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ORCIDNiru Maheswaranathan
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2020 – today
- 2022
[c17]Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein:
Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers. CoLLAs 2022: 142-164- [i18]Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein:
Practical tradeoffs between memory, compute, and performance in learned optimizers. CoRR abs/2203.11860 (2022) - 2021
- [c16]Xuehao Ding, Dongsoo Lee
, Satchel Grant, Heike Stein, Lane McIntosh, Niru Maheswaranathan, Stephen Baccus:
A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics. ACSCC 2021: 287-291 - [c15]Kyle Aitken, Vinay Venkatesh Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan:
The geometry of integration in text classification RNNs. ICLR 2021 - [c14]Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein:
Reverse engineering learned optimizers reveals known and novel mechanisms. NeurIPS 2021: 19910-19922 - [c13]Kyle Aitken, Vinay V. Ramasesh, Yuan Cao, Niru Maheswaranathan:
Understanding How Encoder-Decoder Architectures Attend. NeurIPS 2021: 22184-22195 - [i17]Luke Metz, C. Daniel Freeman, Niru Maheswaranathan, Jascha Sohl-Dickstein:
Training Learned Optimizers with Randomly Initialized Learned Optimizers. CoRR abs/2101.07367 (2021) - [i16]Kyle Aitken, Vinay V. Ramasesh, Yuan Cao, Niru Maheswaranathan:
Understanding How Encoder-Decoder Architectures Attend. CoRR abs/2110.15253 (2021) - 2020
- [c12]Niru Maheswaranathan, David Sussillo:
How recurrent networks implement contextual processing in sentiment analysis. ICML 2020: 6608-6619 - [i15]Luke Metz, Niru Maheswaranathan, Ruoxi Sun, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein:
Using a thousand optimization tasks to learn hyperparameter search strategies. CoRR abs/2002.11887 (2020) - [i14]Niru Maheswaranathan, David Sussillo:
How recurrent networks implement contextual processing in sentiment analysis. CoRR abs/2004.08013 (2020) - [i13]Luke Metz, Niru Maheswaranathan, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein:
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves. CoRR abs/2009.11243 (2020) - [i12]Kyle Aitken, Vinay V. Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan:
The geometry of integration in text classification RNNs. CoRR abs/2010.15114 (2020) - [i11]Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein:
Reverse engineering learned optimizers reveals known and novel mechanisms. CoRR abs/2011.02159 (2020)
2010 – 2019
- 2019
- [c11]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Meta-Learning Update Rules for Unsupervised Representation Learning. ICLR 2019 - [c10]Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein:
Guided evolutionary strategies: augmenting random search with surrogate gradients. ICML 2019: 4264-4273 - [c9]Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein:
Understanding and correcting pathologies in the training of learned optimizers. ICML 2019: 4556-4565 - [c8]Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli:
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction. NeurIPS 2019: 8535-8545 - [c7]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Universality and individuality in neural dynamics across large populations of recurrent networks. NeurIPS 2019: 15603-15615 - [c6]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. NeurIPS 2019: 15670-15679 - [i10]Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk:
Using learned optimizers to make models robust to input noise. CoRR abs/1906.03367 (2019) - [i9]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. CoRR abs/1906.10720 (2019) - [i8]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Universality and individuality in neural dynamics across large populations of recurrent networks. CoRR abs/1907.08549 (2019) - [i7]Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen A. Baccus, Surya Ganguli:
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction. CoRR abs/1912.06207 (2019) - 2018
- [j3]Niru Maheswaranathan, David B. Kastner, Stephen A. Baccus, Surya Ganguli:
Inferring hidden structure in multilayered neural circuits. PLoS Comput. Biol. 14(8) (2018) - [c5]Lane McIntosh, Niru Maheswaranathan, David Sussillo, Jonathon Shlens:
Recurrent Segmentation for Variable Computational Budgets. CVPR Workshops 2018: 1648-1657 - [c4]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Learning to Learn Without Labels. ICLR (Workshop) 2018 - [i6]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Learning Unsupervised Learning Rules. CoRR abs/1804.00222 (2018) - [i5]Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein:
Guided evolutionary strategies: escaping the curse of dimensionality in random search. CoRR abs/1806.10230 (2018) - [i4]Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein:
Learned optimizers that outperform SGD on wall-clock and test loss. CoRR abs/1810.10180 (2018) - 2017
- [j2]Benjamin Naecker, Niru Maheswaranathan, Surya Ganguli, Stephen Baccus:
Pyret: A Python package for analysis of neurophysiology data. J. Open Source Softw. 2(9): 137 (2017) - [c3]Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein:
Learned Optimizers that Scale and Generalize. ICML 2017: 3751-3760 - [i3]Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein:
Learned Optimizers that Scale and Generalize. CoRR abs/1703.04813 (2017) - [i2]Lane McIntosh, David Sussillo, Niru Maheswaranathan, Jonathon Shlens:
Recurrent Segmentation for Variable Computational Budgets. CoRR abs/1711.10151 (2017) - 2016
- [c2]Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus:
Deep Learning Models of the Retinal Response to Natural Scenes. NIPS 2016: 1361-1369 - 2015
- [c1]Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli:
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. ICML 2015: 2256-2265 - [i1]Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli:
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. CoRR abs/1503.03585 (2015) - 2012
- [j1]Niru Maheswaranathan, Silvia Ferrari, Antonius M. J. VanDongen, Craig Henriquez:
Emergent bursting and synchrony in computer simulations of neuronal cultures. Frontiers Comput. Neurosci. 6: 15 (2012)
Coauthor Index
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