Storchastic: A Framework for General Stochastic Automatic Differentiation
Authors: Emile Krieken, Jakub Tomczak, Annette Ten Teije
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 e.van.krieken@vu.nl Jakub M. Tomczak Vrije Universiteit Amsterdam j.m.tomczak@vu.nl Annette ten Teije Vrije Universiteit Amsterdam annette.ten.teije@vu.nl |
| 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'. |