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Found 6 papers, 2 papers with code
Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions
Consequently, the model achieves a linear inference cost over time points and provides an interpretable low-dimensional representation, revealing communication directions across brain regions and separating oscillatory communications into different frequency bands.
A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing
For (1), we propose a new differentiable POGLM, which enables the pathwise gradient estimator, better than the score function gradient estimator used in existing works.
Forward $χ^2$ Divergence Based Variational Importance Sampling
Maximizing the log-likelihood is a crucial aspect of learning latent variable models, and variational inference (VI) stands as the commonly adopted method.
One-hot Generalized Linear Model for Switching Brain State Discovery
We introduce both a Gaussian prior and a one-hot prior over the GLM in each state.
Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models
Specifically, the latent dynamics structures of the source domain are first extracted by a diffusion model.
Inverse Kernel Decomposition
To deal with very noisy data with weak correlations, we propose two solutions -- blockwise and geodesic -- to make use of locally correlated data points and provide better and numerically more stable latent estimations.
