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Found 9 papers, 5 papers with code
Target Score Matching
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 Diffusion Models.
Diffusion Models for Constrained Domains
Denoising diffusion models are a novel class of generative algorithms that achieve state-of-the-art performance across a range of domains, including image generation and text-to-image tasks.
Spectral Diffusion Processes
Score-based generative modelling (SGM) has proven to be a very effective method for modelling densities on finite-dimensional spaces.
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.
Riemannian Score-Based Generative Modelling
Score-based generative models (SGMs) are a powerful class of generative models that exhibit remarkable empirical performance.
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-making systems.
LieTransformer: Equivariant self-attention for Lie Groups
Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency through principled parameter sharing.
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Motivated by objects such as electric fields or fluid streams, we study the problem of learning stochastic fields, i. e. stochastic processes whose samples are fields like those occurring in physics and engineering.
Differentially Private Federated Variational Inference
This setting is known as federated learning, in which privacy is a key concern.
