Deep learning models of the retinal response to natural scenes LT McIntosh, N Maheswaranathan, A Nayebi, S Ganguli, SA Baccus Advances in neural information processing systems 29, 1369, 2016 | 155 | 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 | 132 | 2017 |
Deep unsupervised learning using nonequilibrium thermodynamics J Sohl-Dickstein, E Weiss, N Maheswaranathan, S Ganguli International Conference on Machine Learning, 2256-2265, 2015 | 124 | 2015 |
Learned optimizers that scale and generalize O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ... International Conference on Machine Learning, 3751-3760, 2017 | 119 | 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 | 77 | 2017 |
Meta-learning update rules for unsupervised representation learning L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein arXiv preprint arXiv:1804.00222, 2018 | 50 | 2018 |
Inferring hidden structure in multilayered neural circuits N Maheswaranathan, DB Kastner, SA Baccus, S Ganguli PLoS computational biology 14 (8), e1006291, 2018 | 45 | 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 | 43* | 2019 |
Universality and individuality in neural dynamics across large populations of recurrent networks N Maheswaranathan, AH Williams, MD Golub, S Ganguli, D Sussillo Advances in neural information processing systems 2019, 15629, 2019 | 32 | 2019 |
Learning unsupervised learning rules L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein arXiv preprint arXiv:1804.00222, 8, 2018 | 28 | 2018 |
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 | 25 | 2018 |
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 | 24 | 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 | 22 | 2020 |
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics N Maheswaranathan, AH Williams, MD Golub, S Ganguli, D Sussillo Advances in neural information processing systems 32, 15696, 2019 | 21 | 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 |
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 | 16 | 2018 |
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction H Tanaka, A Nayebi, N Maheswaranathan, L McIntosh, SA Baccus, ... arXiv preprint arXiv:1912.06207, 2019 | 15 | 2019 |
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 | 9 | 2019 |
How recurrent networks implement contextual processing in sentiment analysis N Maheswaranathan, D Sussillo arXiv preprint arXiv:2004.08013, 2020 | 8 | 2020 |
Time-warped PCA: simultaneous alignment and dimensionality reduction of neural data B Poole, A Williams, N Maheswaranathan, B Yu, G Santhanam, S Ryu, ... Frontiers in Neuroscience. Computational and Systems Neuroscience (COSYNE …, 2017 | 8 | 2017 |