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 [1].
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework
Authors: Chao Zhang, Zhijian Li, Xin Du, Hui Qian
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithms on various synthetic and realworld tasks. The empirical results demonstrate the superiority of our dynamic weight strategy over the fixed weight strategy, and our DPVI algorithms constantly outperform their fixedweight counterparts in all the tasks. |
| Researcher Affiliation | Collaboration | Chao Zhang1,2 , Zhijian Li3 , Xin Du3 and Hui Qian1,2 1College of Computer Science and Technology, Zhejiang University 2Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies 3Information Science and Electronic Engineering, Zhejiang University |
| Pseudocode | Yes | Algorithm 1 Dynamic-weight Particle-based Variational Inference (DPVI) Framework |
| Open Source Code | No | No explicit statement or link to open-source code for the methodology was found. |
| Open Datasets | No | The paper mentions using 'LIDAR', 'UCI', and 'LIBSVM' datasets, but does not provide specific links, DOIs, or formal citations with author/year for accessing these datasets as used in the paper. UCI and LIBSVM are large repositories, not specific datasets with concrete access info. |
| Dataset Splits | No | The paper does not provide specific percentages, sample counts, or explicit methodologies for training, validation, or test splits. It refers to 'batchsize' for BNN but not data splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments are provided. |
| Software Dependencies | No | The paper mentions the 'POT library' but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | Yes | In this task, we set the particle number to M = 128 for all the algorithms. For all the datasets, we set the batchsize as 128. |