Wheel Defect Detection With Machine Learning. - Gabriel Krummenacher; Cheng Soon Ong; Stefan Koller; Seijin Kobayashi; Joachim M. Buhmann (IEEE Trans. Intell. Transp. Syst., 2018)

Economical ensembles with hypernetworks. - João Sacramento; Johannes von Oswald; Seijin Kobayashi; Christian Henning; Benjamin F. Grewe (CoRR, 2020)

Neural networks with late-phase weights. - Johannes von Oswald; Seijin Kobayashi; João Sacramento; Alexander Meulemans; Christian Henning; Benjamin F. Grewe (ICLR, 2021)

Posterior Meta-Replay for Continual Learning. - Christian Henning; Maria R. Cervera; Francesco D'Angelo; Johannes von Oswald; Regina Traber; Benjamin Ehret; Seijin Kobayashi; Benjamin F. Grewe; João Sacramento (NeurIPS, 2021)

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)

Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel. - Seijin Kobayashi; Pau Vilimelis Aceituno; Johannes von Oswald (NeurIPS, 2022)

The least-control principle for local learning at equilibrium. - Alexander Meulemans; Nicolas Zucchet; Seijin Kobayashi; Johannes von Oswald; João Sacramento (NeurIPS, 2022)

The least-control principle for learning at equilibrium. - Alexander Meulemans; Nicolas Zucchet; Seijin Kobayashi; Johannes von Oswald; João Sacramento (CoRR, 2022)

Meta-Learning via Classifier(-free) Guidance. - Elvis Nava; Seijin Kobayashi; Yifei Yin; Robert K. Katzschmann; Benjamin F. Grewe (CoRR, 2022)

Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis. - Alexander Meulemans; Simon Schug; Seijin Kobayashi; Nathaniel Daw; Gregory Wayne (NeurIPS, 2023)

Gated recurrent neural networks discover attention. - Nicolas Zucchet; Seijin Kobayashi; Yassir Akram; Johannes von Oswald; Maxime Larcher; Angelika Steger; João Sacramento (CoRR, 2023)

Uncovering mesa-optimization algorithms in Transformers. - Johannes von Oswald; Eyvind Niklasson; Maximilian Schlegel; Seijin Kobayashi; Nicolas Zucchet; Nino Scherrer; Nolan Miller; Mark Sandler 002; Blaise Agüera y Arcas; Max Vladymyrov; Razvan Pascanu; João Sacramento (CoRR, 2023)

Meta-Learning via Classifier(-free) Diffusion Guidance. - Elvis Nava; Seijin Kobayashi; Yifei Yin; Robert K. Katzschmann; Benjamin F. Grewe (Trans. Mach. Learn. Res., 2023)

Discovering modular solutions that generalize compositionally. - Simon Schug; Seijin Kobayashi; Yassir Akram; Maciej Wolczyk; Alexandra Proca; Johannes von Oswald; Razvan Pascanu; João Sacramento; Angelika Steger (ICLR, 2024)

Weight decay induces low-rank attention layers. - Seijin Kobayashi; Yassir Akram; Johannes von Oswald (NeurIPS, 2024)

When can transformers compositionally generalize in-context? - Seijin Kobayashi; Simon Schug; Yassir Akram; Florian Redhardt; Johannes von Oswald; Razvan Pascanu; Guillaume Lajoie; João Sacramento (CoRR, 2024)

Multi-agent cooperation through learning-aware policy gradients. - Alexander Meulemans; Seijin Kobayashi; Johannes von Oswald; Nino Scherrer; Eric Elmoznino; Blake Aaron Richards; Guillaume Lajoie; Blaise Agüera y Arcas; João Sacramento (ICLR, 2025)

Learning Randomized Algorithms with Transformers. - Johannes von Oswald; Seijin Kobayashi; Yassir Akram; Angelika Steger (ICLR, 2025)

Attention as a Hypernetwork. - Simon Schug; Seijin Kobayashi; Yassir Akram; João Sacramento; Razvan Pascanu (ICLR, 2025)

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training. - Johannes von Oswald; Nino Scherrer; Seijin Kobayashi; Luca Versari; Songlin Yang; Maximilian Schlegel; Kaitlin Maile; Yanick Schimpf; Oliver Sieberling; Alexander Meulemans; Rif A. Saurous; Guillaume Lajoie; et al. (CoRR, 2025)