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..
Function Space Particle Optimization for Bayesian Neural Networks
Authors: Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate through extensive experiments that our method successfully overcomes this issue, and outperforms strong baselines in a variety of tasks including prediction, defense against adversarial examples, and reinforcement learning. (Abstract) and In this section, we evaluate our method on a variety of tasks. (Section 5, Evaluation) |
| Researcher Affiliation | Academia | Ziyu Wang, Tongzheng Ren, Jun Zhu , Bo Zhang Department of Computer Science & Technology, Institute for Artificial Intelligence, State Key Lab for Intell. Tech. & Sys., BNRist Center, THBI Lab, Tsinghua University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Function Space POVI for Bayesian Neural Network |
| Open Source Code | Yes | Code for the experiments will be available at https://github.com/thu-ml/fpovi. |
| Open Datasets | Yes | We evaluate the predictive performance on several standard regression and classification datasets: a number of UCI datasets for real-valued regression, and the MNIST dataset for classification. (Section 5.2) and We use the mushroom and wheel bandits from Riquelme et al. (2018). (Section 5.4) |
| Dataset Splits | Yes | We use a 90-10 random traintest split, repeated for 20 times, except for Protein in which we use 5 replicas. (Appendix A.2.1) and We hold out the last 10,000 examples in training set for model selection. (Section 5.2.2, MNIST) |
| Hardware Specification | No | No specific hardware details (like CPU/GPU models, memory) are provided. |
| Software Dependencies | No | The implementation is based on Zhu Suan (Shi et al., 2017). (Section 5). While “Zhu Suan” is a software name, no version number is given for it, nor for any other libraries like Python, PyTorch/TensorFlow, etc. |
| Experiment Setup | Yes | For our method in all datasets, we use the Ada M optimizer with a learning rate of 0.004. For datasets with fewer than 1000 samples, we use a batch size of 100 and train for 500 epochs. For the larger datasets, we set the batch size to 1000, and train for 1000 epochs. (Appendix A.2.1) |