Deep Learning, Neuroscience, and Psychology
Supervisor: Haim Sompolinsky
PhD in Electrical Engineering, Stanford University
Thesis: Deep linear neural networks: A theory of learning in the brain and mind
Advisers: Jay McClelland (primary), Andrew Ng, Christoph Schreiner, and Surya Ganguli
BSE in Electrical Engineering, Princeton University (summa cum laude)
The theory of deep learning and its applications to phenomena in neuroscience and psychology.
Saxe, A.M. (2015, March). A deep learning theory of perceptual learning dynamics. Poster at COSYNE 2015. Salt Lake City.
Lee, R., & Saxe, A.M. (2015, March). The effect of pooling in a deep learning model of perceptual learning. Poster at COSYNE 2015. Salt Lake City.
Goodfellow, I.J., Vinyals, O., & Saxe, A.M. (2015). Qualitatively characterizing neural network optimization problems. In Y. Bengio & Y. LeCun (Eds.), International Conference on Learning Representations. San Diego, CA.
pdf | arxiv
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.
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) 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.
Saxe, A.M., McClelland, J.L., and Ganguli, S. (2013) A Mathematical Theory of Semantic Development. Poster at COSYNE 2013, Salt Lake City.
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., 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.
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.
Goodfellow, I. J., Le, Q. V., Saxe, A. M., Lee, H., & Ng, A.Y. (2009). Measuring invariances in deep networks. In NIPS 2009.
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.
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.
Lecture slides on backpropagation