Diversity-Promoting Bayesian Learning of Latent Variable Models
Authors: Pengtao Xie, Jun Zhu, Eric Xing
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose two approaches that have complementary advantages. One is to define diversity-promoting mutual angular priors... We develop two efficient approximate posterior inference algorithms... The other approach is to impose diversity-promoting regularization directly over the post-data distribution of components. These two methods are applied to the Bayesian mixture of experts model to encourage the experts to be diverse and experimental results demonstrate the effectiveness and efficiency of our methods. |
| Researcher Affiliation | Academia | Pengtao Xie PENGTAOX@CS.CMU.EDU Jun Zhu DCSZJ@TSINGHUA.EDU.CN Eric P. Xing EPXING@CS.CMU.EDU Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213 USA Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList, CBICR Center, Tsinghua University, China |
| Pseudocode | No | The paper does not contain any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links, explicit statements of code release) for its source code. |
| Open Datasets | Yes | We used two binary-classification datasets. The first one is the Adult-9 (Platt et al., 1999) dataset... The other dataset is SUNBuilding compiled from the SUN (Xiao et al., 2010) dataset |
| Dataset Splits | Yes | Adult-9... 33K training instances and 16K testing instances. The other dataset is SUNBuilding... 70% of images are used for training and the rest for testing. All parameters were tuned using 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | All parameters were tuned using 5-fold cross validation. |