Kenji Kawaguchi

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Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Leslie KaelblingVerified email at csail.mit.edu
Vikas VermaAalto University/ MILA/ Google BrainVerified email at google.com
George Em KarniadakisThe Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and EngineeringVerified email at brown.edu
Ameya D. JagtapAssistant Professor of Applied Mathematics (Research), Brown University, USAVerified email at brown.edu
Jiaoyang HuangUniversity of PennsylvaniaVerified email at wharton.upenn.edu
Linjun ZhangAssistant Professor of Statistics, Rutgers UniversityVerified email at rutgers.edu
TOMASO POGGIOMcDermott Professor in Brain Sciences, MITVerified email at ai.mit.edu
Hieu PhamResearch Scientist, Google BrainVerified email at google.com
James ZouStanford UniversityVerified email at stanford.edu
Haihao (Sean) LuAssistant Professor, University of ChicagoVerified email at chicagobooth.edu
Tomas Lozano-PerezProfessor of Computer Science, MITVerified email at mit.edu
David Lopez-PazMeta AIVerified email at fb.com
Quoc V. LeResearch Scientist, Google BrainVerified email at stanford.edu
Minh-Thang LuongStaff Research Scientist at GoogleVerified email at google.com
Anirudh GoyalMila, Université de MontréalVerified email at umontreal.ca
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaVerified email at unige.it
Jack HidaryAlphabet (Google) XVerified email at google.com
Dianbo LiuMILA/Harvard University/MITVerified email at hms.harvard.edu
Michael MozerGoogle Research, Brain Team; University of ColoradoVerified email at colorado.edu

Kenji Kawaguchi

Presidential Young Professor, National University of Singapore

Verified email at nus.edu.sg - Homepage

Deep learning theory Machine learning theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Deep learning without poor local minima K Kawaguchi Advances In Neural Information Processing Systems (NeurIPS), 586-594, 2016	893	2016
Interpolation consistency training for semi-supervised learning V Verma, K Kawaguchi, A Lamb, J Kannala, A Solin, Y Bengio, ... Neural Networks 145, 90-106, 2022	475	2022
Generalization in Deep Learning K Kawaguchi, LP Kaelbling, Y Bengio Mathematical Aspects of Deep Learning, 112 - 148, 2022	436*	2022
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks AD Jagtap, K Kawaguchi, GE Karniadakis Journal of Computational Physics 404, 109136, 2020	310	2020
Theory of Deep Learning III: explaining the non-overfitting puzzle T Poggio, K Kawaguchi, Q Liao, B Miranda, L Rosasco, X Boix, J Hidary, ... Massachusetts Institute of Technology, CBMM Memo No. 073, 2018	136*	2018
Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks AD Jagtap, K Kawaguchi, G Em Karniadakis Proceedings of the Royal Society A 476 (2239), 20200334, 2020	128	2020
How Does Mixup Help With Robustness and Generalization? L Zhang, Z Deng, K Kawaguchi, A Ghorbani, J Zou International Conference on Learning Representations (ICLR), 2021	127	2021
Bayesian optimization with exponential convergence K Kawaguchi, LP Kaelbling, T Lozano-Pérez Advances in Neural Information Processing Systems (NeurIPS) 28, 2809-2817, 2015	114	2015
Depth Creates No Bad Local Minima H Lu, K Kawaguchi arXiv preprint arXiv:1702.08580, 2017	105	2017
GraphMix: Improved Training of GNNs for Semi-Supervised Learning V Verma, M Qu, K Kawaguchi, A Lamb, Y Bengio, J Kannala, J Tang Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021	94	2021
Interpolated adversarial training: Achieving robust neural networks without sacrificing too much accuracy A Lamb, V Verma, K Kawaguchi, A Matyasko, S Khosla, J Kannala, ... Neural Networks, 2022	61	2022
Depth with Nonlinearity Creates No Bad Local Minima in ResNets K Kawaguchi, Y Bengio Neural Networks 118, 167-174, 2019	54	2019
Combined Scaling for Open-Vocabulary Image Classification H Pham, Z Dai, G Ghiasi, K Kawaguchi, H Liu, AW Yu, J Yu, YT Chen, ... arXiv preprint arXiv:2111.10050, 2021	52*	2021
Towards domain-agnostic contrastive learning V Verma, T Luong, K Kawaguchi, H Pham, Q Le International Conference on Machine Learning (ICML), 10530-10541, 2021	51	2021
Elimination of all bad local minima in deep learning K Kawaguchi, L Kaelbling Artificial Intelligence and Statistics (AISTATS), 853-863, 2020	50	2020
Effect of depth and width on local minima in deep learning K Kawaguchi, J Huang, LP Kaelbling Neural computation 31 (7), 1462-1498, 2019	48	2019
Gradient descent finds global minima for generalizable deep neural networks of practical sizes K Kawaguchi, J Huang 2019 57th Annual Allerton Conference on Communication, Control, and …, 2019	46	2019
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions AD Jagtap, Y Shin, K Kawaguchi, GE Karniadakis Neurocomputing 468, 165-180, 2022	45	2022
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization K Kawaguchi, H Lu Artificial Intelligence and Statistics (AISTATS), 669-679, 2020	40	2020
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning Q Liao, K Kawaguchi, T Poggio Massachusetts Institute of Technology, CBMM Memo No. 57, 2016	35	2016

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