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Found 7 papers, 1 papers with code
Modeling Neural Activity with Conditionally Linear Dynamical Systems
Neural population activity exhibits complex, nonlinear dynamics, varying in time, over trials, and across experimental conditions.
Comparing noisy neural population dynamics using optimal transport distances
Biological and artificial neural systems form high-dimensional neural representations that underpin their computational capabilities.
Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport
We study Sinkhorn EM (sEM), a variant of the expectation maximization (EM) algorithm for mixtures based on entropic optimal transport.
Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution
Motion segmentation for natural images commonly relies on dense optic flow to yield point trajectories which can be grouped into clusters through various means including spectral clustering or minimum cost multicuts.
Wasserstein total variation filtering
In this paper, we expand upon the theory of trend filtering by introducing the use of the Wasserstein metric as a means to control the amount of spatiotemporal variation in filtered time series data.
Sinkhorn Permutation Variational Marginal Inference
This problem is known to quickly become intractable as the size of the permutation increases, since its involves the computation of the permanent of a matrix, a #P-hard problem.
Robust approximate linear regression without correspondence
Previous theoretical analysis of the problem has been done in a setting where the responses are a complete permutation of the regressed covariates.
