# Andrew M. Saxe

According to our database

Collaborative distances:

^{1}, Andrew M. Saxe authored at least 24 papers between 2006 and 2019.Collaborative distances:

## Timeline

#### Legend:

Book In proceedings Article PhD thesis Other## Links

#### On csauthors.net:

## Bibliography

2019

Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup.

CoRR, 2019

Generalisation dynamics of online learning in over-parameterised neural networks.

CoRR, 2019

2018

A mathematical theory of semantic development in deep neural networks.

CoRR, 2018

Minnorm training: an algorithm for training over-parameterized deep neural networks.

CoRR, 2018

Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning.

CoRR, 2018

On the Information Bottleneck Theory of Deep Learning.

Proceedings of the 6th International Conference on Learning Representations, 2018

Hierarchical Subtask Discovery with Non-Negative Matrix Factorization.

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

High-dimensional dynamics of generalization error in neural networks.

CoRR, 2017

Hierarchical Subtask Discovery With Non-Negative Matrix Factorization.

CoRR, 2017

Hierarchy Through Composition with Multitask LMDPs.

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Tensor Switching Networks.

CoRR, 2016

Hierarchy through Composition with Linearly Solvable Markov Decision Processes.

CoRR, 2016

Active Long Term Memory Networks.

CoRR, 2016

Tensor Switching Networks.

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Tutorial Workshop on Contemporary Deep Neural Network Models.

Proceedings of the 38th Annual Meeting of the Cognitive Science Society, 2016

2014

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.

Proceedings of the 2nd International Conference on Learning Representations, 2014

Multitask model-free reinforcement learning.

Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 2014

Deep Learning and the Brain.

Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 2014

Modeling Perceptual Learning with Deep Networks.

Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 2014

2013

Learning hierarchical categories in deep neural networks.

Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

2011

Unsupervised learning models of primary cortical receptive fields and receptive field plasticity.

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

On Random Weights and Unsupervised Feature Learning.

Proceedings of the 28th International Conference on Machine Learning, 2011

2009

Measuring Invariances in Deep Networks.

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2006

Prospect Eleven: Princeton University's entry in the 2005 DARPA Grand Challenge.

J. Field Robotics, 2006