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Seijin Kobayashi
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Journal Articles
- 2023
- [j2]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) - 2018
- [j1]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): 1176-1187 (2018)
Conference and Workshop Papers
- 2024
- [c7]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 - 2023
- [c6]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 - 2022
- [c5]Seijin Kobayashi, Pau Vilimelis Aceituno, Johannes von Oswald:
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel. NeurIPS 2022 - [c4]Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento:
The least-control principle for local learning at equilibrium. NeurIPS 2022 - 2021
- [c3]Johannes von Oswald, Seijin Kobayashi, João Sacramento, Alexander Meulemans, Christian Henning, Benjamin F. Grewe:
Neural networks with late-phase weights. ICLR 2021 - [c2]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: 5250-5263 - [c1]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: 14135-14149
Informal and Other Publications
- 2024
- [i13]Simon Schug, Seijin Kobayashi, Yassir Akram, João Sacramento, Razvan Pascanu:
Attention as a Hypernetwork. CoRR abs/2406.05816 (2024) - [i12]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 abs/2407.12275 (2024) - [i11]Johannes von Oswald, Seijin Kobayashi, Yassir Akram, Angelika Steger:
Learning Randomized Algorithms with Transformers. CoRR abs/2408.10818 (2024) - 2023
- [i10]Alexander Meulemans, Simon Schug, Seijin Kobayashi, Nathaniel Daw
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis. CoRR abs/2306.16803 (2023) - [i9]Nicolas Zucchet, Seijin Kobayashi, Yassir Akram, Johannes von Oswald, Maxime Larcher, Angelika Steger, João Sacramento:
Gated recurrent neural networks discover attention. CoRR abs/2309.01775 (2023) - [i8]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 abs/2309.05858 (2023) - [i7]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 abs/2312.15001 (2023) - 2022
- [i6]Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento:
The least-control principle for learning at equilibrium. CoRR abs/2207.01332 (2022) - [i5]Elvis Nava, Seijin Kobayashi, Yifei Yin, Robert K. Katzschmann
Meta-Learning via Classifier(-free) Guidance. CoRR abs/2210.08942 (2022) - [i4]Seijin Kobayashi, Pau Vilimelis Aceituno, Johannes von Oswald:
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel. CoRR abs/2210.09818 (2022) - 2021
- [i3]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 abs/2103.01133 (2021) - [i2]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 abs/2110.14402 (2021) - 2020
- [i1]João Sacramento, Johannes von Oswald, Seijin Kobayashi, Christian Henning, Benjamin F. Grewe:
Economical ensembles with hypernetworks. CoRR abs/2007.12927 (2020)
Coauthor Index
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