Andrew Saxe
Andrew Saxe
Gatsby Unit & Sainsbury Wellcome Centre, UCL
Verified email at - Homepage
Cited by
Cited by
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
AM Saxe, JL McClelland, S Ganguli
arXiv preprint arXiv:1312.6120, 2013
Measuring invariances in deep networks
I Goodfellow, H Lee, Q Le, A Saxe, A Ng
Advances in neural information processing systems 22, 646-654, 2009
On random weights and unsupervised feature learning
AM Saxe, PW Koh, Z Chen, M Bhand, B Suresh, AY Ng
Icml, 2011
Qualitatively characterizing neural network optimization problems
IJ Goodfellow, O Vinyals, AM Saxe
arXiv preprint arXiv:1412.6544, 2014
On the information bottleneck theory of deep learning
AM Saxe, Y Bansal, J Dapello, M Advani, A Kolchinsky, BD Tracey, ...
Journal of Statistical Mechanics: Theory and Experiment 2019 (12), 124020, 2019
A deep learning framework for neuroscience
BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ...
Nature neuroscience 22 (11), 1761-1770, 2019
High-dimensional dynamics of generalization error in neural networks
MS Advani, AM Saxe, H Sompolinsky
Neural Networks 132, 428-446, 2020
Acquisition of decision making criteria: reward rate ultimately beats accuracy
F Balci, P Simen, R Niyogi, A Saxe, JA Hughes, P Holmes, JD Cohen
Attention, Perception, & Psychophysics 73 (2), 640-657, 2011
A mathematical theory of semantic development in deep neural networks
AM Saxe, JL McClelland, S Ganguli
Proceedings of the National Academy of Sciences 116 (23), 11537-11546, 2019
Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices
H Monajemi, S Jafarpour, M Gavish, DL Donoho, ...
Proceedings of the National Academy of Sciences 110 (4), 1181-1186, 2013
Learning hierarchical category structure in deep neural networks
AM Saxe, JL McClelland, S Ganguli
Proceedings of the 35th annual meeting of the Cognitive Science Society …, 0
Multitasking capability versus learning efficiency in neural network architectures
S Musslick, A Saxe, K Özcimder, B Dey, G Henselman, JD Cohen
Cognitive Science Society, 2017
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher–student setup
S Goldt, MS Advani, AM Saxe, F Krzakala, L Zdeborová
Journal of Statistical Mechanics: Theory and Experiment 2020 (12), 124010, 2020
If deep learning is the answer, what is the question?
A Saxe, S Nelli, C Summerfield
Nature Reviews Neuroscience 22 (1), 55-67, 2021
Unsupervised learning models of primary cortical receptive fields and receptive field plasticity
A Saxe, M Bhand, R Mudur, B Suresh, AY Ng
Shawe-Taylor, J.; Zemel, R.; Bartlett, P, 2011
Active long term memory networks
T Furlanello, J Zhao, AM Saxe, L Itti, BS Tjan
arXiv preprint arXiv:1606.02355, 2016
Energy–entropy competition and the effectiveness of stochastic gradient descent in machine learning
Y Zhang, AM Saxe, MS Advani, AA Lee
Molecular Physics 116 (21-22), 3214-3223, 2018
Modeling cortical representational plasticity with unsupervised feature learning
A Saxe, M Bhand, R Mudur, B Suresh, A Ng
Poster presented at COSYNE, 24-27, 2011
Hierarchy through composition with multitask LMDPs
AM Saxe, AC Earle, B Rosman
International Conference on Machine Learning, 3017-3026, 2017
Prospect Eleven: Princeton University's entry in the 2005 DARPA Grand Challenge
AR Atreya, BC Cattle, BM Collins, B Essenburg, GH Franken, AM Saxe, ...
Journal of Field Robotics 23 (9), 745-753, 2006
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