David Saxton

TL;DR: In this paper, the authors use density models to measure uncertainty and derive a pseudo-count from an arbitrary density model, which can be used to improve exploration in non-tabular reinforcement learning.

TL;DR: The notion of containment for independent sets in hypergraphs was introduced in this article, where it was shown that for a given hypergraph G, there exists a relatively small collection of vertex subsets, such that every independent set of G is contained within a member of the subset, and no member is large; the collection reveals an underlying structure to the independent sets.

TL;DR: This work uses density models to measure uncertainty, and proposes a novel algorithm for deriving a pseudo-count from an arbitrary density model, which enables this technique to generalize count-based exploration algorithms to the non-tabular case.

TL;DR: A model, the Memory, RL, and Inference Network (MERLIN), in which memory formation is guided by a process of predictive modeling, demonstrates a single learning agent architecture that can solve canonical behavioural tasks in psychology and neurobiology without strong simplifying assumptions about the dimensionality of sensory input or the duration of experiences.

TL;DR: Conditional Neural Processes (CNPs) as mentioned in this paper combine the benefits of both deep neural networks and Gaussian Processes by using prior knowledge to quickly infer the shape of a new function at test time.