89 citations · 262 across the 16 of their papers we have counts for
6 papers · 1 filter
Data-Driven Koopman Controller Synthesis Based on the Extended Norm Characterization
Daisuke Uchida, Atsushi Yamashita, Hajime Asama
This paper presents a new data-driven controller synthesis based on the Koopman operator and the extended norm characterization of discrete-time linear systems. We…
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…
360 Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron
Ren Komatsu, Hiromitsu Fujii, Yusuke Tamura +2
In this study, we present a method for all-around depth estimation from multiple omnidirectional images for indoor environments. In particular, we focus on plane-sweeping stereo as…
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…