Search Results for author:
Found 7 papers, 4 papers with code
Careful with that Scalpel: Improving Gradient Surgery with an EMA
Beyond minimizing a single training loss, many deep learning estimation pipelines rely on an auxiliary objective to quantify and encourage desirable properties of the model (e. g. performance on another dataset, robustness, agreement with a prior).
Riemannian Diffusion Schrödinger Bridge
Our proposed method generalizes Diffusion Schr\"odinger Bridge introduced in \cite{debortoli2021neurips} to the non-Euclidean setting and extends Riemannian score-based models beyond the first time reversal.
Rethinking Initialization of the Sinkhorn Algorithm
While the optimal transport (OT) problem was originally formulated as a linear program, the addition of entropic regularization has proven beneficial both computationally and statistically, for many applications.
Riemannian Score-Based Generative Modelling
Score-based generative models (SGMs) are a powerful class of generative models that exhibit remarkable empirical performance.
Simulating Diffusion Bridges with Score Matching
This is known to be a challenging problem that has received much attention in the last two decades.
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
In contrast, solving the Schr\"odinger Bridge problem (SB), i. e. an entropy-regularized optimal transport problem on path spaces, yields diffusions which generate samples from the data distribution in finite time.
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models.
