Niru Maheswaranathan

Publication Activity (10 Years)

Years Active: 2012-2022
Publications (10 Years): 35

Top Topics

Unsupervised Learning

Reverse Engineer

Sentiment Analysis

Top Venues

Publications

Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein
Practical tradeoffs between memory, compute, and performance in learned optimizers. CoRR (2022)
Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein
Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers. CoLLAs (2022)
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)
Kyle Aitken, Vinay Venkatesh Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan
The geometry of integration in text classification RNNs. ICLR (2021)
Kyle Aitken, Vinay V. Ramasesh, Yuan Cao, Niru Maheswaranathan
Understanding How Encoder-Decoder Architectures Attend. NeurIPS (2021)
Luke Metz, C. Daniel Freeman, Niru Maheswaranathan, Jascha Sohl-Dickstein
Training Learned Optimizers with Randomly Initialized Learned Optimizers. CoRR (2021)
Kyle Aitken, Vinay V. Ramasesh, Yuan Cao, Niru Maheswaranathan
Understanding How Encoder-Decoder Architectures Attend. CoRR (2021)
Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein
Reverse engineering learned optimizers reveals known and novel mechanisms. NeurIPS (2021)
Niru Maheswaranathan, David Sussillo
How recurrent networks implement contextual processing in sentiment analysis. CoRR (2020)
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 (2020)
Kyle Aitken, Vinay V. Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan
The geometry of integration in text classification RNNs. CoRR (2020)
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 (2020)
Niru Maheswaranathan, David Sussillo
How recurrent networks implement contextual processing in sentiment analysis. ICML (2020)
Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein
Reverse engineering learned optimizers reveals known and novel mechanisms. CoRR (2020)
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein
Understanding and correcting pathologies in the training of learned optimizers. ICML (2019)
Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk
Using learned optimizers to make models robust to input noise. CoRR (2019)
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)
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 (2019)
Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein
Guided evolutionary strategies: augmenting random search with surrogate gradients. ICML (2019)
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)
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein
Meta-Learning Update Rules for Unsupervised Representation Learning. ICLR (2019)
Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. CoRR (2019)
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 (2019)
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)
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein
Learning Unsupervised Learning Rules. CoRR (2018)
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein
Learned optimizers that outperform SGD on wall-clock and test loss. CoRR (2018)
Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein
Guided evolutionary strategies: escaping the curse of dimensionality in random search. CoRR (2018)
Niru Maheswaranathan, David B. Kastner, Stephen A. Baccus, Surya Ganguli
Inferring hidden structure in multilayered neural circuits. PLoS Comput. Biol. 14 (8) (2018)
Lane McIntosh, Niru Maheswaranathan, David Sussillo, Jonathon Shlens
Recurrent Segmentation for Variable Computational Budgets. CVPR Workshops (2018)
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein
Learning to Learn Without Labels. ICLR (Workshop) (2018)
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)
Lane McIntosh, David Sussillo, Niru Maheswaranathan, Jonathon Shlens
Recurrent Segmentation for Variable Computational Budgets. CoRR (2017)
Benjamin Naecker, Niru Maheswaranathan, Surya Ganguli, Stephen Baccus
Pyret: A Python package for analysis of neurophysiology data. J. Open Source Softw. 2 (9) (2017)
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 (2017)
Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus
Deep Learning Models of the Retinal Response to Natural Scenes. NIPS (2016)
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. CoRR (2015)
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. ICML (2015)
Niru Maheswaranathan, Silvia Ferrari, Antonius M. J. VanDongen, Craig Henriquez
Emergent bursting and synchrony in computer simulations of neuronal cultures. Frontiers Comput. Neurosci. 6 (2012)