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..
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework
Authors: Fangyikang Wang, Huminhao Zhu, Chao Zhang, Hanbin Zhao, Hui Qian
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world data demonstrate the faster convergence and reduced approximation error of GAD-PVI methods over the state-of-the-art. We evaluate our algorithms on various synthetic and real-world tasks. The empirical results demonstrate the superiority of our GAD-PVI methods. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Zhejiang University 2Advanced Technology Institute, Zhejiang University 3State Key Lab of CAD&CG, Zhejiang University |
| Pseudocode | Yes | Algorithm 1: General Accelerated Dynamic-weight Particlebased Variational Inference (GAD-PVI) framework |
| Open Source Code | Yes | Our appendix can be downloaded at https://github.com/zituitui/GAD-PVI paper |
| Open Datasets | Yes | We follow the experiment setting in (Chen et al. 2018b), and use the dataset LIDAR which consists of 221 observations. In this experiment, we study a Bayesian regression task with Bayesian neural network on 4 datasets from UCI and LIBSVM. |
| Dataset Splits | Yes | In the BNN task, we split 1/5 of the training set as our validation set to tune the parameters. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'POT library (Flamary et al. 2021)' but does not specify a version number. Other software mentions are generic without version details. |
| Experiment Setup | Yes | For all the algorithms, the particles weights are initialized to be equal. In the BNN task, we split 1/5 of the training set as our validation set to tune the parameters. The position step-size are tuned via grid search for the fixed-weight Par VI algorithms, then used in the corresponding dynamic-weight algorithms. The acceleration parameters and weight adjustment parameters are tuned via grid search for each specific algorithm. We use a one-hidden-layer neural network with 50 hidden units and maintain 128 particles. For all the datasets, we set the batchsize as 128. |