157 citations · 476 across the 27 of their papers we have counts for
5 papers · 1 filter
Neural Ordinary Differential Equations for Intervention Modeling
Daehoon Gwak, Gyuhyeon Sim, Michael Poli +3
By interpreting the forward dynamics of the latent representation of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation (Neural ODE) emerge…
TorchDyn: A Neural Differential Equations Library
Michael Poli, Stefano Massaroli, Atsushi Yamashita +2
Continuous-depth learning has recently emerged as a novel perspective on deep learning, improving performance in tasks related to dynamical systems and density estimation. Core to…
Hypersolvers: Toward Fast Continuous-Depth Models
Michael Poli, Stefano Massaroli, Atsushi Yamashita +2
The infinite-depth paradigm pioneered by Neural ODEs has launched a renaissance in the search for novel dynamical system-inspired deep learning primitives; however, their utilizati…
Stable Neural Flows
Stefano Massaroli, Michael Poli, Michelangelo Bin +3
We introduce a provably stable variant of neural ordinary differential equations (neural ODEs) whose trajectories evolve on an energy functional parametrised by a neural network. S…
Dissecting Neural ODEs
Stefano Massaroli, Michael Poli, Jinkyoo Park +2
Continuous deep learning architectures have recently re-emerged as Neural Ordinary Differential Equations (Neural ODEs). This infinite-depth approach theoretically bridges the gap…