Seijin Kobayashi
Seijin Kobayashi
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Cited by
Cited by
Wheel defect detection with machine learning
G Krummenacher, CS Ong, S Koller, S Kobayashi, JM Buhmann
IEEE Transactions on Intelligent Transportation Systems 19 (4), 1176-1187, 2017
Comparison of hepatic resection and radiofrequency ablation for small hepatocellular carcinoma: a meta-analysis of 16,103 patients
Q Xu, S Kobayashi, X Ye, X Meng
Scientific reports 4 (1), 7252, 2014
Learning where to learn: Gradient sparsity in meta and continual learning
J Von Oswald, D Zhao, S Kobayashi, S Schug, M Caccia, N Zucchet, ...
Advances in Neural Information Processing Systems 34, 5250-5263, 2021
Posterior meta-replay for continual learning
C Henning, M Cervera, F D'Angelo, J Von Oswald, R Traber, B Ehret, ...
Advances in neural information processing systems 34, 14135-14149, 2021
Meta-learning via hypernetworks
D Zhao, S Kobayashi, J Sacramento, J von Oswald
IEEE, 2020
Neural networks with late-phase weights
J Von Oswald, S Kobayashi, A Meulemans, C Henning, BF Grewe, ...
arXiv preprint arXiv:2007.12927, 2020
Uncovering mesa-optimization algorithms in transformers
J von Oswald, E Niklasson, M Schlegel, S Kobayashi, N Zucchet, ...
arXiv preprint arXiv:2309.05858, 2023
The least-control principle for local learning at equilibrium
A Meulemans, N Zucchet, S Kobayashi, J Von Oswald, J Sacramento
Advances in Neural Information Processing Systems 35, 33603-33617, 2022
On the reversed bias-variance tradeoff in deep ensembles
S Kobayashi, J von Oswald, BF Grewe
ICML, 2021
Gated recurrent neural networks discover attention
N Zucchet, S Kobayashi, Y Akram, J Von Oswald, M Larcher, A Steger, ...
arXiv preprint arXiv:2309.01775, 2023
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis
A Meulemans, S Schug, S Kobayashi, G Wayne
Advances in Neural Information Processing Systems 36, 2024
Meta-learning via classifier (-free) diffusion guidance
E Nava, S Kobayashi, Y Yin, RK Katzschmann, BF Grewe
arXiv preprint arXiv:2210.08942, 2022
Discovering modular solutions that generalize compositionally
S Schug, S Kobayashi, Y Akram, M Wołczyk, A Proca, J Von Oswald, ...
arXiv preprint arXiv:2312.15001, 2023
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
S Kobayashi, P Vilimelis Aceituno, J Von Oswald
Advances in Neural Information Processing Systems 35, 25335-25348, 2022
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