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Simon Schug
Simon Schug
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Cited by
Cited by
Year
Learning where to learn: Gradient sparsity in meta and continual learning
J Von Oswald, D Zhao, S Kobayashi, S Schug, M Caccia, N Zucchet, ...
Advances in Neural Information Processing Systems 34, 5250-5263, 2021
572021
A contrastive rule for meta-learning
N Zucchet*, S Schug*, J Von Oswald*, D Zhao, J Sacramento
Advances in Neural Information Processing Systems 35, 25921-25936, 2022
232022
Random initialisations performing above chance and how to find them
F Benzing, S Schug, R Meier, J von Oswald, Y Akram, N Zucchet, ...
arXiv preprint arXiv:2209.07509, 2022
232022
Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
S Schug*, F Benzing*, A Steger
Elife 10, e69884, 2021
162021
Evolving instinctive behaviour in resource-constrained autonomous agents using grammatical evolution
A Hallawa, S Schug, G Iacca, G Ascheid
Applications of Evolutionary Computation: 23rd European Conference …, 2020
132020
Online learning of long-range dependencies
N Zucchet*, R Meier*, S Schug*, A Mujika, J Sacramento
Advances in Neural Information Processing Systems 36, 2024
122024
Discovering modular solutions that generalize compositionally
S Schug*, S Kobayashi*, Y Akram, M Wołczyk, A Proca, J von Oswald, ...
The Twelfth International Conference on Learning Representations, 2024
82024
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis
A Meulemans*, S Schug*, S Kobayashi*, N Daw, G Wayne
Advances in Neural Information Processing Systems 36, 2024
32024
Attention as a Hypernetwork
S Schug, S Kobayashi, Y Akram, J Sacramento, R Pascanu
arXiv preprint arXiv:2406.05816, 2024
12024
When can transformers compositionally generalize in-context?
S Kobayashi*, S Schug*, Y Akram*, F Redhardt, J von Oswald, R Pascanu, ...
arXiv preprint arXiv:2407.12275, 2024
2024
A complementary systems theory of meta-learning
S Schug, N Zucchet, J von Oswald, J Sacramento
Cosyne 2023, 2023
2023
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Articles 1–11