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..
Functional Variational Inference based on Stochastic Process Generators
Authors: Chao Ma, José Miguel Hernández-Lobato
NeurIPS 2021 | Venue PDF | 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 EMAIL José Miguel Hernández-Lobato University of Cambridge Cambridge, UK EMAIL |
| 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). |