NeurIPS 2020oral253 citations
Dissecting Neural ODEs
Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, Hajime Asama
Abstract
Continuous deep learning architectures have recently re-emerged as Neural Ordinary Differential Equations (Neural ODEs). This infinite-depth approach theoretically bridges the gap between deep learning and dynamical systems, offering a novel perspective. However, deciphering the inner working of these models is still an open challenge, as most applications apply them as generic black-box modules. In this work we ``open the box'', further developing the continuous-depth formulation with the aim of clarifying the influence of several design choices on the underlying dynamics.
BibTeX
@inproceedings{NEURIPS2020_293835c2,
author = {Massaroli, Stefano and Poli, Michael and Park, Jinkyoo and Yamashita, Atsushi and Asama, Hajime},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {3952--3963},
publisher = {Curran Associates, Inc.},
title = {Dissecting Neural ODEs},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/293835c2cc75b585649498ee74b395f5-Paper.pdf},
volume = {33},
year = {2020}
}