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Guido F. Montufar
Guido F. Montufar
UCLA Departments of Mathematics and Statistics
Verified email at math.ucla.edu - Homepage
Title
Cited by
Cited by
Year
On the number of linear regions of deep neural networks
GF Montufar, R Pascanu, K Cho, Y Bengio
Advances in neural information processing systems 27, 2014
12172014
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 (ICLR) 2014, Banffá…, 2013
2702013
Refinements of universal approximation results for deep belief networks and restricted Boltzmann machines
G Montufar, N Ay
Neural computation 23 (5), 1306-1319, 2011
1162011
Weisfeiler and lehman go topological: Message passing simplicial networks
C Bodnar, F Frasca, YG Wang, N Otter, G Mont˙far, P Lio, M Bronstein
38th International Conference on Machine Learning (ICML), 1026-1037, 2021
782021
Weisfeiler and lehman go cellular: Cw networks
C Bodnar, F Frasca, N Otter, YG Wang, P Li˛, GF Montufar, M Bronstein
Advances in Neural Information Processing Systems (NeurIPS) 35, 2021
642021
Natural gradient via optimal transport
W Li, G Mont˙far
Information Geometry 1 (2), 181-214, 2018
592018
Expressive power and approximation errors of restricted Boltzmann machines
GF Mont˙far, J Rauh, N Ay
Advances in Neural Information Processing Systems (NeurIPS) 24, 415-423, 2011
582011
Restricted boltzmann machines: Introduction and review
G Mont˙far
Information Geometry and Its Applications IV, 75-115, 2016
502016
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
GF Mont˙far
Neural Computation 26 (7), 1386-1407, 2014
472014
Haar graph pooling
YG Wang, M Li, Z Ma, G Montufar, X Zhuang, Y Fan
37th International conference on machine learning (ICML), 9952-9962, 2020
462020
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
43*2019
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
422015
How framelets enhance graph neural networks
X Zheng, B Zhou, J Gao, YG Wang, P Lio, M Li, G Mont˙far
38th International Conference on Machine Learning (ICML), 12761-12771, 2021
332021
Wasserstein of Wasserstein loss for learning generative models
Y Dukler, W Li, A Tong Lin, G Mont˙far
36th International Conference on Machine Learning (ICML) 97, 1716-1725, 2019
282019
Discrete restricted Boltzmann machines
G Mont˙far, J Morton
Journal of Machine Learning Research 16 (1), 653-672, 2015
282015
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
282014
Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep relu networks
Q Nguyen, M Mondelli, GF Montufar
38th International Conference on Machine Learning (ICML), 8119-8129, 2021
262021
Ricci curvature for parametric statistics via optimal transport
W Li, G Mont˙far
Information Geometry 3 (1), 89-117, 2020
262020
Wasserstein Proximal of GANs
A Tong Lin, W Li, S Osher, G Mont˙far
5th International Conference Geometric Science of Information, 2018
25*2018
Notes on the number of linear regions of deep neural networks
G Mont˙far
eScholarship, University of California, 2017
252017
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