Eibe Frank | DeepAI

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Featured Co-authors

Albert Bifet
33 publications
Moi Hoon Yap
30 publications
Hongyu Wang
28 publications
Jesse Read
28 publications
Henry Gouk
25 publications
Bernhard Pfahringer
24 publications
Neil D. Reeves
8 publications
Bill Cassidy
8 publications
Chaitanya Joshi
8 publications
Michael Mayo
7 publications
Geoffrey Holmes
7 publications

∙ 07/14/2023

Eliciting Informative Priors by Modelling Expert Decision Making

This article introduces a new method for eliciting prior distributions f...

0 Julia R. Falconer, et al. ∙

∙ 09/28/2022

A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal

Online continual learning (OCL) aims to train neural networks incrementa...

0 Yaqian Zhang, et al. ∙

∙ 05/12/2022

Cross-domain Few-shot Meta-learning Using Stacking

Cross-domain few-shot meta-learning (CDFSML) addresses learning problems...

0 Hongyu Wang, et al. ∙

∙ 12/14/2021

Methods for Eliciting Informative Prior Distributions

Eliciting informative prior distributions for Bayesian inference can oft...

0 Julia R. Falconer, et al. ∙

∙ 10/07/2021

Hitting the Target: Stopping Active Learning at the Cost-Based Optimum

Active learning allows machine learning models to be trained using fewer...

1 Zac Pullar-Strecker, et al. ∙

∙ 09/02/2021

Semi-Supervised Learning using Siamese Networks

Neural networks have been successfully used as classification models yie...

0 Attaullah Sahito, et al. ∙

∙ 09/02/2021

Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification

Deep neural networks produce state-of-the-art results when trained on a ...

0 Attaullah Sahito, et al. ∙

∙ 09/02/2021

Better Self-training for Image Classification through Self-supervision

Self-training is a simple semi-supervised learning approach: Unlabelled ...

0 Attaullah Sahito, et al. ∙

∙ 06/11/2021

Bandwidth-Optimal Random Shuffling for GPUs

Linear-time algorithms that are traditionally used to shuffle data on CP...

0 Rory Mitchell, et al. ∙

∙ 04/25/2021

Sampling Permutations for Shapley Value Estimation

Game-theoretic attribution techniques based on Shapley values are used e...

0 Rory Mitchell, et al. ∙

∙ 10/27/2020

GPUTreeShap: Fast Parallel Tree Interpretability

SHAP (SHapley Additive exPlanation) values provide a game theoretic inte...

0 Rory Mitchell, et al. ∙

∙ 05/15/2020

Adaptive XGBoost for Evolving Data Streams

Boosting is an ensemble method that combines base models in a sequential...

0 Jacob Montiel, et al. ∙

∙ 04/24/2020

DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection

Every 20 seconds, a limb is amputated somewhere in the world due to diab...

1 Bill Cassidy, et al. ∙

∙ 04/06/2020

Embedding Java Classes with code2vec: Improvements from Variable Obfuscation

Automatic source code analysis in key areas of software engineering, suc...

1 Rhys Compton, et al. ∙

∙ 12/26/2019

Classifier Chains: A Review and Perspectives

The family of methods collectively known as classifier chains has become...

0 Jesse Read, et al. ∙

∙ 01/23/2019

Stochastic Gradient Trees

We present an online algorithm that induces decision trees using gradien...

0 Henry Gouk, et al. ∙

∙ 09/08/2018

On the Calibration of Nested Dichotomies for Large Multiclass Tasks

Nested dichotomies are used as a method of transforming a multiclass cla...

0 Tim Leathart, et al. ∙

∙ 09/08/2018

Ensembles of Nested Dichotomies with Multiple Subset Evaluation

A system of nested dichotomies is a method of decomposing a multi-class ...

0 Tim Leathart, et al. ∙

∙ 07/31/2018

Probability Calibration Trees

Obtaining accurate and well calibrated probability estimates from classi...

0 Tim Leathart, et al. ∙

∙ 06/29/2018

XGBoost: Scalable GPU Accelerated Learning

We describe the multi-GPU gradient boosting algorithm implemented in the...

0 Rory Mitchell, et al. ∙

∙ 04/16/2018

MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes

Effective regularisation of neural networks is essential to combat overf...

0 Henry Gouk, et al. ∙

∙ 04/12/2018

Regularisation of Neural Networks by Enforcing Lipschitz Continuity

We investigate the effect of explicitly enforcing the Lipschitz continui...

0 Henry Gouk, et al. ∙

∙ 07/16/2017

Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data

Can we evolve better training data for machine learning algorithms? To i...

0 Michael Mayo, et al. ∙

∙ 04/07/2016

Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection

A system of nested dichotomies is a method of decomposing a multi-class ...

0 Tim Leathart, et al. ∙

∙ 10/19/2012

Locally Weighted Naive Bayes

Despite its simplicity, the naive Bayes classifier has surprised machine...

0 Eibe Frank, et al. ∙

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