50 citations · 223 across the 27 of their papers we have counts for
5 papers · 1 filter
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks
Carles Domingo-Enrich, Alberto Bietti, Marylou Gabrié +2
Energy-based models (EBMs) are generative models that are usually trained via maximum likelihood estimation. This approach becomes challenging in generic situations where the train…
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…
On the Universality of Graph Neural Networks on Large Random Graphs
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
We study the approximation power of Graph Neural Networks (GNNs) on latent position random graphs. In the large graph limit, GNNs are known to converge to certain "continuous" mode…
On Energy-Based Models with Overparametrized Shallow Neural Networks
Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden +1
Energy-based models (EBMs) are a simple yet powerful framework for generative modeling. They are based on a trainable energy function which defines an associated Gibbs measure, and…
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective
Alberto Bietti
The empirical success of deep convolutional networks on tasks involving high-dimensional data such as images or audio suggests that they can efficiently approximate certain functio…