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Niru Maheswaranathan
Niru Maheswaranathan
Meta Reality Labs
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Title
Cited by
Cited by
Year
Deep unsupervised learning using nonequilibrium thermodynamics
J Sohl-Dickstein, E Weiss, N Maheswaranathan, S Ganguli
International Conference on Machine Learning, 2256-2265, 2015
3962015
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 29, 2016
2102016
Learned optimizers that scale and generalize
O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ...
International Conference on Machine Learning, 3751-3760, 2017
2042017
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
1802017
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
1072017
Meta-learning update rules for unsupervised representation learning
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 2018
892018
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
78*2019
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 32, 2019
752019
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
672019
Inferring hidden structure in multilayered neural circuits
N Maheswaranathan, DB Kastner, SA Baccus, S Ganguli
PLoS computational biology 14 (8), e1006291, 2018
602018
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 32, 2019
512019
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
482020
The dynamic neural code of the retina for natural scenes
N Maheswaranathan, LT McIntosh, H Tanaka, S Grant, DB Kastner, ...
BioRxiv, 340943, 2019
432019
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 32, 2019
382019
Learning unsupervised learning rules
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 8, 2018
372018
Emergent bursting and synchrony in computer simulations of neuronal cultures
N Maheswaranathan, S Ferrari, AMJ VanDongen, CS Henriquez
Frontiers in computational neuroscience 6, 15, 2012
232012
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
222018
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
L Metz, N Maheswaranathan, CD Freeman, B Poole, J Sohl-Dickstein
arXiv preprint arXiv:2009.11243, 2020
192020
How recurrent networks implement contextual processing in sentiment analysis
N Maheswaranathan, D Sussillo
arXiv preprint arXiv:2004.08013, 2020
172020
Using a thousand optimization tasks to learn hyperparameter search strategies
L Metz, N Maheswaranathan, R Sun, CD Freeman, B Poole, ...
arXiv preprint arXiv:2002.11887, 2020
152020
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