46 citations · 101 across the 5 of their papers we have counts for
7 papers
AlignNet: Unsupervised Entity Alignment
Antonia Creswell, Kyriacos Nikiforou, Oriol Vinyals +8
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision. This opens many new and exciting avenues of research, allowi…
An Explicitly Relational Neural Network Architecture
Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell +3
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations w…
Meta-Learning surrogate models for sequential decision making
Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim +5
We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning.…
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Tiago Ramalho, Marta Garnelo
The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions…
Open-ended Learning in Symmetric Zero-sum Games
David Balduzzi, Marta Garnelo, Yoram Bachrach +4
Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'. If the game is approximately transi…
Attentive Neural Processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz +5
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each…