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2020 – today
- 2023
[i54]Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Auxiliary Learning as an Asymmetric Bargaining Game. CoRR abs/2301.13501 (2023)- [i53]Tianbo Li, Min Lin, Zheyuan Hu, Kunhao Zheng, Giovanni Vignale, Kenji Kawaguchi, A. H. Castro Neto, Kostya S. Novoselov, Shuicheng Yan:
D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory. CoRR abs/2303.00399 (2023) - [i52]Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun:
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. CoRR abs/2303.00633 (2023) - 2022
- [j12]Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis:
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. Neurocomputing 468: 165-180 (2022) - [j11]Apostolos F. Psaros
, Kenji Kawaguchi, George Em Karniadakis:
Meta-learning PINN loss functions. J. Comput. Phys. 458: 111121 (2022) - [j10]Kenji Kawaguchi, Linjun Zhang, Zhun Deng:
Understanding Dynamics of Nonlinear Representation Learning and Its Application. Neural Comput. 34(4): 991-1018 (2022) - [j9]Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz:
Interpolation consistency training for semi-supervised learning. Neural Networks 145: 90-106 (2022) - [j8]Alex Lamb, Vikas Verma, Kenji Kawaguchi, Alexander Matyasko, Savya Khosla, Juho Kannala, Yoshua Bengio:
Interpolated Adversarial Training: Achieving robust neural networks without sacrificing too much accuracy. Neural Networks 154: 218-233 (2022) - [j7]Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi:
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? SIAM J. Sci. Comput. 44(5): 3158- (2022) - [c25]Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang:
Robustness Implies Generalization via Data-Dependent Generalization Bounds. ICML 2022: 10866-10894 - [c24]Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Multi-Task Learning as a Bargaining Game. ICML 2022: 16428-16446 - [c23]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou:
When and How Mixup Improves Calibration. ICML 2022: 26135-26160 - [c22]Siddharth Bhatia, Arjit Jain, Shivin Srivastava, Kenji Kawaguchi, Bryan Hooi:
MemStream: Memory-Based Streaming Anomaly Detection. WWW 2022: 610-621 - [i51]Zhiyuan Liu, Yixin Cao, Fuli Feng, Xiang Wang, Xindi Shang, Jie Tang, Kenji Kawaguchi, Tat-Seng Chua:
Training Free Graph Neural Networks for Graph Matching. CoRR abs/2201.05349 (2022) - [i50]Shivin Srivastava, Kenji Kawaguchi, Vaibhav Rajan:
ExpertNet: A Symbiosis of Classification and Clustering. CoRR abs/2201.06344 (2022) - [i49]Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Multi-Task Learning as a Bargaining Game. CoRR abs/2202.01017 (2022) - [i48]Dianbo Liu, Alex Lamb, Xu Ji, Pascal Notsawo, Michael Mozer, Yoshua Bengio, Kenji Kawaguchi:
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization. CoRR abs/2202.01334 (2022) - [i47]Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao:
EIGNN: Efficient Infinite-Depth Graph Neural Networks. CoRR abs/2202.10720 (2022) - [i46]Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Kenji Kawaguchi, Ankit Vani, Aaron C. Courville:
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification. CoRR abs/2204.00616 (2022) - [i45]Seanie Lee, Andreis Bruno, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang:
Set-based Meta-Interpolation for Few-Task Meta-Learning. CoRR abs/2205.09990 (2022) - [i44]Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang:
Robustness Implies Generalization via Data-Dependent Generalization Bounds. CoRR abs/2206.13497 (2022) - [i43]Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf:
Discrete Key-Value Bottleneck. CoRR abs/2207.11240 (2022) - [i42]Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi:
Self-Distillation for Further Pre-training of Transformers. CoRR abs/2210.02871 (2022) - [i41]Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Xiaokui Xiao:
MGNNI: Multiscale Graph Neural Networks with Implicit Layers. CoRR abs/2210.08353 (2022) - [i40]Dianbo Liu, Moksh Jain, Bonaventure Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio:
GFlowOut: Dropout with Generative Flow Networks. CoRR abs/2210.12928 (2022) - [i39]Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes:
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning. CoRR abs/2211.00247 (2022) - [i38]Savya Khosla, Chew Kin Whye, Jordan T. Ash, Cyril Zhang, Kenji Kawaguchi, Alex Lamb:
Neural Active Learning on Heteroskedastic Distributions. CoRR abs/2211.00928 (2022) - [i37]Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi:
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology. CoRR abs/2211.08939 (2022) - [i36]Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi:
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective. CoRR abs/2212.13381 (2022) - 2021
- [c21]Kenji Kawaguchi, Qingyun Sun:
A Recipe for Global Convergence Guarantee in Deep Neural Networks. AAAI 2021: 8074-8082 - [c20]Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang:
GraphMix: Improved Training of GNNs for Semi-Supervised Learning. AAAI 2021: 10024-10032 - [c19]Zhun Deng, Jiaoyang Huang, Kenji Kawaguchi:
How Shrinking Gradient Noise Helps the Performance of Neural Networks. IEEE BigData 2021: 1002-1007 - [c18]Kenji Kawaguchi:
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers. ICLR 2021 - [c17]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou:
How Does Mixup Help With Robustness and Generalization? ICLR 2021 - [c16]Vikas Verma, Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le:
Towards Domain-Agnostic Contrastive Learning. ICML 2021: 10530-10541 - [c15]Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, Kenji Kawaguchi:
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. ICML 2021: 11592-11602 - [c14]Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling:
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization. NeurIPS 2021: 703-714 - [c13]Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael C. Mozer, Yoshua Bengio:
Discrete-Valued Neural Communication. NeurIPS 2021: 2109-2121 - [c12]Ferran Alet, Dylan Doblar, Allan Zhou, Josh Tenenbaum, Kenji Kawaguchi, Chelsea Finn:
Noether Networks: meta-learning useful conserved quantities. NeurIPS 2021: 16384-16397 - [c11]Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao:
EIGNN: Efficient Infinite-Depth Graph Neural Networks. NeurIPS 2021: 18762-18773 - [c10]Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Y. Zou:
Adversarial Training Helps Transfer Learning via Better Representations. NeurIPS 2021: 25179-25191 - [c9]Ferran Alet, Maria Bauzá, Kenji Kawaguchi, Nurullah Giray Kuru, Tomás Lozano-Pérez, Leslie Pack Kaelbling:
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time. NeurIPS 2021: 29206-29217 - [i35]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou:
When and How Mixup Improves Calibration. CoRR abs/2102.06289 (2021) - [i34]Kenji Kawaguchi:
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers. CoRR abs/2102.07346 (2021) - [i33]Shivin Srivastava, Siddharth Bhatia, Lingxiao Huang, Lim Jun Heng, Kenji Kawaguchi, Vaibhav Rajan:
CAC: A Clustering Based Framework for Classification. CoRR abs/2102.11872 (2021) - [i32]Kenji Kawaguchi, Qingyun Sun:
A Recipe for Global Convergence Guarantee in Deep Neural Networks. CoRR abs/2104.05785 (2021) - [i31]Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, Kenji Kawaguchi:
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. CoRR abs/2105.04550 (2021) - [i30]Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis:
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. CoRR abs/2105.09513 (2021) - [i29]Siddharth Bhatia, Arjit Jain, Shivin Srivastava, Kenji Kawaguchi, Bryan Hooi:
MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift. CoRR abs/2106.03837 (2021) - [i28]Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Zou:
Adversarial Training Helps Transfer Learning via Better Representations. CoRR abs/2106.10189 (2021) - [i27]Kenji Kawaguchi, Linjun Zhang, Zhun Deng:
Understanding Dynamics of Nonlinear Representation Learning and Its Application. CoRR abs/2106.14836 (2021) - [i26]Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio:
Discrete-Valued Neural Communication. CoRR abs/2107.02367 (2021) - [i25]Apostolos F. Psaros, Kenji Kawaguchi, George Em Karniadakis:
Meta-learning PINN loss functions. CoRR abs/2107.05544 (2021) - [i24]Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi:
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? CoRR abs/2109.09444 (2021) - [i23]Ferran Alet, Dylan Doblar, Allan Zhou, Joshua B. Tenenbaum, Kenji Kawaguchi, Chelsea Finn:
Noether Networks: Meta-Learning Useful Conserved Quantities. CoRR abs/2112.03321 (2021) - 2020
- [j6]Ameya D. Jagtap, Kenji Kawaguchi, George Em Karniadakis:
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. J. Comput. Phys. 404 (2020) - [c8]Kenji Kawaguchi, Haihao Lu:
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization. AISTATS 2020: 669-679 - [c7]Kenji Kawaguchi, Leslie Pack Kaelbling:
Elimination of All Bad Local Minima in Deep Learning. AISTATS 2020: 853-863 - [i22]Ferran Alet, Kenji Kawaguchi, Tomás Lozano-Pérez, Leslie Pack Kaelbling:
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time. CoRR abs/2009.10623 (2020) - [i21]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Y. Zou:
How Does Mixup Help With Robustness and Generalization? CoRR abs/2010.04819 (2020) - [i20]Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le:
Towards Domain-Agnostic Contrastive Learning. CoRR abs/2011.04419 (2020)
2010 – 2019
- 2019
- [j5]Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling:
Effect of Depth and Width on Local Minima in Deep Learning. Neural Comput. 31(7): 1462-1498 (2019) - [j4]Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling:
Every Local Minimum Value Is the Global Minimum Value of Induced Model in Nonconvex Machine Learning. Neural Comput. 31(12): 2293-2323 (2019) - [j3]Kenji Kawaguchi, Yoshua Bengio:
Depth with nonlinearity creates no bad local minima in ResNets. Neural Networks 118: 167-174 (2019) - [c6]Kenji Kawaguchi, Jiaoyang Huang:
Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes. Allerton 2019: 92-99 - [i19]Kenji Kawaguchi, Leslie Pack Kaelbling:
Elimination of All Bad Local Minima in Deep Learning. CoRR abs/1901.00279 (2019) - [i18]Jascha Sohl-Dickstein, Kenji Kawaguchi:
Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit. CoRR abs/1901.03909 (2019) - [i17]Kenji Kawaguchi, Leslie Pack Kaelbling:
Every Local Minimum is a Global Minimum of an Induced Model. CoRR abs/1904.03673 (2019) - [i16]Kenji Kawaguchi, Haihao Lu:
A Stochastic First-Order Method for Ordered Empirical Risk Minimization. CoRR abs/1907.04371 (2019) - [i15]Kenji Kawaguchi, Jiaoyang Huang:
Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes. CoRR abs/1908.02419 (2019) - [i14]Ameya D. Jagtap, Kenji Kawaguchi, George E. Karniadakis:
Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks. CoRR abs/1909.12228 (2019) - 2018
- [c5]Kenji Kawaguchi, Bo Xie, Le Song:
Deep Semi-Random Features for Nonlinear Function Approximation. AAAI 2018: 3382-3389 - [i13]Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar:
Theory of Deep Learning III: explaining the non-overfitting puzzle. CoRR abs/1801.00173 (2018) - [i12]Kenji Kawaguchi, Yoshua Bengio:
Generalization in Machine Learning via Analytical Learning Theory. CoRR abs/1802.07426 (2018) - [i11]Kenji Kawaguchi, Yoshua Bengio:
Depth with Nonlinearity Creates No Bad Local Minima in ResNets. CoRR abs/1810.09038 (2018) - [i10]Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling:
Effect of Depth and Width on Local Minima in Deep Learning. CoRR abs/1811.08150 (2018) - 2017
- [i9]Haihao Lu, Kenji Kawaguchi:
Depth Creates No Bad Local Minima. CoRR abs/1702.08580 (2017) - [i8]Kenji Kawaguchi, Bo Xie, Le Song:
Deep Semi-Random Features for Nonlinear Function Approximation. CoRR abs/1702.08882 (2017) - [i7]Kenji Kawaguchi, Leslie Pack Kaelbling, Yoshua Bengio:
Generalization in Deep Learning. CoRR abs/1710.05468 (2017) - 2016
- [j2]Kenji Kawaguchi, Yu Maruyama, Xiaoyu Zheng:
Global Continuous Optimization with Error Bound and Fast Convergence. J. Artif. Intell. Res. 56: 153-195 (2016) - [c4]Kenji Kawaguchi:
Bounded Optimal Exploration in MDP. AAAI 2016: 1758-1764 - [c3]Kenji Kawaguchi:
Deep Learning without Poor Local Minima. NIPS 2016: 586-594 - [i6]Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez:
Bayesian Optimization with Exponential Convergence. CoRR abs/1604.01348 (2016) - [i5]Kenji Kawaguchi:
Bounded Optimal Exploration in MDP. CoRR abs/1604.01350 (2016) - [i4]Kenji Kawaguchi:
Deep Learning without Poor Local Minima. CoRR abs/1605.07110 (2016) - [i3]Kenji Kawaguchi, Yu Maruyama, Xiaoyu Zheng:
Global Continuous Optimization with Error Bound and Fast Convergence. CoRR abs/1607.04817 (2016) - [i2]Qianli Liao, Kenji Kawaguchi, Tomaso A. Poggio:
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. CoRR abs/1610.06160 (2016) - 2015
- [j1]Xiaoyu Zheng, Hiroto Itoh, Kenji Kawaguchi, Hitoshi Tamaki, Yu Maruyama:
Application of Bayesian nonparametric models to the uncertainty and sensitivity analysis of source term in a BWR severe accident. Reliab. Eng. Syst. Saf. 138: 253-262 (2015) - [c2]Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez:
Bayesian Optimization with Exponential Convergence. NIPS 2015: 2809-2817 - 2013
- [c1]Kenji Kawaguchi, Hiroshi Sato:
Prior-Free Exploration Bonus for and beyond Near Bayes-Optimal Behavior. IJCAI 2013: 1437-1443 - [i1]Kenji Kawaguchi, Mauricio Araya-López:
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model. CoRR abs/1303.3163 (2013)
Coauthor Index
George Em Karniadakis
aka: George E. Karniadakis
aka: George E. Karniadakis
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last updated on 2023-03-07 21:54 CET by the dblp team
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