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Louis Kirsch
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2020 – today
- 2024
- [c14]Matthew Thomas Jackson, Chris Lu, Louis Kirsch, Robert Tjarko Lange, Shimon Whiteson, Jakob Nicolaus Foerster:
Discovering Temporally-Aware Reinforcement Learning Algorithms. ICLR 2024 - [c13]Aditya A. Ramesh, Kenny John Young, Louis Kirsch, Jürgen Schmidhuber:
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning. ICML 2024 - [c12]Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber:
GPTSwarm: Language Agents as Optimizable Graphs. ICML 2024 - [i18]Matthew Thomas Jackson, Chris Lu, Louis Kirsch, Robert T. Lange, Shimon Whiteson, Jakob Nicolaus Foerster:
Discovering Temporally-Aware Reinforcement Learning Algorithms. CoRR abs/2402.05828 (2024) - [i17]Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber:
Language Agents as Optimizable Graphs. CoRR abs/2402.16823 (2024) - [i16]Aditya A. Ramesh, Kenny Young, Louis Kirsch, Jürgen Schmidhuber:
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning. CoRR abs/2405.03878 (2024) - 2023
- [c11]Francesco Faccio, Vincent Herrmann, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
Goal-Conditioned Generators of Deep Policies. AAAI 2023: 7503-7511 - [c10]Kenny John Young, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
The Benefits of Model-Based Generalization in Reinforcement Learning. ICML 2023: 40254-40276 - [c9]Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber:
Learning One Abstract Bit at a Time Through Self-invented Experiments Encoded as Neural Networks. IWAI 2023: 254-274 - [i15]Samuel Schmidgall, Jascha Achterberg, Thomas Miconi, Louis Kirsch, Rojin Ziaei, S. Pardis Hajiseyedrazi, Jason Eshraghian:
Brain-inspired learning in artificial neural networks: a review. CoRR abs/2305.11252 (2023) - [i14]Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R. Ashley, Róbert Csordás, Anand Gopalakrishnan, Abdullah Hamdi, Hasan Abed Al Kader Hammoud, Vincent Herrmann, Kazuki Irie, Louis Kirsch, Bing Li, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piekos, Aditya A. Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanic, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber:
Mindstorms in Natural Language-Based Societies of Mind. CoRR abs/2305.17066 (2023) - [i13]Aleksandar Stanic, Dylan R. Ashley, Oleg Serikov, Louis Kirsch, Francesco Faccio, Jürgen Schmidhuber, Thomas Hofmann, Imanol Schlag:
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute. CoRR abs/2309.11197 (2023) - 2022
- [c8]Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen:
Introducing Symmetries to Black Box Meta Reinforcement Learning. AAAI 2022: 7202-7210 - [c7]Aditya A. Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Exploring through Random Curiosity with General Value Functions. NeurIPS 2022 - [i12]Francesco Faccio, Vincent Herrmann, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
Goal-Conditioned Generators of Deep Policies. CoRR abs/2207.01570 (2022) - [i11]Kenny Young, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
The Benefits of Model-Based Generalization in Reinforcement Learning. CoRR abs/2211.02222 (2022) - [i10]Aditya A. Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Exploring through Random Curiosity with General Value Functions. CoRR abs/2211.10282 (2022) - [i9]Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, Luke Metz:
General-Purpose In-Context Learning by Meta-Learning Transformers. CoRR abs/2212.04458 (2022) - [i8]Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber:
Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks. CoRR abs/2212.14374 (2022) - [i7]Louis Kirsch, Jürgen Schmidhuber:
Eliminating Meta Optimization Through Self-Referential Meta Learning. CoRR abs/2212.14392 (2022) - 2021
- [c6]Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber:
Parameter-Based Value Functions. ICLR 2021 - [c5]Louis Kirsch, Jürgen Schmidhuber:
Meta Learning Backpropagation And Improving It. NeurIPS 2021: 14122-14134 - [i6]Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen:
Introducing Symmetries to Black Box Meta Reinforcement Learning. CoRR abs/2109.10781 (2021) - 2020
- [c4]Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. ICLR 2020 - [i5]Louis Kirsch, Jürgen Schmidhuber:
Meta Learning Backpropagation And Improving It. CoRR abs/2012.14905 (2020)
2010 – 2019
- 2019
- [j1]Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
:
Gaussian Mean Field Regularizes by Limiting Learned Information. Entropy 21(8): 758 (2019) - [i4]Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber:
Gaussian Mean Field Regularizes by Limiting Learned Information. CoRR abs/1902.04340 (2019) - [i3]Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. CoRR abs/1910.04098 (2019) - 2018
- [c3]Louis Kirsch, Julius Kunze, David Barber:
Modular Networks: Learning to Decompose Neural Computation. NeurIPS 2018: 2414-2423 - [i2]Louis Kirsch, Julius Kunze, David Barber:
Modular Networks: Learning to Decompose Neural Computation. CoRR abs/1811.05249 (2018) - 2017
- [c2]Louis Kirsch, Niklas Riekenbrauck, Daniel Thevessen, Marcus Pappik, Axel Stebner, Julius Kunze, Alexander Meissner, Arvind Kumar Shekar, Emmanuel Müller:
Framework for Exploring and Understanding Multivariate Correlations. ECML/PKDD (3) 2017: 404-408 - [c1]Julius Kunze, Louis Kirsch, Ilia Kurenkov, Andreas Krug, Jens Johannsmeier, Sebastian Stober:
Transfer Learning for Speech Recognition on a Budget. Rep4NLP@ACL 2017: 168-177 - [i1]Julius Kunze, Louis Kirsch, Ilia Kurenkov, Andreas Krug, Jens Johannsmeier, Sebastian Stober:
Transfer Learning for Speech Recognition on a Budget. CoRR abs/1706.00290 (2017)
Coauthor Index
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last updated on 2024-11-15 19:35 CET by the dblp team
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