10.1k citations · 22.1k across the 78 of their papers we have counts for
12 papers · 1 filter
Dream to Control: Learning Behaviors by Latent Imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba +1
Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible…
LOGAN: Latent Optimisation for Generative Adversarial Networks
Yan Wu, Jeff Donahue, David Balduzzi +2
Training generative adversarial networks requires balancing of delicate adversarial dynamics. Even with careful tuning, training may diverge or end up in a bad equilibrium with dro…
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…
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert +9
Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge suc…
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…
Automated curricula through setter-solver interactions
Sebastien Racaniere, Andrew K. Lampinen, Adam Santoro +3
Reinforcement learning algorithms use correlations between policies and rewards to improve agent performance. But in dynamic or sparsely rewarding environments these correlations a…
What does it mean to understand a neural network?
Timothy P. Lillicrap, Konrad P. Kording
We can define a neural network that can learn to recognize objects in less than 100 lines of code. However, after training, it is characterized by millions of weights that contain…
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…
Deep Compressed Sensing
Yan Wu, Mihaela Rosca, Timothy Lillicrap
Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and re…
Deep Learning without Weight Transport
Mohamed Akrout, Collin Wilson, Peter C. Humphreys +2
Current algorithms for deep learning probably cannot run in the brain because they rely on weight transport, where forward-path neurons transmit their synaptic weights to a feedbac…
Learning to Make Analogies by Contrasting Abstract Relational Structure
Felix Hill, Adam Santoro, David G. T. Barrett +2
Analogical reasoning has been a principal focus of various waves of AI research. Analogy is particularly challenging for machines because it requires relational structures to be re…
An investigation of model-free planning
Arthur Guez, Mehdi Mirza, Karol Gregor +10
The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity. For an RL agent to address these challenges, it is essential that…