Efficient Approximate Inference for Stationary Kernel on Frequency Domain
Authors: Yohan Jung, Kyungwoo Song, Jinkyoo Park
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide the experiments results validating the performances of the proposed model using the various data sets. |
| Researcher Affiliation | Academia | 1Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon , South Korea 2Department of Artificial Intelligence, University of Seoul, Seoul, South Korea. Correspondence to: Yohan Jung <becre1776@kaist.ac.kr>. |
| Pseudocode | Yes | Algorithm 1 Approximate Inference for the spectral density parameters {wq, µq, σq}Q q=1 and noise parameter σϵ. |
| Open Source Code | Yes | We provide our implementation at https: //github.com/becre2021/ABInfer GSM and additional results in Appendix D. |
| Open Datasets | Yes | Passenger data set used in (Wilson & Adams, 2013). ... bike dataset (N=17379, D=17) in UCI benchmark set (Dua & Graff, 2017). |
| Dataset Splits | Yes | We equally divide five partitions of the dataset and randomly select the training, validation, and test set with a ratio of 8:1:1 for each partition. We pick the best kernel parameters with the lowest RMSE on validation set and used them for conducting predictions on test set. |
| Hardware Specification | Yes | We use Py Torch (1.7.0) (Paszke et al., 2019) and employ RTX2080TI-11GB and V100-16GB for GPU. |
| Software Dependencies | Yes | We use Py Torch (1.7.0) (Paszke et al., 2019) |
| Experiment Setup | Yes | For the baseline learning method (maximization of log marginal likelihood known as MLE-Type 2), SVSS, and SVSS-Ws, we use the Adam optimizer (Kingma & Ba, 2014) with the learning rate lr = .005. ... We run 1000 and 1200 iterations for training the Parkinsons and the Bike dataset, respectively. ... We run 1500 iterations for training. |