Neural Information Processing Systems http://nips.cc/

We introduce a method for learning provably stable deep neural network based dynamic models from observed data.

A common feature of these applications is that there are multiple decision makers interacting with each other in a common environment.

Markovian models, estimating the rate of convergence to equilibrium is critical.

Neural Information Processing Systems http://nips.cc/

We study gradient-based optimization methods obtained by directly discretizing a second-order ordinary differential equation (ODE) related to the continuous limit of Nesterov's accelerated gradient method.