273 citations · 988 across the 24 of their papers we have counts for
24 papers
Fast Adaptation with Linearized Neural Networks
Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno +2
The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings. We study the inductive biases of linearizations of neural n…
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Francesco Tonolini, Pablo G. Moreno, Andreas Damianou +1
We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean valu…
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
Shell Xu Hu, Pablo G. Moreno, Yang Xiao +4
We propose a meta-learning approach that learns from multiple tasks in a transductive setting, by leveraging the unlabeled query set in addition to the support set to generate a mo…
Online Constrained Model-based Reinforcement Learning
Benjamin van Niekerk, Andreas Damianou, Benjamin Rosman
Applying reinforcement learning to robotic systems poses a number of challenging problems. A key requirement is the ability to handle continuous state and action spaces while remai…
Similarity of Neural Networks with Gradients
Shuai Tang, Wesley J. Maddox, Charlie Dickens +2
A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights…
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
Bharathan Balaji, Jordan Bell-Masterson, Enes Bilgin +7
Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games. We build on this previous work by applying RL algorithms to a selection of…