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Found 6 papers, 2 papers with code
Parameter Inference of Time Series by Delay Embeddings and Learning Differentiable Operators
We provide a method to identify system parameters of dynamical systems, called ID-ODE -- Inference by Differentiation and Observing Delay Embeddings.
Wasserstein Proximal of GANs
We introduce a new method for training generative adversarial networks by applying the Wasserstein-2 metric proximal on the generators.
Wasserstein-based Projections with Applications to Inverse Problems
To bridge this gap, we present a new algorithm that takes samples from the manifold of true data as input and outputs an approximation of the projection operator onto this manifold.
Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field Games
By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial network (GAN).
Wasserstein Diffusion Tikhonov Regularization
We propose regularization strategies for learning discriminative models that are robust to in-class variations of the input data.
Decentralized Multi-Agents by Imitation of a Centralized Controller
In order to obtain multi-agents that act in a decentralized manner, we introduce a novel algorithm under the popular framework of centralized training, but decentralized execution.
