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ORCIDJohannes von Oswald
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2020 – today
- 2024
[b1]Johannes von Oswald:
Interpretability of Learning Algorithms Encoded in Deep Neural Networks. ETH Zurich, Zürich, Switzerland, 2024- [c10]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 - [i17]Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge:
Linear Transformers are Versatile In-Context Learners. CoRR abs/2402.14180 (2024) - [i16]Maciej Pióro, Maciej Wolczyk, Razvan Pascanu, Johannes von Oswald, João Sacramento:
State Soup: In-Context Skill Learning, Retrieval and Mixing. CoRR abs/2406.08423 (2024) - [i15]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) - [i14]Johannes von Oswald, Seijin Kobayashi, Yassir Akram, Angelika Steger:
Learning Randomized Algorithms with Transformers. CoRR abs/2408.10818 (2024) - 2023
- [c9]Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov:
Transformers Learn In-Context by Gradient Descent. ICML 2023: 35151-35174 - [i13]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) - [i12]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) - [i11]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
- [c8]Seijin Kobayashi, Pau Vilimelis Aceituno, Johannes von Oswald:
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel. NeurIPS 2022 - [c7]Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento:
The least-control principle for local learning at equilibrium. NeurIPS 2022 - [c6]Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento:
A contrastive rule for meta-learning. NeurIPS 2022 - [i10]Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento:
The least-control principle for learning at equilibrium. CoRR abs/2207.01332 (2022) - [i9]Frederik Benzing, Simon Schug, Robert Meier, Johannes von Oswald, Yassir Akram, Nicolas Zucchet, Laurence Aitchison, Angelika Steger:
Random initialisations performing above chance and how to find them. CoRR abs/2209.07509 (2022) - [i8]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) - [i7]Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov:
Transformers learn in-context by gradient descent. CoRR abs/2212.07677 (2022) - 2021
- [c5]Benjamin Ehret, Christian Henning, Maria R. Cervera, Alexander Meulemans, Johannes von Oswald, Benjamin F. Grewe:
Continual learning in recurrent neural networks. ICLR 2021 - [c4]Johannes von Oswald, Seijin Kobayashi, João Sacramento, Alexander Meulemans, Christian Henning, Benjamin F. Grewe:
Neural networks with late-phase weights. ICLR 2021 - [c3]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 - [c2]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 - [i6]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) - [i5]Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento:
A contrastive rule for meta-learning. CoRR abs/2104.01677 (2021) - [i4]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
- [c1]Johannes von Oswald, Christian Henning, João Sacramento, Benjamin F. Grewe:
Continual learning with hypernetworks. ICLR 2020 - [i3]Benjamin Ehret, Christian Henning, Maria R. Cervera, Alexander Meulemans, Johannes von Oswald, Benjamin F. Grewe:
Continual Learning in Recurrent Neural Networks with Hypernetworks. CoRR abs/2006.12109 (2020) - [i2]João Sacramento, Johannes von Oswald, Seijin Kobayashi, Christian Henning, Benjamin F. Grewe:
Economical ensembles with hypernetworks. CoRR abs/2007.12927 (2020)
2010 – 2019
- 2019
- [i1]Johannes von Oswald, Christian Henning, João Sacramento, Benjamin F. Grewe:
Continual learning with hypernetworks. CoRR abs/1906.00695 (2019)
Coauthor Index
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last updated on 2024-09-26 01:55 CEST by the dblp team
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