Skip to content
12-andrew-saxe_bw

Andrew Saxe

Appointment

  • CIFAR Azrieli Global Scholar 2020-2022
  • Learning in Machines & Brains

Connect

website

About

The interactions of billions of neurons ultimately give rise to our thoughts and actions.

Remarkably, much of our behaviour is learned starting in infancy and continuing throughout our lifespan. Andrew Saxe is aiming to develop a mathematical toolkit suitable for analyzing and describing aspects of learning in the brain and mind. His current focus is on the theory of deep learning, a class of artificial neural network models that take inspiration from the brain. Alongside this theoretical work, he develops close collaborations with experimentalists to empirically test principles of learning in biological organisms.

Awards

  • Wellcome-Beit Prize, Wellcome Trust, 2019
  • Sir Henry Dale Fellowship, Wellcome Trust & Royal Society, 2019
  • Robert J. Glushko Outstanding Doctoral Dissertations Prize, Cognitive Science Society, 2016
  • NDSEG Fellowship, 2010

Relevant Publications

  • Saxe, A. M., McClelland, J. L., & Ganguli, S. (2019). A mathematical theory of semantic development in deep neural networks. Proceedings of the National Academy of Sciences, 116(23), 11537–11546. https://doi.org/10.1073/pnas.1820226116
  • Earle, A. C., Saxe, A. M., & Rosman, B. (2018). Hierarchical Subtask Discovery with Non-Negative Matrix Factorization. In Y. Bengio & Y. LeCun (Eds.), International Conference on Learning Representations.
  • Advani*, M., & Saxe*, A. M. (2017). High-dimensional dynamics of generalization error in neural networks. ArXiv.
  • Musslick, S., Saxe, A. M., Ozcimder, K., Dey, B., Henselman, G., & Cohen, J. D. (2017). Multitasking Capability Versus Learning Efficiency in Neural Network Architectures. Annual Meeting of the Cognitive Science Society, 829–834.
  • Saxe, A. M., McClelland, J. L., & Ganguli, S. (2014). Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. In Y. Bengio & Y. LeCun (Eds.), International Conference on Learning Representations.

Support Us

CIFAR is a registered charitable organization supported by the governments of Canada, Alberta, Ontario, and Quebec as well as foundations, individuals, corporations, and international partner organizations.

MaRS Centre, West Tower
661 University Ave., Suite 505
Toronto, ON M5G 1M1 Canada