Weicong Ding

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Prakash IshwarProfessor of Electrical and Computer Engineering at Boston UniversityDirección de correo verificada de bu.edu
Venkatesh SaligramaProfessor, Electrical and Computer Engineering, Boston UniversityDirección de correo verificada de bu.edu
Yifan SunAssistant Prof at Stonybrook CS DeptDirección de correo verificada de stonybrook.edu
Nikhil RaoMicrosoftDirección de correo verificada de microsoft.com
Brian ErikssonAdobeDirección de correo verificada de adobe.com
Mohammad Hossein RohbanComputer Eng. Department, Sharif University of TechnologyDirección de correo verificada de sharif.edu
Hui Xiong, Fellow of AAAS and IEEERutgers University/Baidu/HKUSTDirección de correo verificada de ust.hk
Zijun YaoUniversity of KansasDirección de correo verificada de ku.edu
Chris J. VargoUniversity of Colorado BoulderDirección de correo verificada de colorado.edu
Lei GuoFudan UniversityDirección de correo verificada de fudan.edu.cn
Dinesh GovindarajSenior Applied Scientist, AmazonDirección de correo verificada de amazon.com
William TrouleauData Scientist, Unit8Dirección de correo verificada de epfl.ch
Azin AshkanGoogleDirección de correo verificada de uwaterloo.ca
Christy LinStudent at System Engineering, Boston UniversityDirección de correo verificada de bu.edu
S V N VishwanathanAssociate Professor of Statistics and Computer Science, Purdue UniversityDirección de correo verificada de stat.purdue.edu
Aman AhujaVirginia TechDirección de correo verificada de cs.vt.edu
Chandan K. ReddyProfessor, Computer Science, Virginia TechDirección de correo verificada de cs.vt.edu
Vineeth RakeshResearch Scientist at VisaDirección de correo verificada de visa.com
Vatsal ShahAmazonDirección de correo verificada de utexas.edu
Clem KarlProfessor of Electrical and Computer Engineering and Biomedical Engineering, Boston UniversityDirección de correo verificada de bu.edu

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Weicong Ding

Scientist at Roku Inc.

No hay ninguna dirección de correo electrónico verificada.

Machine Learning Data Mining


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Big social data analytics in journalism and mass communication: Comparing dictionary-based text analysis and unsupervised topic modeling L Guo, CJ Vargo, Z Pan, W Ding, P Ishwar Journalism & Mass Communication Quarterly 93 (2), 332-359, 2016	311	2016
Dynamic word embeddings for evolving semantic discovery Z Yao, Y Sun, W Ding, N Rao, H Xiong Proceedings of the eleventh acm international conference on web search and …, 2018	298	2018
Just One More: Modeling Binge Watching Behavior W Trouleau, A Ashkan, W Ding, B Eriksson 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2016	76	2016
Topic discovery through data dependent and random projections W Ding, MH Rohban, P Ishwar, V Saligrama International Conference on Machine Learning, 1202-1210, 2013	68	2013
A sparse topic model for extracting aspect-specific summaries from online reviews V Rakesh, W Ding, A Ahuja, N Rao, Y Sun, CK Reddy Proceedings of the 2018 World Wide Web Conference, 1573-1582, 2018	27	2018
Efficient distributed topic modeling with provable guarantees W Ding, M Rohban, P Ishwar, V Saligrama Artificial Intelligence and Statistics, 167-175, 2014	27	2014
A topic modeling approach to ranking W Ding, P Ishwar, V Saligrama Artificial Intelligence and Statistics, 214-222, 2015	26	2015
Node Embedding via Word Embedding for Network Community Discovery W Ding, C Lin, P Ishwar arXiv:1611.03028, 2016	21	2016
Whole page optimization with global constraints W Ding, D Govindaraj, SVN Vishwanathan Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019	19	2019
Most large topic models are approximately separable W Ding, P Ishwar, V Saligrama 2015 Information theory and applications workshop (ITA), 199-203, 2015	19	2015
A simple approach to learn polysemous word embeddings Y Sun, N Rao, W Ding arXiv preprint arXiv:1707.01793, 2017	15	2017
Learning mixed membership mallows models from pairwise comparisons W Ding, P Ishwar, V Saligrama arXiv preprint arXiv:1504.00757, 2015	15	2015
Necessary and sufficient conditions for novel word detection in separable topic models W Ding, P Ishwar, MH Rohban, V Saligrama arXiv preprint arXiv:1310.7994, 2013	15	2013
Matrix Factorization with Side and Higher Order Information V Shah, N Rao, W Ding https://arxiv.org/abs/1705.02047, 2017	8	2017
A new geometric approach to latent topic modeling and discovery W Ding, MH Rohban, P Ishwar, V Saligrama 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013	8	2013
A topic modeling approach to rank aggregation W Ding, P Ishwar, V Saligrama Advances in on Neural Information Processing Systems, workshop on Analysis …, 2014	7	2014
Necessary and sufficient conditions and a provably efficient algorithm for separable topic discovery W Ding, P Ishwar, V Saligrama arXiv preprint arXiv:1508.05565, 2015	6	2015
Node embedding for network community discovery C Lin, P Ishwar, W Ding 2017 IEEE international conference on acoustics, speech and signal …, 2017	4	2017
Appliance failure prediction BC Eriksson, A Pudhiyaveetil, Y Sun, W Ding US Patent App. 16/004,353, 2018	3	2018
Matrix completion via factorizing polynomials V Shah, N Rao, W Ding arXiv preprint arXiv:1705.02047, 2017	2	2017

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