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, 1369-1377, 2016 | 160 | 2016 |
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 | 121 | 2017 |
Deep unsupervised learning using nonequilibrium thermodynamics J Sohl-Dickstein, EA Weiss, N Maheswaranathan, S Ganguli arXiv preprint arXiv:1503.03585, 2015 | 110 | 2015 |
Learned optimizers that scale and generalize O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ... arXiv preprint arXiv:1703.04813, 2017 | 109 | 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 | 74 | 2017 |
Inferring hidden structure in multilayered neural circuits N Maheswaranathan, DB Kastner, SA Baccus, S Ganguli PLoS computational biology 14 (8), e1006291, 2018 | 42* | 2018 |
Meta-learning update rules for unsupervised representation learning L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein arXiv preprint arXiv:1804.00222, 2018 | 37 | 2018 |
Learning unsupervised learning rules L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein arXiv preprint arXiv:1804.00222, 8, 2018 | 28 | 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 | 25 | 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, 15629-15641, 2019 | 22 | 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 | 20 | 2020 |
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 | 18 | 2019 |
Emergent bursting and synchrony in computer simulations of neuronal cultures N Maheswaranathan, S Ferrari, AMJ VanDongen, C Henriquez Frontiers in computational neuroscience 6, 15, 2012 | 18 | 2012 |
Deep learning models reveal internal structure and diverse computations in the retina under natural scenes N Maheswaranathan, L McIntosh, DB Kastner, J Melander, L Brezovec, ... bioRxiv, 340943, 2018 | 16 | 2018 |
Guided evolutionary strategies: escaping the curse of dimensionality in random search N Maheswaranathan, L Metz, G Tucker, D Choi, J Sohl-Dickstein | 15 | 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, 15696-15705, 2019 | 13 | 2019 |
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 | 13 | 2018 |
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, 8537-8547, 2019 | 9 | 2019 |
Deep learning models reveal internal structure and diverse computations in the retina under natural scenes. bioRxiv N Maheswaranathan, LT McIntosh, DB Kastner, J Melander, L Brezovec, ... URL: https://www. biorxiv. org/content/early/2018/06/14/340943. http://dx …, 2018 | 8 | 2018 |
Using learned optimizers to make models robust to input noise L Metz, N Maheswaranathan, J Shlens, J Sohl-Dickstein, ED Cubuk arXiv preprint arXiv:1906.03367, 2019 | 7 | 2019 |