275 citations · 642 across the 23 of their papers we have counts for
7 papers · 1 filter
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…
Better Self-training for Image Classification through Self-supervision
Attaullah Sahito, Eibe Frank, Bernhard Pfahringer
Self-training is a simple semi-supervised learning approach: Unlabelled examples that attract high-confidence predictions are labelled with their predictions and added to the train…
Bandwidth-Optimal Random Shuffling for GPUs
Rory Mitchell, Daniel Stokes, Eibe Frank +1
Linear-time algorithms that are traditionally used to shuffle data on CPUs, such as the method of Fisher-Yates, are not well suited to implementation on GPUs due to inherent sequen…
Sampling Permutations for Shapley Value Estimation
Rory Mitchell, Joshua Cooper, Eibe Frank +1
Game-theoretic attribution techniques based on Shapley values are used to interpret black-box machine learning models, but their exact calculation is generally NP-hard, requiring a…