Guido F. Montufar

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Nihat AyHamburg University of Technology, Institute for Data Science FoundationsEmail yang diverifikasi di tuhh.de
Yu Guang WangAssociate Professor, Shanghai Jiao Tong University, University of New South WalesEmail yang diverifikasi di sjtu.edu.cn
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFAREmail yang diverifikasi di umontreal.ca
Razvan PascanuGoogle DeepMindEmail yang diverifikasi di google.com
Nina OtterUniversity of OxfordEmail yang diverifikasi di maths.ox.ac.uk
Johannes RauhInstitut für Qualitätssicherung und Transparenz im GesundheitswesenEmail yang diverifikasi di iqtig.org
Pietro LiòProfessor, University of CambridgeEmail yang diverifikasi di cam.ac.uk
Kyunghyun ChoNew York University, GenentechEmail yang diverifikasi di nyu.edu
Pradeep Kr. BanerjeeInstitute for Data Science Foundations, TUHHEmail yang diverifikasi di tuhh.de
Wuchen Li 李务晨University of South Carolina, Assistant professorEmail yang diverifikasi di mailbox.sc.edu
Keyan Ghazi-ZahediArizona State UniversityEmail yang diverifikasi di asu.edu
Jason MortonAssociate Professor of Mathematics and Statistics, Penn StateEmail yang diverifikasi di math.psu.edu
Fabrizio FrascaPosdoctoral Fellow, Technion – Israel Institute of TechnologyEmail yang diverifikasi di campus.technion.ac.il
Cristian BodnarSilurianEmail yang diverifikasi di silurian.ai
Michael BronsteinDeepMind Professor of AI, University of Oxford / Scientific Director, AITHYRAEmail yang diverifikasi di cs.ox.ac.uk
Kedar KarhadkarUCLAEmail yang diverifikasi di math.ucla.edu
Yonatan DuklerUCLA MathematicsEmail yang diverifikasi di math.ucla.edu
Johannes MüllerRWTH Aachen UniversityEmail yang diverifikasi di mathc.rwth-aachen.de
S osherEmail yang diverifikasi di math.ucla.edu
Hui JinUCLA MathematicsEmail yang diverifikasi di ucla.edu

Ikuti

Guido F. Montufar

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

Email yang diverifikasi di math.ucla.edu - Beranda

machine learning neural networks deep learning graphical models


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
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	2871	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	381	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	308	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	308	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	118	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	89*	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	87	2021
Natural gradient via optimal transport W Li, G Montúfar Information Geometry 1, 181-214, 2018	84	2018
Restricted boltzmann machines: Introduction and review G Montúfar Information Geometry and Its Applications IV, 75-115, 2016	80	2016
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	77	2021
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs K Karhadkar, PK Banerjee, G Montúfar International Conference on Learning Representations (ICLR) 2023, 2022	68	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
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	58*	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	54	2022
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units GF Montúfar Neural Computation 26 (7), 1386-1407, 2014	54	2014
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	47	2019
Notes on the number of linear regions of deep neural networks G Montúfar eScholarship, University of California, 2017	47	2017
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	47	2015
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	45	2023

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