| Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting C Atkinson, B McCane, L Szymanski, A Robins Neurocomputing 428, 291-307, 2021 | 110 | 2021 |
| Deep Networks are Effective Encoders of Periodicity L Szymanski, B McCane IEEE Transactions on Neural Networks and Learning Systems 25 (10), 1816-1827, 2014 | 54 | 2014 |
| Hierarchical Structure from Motion from Endoscopic Video S Mills, L Szymanski, R Johnson Proceedings of the 29th International Conference on Image and Vision …, 2014 | 15 | 2014 |
| Hierarchical structure from motion optical flow algorithms to harvest three-dimensional features from two-dimensional neuro-endoscopic images R Johnson, L Szymanski, S Mills Journal of Clinical Neuroscience 22 (2), 378-382, 2015 | 11 | 2015 |
| Spanning tree algorithm for spare network capacity L Szymanski, OWW Yang Canadian Conference on Electrical and Computer Engineering 2001. Conference …, 2001 | 9 | 2001 |
| Twin bounded large margin distribution machine H Xu, B McCane, L Szymanski AI 2018: Advances in Artificial Intelligence: 31st Australasian Joint …, 2018 | 8 | 2018 |
| Deep Radial Kernel Networks: Approximating Radially Symmetric Functions with Deep Networks B McCane, L Szymanski arXiv preprint arXiv:1703.03470, 2017 | 8 | 2017 |
| Visualising kernel spaces L Szymanski, B McCane Image and Vision Computing New Zealand (IVCNZ), 449-452, 2011 | 8 | 2011 |
| Deep, super-narrow neural network is a universal classifier L Szymanski, B McCane The 2012 International Joint Conference on Neural Networks (IJCNN), 1-8, 2012 | 7 | 2012 |
| Deep Sheep: kinship assignment in livestock from facial images L Szymanski, M Lee 2020 35th International Conference on Image and Vision Computing New Zealand …, 2020 | 6 | 2020 |
| Comb filter decomposition for robust ASR. L Szymanski, M Bouchard InterSpeech, 2645-2648, 2005 | 6 | 2005 |
| Auto-JacoBin: Auto-encoder Jacobian Binary Hashing X Fu, B McCane, S Mills, M Albert, L Szymanski arXiv preprint arXiv:1602.08127, 2016 | 5 | 2016 |
| Learning in deep architectures with folding transformations L Szymanski, B McCane The 2013 International Joint Conference on Neural Networks (IJCNN), 1-8, 2013 | 5 | 2013 |
| Vase: Variational assorted surprise exploration for reinforcement learning H Xu, L Szymanski, B McCane IEEE Transactions on Neural Networks and Learning Systems, 2021 | 4 | 2021 |
| Predicting Cherry Quality Using Siamese Networks Y van Sint Annaland, L Szymanski, S Mills 2020 35th International Conference on Image and Vision Computing New Zealand …, 2020 | 4 | 2020 |
| Efficiency of deep networks for radially symmetric functions B McCane, L Szymanski Neurocomputing 313, 119-124, 2018 | 4 | 2018 |
| Deep networks are efficient for circular manifolds B McCane, L Szymanskic 2016 23rd International Conference on Pattern Recognition (ICPR), 3464-3469, 2016 | 3 | 2016 |
| Conceptual complexity of neural networks L Szymanski, B McCane, C Atkinson Neurocomputing 469, 52-64, 2022 | 2 | 2022 |
| MIME: Mutual Information Minimisation Exploration H Xu, B McCane, L Szymanski, C Atkinson arXiv preprint arXiv:2001.05636, 2020 | 2 | 2020 |
| GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal C Atkinson, B McCane, L Szymanski, A Robins arXiv preprint arXiv:1911.11988, 2019 | 2 | 2019 |
Brendan McCaneUniversity of OtagoVerified email at cs.otago.ac.nz
