Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Authors: Tameem Adel, Zoubin Ghahramani, Adrian Weller
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we achieve state-of-the-art results on three datasets using the two proposed algorithms. |
| Researcher Affiliation | Collaboration | 1University of Cambridge, UK 2Leverhulme CFI, Cambridge, UK 3Uber AI Labs, USA 4The Alan Turing Institute, UK. |
| Pseudocode | Yes | The key steps of the algorithm are shown in Algorithm 1 in the Appendix. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is publicly available, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We qualitatively and quantitatively evaluate the proposed frameworks on three datasets: MNIST, SVHN and Chairs. [...] Side information used with a few of the MNIST images are the digit labels and thickness. Side information for SVHN is the lighting condition and saturation degree, and it comes in the form of azimuth and width for the 3D Chairs data. |
| Dataset Splits | No | The paper mentions 'training size' and 'test set' but does not provide explicit details about a validation set split or how data was partitioned for validation purposes. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not specify the version numbers of any software dependencies used in the experiments. |
| Experiment Setup | No | The paper states 'Details of the datasets and experiments are provided in Sections 10 and 12 of the Appendix, respectively,' indicating that explicit setup details like hyperparameters are not in the main text. |