715 citations · 867 across the 11 of their papers we have counts for
3 papers · 2 filters
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning
Kolby Nottingham, Anand Balakrishnan, Jyotirmoy Deshmukh +1
It is notoriously difficult to control the behavior of reinforcement learning agents. Agents often learn to exploit the environment or reward signal and need to be retrained multip…
Wasserstein Neural Processes
Andrew Carr, Jared Nielsen, David Wingate
Neural Processes (NPs) are a class of models that learn a mapping from a context set of input-output pairs to a distribution over functions. They are traditionally trained using ma…
Graph Neural Processes: Towards Bayesian Graph Neural Networks
Andrew Carr, David Wingate
We introduce Graph Neural Processes (GNP), inspired by the recent work in conditional and latent neural processes. A Graph Neural Process is defined as a Conditional Neural Process…