You can also find my articles on my Google Scholar profile
- “Meta Learning for Supervised and Unsupervised Few-Shot Learning”, A. Antoniou, PhD Thesis
- “Defining Benchmarks for Continual Few-Shot Learning”, A. Antoniou, M. Patacchiola, M. Ochal, A. Storkey, arXiv e-prints, 2020
- “Meta-Learning in Neural Networks: A Survey”, T. Hospedales, A. Antoniou, P. Micaelli and A. Storkey, arXiv e-prints (arXiv:2004.05439), 2020
- “Learning to Learn via Self-Critique”, A. Antoniou and A. Storkey, Neural Information Processing Systems, 2019, Vancouver, Canada
- “Meta-meta-learning for Neural Architecture Search Through arXiv Descent”, A. Antoniou, Nick Pawloski, Jack Turner, James Owers, Joseph Mellor, Elliot J. Crowley, 2019, SIGBOVIK
- “Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation”, A. Antoniou and A. Storkey, arXiv e-prints (arXiv:1902.09884), 2019
- “Dilated DenseNets for Relational Reasoning”, A. Antoniou, A. Słowik, E. J. Crowley, and A. Storkey, arXiv e-prints (arXiv:1811.00410), 2018.
- “How to train your MAML,” A. Antoniou, H. Edwards, and A. Storkey, In International Conference in Learning Representations, 2019.
- “CINIC-10 is not ImageNet or CIFAR-10,” L.N. Darlow, E. J. Crowley, A. Antoniou, and A. J. Storkey, arXiv e-prints (arXiv:1810.03505), 2018.
“Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks,” A. Antoniou, A. Storkey, and H. Edwards, International Conference on Artificial Neural Networks and Machine Learning, 2018
- “Data augmentation generative adversarial networks,” A. Antoniou, A. Storkey, and H. Edwards, arXiv preprint (arXiv:1711.04340), 2017.
- “A general purpose intelligent surveillance system for mobile devices using deep learning,” A. Antoniou and P. Angelov, in Neural Networks (IJCNN), 2016 International Joint Conference on, 2016, pp. 2879–2886.