89 citations · 262 across the 16 of their papers we have counts for
17 papers
Motion Degeneracy in Self-supervised Learning of Elevation Angle Estimation for 2D Forward-Looking Sonar
Yusheng Wang, Yonghoon Ji, Chujie Wu +3
2D forward-looking sonar is a crucial sensor for underwater robotic perception. A well-known problem in this field is estimating missing information in the elevation direction duri…
2D Forward Looking Sonar Simulation with Ground Echo Modeling
Yusheng Wang, Chujie Wu, Yonghoon Ji +3
Imaging sonar produces clear images in underwater environments, independent of water turbidity and lighting conditions. The next generation 2D forward looking sonars are compact in…
Learning Pseudo Front Depth for 2D Forward-Looking Sonar-based Multi-view Stereo
Yusheng Wang, Yonghoon Ji, Hiroshi Tsuchiya +2
Retrieving the missing dimension information in acoustic images from 2D forward-looking sonar is a well-known problem in the field of underwater robotics. There are works attemptin…
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…
Stereo Camera Visual SLAM with Hierarchical Masking and Motion-state Classification at Outdoor Construction Sites Containing Large Dynamic Objects
Runqiu Bao, Ren Komatsu, Renato Miyagusuku +3
At modern construction sites, utilizing GNSS (Global Navigation Satellite System) to measure the real-time location and orientation (i.e. pose) of construction machines and navigat…
Optimal Energy Shaping via Neural Approximators
Stefano Massaroli, Michael Poli, Federico Califano +3
We introduce optimal energy shaping as an enhancement of classical passivity-based control methods. A promising feature of passivity theory, alongside stability, has traditionally…
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…
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…
Port-Hamiltonian Approach to Neural Network Training
Stefano Massaroli, Michael Poli, Federico Califano +4
Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations. Recent work proposes ne…