Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
Authors: Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We answer this question by showing a convenient criterion: a CF-INN is universal if its layers contain affine coupling and invertible linear functions as special cases. As its corollary, we can affirmatively resolve a previously unsolved problem: whether normalizing flow models based on affine coupling can be universal distributional approximators. In the course of proving the universality, we prove a general theorem to show the equivalence of the universality for certain diffeomorphism classes, a theoretical insight that is of interest by itself. |
| Researcher Affiliation | Academia | Takeshi Teshima The University of Tokyo, RIKEN EMAIL Isao Ishikawa* Ehime University, RIKEN EMAIL Koichi Tojo RIKEN EMAIL Kenta Oono The University of Tokyo EMAIL Masahiro Ikeda RIKEN EMAIL Masashi Sugiyama RIKEN, The University of Tokyo EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit statement of code release) for the source code of the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe experiments that use training datasets. Therefore, it does not provide concrete access information for a publicly available or open dataset used for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments that involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not mention specific ancillary software details with version numbers needed to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments, thus no experimental setup details like hyperparameters or training configurations are provided. |