Iterative Project Quasi-Newton Algorithm for Training RBM
Authors: Shuai Mi, Xiaozhao Zhao, Yuexian Hou, Peng Zhang, Wenjie Li, Dawei Song
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate IPQN in a series of density estimation experiments on the artificial dataset and the MNIST digit dataset. Experimental results indicate that IPQN achieves an improved convergent performance over the traditional CD method. Experimental Study We experimentally investigate the IPQN algorithm in density estimation tasks for restricted Boltzmann machines. |
| Researcher Affiliation | Academia | Shuai Mi Tianjin University Tianjin, China watermelon0519@163.com Xiaozhao Zhao Tianjin University Tianjin, China 0.25eye@gmail.com Yuexian Hou Tianjin University Tianjin, China yxhou@tju.edu.cn Peng Zhang Tianjin University Tianjin, China pzhang@tju.edu.cn Wenjie Li The Hong Kong Polytechnic University Hong Kong cswjli@comp.polyu.edu.hk Dawei Song Tianjin University Tianjin, China dwsong@tju.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | In our experiments, we used the artificial data and the MNIST digit data. In this subsection, we experimentally investigate the performance of IPQN on real-world datasets in the context of density estimation. We used a RBM that contains 10 hidden units to learn the distribution density over the MNIST digit data. |
| Dataset Splits | No | In our experiments, the training set consists of 1000 cases and the test set consists of 1000 cases. No specific information about a validation set or detailed splitting methodology is provided beyond train/test split numbers. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions aspects like dataset dimensionality and the number of hidden units in the RBM, but it does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or training configurations. |