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
L McIntosh, N Maheswaranathan, A Nayebi, S Ganguli, S Baccus
Advances in neural information processing systems, 1369-1377, 2016
1572016
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
1132017
Learned optimizers that scale and generalize
O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ...
arXiv preprint arXiv:1703.04813, 2017
1072017
Deep unsupervised learning using nonequilibrium thermodynamics
J Sohl-Dickstein, EA Weiss, N Maheswaranathan, S Ganguli
arXiv preprint arXiv:1503.03585, 2015
1032015
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
682017
Inferring hidden structure in multilayered neural circuits
N Maheswaranathan, DB Kastner, SA Baccus, S Ganguli
PLoS computational biology 14 (8), e1006291, 2018
39*2018
Meta-learning update rules for unsupervised representation learning
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 2018
362018
Learning unsupervised learning rules
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 8, 2018
282018
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
242019
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, 15629-15641, 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
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
172020
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
162019
Guided evolutionary strategies: escaping the curse of dimensionality in random search
N Maheswaranathan, L Metz, G Tucker, D Choi, J Sohl-Dickstein
152018
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
122018
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
112018
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, 8537-8547, 2019
102019
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
N Maheswaranathan, A Williams, M Golub, S Ganguli, D Sussillo
Advances in Neural Information Processing Systems, 15696-15705, 2019
102019
Deep learning models reveal internal structure and diverse computations in the retina under natural scenes. bioRxiv
N Maheswaranathan, LT McIntosh, DB Kastner, J Melander, L Brezovec, ...
URL: https://www. biorxiv. org/content/early/2018/06/14/340943. http://dx …, 2018
82018
How recurrent networks implement contextual processing in sentiment analysis
N Maheswaranathan, D Sussillo
arXiv preprint arXiv:2004.08013, 2020
62020
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Articles 1–20