Seijin Kobayashi

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 21

Top Topics

Decision Stumps

Ensemble Selection

Inductive Learning

Top Venues

IEEE Trans. Intell. Transp. Syst.

Publications

Simon Schug, Seijin Kobayashi, Yassir Akram, João Sacramento, Razvan Pascanu
Attention as a Hypernetwork. CoRR (2024)
Seijin Kobayashi, Simon Schug, Yassir Akram, Florian Redhardt, Johannes von Oswald, Razvan Pascanu, Guillaume Lajoie, João Sacramento
When can transformers compositionally generalize in-context? CoRR (2024)
Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wolczyk, Alexandra Proca, Johannes von Oswald, Razvan Pascanu, João Sacramento, Angelika Steger
Discovering modular solutions that generalize compositionally. ICLR (2024)
Alexander Meulemans, Simon Schug, Seijin Kobayashi, Nathaniel Daw, Gregory Wayne
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis. NeurIPS (2023)
Alexander Meulemans, Simon Schug, Seijin Kobayashi, Nathaniel Daw, Gregory Wayne
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis. CoRR (2023)
Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wolczyk, Alexandra Proca, Johannes von Oswald, Razvan Pascanu, João Sacramento, Angelika Steger
Discovering modular solutions that generalize compositionally. CoRR (2023)
Nicolas Zucchet, Seijin Kobayashi, Yassir Akram, Johannes von Oswald, Maxime Larcher, Angelika Steger, João Sacramento
Gated recurrent neural networks discover attention. CoRR (2023)
Elvis Nava, Seijin Kobayashi, Yifei Yin, Robert K. Katzschmann, Benjamin F. Grewe
Meta-Learning via Classifier(-free) Diffusion Guidance. Trans. Mach. Learn. Res. 2023 (2023)
Johannes von Oswald, Eyvind Niklasson, Maximilian Schlegel, Seijin Kobayashi, Nicolas Zucchet, Nino Scherrer, Nolan Miller, Mark Sandler, Blaise Agüera y Arcas, Max Vladymyrov, Razvan Pascanu, João Sacramento
Uncovering mesa-optimization algorithms in Transformers. CoRR (2023)
Elvis Nava, Seijin Kobayashi, Yifei Yin, Robert K. Katzschmann, Benjamin F. Grewe
Meta-Learning via Classifier(-free) Guidance. CoRR (2022)
Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento
The least-control principle for local learning at equilibrium. NeurIPS (2022)
Seijin Kobayashi, Pau Vilimelis Aceituno, Johannes von Oswald
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel. NeurIPS (2022)
Seijin Kobayashi, Pau Vilimelis Aceituno, Johannes von Oswald
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel. CoRR (2022)
Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento
The least-control principle for learning at equilibrium. CoRR (2022)
Christian Henning, Maria R. Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, João Sacramento, Benjamin F. Grewe
Posterior Meta-Replay for Continual Learning. CoRR (2021)
Johannes von Oswald, Seijin Kobayashi, João Sacramento, Alexander Meulemans, Christian Henning, Benjamin F. Grewe
Neural networks with late-phase weights. ICLR (2021)
Christian Henning, Maria R. Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F. Grewe, João Sacramento
Posterior Meta-Replay for Continual Learning. NeurIPS (2021)
Johannes von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento
Learning where to learn: Gradient sparsity in meta and continual learning. NeurIPS (2021)
Johannes von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento
Learning where to learn: Gradient sparsity in meta and continual learning. CoRR (2021)
João Sacramento, Johannes von Oswald, Seijin Kobayashi, Christian Henning, Benjamin F. Grewe
Economical ensembles with hypernetworks. CoRR (2020)
Gabriel Krummenacher, Cheng Soon Ong, Stefan Koller, Seijin Kobayashi, Joachim M. Buhmann
Wheel Defect Detection With Machine Learning. IEEE Trans. Intell. Transp. Syst. 19 (4) (2018)