Graph Neural Ordinary Differential Equations M Poli, S Massaroli, J Park, H Asama, A Yamashita, J Park arXiv preprint arXiv:1911.07532, 2019 | 406 | 2019 |
Dissecting Neural ODEs S Massaroli, M Poli, P Jinkyoo, A Yamashita, H Asama Advances in Neural Information Processing Systems 34, 2020 | 190 | 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 | 167 | 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 | 93 | 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 | 54 | 2020 |
Stable Neural Flows S Massaroli, M Poli, M Bin, P Jinkyoo, A Yamashita, H Asama arXiv preprint arXiv:2003.08063, 2020 | 44 | 2020 |
Torchdyn: A neural differential equations library M Poli, S Massaroli, A Yamashita, H Asama, J Park arXiv preprint arXiv:2009.09346, 2020 | 29 | 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 | 20 | 2021 |
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 | 18 | 2023 |
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 | 16 | 2022 |
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 | 16 | 2019 |
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 |
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 | 14 | 2019 |
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 | 13 | 2022 |
Laughing hyena distillery: Extracting compact recurrences from convolutions S Massaroli, M Poli, D Fu, H Kumbong, R Parnichkun, D Romero, ... Advances in Neural Information Processing Systems 36, 2024 | 11 | 2024 |
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 | 11 | 2021 |
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 |
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 | 8 | 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 | 8 | 2020 |
Mechanistic design and scaling of hybrid architectures M Poli, AW Thomas, E Nguyen, P Ponnusamy, B Deiseroth, K Kersting, ... arXiv preprint arXiv:2403.17844, 2024 | 6 | 2024 |