Sacramento, João

A deep learning framework for neuroscience

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., et al. (2019) · Nature Neuroscience

Neural Networks and ApplicationsNeural dynamics and brain function

Cited by 1,027

Transformers learn in-context by gradient descent

von Oswald, J., Niklasson, E., Randazzo, E., Sacramento, J., Mordvintsev, A., Zhmoginov, A., & Vladymyrov, M. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 87

Uncovering mesa-optimization algorithms in Transformers

von Oswald, J., Schlegel, M., Meulemans, A., Kobayashi, S., Niklasson, E., Zucchet, N., Scherrer, N., Miller, N., Sandler, M., Arcas, B. A. Y., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 5

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training

von Oswald, J., Scherrer, N., Kobayashi, S., Versari, L., Yang, S., Mittal, S., Schlegel, M., Maile, K., Schimpf, Y., Sieberling, O., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

State Soup: In-Context Skill Learning, Retrieval and Mixing

Pióro, M., Wołczyk, M., Pascanu, R., von Oswald, J., & Sacramento, J. (2024) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 0