Papers › Augmented Neural ODEs
Augmented Neural ODEs
Emilien Dupont, Arnaud Doucet, Yee Whye Teh
We show that Neural Ordinary Differential Equations (ODEs) learn representations that preserve the topology of the input space and prove that this implies the existence of functions Neural ODEs cannot represent. To address these limitations, we introduce Augmented Neural ODEs which, in addition to being more expressive models, are empirically more stable, generalize better and have a lower computational cost than Neural ODEs.
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Code
Syntology Ran 2 of 9 code samples harvested from 1 repository linked to this paper; 7 have no recorded run. Of those that ran: 2 ran · our draft was wrong.
By repository: official repository: 7 samples from 1 repository, 0 ran; 2 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
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Code Syntology ran Syntology
9 samples harvested; 2 ran; 0 honoured the contract we drafted; 7 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 2 of the 9 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from EmilienDupont/augmented-neural-odes. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | ANODE | Percentage correct | 60.6 | #262 of 265 | Archive leaderboard | report |
| Image Classification | MNIST | Augmented Neural Ordinary Differential Equation | Accuracy | 99.63 | #23 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | Augmented Neural Ordinary Differential Equation | Percentage error | 0.37 | #23 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | ANODE | Accuracy | 98.2 | #55 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | ANODE | Percentage error | 1.8 | #55 of 81 | Archive leaderboard | report |
| Image Classification | SVHN | ANODE | Percentage error | 16.5 | #48 of 62 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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