| Hamiltonian Neural Networks S Greydanus, M Dzamba, J Yosinski Neural Information Processing Systems, 2019 | 896 | 2019 |
| Lagrangian Neural Networks M Cranmer, S Greydanus, S Hoyer, P Battaglia, D Spergel, S Ho ICML Deep Differential Equations Workshop, 2020 | 444 | 2020 |
| Visualizing and Understanding Atari Agents S Greydanus, A Koul, J Dodge, A Fern International Conference on Machine Learning, 2018 | 371 | 2018 |
| Learning Finite State Representations of Recurrent Policy Networks A Koul, S Greydanus, A Fern International Conference on Learning Representations, 72, 2019 | 88 | 2019 |
| Neural Reparameterization Improves Structural Optimization S Hoyer, J Sohl-Dickstein, S Greydanus Neural Information Processing Systems: Deep Inverse Workshop, 2019 | 80 | 2019 |
| Learning the Enigma with Recurrent Neural Networks S Greydanus arXiv preprint arXiv:1708.07576, 2017 | 38 | 2017 |
| Self-classifying MNIST digits E Randazzo, A Mordvintsev, E Niklasson, M Levin, S Greydanus Distill 5 (8), e00027. 002, 2020 | 34 | 2020 |
| Dissipative hamiltonian neural networks: Learning dissipative and conservative dynamics separately S Greydanus, A Sosanya arXiv preprint arXiv:2201.10085, 2022 | 27 | 2022 |
| Advances in Neural Information Processing Systems S Greydanus, M Dzamba, J Yosinski Curran Associates, Inc., 2019 | 21 | 2019 |
| Meta-Learning Biologically Plausible Semi-Supervised Update Rules K Gu, S Greydanus, L Metz, N Maheswaranathan, J Sohl-Dickstein Cosyne, 2019 | 18 | 2019 |
| Scaling down Deep Learning S Greydanus arXiv preprint arXiv:2011.14439, 2020 | 15 | 2020 |
| Lagrangian neural networks (2020) M Cranmer, S Greydanus, S Hoyer, P Battaglia, D Spergel, S Ho arXiv preprint arXiv:2003.04630, 2003 | 13 | 2003 |
| Hamiltonian neural networks. arXiv 2019 S Greydanus, M Dzamba, J Yosinski arXiv preprint arXiv:1906.01563, 0 | 13 | |
| Self-classifying mnist digits. Distill E Randazzo, A Mordvintsev, E Niklasson, M Levin, S Greydanus | 8 | 2020 |
| Piecewise-constant neural ODEs S Greydanus, S Lee, A Fern arXiv preprint arXiv:2106.06621, 2021 | 6 | 2021 |
| Thread: Differentiable Self-organizing Systems A Mordvintsev, E Randazzo, E Niklasson, M Levin, S Greydanus Distill 5 (8), e27, 2020 | 5 | 2020 |
| Neural Reparameterization for Optimization of Physical Designs SG Stephan Hoyer, Jascha Sohl-Dickstein US Patent US20210192111A1, 2021 | 4* | 2021 |
| Nature's Cost Function: Simulating Physics by Minimizing the Action SJ Greydanus, TD Strang, I Caruso ICLR 2023 Workshop on Physics for Machine Learning, 2023 | 1 | 2023 |
| The Paths Perspective on Value Learning S Greydanus, C Olah Distill 4 (9), e20, 2019 | 1 | 2019 |
| A Tutorial on Structural Optimization S Greydanus arXiv preprint arXiv:2205.08966, 2022 | | 2022 |
Misko DzambaVerified email at cs.toronto.edu
