mlsanityData to 7 Oct 2026

Neural Ordinary Differential Equations

arXiv 1806.07366 ↗First posted 19 Jun 2018cs.LG cs.AI stat.MLcited by 3,733

Not on the map. Why?

Cited by 568 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-box differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.

Key facts. Cited by 3,733 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2018.

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Citing papers per year: 2020: 1; 2021: 415; 2022: 463; 2023: 585; 2024: 620; 2025: 841; 2026: 808.

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