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..
Faster Relative Entropy Coding with Greedy Rejection Coding
Authors: Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate GRC in a variational autoencoderbased compression pipeline on MNIST, and show that a modified ELBO and an index-compression method can further improve compression efficiency. |
| Researcher Affiliation | Academia | Gergely Flamich Department of Engineering University of Cambridge EMAIL Stratis Markou Department of Engineering University of Cambridge EMAIL José Miguel Hernández Lobato Department of Engineering University of Cambridge EMAIL |
| Pseudocode | Yes | Algorithm 1 Harsha et al. s rejection algorithm; equivalent to GRC with a global partition Algorithm 2 GRC with partition process Z; differences to Harsha et al. s algorithm shown in green |
| Open Source Code | Yes | Our code is available at https://github. com/cambridge-mlg/fast-rec-with-grc. |
| Open Datasets | Yes | Finally, we evaluate GRC in a variational autoencoderbased compression pipeline on MNIST |
| Dataset Splits | No | The paper mentions training on MNIST and evaluating on the MNIST test set, but it does not specify a separate validation split or its size. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | We trained our VAE with L 2 {20, 50, 100} latent dimensions optimized using the negative ELBO and its modified version in Equation (12), and experimented with encoding the heap indices of GRCD with both δ and coding. |