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Simon Schug
Simon Schug
ETH Zurich
Zweryfikowany adres z ethz.ch - Strona główna
Tytuł
Cytowane przez
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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
502021
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
192022
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
192022
Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
S Schug, F Benzing, A Steger
Elife 10, e69884, 2021
122021
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
122020
Online learning of long-range dependencies
N Zucchet, R Meier, S Schug, A Mujika, J Sacramento
Advances in Neural Information Processing Systems 36, 2024
62024
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
22024
Discovering modular solutions that generalize compositionally
S Schug, S Kobayashi, Y Akram, M Wołczyk, A Proca, J von Oswald, ...
arXiv preprint arXiv:2312.15001, 2023
12023
A complementary systems theory of meta-learning
S Schug, N Zucchet, J von Oswald, J Sacramento
Cosyne 2023, 2023
2023
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