Louis Kirsch

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 31

Top Topics

Eligibility Traces

Reinforcement Learning

Maximum Entropy Models

Neural Computation

Top Venues

Publications

Matthew Thomas Jackson, Chris Lu, Louis Kirsch, Robert Tjarko Lange, Shimon Whiteson, Jakob Nicolaus Foerster
Discovering Temporally-Aware Reinforcement Learning Algorithms. ICLR (2024)
Matthew Thomas Jackson, Chris Lu, Louis Kirsch, Robert Tjarko Lange, Shimon Whiteson, Jakob Nicolaus Foerster
Discovering Temporally-Aware Reinforcement Learning Algorithms. CoRR (2024)
Aditya A. Ramesh, Kenny Young, Louis Kirsch, Jürgen Schmidhuber
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning. CoRR (2024)
Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber
Language Agents as Optimizable Graphs. CoRR (2024)
Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber
Learning One Abstract Bit at a Time Through Self-invented Experiments Encoded as Neural Networks. IWAI (2023)
Francesco Faccio, Vincent Herrmann, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber
Goal-Conditioned Generators of Deep Policies. AAAI (2023)
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 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 (2023)
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 (2023)
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 (2023)
Kenny John Young, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber
The Benefits of Model-Based Generalization in Reinforcement Learning. ICML (2023)
Francesco Faccio, Vincent Herrmann, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber
Goal-Conditioned Generators of Deep Policies. CoRR (2022)
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen
Introducing Symmetries to Black Box Meta Reinforcement Learning. AAAI (2022)
Kenny Young, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber
The Benefits of Model-Based Generalization in Reinforcement Learning. CoRR (2022)
Louis Kirsch, Jürgen Schmidhuber
Eliminating Meta Optimization Through Self-Referential Meta Learning. CoRR (2022)
Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, Luke Metz
General-Purpose In-Context Learning by Meta-Learning Transformers. CoRR (2022)
Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber
Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks. CoRR (2022)
Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber
Exploring through Random Curiosity with General Value Functions. NeurIPS (2022)
Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber
Exploring through Random Curiosity with General Value Functions. CoRR (2022)
Louis Kirsch, Jürgen Schmidhuber
Meta Learning Backpropagation And Improving It. NeurIPS (2021)
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen
Introducing Symmetries to Black Box Meta Reinforcement Learning. CoRR (2021)
Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber
Parameter-Based Value Functions. ICLR (2021)
Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. ICLR (2020)
Louis Kirsch, Jürgen Schmidhuber
Meta Learning Backpropagation And Improving It. CoRR (2020)
Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
Gaussian Mean Field Regularizes by Limiting Learned Information. CoRR (2019)
Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
Gaussian Mean Field Regularizes by Limiting Learned Information. Entropy 21 (8) (2019)
Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. CoRR (2019)
Louis Kirsch, Julius Kunze, David Barber
Modular Networks: Learning to Decompose Neural Computation. NeurIPS (2018)
Louis Kirsch, Julius Kunze, David Barber
Modular Networks: Learning to Decompose Neural Computation. CoRR (2018)
Julius Kunze, Louis Kirsch, Ilia Kurenkov, Andreas Krug, Jens Johannsmeier, Sebastian Stober
Transfer Learning for Speech Recognition on a Budget. Rep4NLP@ACL (2017)
Julius Kunze, Louis Kirsch, Ilia Kurenkov, Andreas Krug, Jens Johannsmeier, Sebastian Stober
Transfer Learning for Speech Recognition on a Budget. CoRR (2017)
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)