Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
Authors: Yunfei Long, Zilin Tian, Liguo Zhang, Huosheng Xu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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. |