Faster Relative Entropy Coding with Greedy Rejection Coding
Authors: Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 gf332@cam.ac.uk Stratis Markou Department of Engineering University of Cambridge em626@cam.ac.uk José Miguel Hernández Lobato Department of Engineering University of Cambridge jmh233@cam.ac.uk |
| 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. |