Accessibility Skip to Global Navigation Skip to Local Navigation Skip to Content Skip to Search Skip to Site Map Menu

Lech Szymanski - Lecturer

Lech_226

Owheo Building, Room 249
Phone: +64 3 479 5691
Email: lech.szymanski@otago.ac.nz

My research interests are machine learning, deep representation and connectionist models. Our state of the art computation models can learn from examples, but not without the heavy involvement of an expert user, making critical decisions about system architecture, parameters, and appropriate representation. My ultimate objective is to make machine learning into a tool that is easier to use by an average user. However, in order for that to happen, we need to develop truly autonomous machine learning algorithms, capable of forming an appropriate model for the task at hand, completely on their own.

Before my PhD, which I completed in 2012, I worked as a software engineer for a wireless telecommunications company in Ottawa, Canada. My background is in computer and electrical engineering, with a focus on embedded programming and digital signal processing. My interest in artificial neural network models dates back to summer employment, while still an undergraduate student, developing programs for data analysis in a neurobiology laboratory at the National Research Council Canada. Since then I have worked on several aspects of modelling and machine learning, including speech recognition, classification, learning theory and object recognition from images.

For more information and selected publications, see this page.

^ Top of page

Publications

Szymanski, L., McCane, B., & Atkinson, C. (2022). Conceptual complexity of neural networks. Neurocomputing, 469, 52-64. doi: 10.1016/j.neucom.2021.10.063

Szymanski, L., & Lee, M. (2021). Coarse facial feature detection in sheep. Proceedings of the 36th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ54163.2021.9653248

Xu, H., Szymanski, L., & McCane, B. (2021). VASE: Variational assorted surprise exploration for reinforcement learning. IEEE Transactions on Neural Networks & Learning Systems. Advance online publication. doi: 10.1109/TNNLS.2021.3105140

Atkinson, C., McCane, B., Szymanski, L., & Robins, A. (2021). Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting. Neurocomputing, 428, 291-307. doi: 10.1016/j.neucom.2020.11.050

van Sint Annaland, Y., Szymanski, L., & Mills, S. (2020). Predicting cherry quality using siamese networks. 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ51579.2020.9290674

Szymanski, L., McCane, B., & Atkinson, C. (2022). Conceptual complexity of neural networks. Neurocomputing, 469, 52-64. doi: 10.1016/j.neucom.2021.10.063

Journal - Research Article

Atkinson, C., McCane, B., Szymanski, L., & Robins, A. (2021). Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting. Neurocomputing, 428, 291-307. doi: 10.1016/j.neucom.2020.11.050

Journal - Research Article

Xu, H., Szymanski, L., & McCane, B. (2021). VASE: Variational assorted surprise exploration for reinforcement learning. IEEE Transactions on Neural Networks & Learning Systems. Advance online publication. doi: 10.1109/TNNLS.2021.3105140

Journal - Research Article

McCane, B., & Szymanski, L. (2018). Efficiency of deep networks for radially symmetric functions. Neurocomputing, 313, 119-124. doi: 10.1016/j.neucom.2018.06.003

Journal - Research Article

Johnson, R., Szymanski, L., & Mills, S. (2015). Hierarchical structure from motion optical flow algorithms to harvest three-dimensional features from two-dimensional neuro-endoscopic images. Journal of Clinical Neuroscience, 22(2), 378-382. doi: 10.1016/j.jocn.2014.08.004

Journal - Research Article

Szymanski, L., & McCane, B. (2014). Deep networks are effective encoders of periodicity. IEEE Transactions on Neural Networks & Learning Systems, 25(10), 1816-1827. doi: 10.1109/TNNLS.2013.2296046

Journal - Research Article

Szymanski, L., & Lee, M. (2021). Coarse facial feature detection in sheep. Proceedings of the 36th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ54163.2021.9653248

Conference Contribution - Published proceedings: Full paper

Szymanski, L., & Lee, M. (2020). Deep sheep: Kinship assignment in livestock from facial images. Proceedings of the 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ51579.2020.9290558

Conference Contribution - Published proceedings: Full paper

van Sint Annaland, Y., Szymanski, L., & Mills, S. (2020). Predicting cherry quality using siamese networks. 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ51579.2020.9290674

Conference Contribution - Published proceedings: Full paper

Clark-Younger, H., Mills, S., & Szymanski, L. (2019). Stacked hourglass CNN for handwritten character location. Proceedings of the International Conference Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ.2018.8634694

Conference Contribution - Published proceedings: Full paper

Atkinson, C., McCane, B., & Szymanski, L. (2018). Increasing the accuracy of convolutional neural networks with progressive reinitialisation. Proceedings of the International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ.2017.8402457

Conference Contribution - Published proceedings: Full paper

Szymanski, L., & Mills, S. (2018). CNN for historic handwritten document search. Proceedings of the International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ.2017.8402461

Conference Contribution - Published proceedings: Full paper

Xu, H., McCane, B., & Szymanski, L. (2018). Twin bounded large margin distribution machine. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artifical intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 718-729). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_64

Conference Contribution - Published proceedings: Full paper

McCane, B., & Szymanski, L. (2017). Deep networks are efficient for circular manifolds. Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). (pp. 3464-3469). IEEE. doi: 10.1109/ICPR.2016.7900170

Conference Contribution - Published proceedings: Full paper

Johnson, R., Mills, S., & Szymanski, L. (2014). Optical flow algorithms to recover 3D information from 2D endoscopic images. Proceedings of the 6th World Congress for Endoscopic Surgery of the Brain and Spine and Second Global Update on FESS, the Sinuses and the Nose (Endomilano). (pp. 76-77). Turin, Italy: Edizioni Minerva Medica. [Full Paper]

Conference Contribution - Published proceedings: Full paper

Mills, S., Szymanski, L., & Johnson, R. (2014). Hierarchical structure from motion from endoscopic video. Proceedings of the 29th International Conference on Image and Vision Computing New Zealand (IVCNZ). (pp. 102-107). New York: ACM. doi: 10.1145/2683405.2683411

Conference Contribution - Published proceedings: Full paper

Martin, S., & Szymanski, L. (2013). Singularity resolution for dimension reduction. Proceedings of the 28th International Conference of Image and Vision Computing New Zealand (IVCNZ). (pp. 19-24). IEEE. doi: 10.1109/ivcnz.2013.6726986

Conference Contribution - Published proceedings: Full paper

Szymanski, L., & McCane, B. (2013). Learning in deep architectures with folding transformations. Proceedings of the International Joint Conference on Neural Networks (IJCNN). IEEE. doi: 10.1109/IJCNN.2013.6706945

Conference Contribution - Published proceedings: Full paper

Szymanski, L., & McCane, B. (2012). Deep, super-narrow neural network is a universal classifier. Proceedings of the International Joint Conference on Neural Networks (IJCNN). IEEE. doi: 10.1109/IJCNN.2012.6252513

Conference Contribution - Published proceedings: Full paper

Szymanski, L., & McCane, B. (2012). Push-pull separability objective for supervised layer-wise training of neural networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN). IEEE. doi: 10.1109/IJCNN.2012.6252366

Conference Contribution - Published proceedings: Full paper

Szymanski, L., & McCane, B. (2011). Visualising kernel spaces. In P. Delmas, B. Wuensche & J. James (Eds.), Proceedings of the Image and Vision Computing New Zealand (IVCNZ) Conference. (pp. 449-452). Auckland, New Zealand: IVCNZ. [Full Paper]

Conference Contribution - Published proceedings: Full paper

Szymanski, L., McCane, B., & Rountree, N. (2008). Maximum margin perceptron: Towards optimal and deterministic neural network architectures. Proceedings of the New Zealand Computer Science Research Student Conference. (pp. 266-269). [Full Paper]

Conference Contribution - Published proceedings: Full paper

Szymanski, L., & Bouchard, M. (2005). Comb filter decomposition for robust ASR. Proceedings of the 6th Interspeech and 9th European Conference on Speech Communication and Technology (EUROSPEECH). (pp. 2645-2648). [Full Paper]

Conference Contribution - Published proceedings: Full paper

Szymanski, L., & Yang, O. W. W. (2001). Spanning tree algorithm for spare network capacity. Proceedings of the Canadian Conference on Electrical and Computer Engineering. (pp. 447-452). IEEE. doi: 10.1109/CCECE.2001.933725

Conference Contribution - Published proceedings: Full paper

Atkinson, C., McCane, B., & Szymanski, L. (2017, December). Increasing the accuracy of convolutional neural networks with progressive reinitialisation. Verbal presentation at the Electronics New Zealand Conference (ENZCon), Christchurch, New Zealand.

Conference Contribution - Verbal presentation and other Conference outputs

Knott, A., Szymanski, L., Gorman, C., & Takac, M. (2015, December). Predicative sentences and perceptual mechanisms. Verbal presentation at the Linguistic Society of New Zealand Conference, Dunedin, New Zealand.

Conference Contribution - Verbal presentation and other Conference outputs

Szymanski, L., McCane, B., & Atkinson, C. (2019). Switched linear projections and inactive state sensitivity for deep neural network interpretability. arXiv. Retrieved from https://arxiv.org/abs/1909.11275

Working Paper; Discussion Paper; Technical Report

Atkinson, C., McCane, B., Szymanski, L., & Robins, A. (2018). Pseudo-recursal: Solving the catastrophic forgetting problem in deep neural networks (v2). arXiv. Retrieved from https://arxiv.org/abs/1802.03875

Working Paper; Discussion Paper; Technical Report

Atkinson, C., McCane, B., Szymanski, L., & Robins, A. (2018). Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting. arXiv. 8p. Retrieved from https://arxiv.org/abs/1812.02464

Working Paper; Discussion Paper; Technical Report

Fu, X., McCane, B., Mills, S., Albert, M., & Szymanski, L. (2016). Auto-JacoBin: Auto-encoder Jacobian binary hashing. arXiv. 17p. Retrieved from https://arxiv.org/abs/1602.08127

Working Paper; Discussion Paper; Technical Report

Szymanski, L., & Eyers, D. (2014). Practical use of SELinux for enhancing the security of web applications [Technical Report OUCS-2014-02]. Dunedin, New Zealand: Department of Computer Science, University of Otago. 78p. Retrieved from http://www.cs.otago.ac.nz/research/techreports.php

Working Paper; Discussion Paper; Technical Report

Szymanski, L. (2019, March). Folding, surprise and playing games: Deep learning at the CS department. Mathematics Seminar Series, Department of Mathematics & Statistics, University of Otago, Dunedin, New Zealand. [Department Seminar (University of Otago)].

Other Research Output

Szymanski, L. (2012). Deep architectures and classification by intermediary transformations (PhD). University of Otago, Dunedin, New Zealand. Retrieved from http://hdl.handle.net/10523/2129

Awarded Doctoral Degree

More publications...