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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
54202015
Learned optimizers that scale and generalize
O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ...
International conference on machine learning, 3751-3760, 2017
3172017
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
2962016
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
2802017
Social control of hypothalamus-mediated male aggression
T Yang, CF Yang, MD Chizari, N Maheswaranathan, KJ Burke, M Borius, ...
Neuron 95 (4), 955-970. e4, 2017
1582017
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
1482019
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
1422019
Meta-learning update rules for unsupervised representation learning
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 2018
1402018
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
121*2019
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
952020
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
892019
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
752019
Inferring hidden structure in multilayered neural circuits
N Maheswaranathan, DB Kastner, SA Baccus, S Ganguli
PLoS computational biology 14 (8), e1006291, 2018
732018
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
572020
Learning unsupervised learning rules
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
International Conference on Learning Representations, 2019
482019
Deep learning models reveal internal structure and diverse computations in the retina under natural scenes
N Maheswaranathan, LT McIntosh, DB Kastner, JB Melander, L Brezovec, ...
BioRxiv, 340943, 2018
412018
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
382020
How recurrent networks implement contextual processing in sentiment analysis
N Maheswaranathan, D Sussillo
arXiv preprint arXiv:2004.08013, 2020
282020
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
282012
Practical tradeoffs between memory, compute, and performance in learned optimizers
L Metz, CD Freeman, J Harrison, N Maheswaranathan, J Sohl-Dickstein
Conference on Lifelong Learning Agents, 142-164, 2022
272022
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