Guido F. Montufar
Guido F. Montufar
UCLA Math / Stat
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Cited by
Cited by
On the number of linear regions of deep neural networks
G Mont˙far, R Pascanu, K Cho, Y Bengio
arXiv preprint arXiv:1402.1869, 2014
On the number of response regions of deep feed forward networks with piece-wise linear activations
R Pascanu, G Montufar, Y Bengio
International Conference on Learning Representations 2014 (ICLR 2014), Banffá…, 2013
Refinements of universal approximation results for deep belief networks and restricted Boltzmann machines
G Montufar, N Ay
Neural computation 23 (5), 1306-1319, 2011
Expressive power and approximation errors of restricted Boltzmann machines
GF Mont˙far, J Rauh, N Ay
Advances in Neural Information Processing Systems 24, 415-423, 2011
Natural gradient via optimal transport
W Li, G Mont˙far
Information Geometry 1 (2), 181-214, 2018
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
GF Mont˙far
Neural Computation 26 (7), 1386-1407, 2014
When Does a Mixture of Products Contain a Product of Mixtures?
GF Mont˙far, J Morton
SIAM Journal on Discrete Mathematics 29 (1), 321-347, 2015
Restricted boltzmann machines: Introduction and review
G Mont˙far
Information Geometry and Its Applications IV, 75-115, 2016
Optimal Transport to a Variety
TÍ ăelik, A Jamneshan, G Montufar, B Sturmfels, L Venturello
Mathematical Aspects of Computer and Information Sciences, 364-381, 2019
Discrete restricted Boltzmann machines
G Mont˙far, J Morton
Journal of Machine Learning Research 16 (1), 653-672, 2015
Geometry and Expressive Power of Conditional Restricted Boltzmann Machines
G Mont˙far, N Ay, K Ghazi-Zahedi
Journal of Machine Learning Research 16, 2405--2436, 2015
Mixture decompositions of exponential families using a decomposition of their sample spaces
G Mont˙far
Kybernetika 49 (1), 23-39, 2013
A Theory of Cheap Control in Embodied Systems
G Montufar, N Ay, K Ghazi-Zahedi
PLoS Computational Biololgy 11 (9), doi: 10.1371/journal.pcbi.1004, 2014
Haar graph pooling
YG Wang, M Li, Z Ma, G Montufar, X Zhuang, Y Fan
International conference on machine learning, 9952-9962, 2020
Ricci curvature for parametric statistics via optimal transport
W Li, G Mont˙far
Information Geometry 3 (1), 89-117, 2020
Selection criteria for neuromanifolds of stochastic dynamics
N Ay, G Mont˙far, J Rauh
Advances in Cognitive Neurodynamics (III), 147-154, 2013
Notes on the number of linear regions of deep neural networks
G Mont˙far
eScholarship, University of California, 2017
Evaluating morphological computation in muscle and dc-motor driven models of hopping movements
K Ghazi-Zahedi, DFB Haeufle, G Mont˙far, S Schmitt, N Ay
Frontiers in Robotics and AI 3, 42, 2016
Wasserstein of Wasserstein loss for learning generative models
Y Dukler, W Li, A Tong Lin, G Mont˙far
Proceedings of the 36th International Conference on Machine Learning 97á…, 2019
Computing the unique information
PK Banerjee, J Rauh, G Mont˙far
2018 IEEE International Symposium on Information Theory (ISIT), 141-145, 2018
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