172 citations · 189 across the 8 of their papers we have counts for
4 papers · 1 filter
Adversarial Attacks on the Interpretation of Neuron Activation Maximization
Geraldin Nanfack, Alexander Fulleringer, Jonathan Marty +2
The internal functional behavior of trained Deep Neural Networks is notoriously difficult to interpret. Activation-maximization approaches are one set of techniques used to interpr…
Can Forward Gradient Match Backpropagation?
Louis Fournier, Stéphane Rivaud, Eugene Belilovsky +2
Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable for neural network training while avoiding…
Local Learning with Neuron Groups
Adeetya Patel, Michael Eickenberg, Eugene Belilovsky
Traditional deep network training methods optimize a monolithic objective function jointly for all the components. This can lead to various inefficiencies in terms of potential par…
Machine Learning for Neuroimaging with Scikit-Learn
Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg +6
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate…