João Sacramento

Orcid: 0000-0002-2837-9695

According to our database¹, João Sacramento authored at least 28 papers between 2011 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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PhD thesis

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On csauthors.net:

Bibliography

2023

Discovering modular solutions that generalize compositionally.

[BibT_eX]

[DOI]

CoRR, 2023

Uncovering mesa-optimization algorithms in Transformers.

[BibT_eX]

[DOI]

Blaise Agüera y Arcas

Max Vladymyrov

Razvan Pascanu

João Sacramento

CoRR, 2023

Gated recurrent neural networks discover attention.

[BibT_eX]

[DOI]

CoRR, 2023

Online learning of long-range dependencies.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Transformers Learn In-Context by Gradient Descent.

[BibT_eX]

[DOI]

Alexander Mordvintsev

Andrey Zhmoginov

Max Vladymyrov

Proceedings of the International Conference on Machine Learning, 2023

2022

Beyond Backpropagation: Bilevel Optimization Through Implicit Differentiation and Equilibrium Propagation.

[BibT_eX]

[DOI]

Nicolas Zucchet

João Sacramento

Neural Comput., 2022

The least-control principle for learning at equilibrium.

[BibT_eX]

[DOI]

CoRR, 2022

Beyond backpropagation: implicit gradients for bilevel optimization.

[BibT_eX]

[DOI]

Nicolas Zucchet

João Sacramento

CoRR, 2022

A contrastive rule for meta-learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The least-control principle for local learning at equilibrium.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Minimizing Control for Credit Assignment with Strong Feedback.

[BibT_eX]

[DOI]

Alexander Meulemans

Matilde Tristany Farinha

Maria R. Cervera

João Sacramento

Benjamin F. Grewe

Proceedings of the International Conference on Machine Learning, 2022

2021

Learning where to learn: Gradient sparsity in meta and continual learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Credit Assignment in Neural Networks through Deep Feedback Control.

[BibT_eX]

[DOI]

Alexander Meulemans

Matilde Tristany Farinha

Javier García Ordóñez

Pau Vilimelis Aceituno

João Sacramento

Benjamin F. Grewe

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Posterior Meta-Replay for Continual Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural networks with late-phase weights.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Economical ensembles with hypernetworks.

[BibT_eX]

[DOI]

CoRR, 2020

A Theoretical Framework for Target Propagation.

[BibT_eX]

[DOI]

Alexander Meulemans

Francesco S. Carzaniga

Johan A. K. Suykens

João Sacramento

Benjamin F. Grewe

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Conductance-based dendrites perform reliability-weighted opinion pooling.

[BibT_eX]

[DOI]

Proceedings of the NICE '20: Neuro-inspired Computational Elements Workshop, 2020

Continual learning with hypernetworks.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

2018

Dendritic error backpropagation in deep cortical microcircuits.

[BibT_eX]

[DOI]

CoRR, 2018

Dendritic cortical microcircuits approximate the backpropagation algorithm.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2016

Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible.

[BibT_eX]

[DOI]

CoRR, 2016

2015

Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules.

[BibT_eX]

[DOI]

João Sacramento

Andreas Wichert

Mark C. W. van Rossum

PLoS Comput. Biol., 2015

2014

Taxonomical Associative Memory.

[BibT_eX]

[DOI]

Diogo Rendeiro

João Sacramento

Andreas Wichert

Cogn. Comput., 2014

2012

Regarding the temporal requirements of a hierarchical Willshaw network.

[BibT_eX]

[DOI]

João Sacramento

Francisco Burnay

Andreas Wichert

Neural Networks, 2012

Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory.

[BibT_eX]

[DOI]

João Sacramento

Andreas Wichert

Biol. Cybern., 2012

2011

Tree-like hierarchical associative memory structures.

[BibT_eX]

[DOI]

João Sacramento

Andreas Wichert

Neural Networks, 2011

João Sacramento

Timeline

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