Search Results for author: Pau Vilimelis Aceituno

Found 4 papers, 2 papers with code

Bio-Inspired, Task-Free Continual Learning through Activity Regularization

no code implementations8 Dec 2022 Francesco Lässig, Pau Vilimelis Aceituno, Martino Sorbaro, Benjamin F. Grewe

We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation.

Continual Learning Split-MNIST

Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel

no code implementations18 Oct 2022 Seijin Kobayashi, Pau Vilimelis Aceituno, Johannes von Oswald

Identifying unfamiliar inputs, also known as out-of-distribution (OOD) detection, is a crucial property of any decision making process.

Decision Making Inductive Bias +1

Credit Assignment in Neural Networks through Deep Feedback Control

3 code implementations NeurIPS 2021 Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe

The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output.

Tailoring Artificial Neural Networks for Optimal Learning

1 code implementation8 Jul 2017 Pau Vilimelis Aceituno, Yan Gang, Yang-Yu Liu

As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine, finance, and language processing.

Time Series Analysis

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