Moonshine: Distilling with cheap convolutions EJ Crowley, G Gray, AJ Storkey Advances in Neural Information Processing Systems 31, 2018 | 137 | 2018 |
Blockswap: Fisher-guided block substitution for network compression on a budget J Turner, EJ Crowley, M O'Boyle, A Storkey, G Gray arXiv preprint arXiv:1906.04113, 2019 | 46 | 2019 |
Separable Layers Enable Structured Efficient Linear Substitutions G Gray, EJ Crowley, A Storkey arXiv preprint arXiv:1906.00859, 2019 | 2 | 2019 |
Spectral Analysis for Dance Movement Query and Interpolation E Napier, G Gray, S Oore Proceedings of the 8th International Conference on Movement and Computing, 1-7, 2022 | 1 | 2022 |
Sequence Modeling of Motion-Captured Dance E Napier, G Gray, S Oore Workshop on Machine Learning for Creativity and Design NeurIPS 2022, 2022 | 1 | 2022 |
Substituting convolutions for neural network compression EJ Crowley, G Gray, J Turner, A Storkey IEEE Access 9, 83199-83213, 2021 | 1 | 2021 |
Efficient and Approximate Per-Example Gradient Norms for Gradient Noise Scale G Gray, A Samar, J Hestness Workshop on Advancing Neural Network Training: Computational Efficiency …, 2023 | | 2023 |
Transferring Movement Understanding for Parkinson’s Therapy by Generative Pre-Training E Napier, G Gray, T Loria, V Vuong, M Thaut, S Oore Deep Generative Models for Health Workshop NeurIPS 2023, 2023 | | 2023 |
Test time cost sensitivity in machine learning GDB Gray The University of Edinburgh, 2019 | | 2019 |
Training Structured Efficient Convolutional Layers G Gray, E Crowley, A Storkey | | 2018 |
Resource-Efficient Feature Gathering at Test Time G Gray, A Storkey Reliable Machine Learning in the Wild: NIPS 2016 Workshop, 2016 | | 2016 |