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Guido F. Montufar
Guido F. Montufar
UCLA, Mathematics and Statistics & Data Science, Max Planck Institute MiS
Bestätigte E-Mail-Adresse bei math.ucla.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
27992014
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
3672013
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
2712021
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
2702021
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
982020
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
80*2019
Natural gradient via optimal transport
W Li, G Montúfar
Information Geometry 1, 181-214, 2018
792018
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
772021
Restricted boltzmann machines: Introduction and review
G Montúfar
Information Geometry and Its Applications: On the Occasion of Shun-ichi …, 2018
762018
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
702021
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
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
GF Montúfar
Neural Computation 26 (7), 1386-1407, 2014
532014
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
K Karhadkar, PK Banerjee, G Montúfar
International Conference on Learning Representations (ICLR) 2023, 2022
502022
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
49*2022
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
462015
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
452022
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
442019
Wasserstein Proximal of GANs
A Tong Lin, W Li, S Osher, G Montúfar
5th International Conference Geometric Science of Information, 2018
44*2018
Notes on the number of linear regions of deep neural networks
G Montúfar
eScholarship, University of California, 2017
432017
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