mlsanityData to 7 Oct 2026

Deep learning with differential Gaussian process flows

arXiv 1810.04066 ↗First posted 9 Oct 2018cs.LG stat.MLcited by 11

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Abstract (arXiv)

We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regression function. The key property of differential Gaussian processes is the warping of inputs through infinitely deep, but infinitesimal, differential fields, that generalise discrete layers into a dynamical system. We demonstrate state-of-the-art results that exceed the performance of deep Gaussian processes and neural networks

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Oct 2018.

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Citing papers per year: 2021: 7; 2022: 1; 2023: 2; 2024: 1.

11 papers.

  1. 2021Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equationscited by 38
  2. 2021Constructing Neural Network-Based Models for Simulating Dynamical Systemscited by 29
  3. 2021Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equationcited by 14
  4. 2021Inferring the Structure of Ordinary Differential Equationscited by 7
  5. 2023Bayesian Gaussian Process ODEs via Double Normalizing Flowscited by 7
  6. 2024Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equationscited by 4
  7. 2023A General Framework for Uncertainty Quantification via Neural SDE-RNNcited by 2
  8. 2021Approximate Latent Force Model Inferencecited by 1
  9. 2021Aligned Multi-Task Gaussian Processcited by 1
  10. 2022Shallow and Deep Nonparametric Convolutions for Gaussian Processescited by 1
  11. 2021Traversing Time with Multi-Resolution Gaussian Process State-Space Modelscited by 0