3 citations · 3 across the 5 of their papers we have counts for
6 papers · 1 filter
The Ups and Downs of Backprop Weights
Giuseppe Chindemi, Benjamin F. Grewe
Backpropagation (BP) has driven the remarkable success of modern deep learning by enabling large hierarchical networks to learn complex functions end-to-end. Yet it does not by its…
A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs
Nicolas Stalder, Benjamin F. Grewe, Matteo Saponati +1
The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment. Existing techniques to enhance deep network robustness re…
Learning Compositional Latent Structure with Vector Networks
Niclas Pokel, Benjamin F. Grewe
Deep networks are powerful function approximators, but they typically store many different computations in shared weight matrices, making it difficult to selectively reuse or adapt…
Continual Learning through Control Minimization
Sander de Haan, Yassine Taoudi-Benchekroun, Pau Vilimelis Aceituno +1
Catastrophic forgetting remains a fundamental challenge for neural networks when tasks are trained sequentially. In this work, we reformulate continual learning as a control proble…
Guided Transfer Learning for Discrete Diffusion Models
Julian Kleutgens, Claudio Battiloro, Lingkai Kong +3
Discrete diffusion models (DMs) have achieved strong performance in language and other discrete domains, offering a compelling alternative to autoregressive modeling. Yet this perf…
Intrinsic Biologically Plausible Adversarial Robustness
Matilde Tristany Farinha, Thomas Ortner, Giorgia Dellaferrera +2
Artificial Neural Networks (ANNs) trained with Backpropagation (BP) excel in different daily tasks but have a dangerous vulnerability: inputs with small targeted perturbations, als…