ETH Zürich
Seijin Koayashi
Institut für Theoretische Informatik
CAB J 21.2
Universitätstrasse 6
8092 Zürich
The least-control principle for learning at equilibrium
Alexander Meulemans*, Nicolas Zucchet*, Seijin Kobayashi*, Johannes von Oswald, João Sacramento,
NeurIPS, 2022.
Learning where to learn: Gradient sparsity in meta and continual learning
Johannes Von Oswald*, Dominic Zhao*, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento,
NeurIPS, 2021.
Posterior meta-replay for continual learning
Christian Henning*, Maria Cervera*, Francesco D'Angelo, Johannes Von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F Grewe, João Sacramento,
NeurIPS, 2021.
On the reversed bias-variance tradeoff in deep ensembles
Seijin Kobayashi*, Johannes Von Oswald*, Benjamin F. Grewe,
ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning, 2021.
Neural networks with late-phase weights
Johannes von Oswald*, Seijin Kobayashi*,Alexander Meulemans, Christian Henning, Benjamin F. Grewe, João Sacramento
ICLR, 2021.
Meta-learning via hypernetworks
Dominic Zhao, Seijin Kobayashi, João Sacramento, Johannes von Oswald,
NeurIPS Workshop on Meta-Learning, 2020.