ETH Zürich
Seijin Koayashi
Institut für Theoretische Informatik
OAT Z 11
Andreasstrasse 5
8050 Zürich
Uncovering mesa-optimization algorithms in Transformers
J. von Oswald*, E. Niklasson*, M. Schlegel*, S. Kobayashi, N. Zucchet, N. Scherrer, N. Miller, M. Sandler, B. Agüera y Arcas, M. Vladymyrov, R. Pascanu, J. Sacramento
Gated recurrent neural networks discover attention
N. Zucchet*, S. Kobayashi*, Y. Akram*, J. von Oswald, M. Larcher, A. Steger, J. Sacramento
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis
A. Meulemans*, S. Schug*, S. Kobayashi*, N. Daw, G. Wayne
37th Conference on Neural Information Processing Systems (NeurIPS) , 2023.
Meta-Learning via Classifier(-free) Diffusion Guidance
E. Nava, S. Kobayashi*, Y. Yin, R. K. Katzschmann, B. F. Grewe
Transactions on Machine Learning Research (TMLR), 2023.
The least-control principle for learning at equilibrium
A. Meulemans*, N. Zucchet*, S. Kobayashi*, J. von Oswald, J. Sacramento
36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
S. Kobayashi*, P. Vilimelis Aceituno, J. von Oswald
36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
Learning where to learn: Gradient sparsity in meta and continual learning
J. Von Oswald*, D. Zhao*, S. Kobayashi, S. Schug, M. Caccia, N. Zucchet, J. Sacramento
35th Conference on Neural Information Processing Systems (NeurIPS), 2021.
Posterior meta-replay for continual learning
C. Henning*, M. Cervera*, F. D'Angelo, J. Von Oswald, R. Traber, B. Ehret, S. Kobayashi, B. F Grewe, J. Sacramento
35th Conference on Neural Information Processing Systems (NeurIPS), 2021.
Neural networks with late-phase weights
J. von Oswald*, S. Kobayashi*, A. Meulemans, C. Henning, B. F. Grewe, J. Sacramento
International Conference on Learning Representations (ICLR), 2021.
On the reversed bias-variance tradeoff in deep ensembles
S. Kobayashi*, J. Von Oswald*, B. F. Grewe
ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning, 2021.
Meta-learning via hypernetworks
D. Zhao, S. Kobayashi, J. Sacramento, J. von Oswald
NeurIPS Workshop on Meta-Learning, 2020.