1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Muscle Crossbridge Theory With Internal Crossbridge Dynamics
Mengjian Hua, Charles S. Peskin
We describe in this paper a crossbridge model in which an attached crossbridge behaves like a linear spring with a variable rest length. We assume in particular that the rest lengt…
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…
Heading perception and the structure of the optic acceleration field
Charlie S. Burlingham, Mengjian Hua, Oliver Xu +2
Visual estimation of heading in the human brain is widely believed to be based on instantaneous optic flow, the velocity of retinal image motion. However, we previously found that…