672 citations · 2.2k across the 21 of their papers we have counts for
7 papers · 1 filter
Compressive Transformers for Long-Range Sequence Modelling
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar +1
We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Compressive Transformer obtains sta…
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
Po-Sen Huang, Huan Zhang, Ray Jiang +6
Advances in language modeling architectures and the availability of large text corpora have driven progress in automatic text generation. While this results in models capable of ge…
Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto, H. Francis Song, Jack W. Rae +10
Owing to their ability to both effectively integrate information over long time horizons and scale to massive amounts of data, self-attention architectures have recently shown brea…
Meta-Learning Deep Energy-Based Memory Models
Sergey Bartunov, Jack W Rae, Simon Osindero +1
We study the problem of learning associative memory -- a system which is able to retrieve a remembered pattern based on its distorted or incomplete version. Attractor networks prov…
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg +11
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy…
Meta-Learning Neural Bloom Filters
Jack W Rae, Sergey Bartunov, Timothy P Lillicrap
There has been a recent trend in training neural networks to replace data structures that have been crafted by hand, with an aim for faster execution, better accuracy, or greater c…