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..
Exact Rate-Distortion in Autoencoders via Echo Noise
Authors: Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We proceed to analyse the log-likelihood performance of relevant models on three image datasets: static Binary MNIST [38], Omniglot [24] as adapted by Burda et al. [8], and Fashion MNIST (f MNIST) [44]. All models are trained with 32 latent variables using the same convolutional architecture as in Alemi et al. [3] except with Re LU activations. We trained using Adam optimization for 200 epochs, with an initial learning rate of 0.0003 decaying linearly to 0 over the last 100 epochs. Results are averaged from ten runs of each model after removing the highest and lowest outliers. |
| Researcher Affiliation | Academia | Information Sciences Institute University of Southern California Marina del Rey, CA 90292 brekelma, EMAIL; galstyan, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology it describes. |
| Open Datasets | Yes | We proceed to analyse the log-likelihood performance of relevant models on three image datasets: static Binary MNIST [38], Omniglot [24] as adapted by Burda et al. [8], and Fashion MNIST (f MNIST) [44]. |
| Dataset Splits | No | The paper mentions training and testing but does not specify explicit training/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. While it evaluates on 'test' sets, it does not specify how the data was partitioned for validation. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimization' and 'Re LU activations' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | All models are trained with 32 latent variables using the same convolutional architecture as in Alemi et al. [3] except with Re LU activations. We trained using Adam optimization for 200 epochs, with an initial learning rate of 0.0003 decaying linearly to 0 over the last 100 epochs. |