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Seijin Kobayashi
Seijin Kobayashi
Google
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Titel
Zitiert von
Zitiert von
Jahr
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
2762017
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
1442014
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
752021
Meta-learning via hypernetworks
D Zhao, S Kobayashi, J Sacramento, J von Oswald
IEEE, 2020
632020
Uncovering mesa-optimization algorithms in transformers
J Von Oswald, M Schlegel, A Meulemans, S Kobayashi, E Niklasson, ...
arXiv preprint arXiv:2309.05858, 2023
62*2023
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
612021
Neural networks with late-phase weights
J Von Oswald, S Kobayashi, A Meulemans, C Henning, BF Grewe, ...
arXiv preprint arXiv:2007.12927, 2020
382020
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
282022
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
132023
Meta-learning via classifier (-free) diffusion guidance
E Nava, S Kobayashi, Y Yin, RK Katzschmann, BF Grewe
arXiv preprint arXiv:2210.08942, 2022
11*2022
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
102023
On the reversed bias-variance tradeoff in deep ensembles
S Kobayashi, J von Oswald, BF Grewe
ICML, 2021
92021
Attention as a hypernetwork
S Schug, S Kobayashi, Y Akram, J Sacramento, R Pascanu
arXiv preprint arXiv:2406.05816, 2024
52024
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, 68685-68735, 2023
52023
Weight decay induces low-rank attention layers
S Kobayashi, Y Akram, J Von Oswald
Advances in Neural Information Processing Systems 37, 4481-4510, 2024
32024
Multi-agent cooperation through learning-aware policy gradients
A Meulemans, S Kobayashi, J Von Oswald, N Scherrer, E Elmoznino, ...
arXiv preprint arXiv:2410.18636, 2024
12024
When can transformers compositionally generalize in-context?
S Kobayashi, S Schug, Y Akram, F Redhardt, J von Oswald, R Pascanu, ...
arXiv preprint arXiv:2407.12275, 2024
12024
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
12022
Learning Randomized Algorithms with Transformers
J von Oswald, S Kobayashi, Y Akram, A Steger
arXiv preprint arXiv:2408.10818, 2024
2024
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