Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Authors: Martin Jørgensen, Soren Hauberg
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |