| Graph Neural Ordinary Differential Equations M Poli, S Massaroli, J Park, H Asama, A Yamashita, J Park arXiv preprint arXiv:1911.07532, 2019 | 373 | 2019 |
| Dissecting Neural ODEs S Massaroli, M Poli, P Jinkyoo, A Yamashita, H Asama Advances in Neural Information Processing Systems 34, 2020 | 180 | 2020 |
| Hyena hierarchy: Towards larger convolutional language models M Poli, S Massaroli, E Nguyen, DY Fu, T Dao, S Baccus, Y Bengio, ... International Conference on Machine Learning, 28043-28078, 2023 | 119 | 2023 |
| Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution E Nguyen, M Poli, M Faizi, A Thomas, M Wornow, C Birch-Sykes, ... Advances in neural information processing systems 36, 2024 | 57 | 2024 |
| Hypersolvers: Toward Fast Continuous-Depth Models M Poli, S Massaroli, A Yamashita, H Asama, J Park Advances in Neural Information Processing Systems 34, 2020 | 51 | 2020 |
| Stable Neural Flows S Massaroli, M Poli, M Bin, P Jinkyoo, A Yamashita, H Asama arXiv preprint arXiv:2003.08063, 2020 | 45 | 2020 |
| Torchdyn: A neural differential equations library M Poli, S Massaroli, A Yamashita, H Asama, J Park arXiv preprint arXiv:2009.09346, 2020 | 26 | 2020 |
| Differentiable multiple shooting layers S Massaroli, M Poli, S Sonoda, T Suzuki, J Park, A Yamashita, H Asama Advances in Neural Information Processing Systems 34, 16532-16544, 2021 | 18 | 2021 |
| Port–hamiltonian approach to neural network training S Massaroli, M Poli, F Califano, A Faragasso, J Park, A Yamashita, ... 2019 IEEE 58th Conference on Decision and Control (CDC), 6799-6806, 2019 | 15 | 2019 |
| Pedestrian trajectory prediction using BiRNN encoder–decoder framework J Wu, H Woo, Y Tamura, A Moro, S Massaroli, A Yamashita, H Asama Advanced Robotics 33 (18), 956-969, 2019 | 15 | 2019 |
| Transform once: Efficient operator learning in frequency domain M Poli, S Massaroli, F Berto, J Park, T Dao, C Ré, S Ermon Advances in Neural Information Processing Systems 35, 7947-7959, 2022 | 14 | 2022 |
| Neural ordinary differential equations for intervention modeling D Gwak, G Sim, M Poli, S Massaroli, J Choo, E Choi arXiv preprint arXiv:2010.08304, 2020 | 14 | 2020 |
| Deep latent state space models for time-series generation L Zhou, M Poli, W Xu, S Massaroli, S Ermon International Conference on Machine Learning, 42625-42643, 2023 | 13 | 2023 |
| Optimal energy shaping via neural approximators S Massaroli, M Poli, F Califano, J Park, A Yamashita, H Asama SIAM Journal on Applied Dynamical Systems 21 (3), 2126-2147, 2022 | 12 | 2022 |
| Neural hybrid automata: Learning dynamics with multiple modes and stochastic transitions M Poli, S Massaroli, L Scimeca, S Chun, SJ Oh, A Yamashita, H Asama, ... Advances in Neural Information Processing Systems 34, 9977-9989, 2021 | 10 | 2021 |
| TorchDyn: implicit models and neural numerical methods in PyTorch M Poli, S Massaroli, A Yamashita, H Asama, J Park, S Ermon Neural Information Processing Systems, Workshop on Physical Reasoning and …, 2021 | 8 | 2021 |
| Continuous-depth neural models for dynamic graph prediction M Poli, S Massaroli, CM Rabideau, J Park, A Yamashita, H Asama, J Park arXiv preprint arXiv:2106.11581, 2021 | 6 | 2021 |
| Learning stochastic optimal policies via gradient descent S Massaroli, M Poli, S Peluchetti, J Park, A Yamashita, H Asama IEEE Control Systems Letters 6, 1094-1099, 2021 | 6 | 2021 |
| Port-Hamiltonian Gradient Flows M Poli, S Massaroli, A Yamashita, H Asama, J Park ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | 6 | 2020 |
| Neural solvers for fast and accurate numerical optimal control F Berto, S Massaroli, M Poli, J Park arXiv preprint arXiv:2203.08072, 2022 | 5 | 2022 |
Michael PoliStanford UniversityVerified email at stanford.edu
