid Networks | The recently introduced panoptic segmentation task has renewed our
community's interest in unifying the tasks of instance segmentation (for thing
classes) and semantic segmentation (for stuff classes). However, current
state-of-the-art methods for this joint task use separate and dissimilar
networks for instance and se... | ['Piotr Dollár', 'Kaiming He', 'Ross Girshick', 'Alexander Kirillov'] | 2019-01-08 | panoptic-feature-pyramid-networks-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kirillov_Panoptic_Feature_Pyramid_Networks_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kirillov_Panoptic_Feature_Pyramid_Networks_CVPR_2019_paper.pdf | cvpr-2019-6 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 4.52878982e-01 2.01739147e-01 -3.33236158e-01 -4.23319399e-01
-5.43824792e-01 -8.65080714e-01 6.21741951e-01 -2.54937381e-01
-2.64649510e-01 1.52532026e-01 8.35049972e-02 -2.95610458e-01
8.16378519e-02 -1.02909064e+00 -5.25105059e-01 -5.77869654e-01
1.07893862e-01 4.80882436e-01 5.87774158e-01 -8.07003379... | [9.579144477844238, 0.37181025743484497] |
7f71fced-cf44-4738-8d0e-7137d97e080e | procedural-editing-of-3d-building-point | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Demir_Procedural_Editing_of_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Demir_Procedural_Editing_of_ICCV_2015_paper.pdf | Procedural Editing of 3D Building Point Clouds | Thanks to the recent advances in computational photography and remote sensing, point clouds of buildings are becoming increasingly available, yet their processing poses various challenges. In our work, we tackle the problem of point cloud completion and editing and we approach it via inverse procedural modeling. Contra... | ['Daniel G. Aliaga', 'Bedrich Benes', 'Ilke Demir'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['point-cloud-completion'] | ['computer-vision'] | [ 7.51219153e-01 -7.50520602e-02 5.11125565e-01 -2.97063440e-01
-6.25567615e-01 -8.20419371e-01 6.31757379e-01 5.78687847e-01
-2.91630507e-01 2.84197897e-01 -5.96886039e-01 -5.42230487e-01
-2.10420221e-01 -1.18687546e+00 -5.83015740e-01 -1.75782412e-01
2.09398400e-02 9.09085810e-01 6.49814010e-01 -1.74854830... | [8.311504364013672, -2.723179578781128] |
cd54cc05-dfb7-4e57-a021-458aa73f8dca | statistically-consistent-k-mer-methods-for | 1511.01956 | null | http://arxiv.org/abs/1511.01956v2 | http://arxiv.org/pdf/1511.01956v2.pdf | Statistically-Consistent k-mer Methods for Phylogenetic Tree Reconstruction | Frequencies of $k$-mers in sequences are sometimes used as a basis for
inferring phylogenetic trees without first obtaining a multiple sequence
alignment. We show that a standard approach of using the squared-Euclidean
distance between $k$-mer vectors to approximate a tree metric can be
statistically inconsistent. To r... | [] | 2016-01-14 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 7.02633619e-01 -4.26124722e-01 -1.89492553e-01 -6.09955490e-01
-7.70703733e-01 -9.74630237e-01 1.10530213e-01 3.68714392e-01
-7.18192160e-01 1.00712132e+00 -3.93839777e-01 -7.69572198e-01
-1.53066859e-01 -5.04011333e-01 -5.91491520e-01 -8.89010131e-01
-2.91795939e-01 6.01601243e-01 1.62553087e-01 -6.80284435... | [4.820062160491943, 5.137710094451904] |
2111d4c4-be48-4310-a3bd-7737a9034c65 | phrase-detectives-corpus-10-crowdsourced | null | null | https://aclanthology.org/L16-1323 | https://aclanthology.org/L16-1323.pdf | Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference. | Natural Language Engineering tasks require large and complex annotated datasets to build more advanced models of language. Corpora are typically annotated by several experts to create a gold standard; however, there are now compelling reasons to use a non-expert crowd to annotate text, driven by cost, speed and scalabi... | ['Udo Kruschwitz', 'Jon Chamberlain', 'Massimo Poesio'] | 2016-05-01 | phrase-detectives-corpus-10-crowdsourced-1 | https://aclanthology.org/L16-1323 | https://aclanthology.org/L16-1323.pdf | lrec-2016-5 | ['text-annotation'] | ['natural-language-processing'] | [-3.03886712e-01 7.13879049e-01 -1.85680583e-01 -5.41826129e-01
-1.14068151e+00 -1.00374413e+00 8.62995028e-01 6.67318523e-01
-9.74332750e-01 9.34080064e-01 1.06337333e+00 1.04493238e-01
1.67113051e-01 -3.23169023e-01 -2.12487072e-01 -2.21686974e-01
7.40978837e-01 1.44909573e+00 7.93198228e-01 -5.06147087... | [9.515403747558594, 9.233325004577637] |
fd0e4013-bd82-4ed7-a9a4-06f78fc39417 | face-recognition-using-multi-modal-low-rank | 1703.04853 | null | http://arxiv.org/abs/1703.04853v1 | http://arxiv.org/pdf/1703.04853v1.pdf | Face Recognition using Multi-Modal Low-Rank Dictionary Learning | Face recognition has been widely studied due to its importance in different
applications; however, most of the proposed methods fail when face images are
occluded or captured under illumination and pose variations. Recently several
low-rank dictionary learning methods have been proposed and achieved promising
results f... | ['Moein Shakeri', 'Nilanjan Ray', 'Homa Foroughi', 'Hong Zhang'] | 2017-03-15 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 3.87659341e-01 -7.12357700e-01 -2.40555286e-01 -5.79400003e-01
-9.86148119e-01 -3.94087791e-01 6.52437866e-01 -4.78005767e-01
-1.47397518e-01 7.27429450e-01 2.57289886e-01 5.80009341e-01
-4.47686017e-01 -3.08810353e-01 -4.52195466e-01 -1.22698903e+00
1.07659474e-01 2.23773792e-01 -3.62317771e-01 2.22970739... | [12.653082847595215, 0.3810901343822479] |
29d378d8-79b0-4513-b0c1-84ae0f9e21e5 | an-analysis-of-universal-differential | 2306.10335 | null | https://arxiv.org/abs/2306.10335v1 | https://arxiv.org/pdf/2306.10335v1.pdf | An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations | In the last decade, the scientific community has devolved its attention to the deployment of data-driven approaches in scientific research to provide accurate and reliable analysis of a plethora of phenomena. Most notably, Physics-informed Neural Networks and, more recently, Universal Differential Equations (UDEs) prov... | ['Michele Lombardi', 'Eleonora Misino', 'Federico Baldo', 'Mattia Silvestri'] | 2023-06-17 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 8.41694102e-02 -1.89619660e-01 1.12966329e-01 2.85474867e-01
-2.25436285e-01 -4.70089078e-01 7.85847425e-01 2.62114644e-01
-1.01486787e-01 1.03093326e+00 -3.35464984e-01 -5.96522808e-01
-7.36848831e-01 -6.15580261e-01 -4.74118829e-01 -8.01628768e-01
-2.61414081e-01 3.45917314e-01 -2.43065640e-01 -4.10035968... | [6.379023551940918, 3.4963746070861816] |
8f946eb0-89b1-4eab-b340-44b71ee98936 | optimization-of-the-area-under-the-roc-curve | 1901.11332 | null | http://arxiv.org/abs/1901.11332v2 | http://arxiv.org/pdf/1901.11332v2.pdf | Optimization of the Area Under the ROC Curve using Neural Network Supervectors for Text-Dependent Speaker Verification | This paper explores two techniques to improve the performance of
text-dependent speaker verification systems based on deep neural networks.
Firstly, we propose a general alignment mechanism to keep the temporal
structure of each phrase and obtain a supervector with the speaker and phrase
information, since both are rel... | ['Antonio Miguel', 'Alfonso Ortega', 'Victoria Mingote', 'Eduardo Lleida'] | 2019-01-31 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 1.01617925e-01 2.53182091e-03 3.07337791e-01 -7.74097741e-01
-1.09733045e+00 -5.44730902e-01 5.70861220e-01 6.61998838e-02
-8.30451965e-01 4.69669998e-01 -1.31812990e-01 -3.38153720e-01
-2.98141036e-02 -2.70645738e-01 -6.73630774e-01 -9.44491208e-01
-1.12735413e-01 4.14318591e-01 1.09842874e-01 -3.53631526... | [14.306634902954102, 6.079519271850586] |
76a21fee-0637-448e-a57b-14e528b19cc0 | investigating-the-effect-of-residual-and | null | null | https://openreview.net/forum?id=rkzeXBDos7 | https://openreview.net/pdf?id=rkzeXBDos7 | Investigating the effect of residual and highway connections in speech enhancement models | Residual and skip connections play an important role in many current
generative models. Although their theoretical and numerical advantages
are understood, their role in speech enhancement systems has not been
investigated so far. When performing spectral speech enhancement,
residual connections are very simi... | ['Anonymous'] | 2018-10-22 | null | null | null | nips-workshop-irasl-2018 | ['speech-denoising'] | ['speech'] | [ 5.70671499e-01 1.14605926e-01 6.17946386e-01 -1.78348169e-01
-4.13446240e-02 -3.56734723e-01 7.58913636e-01 -4.17817421e-02
-5.81291318e-01 5.83014011e-01 3.34858328e-01 -4.59372491e-01
-1.68399036e-01 -7.64846325e-01 -6.26975834e-01 -1.04946947e+00
1.03685603e-01 -2.42442861e-01 1.48832694e-01 -5.93412936... | [15.086625099182129, 5.949264049530029] |
62f23506-1d54-4f54-aeb8-b51bff5a26c5 | modeling-spatial-and-temporal-cues-for-multi | 1608.00911 | null | http://arxiv.org/abs/1608.00911v1 | http://arxiv.org/pdf/1608.00911v1.pdf | Modeling Spatial and Temporal Cues for Multi-label Facial Action Unit Detection | Facial action units (AUs) are essential to decode human facial expressions.
Researchers have focused on training AU detectors with a variety of features
and classifiers. However, several issues remain. These are spatial
representation, temporal modeling, and AU correlation. Unlike most studies that
tackle these issues ... | ['Fernando de la Torre', 'Wen-Sheng Chu', 'Jeffrey F. Cohn'] | 2016-08-02 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 1.76417455e-01 -1.20203823e-01 -2.05786228e-01 -4.93706524e-01
-6.13852262e-01 -1.22430377e-01 6.07922673e-01 -3.40723068e-01
-3.94729048e-01 4.45482969e-01 3.10053259e-01 2.54900783e-01
3.47671986e-01 -6.46502793e-01 -8.09971631e-01 -6.43431127e-01
-2.21758321e-01 -1.91331670e-01 6.33109175e-03 -1.03359818... | [13.558670997619629, 1.6083437204360962] |
b87c951d-d28d-44a6-ba30-f1381c829b1a | mask2cad-3d-shape-prediction-by-learning-to | 2007.13034 | null | https://arxiv.org/abs/2007.13034v1 | https://arxiv.org/pdf/2007.13034v1.pdf | Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve | Object recognition has seen significant progress in the image domain, with focus primarily on 2D perception. We propose to leverage existing large-scale datasets of 3D models to understand the underlying 3D structure of objects seen in an image by constructing a CAD-based representation of the objects and their poses. ... | ['Wei-cheng Kuo', 'Tsung-Yi Lin', 'Anelia Angelova', 'Angela Dai'] | 2020-07-26 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/193_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480273.pdf | eccv-2020-8 | ['image-to-3d'] | ['computer-vision'] | [ 3.26827317e-01 4.82021645e-03 5.56370132e-02 -5.50663471e-01
-6.72740698e-01 -8.00388157e-01 7.54065990e-01 -1.70690879e-01
-2.84522027e-02 -3.13430607e-01 6.30602986e-02 -1.41085073e-01
1.56351447e-01 -8.01734805e-01 -8.70917797e-01 -2.84579039e-01
-5.39041264e-03 9.69081521e-01 5.74447215e-01 -1.22125491... | [7.775272369384766, -2.701007843017578] |
284244d8-635a-4379-92bc-d0507242ca6c | prompt-performance-prediction-for-generative | 2306.08915 | null | https://arxiv.org/abs/2306.08915v1 | https://arxiv.org/pdf/2306.08915v1.pdf | Prompt Performance Prediction for Generative IR | The ability to predict the performance of a query in Information Retrieval (IR) systems has been a longstanding challenge. In this paper, we introduce a novel task called "Prompt Performance Prediction" that aims to predict the performance of a query, referred to as a prompt, before obtaining the actual search results.... | ['Olivier Risser-Maroix', 'Ihab Bendidi', 'Nicolas Bizzozzero'] | 2023-06-15 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [ 3.87888312e-01 -1.27366081e-01 -3.91278982e-01 -6.70454562e-01
-1.65180230e+00 -6.88933969e-01 1.09815979e+00 -8.18402991e-02
-2.64910549e-01 9.39240977e-02 4.25649881e-01 -1.05811082e-01
-4.19057608e-01 -3.60356808e-01 -5.62233329e-01 -4.33209032e-01
2.76960999e-01 5.96214712e-01 -2.79678941e-01 -5.79798315... | [11.579974174499512, 7.690279483795166] |
58d5abd5-245c-4818-a68c-1a28cb758ada | evaluation-of-machine-learning-architectures | 2306.15159 | null | https://arxiv.org/abs/2306.15159v1 | https://arxiv.org/pdf/2306.15159v1.pdf | Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems | Machine learning methods for the construction of data-driven reduced order model models are used in an increasing variety of engineering domains, especially as a supplement to expensive computational fluid dynamics for design problems. An important check on the reliability of surrogate models is Uncertainty Quantificat... | ['Themistoklis P. Sapsis', 'Alireza Mojahed', 'Stephen Guth'] | 2023-06-27 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 2.23008431e-02 -6.12304360e-02 5.35958767e-01 -9.59717557e-02
-7.19665289e-01 -4.44615811e-01 6.88249290e-01 2.72192150e-01
-3.94200265e-01 1.16961670e+00 9.84652061e-03 -6.49815321e-01
-8.24811757e-01 -6.92815006e-01 -4.07318115e-01 -9.85688090e-01
-2.92698741e-01 5.53400815e-01 -4.44743186e-02 -7.48618692... | [6.5775146484375, 3.277268648147583] |
9896c376-04fe-4232-b704-db5e03b19255 | objective-based-hierarchical-clustering-of | 2012.08466 | null | https://arxiv.org/abs/2012.08466v2 | https://arxiv.org/pdf/2012.08466v2.pdf | Objective-Based Hierarchical Clustering of Deep Embedding Vectors | We initiate a comprehensive experimental study of objective-based hierarchical clustering methods on massive datasets consisting of deep embedding vectors from computer vision and NLP applications. This includes a large variety of image embedding (ImageNet, ImageNetV2, NaBirds), word embedding (Twitter, Wikipedia), and... | ['Dmitrii Avdiukhin', 'Grigory Yaroslavtsev', 'Stanislav Naumov'] | 2020-12-15 | null | null | null | null | ['2048'] | ['playing-games'] | [-0.32445398 0.17707783 -0.14514005 -0.15077372 -0.95190954 -0.50161207
0.28560966 0.6186595 -0.8887861 0.5546027 0.20934168 -0.11331315
-0.40465292 -0.7677696 -0.7152626 -0.793329 -0.68480027 0.7792713
0.22196846 -0.09742954 0.34052205 0.35896432 -1.2863399 -0.16604407
0.41421303 0.9941652 0.1... | [7.018632411956787, 5.280104637145996] |
eb7aff25-320b-472c-8aa9-aef9789892d2 | small-object-detection-based-on-modified-fssd | 2108.10503 | null | https://arxiv.org/abs/2108.10503v1 | https://arxiv.org/pdf/2108.10503v1.pdf | Small Object Detection Based on Modified FSSD and Model Compression | Small objects have relatively low resolution, the unobvious visual features which are difficult to be extracted, so the existing object detection methods cannot effectively detect small objects, and the detection speed and stability are poor. Thus, this paper proposes a small object detection algorithm based on FSSD, m... | ['Qinqin Zhou', 'Xianggong Hong', 'Hao Zhang', 'Qingcai Wang'] | 2021-08-24 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 4.60929945e-02 -5.77792048e-01 -6.47191778e-02 -4.82156165e-02
-6.89659491e-02 1.18791901e-01 -5.92450649e-02 5.48739098e-02
-6.66950703e-01 2.97635086e-02 -4.83859926e-01 1.45344064e-02
3.07270825e-01 -9.93475914e-01 -2.58501768e-01 -8.61669660e-01
2.26960093e-01 -1.66967899e-01 1.15719652e+00 1.05698116... | [8.76840591430664, -0.5999980568885803] |
37b5f24f-45a2-4133-ae75-9c54e3619295 | cgmn-a-contrastive-graph-matching-network-for | 2205.15083 | null | https://arxiv.org/abs/2205.15083v2 | https://arxiv.org/pdf/2205.15083v2.pdf | CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning | Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As most of the existing graph neural networks yield effective graph representations of a single graph, li... | ['Shirui Pan', 'Wei Lin', 'Fei Jiang', 'Xiang Li', 'Yizhen Zheng', 'Luzhi Wang', 'Di Jin'] | 2022-05-30 | null | null | null | null | ['visual-tracking', 'graph-similarity'] | ['computer-vision', 'graphs'] | [ 1.12609476e-01 3.77530009e-02 -5.87392598e-02 -2.69689053e-01
-1.34174228e-01 -4.39130843e-01 6.10703170e-01 6.62486076e-01
-2.96598282e-02 1.10275045e-01 1.07550733e-01 -7.53948018e-02
-3.46730441e-01 -1.07415962e+00 -2.53348351e-01 -6.33536518e-01
7.88667649e-02 1.95999861e-01 4.75844085e-01 -1.53132647... | [7.232662200927734, 6.205608367919922] |
cff4c907-0c4a-42fd-9d81-b469fa68bb5e | learning-representations-for-counterfactual | 1605.03661 | null | http://arxiv.org/abs/1605.03661v3 | http://arxiv.org/pdf/1605.03661v3.pdf | Learning Representations for Counterfactual Inference | Observational studies are rising in importance due to the widespread
accumulation of data in fields such as healthcare, education, employment and
ecology. We consider the task of answering counterfactual questions such as,
"Would this patient have lower blood sugar had she received a different
medication?". We propose ... | ['Fredrik D. Johansson', 'Uri Shalit', 'David Sontag'] | 2016-05-12 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.54033321e-01 4.46341187e-01 -8.59940767e-01 -5.13324082e-01
-4.66943473e-01 -9.96249467e-02 9.62459981e-01 5.62747896e-01
-4.13875490e-01 1.54817343e+00 1.10203505e+00 -9.44973648e-01
-5.47258556e-01 -9.16492343e-01 -1.06466150e+00 -4.30324405e-01
-2.82976478e-01 5.11203587e-01 -6.50096536e-01 -1.40333742... | [8.070560455322266, 5.377635955810547] |
8e45506e-148f-4c59-a1b4-5a4349330a12 | uvid-net-enhanced-semantic-segmentation-of | 2011.14284 | null | https://arxiv.org/abs/2011.14284v2 | https://arxiv.org/pdf/2011.14284v2.pdf | UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information | Semantic segmentation of aerial videos has been extensively used for decision making in monitoring environmental changes, urban planning, and disaster management. The reliability of these decision support systems is dependent on the accuracy of the video semantic segmentation algorithms. The existing CNN based video se... | ['Radhika Pai', 'Manohara Pai M M', 'Ujjwal Verma', 'Girisha S'] | 2020-11-29 | null | null | null | null | ['aerial-video-semantic-segmentation'] | ['computer-vision'] | [ 2.61978269e-01 -3.64001021e-02 1.00712314e-01 -2.77724743e-01
-4.76330854e-02 -4.85598654e-01 3.10444355e-01 3.79929543e-02
-6.73221946e-01 6.13278508e-01 -2.31464714e-01 -1.10428900e-01
-1.55561149e-01 -8.11219633e-01 -5.81794739e-01 -6.30376339e-01
-1.35685205e-01 -1.34854969e-02 8.46867740e-01 -1.54700071... | [9.05648136138916, -0.9683645367622375] |
3f4c0cb9-a8d5-478b-aea1-4e835048ae92 | a-causal-framework-for-decomposing-spurious | 2306.05071 | null | https://arxiv.org/abs/2306.05071v1 | https://arxiv.org/pdf/2306.05071v1.pdf | A Causal Framework for Decomposing Spurious Variations | One of the fundamental challenges found throughout the data sciences is to explain why things happen in specific ways, or through which mechanisms a certain variable $X$ exerts influences over another variable $Y$. In statistics and machine learning, significant efforts have been put into developing machinery to estima... | ['Elias Bareinboim', 'Drago Plecko'] | 2023-06-08 | null | null | null | null | ['causal-inference', 'epidemiology', 'causal-inference'] | ['knowledge-base', 'medical', 'miscellaneous'] | [ 2.73062795e-01 1.35058433e-01 -8.07602942e-01 -4.26357776e-01
-1.88949093e-01 -3.27095568e-01 4.62550014e-01 8.12405571e-02
1.65842357e-03 1.03255880e+00 2.05009520e-01 -6.66800320e-01
-6.97162330e-01 -8.38074565e-01 -8.15286696e-01 -6.92581415e-01
-3.55845451e-01 3.19976330e-01 -3.08819443e-01 1.97224453... | [7.869596004486084, 5.3755903244018555] |
5763216e-f16d-4c34-88e7-31bddb6e8c35 | helpfulness-and-fairness-of-task-oriented | 2205.12554 | null | https://arxiv.org/abs/2205.12554v3 | https://arxiv.org/pdf/2205.12554v3.pdf | Helpfulness and Fairness of Task-Oriented Dialogue Systems | Goal-oriented dialogue systems aim to help users achieve certain goals. Therefore, how humans perceive their helpfulness is important. However, neither the human-perceived helpfulness of goal-oriented dialogue systems nor its fairness implication has been well studied. In this paper, we study computational measurements... | ['Nanyun Peng', 'Jiin Kim', 'Yu Hou', 'Jiao Sun'] | 2022-05-25 | null | null | null | null | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-3.78190160e-01 8.61502647e-01 -1.22502185e-02 -6.97287798e-01
-4.35127825e-01 -6.82767451e-01 9.38912809e-01 4.96257216e-01
-5.84513664e-01 7.01057732e-01 6.90965056e-01 -2.75332570e-01
1.16545483e-02 -5.20049751e-01 6.72889888e-01 -3.31559330e-01
2.53791511e-01 4.40235108e-01 9.47532058e-03 -9.58083153... | [12.770588874816895, 8.126909255981445] |
ca164e46-7f5e-4d32-9cef-dd9f2050dc8c | hybrid-transformers-for-music-source | 2211.08553 | null | https://arxiv.org/abs/2211.08553v1 | https://arxiv.org/pdf/2211.08553v1.pdf | Hybrid Transformers for Music Source Separation | A natural question arising in Music Source Separation (MSS) is whether long range contextual information is useful, or whether local acoustic features are sufficient. In other fields, attention based Transformers have shown their ability to integrate information over long sequences. In this work, we introduce Hybrid Tr... | ['Alexandre Défossez', 'Francisco Massa', 'Simon Rouard'] | 2022-11-15 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 2.79858232e-01 -2.12566108e-01 9.77528095e-03 5.08403108e-02
-1.54536283e+00 -7.23140001e-01 2.48134226e-01 -9.44958031e-02
-3.00071150e-01 5.47759116e-01 6.53699398e-01 -2.12614685e-02
-1.07632391e-01 -3.69417012e-01 -8.05197001e-01 -5.30127287e-01
-2.58355960e-02 1.85299829e-01 3.03571582e-01 -3.29035044... | [15.403169631958008, 5.467497825622559] |
497511f8-4d30-472d-9ede-b2fd3b8ac5cc | fmri-from-eeg-is-only-deep-learning-away-the | 2211.02024 | null | https://arxiv.org/abs/2211.02024v2 | https://arxiv.org/pdf/2211.02024v2.pdf | fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships | The access to activity of subcortical structures offers unique opportunity for building intention dependent brain-computer interfaces, renders abundant options for exploring a broad range of cognitive phenomena in the realm of affective neuroscience including complex decision making processes and the eternal free-will ... | ['Alexei Ossadtchi', 'Ilia Mikheev', 'Alexander Kovalev'] | 2022-10-23 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 1.78363442e-01 1.69185847e-01 2.69690126e-01 -4.98209506e-01
-1.56903431e-01 -3.95706683e-01 2.88835645e-01 5.45773059e-02
-6.83417916e-01 1.13613284e+00 3.32306117e-01 1.18706021e-02
-5.84835649e-01 -3.30861002e-01 -1.14487953e-01 -8.14286470e-01
-5.68477571e-01 5.93988895e-01 -1.86892375e-01 -1.15085587... | [12.973811149597168, 3.415456771850586] |
63e8cbde-0207-461a-a807-35aa28ca29cb | conditional-image-generation-with-score-based | 2111.13606 | null | https://arxiv.org/abs/2111.13606v1 | https://arxiv.org/pdf/2111.13606v1.pdf | Conditional Image Generation with Score-Based Diffusion Models | Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling. In this work we conduct a systematic comparison and theoretical analysis of different approaches to learning conditional probability distributions with score-based diffusion models. In particular, we prove r... | ['Christian Etmann', 'Carola-Bibiane Schönlieb', 'Jan Stanczuk', 'Georgios Batzolis'] | 2021-11-26 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [-1.70088604e-01 -7.20018446e-02 -1.23988636e-01 -3.06751132e-01
-9.02996361e-01 -3.34648460e-01 1.00798273e+00 2.32580174e-02
-2.93720812e-01 5.84217429e-01 7.16652796e-02 -2.92161852e-01
-5.16617239e-01 -1.10302758e+00 -3.12607437e-01 -8.91340077e-01
-1.44674733e-01 7.76347816e-01 4.54647750e-01 -9.68141388... | [11.25130558013916, -0.10414239764213562] |
b4cb1e46-4cc8-4290-a469-7c47309e14a5 | conceptron-a-probabilistic-deep-one-class | null | null | https://openreview.net/forum?id=q58E59ZPLp | https://openreview.net/pdf?id=q58E59ZPLp | Conceptron: a probabilistic deep one-class classification method | One-class learning through deep architectures is a particularly challenging task; in this scenario the crasis of kernel methods and deep networks can represent a viable strategy to empower already effective methods. In this contribution we present Conceptron, a probabilistic and deep one-class classification method. Th... | ['Sergio Decherchi', 'Andrea Cavalli', 'Erika Gardini'] | 2021-09-29 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [-5.49085811e-03 -3.13409418e-02 -2.56886929e-01 -6.35454118e-01
-6.77214265e-01 -3.75498712e-01 1.09338367e+00 9.62124318e-02
-7.87196696e-01 1.11920238e+00 -2.85069913e-01 -3.48186076e-01
-5.30378699e-01 -6.88058257e-01 -6.51888192e-01 -1.08142221e+00
-9.03599113e-02 1.01992583e+00 2.16797531e-01 1.01982392... | [7.361118316650391, 3.7742092609405518] |
d65be4fb-e49d-405e-ac91-824cb9615e9d | multi-label-network-classification-via | 1902.09294 | null | http://arxiv.org/abs/1902.09294v1 | http://arxiv.org/pdf/1902.09294v1.pdf | Multi-Label Network Classification via Weighted Personalized Factorizations | Multi-label network classification is a well-known task that is being used in
a wide variety of web-based and non-web-based domains. It can be formalized as
a multi-relational learning task for predicting nodes labels based on their
relations within the network. In sparse networks, this prediction task can be
very chal... | ['Lars Schmidt-Thieme', 'Josif Grabocka', 'Ahmed Rashed'] | 2019-02-25 | null | null | null | null | ['implicit-relations'] | ['natural-language-processing'] | [ 2.90623993e-01 4.69144315e-01 -8.48968208e-01 -6.38308287e-01
2.89097447e-02 -3.47180992e-01 4.71816778e-01 6.30840898e-01
-2.57110000e-01 7.20268786e-01 4.20734584e-01 -1.27486721e-01
-8.20670784e-01 -1.14399219e+00 -3.01835775e-01 -5.11666000e-01
-1.86799332e-01 8.71889114e-01 3.95774722e-01 -2.95668304... | [7.5199713706970215, 6.488358974456787] |
6d9c523a-0af0-4ab9-8b1c-19ed8b7f0337 | ddr-id-dual-deep-reconstruction-networks | 2007.09431 | null | https://arxiv.org/abs/2007.09431v1 | https://arxiv.org/pdf/2007.09431v1.pdf | DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection | One pivot challenge for image anomaly (AD) detection is to learn discriminative information only from normal class training images. Most image reconstruction based AD methods rely on the discriminative capability of reconstruction error. This is heuristic as image reconstruction is unsupervised without incorporating no... | ['Yiqun Li', 'Shudong Xie', 'Tin Lay Nwe', 'Sheng Dong', 'Dongyun Lin'] | 2020-07-18 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 3.89156938e-01 -1.45552069e-01 1.66106477e-01 -2.37895653e-01
-6.38167381e-01 -2.48664021e-01 3.79103452e-01 2.46572196e-01
-4.15139884e-01 1.56619936e-01 -6.20579049e-02 -2.72772282e-01
1.01155221e-01 -6.50643170e-01 -9.27026510e-01 -1.04589593e+00
-1.06427610e-01 1.93197161e-01 -1.52134985e-01 -1.60438493... | [7.697807312011719, 2.0797438621520996] |
5fa704b0-6fd0-483f-b38d-4aa104892a86 | avt-audio-video-transformer-for-multimodal | null | null | https://openreview.net/pdf?id=yFuHxmSwGus | https://openreview.net/pdf?id=yFuHxmSwGus | AVT: Audio-Video Transformer for Multimodal Action Recognition | Action recognition is an essential field for video understanding. To learn from heterogeneous data sources effectively, in this work, we propose a novel multimodal action recognition approach termed Audio-Video Transformer (AVT). AVT uses a combination of video and audio signals to improve action recognition accuracy, ... | ['Mohamed Omar', 'Linda Liu', 'Xiang Hao', 'Xiaohang Sun', 'Kevin Hsu', 'Jingru Yi', 'Wentao Zhu'] | 2022-09-22 | null | null | null | submitted-to-iclr-2022-9 | ['audio-classification', 'video-understanding', 'multi-modal-classification'] | ['audio', 'computer-vision', 'miscellaneous'] | [ 5.44478476e-01 -4.19963509e-01 -1.95054621e-01 -5.73687516e-02
-1.38051140e+00 -4.89642471e-01 3.80621433e-01 -9.58342031e-02
-4.18544829e-01 3.74731034e-01 3.45224857e-01 1.20016702e-01
-1.46641746e-01 -4.17649865e-01 -9.51428413e-01 -6.75048172e-01
9.60826427e-02 2.91544665e-02 3.90622020e-01 5.71292713... | [9.559859275817871, 1.073599934577942] |
c7dec5f7-883e-4bca-8e11-9996f6c4e148 | hope-net-a-graph-based-model-for-hand-object | 2004.0006 | null | https://arxiv.org/abs/2004.00060v1 | https://arxiv.org/pdf/2004.00060v1.pdf | HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation | Hand-object pose estimation (HOPE) aims to jointly detect the poses of both a hand and of a held object. In this paper, we propose a lightweight model called HOPE-Net which jointly estimates hand and object pose in 2D and 3D in real-time. Our network uses a cascade of two adaptive graph convolutional neural networks, o... | ['Bardia Doosti', 'Majid Mirbagheri', 'David Crandall', 'Shujon Naha'] | 2020-03-31 | hope-net-a-graph-based-model-for-hand-object-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Doosti_HOPE-Net_A_Graph-Based_Model_for_Hand-Object_Pose_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Doosti_HOPE-Net_A_Graph-Based_Model_for_Hand-Object_Pose_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['hand-object-pose'] | ['computer-vision'] | [-5.03641903e-01 5.19811995e-02 -7.70441517e-02 1.57204773e-02
-5.52921951e-01 -6.71822190e-01 4.75125313e-01 -1.63925320e-01
-3.52422357e-01 -5.45642264e-02 3.51248160e-02 -5.24230264e-02
1.00772612e-01 -3.31716210e-01 -6.29410982e-01 -2.46561170e-01
-3.17860991e-01 1.03866768e+00 4.06312555e-01 1.68307319... | [6.587416172027588, -0.8649260401725769] |
8f6e8417-4b5d-46fd-be6d-ace06cecc3ac | real-time-visual-tracking-using-spatial-aware | 1908.00692 | null | https://arxiv.org/abs/1908.00692v1 | https://arxiv.org/pdf/1908.00692v1.pdf | Real Time Visual Tracking using Spatial-Aware Temporal Aggregation Network | More powerful feature representations derived from deep neural networks benefit visual tracking algorithms widely. However, the lack of exploitation on temporal information prevents tracking algorithms from adapting to appearances changing or resisting to drift. This paper proposes a correlation filter based tracking m... | ['Xian-Ming Liu', 'Lichao Huang', 'Tao Hu', 'Han Shen'] | 2019-08-02 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [-4.08854961e-01 -7.25830078e-01 -8.01479369e-02 -8.68754834e-02
-2.52508551e-01 -5.17642200e-01 4.86439407e-01 -3.72745782e-01
-4.81868833e-01 6.02379382e-01 -1.02450773e-01 9.25609916e-02
1.81212667e-02 -5.60236037e-01 -6.19479060e-01 -6.01061881e-01
-2.46429503e-01 -1.35953590e-01 6.61976159e-01 -4.37767506... | [6.317809581756592, -2.1318702697753906] |
16c53109-16ec-444c-ba53-abc3d07618b6 | evaluating-xgboost-for-balanced-and | 2303.15218 | null | https://arxiv.org/abs/2303.15218v1 | https://arxiv.org/pdf/2303.15218v1.pdf | Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection | This paper evaluates XGboost's performance given different dataset sizes and class distributions, from perfectly balanced to highly imbalanced. XGBoost has been selected for evaluation, as it stands out in several benchmarks due to its detection performance and speed. After introducing the problem of fraud detection, t... | ['Vaibhav Joshi', 'Khushboo Sharma', 'Anuj Deshmunkh', 'Sanjay Deshmane', 'Anindya Sudhir', 'Gissel Velarde'] | 2023-03-27 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-3.27332407e-01 -5.25142774e-02 -5.95673621e-01 -3.91228437e-01
-4.75306690e-01 -2.87537307e-01 4.69963729e-01 6.11346543e-01
-3.50270301e-01 8.79276633e-01 4.54633944e-02 -4.53669965e-01
-3.67311329e-01 -1.25677657e+00 -3.54905307e-01 -5.73521316e-01
-1.37038991e-01 8.36703658e-01 -2.54531465e-02 -3.22820365... | [8.756759643554688, 4.27420711517334] |
ecfd0ecf-b724-41b2-aada-5ddb4a8052a6 | ab3dmot-a-baseline-for-3d-multi-object | 2008.08063 | null | https://arxiv.org/abs/2008.08063v1 | https://arxiv.org/pdf/2008.08063v1.pdf | AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics | 3D multi-object tracking (MOT) is essential to applications such as autonomous driving. Recent work focuses on developing accurate systems giving less attention to computational cost and system complexity. In contrast, this work proposes a simple real-time 3D MOT system with strong performance. Our system first obtains... | ['Jianren Wang', 'David Held', 'Kris Kitani', 'Xinshuo Weng'] | 2020-08-18 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-3.15632433e-01 -5.47567368e-01 -6.52964711e-02 -2.36848533e-01
-4.34665263e-01 -3.66836607e-01 5.45068204e-01 -2.06630290e-01
-6.06089652e-01 5.92905998e-01 -8.19343388e-01 -5.98725736e-01
4.51563904e-03 -8.47409785e-01 -6.16119266e-01 -5.59115112e-01
-5.21437563e-02 9.98746157e-01 9.61024165e-01 -3.12178761... | [6.6661763191223145, -2.2181155681610107] |
9df03d62-d71b-46e5-91fb-d4d555e611a9 | quantum-machine-learning-for-image | 2304.09224 | null | https://arxiv.org/abs/2304.09224v1 | https://arxiv.org/pdf/2304.09224v1.pdf | Quantum machine learning for image classification | Image recognition and classification are fundamental tasks with diverse practical applications across various industries, making them critical in the modern world. Recently, machine learning models, particularly neural networks, have emerged as powerful tools for solving these problems. However, the utilization of quan... | ['Alexey Melnikov', 'Asel Sagingalieva', 'Alexander Sedykh', 'Arsenii Senokosov'] | 2023-04-18 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 5.70105135e-01 -1.46198899e-01 -3.92587185e-01 -2.61734277e-01
-6.89197421e-01 -2.10768953e-01 5.27885079e-01 -2.27208771e-02
-5.67645490e-01 5.93957841e-01 -5.33221185e-01 -3.27061743e-01
-9.88752916e-02 -1.00801671e+00 -5.84475160e-01 -1.42060709e+00
3.79957348e-01 2.47887284e-01 9.58226547e-02 -4.87293810... | [5.556402683258057, 4.974667072296143] |
44ab959b-2be9-4305-ac85-c2a8e1095501 | self-supervised-geometric-correspondence-for | 2210.07199 | null | https://arxiv.org/abs/2210.07199v3 | https://arxiv.org/pdf/2210.07199v3.pdf | Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild | While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations. The problem becomes even more challenging when moving to category-level 6D pose, which requires generalization to unseen instances. Current approaches are restricte... | ['Xiaolong Wang', 'Fatih Porikli', 'Hong Cai', 'Shubhankar Borse', 'Yang Fu', 'Kaifeng Zhang'] | 2022-10-13 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.74758881e-02 1.07654274e-01 -1.16575234e-01 -2.79674232e-01
-9.62957382e-01 -8.43679368e-01 5.92358291e-01 -6.39477149e-02
-3.93403709e-01 2.32419088e-01 -7.35333264e-02 8.47026780e-02
-1.17181055e-02 -2.84081697e-01 -1.18017209e+00 -4.54412669e-01
-1.09528877e-01 9.18888509e-01 3.83929640e-01 1.79828219... | [7.74999475479126, -2.627852201461792] |
36401bc5-a926-48b6-8ffa-a3c2db7628fb | resource-allocation-of-federated-learning-for | 2211.08705 | null | https://arxiv.org/abs/2211.08705v2 | https://arxiv.org/pdf/2211.08705v2.pdf | Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality | The Metaverse has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with the real world. Federated learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics.... | ['Jun Zhao', 'Chang Liu', 'Xinyu Zhou'] | 2022-11-16 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-4.35475539e-03 -3.71052563e-01 -5.46069086e-01 -1.90017983e-01
-8.89658928e-01 -5.39838850e-01 -2.51154561e-04 -8.64232779e-02
-4.11475688e-01 7.17244983e-01 -2.45295390e-01 -3.90923887e-01
-2.62865037e-01 -6.69440031e-01 -6.79650366e-01 -8.28683555e-01
-1.24603540e-01 -2.72604488e-02 -6.64542019e-02 4.78298038... | [5.908616065979004, 5.931375026702881] |
066fd5b8-3228-4d16-a4fe-f7a16cae739d | unleashing-the-potential-of-unsupervised-deep | 2305.16777 | null | https://arxiv.org/abs/2305.16777v1 | https://arxiv.org/pdf/2305.16777v1.pdf | Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training Stopping | Outlier detection (OD) has received continuous research interests due to its wide applications. With the development of deep learning, increasingly deep OD algorithms are proposed. Despite the availability of numerous deep OD models, existing research has reported that the performance of deep models is extremely sensit... | ['Xuemin Lin', 'Liping Wang', 'Yuang Zhang', 'Yihong Huang'] | 2023-05-26 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [-2.64773071e-01 -3.53596389e-01 -1.27257824e-01 -3.04364502e-01
-4.06421095e-01 -3.00195813e-01 4.44028854e-01 3.70997250e-01
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-3.01530719e-01 -6.03902936e-01 -6.18522942e-01 -7.50849426e-01
-1.15490995e-01 4.11051154e-01 4.37341452e-01 2.13054165... | [7.7150092124938965, 2.6090035438537598] |
5e07f78f-c193-4bb3-a53d-16c189896409 | uniqueness-of-iris-pattern-based-on-ar-model | 2306.12572 | null | https://arxiv.org/abs/2306.12572v1 | https://arxiv.org/pdf/2306.12572v1.pdf | Uniqueness of Iris Pattern Based on AR Model | The assessment of iris uniqueness plays a crucial role in analyzing the capabilities and limitations of iris recognition systems. Among the various methodologies proposed, Daugman's approach to iris uniqueness stands out as one of the most widely accepted. According to Daugman, uniqueness refers to the iris recognition... | ['Matthew C. Valenti', 'Joseph Skufca', 'Stephanie Schuckers', 'Natalia A. Schmid', 'Priyanka Das', 'Jinyu Zuo', 'Katelyn M. Hampel'] | 2023-06-21 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 3.75144064e-01 -4.28524576e-02 -2.43745521e-01 -4.17546555e-02
-2.36766145e-01 -6.12467825e-01 2.59057432e-01 3.91285300e-01
-3.34929496e-01 4.11074936e-01 5.02434336e-02 -5.98060548e-01
-7.21465766e-01 -5.50330997e-01 -1.97038531e-01 -7.58846045e-01
-9.12117288e-02 2.91140020e-01 -2.30287880e-01 1.37529880... | [3.7472660541534424, -3.624019145965576] |
ca28230e-56d9-470b-bcc5-04e3ea6dc4b7 | pc-dan-point-cloud-based-deep-affinity | 2106.07552 | null | https://arxiv.org/abs/2106.07552v1 | https://arxiv.org/pdf/2106.07552v1.pdf | PC-DAN: Point Cloud based Deep Affinity Network for 3D Multi-Object Tracking (Accepted as an extended abstract in JRDB-ACT Workshop at CVPR21) | In recent times, the scope of LIDAR (Light Detection and Ranging) sensor-based technology has spread across numerous fields. It is popularly used to map terrain and navigation information into reliable 3D point cloud data, potentially revolutionizing the autonomous vehicles and assistive robotic industry. A point cloud... | ['Ajmal Mian', 'Mubarak Shah', 'Jyoti Kini', 'Aakash Kumar'] | 2021-06-03 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [ 7.70702735e-02 -4.88406062e-01 -3.22425395e-01 -3.67833018e-01
-6.92231506e-02 -5.29581606e-01 6.81484520e-01 1.13870114e-01
-4.20275360e-01 5.23793936e-01 -4.16281402e-01 -4.80344236e-01
-9.90058035e-02 -1.27589560e+00 -4.17238981e-01 -2.92031258e-01
-2.60259639e-02 9.49415445e-01 9.35459375e-01 -5.19037127... | [7.641634941101074, -2.4727327823638916] |
a7548e38-8968-4fc1-b204-cb8a95620334 | unified-2d-and-3d-pre-training-of-molecular | 2207.08806 | null | https://arxiv.org/abs/2207.08806v1 | https://arxiv.org/pdf/2207.08806v1.pdf | Unified 2D and 3D Pre-Training of Molecular Representations | Molecular representation learning has attracted much attention recently. A molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds, and can also be represented by a 3D conformation with 3-dimensional coordinates of all atoms. We note that most previous work handles 2D and 3D information separatel... | ['Tie-Yan Liu', 'Houqiang Li', 'Wengang Zhou', 'Tao Qin', 'Shufang Xie', 'Lijun Wu', 'Yingce Xia', 'Jinhua Zhu'] | 2022-07-14 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 4.63907182e-01 3.61845076e-01 -5.12845516e-01 -3.18518937e-01
-8.16881120e-01 -5.79409420e-01 6.83688164e-01 5.28572619e-01
-9.48863402e-02 8.83409441e-01 3.87672782e-01 -6.58224046e-01
1.78710997e-01 -9.90623772e-01 -9.66782391e-01 -9.65804875e-01
-3.56096774e-01 5.64204633e-01 -1.38745874e-01 -1.70532018... | [5.094351768493652, 5.78941011428833] |
5f4f7c81-f593-4619-9736-3ca8f6dcdd33 | efficient-distributed-framework-for | 2205.05248 | null | https://arxiv.org/abs/2205.05248v1 | https://arxiv.org/pdf/2205.05248v1.pdf | Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning | Multi-agent reinforcement learning for incomplete information environments has attracted extensive attention from researchers. However, due to the slow sample collection and poor sample exploration, there are still some problems in multi-agent reinforcement learning, such as unstable model iteration and low training ef... | ['Jing Xiao', 'Xuan Wang', 'Jiajia Zhang', 'Xiaohan Hou', 'Shuhao Zhang', 'Shuhan Qi'] | 2022-05-11 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.46991473e-01 -5.07447243e-01 -2.72392392e-01 1.50999442e-01
-5.37324786e-01 -3.04955512e-01 1.83289289e-01 1.64587691e-01
-8.96070600e-01 1.07177031e+00 -1.37424812e-01 -1.77662671e-01
-2.88003504e-01 -8.35036814e-01 -1.98473722e-01 -8.89187813e-01
-2.63794780e-01 6.56546891e-01 2.50645936e-01 -3.61615509... | [3.7989656925201416, 2.026935577392578] |
d982d8c6-fbdb-4cc2-80d6-85306b547cf6 | asymmetric-modality-translation-for-face | 2110.09108 | null | https://arxiv.org/abs/2110.09108v2 | https://arxiv.org/pdf/2110.09108v2.pdf | Asymmetric Modality Translation For Face Presentation Attack Detection | Face presentation attack detection (PAD) is an essential measure to protect face recognition systems from being spoofed by malicious users and has attracted great attention from both academia and industry. Although most of the existing methods can achieve desired performance to some extent, the generalization issue of ... | ['Alex C. Kot', 'Kwok-Yan Lam', 'Yongjian Hu', 'Xin Luo', 'Haoliang Li', 'Zhi Li'] | 2021-10-18 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 7.24935472e-01 -3.61874819e-01 -1.48043588e-01 -2.60984242e-01
-6.38804317e-01 -6.95763111e-01 7.79064298e-01 -3.82094204e-01
-5.10696881e-02 3.74347955e-01 -1.96010530e-01 -2.88819671e-01
-1.61274746e-02 -7.00595975e-01 -6.63966119e-01 -1.21847200e+00
2.28557944e-01 -1.43709868e-01 1.23383805e-01 -2.60525018... | [13.044256210327148, 1.1708290576934814] |
0504310b-f0d4-4f33-a256-13e6d46990b3 | synthesis-of-opacity-enforcing-winning | 2304.01286 | null | https://arxiv.org/abs/2304.01286v1 | https://arxiv.org/pdf/2304.01286v1.pdf | Synthesis of Opacity-Enforcing Winning Strategies Against Colluded Opponent | This paper studies a language-based opacity enforcement in a two-player, zero-sum game on a graph. In this game, player 1 (P1) wins if it can achieve a secret temporal goal described by the language of a finite automaton, no matter what strategy the opponent player 2 (P2) selects. In addition, P1 aims to win while maki... | ['Jie Fu', 'Hazhar Rahmani', 'Abhishek N. Kulkarni', 'Chongyang Shi'] | 2023-04-03 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 4.11234051e-01 1.22597146e+00 4.63492796e-02 4.32712376e-01
-6.41750693e-01 -9.12773252e-01 4.30611551e-01 -8.91495775e-03
-5.89601040e-01 8.34384680e-01 -3.92650329e-02 -6.60003304e-01
2.35987082e-01 -1.17144334e+00 -6.19023740e-01 -7.36638784e-01
-3.52566570e-01 4.05554295e-01 8.51712644e-01 -1.57576978... | [4.402095794677734, 2.091395616531372] |
351e5f1c-5543-454d-82ac-89a8fea3afcb | self-governing-neural-networks-for-on-device | null | null | https://aclanthology.org/D18-1105 | https://aclanthology.org/D18-1105.pdf | Self-Governing Neural Networks for On-Device Short Text Classification | Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity... | ['Zornitsa Kozareva', 'Sujith Ravi'] | 2018-10-01 | self-governing-neural-networks-for-on-device-1 | https://aclanthology.org/D18-1092 | https://aclanthology.org/D18-1092.pdf | emnlp-2018-10 | ['dialog-act-classification', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-1.86267406e-01 1.26554817e-01 -3.23463500e-01 -5.89871228e-01
-4.05333847e-01 -3.30384493e-01 6.79933131e-01 3.19652483e-02
-9.18083489e-01 4.02313054e-01 6.34050012e-01 -2.88705766e-01
1.95286304e-01 -7.54881144e-01 -2.00523794e-01 -4.68310535e-01
2.71713406e-01 5.33681452e-01 2.81873822e-01 -4.34732407... | [10.798141479492188, 7.905714511871338] |
fc97a451-9a19-4726-b60c-f531cc992d26 | deep-learning-and-image-super-resolution | 2305.13929 | null | https://arxiv.org/abs/2305.13929v1 | https://arxiv.org/pdf/2305.13929v1.pdf | Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks | In this paper, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming at the base station (BS). The following persisting challenges motivated our research: (i) User and vehicular mobility, as well as redundant... | ['Tony Q. S. Quek', 'Setareh Maghsudi', 'Tomoaki Ohtsuki', 'Yuwen Cao'] | 2023-05-08 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [-1.31159902e-01 -1.86242864e-01 -1.58820584e-01 -9.20426399e-02
-7.07946777e-01 -2.73979604e-01 5.44611663e-02 -4.43658888e-01
-8.35853964e-02 9.40523446e-01 3.36564869e-01 -5.50722301e-01
-6.94765449e-01 -1.11589742e+00 -2.82610297e-01 -1.15569484e+00
-2.73629993e-01 2.28172243e-02 -1.57506526e-01 -1.39102727... | [6.23029899597168, 1.3396475315093994] |
1a0073e8-a149-4438-aa8c-357d65ce0b34 | lepus-prompt-based-unsupervised-multi-hop | 2205.1265 | null | https://arxiv.org/abs/2205.12650v3 | https://arxiv.org/pdf/2205.12650v3.pdf | Few-shot Reranking for Multi-hop QA via Language Model Prompting | We study few-shot reranking for multi-hop QA with open-domain questions. To alleviate the need for a large number of labeled question-document pairs for retriever training, we propose PromptRank, which relies on large language models prompting for multi-hop path reranking. PromptRank first constructs an instruction-bas... | ['Lu Wang', 'Honglak Lee', 'Moontae Lee', 'Lajanugen Logeswaran', 'Muhammad Khalifa'] | 2022-05-25 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [-2.35429525e-01 5.12691401e-02 -2.14446008e-01 -2.89607525e-01
-1.99569893e+00 -7.42790878e-01 8.17499459e-01 5.07108331e-01
-7.08176851e-01 6.78834736e-01 6.91443682e-01 -4.95776892e-01
-4.68946576e-01 -5.94568789e-01 -5.10082066e-01 -1.00326248e-01
2.10994959e-01 1.37328792e+00 7.41930544e-01 -8.74358535... | [11.436332702636719, 7.751377105712891] |
8c15c917-1863-4705-8610-b621a6750189 | improving-action-localization-by-progressive-1 | 1905.11575 | null | https://arxiv.org/abs/1905.11575v1 | https://arxiv.org/pdf/1905.11575v1.pdf | Improving Action Localization by Progressive Cross-stream Cooperation | Spatio-temporal action localization consists of three levels of tasks: spatial localization, action classification, and temporal segmentation. In this work, we propose a new Progressive Cross-stream Cooperation (PCSC) framework to use both region proposals and features from one stream (i.e. Flow/RGB) to help another st... | ['Rui Su', 'Wanli Ouyang', 'Luping Zhou', 'Dong Xu'] | 2019-05-28 | improving-action-localization-by-progressive | http://openaccess.thecvf.com/content_CVPR_2019/html/Su_Improving_Action_Localization_by_Progressive_Cross-Stream_Cooperation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Su_Improving_Action_Localization_by_Progressive_Cross-Stream_Cooperation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 3.84073615e-01 -3.99437338e-01 -4.93622392e-01 -1.64202869e-01
-9.22640681e-01 -4.14599627e-01 3.29492211e-01 2.55580395e-01
-5.57660162e-01 6.53440475e-01 1.36216715e-01 1.37891948e-01
1.84178818e-02 -8.06140602e-01 -6.40615165e-01 -8.59080076e-01
-8.72733667e-02 9.90355760e-02 8.95837963e-01 9.29720029... | [8.470512390136719, 0.6310554146766663] |
b859e83b-f667-4ec7-8161-697800d7857c | from-abstractions-to-natural-languages-for | 1905.00517 | null | https://arxiv.org/abs/1905.00517v2 | https://arxiv.org/pdf/1905.00517v2.pdf | From Abstractions to "Natural Languages" for Coordinating Planning Agents | Despite significant advancements in developing autonomous agents, communication between them often relies on a set of pre-specified symbols for a given domain. In this paper, we investigate the automatic construction of these symbols from abstractions to form "natural languages" for such agents. The focus of this initi... | ['Li Wang', 'Yu Zhang'] | 2019-05-01 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 3.25467348e-01 6.11924291e-01 1.48352727e-01 -1.90939739e-01
-6.11972570e-01 -8.19241405e-01 8.87279391e-01 2.60882676e-01
-3.66934866e-01 7.99939036e-01 2.91226268e-01 -5.51472247e-01
-1.54080078e-01 -1.05824935e+00 -6.45784199e-01 -5.44661403e-01
-3.77295822e-01 9.48057353e-01 3.91667038e-01 -4.91036952... | [4.440442085266113, 1.1946226358413696] |
870cd6af-f8d1-460e-b93d-3f1fde007268 | conditional-and-residual-methods-in-scalable | 2305.02562 | null | https://arxiv.org/abs/2305.02562v2 | https://arxiv.org/pdf/2305.02562v2.pdf | Conditional and Residual Methods in Scalable Coding for Humans and Machines | We present methods for conditional and residual coding in the context of scalable coding for humans and machines. Our focus is on optimizing the rate-distortion performance of the reconstruction task using the information available in the computer vision task. We include an information analysis of both approaches to pr... | ['Ivan V. Bajić', 'Yalda Foroutan', 'Alon Harell', 'Anderson de Andrade'] | 2023-05-04 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.67498112e-01 7.84333110e-01 1.22094765e-01 -4.15115923e-01
-1.20367825e+00 -5.65037131e-01 9.70759988e-01 1.25771701e-01
-5.41473567e-01 4.03699547e-01 6.18852973e-01 -2.82487392e-01
1.72896370e-01 -4.40631777e-01 -6.97654545e-01 -5.43290377e-01
4.93871383e-02 4.51191336e-01 2.95631826e-01 2.88285881... | [10.917855262756348, 0.10810727626085281] |
c0bd3c0e-d056-4d11-8101-aa9a8035fd6f | joint-span-segmentation-and-rhetorical-role | 2302.06448 | null | https://arxiv.org/abs/2302.06448v1 | https://arxiv.org/pdf/2302.06448v1.pdf | Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents | Segmentation and Rhetorical Role Labeling of legal judgements play a crucial role in retrieval and adjacent tasks, including case summarization, semantic search, argument mining etc. Previous approaches have formulated this task either as independent classification or sequence labeling of sentences. In this work, we re... | ['Matthias Grabmair', 'Philipp Bock', 'T. Y. S. S. Santosh'] | 2023-02-13 | null | null | null | null | ['argument-mining'] | ['natural-language-processing'] | [ 7.48991966e-01 5.01586556e-01 -7.65428603e-01 -4.91291642e-01
-1.34687078e+00 -8.21746290e-01 7.32525229e-01 5.79392433e-01
-5.41689038e-01 1.24010932e+00 9.35923398e-01 -6.26048446e-01
-1.88898876e-01 -3.33854735e-01 -3.23346138e-01 -2.67153651e-01
2.42921770e-01 5.53232908e-01 2.84460992e-01 -1.36954814... | [10.873517036437988, 9.439335823059082] |
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