Hyun Oh Song

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Stefanie JegelkaTUM and MITPatvirtintas el. paštas mit.edu
Jang-Hyun KimSeoul National UniversityPatvirtintas el. paštas snu.ac.kr
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyPatvirtintas el. paštas eecs.berkeley.edu
Silvio SavareseAssociate Professor of Computer Science at Stanford UniversityPatvirtintas el. paštas stanford.edu
Seungyong MoonSeoul National UniversityPatvirtintas el. paštas mllab.snu.ac.kr
Yeonwoo JeongSeoul National UniversityPatvirtintas el. paštas mllab.snu.ac.kr
Ross GirshickResearch Scientist, Allen Institute for Artificial Intelligence (AI2)Patvirtintas el. paštas allenai.org
Kevin MurphyResearch Scientist, GooglePatvirtintas el. paštas google.com
Vivek RathodSoftware Engineer, GooglePatvirtintas el. paštas google.com
Yu XiangAssistant Professor, University of Texas at DallasPatvirtintas el. paštas utdallas.edu
Mario FritzFaculty CISPA Helmholtz Center for Information Security; Professor Saarland UniversityPatvirtintas el. paštas cispa.de
Ozan SenerApplePatvirtintas el. paštas cs.stanford.edu
Ashutosh SaxenaFormer Faculty, Computer Science, Cornell University. Stanford UniversityPatvirtintas el. paštas cs.stanford.edu
Sergey LevineUC Berkeley, Physical IntelligencePatvirtintas el. paštas eecs.berkeley.edu
Jaekyeom KimLG AI ResearchPatvirtintas el. paštas lgresearch.ai
Stefan ZickleriRobot Corp.Patvirtintas el. paštas irobot.com
Pedro FelzenszwalbBrown UniversityPatvirtintas el. paštas brown.edu
Christopher GeyerBerkshire GreyPatvirtintas el. paštas berkshiregrey.com
Julien MairalInria - Univ. Grenoble AlpesPatvirtintas el. paštas inria.fr
Zaid HarchaouiUniversity of WashingtonPatvirtintas el. paštas uw.edu

Stebėti

Hyun Oh Song

Associate Professor of Computer Science, Seoul National University

Patvirtintas el. paštas snu.ac.kr - Pagrindinis puslapis

Machine Learning Optimization Deep Learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Deep metric learning via lifted structured feature embedding HO Song, Y Xiang, S Jegelka, S Savarese Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016	2069	2016
Puzzle mix: Exploiting saliency and local statistics for optimal mixup JH Kim, W Choo, HO Song International conference on machine learning, 5275-5285, 2020	414	2020
Deep Metric Learning via Facility Location HO Song, S Jegelka, V Rathod, K Murphy Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017	385	2017
Learning transferrable representations for unsupervised domain adaptation O Sener, HO Song, A Saxena, S Savarese Advances in neural information processing systems 29, 2016	348	2016
On learning to localize objects with minimal supervision HO Song, R Girshick, S Jegelka, J Mairal, Z Harchaoui, T Darrell Proceedings of the 31th International Conference on Machine Learning (ICML-14), 2014	291	2014
Uncertainty-based offline reinforcement learning with diversified q-ensemble G An, S Moon, JH Kim, HO Song Advances in neural information processing systems 34, 7436-7447, 2021	290	2021
Semantic instance segmentation via deep metric learning A Fathi, Z Wojna, V Rathod, P Wang, HO Song, S Guadarrama, ... arXiv preprint arXiv:1703.10277, 2017	243	2017
Co-mixup: Saliency guided joint mixup with supermodular diversity JH Kim, W Choo, H Jeong, HO Song arXiv preprint arXiv:2102.03065, 2021	195	2021
Weakly-supervised discovery of visual pattern configurations HO Song, YJ Lee, S Jegelka, T Darrell Advances in Neural Information Processing Systems 27, 2014	195	2014
Dataset condensation via efficient synthetic-data parameterization JH Kim, J Kim, SJ Oh, S Yun, H Song, J Jeong, JW Ha, HO Song International Conference on Machine Learning, 11102-11118, 2022	155	2022
Parsimonious black-box adversarial attacks via efficient combinatorial optimization S Moon, G An, HO Song International Conference on Machine Learning, 4636-4645, 2019	154	2019
Emi: Exploration with mutual information H Kim, J Kim, Y Jeong, S Levine, HO Song International Conference on Machine Learning, 3360-3369, 2019	126	2019
Sparselet models for efficient multiclass object detection HO Song, S Zickler, T Althoff, R Girshick, M Fritz, C Geyer, P Felzenszwalb, ... Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012	105	2012
Learning to detect visual grasp affordance HO Song, M Fritz, D Goehring, T Darrell IEEE Transactions on Automation Science and Engineering 13 (2), 798-809, 2015	67	2015
Query-efficient and scalable black-box adversarial attacks on discrete sequential data via bayesian optimization D Lee, S Moon, J Lee, HO Song International Conference on Machine Learning, 12478-12497, 2022	47	2022
Learning discrete and continuous factors of data via alternating disentanglement Y Jeong, HO Song International Conference on Machine Learning, 3091-3099, 2019	43	2019
Discriminatively activated sparselets R Girshick, HO Song, T Darrell International Conference on Machine Learning, 196-204, 2013	33	2013
Detection bank: an object detection based video representation for multimedia event recognition T Althoff, HO Song, T Darrell Proceedings of the 20th ACM international conference on Multimedia, 1065-1068, 2012	32	2012
Visual grasp affordances from appearance-based cues HO Song, M Fritz, C Gu, T Darrell 2011 IEEE International Conference on Computer Vision Workshops (ICCV …, 2011	26	2011
Generalized sparselet models for real-time multiclass object recognition HO Song, R Girshick, S Zickler, C Geyer, P Felzenszwalb, T Darrell IEEE transactions on pattern analysis and machine intelligence 37 (5), 1001-1012, 2014	25	2014

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