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Found 4 papers, 0 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.
Forecasting battery capacity and power degradation with multi-task learning
In this paper, we propose a data-driven prognostics framework to predict both capacity and power fade simultaneously with multi-task learning.
