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..
Hierarchical Quantized Autoencoders
Authors: Will Williams, Sam Ringer, Tom Ash, David MacLeod, Jamie Dougherty, John Hughes
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide qualitative and quantitative evaluations on the Celeb A and MNIST datasets. and 6 Experiments |
| Researcher Affiliation | Industry | Will Williams EMAIL Sam Ringer EMAIL John Hughes EMAIL Tom Ash EMAIL David Mac Leod EMAIL Jamie Dougherty EMAIL |
| Pseudocode | Yes | Algorithm 1 Lossy Compression Pseudo-code Using A Quantized Hierarchy |
| Open Source Code | Yes | Code available at https://github.com/speechmatics/hqa |
| Open Datasets | Yes | We provide qualitative and quantitative evaluations on the Celeb A and MNIST datasets. and provides citations [21] and [18] which point to the public datasets. E.g., for Celeb A: Z. Liu, P. Luo, X. Wang, and X. Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015. URL http:// mmlab.ie.cuhk.edu.hk/projects/Celeb A.html. |
| Dataset Splits | No | The paper mentions evaluating on 'Celeb A test examples' and 'MNIST 10k test samples' but does not provide specific training, validation, and test dataset split percentages or counts for reproducibility beyond the test set size. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various techniques and frameworks (e.g., VAEs, VQ-VAEs, Gumbel Softmax) but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | While training HQA, we linearly decay the Gumbel Softmax temperature to 0 so the soft quantization operation closely resembles hard quantization... and We control for the number of parameters (∼1M) in each system, training each with codebook size 256 and dimension 64. |