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..
SpHMC: Spectral Hamiltonian Monte Carlo
Authors: Haoyi Xiong, Kafeng Wang, Jiang Bian, Zhanxing Zhu, Cheng-Zhong Xu, Zhishan Guo, Jun Huan5516-5524
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been conducted to evaluate the proposed algorithm using real-world datasets. The performance comparisons on three real-world applications demonstrate the superior performance of Sp HMC beyond baseline methods. |
| Researcher Affiliation | Collaboration | 1Big Data Lab, Baidu Inc. & National Engineering Laboratory for Deep Learning Technology and Applications, China 2Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, China 3Department of Electrical and Computer Engineering, University of Central Florida, United States 4Deep Learning Laboratory, Peking University & Beijing Institute of Big Data Research, China |
| Pseudocode | Yes | Algorithm 1 Spectral Sampler using Stochastic Gradient Hamiltonian Monte Carlo |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We propose to evaluate Sp HMC using three real-world benchmark datasets including MNIST, Fashion MNIST, and EMNIST |
| Dataset Splits | No | While the paper mentions "10 folder cross-validation on the training set" for hyper-parameter tuning, it does not explicitly provide the specific percentages or counts for training, validation, and test dataset splits needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | All other parameters such as regularization term λ, step size η and batch size m for both Sp HMC and baselines are all tuned best with repeat trials. |