Timezone: »
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.
Author Information
Stefano Massaroli (The University of Tokyo)
Michael Poli (KAIST)
My work spans topics in deep learning, dynamical systems, variational inference and numerical methods. I am broadly interested in ensuring the successes achieved by deep learning methods in computer vision and natural language are extended to other engineering domains.
Jinkyoo Park (KAIST)
Atsushi Yamashita (The University of Tokyo)
Hajime Asama (The University of Tokyo)
Related Events (a corresponding poster, oral, or spotlight)
-
2020 Poster: Dissecting Neural ODEs »
Tue. Dec 8th 05:00 -- 07:00 PM Room Poster Session 1 #222
More from the Same Authors
-
2022 : Scale-conditioned Adaptation for Large Scale Combinatorial Optimization »
Minsu Kim · Jiwoo SON · Hyeonah Kim · Jinkyoo Park -
2022 : Collaborative symmetricity exploitation for offline learning of hardware design solver »
HAEYEON KIM · Minsu Kim · joungho kim · Jinkyoo Park -
2022 : Neural Coarsening Process for Multi-level Graph Combinatorial Optimization »
Hyeonah Kim · Minsu Kim · Changhyun Kwon · Jinkyoo Park -
2022 Workshop: The Symbiosis of Deep Learning and Differential Equations II »
Michael Poli · Winnie Xu · Estefany Kelly Buchanan · Maryam Hosseini · Luca Celotti · Martin Magill · Ermal Rrapaj · Qiyao Wei · Stefano Massaroli · Patrick Kidger · Archis Joglekar · Animesh Garg · David Duvenaud -
2022 Poster: Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization »
Minsu Kim · Junyoung Park · Jinkyoo Park -
2022 Poster: Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning »
HYUNWOOK KANG · Taehwan Kwon · Jinkyoo Park · James R. Morrison -
2022 Poster: Transform Once: Efficient Operator Learning in Frequency Domain »
Michael Poli · Stefano Massaroli · Federico Berto · Jinkyoo Park · Tri Dao · Christopher RĂ© · Stefano Ermon -
2021 : Neural Solvers for Fast and Accurate Numerical Optimal Control »
Federico Berto · Stefano Massaroli · Michael Poli · Jinkyoo Park -
2021 : TorchDyn: Implicit Models and Neural Numerical Methods in PyTorch »
Michael Poli · Stefano Massaroli · Atsushi Yamashita · Hajime Asama · Jinkyoo Park · Stefano Ermon -
2021 Workshop: The Symbiosis of Deep Learning and Differential Equations »
Luca Celotti · Kelly Buchanan · Jorge Ortiz · Patrick Kidger · Stefano Massaroli · Michael Poli · Lily Hu · Ermal Rrapaj · Martin Magill · Thorsteinn Jonsson · Animesh Garg · Murtadha Aldeer -
2021 Poster: Differentiable Multiple Shooting Layers »
Stefano Massaroli · Michael Poli · Sho Sonoda · Taiji Suzuki · Jinkyoo Park · Atsushi Yamashita · Hajime Asama -
2021 Poster: Learning Collaborative Policies to Solve NP-hard Routing Problems »
Minsu Kim · Jinkyoo Park · joungho kim -
2021 Poster: Neural Hybrid Automata: Learning Dynamics With Multiple Modes and Stochastic Transitions »
Michael Poli · Stefano Massaroli · Luca Scimeca · Sanghyuk Chun · Seong Joon Oh · Atsushi Yamashita · Hajime Asama · Jinkyoo Park · Animesh Garg -
2020 Poster: Hypersolvers: Toward Fast Continuous-Depth Models »
Michael Poli · Stefano Massaroli · Atsushi Yamashita · Hajime Asama · Jinkyoo Park