| Dissecting neural ODEs S Massaroli, M Poli, J Park, A Yamashita, H Asama Advances in Neural Information Processing Systems 33, 2020 | 81 | 2020 |
| Graph neural ordinary differential equations M Poli, S Massaroli, J Park, A Yamashita, H Asama, J Park Workshop on Deep Learning on Graphs: Methodologies and Applications (DLGMA’20), 2019 | 36 | 2019 |
| Hypersolvers: Toward fast continuous-depth models M Poli, S Massaroli, A Yamashita, H Asama, J Park Advances in Neural Information Processing Systems 33, 2020 | 29 | 2020 |
| Stable neural flows S Massaroli, M Poli, M Bin, J Park, A Yamashita, H Asama arXiv preprint arXiv:2003.08063, 2020 | 27 | 2020 |
| TorchDyn: Implicit models and neural numerical methods in PyTorch M Poli, S Massaroli, A Yamashita, H Asama, J Park, S Ermon | 11* | |
| 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 | 10 | 2019 |
| Which shortcut cues will dnns choose? a study from the parameter-space perspective L Scimeca, SJ Oh, S Chun, M Poli, S Yun International Conference on Learning Representations, ICLR 2022, 2021 | 6 | 2021 |
| 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, 2021 | 6 | 2021 |
| 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 | 5 | 2020 |
| 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 | 3 | 2021 |
| Optimal energy shaping via neural approximators S Massaroli, M Poli, F Califano, J Park, A Yamashita, H Asama SIAM Journal on Applied Dynamical Systems (SIADS), 2021 | 3 | 2021 |
| WATTNet: Learning to trade FX via hierarchical spatio-temporal representation of highly multivariate time series M Poli, J Park, I Ilievski 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020 | 2 | 2020 |
| Monarch: Expressive Structured Matrices for Efficient and Accurate Training T Dao, B Chen, N Sohoni, A Desai, M Poli, J Grogan, A Liu, A Rao, ... arXiv preprint arXiv:2204.00595, 2022 | 1 | 2022 |
| Neural solvers for fast and accurate numerical optimal control F Berto, S Massaroli, M Poli, J Park International Conference on Learning Representations, ICLR 2022, 2021 | 1 | 2021 |
| Neural Hybrid Automata: Learning dynamics with multiple modes and stochastic transitions M Poli, S Massaroli, L Scimeca, SJ Oh, S Chun, A Yamashita, H Asama, ... Advances in Neural Information Processing Systems 34, 2021 | 1 | 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 | 1 | 2021 |
| Efficient Continuous Spatio-Temporal Simulation with Graph Spline Networks C Hua, F Berto, M Poli, S Massaroli, J Park ICML 2022 2nd AI for Science Workshop, 2022 | | 2022 |
| Transform Once: Efficient Operator Learning in Frequency Domain M Poli, S Massaroli, F Berto, J Park, T Dao, C Re, S Ermon ICML 2022 2nd AI for Science Workshop, 2022 | | 2022 |
| Self-similarity priors: Neural collages as differentiable fractal representations M Poli, W Xu, S Massaroli, C Meng, K Kim, S Ermon arXiv preprint arXiv:2204.07673, 2022 | | 2022 |
| Continuous–depth value networks for parametrized actions S Massaroli, M Poli, S Bakhtiyarov, A Yamashita, H Asama, J Park ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | | 2020 |
Stefano MassaroliThe University of TokyoVerified email at robot.t.u-tokyo.ac.jp
