| Unsupervised neural network models of the ventral visual stream C Zhuang, S Yan, A Nayebi, M Schrimpf, MC Frank, JJ DiCarlo, ... Proceedings of the National Academy of Sciences 118 (3), e2014196118, 2021 | 410 | 2021 |
| 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 (NIPS 2016) 29, 1369--1377, 2016 | 305 | 2016 |
| Brain-like object recognition with high-performing shallow recurrent ANNs J Kubilius, M Schrimpf, K Kar, R Rajalingham, H Hong, N Majaj, E Issa, ... Advances in neural information processing systems 32, 2019 | 301 | 2019 |
| Task-driven convolutional recurrent models of the visual system A Nayebi, D Bear, J Kubilius, K Kar, S Ganguli, D Sussillo, JJ DiCarlo, ... Advances in neural information processing systems 31, 2018 | 186 | 2018 |
| Brain-like object recognition with high-performing shallow recurrent ANNs J Kubilius, M Schrimpf, K Kar, R Rajalingham, H Hong, N Majaj, E Issa, ... Advances in neural information processing systems 32, 2019 | 182* | 2019 |
| Biologically inspired protection of deep networks from adversarial attacks A Nayebi, S Ganguli arXiv preprint arXiv:1703.09202, 2017 | 136 | 2017 |
| Learning physical graph representations from visual scenes D Bear, C Fan, D Mrowca, Y Li, S Alter, A Nayebi, J Schwartz, LF Fei-Fei, ... Advances in Neural Information Processing Systems 33, 6027-6039, 2020 | 94 | 2020 |
| 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 | 78 | 2019 |
| Interpreting the retinal neural code for natural scenes: From computations to neurons N Maheswaranathan, LT McIntosh, H Tanaka, S Grant, DB Kastner, ... Neuron 111 (17), 2742-2755. e4, 2023 | 74* | 2023 |
| Gruv: Algorithmic music generation using recurrent neural networks A Nayebi, M Vitelli Course CS224D: Deep Learning for Natural Language Processing (Stanford) 52, 2015 | 72 | 2015 |
| Recurrent connections in the primate ventral visual stream mediate a trade-off between task performance and network size during core object recognition A Nayebi, J Sagastuy-Brena, DM Bear, K Kar, J Kubilius, S Ganguli, ... Neural Computation 34 (8), 1652-1675, 2022 | 59* | 2022 |
| Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation A Nayebi, NCL Kong, C Zhuang, JL Gardner, AM Norcia, DLK Yamins PLOS Computational Biology 19 (10), e1011506, 2023 | 51* | 2023 |
| Carma: A deep reinforcement learning approach to autonomous driving M Vitelli, A Nayebi Tech. rep. Stanford University, Tech. Rep., 2016 | 45 | 2016 |
| Two routes to scalable credit assignment without weight symmetry D Kunin, A Nayebi, J Sagastuy-Brena, S Ganguli, J Bloom, D Yamins International Conference on Machine Learning, 5511-5521, 2020 | 42 | 2020 |
| Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks A Nayebi, A Attinger, M Campbell, K Hardcastle, I Low, CS Mallory, G Mel, ... Advances in Neural Information Processing Systems 34, 12167-12179, 2021 | 37 | 2021 |
| Identifying learning rules from neural network observables A Nayebi, S Srivastava, S Ganguli, DL Yamins Advances in Neural Information Processing Systems 33, 2639-2650, 2020 | 33 | 2020 |
| Quantum lower bound for inverting a permutation with advice A Nayebi, S Aaronson, A Belovs, L Trevisan arXiv preprint arXiv:1408.3193, 2014 | 32 | 2014 |
| Neural foundations of mental simulation: Future prediction of latent representations on dynamic scenes A Nayebi, R Rajalingham, M Jazayeri, GR Yang Advances in Neural Information Processing Systems 36, 2024 | 19 | 2024 |
| Distinct in vivo dynamics of excitatory synapses onto cortical pyramidal neurons and parvalbumin-positive interneurons JB Melander, A Nayebi, BC Jongbloets, DA Fortin, M Qin, S Ganguli, ... Cell reports 37 (6), 2021 | 18* | 2021 |
| Fast matrix multiplication techniques based on the Adleman-Lipton model A Nayebi arXiv preprint arXiv:0912.0750, 2009 | 11 | 2009 |
Daniel YaminsAssociate Professor of Computer Science and of Psychology, Stanford UniversityVerified email at stanford.edu
