Blog

Interpretating Bayes’ Law via Optimal Transport for Filtering Problems

An unbiased Monte Carlo sampler for implicit trace estimation

Can a Transformer represent a Kalman filter?

A Monte Carlo sampler for radial basis function kernels and positional embedding

A scalable template for high-dimensional time series predictions

The pioneering normalizing flows within generative models.

Does a straighter flow always yield more efficient transport?

The optimal penalty can be zero or negative for real-world high dimensional data.

Connections between Probability Flows, Diffusions, and PDEs.

A general coupling technique for characterizing a broad range of diffusions.

An application of Girsanov theorem in parameter estimation.

A framework that unifies optimal transport, FB-SDEs, fluid dynamics, and stochastic control.

An elegant sampler that utilizes Hamiltonian dynamics to propose new states in simulations.

A family of techniques to understand the convergence of random variables.

An elegant functional inequality that unifies ODEs, PDEs, SDEs, functional analysis, and Riemannian geometry.

Running multiple MCMCs at different temperatures to explore the solution thoroughly.

Negative learning rate helps escape local traps.