44 citations · 162 across the 10 of their papers we have counts for
10 papers
On the Sample Complexity of Learning under Invariance and Geometric Stability
Alberto Bietti, Luca Venturi, Joan Bruna
Many supervised learning problems involve high-dimensional data such as images, text, or graphs. In order to make efficient use of data, it is often useful to leverage certain geom…
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems
Terrence Alsup, Luca Venturi, Benjamin Peherstorfer
This work presents a multilevel variant of Stein variational gradient descent to more efficiently sample from target distributions. The key ingredient is a sequence of distribution…
Depth separation beyond radial functions
Luca Venturi, Samy Jelassi, Tristan Ozuch +1
High-dimensional depth separation results for neural networks show that certain functions can be efficiently approximated by two-hidden-layer networks but not by one-hidden-layer o…
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems
Donsub Rim, Luca Venturi, Joan Bruna +1
Classical reduced models are low-rank approximations using a fixed basis designed to achieve dimensionality reduction of large-scale systems. In this work, we introduce reduced dee…
Diffusion-based Deep Active Learning
Dan Kushnir, Luca Venturi
The remarkable performance of deep neural networks depends on the availability of massive labeled data. To alleviate the load of data annotation, active deep learning aims to selec…
Weighted reduced order methods for parametrized partial differential equations with random inputs
Luca Venturi, Davide Torlo, Francesco Ballarin +1
In this manuscript we discuss weighted reduced order methods for stochastic partial differential equations. Random inputs (such as forcing terms, equation coefficients, boundary co…