Hanie Sedghi

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Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerified email at caltech.edu
Majid JanzaminTwitter Inc.Verified email at twitter.com
Behnam NeyshaburSenior Staff Research Scientist, GoogleVerified email at google.com
Philip LongGoogleVerified email at google.com
Edmond JonckheereProfessor of Electrical Engineering, University of Southern CaliforniaVerified email at usc.edu
Vineet GuptaGoogle IncVerified email at google.com
Chiyuan ZhangGoogle BrainVerified email at google.com
Samira AbnarApple ML ResearchVerified email at apple.com
Mostafa DehghaniResearch Scientist, Google BrainVerified email at google.com
Preetum NakkiranApple ML ResearchVerified email at cs.harvard.edu
Ashish SabharwalAllen Institute for AI (AI2)Verified email at allenai.org
Giacomo ComoProfessor, Politecnico di TorinoVerified email at polito.it
Niladri S. ChatterjiPostdoctoral Researcher, Department of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Olga SaukhTU Graz / CSH ViennaVerified email at tugraz.at
Rahim EntezariTU Graz / CSH ViennaVerified email at tugraz.at
Saurabh GargPhD Student, Machine Learning Department, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Zachary C. LiptonAssistant Professor of Machine Learning at CMUVerified email at cmu.edu
Sivaraman BalakrishnanCarnegie Mellon UniversityVerified email at stat.cmu.edu
Golnaz GhiasiGoogle BrainVerified email at google.com
Rianne van den BergPrincipal researcher, Microsoft ResearchVerified email at microsoft.com

Hanie Sedghi

Senior Research Scientist, Google Brain

Verified email at google.com - Homepage

Machine learning Deep neural networks Artificial Intelligence


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Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods M Janzamin, H Sedghi, A Anandkumar arXiv preprint arXiv:1506.08473, 2015	222	2015
The Singular Values of Convolutional Layers H Sedghi, V Gupta, PM Long arXiv preprint arXiv:1805.10408, 2018	145	2018
What is being transferred in transfer learning? B Neyshabur, H Sedghi, C Zhang Neural Information Processing Systems (NeurIPS), 2020	127	2020
Provable tensor methods for learning mixtures of generalized linear models H Sedghi, M Janzamin, A Anandkumar Artificial Intelligence and Statistics, 1223-1231, 2016	80	2016
Provable Methods for Training Neural Networks with Sparse Connectivity H Sedghi, A Anandkumar arXiv preprint arXiv:1412.2693, 2014	73	2014
Statistical Structure Learning to Ensure Data Integrity in Smart Grid H Sedghi, E Jonckheere IEEE Transactions on Smart Grid 6 (4), 1924-1933, 2015	59	2015
Statistical structure learning of smart grid for detection of false data injection H Sedghi, E Jonckheere 2013 IEEE Power & Energy Society General Meeting, 1-5, 2013	42	2013
Score Function Features for Discriminative Learning: Matrix and Tensor Framework M Janzamin, H Sedghi, A Anandkumar arXiv preprint arXiv:1412.2863, 2014	40	2014
Generalization bounds for deep convolutional neural networks PM Long, H Sedghi International Conference on Learning Representations, 2020	38	2020
A game-theoretic approach for power allocation in bidirectional cooperative communication M Janzamin, MR Pakravan, H Sedghi 2010 IEEE Wireless Communication and Networking Conference, 1-6, 2010	27	2010
The intriguing role of module criticality in the generalization of deep networks NS Chatterji, B Neyshabur, H Sedghi International Conference on Learning Representations, 2020	26	2020
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers P Nakkiran, B Neyshabur, H Sedghi International Conference on Learning Representations, 2021	25*	2021
Exploring the Limits of Large Scale Pre-training S Abnar, M Dehghani, B Neyshabur, H Sedghi International Conference on Learning Representations, 2022	23	2022
Sysml: The new frontier of machine learning systems A Ratner, D Alistarh, G Alonso, DG Andersen, P Bailis, S Bird, N Carlini, ...	21	2019
Training Input-Output Recurrent Neural Networks through Spectral Methods H Sedghi, A Anandkumar arXiv preprint arXiv:1603.00954, 2016	21	2016
Generalization bounds for neural networks through tensor factorization M Janzamin, H Sedghi, A Anandkumar CoRR, abs/1506.08473 1, 2015	21	2015
Size-free generalization bounds for convolutional neural networks PM Long, H Sedghi	18	2019
Multi-step stochastic ADMM in high dimensions: Applications to sparse optimization and matrix decomposition H Sedghi, A Anandkumar, E Jonckheere Advances in neural information processing systems 3 (January), 2771-2779, 2014	17	2014
Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods A Anandkumar, H Sedghi arXiv preprint arXiv:1503.04567, 2015	15	2015
Provable tensor methods for learning mixtures of classifiers H Sedghi, A Anandkumar arXiv preprint arXiv:1412.3046, 2014	15	2014

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