Search Results for author:
Found 8 papers, 7 papers with code
Applications of No-Collision Transportation Maps in Manifold Learning
In this work, we investigate applications of no-collision transportation maps introduced in [Nurbekyan et.
Taming Hyperparameter Tuning in Continuous Normalizing Flows Using the JKO Scheme
Instead of tuning $\alpha$, we repeatedly solve the optimization problem for a fixed $\alpha$ effectively performing a JKO update with a time-step $\alpha$.
Efficient Natural Gradient Descent Methods for Large-Scale PDE-Based Optimization Problems
We propose efficient numerical schemes for implementing the natural gradient descent (NGD) for a broad range of metric spaces with applications to PDE-based optimization problems.
A Neural Network Approach Applied to Multi-Agent Optimal Control
Our approach is grid-free and scales efficiently to dimensions where grids become impractical or infeasible.
Optimization and Control
Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field Games
By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial network (GAN).
How to train your neural ODE: the world of Jacobian and kinetic regularization
Training neural ODEs on large datasets has not been tractable due to the necessity of allowing the adaptive numerical ODE solver to refine its step size to very small values.
Ranked #1 on
Density Estimation
on CelebA-HQ 256x256
No-collision Transportation Maps
Transportation maps between probability measures are critical objects in numerous areas of mathematics and applications such as PDE, fluid mechanics, geometry, machine learning, computer science, and economics.
Optimization and Control 49M27,
A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems
State-of-the-art numerical methods for solving such problems utilize spatial discretization that leads to a curse-of-dimensionality.
