Spectral Methods for Indian Buffet Process Inference
Authors: Hsiao-Yu Tung, Alexander J Smola
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments We evaluate the algorithm on a number of problems suitable for the two models of (2) and (3). The problems are largely identical to those put forward in [18] in order to keep our results comparable with a more traditional inference approach. We demonstrate that our algorithm is faster, simpler, and achieves comparable or superior accuracy. ... Figure 1 shows that our algorithm is faster and comparatively accurate. |
| Researcher Affiliation | Collaboration | Hsiao-Yu Fish Tung Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 Alexander J. Smola Machine Learning Department Carnegie Mellon University and Google Pittsburgh, PA 15213 |
| Pseudocode | Yes | Algorithm 1 Excess Correlation Analysis for Linear-Gaussian model with IBP prior |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | For a more realistic analysis we used a microarray dataset. The data consisted of 587 mouse liver samples detecting 8565 gene probes, available as dataset GSE2187 as part of NCBI s Gene Expression Omnibus www.ncbi.nlm.nih.gov/geo. |
| Dataset Splits | No | The paper mentions sample sizes and training on N=500 samples, but does not explicitly provide training/validation/test dataset splits or cross-validation details. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Using an additive noise variance of σ2 = 0.5 we are able to recover the original signal quite accurately... We used 10 initial iterations 50 random seeds and 30 final iterations 50 in the Robust Power Tensor Method. |