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..
A Category-theoretical Meta-analysis of Definitions of Disentanglement
Authors: Yivan Zhang, Masashi Sugiyama
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Although large-scale experimental studies exist (Locatello et al., 2019), theoretical analyses and systematic comparisons are limited (Sepliarskaia et al., 2019; Carbonneau et al., 2022). It is worth clarifying that this paper does not discuss metrics, models, methods, supervision, and learnability. |
| Researcher Affiliation | Academia | 1The University of Tokyo, Tokyo, Japan 2RIKEN AIP, Tokyo, Japan. |
| Pseudocode | No | The paper contains mathematical definitions, propositions, theorems, and various diagrams (commutative diagrams and string diagrams) but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper explicitly states, "It is worth clarifying that this paper does not discuss metrics, models, methods, supervision, and learnability." As it's a theoretical meta-analysis and does not propose a new implementable method, there is no source code for its own methodology to be provided. |
| Open Datasets | No | The paper is a theoretical meta-analysis and does not involve conducting experiments or training models on datasets. Thus, it does not specify any dataset for training. |
| Dataset Splits | No | The paper is a theoretical meta-analysis and does not involve conducting experiments or data splits for validation. No information on dataset splits (train/validation/test) is provided. |
| Hardware Specification | No | The paper is a theoretical work and does not describe any computational experiments. Therefore, no hardware specifications for running experiments are provided. |
| Software Dependencies | No | The paper is a theoretical work and does not describe any computational experiments. Therefore, no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is a theoretical work and does not describe any experiments, models, or training procedures. Therefore, no experimental setup details such as hyperparameters or training settings are provided. |