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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
28612014
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, Banff …, 2013
3792013
Weisfeiler and lehman go topological: Message passing simplicial networks
C Bodnar, F Frasca, YG Wang, N Otter, G Montúfar, P Lio, M Bronstein
38th International Conference on Machine Learning (ICML), 1026-1037, 2021
3052021
Weisfeiler and lehman go cellular: Cw networks
C Bodnar, F Frasca, N Otter, YG Wang, P Liò, GF Montufar, M Bronstein
Advances in Neural Information Processing Systems (NeurIPS) 35, 2021
3032021
Refinements of universal approximation results for deep belief networks and restricted Boltzmann machines
G Montufar, N Ay
Neural computation 23 (5), 1306-1319, 2011
1172011
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
1042020
Optimal Transport to a Variety
TÖ Çelik, A Jamneshan, G Montufar, B Sturmfels, L Venturello
Mathematical Aspects of Computer and Information Sciences, 364-381, 2019
88*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
842021
Natural gradient via optimal transport
W Li, G Montúfar
Information Geometry 1, 181-214, 2018
832018
Restricted boltzmann machines: Introduction and review
G Montúfar
Information Geometry and Its Applications: On the Occasion of Shun-ichi …, 2018
802018
How framelets enhance graph neural networks
X Zheng, B Zhou, J Gao, YG Wang, P Lio, M Li, G Montúfar
38th International Conference on Machine Learning (ICML), 12761-12771, 2021
772021
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
K Karhadkar, PK Banerjee, G Montúfar
International Conference on Learning Representations (ICLR) 2023, 2022
672022
Expressive power and approximation errors of restricted Boltzmann machines
GF Montúfar, J Rauh, N Ay
Advances in Neural Information Processing Systems (NeurIPS) 24, 415-423, 2011
622011
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
57*2022
Oversquashing in GNNs through the lens of information contraction and graph expansion
PK Banerjee, K Karhadkar, YG Wang, U Alon, G Montúfar
58th Annual Allerton Conference on Communication, Control, and Computing, 2022
542022
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
GF Montúfar
Neural Computation 26 (7), 1386-1407, 2014
542014
Wasserstein of Wasserstein loss for learning generative models
Y Dukler, W Li, A Tong Lin, G Montúfar
36th International Conference on Machine Learning (ICML) 97, 1716-1725, 2019
472019
Notes on the number of linear regions of deep neural networks
G Montúfar
eScholarship, University of California, 2017
472017
When Does a Mixture of Products Contain a Product of Mixtures?
GF Montúfar, J Morton
SIAM Journal on Discrete Mathematics 29 (1), 321-347, 2015
472015
Wasserstein Proximal of GANs
A Tong Lin, W Li, S Osher, G Montúfar
5th International Conference Geometric Science of Information, 2018
44*2018
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