Approximate inference

Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

We extend ConvCNPs to Construct and meta-learn translation equivariant maps from the space of data sets to predictive stochastic processes.

Predictive Complexity Priors

Defining priors for machine learning models via complexity considerations in function space.

Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes

Powerful meta-learning system based on the neural process framework (Spotlight)

Bayesian Batch Active Learning as Sparse Subset Approximation

We reconsider the active learning problem, leveraging advances in Bayesian coresets to relieve the standard myopic assumption.

Combining Deep Generative and Discriminative Models for Bayesian Semi-Supervised Learning

We introduce a framework for combining deep generative and discriminative models for semi-supervised learning.

Refining the Variational Posterior Through Iterative Optimization

Iteratively improving variational posteriors for BNNs with gradient descent and auxiliary variables.

Consolidating the Meta-Learning Zoo: A Unifying Perspective as Posterior Predictive Inference

A unifying perspective on meta-learning algorithms based on posterior predictive inference.

VERSA: Versatile and Efficient Few-shot Learning

Introducing VERSA, an efficient and flexible few-shot learner based on amortized inference.

Probabilistic Neural Architecture Search

A probabilistic and differentiable framework for neural architecture search that improves speed and memory efficiency.