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Simon Schug
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2020 – today
- 2024
- [c6]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 - [i7]Simon Schug, Seijin Kobayashi, Yassir Akram, João Sacramento, Razvan Pascanu:
Attention as a Hypernetwork. CoRR abs/2406.05816 (2024) - 2023
- [c5]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 - [c4]Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika, João Sacramento:
Online learning of long-range dependencies. NeurIPS 2023 - [i6]Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika, João Sacramento:
Online learning of long-range dependencies. CoRR abs/2305.15947 (2023) - [i5]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 abs/2306.16803 (2023) - [i4]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
- [c3]Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento:
A contrastive rule for meta-learning. NeurIPS 2022 - [i3]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) - 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 - [i2]Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento:
A contrastive rule for meta-learning. CoRR abs/2104.01677 (2021) - [i1]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]Ahmed Hallawa, Simon Schug, Giovanni Iacca
, Gerd Ascheid:
Evolving Instinctive Behaviour in Resource-Constrained Autonomous Agents Using Grammatical Evolution. EvoApplications 2020: 369-383
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