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Found 4 papers, 2 papers with code
Sparse Gaussian Neural Processes
Despite significant recent advances in probabilistic meta-learning, it is common for practitioners to avoid using deep learning models due to a comparative lack of interpretability.
Structured Partial Stochasticity in Bayesian Neural Networks
Bayesian neural network posterior distributions have a great number of modes that correspond to the same network function.
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning
The global inducing point variational approximation for BNNs is based on using a set of inducing inputs to construct a series of conditional distributions that accurately approximate the conditionals of the true posterior distribution.
Amortised Inference in Bayesian Neural Networks
Meta-learning is a framework in which machine learning models train over a set of datasets in order to produce predictions on new datasets at test time.
