Interests: Reinforcement Learning, Support Vector Machines, Convolutional Neural Networks, Deep Learning, Catastrophic Forgetting, Learning Theory, Models of visual perception
Brendan's Publications
H. Xu, L. Szymanski and B. McCane. VASE: Variational Assorted Surprise Exploration for Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems, 34(3):1243-1252, 2023. 
@article{xu.etal:2023,
author={Xu, Haitao and Szymanski, Lech and McCane, Brendan},
journal={IEEE Transactions on Neural Networks and Learning Systems},
title={VASE: Variational Assorted Surprise Exploration for Reinforcement Learning},
year={2023},
volume={34},
number={3},
pages={1243--1252},
url={https://doi.org/10.1109/TNNLS.2021.3105140},
doi={10.1109/TNNLS.2021.3105140}
}
Bibtex has been copied to clipboard.
L. Szymanski, B. McCane and C. Atkinson. Conceptual complexity of neural networks. Neurocomputing, 469:52-64, 2022.
@article{Szymanski.etal:2021,
title = {Conceptual complexity of neural networks},
journal = {Neurocomputing},
volume = {469},
pages = {52-64},
year = {2022},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2021.10.063},
url = {https://doi.org/10.1016/j.neucom.2021.10.063},
author = {Lech Szymanski and Brendan McCane and Craig Atkinson},
keywords = {deep learning, learning theory, complexity measures},
}
Bibtex has been copied to clipboard.
C. Atkinson, B. McCane, L. Szymanski and A. Robins. Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting. Neurocomputing, 428:291 - 307, 2021.
@article{atkinson2020pseudo,
title = "Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting",
journal = "Neurocomputing",
volume = "428",
pages = "291 - 307",
year = "2021",
issn = "0925-2312",
doi = "https://doi.org/10.1016/j.neucom.2020.11.050",
url = "http://www.sciencedirect.com/science/article/pii/S0925231220318439",
author = "Craig Atkinson and Brendan McCane and Lech Szymanski and Anthony Robins",
}
Bibtex has been copied to clipboard.
H. Xu, B. McCane, L. Szymanski and C. Atkinson. MIME: Mutual Information Minimisation Exploration. arXiv preprint arXiv:2001.05636, 2020.
@article{xu.etal:2020,
title={MIME: Mutual Information Minimisation Exploration},
author={Haitao Xu and
Brendan McCane and
Lech Szymanski and
Craig Atkinson},
journal={arXiv preprint arXiv:2001.05636},
url={https://arxiv.org/abs/2001.05636},
year={2020}
}
Bibtex has been copied to clipboard.
L. Szymanski, B. McCane and C. Atkinson. Switched linear projections for neural network interpretability. arXiv preprint arXiv:1909.11275, 2020.
@article{lechszym.etal:2020,
title={Switched linear projections for neural network interpretability},
author={Szymanski, Lech and McCane, Brendan and Atkinson, Craig},
journal={arXiv preprint arXiv:1909.11275},
url={https://arxiv.org/abs/1909.11275},
year={2020}
}
Bibtex has been copied to clipboard.
C. Atkinson, B. McCane, L. Szymanski and A. Robins. GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal. arXiv preprint arXiv:1911.11988, 2019.
@article{atkinson.etal:2019,
title={GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal},
author={Craig Atkinson and
Brendan McCane and
Lech Szymanski and
Anthony Robins},
journal={arXiv preprint arXiv:1911.11988},
url={https://arxiv.org/abs/1911.11988},
year={2019}
}
Bibtex has been copied to clipboard.
H. Xu, B. McCane and L. Szymanski. Twin Bounded Large Margin Distribution Machine. In Australasian Joint Conference on Artificial Intelligence, pp. 718-729, 2018.
@inproceedings{xu2018twin,
title={Twin Bounded Large Margin Distribution Machine},
author={Xu, Haitao and McCane, Brendan and Szymanski, Lech},
booktitle={Australasian Joint Conference on Artificial Intelligence},
pages={718--729},
year={2018},
url={https://link.springer.com/chapter/10.1007/978-3-030-03991-2_64},
organization={Springer}
}
Bibtex has been copied to clipboard.
C. Atkinson, B. McCane, L. Szymanski and A. Robins. Pseudo-recursal: Solving the catastrophic forgetting problem in deep neural networks. arXiv preprint arXiv:1802.03875, 2018.
@article{atkinson2018pseudo-recursal,
title={Pseudo-recursal: Solving the catastrophic forgetting problem in deep neural networks},
author={Atkinson, Craig and McCane, Brendan and Szymanski, Lech and Robins, Anthony},
journal={arXiv preprint arXiv:1802.03875},
url={https://arxiv.org/abs/1802.03875},
year={2018}
}
Bibtex has been copied to clipboard.
L. Szymanski, C. Gorman, A. Knott, B. McCane and M. Takac. On Learning Object Properties in Convolutional Neural Networks via an Inhibition of Return (IOR) Mechanism. Tech report: OUCS-2018-04, Department of Computer Science, University of Otago, 2018.
@techreport{Szymanski.etal2012b,
author = {Lech Szymanski and Chris Gorman and Alistair Knott and Brendan McCane and Martin Takac},
title = {On Learning Object Properties in Convolutional Neural Networks via an Inhibition of Return (IOR) Mechanism},
number = {OUCS-2018-04},
institution = {Department of Computer Science, University of Otago},
year = {2018},
url = {https://www.otago.ac.nz/computer-science/otago702113.pdf}
}
Bibtex has been copied to clipboard.
L. Szymanski, B. McCane and M. Albert. The effect of the choice of neural network depth and breadth on the
size of its hypothesis space. CoRR, abs/1806.02460, 2018.
@article{Szymanski.etal2018a,
author = {Lech Szymanski and Brendan McCane and Michael Albert},
title = {The effect of the choice of neural network depth and breadth on the
size of its hypothesis space},
journal = {CoRR},
volume = {abs/1806.02460},
year = {2018},
url = {http://arxiv.org/abs/1806.02460},
archivePrefix = {arXiv},
eprint = {1806.02460},
timestamp = {Mon, 13 Aug 2018 16:47:32 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1806-02460},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Bibtex has been copied to clipboard.
B. McCane and L. Szymanski. Efficiency of deep networks for radially symmetric functions. Neurocomputing, 313:119-124, 2017.
@article{mccane.etal2017a,
author = {Brendan McCane and Lech Szymanski},
title = {Efficiency of deep networks for radially symmetric functions},
journal = {Neurocomputing},
volume = {313},
pages = {119--124},
year = {2017},
url = {https://doi.org/10.1016/j.neucom.2018.06.003}
}
Bibtex has been copied to clipboard.
C. Atkinson, B. McCane and L. Szymanski. Increasing the accuracy of convolutional neural networks with progressive reinitialisation. In 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1-5, 2017.
@inproceedings{atkinson2017increasing,
title={Increasing the accuracy of convolutional neural networks with progressive reinitialisation},
author={Atkinson, Craig and McCane, Brendan and Szymanski, Lech},
booktitle={2017 International Conference on Image and Vision Computing New Zealand (IVCNZ)},
pages={1--5},
year={2017},
url={https://doi.org/10.1109/IVCNZ.2017.8402457},
organization={IEEE}
}
Bibtex has been copied to clipboard.
L. Szymanski, B. McCane, W. Gao and Z. Zhou. Effects of the optimisation of the margin distribution on generalisation
in deep architectures. CoRR, abs/1704.05646, 2017.
@article{Szymanski.etal:2017b,
author = {Lech Szymanski and
Brendan McCane and
Wei Gao and
Zhi{-}Hua Zhou},
title = {Effects of the optimisation of the margin distribution on generalisation
in deep architectures},
journal = {CoRR},
volume = {abs/1704.05646},
year = {2017},
url = {http://arxiv.org/abs/1704.05646},
archivePrefix = {arXiv},
eprint = {1704.05646},
timestamp = {Mon, 13 Aug 2018 16:47:28 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/SzymanskiMGZ17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Bibtex has been copied to clipboard.
B. McCane and L. Szymanski. Deep networks are efficient for circular manifolds. In 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3464-3469, 2016.
@inproceedings{McCane.etal:2016a,
author={B. McCane and L. Szymanski},
booktitle={2016 23rd International Conference on Pattern Recognition (ICPR)},
title={Deep networks are efficient for circular manifolds},
year={2016},
pages={3464-3469},
url={https://doi.org/10.1109/ICPR.2016.7900170}
}
Bibtex has been copied to clipboard.
L. Szymanski and B. McCane. Deep Networks are Effective Encoders of Periodicity. IEEE Transactions on Neural Networks and Learning Systems, 25(10):1816-1827, 2014.
@article{Szymanski.etal:2014,
author={L. Szymanski and B. McCane},
journal={IEEE Transactions on Neural Networks and Learning Systems},
title={Deep Networks are Effective Encoders of Periodicity},
year={2014},
volume={25},
number={10},
pages={1816-1827},
doi={10.1109/TNNLS.2013.2296046},
url={https://doi.org/10.1109/TNNLS.2013.2296046}
}
Bibtex has been copied to clipboard.
L. Szymanski and B. McCane. Learning in deep architectures with folding transformations. In The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2013.
@INPROCEEDINGS{Szymanski.etal:2013a,
author={L. Szymanski and B. McCane},
booktitle={The 2013 International Joint Conference on Neural Networks (IJCNN)},
title={Learning in deep architectures with folding transformations},
year={2013},
pages={1-8},
url={https://doi.org/10.1109/IJCNN.2013.6706945}
}
Bibtex has been copied to clipboard.
L. Szymanski and B. McCane. Push-pull separability objective for supervised layer-wise training of neural networks. In The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2012.
@INPROCEEDINGS{Szymanski.etal:2012b,
author={L. Szymanski and B. McCane},
booktitle={The 2012 International Joint Conference on Neural Networks (IJCNN)},
title={Push-pull separability objective for supervised layer-wise training of neural networks},
year={2012},
pages={1-8},
url = {https://doi.org/10.1109/IJCNN.2012.6252366}
}
Bibtex has been copied to clipboard.
L. Szymanski and B. McCane. Deep, super-narrow neural network is a universal classifier. In The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2012.
@INPROCEEDINGS{Szymanski.etal:2012a,
author={L. Szymanski and B. McCane},
booktitle={The 2012 International Joint Conference on Neural Networks (IJCNN)},
title={Deep, super-narrow neural network is a universal classifier},
year={2012},
pages={1-8},
url={https://doi.org/10.1109/IJCNN.2012.6252513}
}
Bibtex has been copied to clipboard.
L. Szymanski and B. McCane. Visualising Kernel Spaces. In Proceedings of Image and Vision Computing New Zealand, pp. 449-452, 2011.
@inproceedings{Szymanski.etal:2011c,
Author = {Lech Szymanski and Brendan McCane},
Booktitle = {Proceedings of Image and Vision Computing New Zealand},
Pages = {449-452},
Title = {Visualising Kernel Spaces},
Year = {2011}
}
Bibtex has been copied to clipboard.
More of Brendan's publications can be found
here.
