1k citations · 5.3k across the 132 of their papers we have counts for
14 papers · 2 filters
MetaFun: Meta-Learning with Iterative Functional Updates
Jin Xu, Jean-Francois Ton, Hyunjik Kim +2
We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a fin…
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
Adam Foster, Martin Jankowiak, Matthew O'Meara +2
We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED). Our approach utilizes variational lower bounds on the expected information g…
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Yuan Zhou, Hongseok Yang, Yee Whye Teh +1
Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models. They further attempt to automate the process of drawing in…
Task Agnostic Continual Learning via Meta Learning
Xu He, Jakub Sygnowski, Alexandre Galashov +3
While neural networks are powerful function approximators, they suffer from catastrophic forgetting when the data distribution is not stationary. One particular formalism that stud…
Stacked Capsule Autoencoders
Adam R. Kosiorek, Sara Sabour, Yee Whye Teh +1
Objects are composed of a set of geometrically organized parts. We introduce an unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships between parts…
Random Tessellation Forests
Shufei Ge, Shijia Wang, Yee Whye Teh +2
Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursi…