Niru Maheswaranathan

According to our database, Niru Maheswaranathan authored at least 27 papers between 2012 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers.

Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein

Proceedings of the Conference on Lifelong Learning Agents, 2022

2021

Training Learned Optimizers with Randomly Initialized Learned Optimizers.

Luke Metz, C. Daniel Freeman, Niru Maheswaranathan, Jascha Sohl-Dickstein

CoRR, 2021

Reverse engineering learned optimizers reveals known and novel mechanisms.

Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Understanding How Encoder-Decoder Architectures Attend.

Kyle Aitken, Vinay V. Ramasesh, Yuan Cao, Niru Maheswaranathan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The geometry of integration in text classification RNNs.

Kyle Aitken, Vinay Venkatesh Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan

Proceedings of the 9th International Conference on Learning Representations, 2021

A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics.

Xuehao Ding, Dongsoo Lee, Satchel Grant, Heike Stein, Lane McIntosh, Niru Maheswaranathan, Stephen Baccus

Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

2020

Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves.

Luke Metz, Niru Maheswaranathan, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein

CoRR, 2020

Using a thousand optimization tasks to learn hyperparameter search strategies.

Luke Metz, Niru Maheswaranathan, Ruoxi Sun, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein

CoRR, 2020

How recurrent networks implement contextual processing in sentiment analysis.

Niru Maheswaranathan, David Sussillo

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Using learned optimizers to make models robust to input noise.

Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk

CoRR, 2019

From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction.

Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics.

Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Universality and individuality in neural dynamics across large populations of recurrent networks.

Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Understanding and correcting pathologies in the training of learned optimizers.

Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein

Proceedings of the 36th International Conference on Machine Learning, 2019

Guided evolutionary strategies: augmenting random search with surrogate gradients.

Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein

Proceedings of the 36th International Conference on Machine Learning, 2019

Meta-Learning Update Rules for Unsupervised Representation Learning.

Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Inferring hidden structure in multilayered neural circuits.

Niru Maheswaranathan, David B. Kastner, Stephen A. Baccus, Surya Ganguli

PLoS Comput. Biol., 2018

Learned optimizers that outperform SGD on wall-clock and test loss.

Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein

CoRR, 2018

Guided evolutionary strategies: escaping the curse of dimensionality in random search.

Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein

CoRR, 2018

Learning Unsupervised Learning Rules.

Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein

CoRR, 2018

Learning to Learn Without Labels.

Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein

Proceedings of the 6th International Conference on Learning Representations, 2018

Recurrent Segmentation for Variable Computational Budgets.

Lane McIntosh, Niru Maheswaranathan, David Sussillo, Jonathon Shlens

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018

2017

Pyret: A Python package for analysis of neurophysiology data.

Benjamin Naecker, Niru Maheswaranathan, Surya Ganguli, Stephen Baccus

J. Open Source Softw., 2017

Learned Optimizers that Scale and Generalize.

Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Deep Learning Models of the Retinal Response to Natural Scenes.

Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli

Proceedings of the 32nd International Conference on Machine Learning, 2015

2012

Emergent bursting and synchrony in computer simulations of neuronal cultures.

Niru Maheswaranathan, Silvia Ferrari, Antonius M. J. VanDongen, Craig Henriquez

Frontiers Comput. Neurosci., 2012