On Slicing Optimality for Mutual Information
Authors: Ammar Fayad, Majd Ibrahim
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through comprehensive experiments in benchmark domains, we demonstrate significant gains in our information measure than state-of-the-art baselines. |
| Researcher Affiliation | Academia | Ammar Fayad MIT Majd Ibrahim HIAST afayad@mit.edu |
| Pseudocode | Yes | We refer to Appendix C for the pseudocode of SI W. |
| Open Source Code | No | We refer to Appendix B for further analysis experiments and Appendix D for implementation details and hyperparameters provided at https://bit.ly/3fo Lke2. The paper does not explicitly state that source code is provided, only "implementation details and hyperparameters." |
| Open Datasets | Yes | We test these three methods along with Bi GAN (Donahue et al., 2016) on the STL-10 (Coates et al., 2011) and CIFAR10 (Krizhevsky et al., 2009) datasets, which consist of high-dimensional images. |
| Dataset Splits | No | The paper uses datasets like STL-10 and CIFAR10 and performs experiments but does not explicitly provide details on how the datasets were split into training, validation, and test sets or mention specific percentages/counts for reproduction. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for its experiments, such as GPU or CPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper does not explicitly provide specific software dependencies, such as library names with version numbers, required to replicate the experiments. |
| Experiment Setup | Yes | We refer to Appendix B for further analysis experiments and Appendix D for implementation details and hyperparameters provided at https://bit.ly/3fo Lke2. |