Niru Maheswaranathan
Niru Maheswaranathan
Google Brain
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TitleCited byYear
Deep learning models of the retinal response to natural scenes
L McIntosh, N Maheswaranathan, A Nayebi, S Ganguli, S Baccus
Advances in neural information processing systems, 1369-1377, 2016
Deep unsupervised learning using nonequilibrium thermodynamics
J Sohl-Dickstein, EA Weiss, N Maheswaranathan, S Ganguli
arXiv preprint arXiv:1503.03585, 2015
A multiplexed, heterogeneous, and adaptive code for navigation in medial entorhinal cortex
K Hardcastle, N Maheswaranathan, S Ganguli, LM Giocomo
Neuron 94 (2), 375-387. e7, 2017
Learned optimizers that scale and generalize
O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ...
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
Social control of hypothalamus-mediated male aggression
T Yang, CF Yang, MD Chizari, N Maheswaranathan, KJ Burke Jr, ...
Neuron 95 (4), 955-970. e4, 2017
Inferring hidden structure in multilayered neural circuits
N Maheswaranathan, DB Kastner, SA Baccus, S Ganguli
PLoS computational biology 14 (8), e1006291, 2018
Learning unsupervised learning rules
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 2018
Emergent bursting and synchrony in computer simulations of neuronal cultures
N Maheswaranathan, S Ferrari, AMJ VanDongen, C Henriquez
Frontiers in computational neuroscience 6, 15, 2012
Guided evolutionary strategies: escaping the curse of dimensionality in random search
N Maheswaranathan, L Metz, G Tucker, D Choi, J Sohl-Dickstein
Deep learning models reveal internal structure and diverse computations in the retina under natural scenes
N Maheswaranathan, LT McIntosh, DB Kastner, J Melander, L Brezovec, ...
bioRxiv, 340943, 2018
Meta-learning update rules for unsupervised representation learning
L Metz, N Maheswaranathan, B Cheung, J Sohl-dickstein
Recurrent segmentation for variable computational budgets
L McIntosh, N Maheswaranathan, D Sussillo, J Shlens
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
Guided evolutionary strategies: Augmenting random search with surrogate gradients
N Maheswaranathan, L Metz, G Tucker, D Choi, J Sohl-Dickstein
arXiv preprint arXiv:1806.10230, 2018
Inferring hidden structure in multilayered neural circuits. bioRxiv
N Maheswaranathan, SA Baccus, S Ganguli
Understanding and correcting pathologies in the training of learned optimizers
L Metz, N Maheswaranathan, J Nixon, D Freeman, J Sohl-Dickstein
International Conference on Machine Learning, 4556-4565, 2019
Time-warped PCA: simultaneous alignment and dimensionality reduction of neural data
B Poole, A Williams, N Maheswaranathan, B Yu, G Santhanam, S Ryu, ...
Frontiers in Neuroscience. Computational and Systems Neuroscience (COSYNE …, 2017
Pyret: A Python package for analysis of neurophysiology data.
B Naecker, N Maheswaranathan, S Ganguli, S Baccus
J. Open Source Software 2 (9), 137, 2017
Going Deeper with Point Networks
ET Le, I Kokkinos, NJ Mitra
arXiv preprint arXiv:1907.00960, 2019
Learned optimizers that outperform SGD on wall-clock and validation loss
L Metz, N Maheswaranathan, J Nixon, CD Freeman, J Sohl-Dickstein
arXiv preprint arXiv:1810.10180, 2018
A Deep Learning Model of the Retina
L McIntosh, N Maheswaranathan
Stanford, CA, 2015
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