8.9k citations · 11.5k across the 56 of their papers we have counts for
5 papers · 1 filter
Population Based Training of Neural Networks
Max Jaderberg, Valentin Dalibard, Simon Osindero +9
Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model a…
Beautiful and damned. Combined effect of content quality and social ties on user engagement
Luca M. Aiello, Rossano Schifanella, Miriam Redi +3
User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects…
Sobolev Training for Neural Networks
Wojciech Marian Czarnecki, Simon Osindero, Max Jaderberg +2
At the heart of deep learning we aim to use neural networks as function approximators - training them to produce outputs from inputs in emulation of a ground truth function or data…
FeUdal Networks for Hierarchical Reinforcement Learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul +4
We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan a…
Understanding Synthetic Gradients and Decoupled Neural Interfaces
Wojciech Marian Czarnecki, Grzegorz Świrszcz, Max Jaderberg +3
When training neural networks, the use of Synthetic Gradients (SG) allows layers or modules to be trained without update locking - without waiting for a true error gradient to be b…