no code implementations • 26 May 2023 • Fabio Anselmi, Mauro Castelli, Alberto D'Onofrio, Luca Manzoni, Luca Mariot, Martina Saletta
In recent years, a new mutation operator, Geometric Semantic Mutation with Local Search (GSM-LS), has been proposed to include a local search step in the mutation process based on the idea that performing a linear regression during the mutation can allow for a faster convergence to good-quality solutions.
1 code implementation • 24 May 2023 • Lorenzo Basile, Nikos Karantzas, Alberto D'Onofrio, Luca Bortolussi, Alex Rodriguez, Fabio Anselmi
Despite their impressive performance in classification, neural networks are known to be vulnerable to adversarial attacks.
1 code implementation • 16 Mar 2022 • Nikos Karantzas, Emma Besier, Josue Ortega Caro, Xaq Pitkow, Andreas S. Tolias, Ankit B. Patel, Fabio Anselmi
Our results also indicate that the essential frequencies in question are effectively the ones used to achieve generalization in the first place.
no code implementations • 19 Jun 2020 • Josue Ortega Caro, Yilong Ju, Ryan Pyle, Sourav Dey, Wieland Brendel, Fabio Anselmi, Ankit Patel
Inspired by theoretical work on linear full-width convolutional models, we hypothesize that the local (i. e. bounded-width) convolutional operations commonly used in current neural networks are implicitly biased to learn high frequency features, and that this is one of the root causes of high frequency adversarial examples.
no code implementations • 18 Feb 2019 • Fabio Anselmi, Micah M. Murray, Benedetta Franceschiello
Grid cells in the entorhinal cortex, together with head direction, place, speed and border cells, are major contributors to the organization of spatial representations in the brain.
no code implementations • 5 Jun 2016 • Joel Z. Leibo, Qianli Liao, Winrich Freiwald, Fabio Anselmi, Tomaso Poggio
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving transformations like depth-rotations.
no code implementations • 5 Aug 2015 • Fabio Anselmi, Lorenzo Rosasco, Cheston Tan, Tomaso Poggio
In i-theory a typical layer of a hierarchical architecture consists of HW modules pooling the dot products of the inputs to the layer with the transformations of a few templates under a group.
no code implementations • 19 Mar 2015 • Fabio Anselmi, Lorenzo Rosasco, Tomaso Poggio
We discuss data representation which can be learned automatically from data, are invariant to transformations, and at the same time selective, in the sense that two points have the same representation only if they are one the transformation of the other.
no code implementations • 17 Nov 2013 • Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso Poggio
It also suggests that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and discriminative for recognition---and that this representation may be continuously learned in an unsupervised way during development and visual experience.