Emergent bursting and synchrony in computer simulations of neuronal cultures. - Niru Maheswaranathan; Silvia Ferrari; Antonius M. J. VanDongen; Craig Henriquez (Frontiers Comput. Neurosci., 2012)

Deep Unsupervised Learning using Nonequilibrium Thermodynamics. - Jascha Sohl-Dickstein; Eric A. Weiss; Niru Maheswaranathan; Surya Ganguli (ICML, 2015)

Deep Learning Models of the Retinal Response to Natural Scenes. - Lane McIntosh; Niru Maheswaranathan; Aran Nayebi; Surya Ganguli; Stephen Baccus (NIPS, 2016)

Learned Optimizers that Scale and Generalize. - Olga Wichrowska; Niru Maheswaranathan; Matthew W. Hoffman; Sergio Gomez Colmenarejo; Misha Denil; Nando de Freitas; Jascha Sohl-Dickstein (ICML, 2017)

Pyret: A Python package for analysis of neurophysiology data. - Benjamin Naecker; Niru Maheswaranathan; Surya Ganguli; Stephen Baccus (J. Open Source Softw., 2017)

Recurrent Segmentation for Variable Computational Budgets. - Lane McIntosh; Niru Maheswaranathan; David Sussillo; Jonathon Shlens (CVPR Workshops, 2018)

Learning to Learn Without Labels. - Luke Metz; Niru Maheswaranathan; Brian Cheung; Jascha Sohl-Dickstein (ICLR (Workshop), 2018)

Learning Unsupervised Learning Rules. - Luke Metz; Niru Maheswaranathan; Brian Cheung; 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)

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)

Inferring hidden structure in multilayered neural circuits. - Niru Maheswaranathan; David B. Kastner; Stephen A. Baccus; Surya Ganguli (PLoS Comput. Biol., 2018)

Meta-Learning Update Rules for Unsupervised Representation Learning. - Luke Metz; Niru Maheswaranathan; Brian Cheung; Jascha Sohl-Dickstein (ICLR, 2019)

Guided evolutionary strategies: augmenting random search with surrogate gradients. - Niru Maheswaranathan; Luke Metz; George Tucker; Dami Choi; Jascha Sohl-Dickstein (ICML, 2019)

Understanding and correcting pathologies in the training of learned optimizers. - Luke Metz; Niru Maheswaranathan; Jeremy Nixon; C. Daniel Freeman; Jascha Sohl-Dickstein (ICML, 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 (NeurIPS, 2019)

Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. - Niru Maheswaranathan; Alex H. Williams; Matthew D. Golub; Surya Ganguli; David Sussillo (NeurIPS, 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 (NeurIPS, 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)

How recurrent networks implement contextual processing in sentiment analysis. - Niru Maheswaranathan; David Sussillo (ICML, 2020)

Using a thousand optimization tasks to learn hyperparameter search strategies. - Luke Metz; Niru Maheswaranathan; Ruoxi Sun 002; C. Daniel Freeman; Ben Poole; Jascha Sohl-Dickstein (CoRR, 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)

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 (ACSCC, 2021)

The geometry of integration in text classification RNNs. - Kyle Aitken; Vinay Venkatesh Ramasesh; Ankush Garg; Yuan Cao; David Sussillo; Niru Maheswaranathan (ICLR, 2021)

Understanding How Encoder-Decoder Architectures Attend. - Kyle Aitken; Vinay V. Ramasesh; Yuan Cao; Niru Maheswaranathan (NeurIPS, 2021)

Reverse engineering learned optimizers reveals known and novel mechanisms. - Niru Maheswaranathan; David Sussillo; Luke Metz; Ruoxi Sun 002; Jascha Sohl-Dickstein (NeurIPS, 2021)

Training Learned Optimizers with Randomly Initialized Learned Optimizers. - Luke Metz; C. Daniel Freeman; Niru Maheswaranathan; Jascha Sohl-Dickstein (CoRR, 2021)

Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers. - Luke Metz; C. Daniel Freeman; James Harrison; Niru Maheswaranathan; Jascha Sohl-Dickstein (CoLLAs, 2022)