mlsanityData to 7 Oct 2026

Funnels: Exact maximum likelihood with dimensionality reduction

arXiv 2112.08069 ↗First posted 15 Dec 2021cs.LG stat.MLcited by 4

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Abstract (arXiv)

Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model. We use the SurVAE framework to construct dimension reducing surjective flows via a new layer, known as the funnel. We demonstrate its efficacy on a variety of datasets, and show it improves upon or matches the performance of existing flows while having a reduced latent space size. The funnel layer can be constructed from a wide range of transformations including restricted convolution and feed forward layers.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 41 references that are in the map. First posted on arXiv 15 Dec 2021.

Builds on

23 of 41 references.

References · 23

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11 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 1.

4 papers.

  1. 2023Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimationcited by 8
  2. 2022Flowification: Everything is a Normalizing Flowcited by 5
  3. 2023Uncertainty quantification and out-of-distribution detection using surjective normalizing flowscited by 0
  4. 2026Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learningcited by 0