%0 Generic %1 massaroli2020dissecting %A Massaroli, Stefano %A Poli, Michael %A Park, Jinkyoo %A Yamashita, Atsushi %A Asama, Hajime %D 2020 %K deeplearning neuralode ode %T Dissecting Neural ODEs %U http://arxiv.org/abs/2002.08071 %X 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 öpen the box", further developing the continuous-depth formulation with the aim of clarifying the influence of several design choices on the underlying dynamics.

@misc{massaroli2020dissecting, 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.}, added-at = {2021-09-21T09:12:09.000+0200}, author = {Massaroli, Stefano and Poli, Michael and Park, Jinkyoo and Yamashita, Atsushi and Asama, Hajime}, biburl = {https://www.bibsonomy.org/bibtex/2d9d569e827454b8ea7b9e39b3b71fa0d/annakrause}, description = {2002.08071.pdf}, interhash = {132078e96a914edcaa7367f0f9cf75d4}, intrahash = {d9d569e827454b8ea7b9e39b3b71fa0d}, keywords = {deeplearning neuralode ode}, note = {cite arxiv:2002.08071}, timestamp = {2021-09-21T09:12:09.000+0200}, title = {Dissecting Neural ODEs}, url = {http://arxiv.org/abs/2002.08071}, year = 2020 }