| Activation function design for deep networks: linearity and effective initialisation M Murray, V Abrol, J Tanner Applied and Computational Harmonic Analysis 59, 117-154, 2022 | 27 | 2022 |
| Characterizing the Spectrum of the NTK via a Power Series Expansion M Murray, H Jin, B Bowman, G Montufar International Conference on Learning Representations (ICLR) 2023, 2022 | 13 | 2022 |
| Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign? E George, M Murray, W Swartworth, D Needell Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS …, 2023 | 9 | 2023 |
| Representation Learning for High-Dimensional Data Collection under Local Differential Privacy A Mansbridge, G Barbour, D Piras, M Murray, C Frye, I Feige, D Barber arXiv preprint arXiv:2010.12464, 2020 | 7* | 2020 |
| Mildly overparameterized relu networks have a favorable loss landscape K Karhadkar, M Murray, H Tseran, G Montúfar Transactions on Machine Learning Research, 2023 | 6 | 2023 |
| Benign overfitting in leaky ReLU networks with moderate input dimension K Karhadkar, E George, M Murray, G Montúfar, D Needell The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | 4 | 2024 |
| Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension K Karhadkar, M Murray, G Montúfar The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | 1 | 2024 |
| Towards an understanding of CNNs: analysing the recovery of activation pathways via Deep Convolutional Sparse Coding M Murray, J Tanner 2018 IEEE Data Science Workshop, 2018 | 1* | 2018 |
| Encoder blind combinatorial compressed sensing M Murray, J Tanner IEEE Transactions on Information Theory, 2022 | | 2022 |
| From matrix factorisation to signal propagation in deep learning: algorithms and guarantees M Murray University of Oxford, 2021 | | 2021 |
Jared TannerProfessor of Mathematics of Information, University of OxfordVerified email at maths.ox.ac.uk
