Niru Maheswaranathan
Niru Maheswaranathan
Google Brain
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Deep learning models of the retinal response to natural scenes
LT McIntosh, N Maheswaranathan, A Nayebi, S Ganguli, SA Baccus
Advances in neural information processing systems 29, 1369, 2016
1562016
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
1332017
Deep unsupervised learning using nonequilibrium thermodynamics
J Sohl-Dickstein, E Weiss, N Maheswaranathan, S Ganguli
International Conference on Machine Learning, 2256-2265, 2015
1272015
Learned optimizers that scale and generalize
O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ...
International Conference on Machine Learning, 3751-3760, 2017
1202017
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
782017
Meta-learning update rules for unsupervised representation learning
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 2018
502018
Inferring hidden structure in multilayered neural circuits
N Maheswaranathan, DB Kastner, SA Baccus, S Ganguli
PLoS computational biology 14 (8), e1006291, 2018
452018
Guided evolutionary strategies: Augmenting random search with surrogate gradients
N Maheswaranathan, L Metz, G Tucker, D Choi, J Sohl-Dickstein
International Conference on Machine Learning, 4264-4273, 2019
44*2019
Universality and individuality in neural dynamics across large populations of recurrent networks
N Maheswaranathan, AH Williams, MD Golub, S Ganguli, D Sussillo
Advances in neural information processing systems 2019, 15629, 2019
332019
Learning unsupervised learning rules
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 8, 2018
282018
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
262019
Deep learning models reveal internal structure and diverse computations in the retina under natural scenes
N Maheswaranathan, L McIntosh, DB Kastner, J Melander, L Brezovec, ...
BioRxiv, 340943, 2018
262018
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
222020
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
N Maheswaranathan, AH Williams, MD Golub, S Ganguli, D Sussillo
Advances in neural information processing systems 32, 15696, 2019
212019
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
182012
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
162018
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
H Tanaka, A Nayebi, N Maheswaranathan, L McIntosh, SA Baccus, ...
arXiv preprint arXiv:1912.06207, 2019
152019
Using learned optimizers to make models robust to input noise
L Metz, N Maheswaranathan, J Shlens, J Sohl-Dickstein, ED Cubuk
arXiv preprint arXiv:1906.03367, 2019
92019
How recurrent networks implement contextual processing in sentiment analysis
N Maheswaranathan, D Sussillo
arXiv preprint arXiv:2004.08013, 2020
82020
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
82017
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Articles 1–20