| Transfer Learning for Speech Recognition on a Budget J Kunze, L Kirsch, I Kurenkov, A Krug, J Johannsmeier, S Stober ACL 2017, 168, 2017 | 161 | 2017 |
| Improving Generalization in Meta Reinforcement Learning using Learned Objectives L Kirsch, S van Steenkiste, J Schmidhuber arXiv preprint arXiv:1910.04098, 2019 | 120 | 2019 |
| Modular Networks: Learning to Decompose Neural Computation L Kirsch, J Kunze, D Barber Advances in Neural Information Processing Systems, 2408-2418, 2018 | 113 | 2018 |
| Meta Learning Backpropagation And Improving It L Kirsch, J Schmidhuber 4th Workshop on Meta-Learning at NeurIPS 2020, 2020 | 54 | 2020 |
| General-Purpose In-Context Learning by Meta-Learning Transformers L Kirsch, J Harrison, J Sohl-Dickstein, L Metz arXiv preprint arXiv:2212.04458, 2022 | 24 | 2022 |
| 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 | 19 | 2022 |
| Parameter-based value functions F Faccio, L Kirsch, J Schmidhuber arXiv preprint arXiv:2006.09226, 2020 | 18 | 2020 |
| Goal-conditioned generators of deep policies F Faccio, V Herrmann, A Ramesh, L Kirsch, J Schmidhuber Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7503-7511, 2023 | 8 | 2023 |
| Brain-inspired learning in artificial neural networks: a review S Schmidgall, J Achterberg, T Miconi, L Kirsch, R Ziaei, S Hajiseyedrazi, ... arXiv preprint arXiv:2305.11252, 2023 | 8 | 2023 |
| Self-Referential Meta Learning L Kirsch, J Schmidhuber First Conference on Automated Machine Learning (Late-Breaking Workshop), 2022 | 8 | 2022 |
| Mindstorms in Natural Language-Based Societies of Mind M Zhuge, H Liu, F Faccio, DR Ashley, R Csordás, A Gopalakrishnan, ... arXiv preprint arXiv:2305.17066, 2023 | 7 | 2023 |
| The Benefits of Model-Based Generalization in Reinforcement Learning K Young, A Ramesh, L Kirsch, J Schmidhuber arXiv preprint arXiv:2211.02222, 2022 | 5 | 2022 |
| Eliminating Meta Optimization Through Self-Referential Meta Learning L Kirsch, J Schmidhuber arXiv preprint arXiv:2212.14392, 2022 | 4 | 2022 |
| Exploring through random curiosity with general value functions A Ramesh, L Kirsch, S van Steenkiste, J Schmidhuber Advances in Neural Information Processing Systems 35, 18733-18748, 2022 | 4 | 2022 |
| Gaussian mean field regularizes by limiting learned information J Kunze, L Kirsch, H Ritter, D Barber Entropy 21 (8), 758, 2019 | 3 | 2019 |
| Scaling Neural Networks Through Sparsity L Kirsch Tech. rep, 2018 | 1 | 2018 |
| 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 | 1 | 2017 |
| The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute A Stanić, D Ashley, O Serikov, L Kirsch, F Faccio, J Schmidhuber, ... arXiv preprint arXiv:2309.11197, 2023 | | 2023 |
| 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 |
| Noisy Information Bottlenecks for Generalization J Kunze, L Kirsch, H Ritter, D Barber | | 2018 |
Juergen SchmidhuberKing Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of LuganoVerified email at kaust.edu.sa
