Guido F. Montufar

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Nihat AyMax Planck Institute for Mathematics in the SciencesVerified email at mis.mpg.de
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Razvan PascanuResearch Scientist at Google DeepMindVerified email at google.com
Yu Guang WangShanghai Jiao Tong University, University of New South WalesVerified email at sjtu.edu.cn
Johannes RauhInstitut für Qualitätssicherung und Transparenz im GesundheitswesenVerified email at iqtig.org
Kyunghyun ChoNew York University, GenentechVerified email at nyu.edu
Nina OtterUniversity of OxfordVerified email at maths.ox.ac.uk
Wuchen Li 李务晨University of South Carolina, Assistant professorVerified email at mailbox.sc.edu
Keyan Ghazi-ZahediArizona State UniversityVerified email at asu.edu
P. Lió, Pietro Lio, P. Lio, Pietro Liò, Pie...University of CambridgeVerified email at cam.ac.uk
Jason MortonAssociate Professor of Mathematics and Statistics, Penn StateVerified email at math.psu.edu
Pradeep Kr. BanerjeeMPI MiSVerified email at mis.mpg.de
Fabrizio FrascaPhD Candidate, Imperial College LondonVerified email at ic.ac.uk
Cristian BodnarSenior Researcher, Microsoft ResearchVerified email at microsoft.com
Michael BronsteinDeepMind Professor of AI, University of OxfordVerified email at cs.ox.ac.uk
S osherVerified email at math.ucla.edu
Yonatan DuklerUCLA MathematicsVerified email at math.ucla.edu
Hui JinUCLA MathematicsVerified email at ucla.edu
Thomas MerkhQ-CTRLVerified email at q-ctrl.com
Lorenzo VenturelloResearcher, Università di PisaVerified email at unipi.it

Guido F. Montufar

UCLA, Mathematics and Statistics & Data Science, Max Planck Institute MiS

Verified email at math.ucla.edu - Homepage

machine learning neural networks deep learning graphical models


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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	2593	2014
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	320	2013
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	157	2021
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	144	2021
Refinements of universal approximation results for deep belief networks and restricted Boltzmann machines G Montufar, N Ay Neural computation 23 (5), 1306-1319, 2011	114	2011
Natural gradient via optimal transport W Li, G Montúfar Information Geometry 1, 181-214, 2018	68	2018
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	65	2020
Restricted boltzmann machines: Introduction and review G Montúfar Information Geometry and Its Applications: On the Occasion of Shun-ichi …, 2018	63	2018
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	60	2011
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	59*	2019
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	51	2021
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units GF Montúfar Neural Computation 26 (7), 1386-1407, 2014	50	2014
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	45	2021
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	44	2015
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	38	2019
Wasserstein Proximal of GANs A Tong Lin, W Li, S Osher, G Montúfar 5th International Conference Geometric Science of Information, 2018	36*	2018
Notes on the number of linear regions of deep neural networks G Montúfar eScholarship, University of California, 2017	33	2017
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	31	2014
Discrete restricted Boltzmann machines G Montúfar, J Morton Journal of Machine Learning Research 16 (1), 653-672, 2015	29	2015
Ricci curvature for parametric statistics via optimal transport W Li, G Montúfar Information Geometry 3 (1), 89-117, 2020	28	2020

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