| Transfer Learning for Speech Recognition on a Budget J Kunze, L Kirsch, I Kurenkov, A Krug, J Johannsmeier, S Stober ACL 2017, 168, 2017 | 202 | 2017 |
| Improving Generalization in Meta Reinforcement Learning using Learned Objectives L Kirsch, S van Steenkiste, J Schmidhuber arXiv preprint arXiv:1910.04098, 2019 | 157 | 2019 |
| Modular Networks: Learning to Decompose Neural Computation L Kirsch, J Kunze, D Barber Advances in Neural Information Processing Systems, 2408-2418, 2018 | 141 | 2018 |
| Meta Learning Backpropagation And Improving It L Kirsch, J Schmidhuber 4th Workshop on Meta-Learning at NeurIPS 2020, 2020 | 82 | 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 | 71 | 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 | 69 | 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 | 62 | 2023 |
| 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 | 36 | 2022 |
| Parameter-based value functions F Faccio, L Kirsch, J Schmidhuber arXiv preprint arXiv:2006.09226, 2020 | 25 | 2020 |
| The Benefits of Model-Based Generalization in Reinforcement Learning K Young, A Ramesh, L Kirsch, J Schmidhuber arXiv preprint arXiv:2211.02222, 2022 | 24 | 2022 |
| GPTSwarm: Language Agents as Optimizable Graphs M Zhuge, W Wang, L Kirsch, F Faccio, D Khizbullin, J Schmidhuber Forty-first International Conference on Machine Learning, 0 | 19 | |
| Language agents as optimizable graphs M Zhuge, W Wang, L Kirsch, F Faccio, D Khizbullin, J Schmidhuber arXiv preprint arXiv:2402.16823, 2024 | 18 | 2024 |
| Discovering Temporally-Aware Reinforcement Learning Algorithms MT Jackson, C Lu, L Kirsch, RT Lange, S Whiteson, JN Foerster Second Agent Learning in Open-Endedness Workshop, 2023 | 16 | 2023 |
| 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 | 14 | 2023 |
| Eliminating Meta Optimization Through Self-Referential Meta Learning L Kirsch, J Schmidhuber arXiv preprint arXiv:2212.14392, 2022 | 11 | 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 | 10 | 2022 |
| Towards General-Purpose In-Context Learning Agents L Kirsch, J Harrison, C Freeman, J Sohl-Dickstein, J Schmidhuber NeurIPS 2023 Foundation Models for Decision Making Workshop, 2023 | 9 | 2023 |
| Self-Referential Meta Learning L Kirsch, J Schmidhuber First Conference on Automated Machine Learning (Late-Breaking Workshop), 2022 | 9 | 2022 |
| 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 | 8 | 2023 |
| Learning one abstract bit at a time through self-invented experiments encoded as neural networks V Herrmann, L Kirsch, J Schmidhuber International Workshop on Active Inference, 254-274, 2023 | 5 | 2023 |
Juergen SchmidhuberKing Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of LuganoDirección de correo verificada de kaust.edu.sa
