Code of Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
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Updated
Mar 31, 2020 - Python
Code of Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022
Code for reproducing the experiments in the paper Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference.
Learning Diffusion Priors from Observations by Expectation Maximization
Code and Analyses using the Hierarchical Empirical Bayes Autoencoder (HEBAE)
Hierarchical Empirical Bayes Auto-Encoder
Python codes for Paper: An Efficient and Flexible Spike Train Model via Empirical Bayes
General-purpose library for fitting models to data with correlated Gaussian-distributed noise
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