Jack W. Rae

TL;DR: A deep learning approach that predicts the risk of acute kidney injury and provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests are developed.

TL;DR: The Compressive Transformer is presented, an attentive sequence model which compresses past memories for long-range sequence learning and can model high-frequency speech effectively and can be used as a memory mechanism for RL, demonstrated on an object matching task.

TL;DR: This work demonstrates that a simple model of hippocampal episodic control can learn to solve difficult sequential decision-making tasks and attains a highly rewarding strategy significantly faster than state-of-the-art deep reinforcement learning algorithms, but also achieves a higher overall reward on some of the more challenging domains.

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: The proposed architecture, the Gated Transformer-XL (GTrXL), surpasses LSTMs on challenging memory environments and achieves state-of-the-art results on the multi-task DMLab-30 benchmark suite, exceeding the performance of an external memory architecture.