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2020 – today
- 2024
[c14]Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wolczyk, Alexandra Proca, Johannes von Oswald, Razvan Pascanu, João Sacramento, Angelika Steger:
Discovering modular solutions that generalize compositionally. ICLR 2024- [i22]Simon Schug, Seijin Kobayashi, Yassir Akram, João Sacramento, Razvan Pascanu:
Attention as a Hypernetwork. CoRR abs/2406.05816 (2024) - [i21]Maciej Pióro, Maciej Wolczyk, Razvan Pascanu, Johannes von Oswald, João Sacramento:
State Soup: In-Context Skill Learning, Retrieval and Mixing. CoRR abs/2406.08423 (2024) - [i20]Seijin Kobayashi, Simon Schug, Yassir Akram, Florian Redhardt, Johannes von Oswald, Razvan Pascanu, Guillaume Lajoie, João Sacramento:
When can transformers compositionally generalize in-context? CoRR abs/2407.12275 (2024) - 2023
- [c13]Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov:
Transformers Learn In-Context by Gradient Descent. ICML 2023: 35151-35174 - [c12]Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika, João Sacramento:
Online learning of long-range dependencies. NeurIPS 2023 - [i19]Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika, João Sacramento:
Online learning of long-range dependencies. CoRR abs/2305.15947 (2023) - [i18]Nicolas Zucchet, Seijin Kobayashi, Yassir Akram, Johannes von Oswald, Maxime Larcher, Angelika Steger, João Sacramento:
Gated recurrent neural networks discover attention. CoRR abs/2309.01775 (2023) - [i17]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:
Uncovering mesa-optimization algorithms in Transformers. CoRR abs/2309.05858 (2023) - [i16]Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wolczyk, Alexandra Proca, Johannes von Oswald, Razvan Pascanu, João Sacramento, Angelika Steger:
Discovering modular solutions that generalize compositionally. CoRR abs/2312.15001 (2023) - 2022
- [j6]Nicolas Zucchet, João Sacramento:
Beyond Backpropagation: Bilevel Optimization Through Implicit Differentiation and Equilibrium Propagation. Neural Comput. 34(12): 2309-2346 (2022) - [c11]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 - [c10]Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento:
The least-control principle for local learning at equilibrium. NeurIPS 2022 - [c9]Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento:
A contrastive rule for meta-learning. NeurIPS 2022 - [i15]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) - [i14]Nicolas Zucchet, João Sacramento:
Beyond backpropagation: implicit gradients for bilevel optimization. CoRR abs/2205.03076 (2022) - [i13]Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento:
The least-control principle for learning at equilibrium. CoRR abs/2207.01332 (2022) - [i12]Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov:
Transformers learn in-context by gradient descent. CoRR abs/2212.07677 (2022) - 2021
- [c8]Johannes von Oswald, Seijin Kobayashi, João Sacramento, Alexander Meulemans, Christian Henning, Benjamin F. Grewe:
Neural networks with late-phase weights. ICLR 2021 - [c7]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 - [c6]Johannes von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento:
Learning where to learn: Gradient sparsity in meta and continual learning. NeurIPS 2021: 5250-5263 - [c5]Christian Henning, Maria R. Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F. Grewe, João Sacramento:
Posterior Meta-Replay for Continual Learning. NeurIPS 2021: 14135-14149 - [i11]Christian Henning, Maria R. Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, João Sacramento, Benjamin F. Grewe:
Posterior Meta-Replay for Continual Learning. CoRR abs/2103.01133 (2021) - [i10]Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento:
A contrastive rule for meta-learning. CoRR abs/2104.01677 (2021) - [i9]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) - [i8]Johannes von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento:
Learning where to learn: Gradient sparsity in meta and continual learning. CoRR abs/2110.14402 (2021) - 2020
- [c4]Johannes von Oswald, Christian Henning, João Sacramento, Benjamin F. Grewe:
Continual learning with hypernetworks. ICLR 2020 - [c3]Jakob Jordan, Mihai A. Petrovici, Walter Senn, João Sacramento:
Conductance-based dendrites perform reliability-weighted opinion pooling. NICE 2020: 11:1-11:3 - [c2]Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe:
A Theoretical Framework for Target Propagation. NeurIPS 2020 - [i7]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) - [i6]João Sacramento, Johannes von Oswald, Seijin Kobayashi, Christian Henning, Benjamin F. Grewe:
Economical ensembles with hypernetworks. CoRR abs/2007.12927 (2020)
2010 – 2019
- 2019
- [i5]Johannes von Oswald, Christian Henning, João Sacramento, Benjamin F. Grewe:
Continual learning with hypernetworks. CoRR abs/1906.00695 (2019) - [i4]Thomas Mesnard, Gaëtan Vignoud, João Sacramento, Walter Senn, Yoshua Bengio:
Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks. CoRR abs/1911.08585 (2019) - 2018
- [c1]João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn:
Dendritic cortical microcircuits approximate the backpropagation algorithm. NeurIPS 2018: 8735-8746 - [i3]João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn:
Dendritic error backpropagation in deep cortical microcircuits. CoRR abs/1801.00062 (2018) - [i2]João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn:
Dendritic cortical microcircuits approximate the backpropagation algorithm. CoRR abs/1810.11393 (2018) - 2016
- [i1]Yoshua Bengio, Benjamin Scellier, Olexa Bilaniuk, João Sacramento, Walter Senn:
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible. CoRR abs/1606.01651 (2016) - 2015
- [j5]João Sacramento, Andreas Wichert, Mark C. W. van Rossum:
Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules. PLoS Comput. Biol. 11(6) (2015) - 2014
- [j4]Diogo Rendeiro, João Sacramento, Andreas Wichert:
Taxonomical Associative Memory. Cogn. Comput. 6(1): 45-65 (2014) - 2012
- [j3]João Sacramento, Andreas Wichert:
Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory. Biol. Cybern. 106(2): 123-133 (2012) - [j2]João Sacramento, Francisco Burnay, Andreas Wichert:
Regarding the temporal requirements of a hierarchical Willshaw network. Neural Networks 25: 84-93 (2012) - 2011
- [j1]João Sacramento, Andreas Wichert:
Tree-like hierarchical associative memory structures. Neural Networks 24(2): 143-147 (2011)
Coauthor Index
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