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..
Storchastic: A Framework for General Stochastic Automatic Differentiation
Authors: Emile Krieken, Jakub Tomczak, Annette Ten Teije
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we implement Storchastic as a Py Torch library at github.com/HEmile/storchastic. ... We run this model on our currently implemented set of gradient estimation methods for discrete variables in Appendix E and report the results, which are meant purely to illustrate the case study. |
| Researcher Affiliation | Academia | Emile van Krieken Vrije Universiteit Amsterdam EMAIL Jakub M. Tomczak Vrije Universiteit Amsterdam EMAIL Annette ten Teije Vrije Universiteit Amsterdam EMAIL |
| Pseudocode | Yes | Algorithm 1 The Storchastic framework: Compute a Monte Carlo estimate of the n-th order gradient given k gradient estimators qi, wi, li, ai . |
| Open Source Code | Yes | Finally, we implement Storchastic as a Py Torch library at github.com/HEmile/storchastic. ... We implemented Storchastic as an open source Py Torch [39] library 1. 1Code is available at github.com/HEmile/storchastic. |
| Open Datasets | Yes | 2.3 Example: Discrete Variational Autoencoder ... We run this model on our currently implemented set of gradient estimation methods for discrete variables in Appendix E and report the results, which are meant purely to illustrate the case study. ... For our experiments, we use the binarized MNIST dataset. |
| Dataset Splits | No | The paper describes using a dataset (MNIST) but does not provide specific details on how it was split into training, validation, and test sets. Appendix E mentions 'We train all models for 100 epochs with Adam [24] and a learning rate of 1e-3' but no explicit splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or cloud instance specifications). |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number. No other software dependencies with specific version numbers are listed. |
| Experiment Setup | Yes | In Figure 4, we show how to implement the discrete VAE. The implementation directly follows the SCG shown in Figure 2. ... In Appendix E, it states: 'We train all models for 100 epochs with Adam [24] and a learning rate of 1e-3'. |