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Majid Janzamin
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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
2712015
Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank- Updates
A Anandkumar, R Ge, M Janzamin
arXiv preprint arXiv:1402.5180, 2014
1512014
Provable tensor methods for learning mixtures of generalized linear models
H Sedghi, M Janzamin, A Anandkumar
Artificial Intelligence and Statistics, 1223-1231, 2016
1022016
When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity
A Anandkumar, DJ Hsu, M Janzamin, SM Kakade
Advances in neural information processing systems 26, 2013
622013
Learning overcomplete latent variable models through tensor methods
A Anandkumar, R Ge, M Janzamin
Conference on Learning Theory, 36-112, 2015
592015
Analyzing tensor power method dynamics in overcomplete regime
A Anandkumar, R Ge, M Janzamin
Journal of Machine Learning Research 18 (22), 1-40, 2017
562017
Spectral learning on matrices and tensors
M Janzamin, R Ge, J Kossaifi, A Anandkumar
Foundations and Trends® in Machine Learning 12 (5-6), 393-536, 2019
502019
Score function features for discriminative learning: Matrix and tensor framework
M Janzamin, H Sedghi, A Anandkumar
arXiv preprint arXiv:1412.2863, 2014
472014
Analyzing tensor power method dynamics: Applications to learning overcomplete latent variable models
A Anandkumar, R Ge, M Janzamin
arXiv preprint arXiv:1411.1488 98, 2014
302014
Sample complexity analysis for learning overcomplete latent variable models through tensor methods
A Anandkumar, R Ge, M Janzamin
arXiv preprint arXiv:1408.0553, 2014
30*2014
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
282010
High-dimensional covariance decomposition into sparse Markov and independence models
M Janzamin, A Anandkumar
The Journal of Machine Learning Research 15 (1), 1549-1591, 2014
92014
High-dimensional covariance decomposition into sparse Markov and independence domains
M Janzamin, A Anandkumar
arXiv preprint arXiv:1206.6382, 2012
42012
Feast at play: Feature extraction using score function tensors
M Janzamin, H Sedghi, UN Niranjan, A Anandkumar
Feature Extraction: Modern Questions and Challenges, 130-144, 2015
22015
Score function features for discriminative learning
M Janzamin, H Sedghi, A Anandkumar
arXiv preprint arXiv:1412.6514, 2014
12014
Non-convex Optimization in Machine Learning: Provable Guarantees Using Tensor Methods
M Janzamin
University of California, Irvine, 2016
2016
Matrix and Tensor Features for Discriminative Learning
M Janzamin, H Sedghi, A Anandkumar
arXiv preprint arXiv:1412.2863, 2014
2014
When are Overcomplete Representations Identifiable? Uniqueness of Tensor Decompositions Under Expansion Constraints
A Anandkumar, D Hsu, M Janzamin, S Kakade, ...
2013
Supplementary Material for the AISTATS 2016 Paper: Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
H Sedghi, M Janzamin, A Anandkumar
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Artículos 1–19