| Deep unsupervised learning using nonequilibrium thermodynamics J Sohl-Dickstein, E Weiss, N Maheswaranathan, S Ganguli International Conference on Machine Learning, 2256-2265, 2015 | 309 | 2015 |
| 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 | 202 | 2016 |
| Learned optimizers that scale and generalize O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ... International Conference on Machine Learning, 3751-3760, 2017 | 192 | 2017 |
| 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 | 171 | 2017 |
| Social control of hypothalamus-mediated male aggression T Yang, CF Yang, MD Chizari, N Maheswaranathan, KJ Burke Jr, ... Neuron 95 (4), 955-970. e4, 2017 | 100 | 2017 |
| Meta-learning update rules for unsupervised representation learning L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein arXiv preprint arXiv:1804.00222, 2018 | 83 | 2018 |
| 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 | 71* | 2019 |
| 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 | 69 | 2019 |
| 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 | 59 | 2019 |
| Inferring hidden structure in multilayered neural circuits N Maheswaranathan, DB Kastner, SA Baccus, S Ganguli PLoS computational biology 14 (8), e1006291, 2018 | 56 | 2018 |
| 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 | 49 | 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 | 46 | 2020 |
| The dynamic neural code of the retina for natural scenes N Maheswaranathan, LT McIntosh, H Tanaka, S Grant, DB Kastner, ... BioRxiv, 340943, 2019 | 43 | 2019 |
| 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 | 36 | 2019 |
| Learning unsupervised learning rules L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein arXiv preprint arXiv:1804.00222, 8, 2018 | 35 | 2018 |
| Recurrent segmentation for variable computational budgets L McIntosh, N Maheswaranathan, D Sussillo, J Shlens Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 22 | 2018 |
| 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 | 22 | 2012 |
| 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 | 17 | 2020 |
| How recurrent networks implement contextual processing in sentiment analysis N Maheswaranathan, D Sussillo arXiv preprint arXiv:2004.08013, 2020 | 16 | 2020 |
| 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 | 14 | 2020 |
Jascha Sohl-DicksteinGoogle BrainVerified email at google.com
