Functional Variational Inference based on Stochastic Process Generators
Authors: Chao Ma, José Miguel Hernández-Lobato
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that FVI consistently outperforms weight-space and function-space VI methods on several tasks, which validates the effectiveness of our approach. |
| Researcher Affiliation | Academia | Chao Ma University of Cambridge Cambridge, UK cm905@cam.ac.uk José Miguel Hernández-Lobato University of Cambridge Cambridge, UK jmh233@cam.ac.uk |
| Pseudocode | Yes | Algorithm 1 Functional Variational Inference (FVI) |
| Open Source Code | No | The paper mentions that 'F-BNNs are based on the code kindly open-sourced by [47]' which refers to a third-party's code, not the code for the authors' own method (FVI). |
| Open Datasets | Yes | We consider multivariate regression tasks based on 9 different UCI datasets. We also compare with three function-space BNN inference methods: VIP-BNNs, VIP-Neural processes [28], and f-BNNs. Finally, we include comparisons to function space particle optimization [50] in Appendix C.7 for reference purpose. All inference methods are based on the same BNN priors whenever applicable. For experimental settings, we follow [28]. Each dataset was randomly split into train (90%) and test sets (10%). This was repeated 10 times and results were averaged. |
| Dataset Splits | Yes | Each dataset was randomly split into train (90%) and test sets (10%). |
| Hardware Specification | No | The paper does not specify any hardware details like GPU/CPU models, memory, or specific cloud instance types used for experiments. |
| Software Dependencies | No | The paper mentions various software components and methods (e.g., 'Bayes-by-Backprop [4]', 'PyTorch' in general context), but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | The hyperparameter settings are consistent with [47] except that we used a smaller batchsize (32). |