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..
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning
Authors: Veit David Wild, Robert Hu, Dino Sejdinovic
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method obtains state-of-the-art performance on several benchmark datasets. |
| Researcher Affiliation | Collaboration | Veit D. Wild Department of Statistics University of Oxford EMAIL Robert Hu Amazon EMAIL Dino Sejdinovic School of Computer and Mathematical Sciences University of Adelaide EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Codebase: https://github.com/Mr Huff/GWI |
| Open Datasets | Yes | UCI Regression... Fashion MNIST [Xiao et al., 2017] and CIFAR-10 [Krizhevsky et al., 2009] |
| Dataset Splits | No | The paper mentions 'We train on random 90% of the data and predict on 10%' for training and testing, but does not explicitly provide details for a separate validation split in the main text. |
| Hardware Specification | Yes | All experiments were performed on a single NVIDIA GeForce RTX 3090 GPU with 24GB of memory. |
| Software Dependencies | No | The paper mentions using 'deepobs library [Schneider et al., 2019]' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | For the UCI experiments, we used a single hidden layer MLP with 50 units and ReLU activations. We used a batch size of 128 and trained for 200 epochs using the Adam optimizer with a learning rate of 0.001. |