What is recorded here?
Parameterized the derivative of hidden state with a neural network and used differential-equation solvers to define continuous-depth models, latent time-series models and continuous normalizing flows.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact 2018 NeurIPS paper. Classical ODE solvers, residual networks, later augmented neural ODEs and claims about universal superiority or physical discovery remain separate.
Read the original work
- NeurIPS, Neural Ordinary Differential Equations
- Chen et al., Neural Ordinary Differential Equations preprint
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Ricky T. Q. Chen; Yulia Rubanova; Jesse Bettencourt; David K. Duvenaud
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
Research locations and affiliations
Documented mapping · Included in country comparison
Documented countries / regions: Canada
Documented research locations or paper affiliations for this contribution. Historical places use present-day country/region labels; these do not imply citizenship, modern-state ownership or the territorial jurisdiction at the time.
English catalogue narrative
Chen and colleagues: neural ordinary differential equations
Parameterized the derivative of hidden state with a neural network and used differential-equation solvers to define continuous-depth models, latent time-series models and continuous normalizing flows.
Scientific scope
The record is the exact 2018 NeurIPS paper. Classical ODE solvers, residual networks, later augmented neural ODEs and claims about universal superiority or physical discovery remain separate.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Ricky T. Q. Chen; Yulia Rubanova; Jesse Bettencourt; David K. Duvenaud
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.