48 citations · 101 across the 11 of their papers we have counts for
4 papers · 1 filter
Deep MMD Gradient Flow without adversarial training
Alexandre Galashov, Valentin de Bortoli, Arthur Gretton
We propose a gradient flow procedure for generative modeling by transporting particles from an initial source distribution to a target distribution, where the gradient field on the…
Dynamical Regimes of Diffusion Models
Giulio Biroli, Tony Bonnaire, Valentin de Bortoli +1
Using statistical physics methods, we study generative diffusion models in the regime where the dimension of space and the number of data are large, and the score function has been…
Target Score Matching
Valentin De Bortoli, Michael Hutchinson, Peter Wirnsberger +1
Denoising Score Matching estimates the score of a noised version of a target distribution by minimizing a regression loss and is widely used to train the popular class of Denoising…
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
Pierre Marion, Anna Korba, Peter Bartlett +6
We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions. Doing so allows us to modify the outcome distribution of sampling pr…