89 citations · 262 across the 16 of their papers we have counts for
9 papers · 1 filter
Continuous-Depth Neural Models for Dynamic Graph Prediction
Michael Poli, Stefano Massaroli, Clayton M. Rabideau +4
We introduce the framework of continuous-depth graph neural networks (GNNs). Neural graph differential equations (Neural GDEs) are formalized as the counterpart to GNNs where the i…
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Michael Poli, Stefano Massaroli, Luca Scimeca +6
Effective control and prediction of dynamical systems often require appropriate handling of continuous-time and discrete, event-triggered processes. Stochastic hybrid systems (SHSs…
Differentiable Multiple Shooting Layers
Stefano Massaroli, Michael Poli, Sho Sonoda +4
We detail a novel class of implicit neural models. Leveraging time-parallel methods for differential equations, Multiple Shooting Layers (MSLs) seek solutions of initial value prob…
Learning Stochastic Optimal Policies via Gradient Descent
Stefano Massaroli, Michael Poli, Stefano Peluchetti +3
We systematically develop a learning-based treatment of stochastic optimal control (SOC), relying on direct optimization of parametric control policies. We propose a derivation of…
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…
Graph Neural Ordinary Differential Equations
Michael Poli, Stefano Massaroli, Junyoung Park +3
We introduce the framework of continuous--depth graph neural networks (GNNs). Graph neural ordinary differential equations (GDEs) are formalized as the counterpart to GNNs where th…