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Found 7 papers, 0 papers with code
Closing the ODE-SDE gap in score-based diffusion models through the Fokker-Planck equation
In this paper we rigorously describe the range of dynamics and approximations that arise when training score-based diffusion models, including the true SDE dynamics, the neural approximations, the various approximate particle dynamics that result, as well as their associated Fokker--Planck equations and the neural network approximations of these Fokker--Planck equations.
Generalised Gillespie Algorithms for Simulations in a Rule-Based Epidemiological Model Framework
These rule-based approaches are motivated by chemical reaction rules which are traditionally solved numerically with the standard Gillespie algorithm proposed in the context of molecular dynamics.
Models for information propagation on graphs
We propose and unify classes of different models for information propagation over graphs.
A rule-based epidemiological framework for modelling and simulation in the context of the covid-19 pandemic
Here we stress that we do not have a specific model in mind, but a whole collection of models which can be transformed into each other, or represent different aspects of the pandemic.
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)
Wasserstein GANs are based on the idea of minimising the Wasserstein distance between a real and a generated distribution.
On anisotropic diffusion equations for label propagation
In many problems in data classification one wishes to assign labels to points in a point cloud with a certain number of them being already correctly labeled.
A Deterministic Gradient-Based Approach to Avoid Saddle Points
First-order methods such as gradient descent are usually the methods of choice for training machine learning models.
