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..
Fast Relative Entropy Coding with A* coding
Authors: Gergely Flamich, Stratis Markou, Jose Miguel Hernandez-Lobato
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experimental evidence that AD* also has O(D [Q P]) expected runtime. We prove that AS* and AD* achieve an expected codelength of O(DKL[Q P]). Further, we introduce DAD*, an approximate algorithm based on AD* which retains its favourable runtime and has bias similar to that of alternative methods. Focusing on VAEs, we propose the Iso KL VAE (IKVAE), which can be used with DAD* to further improve compression efficiency. We evaluate A* coding with (IK)VAEs on MNIST, showing that it can losslessly compress images near the theoretically optimal limit. |
| Researcher Affiliation | Collaboration | 1Department of Engineering, University of Cambridge, Cambridge, UK 2Microsoft Research, Cambridge, UK 3Alan Turing Institute, London, UK. |
| Pseudocode | Yes | Algorithm 1: A* coding. Blue parts show modifications of A* sampling (Maddison et al., 2014). |
| Open Source Code | Yes | We make this approximatant method available in our code repository. |
| Open Datasets | Yes | We evaluate A* coding with (IK)VAEs on MNIST, showing that it can losslessly compress images near the theoretically optimal limit. |
| Dataset Splits | No | The paper mentions using MNIST for image compression experiments but does not explicitly provide specific details on training, validation, or test splits (percentages or counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | For DAD*, we set Îș = 2 based on preliminary experiments. We compared the performance of AD* and DAD* on image compression experiments on MNIST, using the feedforward VAE architecture of Townsend et al. (2018), with Gaussian Q and P. |