Portrait

Department of Electrical Engineering

Trainee, Center for Mind, Brain, and Computation

Research Associate, Keck Center for Integrative Neuroscience, UCSF

Advisors: Jay McClelland, Andrew Ng, and Christoph Schreiner

Support: MBC, NDSEG, and Stanford Graduate fellowships

Education: BSE in Electrical Engineering, Princeton University (summa cum laude)

Research

The theory of deep learning and its applications to phenomena in neuroscience and psychology.

Publications

Saxe, A.M. (2014, July) Multitask model-free reinforcement learning. Poster at CogSci 2014, Quebec City, Canada.
pdf | code upon request

Lee, R., Saxe, A., McClelland, J. L. (2014, July). Modeling perceptual learning with deep networks. Poster at CogSci 2014, Quebec City, Canada.
pdf

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. Banff, Canada.
pdf | arxiv

Saxe, A.M., McClelland, J.L., and Ganguli, S. (2013) Dynamics of learning in deep linear neural networks. In NIPS Workshop on Deep Learning 2013.
pdf | supplementary material

Saxe, A.M., McClelland, J.L., and Ganguli, S. (2013) Learning hierarchical category structure in deep networks. In M. Knauff, M. Paulen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th annual meeting of the Cognitive Science Society. (pp. 1271-1276). Austin, TX: Cognitive Science Society.
pdf

Saxe, A.M., McClelland, J.L., and Ganguli, S. (2013) A Mathematical Theory of Semantic Development. Poster at COSYNE 2013, Salt Lake City.
pdf

Saxe, A., Bhand, M., Mudur, R., Suresh, B., & Ng, A. (2011) Unsupervised learning models of primary cortical receptive fields and receptive field plasticity. In NIPS 2011.
pdf | supplementary material | data upon request

Saxe, A., Koh, P.W., Chen, Z., Bhand, M., Suresh, B., & Ng, A. (2011). On random weights and unsupervised feature learning. In ICML 2011.
pdf | code

Saxe, A., Bhand, M., Mudur, R., Suresh, B., & Ng, A. (2011, February). Modeling cortical representational plasticity with unsupervised feature learning. Poster at COSYNE 2011, Salt Lake City.
pdf

Saxe, A., Koh, P.W., Chen, Z., Bhand, M., Suresh, B., & Ng, A. (2010). On random weights and unsupervised feature learning. In NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning.
pdf | supplementary material | code

Balci, F., Simen, P., Niyogi, R., Saxe, A., Hughes, J. A., Holmes, P., Cohen, J.D. (2010). Acquisition of decision making criteria: reward rate ultimately beats accuracy. Attention, Perception, & Psychophysics, 1–18.
pdf

Goodfellow, I. J., Le, Q. V., Saxe, A. M., Lee, H., & Ng, A. Y. (2009). Measuring invariances in deep networks. In NIPS 2009.
pdf

Baldassano, C. A., Franken, G. H., Mayer, J. R., Saxe, A. M., & Yu, D. D. (2009). Kratos: Princeton University’s entry in the 2008 Intelligent Ground Vehicle Competition. Proceedings of SPIE.
pdf

Atreya, A.R., Cattle, B.C., Collins, B.M., Essenburg, B., Franken, G. H., Saxe, A. M., et al. (2006). Prospect Eleven: Princeton University’s entry in the 2005 DARPA Grand Challenge. Journal of Field Robotics, 23(9), 745-753.
pdf

Teaching

Lecture slides on backpropagation

Software

Object recognition with features from random weight TCNNs

Matlab maximally informative dimension solver