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..
Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
Authors: Yunfei Long, Zilin Tian, Liguo Zhang, Huosheng Xu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate the effectiveness of the proposed RIS method in three critical aspects: improved convergence, enhanced predictive performance, and successful uncertainty estimation for out-of-distribution data. |
| Researcher Affiliation | Academia | 1College Of Computer Science And Technology, Harbin Engineering University, Harbin, Heilongjiang, China 2Modeling and Emulation in E-Government National Engineering Laboratory, Harbin Engineering University, Harbin, Heilongjiang, China. |
| Pseudocode | Yes | Algorithm 1 The first moment propagation in l-th via Reparameterized Importcance Sampling |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | Our experiments on real-world applications include Le Net architecture (Le Cun et al., 1998) for MNIST digit dataset, Res Net20, Res Net56 architecture(He et al., 2016), on CIFAR-10 and CIFAR-100 datasets (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | The accuracies represent how well the model recognizes the validation set during training. |
| Hardware Specification | Yes | We implement the above Bayesian architecture and train them with RIS and with standard Monte Carlo sampling (...), under the Py Torch framework, on a Titan RTX 28G device, and using the same random seeds. |
| Software Dependencies | No | The paper mentions using the 'Py Torch framework' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | We optimize the two loss objectives using adam (Kingma & Ba, 2014) for same step size. |