275 citations · 642 across the 23 of their papers we have counts for
27 papers
Eliciting Informative Priors by Modelling Expert Decision Making
Julia R. Falconer, Eibe Frank, Devon L. L. Polaschek +1
This article introduces a new method for eliciting prior distributions from experts. The method models an expert decision-making process to infer a prior probability distribution f…
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
Yaqian Zhang, Bernhard Pfahringer, Eibe Frank +3
Online continual learning (OCL) aims to train neural networks incrementally from a non-stationary data stream with a single pass through data. Rehearsal-based methods attempt to ap…
Methods for Eliciting Informative Prior Distributions: A Critical Review
Julia R. Falconer, Eibe Frank, Devon L. L. Polaschek +1
Eliciting informative prior distributions for Bayesian inference can often be complex and challenging. While popular methods rely on asking experts probability based questions to q…
Hitting the Target: Stopping Active Learning at the Cost-Based Optimum
Zac Pullar-Strecker, Katharina Dost, Eibe Frank +1
Active learning allows machine learning models to be trained using fewer labels while retaining similar performance to traditional supervised learning. An active learner selects th…
Semi-Supervised Learning using Siamese Networks
Attaullah Sahito, Eibe Frank, Bernhard Pfahringer
Neural networks have been successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples. These models, however, are…
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification
Attaullah Sahito, Eibe Frank, Bernhard Pfahringer
Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for tra…