SpHMC: Spectral Hamiltonian Monte Carlo
Authors: Haoyi Xiong, Kafeng Wang, Jiang Bian, Zhanxing Zhu, Cheng-Zhong Xu, Zhishan Guo, Jun Huan5516-5524
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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. |