Niru Maheswaranathan
Niru Maheswaranathan
Google Brain
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Cited by
Cited by
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
Meta-learning update rules for unsupervised representation learning
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 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
Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping
AH Williams, B Poole, N Maheswaranathan, AK Dhawale, T Fisher, ...
Neuron 105 (2), 246-259. e8, 2020
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
Understanding and correcting pathologies in the training of learned optimizers
L Metz, N Maheswaranathan, J Nixon, CD Freeman, J Sohl-Dickstein
arXiv preprint arXiv:1810.10180, 2018
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
Universality and individuality in neural dynamics across large populations of recurrent networks
N Maheswaranathan, A Williams, M Golub, S Ganguli, D Sussillo
Advances in neural information processing systems, 15603-15615, 2019
Learned optimizers that outperform SGD on wall-clock and test loss
L Metz, N Maheswaranathan, J Nixon, D Freeman, J Sohl-Dickstein
Proceedings of the 2nd Workshop on Meta-Learning. MetaLearn, 2018
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
H Tanaka, A Nayebi, N Maheswaranathan, L McIntosh, S Baccus, ...
Advances in Neural Information Processing Systems, 8535-8545, 2019
Pyret: A Python package for analysis of neurophysiology data
B Naecker, N Maheswaranathan, S Ganguli, S Baccus
Journal of Open Source Software 2 (9), 137, 2017
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
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