139 citations · 305 across the 11 of their papers we have counts for
12 papers
Interpreting convolutional neural networks' low dimensional approximation to quantum spin systems
Yilong Ju, Shah Saad Alam, Jonathan Minoff +3
Convolutional neural networks (CNNs) have been employed along with Variational Monte Carlo methods for finding the ground state of quantum many-body spin systems with great success…
Understanding robustness and generalization of artificial neural networks through Fourier masks
Nikos Karantzas, Emma Besier, Josue Ortega Caro +4
Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generaliz…
Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples
Josue Ortega Caro, Yilong Ju, Ryan Pyle +4
Adversarial Attacks are still a significant challenge for neural networks. Recent work has shown that adversarial perturbations typically contain high-frequency features, but the r…
A computational model for grid maps in neural populations
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…
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
Joel Z. Leibo, Qianli Liao, Winrich Freiwald +2
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 rela…
Deep Convolutional Networks are Hierarchical Kernel Machines
Fabio Anselmi, Lorenzo Rosasco, Cheston Tan +1
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 un…