Kenji Kawaguchi

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Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARPatvirtintas el. paštas umontreal.ca
George Em KarniadakisThe Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and EngineeringPatvirtintas el. paštas brown.edu
Leslie KaelblingPatvirtintas el. paštas csail.mit.edu
Vikas VermaAalto University/ MILA/ Google BrainPatvirtintas el. paštas google.com
Anirudh GoyalGoogle DeepMind, Mila, Université de MontréalPatvirtintas el. paštas umontreal.ca
Jiaoyang HuangUniversity of PennsylvaniaPatvirtintas el. paštas wharton.upenn.edu
James ZouStanford UniversityPatvirtintas el. paštas stanford.edu
Linjun ZhangAssociate Professor of Statistics, Rutgers UniversityPatvirtintas el. paštas rutgers.edu
David Lopez-PazFAIR at MetaPatvirtintas el. paštas fb.com
Quoc V. LeResearch Scientist, GooglePatvirtintas el. paštas stanford.edu
Minh-Thang LuongSenior Staff Research Scientist at GooglePatvirtintas el. paštas google.com
Dianbo LiuAssistant professor, National University of SingaporePatvirtintas el. paštas nus.edu.sg
Michael MozerGoogle DeepMind; University of ColoradoPatvirtintas el. paštas colorado.edu
Tomas Lozano-PerezProfessor of Computer Science, MITPatvirtintas el. paštas mit.edu
TOMASO POGGIOMcDermott Professor in Brain Sciences, MITPatvirtintas el. paštas ai.mit.edu
Ferran AletDeepMindPatvirtintas el. paštas deepmind.com
Stefanie JegelkaTUM and MITPatvirtintas el. paštas mit.edu
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityPatvirtintas el. paštas cs.nyu.edu
Chelsea FinnStanford University, Physical IntelligencePatvirtintas el. paštas cs.stanford.edu
Jascha Sohl-DicksteinAnthropicPatvirtintas el. paštas anthropic.com

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Kenji Kawaguchi

Presidential Young Professor, National University of Singapore

Patvirtintas el. paštas nus.edu.sg - Pagrindinis puslapis

LLMs Large language model Deep learning AI


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Deep learning without poor local minima K Kawaguchi Advances In Neural Information Processing Systems (NeurIPS), 586-594, 2016	1121	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	895	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	852	2020
Generalization in Deep Learning K Kawaguchi, LP Kaelbling, Y Bengio Cambridge University Press, 2022	660*	2022
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	342	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	281	2021
Combined scaling for zero-shot transfer learning H Pham, Z Dai, G Ghiasi, K Kawaguchi, H Liu, AW Yu, J Yu, YT Chen, ... Neurocomputing 555, 126658, 2023	243*	2023
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	185	2021
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	172*	2018
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	155	2022
Towards domain-agnostic contrastive learning V Verma, T Luong, K Kawaguchi, H Pham, Q Le International Conference on Machine Learning (ICML), 10530-10541, 2021	135	2021
Bayesian optimization with exponential convergence K Kawaguchi, LP Kaelbling, T Lozano-Pérez Advances in Neural Information Processing Systems (NeurIPS) 28, 2809-2817, 2015	134	2015
Multi-Task Learning as a Bargaining Game A Navon, A Shamsian, I Achituve, H Maron, K Kawaguchi, G Chechik, ... International Conference on Machine Learning (ICML), 2022	133	2022
Depth Creates No Bad Local Minima H Lu, K Kawaguchi arXiv preprint arXiv:1702.08580, 2017	131	2017
Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding Y Xie, K Kawaguchi, Y Zhao, X Zhao, MY Kan, J He, Q Xie Advances in Neural Information Processing Systems (NeurIPS), 2023	126*	2023
Meta-learning PINN loss functions AF Psaros, K Kawaguchi, GE Karniadakis Journal of Computational Physics 458, 111121, 2022	115	2022
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? Z Hu, AD Jagtap, GE Karniadakis, K Kawaguchi SIAM Journal on Scientific Computing 44 (5), A3158-A3182, 2022	113	2022
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	112	2022
Optimization of graph neural networks: Implicit acceleration by skip connections and more depth K Xu, M Zhang, S Jegelka, K Kawaguchi International Conference on Machine Learning (ICML), 11592-11602, 2021	92	2021
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks Z Hu, K Shukla, GE Karniadakis, K Kawaguchi Neural Networks, 2024	80	2024

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