Kernel Bayesian Inference with Posterior Regularization
Authors: Yang Song, Jun Zhu, Yong Ren
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare the results of k Reg Bayes and several other baselines for two state-space filtering tasks. The results are summarized in Fig. 5. p KBR has lower errors compared to KBR, which means the thresholding regularization is practically no worse than the original squared regularization. The lower MSE of k Reg Bayes compared with p KBR shows that the posterior regularization successfully incorporates information from equations of the dynamics. |
| Researcher Affiliation | Academia | Yang Song , Jun Zhu , Yong Ren Dept. of Physics, Tsinghua University, Beijing, China Dept. of Comp. Sci. & Tech., TNList Lab; Center for Bio-Inspired Computing Research State Key Lab for Intell. Tech. & Systems, Tsinghua University, Beijing, China yangsong@cs.stanford.edu; {dcszj@, renyong15@mails}.tsinghua.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The data points {(θt, xt, yt)} are generated from the dynamics... There are 1000 training data and 200 validation/test data for each algorithm. For training data, we set R1 = 0 and R2 = 10 while for validation data and test data we set R1 = 5 and R2 = 7. The paper describes how the data was generated but does not provide concrete access information (link, DOI, repository, or formal citation to a pre-existing public dataset) for the specific datasets used. |
| Dataset Splits | Yes | There are 1000 training data and 200 validation/test data for each algorithm. For training data, we set R1 = 0 and R2 = 10 while for validation data and test data we set R1 = 5 and R2 = 7. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We follow [5] to set our parameters. In all experiments, we flatten the images to a column vector and apply Gaussian RBF kernels if needed. The kernel band widths are set to be the median distances in the training data. Based on experiments on the validation dataset, we set λT = 1e 6 = 2δT and µT = 1e 5. |