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..
Deterministic Variational Inference for Robust Bayesian Neural Networks
Authors: Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement deterministic variational inference (DVI) as described above to train small Re LU networks on UCI regression datasets (Dheeru & Karra Taniskidou, 2017). The experiments address the claims that our methods for eliminating gradient variance and automatic tuning of the prior improve the performance of the final trained model. |
| Researcher Affiliation | Collaboration | 1 Princeton Neuroscience Institute, Princeton University 2 Google AI Berlin 3 Department of Engineering, University of Cambridge 4 Microsoft Research, Cambridge |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Our implementation in Tensor Flow is available at https://github.com/Microsoft/deterministic-variational-inference |
| Open Datasets | Yes | We implement deterministic variational inference (DVI) as described above to train small Re LU networks on UCI regression datasets (Dheeru & Karra Taniskidou, 2017). |
| Dataset Splits | No | Each dataset is split into random training and test sets with 90% and 10% of the data respectively. This splitting process is repeated 20 times and the average test performance of each method at convergence is reported in table 2 |
| Hardware Specification | Yes | Figure 4 shows the time required to propagate activations through a single layer using the MCVI, DVI and d DVI methods on a Tesla V100 GPU. |
| Software Dependencies | No | Our implementation in Tensor Flow is available at https://github.com/Microsoft/deterministic-variational-inference (TensorFlow is mentioned, but no specific version number is provided.) |
| Experiment Setup | Yes | The same model is used for each inference method: a single hidden layer of 50 units for each dataset considered, extending this to 100 units in the special case of the larger protein structure dataset, prot. |