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stat.ML2022★ 1 cited
Loss-calibrated expectation propagation for approximate Bayesian decision-making
Michael J. Morais, Jonathan W. Pillow
Approximate Bayesian inference methods provide a powerful suite of tools for finding approximations to intractable posterior distributions. However, machine learning applications t…
stat.ML2019
Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations
Stephen L. Keeley, David M. Zoltowski, Yiyi Yu +3
Gaussian Process Factor Analysis (GPFA) has been broadly applied to the problem of identifying smooth, low-dimensional temporal structure underlying large-scale neural recordings.…