Louis Kirsch

According to our database, Louis Kirsch authored at least 21 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Language Agents as Optimizable Graphs.

Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber

CoRR, 2024

GPTSwarm: Language Agents as Optimizable Graphs.

Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning.

Aditya A. Ramesh, Kenny John Young, Louis Kirsch, Jürgen Schmidhuber

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Discovering Temporally-Aware Reinforcement Learning Algorithms.

Matthew Thomas Jackson, Chris Lu, Louis Kirsch, Robert Tjarko Lange, Shimon Whiteson, Jakob Nicolaus Foerster

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute.

Aleksandar Stanic, Dylan R. Ashley, Oleg Serikov, Louis Kirsch, Francesco Faccio, Jürgen Schmidhuber, Thomas Hofmann, Imanol Schlag

CoRR, 2023

Mindstorms in Natural Language-Based Societies of Mind.

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

CoRR, 2023

Brain-inspired learning in artificial neural networks: a review.

Samuel Schmidgall, Jascha Achterberg, Thomas Miconi, Louis Kirsch, Rojin Ziaei, S. Pardis Hajiseyedrazi, Jason Eshraghian

CoRR, 2023

Learning One Abstract Bit at a Time Through Self-invented Experiments Encoded as Neural Networks.

Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber

Proceedings of the Active Inference - 4th International Workshop, 2023

The Benefits of Model-Based Generalization in Reinforcement Learning.

Kenny John Young, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber

Proceedings of the International Conference on Machine Learning, 2023

Goal-Conditioned Generators of Deep Policies.

Francesco Faccio, Vincent Herrmann, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Eliminating Meta Optimization Through Self-Referential Meta Learning.

Louis Kirsch, Jürgen Schmidhuber

CoRR, 2022

General-Purpose In-Context Learning by Meta-Learning Transformers.

Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, Luke Metz

CoRR, 2022

Exploring through Random Curiosity with General Value Functions.

Aditya A. Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Introducing Symmetries to Black Box Meta Reinforcement Learning.

Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Meta Learning Backpropagation And Improving It.

Louis Kirsch, Jürgen Schmidhuber

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Parameter-Based Value Functions.

Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Improving Generalization in Meta Reinforcement Learning using Learned Objectives.

Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Gaussian Mean Field Regularizes by Limiting Learned Information.

Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber

Entropy, 2019

2018

Modular Networks: Learning to Decompose Neural Computation.

Louis Kirsch, Julius Kunze, David Barber

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Transfer Learning for Speech Recognition on a Budget.

Julius Kunze, Louis Kirsch, Ilia Kurenkov, Andreas Krug, Jens Johannsmeier, Sebastian Stober

Proceedings of the 2nd Workshop on Representation Learning for NLP, 2017

Framework for Exploring and Understanding Multivariate Correlations.

Louis Kirsch, Niklas Riekenbrauck, Daniel Thevessen, Marcus Pappik, Axel Stebner, Julius Kunze, Alexander Meissner, Arvind Kumar Shekar, Emmanuel Müller

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017