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Louis Kirsch
Louis Kirsch
The Swiss AI Lab IDSIA
Zweryfikowany adres z idsia.ch - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Transfer Learning for Speech Recognition on a Budget
J Kunze, L Kirsch, I Kurenkov, A Krug, J Johannsmeier, S Stober
ACL 2017, 168, 2017
1332017
Improving Generalization in Meta Reinforcement Learning using Learned Objectives
L Kirsch, S van Steenkiste, J Schmidhuber
arXiv preprint arXiv:1910.04098, 2019
872019
Modular Networks: Learning to Decompose Neural Computation
L Kirsch, J Kunze, D Barber
Advances in Neural Information Processing Systems, 2408-2418, 2018
802018
Meta Learning Backpropagation And Improving It
L Kirsch, J Schmidhuber
4th Workshop on Meta-Learning at NeurIPS 2020, 2020
412020
Introducing symmetries to black box meta reinforcement learning
L Kirsch, S Flennerhag, H van Hasselt, A Friesen, J Oh, Y Chen
Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7202-7210, 2022
152022
Parameter-based value functions
F Faccio, L Kirsch, J Schmidhuber
arXiv preprint arXiv:2006.09226, 2020
152020
Self-Referential Meta Learning
L Kirsch, J Schmidhuber
First Conference on Automated Machine Learning (Late-Breaking Workshop), 2022
82022
Goal-Conditioned Generators of Deep Policies
F Faccio, V Herrmann, A Ramesh, L Kirsch, J Schmidhuber
arXiv preprint arXiv:2207.01570, 2022
62022
Exploring through Random Curiosity with General Value Functions
A Ramesh, L Kirsch, S van Steenkiste, J Schmidhuber
arXiv preprint arXiv:2211.10282, 2022
22022
Gaussian mean field regularizes by limiting learned information
J Kunze, L Kirsch, H Ritter, D Barber
Entropy 21 (8), 758, 2019
22019
General-Purpose In-Context Learning by Meta-Learning Transformers
L Kirsch, J Harrison, J Sohl-Dickstein, L Metz
arXiv preprint arXiv:2212.04458, 2022
12022
Scaling Neural Networks Through Sparsity
L Kirsch
Tech. rep, 2018
12018
Framework for Exploring and Understanding Multivariate Correlations
L Kirsch, N Riekenbrauck, D Thevessen, M Pappik, A Stebner, J Kunze, ...
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
12017
Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks
V Herrmann, L Kirsch, J Schmidhuber
arXiv preprint arXiv:2212.14374, 2022
2022
Eliminating Meta Optimization Through Self-Referential Meta Learning
L Kirsch, J Schmidhuber
arXiv preprint arXiv:2212.14392, 2022
2022
The Benefits of Model-Based Generalization in Reinforcement Learning
K Young, A Ramesh, L Kirsch, J Schmidhuber
arXiv preprint arXiv:2211.02222, 2022
2022
Noisy Information Bottlenecks for Generalization
J Kunze, L Kirsch, H Ritter, D Barber
2018
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