5 papers
Machine Learning Methods as Robust Quantum Noise Estimators
Jon Gardeazabal-Gutierrez, Erik B. Terres-Escudero, Pablo García Bringas
Access to quantum computing is steadily increasing each year as the speed advantage of quantum computers solidifies with the growing number of usable qubits. However, the inherent…
On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization
Erik B. Terres-Escudero, Javier Del Ser, Pablo Garcia-Bringas
Forward-only learning algorithms have recently gained attention as alternatives to gradient backpropagation, replacing the backward step of this latter solver with an additional co…
A Contrastive Symmetric Forward-Forward Algorithm (SFFA) for Continual Learning Tasks
Erik B. Terres-Escudero, Javier Del Ser, Pablo Garcia Bringas
The so-called Forward-Forward Algorithm (FFA) has recently gained momentum as an alternative to the conventional back-propagation algorithm for neural network learning, yielding co…
Forward-Forward Learning achieves Highly Selective Latent Representations for Out-of-Distribution Detection in Fully Spiking Neural Networks
Erik B. Terres-Escudero, Javier Del Ser, Aitor Martínez-Seras +1
In recent years, Artificial Intelligence (AI) models have achieved remarkable success across various domains, yet challenges persist in two critical areas: ensuring robustness agai…
Emerging NeoHebbian Dynamics in Forward-Forward Learning: Implications for Neuromorphic Computing
Erik B. Terres-Escudero, Javier Del Ser, Pablo García-Bringas
Advances in neural computation have predominantly relied on the gradient backpropagation algorithm (BP). However, the recent shift towards non-stationary data modeling has highligh…