3 papers
cs.LG2026
Accelerated Gradient Descent for Faster Convergence with Minimal Overhead
Manuel Graca, L. Miguel Silveira, Arlindo Oliveira +1
In this paper, we present CT-AGD (Curvature-Tuned Accelerated Gradient Descent), an optimization method for non-convex optimization problems in deep learning training tasks. CT-AGD…
cs.CV2025
Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness
Lucas Piper, Arlindo L. Oliveira, Tiago Marques
Convolutional neural networks (CNNs) trained on object recognition achieve high task performance but continue to exhibit vulnerability under a range of visual perturbations and out…
cs.CV2024
Explicitly Modeling Pre-Cortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness
Lucas Piper, Arlindo L. Oliveira, Tiago Marques
While convolutional neural networks (CNNs) excel at clean image classification, they struggle to classify images corrupted with different common corruptions, limiting their real-wo…