15 citations · 28 across the 9 of their papers we have counts for
15 papers
Knowledge is reward: Learning optimal exploration by predictive reward cashing
Luca Ambrogioni
There is a strong link between the general concept of intelligence and the ability to collect and use information. The theory of Bayes-adaptive exploration offers an attractive opt…
Automatic variational inference with cascading flows
Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven
The automation of probabilistic reasoning is one of the primary aims of machine learning. Recently, the confluence of variational inference and deep learning has led to powerful an…
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…
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…