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Found 5 papers, 0 papers with code
Normalizing flows as approximations of optimal transport maps via linear-control neural ODEs
The term "Normalizing Flows" is related to the task of constructing invertible transport maps between probability measures by means of deep neural networks.
A minimax optimal control approach for robust neural ODEs
In this paper, we address the adversarial training of neural ODEs from a robust control perspective.
From NeurODEs to AutoencODEs: a mean-field control framework for width-varying Neural Networks
The connection between Residual Neural Networks (ResNets) and continuous-time control systems (known as NeurODEs) has led to a mathematical analysis of neural networks which has provided interesting results of both theoretical and practical significance.
Losing momentum in continuous-time stochastic optimisation
This model is a piecewise-deterministic Markov process that represents the particle movement by an underdamped dynamical system and the data subsampling through a stochastic switching of the dynamical system.
Deep Learning Approximation of Diffeomorphisms via Linear-Control Systems
We consider a control system of the form $\dot x = \sum_{i=1}^lF_i(x)u_i$, with linear dependence in the controls, and we use the corresponding flow to approximate the action of a diffeomorphism on a compact ensemble of points.
