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Guido F. Montufar
Guido F. Montufar
UCLA, Mathematics and Statistics & Data Science, Max Planck Institute MiS
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
27502014
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
3562013
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
2342021
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
2312021
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
Advances in neural information processing systems
GF Montufar, R Pascanu, K Cho, Y Bengio, Z Ghahramani, M Welling, ...
Curran Associates, Inc., 2014
932014
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
902020
Natural gradient via optimal transport
W Li, G Mont˙far
Information Geometry 1, 181-214, 2018
772018
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
75*2019
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
712021
Restricted boltzmann machines: Introduction and review
G Mont˙far
Information Geometry and Its Applications: On the Occasion of Shun-ichiá…, 2018
712018
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
662021
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
622011
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
GF Mont˙far
Neural Computation 26 (7), 1386-1407, 2014
512014
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
452015
Wasserstein Proximal of GANs
A Tong Lin, W Li, S Osher, G Mont˙far
5th International Conference Geometric Science of Information, 2018
43*2018
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
422019
Cell graph neural networks enable the precise prediction of patient survival in gastric cancer
Y Wang, YG Wang, C Hu, M Li, Y Fan, N Otter, I Sam, H Gou, Y Hu, ...
NPJ precision oncology 6 (1), 45, 2022
40*2022
Notes on the number of linear regions of deep neural networks
G Mont˙far
eScholarship, University of California, 2017
402017
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
K Karhadkar, PK Banerjee, G Mont˙far
International Conference on Learning Representations (ICLR) 2023, 2022
382022
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