Alexander Hepburn
Cited by
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bLIMEy: surrogate prediction explanations beyond LIME
K Sokol, A Hepburn, R Santos-Rodriguez, P Flach
arXiv preprint arXiv:1910.13016, 2019
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems
K Sokol, A Hepburn, R Poyiadzi, M Clifford, R Santos-Rodriguez, P Flach
Journal of Open Source Software 5, 1904, 2020
PerceptNet: a human visual system inspired neural net for estimating perceptual distance
A Hepburn, V Laparra, J Malo, R McConville, R Santos
IEEE International Conference on Image Processing (ICIP) 2020, 121-125, 2020
On the relation between statistical learning and perceptual distances
A Hepburn, V Laparra, R Santos-Rodriguez, J Ballé, J Malo
10th International Conference on Learning Representations, ICLR 2022, 2022
Enforcing perceptual consistency on Generative Adversarial Networks by using the Normalised Laplacian Pyramid Distance
A Hepburn, V Laparra, R McConville, R Santos-Rodriguez
Proceedings of the Northern Lights Deep Learning Workshop 1, 6--6, 2020
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions Through Perception
A Hepburn, R Santos-Rodriguez
IEEE International Conference on Image Processing (ICIP) 2021, 3717-3721, 2021
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components
K Sokol, A Hepburn, R Santos-Rodriguez, P Flach, 2020
Album cover generation from genre tags
A Hepburn, McConville, Ryan, R Santos-Rodriguez
Proceedings of 10th International Workshop on Machine Learning and Music, 25-31, 2017
Identification, explanation and clinical evaluation of hospital patient subtypes
E Werner, JN Clark, RS Bhamber, M Ambler, CP Bourdeaux, A Hepburn, ...
International Workshop on Health Intelligence, 137-149, 2023
Surrogate Prediction Explanations Beyond LIME
K Sokol, R Santos-rodriguez, A Hepburn, P Flach
no. HCML, 2019
What you hear is what you see: Audio quality metrics from image quality metrics
T Namgyal, A Hepburn, R Santos-Rodriguez, V Laparra, J Malo
arXiv preprint arXiv:2305.11582, 2023
Orthonormal Convolutions for the Rotation Based Iterative Gaussianization
V Laparra, A Hepburn, JE Johnson, J Malo
IEEE International Conference on Image Processing (ICIP) 2022, 4018-4022, 2022
Proper losses for learning with example-dependent costs
A Hepburn, R McConville, R Santos-Rodríguezo, J Cid-Sueiro, ...
Second International Workshop on Learning with Imbalanced Domains: Theory …, 2018
Explainable hierarchical clustering for patient subtyping and risk prediction
E Werner, JN Clark, A Hepburn, RS Bhamber, M Ambler, CP Bourdeaux, ...
Experimental Biology and Medicine, 15353702231214253, 2023
Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers
R Kleinlein, A Hepburn, R Santos-Rodríguez, F Fernández-Martínez
33rd British Machine Vision Conference 2022, 2022
An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations
J Erskine, M Clifford, A Hepburn, R Santos-Rodríguez
arXiv preprint arXiv:2403.19339, 2024
Evaluating Perceptual Distances by Fitting Binomial Distributions to Two-Alternative Forced Choice Data
A Hepburn, R Santos-Rodriguez, J Portilla
arXiv preprint arXiv:2403.10390, 2024
Data is Overrated: Perceptual Metrics Can Lead Learning in the Absence of Training Data
T Namgyal, A Hepburn, R Santos-Rodriguez, V Laparra, J Malo
arXiv preprint arXiv:2312.03455, 2023
Reconciling Training and Evaluation Objectives in Location Agnostic Surrogate Explainers
M Clifford, J Erskine, A Hepburn, P Flach, R Santos-Rodríguez
Proceedings of the 32nd ACM International Conference on Information and …, 2023
Disentangling the Link Between Image Statistics and Human Perception
A Hepburn, V Laparra, R Santos-Rodriguez, J Malo
arXiv preprint arXiv:2303.09874, 2023
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