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On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data
Aitor Martinez-Seras, Javier Del Ser, Aitzol Olivares-Rad +2
Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capabilit…
Resilience to the Flowing Unknown: an Open Set Recognition Framework for Data Streams
Marcos Barcina-Blanco, Jesus L. Lobo, Pablo Garcia-Bringas +1
Modern digital applications extensively integrate Artificial Intelligence models into their core systems, offering significant advantages for automated decision-making. However, th…
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…
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…