26 citations · 45 across the 8 of their papers we have counts for
9 papers
Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains
Xin Xu, Eibe Frank, Geoffrey Holmes
We investigate cross-domain few-shot learning under the constraint that fine-tuning of backbones (i.e., feature extractors) is impossible or infeasible -- a scenario that is increa…
Multiple Instance Verification
Xin Xu, Eibe Frank, Geoffrey Holmes
We explore multiple instance verification, a problem setting in which a query instance is verified against a bag of target instances with heterogeneous, unknown relevancy. We show…
Feature Extractor Stacking for Cross-domain Few-shot Learning
Hongyu Wang, Eibe Frank, Bernhard Pfahringer +2
Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain wit…
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…
GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles
Rory Mitchell, Eibe Frank, Geoffrey Holmes
SHAP (SHapley Additive exPlanation) values provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values. While exact calculation of…