Прати
Guido F. Montufar
Guido F. Montufar
UCLA, Mathematics and Statistics & Data Science, Max Planck Institute MiS
Верификована је имејл адреса на math.ucla.edu - Почетна страница
Наслов
Навело
Навело
Година
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
28872014
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
3852013
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
3182021
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
3152021
Refinements of universal approximation results for deep belief networks and restricted Boltzmann machines
G Montufar, N Ay
Neural computation 23 (5), 1306-1319, 2011
1212011
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
91*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
892021
Natural gradient via optimal transport
W Li, G Montúfar
Information Geometry 1, 181-214, 2018
842018
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
K Karhadkar, PK Banerjee, G Montúfar
International Conference on Learning Representations (ICLR) 2023, 2022
802022
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
792021
Restricted boltzmann machines: Introduction and review
G Montúfar
Information Geometry and Its Applications IV, 75-115, 2016
792016
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
61*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
592022
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
GF Montúfar
Neural Computation 26 (7), 1386-1407, 2014
532014
Implicit bias of gradient descent for mean squared error regression with two-layer wide neural networks
H Jin, G Montúfar
Journal of Machine Learning Research 24 (137), 1-97, 2023
492023
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
462019
Notes on the number of linear regions of deep neural networks
G Montúfar
eScholarship, University of California, 2017
462017
Wasserstein Proximal of GANs
A Tong Lin, W Li, S Osher, G Montúfar
5th International Conference Geometric Science of Information, 2018
45*2018
Систем тренутно не може да изврши ову радњу. Пробајте поново касније.
Чланци 1–20