Stochastic Fractional Hamiltonian Monte Carlo
Authors: Nanyang Ye, Zhanxing Zhu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the proposed method on both sampling and optimization, we conduct experiments on synthetic examples and mnist classification task. For sampling, we compare our method with FLD, HMC and LD. For training deep neural networks, we compare our method with popular optimization methods-SGD, Adam, RMSprop. The same parameter initialization is used for all methods. |
| Researcher Affiliation | Academia | Nanyang Ye1, Zhanxing Zhu 2,3 1 University of Cambridge, Cambridge, United Kingdom 2 Center for Data Science, Peking University, Beijing, China 3 Beijing Institute of Big Data Research (BIBDR) |
| Pseudocode | Yes | Algorithm 1 (Stochastic Gradient) Fractional Hamiltonian Monte Carlo |
| Open Source Code | No | Our implementation is adapted from https://github.com/hwalsuklee/tensorflow-mnist-VAE. The paper does not explicitly provide a link to the authors' own implementation of the proposed FHMC/SGFHMC methodology. |
| Open Datasets | Yes | We used the training set of MNIST dataset consisting of 60000 training images for this task. |
| Dataset Splits | No | During training, the dataset is split into training, validation and test dataset. (No specific percentages or counts are given for these splits to be reproducible). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Tensorflow' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The best parameter setting for each method are: SGFHMC(learning rate is 0.03, momentum is 0.9, α is 1.6), SGD(learning rate is 0.003, momentum is 0.2), Adam(learning rate is 0.0001), RMSprop (learning rate is 0.0001). |