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07/14/2023
Eliciting Informative Priors by Modelling Expert Decision Making
This article introduces a new method for eliciting prior distributions f...
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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...
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05/12/2022
Cross-domain Few-shot Meta-learning Using Stacking
Cross-domain few-shot meta-learning (CDFSML) addresses learning problems...
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12/14/2021
Methods for Eliciting Informative Prior Distributions
Eliciting informative prior distributions for Bayesian inference can oft...
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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...
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09/02/2021
Semi-Supervised Learning using Siamese Networks
Neural networks have been successfully used as classification models yie...
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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 ...
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09/02/2021
Better Self-training for Image Classification through Self-supervision
Self-training is a simple semi-supervised learning approach: Unlabelled ...
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06/11/2021
Bandwidth-Optimal Random Shuffling for GPUs
Linear-time algorithms that are traditionally used to shuffle data on CP...
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04/25/2021
Sampling Permutations for Shapley Value Estimation
Game-theoretic attribution techniques based on Shapley values are used e...
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10/27/2020
GPUTreeShap: Fast Parallel Tree Interpretability
SHAP (SHapley Additive exPlanation) values provide a game theoretic inte...
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05/15/2020
Adaptive XGBoost for Evolving Data Streams
Boosting is an ensemble method that combines base models in a sequential...
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04/24/2020
DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection
Every 20 seconds, a limb is amputated somewhere in the world due to diab...
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04/06/2020
Embedding Java Classes with code2vec: Improvements from Variable Obfuscation
Automatic source code analysis in key areas of software engineering, suc...
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12/26/2019
Classifier Chains: A Review and Perspectives
The family of methods collectively known as classifier chains has become...
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01/23/2019
Stochastic Gradient Trees
We present an online algorithm that induces decision trees using gradien...
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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...
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09/08/2018
Ensembles of Nested Dichotomies with Multiple Subset Evaluation
A system of nested dichotomies is a method of decomposing a multi-class ...
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07/31/2018
Probability Calibration Trees
Obtaining accurate and well calibrated probability estimates from classi...
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06/29/2018
XGBoost: Scalable GPU Accelerated Learning
We describe the multi-GPU gradient boosting algorithm implemented in the...
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04/16/2018
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes
Effective regularisation of neural networks is essential to combat overf...
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04/12/2018
Regularisation of Neural Networks by Enforcing Lipschitz Continuity
We investigate the effect of explicitly enforcing the Lipschitz continui...
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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...
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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 ...
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10/19/2012