17 citations · 92 across the 25 of their papers we have counts for
5 papers · 1 filter
Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations
Gabriëlle Ras, Luca Ambrogioni, Pim Haselager +2
In this paper we introduce the temporally factorized 3D convolution (3TConv) as an interpretable alternative to the regular 3D convolution (3DConv). In a 3TConv the 3D convolutiona…
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni
Traditional backpropagation of error, though a highly successful algorithm for learning in artificial neural network models, includes features which are biologically implausible fo…
Overcoming the Weight Transport Problem via Spike-Timing-Dependent Weight Inference
Nasir Ahmad, Luca Ambrogioni, Marcel A. J. van Gerven
We propose a solution to the weight transport problem, which questions the biological plausibility of the backpropagation algorithm. We derive our method based upon a theoretical a…
Automatic structured variational inference
Luca Ambrogioni, Kate Lin, Emily Fertig +4
Stochastic variational inference offers an attractive option as a default method for differentiable probabilistic programming. However, the performance of the variational approach…
The Indian Chefs Process
Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier +6
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes India…