708 citations · 2.8k across the 48 of their papers we have counts for
3 papers · 1 filter
Conditionally Strongly Log-Concave Generative Models
Florentin Guth, Etienne Lempereur, Joan Bruna +1
There is a growing gap between the impressive results of deep image generative models and classical algorithms that offer theoretical guarantees. The former suffer from mode collap…
Particle gradient descent model for point process generation
Antoine Brochard, Bartłomiej Błaszczyszyn, Stéphane Mallat +1
This paper presents a statistical model for stationary ergodic point processes, estimated from a single realization observed in a square window. With existing approaches in stochas…
Understanding Deep Convolutional Networks
Stéphane Mallat
Deep convolutional networks provide state of the art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data…