João Sacramento

51 papers A* 15Journal 35Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Multi-agent cooperation through in-context co-player inference. CoRR Marissa A. Weis, Maciej Wolczyk, Rajai Nasser, Rif A. Saurous, Blaise Agüera y Arcas, João Sacramento, Alexander Meulemans
2025	A*	`conf`	Attention as a Hypernetwork. ICLR Simon Schug, Seijin Kobayashi, Yassir Akram, João Sacramento, Razvan Pascanu
2025	J	`jnl`	Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning. CoRR Alexander Meulemans, Rajai Nasser, Maciej Wolczyk, Marissa A. Weis, Seijin Kobayashi, Blake A. Richards, Guillaume Lajoie, Angelika Steger, Marcus Hutter, James Manyika, Rif A. Saurous, João Sacramento, Blaise Agüera y Arcas
2025	J	`jnl`	Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning. CoRR Seijin Kobayashi, Yanick Schimpf, Maximilian Schlegel, Angelika Steger, Maciej Wolczyk, Johannes von Oswald, Nino Scherrer, Kaitlin Maile, Guillaume Lajoie, Blake A. Richards, Rif A. Saurous, James Manyika, Blaise Agüera y Arcas, Alexander Meulemans, João Sacramento
2025	J	`jnl`	MesaNet: Sequence Modeling by Locally Optimal Test-Time Training. CoRR 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, Charlotte Frenkel, Razvan Pascanu, Blaise Agüera y Arcas, João Sacramento
2025	A*	`conf`	Multi-agent cooperation through learning-aware policy gradients. ICLR Alexander Meulemans, Seijin Kobayashi, Johannes von Oswald, Nino Scherrer, Eric Elmoznino, Blake Aaron Richards, Guillaume Lajoie, Blaise Agüera y Arcas, João Sacramento
2025	J	`jnl`	Teaching signal synchronization in deep neural networks with prospective neurons. CoRR Nicolas Zucchet, Qianqian Feng, Axel Laborieux, Friedemann Zenke, Walter Senn, João Sacramento
2024	J	`jnl`	Attention as a Hypernetwork. CoRR Simon Schug, Seijin Kobayashi, Yassir Akram, João Sacramento, Razvan Pascanu
2024	J	`jnl`	Conductance-based dendrites perform Bayes-optimal cue integration. PLoS Comput. Biol. Jakob Jordan, João Sacramento, Willem A. M. Wybo, Mihai A. Petrovici, Walter Senn
2024	A*	`conf`	Discovering modular solutions that generalize compositionally. ICLR Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wolczyk, Alexandra Maria Proca, Johannes von Oswald, Razvan Pascanu, João Sacramento, Angelika Steger
2024	J	`jnl`	Multi-agent cooperation through learning-aware policy gradients. CoRR Alexander Meulemans, Seijin Kobayashi, Johannes von Oswald, Nino Scherrer, Eric Elmoznino, Blake A. Richards, Guillaume Lajoie, Blaise Agüera y Arcas, João Sacramento
2024	J	`jnl`	State Soup: In-Context Skill Learning, Retrieval and Mixing. CoRR Maciej Pióro, Maciej Wolczyk, Razvan Pascanu, Johannes von Oswald, João Sacramento
2024	J	`jnl`	When can transformers compositionally generalize in-context? CoRR Seijin Kobayashi, Simon Schug, Yassir Akram, Florian Redhardt, Johannes von Oswald, Razvan Pascanu, Guillaume Lajoie, João Sacramento
2023	J	`jnl`	Discovering modular solutions that generalize compositionally. CoRR Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wolczyk, Alexandra Maria Proca, Johannes von Oswald, Razvan Pascanu, João Sacramento, Angelika Steger
2023	J	`jnl`	Gated recurrent neural networks discover attention. CoRR Nicolas Zucchet, Seijin Kobayashi, Yassir Akram, Johannes von Oswald, Maxime Larcher, Angelika Steger, João Sacramento
2023	A*	`conf`	Online learning of long-range dependencies. NeurIPS Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika, João Sacramento
2023	J	`jnl`	Online learning of long-range dependencies. CoRR Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika, João Sacramento
2023	A*	`conf`	Transformers Learn In-Context by Gradient Descent. ICML Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov
2023	J	`jnl`	Uncovering mesa-optimization algorithms in Transformers. CoRR Johannes von Oswald, Eyvind Niklasson, Maximilian Schlegel, Seijin Kobayashi, Nicolas Zucchet, Nino Scherrer, Nolan Miller, Mark Sandler, Blaise Agüera y Arcas, Max Vladymyrov, Razvan Pascanu, João Sacramento
2022	A*	`conf`	A contrastive rule for meta-learning. NeurIPS Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento
2022	J	`jnl`	Beyond Backpropagation: Bilevel Optimization Through Implicit Differentiation and Equilibrium Propagation. Neural Comput. Nicolas Zucchet, João Sacramento
2022	J	`jnl`	Beyond backpropagation: implicit gradients for bilevel optimization. CoRR Nicolas Zucchet, João Sacramento
2022	A*	`conf`	Minimizing Control for Credit Assignment with Strong Feedback. ICML Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera, João Sacramento, Benjamin F. Grewe
2022	J	`jnl`	Minimizing Control for Credit Assignment with Strong Feedback. CoRR Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera, João Sacramento, Benjamin F. Grewe
2022	J	`jnl`	The least-control principle for learning at equilibrium. CoRR Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento
2022	A*	`conf`	The least-control principle for local learning at equilibrium. NeurIPS Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento
2022	J	`jnl`	Transformers learn in-context by gradient descent. CoRR Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov
2021	J	`jnl`	A contrastive rule for meta-learning. CoRR Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento
2021	A*	`conf`	Credit Assignment in Neural Networks through Deep Feedback Control. NeurIPS Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe
2021	J	`jnl`	Credit Assignment in Neural Networks through Deep Feedback Control. CoRR Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe
2021	A*	`conf`	Learning where to learn: Gradient sparsity in meta and continual learning. NeurIPS Johannes von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento
2021	J	`jnl`	Learning where to learn: Gradient sparsity in meta and continual learning. CoRR Johannes von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento
2021	A*	`conf`	Neural networks with late-phase weights. ICLR Johannes von Oswald, Seijin Kobayashi, João Sacramento, Alexander Meulemans, Christian Henning, Benjamin F. Grewe
2021	A*	`conf`	Posterior Meta-Replay for Continual Learning. NeurIPS Christian Henning, Maria R. Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F. Grewe, João Sacramento
2021	J	`jnl`	Posterior Meta-Replay for Continual Learning. CoRR Christian Henning, Maria R. Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, João Sacramento, Benjamin F. Grewe
2020	A*	`conf`	A Theoretical Framework for Target Propagation. NeurIPS Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe
2020	J	`jnl`	A Theoretical Framework for Target Propagation. CoRR Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe
2020	—	`conf`	Conductance-based dendrites perform reliability-weighted opinion pooling. NICE Jakob Jordan, Mihai A. Petrovici, Walter Senn, João Sacramento
2020	A*	`conf`	Continual learning with hypernetworks. ICLR Johannes von Oswald, Christian Henning, João Sacramento, Benjamin F. Grewe
2020	J	`jnl`	Economical ensembles with hypernetworks. CoRR João Sacramento, Johannes von Oswald, Seijin Kobayashi, Christian Henning, Benjamin F. Grewe
2019	J	`jnl`	Continual learning with hypernetworks. CoRR Johannes von Oswald, Christian Henning, João Sacramento, Benjamin F. Grewe
2019	J	`jnl`	Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks. CoRR Thomas Mesnard, Gaëtan Vignoud, João Sacramento, Walter Senn, Yoshua Bengio
2018	A*	`conf`	Dendritic cortical microcircuits approximate the backpropagation algorithm. NeurIPS João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn
2018	J	`jnl`	Dendritic cortical microcircuits approximate the backpropagation algorithm. CoRR João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn
2018	J	`jnl`	Dendritic error backpropagation in deep cortical microcircuits. CoRR João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn
2016	J	`jnl`	Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible. CoRR Yoshua Bengio, Benjamin Scellier, Olexa Bilaniuk, João Sacramento, Walter Senn
2015	J	`jnl`	Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules. PLoS Comput. Biol. João Sacramento, Andreas Wichert, Mark C. W. van Rossum
2014	J	`jnl`	Taxonomical Associative Memory. Cogn. Comput. Diogo Rendeiro, João Sacramento, Andreas Wichert
2012	J	`jnl`	Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory. Biol. Cybern. João Sacramento, Andreas Wichert
2012	J	`jnl`	Regarding the temporal requirements of a hierarchical Willshaw network. Neural Networks João Sacramento, Francisco Burnay, Andreas Wichert
2011	J	`jnl`	Tree-like hierarchical associative memory structures. Neural Networks João Sacramento, Andreas Wichert