10 citations · 14 across the 3 of their papers we have counts for
5 papers
Learning Stochastic Optimal Policies via Gradient Descent
Stefano Massaroli, Michael Poli, Stefano Peluchetti +3
We systematically develop a learning-based treatment of stochastic optimal control (SOC), relying on direct optimization of parametric control policies. We propose a derivation of…
A Bayesian nonparametric approach to count-min sketch under power-law data streams
Emanuele Dolera, Stefano Favaro, Stefano Peluchetti
The count-min sketch (CMS) is a randomized data structure that provides estimates of tokens' frequencies in a large data stream using a compressed representation of the data by ran…
Stable behaviour of infinitely wide deep neural networks
Stefano Favaro, Sandra Fortini, Stefano Peluchetti
We consider fully connected feed-forward deep neural networks (NNs) where weights and biases are independent and identically distributed as symmetric centered stable distributions.…
An empirical study of pretrained representations for few-shot classification
Tiago Ramalho, Thierry Sousbie, Stefano Peluchetti
Recent algorithms with state-of-the-art few-shot classification results start their procedure by computing data features output by a large pretrained model. In this paper we system…
Infinitely deep neural networks as diffusion processes
Stefano Peluchetti, Stefano Favaro
When the parameters are independently and identically distributed (initialized) neural networks exhibit undesirable properties that emerge as the number of layers increases, e.g. a…