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Found 10 papers, 3 papers with code
Stable generative modeling using diffusion maps
Diffusion maps are used to approximate the drift term from the available training samples, which is then implemented in a discrete-time Langevin sampler to generate new samples.
Sampling via Gradient Flows in the Space of Probability Measures
Our third contribution is to study, and develop efficient algorithms based on Gaussian approximations of the gradient flows; this leads to an alternative to particle methods.
Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in ReLU Networks
We consider the problem of performing Bayesian inference for logistic regression using appropriate extensions of the ensemble Kalman filter.
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
The flow in the Gaussian space may be understood as a Gaussian approximation of the flow.
Infinite-Dimensional Diffusion Models
For image distributions, these guidelines are in line with the canonical choices currently made for diffusion models.
Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations
We present a supervised learning method to learn the propagator map of a dynamical system from partial and noisy observations.
Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning
We identify effective stochastic differential equations (SDE) for coarse observables of fine-grained particle- or agent-based simulations; these SDE then provide useful coarse surrogate models of the fine scale dynamics.
Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation
The machine-learning model is learned sequentially by incorporating incoming noisy observations.
Spectral convergence of diffusion maps: improved error bounds and an alternative normalisation
We also introduce an alternative normalisation for diffusion maps based on Sinkhorn weights.
A Mathematical Model of Local and Global Attention in Natural Scene Viewing
Thus, the main contribution of our modeling approach is two--fold; first, we propose a new model for saccade generation in scene viewing.
Neurons and Cognition
