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..
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Authors: Martin Jørgensen, Soren Hauberg
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments first on a classical toy dataset and on the image datasets COIL20 and MNIST. |
| Researcher Affiliation | Academia | 1Department of Engineering Science, University of Oxford 2Department of Mathematics and Computer Science, Technical University of Denmark. |
| Pseudocode | No | The paper provides mathematical descriptions and equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We perform experiments first on a classical toy dataset and on the image datasets COIL20 and MNIST. |
| Dataset Splits | No | The paper does not specify explicit train/validation/test dataset splits, percentages, or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU/CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions the 'Adam-optimizer' and 'ARD-kernel' but does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | Yes | We use the Adam-optimizer (Kingma & Ba, 2014) with a learning rate of 3 10 3 and optimize sequentially q(z) and q(u) separately. We use m = 100 inducing points for q(u) and an ARD-kernel as covariance function. |