164 citations · 227 across the 5 of their papers we have counts for
5 papers
Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning
Trevor McInroe, Adam Jelley, Stefano V. Albrecht +1
Offline pretraining with a static dataset followed by online fine-tuning (offline-to-online, or OtO) is a paradigm well matched to a real-world RL deployment process. In this scena…
Robust and efficient computation of retinal fractal dimension through deep approximation
Justin Engelmann, Ana Villaplana-Velasco, Amos Storkey +1
A retinal trait, or phenotype, summarises a specific aspect of a retinal image in a single number. This can then be used for further analyses, e.g. with statistical methods. Howeve…
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley +2
Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a s…
CINIC-10 is not ImageNet or CIFAR-10
Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou +1
In this brief technical report we introduce the CINIC-10 dataset as a plug-in extended alternative for CIFAR-10. It was compiled by combining CIFAR-10 with images selected and down…
GINN: Geometric Illustration of Neural Networks
Luke N. Darlow, Amos J. Storkey
This informal technical report details the geometric illustration of decision boundaries for ReLU units in a three layer fully connected neural network. The network is designed and…