1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Simulation-Free Differential Dynamics through Neural Conservation Laws
Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen
We present a novel simulation-free framework for training continuous-time diffusion processes over very general objective functions. Existing methods typically involve either presc…
An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
Mengjian Hua, Mathieu Laurière, Eric Vanden-Eijnden
We present a novel on-policy algorithm for solving stochastic optimal control (SOC) problems. By leveraging the Girsanov theorem, our method directly computes on-policy gradients o…
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Yifan Chen, Mark Goldstein, Mengjian Hua +3
We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasti…
Efficient Training of Energy-Based Models Using Jarzynski Equality
Davide Carbone, Mengjian Hua, Simon Coste +1
Energy-based models (EBMs) are generative models inspired by statistical physics with a wide range of applications in unsupervised learning. Their performance is best measured by t…