Guido F. Montufar

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Nihat AyHamburg University of Technology, Institute for Data Science FoundationsVerified email at tuhh.de
Yu Guang WangAssociate Professor, Shanghai Jiao Tong University, University of New South WalesVerified email at sjtu.edu.cn
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Razvan PascanuGoogle DeepMindVerified email at google.com
Nina OtterUniversity of OxfordVerified email at maths.ox.ac.uk
Johannes RauhInstitut für Qualitätssicherung und Transparenz im GesundheitswesenVerified email at iqtig.org
Pietro LiòProfessor, University of CambridgeVerified email at cam.ac.uk
Kyunghyun ChoNew York University, GenentechVerified email at nyu.edu
Pradeep Kr. BanerjeeInstitute for Data Science Foundations, TUHHVerified email at tuhh.de
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
Jason MortonAssociate Professor of Mathematics and Statistics, Penn StateVerified email at math.psu.edu
Fabrizio FrascaPosdoctoral Fellow, Technion – Israel Institute of TechnologyVerified email at campus.technion.ac.il
Cristian BodnarSilurianVerified email at silurian.ai
Michael BronsteinDeepMind Professor of AI, University of Oxford / Scientific Director, AITHYRAVerified email at cs.ox.ac.uk
Kedar KarhadkarUCLAVerified email at math.ucla.edu
Johannes MüllerTU BerlinVerified email at mathc.rwth-aachen.de
S osherVerified email at math.ucla.edu
Yonatan DuklerUCLA MathematicsVerified email at math.ucla.edu
Hui JinHuawei Noah's Ark LabVerified email at ucla.edu

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 G Montúfar, R Pascanu, K Cho, Y Bengio Advances in neural information processing systems 27, 2014	2901	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	386	2013
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	328	2021
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	321	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	122	2011
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	104	2020
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	95*	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	89	2021
Natural gradient via optimal transport W Li, G Montúfar Information Geometry 1, 181-214, 2018	85	2018
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs K Karhadkar, PK Banerjee, G Montúfar International Conference on Learning Representations (ICLR) 2023, 2022	84	2022
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	80	2021
Restricted boltzmann machines: Introduction and review G Montúfar Information Geometry and Its Applications IV, 75-115, 2016	80	2016
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	64*	2022
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	62	2011
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	60	2022
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units GF Montúfar Neural Computation 26 (7), 1386-1407, 2014	53	2014
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	49	2023
Notes on the number of linear regions of deep neural networks G Montúfar eScholarship, University of California, 2017	48	2017
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	46	2019
Wasserstein Proximal of GANs A Tong Lin, W Li, S Osher, G Montúfar 5th International Conference Geometric Science of Information, 2018	46*	2018

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