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Louis Kirsch

Transfer Learning for Speech Recognition on a Budget
2017 · Julius Kunze, Louis Kirsch, et al. · Workshop on Representation Learning for NLP (RepL4NLP) · 149
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Brain-inspired learning in artificial neural networks: A review
2024 · Samuel Schmidgall, Rojin Ziaei, et al. · APL Machine Learning · 124
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Improving Generalization in Meta Reinforcement Learning using Learned Objectives
2019 · Kirsch, Louis, van Steenkiste, Sjoerd, et al. · arXiv (Cornell University) · 59
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Modular Networks: Learning to Decompose Neural Computation
2018 · Kirsch, Louis, Kunze, Julius, et al. · arXiv (Cornell University) · 58
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Mindstorms in Natural Language-Based Societies of Mind
2025 · Mingchen Zhuge, Haozhe Liu, et al. · Computational Visual Media · 24
Meta Learning Backpropagation And Improving It
2020 · Kirsch, Louis, Schmidhuber, Jürgen · arXiv (Cornell University) · 19
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Introducing Symmetries to Black Box Meta Reinforcement Learning
2022 · Louis Kirsch, Sebastian Flennerhag, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 16
Improving Generalization in Meta Reinforcement Learning using Neural Objectives
2020 · Louis Kirsch, Sjoerd van Steenkiste, et al. · arXiv (Cornell University) · 13
General-Purpose In-Context Learning by Meta-Learning Transformers
2022 · Kirsch, Louis, Harrison, James, et al. · arXiv (Cornell University) · 9
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Parameter-Based Value Functions
2020 · Faccio, Francesco, Kirsch, Louis, et al. · arXiv (Cornell University) · 6
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Automating the Search for Artificial Life With Foundation Models
2025 · Akarsh Kumar, Chris Lu, et al. · Artificial Life · 5
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Goal-Conditioned Generators of Deep Policies
2023 · Francesco Faccio, Vincent Herrmann, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 4
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The Benefits of Model-Based Generalization in Reinforcement Learning
2022 · Young, Kenny, Ramesh, Aditya, et al. · arXiv (Cornell University) · 4
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Language Agents as Optimizable Graphs
2024 · Zhuge, Mingchen, Wang, Wenyi, et al. · arXiv (Cornell University) · 3
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Discovering Temporally-Aware Reinforcement Learning Algorithms
2024 · Jackson, Matthew Thomas, Lu, Chris, et al. · arXiv (Cornell University) · 2
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Learning One Abstract Bit at a Time Through Self-invented Experiments Encoded as Neural Networks
2023 · Vincent Herrmann, Louis Kirsch, et al. · Communications in computer and information science · 2
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The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute
2023 · Stanić, Aleksandar, Ashley, Dylan, et al. · arXiv (Cornell University) · 2
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Eliminating Meta Optimization Through Self-Referential Meta Learning
2022 · Kirsch, Louis, Schmidhuber, Jürgen · arXiv (Cornell University) · 2
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Gaussian mean field regularizes by limiting learned information
2019 · Kunze, Julius, Kirsch, Louis, et al. · reroDoc Digital Library · 2
Exploring Through Random Curiosity with General Value Functions
2022 · Aditya Ramesh, Louis Kirsch, et al. · neural information processing systems · 1
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Noisy Information Bottlenecks for Generalization
2018 · Julius Kunze, Louis Kirsch, et al. · 1
Framework for Exploring and Understanding Multivariate Correlations
2017 · Louis Kirsch, Niklas Riekenbrauck, et al. · Lecture notes in computer science · 1
Training AI Scientists to Replicate Research
2026 · Falck, Damon, Sabri, Samer, et al. · arXiv (Cornell University)
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Publisher’s Note: “Erratum: ‘Brain-inspired learning in artificial neural networks: A review’ [APL Mach. Learn. 2, 021501 (2024)]” [APL Mach. Learn. 3, 039901 (2025)]
2026 · Samuel Schmidgall, Rojin Ziaei, et al. · APL Machine Learning
Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens
2024 · Anwar, Usman, Von Oswald, Johannes, et al. · arXiv (Cornell University)
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Works count: 30
Citation count: 506
H-index: 9
I10-index: 8
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