Tree-like hierarchical associative memory structures. - João Sacramento; Andreas Wichert (Neural Networks, 2011)

Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory. - João Sacramento; Andreas Wichert (Biol. Cybern., 2012)

Regarding the temporal requirements of a hierarchical Willshaw network. - João Sacramento; Francisco Burnay; Andreas Wichert (Neural Networks, 2012)

Taxonomical Associative Memory. - Diogo Rendeiro; João Sacramento; Andreas Wichert (Cogn. Comput., 2014)

Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules. - João Sacramento; Andreas Wichert; Mark C. W. van Rossum (PLoS Comput. Biol., 2015)

Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible. - Yoshua Bengio; Benjamin Scellier; Olexa Bilaniuk; João Sacramento; Walter Senn (CoRR, 2016)

Dendritic cortical microcircuits approximate the backpropagation algorithm. - João Sacramento; Rui Ponte Costa; Yoshua Bengio; Walter Senn (NeurIPS, 2018)

Dendritic error backpropagation in deep cortical microcircuits. - João Sacramento; Rui Ponte Costa; Yoshua Bengio; Walter Senn (CoRR, 2018)

Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks. - Thomas Mesnard; Gaëtan Vignoud; João Sacramento; Walter Senn; Yoshua Bengio (CoRR, 2019)

Continual learning with hypernetworks. - Johannes von Oswald; Christian Henning; João Sacramento; Benjamin F. Grewe (ICLR, 2020)

Conductance-based dendrites perform reliability-weighted opinion pooling. - Jakob Jordan; Mihai A. Petrovici; Walter Senn; João Sacramento (NICE, 2020)

A Theoretical Framework for Target Propagation. - Alexander Meulemans; Francesco S. Carzaniga; Johan A. K. Suykens; João Sacramento; Benjamin F. Grewe (NeurIPS, 2020)

Economical ensembles with hypernetworks. - João Sacramento; Johannes von Oswald; Seijin Kobayashi; Christian Henning; Benjamin F. Grewe (CoRR, 2020)

Neural networks with late-phase weights. - Johannes von Oswald; Seijin Kobayashi; João Sacramento; Alexander Meulemans; Christian Henning; Benjamin F. Grewe (ICLR, 2021)

Posterior Meta-Replay for Continual Learning. - Christian Henning; Maria R. Cervera; Francesco D'Angelo; Johannes von Oswald; Regina Traber; Benjamin Ehret; Seijin Kobayashi; Benjamin F. Grewe; João Sacramento (NeurIPS, 2021)

Credit Assignment in Neural Networks through Deep Feedback Control. - Alexander Meulemans; Matilde Tristany Farinha; Javier García Ordóñez; Pau Vilimelis Aceituno; João Sacramento; Benjamin F. Grewe (NeurIPS, 2021)

Learning where to learn: Gradient sparsity in meta and continual learning. - Johannes von Oswald; Dominic Zhao; Seijin Kobayashi; Simon Schug; Massimo Caccia; Nicolas Zucchet; João Sacramento (NeurIPS, 2021)

Minimizing Control for Credit Assignment with Strong Feedback. - Alexander Meulemans; Matilde Tristany Farinha; Maria R. Cervera; João Sacramento; Benjamin F. Grewe (ICML, 2022)

The least-control principle for local learning at equilibrium. - Alexander Meulemans; Nicolas Zucchet; Seijin Kobayashi; Johannes von Oswald; João Sacramento (NeurIPS, 2022)

A contrastive rule for meta-learning. - Nicolas Zucchet; Simon Schug; Johannes von Oswald; Dominic Zhao; João Sacramento (NeurIPS, 2022)

Beyond backpropagation: implicit gradients for bilevel optimization. - Nicolas Zucchet; João Sacramento (CoRR, 2022)

The least-control principle for learning at equilibrium. - Alexander Meulemans; Nicolas Zucchet; Seijin Kobayashi; Johannes von Oswald; João Sacramento (CoRR, 2022)

Beyond Backpropagation: Bilevel Optimization Through Implicit Differentiation and Equilibrium Propagation. - Nicolas Zucchet; João Sacramento (Neural Comput., 2022)

Transformers Learn In-Context by Gradient Descent. - Johannes von Oswald; Eyvind Niklasson; Ettore Randazzo; João Sacramento; Alexander Mordvintsev; Andrey Zhmoginov; Max Vladymyrov (ICML, 2023)

Online learning of long-range dependencies. - Nicolas Zucchet; Robert Meier; Simon Schug; Asier Mujika; João Sacramento (NeurIPS, 2023)

Gated recurrent neural networks discover attention. - Nicolas Zucchet; Seijin Kobayashi; Yassir Akram; Johannes von Oswald; Maxime Larcher; Angelika Steger; João Sacramento (CoRR, 2023)

Uncovering mesa-optimization algorithms in Transformers. - Johannes von Oswald; Eyvind Niklasson; Maximilian Schlegel; Seijin Kobayashi; Nicolas Zucchet; Nino Scherrer; Nolan Miller; Mark Sandler 002; Blaise Agüera y Arcas; Max Vladymyrov; Razvan Pascanu; João Sacramento (CoRR, 2023)

Discovering modular solutions that generalize compositionally. - Simon Schug; Seijin Kobayashi; Yassir Akram; Maciej Wolczyk; Alexandra Proca; Johannes von Oswald; Razvan Pascanu; João Sacramento; Angelika Steger (ICLR, 2024)

State Soup: In-Context Skill Learning, Retrieval and Mixing. - Maciej Pióro; Maciej Wolczyk; Razvan Pascanu; Johannes von Oswald; João Sacramento (CoRR, 2024)

When can transformers compositionally generalize in-context? - Seijin Kobayashi; Simon Schug; Yassir Akram; Florian Redhardt; Johannes von Oswald; Razvan Pascanu; Guillaume Lajoie; João Sacramento (CoRR, 2024)

Conductance-based dendrites perform Bayes-optimal cue integration. - Jakob Jordan; João Sacramento; Willem A. M. Wybo; Mihai A. Petrovici; Walter Senn (PLoS Comput. Biol., 2024)

Multi-agent cooperation through learning-aware policy gradients. - Alexander Meulemans; Seijin Kobayashi; Johannes von Oswald; Nino Scherrer; Eric Elmoznino; Blake Aaron Richards; Guillaume Lajoie; Blaise Agüera y Arcas; João Sacramento (ICLR, 2025)

Attention as a Hypernetwork. - Simon Schug; Seijin Kobayashi; Yassir Akram; João Sacramento; Razvan Pascanu (ICLR, 2025)

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training. - Johannes von Oswald; Nino Scherrer; Seijin Kobayashi; Luca Versari; Songlin Yang; Maximilian Schlegel; Kaitlin Maile; Yanick Schimpf; Oliver Sieberling; Alexander Meulemans; Rif A. Saurous; Guillaume Lajoie; et al. (CoRR, 2025)