A control perspective towards continuous-time learning

In this talk, we consider continuous-time learning schemes motivated by optimal control. We first discuss the inference of stochastic dynamical systems through only (nonlinear) noisy measurements. Building on a stochastic control formulation, we construct a generative model that maps the reference measure to the posterior measure through variational inference of a controlled diffusion process. This enables efficient generations of data-assimilated trajectories with applications in system identification and time series prediction. In the second part of the talk, we discuss a mixed precision explicit ODE solver and a custom backpropagation scheme and show their effectiveness in a range of learning tasks. Our scheme uses low-precision computations for evaluating the velocity, parameterized by the neural network, while stability is provided by a custom dynamic adjoint scaling and by accumulating the solution and gradients in higher precision.

Bio
https://nicoletyang.github.io