Discretely Relaxing Continuous Variables for tractable Variational Inference
Authors: Trefor Evans, Prasanth Nair
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate our approaches in section 5...Our empirical studies demonstrate superior performance relative to competing SVI methods on problems with as many as 2 million training points. In this section, we consider discretely relaxing a continuous Gaussian prior on the weights of a generalized linear regression model. This allows us to compare performance between a reparameterization gradient estimator for a continuous prior and our DIRECT method for a relaxed, discrete prior. Considering regression datasets from the UCI repository, we report the mean and standard deviation of the root mean squared error (RMSE) from 10-fold cross validation... In table 1, we see the results of our studies across several model-types. |
| Researcher Affiliation | Academia | Trefor W. Evans University of Toronto trefor.evans@mail.utoronto.ca Prasanth B. Nair University of Toronto pbn@utias.utoronto.ca |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our full code is available at https://github.com/treforevans/direct. |
| Open Datasets | Yes | Considering regression datasets from the UCI repository, we report the mean and standard deviation of the root mean squared error (RMSE) from 10-fold cross validation. Also presented is the mean training time per fold on a machine with two E5-2680 v3 processors and 128Gb of RAM, and the expected sparsity (percentage of zeros) within a posterior sample. All models use b 2000 basis functions. Further details of the experimental setup can be found in appendix E. In table 1, we see the results of our studies across several model-types. |
| Dataset Splits | Yes | we report the mean and standard deviation of the root mean squared error (RMSE) from 10-fold cross validation... 390% train, 10% test per fold. |
| Hardware Specification | Yes | Also presented is the mean training time per fold on a machine with two E5-2680 v3 processors and 128Gb of RAM |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not specify a version number or other software dependencies with version information. |
| Experiment Setup | Yes | Further details of the experimental setup can be found in appendix E. |