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Alexander Meulemans
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2020 – today
- 2024
- [j1]Gaspard Oliviers, Rafal Bogacz, Alexander Meulemans:
Learning probability distributions of sensory inputs with Monte Carlo predictive coding. PLoS Comput. Biol. 20(10): 1012532 (2024) - [c9]Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra:
Learning to Extract Structured Entities Using Language Models. EMNLP 2024: 6817-6834 - [i9]Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra:
Structured Entity Extraction Using Large Language Models. CoRR abs/2402.04437 (2024) - [i8]Alexander Meulemans, Seijin Kobayashi, Johannes von Oswald, Nino Scherrer, Eric Elmoznino, Blake A. Richards, Guillaume Lajoie, Blaise Agüera y Arcas, João Sacramento:
Multi-agent cooperation through learning-aware policy gradients. CoRR abs/2410.18636 (2024) - 2023
- [c8]Alexander Meulemans, Simon Schug, Seijin Kobayashi, Nathaniel Daw, Gregory Wayne:
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis. NeurIPS 2023 - [i7]Alexander Meulemans, Simon Schug, Seijin Kobayashi, Nathaniel Daw, Gregory Wayne:
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis. CoRR abs/2306.16803 (2023) - 2022
- [c7]Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera, João Sacramento, Benjamin F. Grewe:
Minimizing Control for Credit Assignment with Strong Feedback. ICML 2022: 15458-15483 - [c6]Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento:
The least-control principle for local learning at equilibrium. NeurIPS 2022 - [i6]Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera, João Sacramento, Benjamin F. Grewe:
Minimizing Control for Credit Assignment with Strong Feedback. CoRR abs/2204.07249 (2022) - [i5]Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento:
The least-control principle for learning at equilibrium. CoRR abs/2207.01332 (2022) - 2021
- [c5]David Lindner, Kyle Matoba, Alexander Meulemans:
Challenges for Using Impact Regularizers to Avoid Negative Side Effects. SafeAI@AAAI 2021 - [c4]Benjamin Ehret, Christian Henning, Maria R. Cervera, Alexander Meulemans, Johannes von Oswald, Benjamin F. Grewe:
Continual learning in recurrent neural networks. ICLR 2021 - [c3]Johannes von Oswald, Seijin Kobayashi, João Sacramento, Alexander Meulemans, Christian Henning, Benjamin F. Grewe:
Neural networks with late-phase weights. ICLR 2021 - [c2]Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe:
Credit Assignment in Neural Networks through Deep Feedback Control. NeurIPS 2021: 4674-4687 - [i4]David Lindner, Kyle Matoba, Alexander Meulemans:
Challenges for Using Impact Regularizers to Avoid Negative Side Effects. CoRR abs/2101.12509 (2021) - [i3]Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe:
Credit Assignment in Neural Networks through Deep Feedback Control. CoRR abs/2106.07887 (2021) - 2020
- [c1]Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe:
A Theoretical Framework for Target Propagation. NeurIPS 2020 - [i2]Benjamin Ehret, Christian Henning, Maria R. Cervera, Alexander Meulemans, Johannes von Oswald, Benjamin F. Grewe:
Continual Learning in Recurrent Neural Networks with Hypernetworks. CoRR abs/2006.12109 (2020) - [i1]Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe:
A Theoretical Framework for Target Propagation. CoRR abs/2006.14331 (2020)
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