Meta learning

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.

TaskNorm: Rethinking Batch Normalization for Meta-Learning

Deriving a form of batch normalization tailored towards the meta-learning models.

Convolutional Conditional Neural Processes

We extend deep sets to functional embeddings and Neural Processes to include translation equivariant members (Oral Presentaiton).

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

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

Meta Learning Probabilistic Inference for Prediction

We introduce ML-PIP, a general probabilistic framework for meta-learning

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.