Completing State Representations using Spectral Learning
Authors: Nan Jiang, Alex Kulesza, Satinder Singh
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation on synthetic HMMs, an aircraft identification domain, and a gene splice dataset shows that, even with weak domain knowledge, the algorithm can significantly outperform standard PSR learning. |
| Researcher Affiliation | Collaboration | UIUC Urbana, IL nanjiang@illinois.edu Alex Kulesza Google Research New York, NY kulesza@google.com Satinder Singh University of Michigan Ann Arbor, MI baveja@umich.edu |
| Pseudocode | Yes | Algorithm 1 Template for learning transformed PSR-fs; Algorithm 2 A basic spectral algorithm for PSR-f; Algorithm 3 Canonical angle algorithm for PSR-f |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology, such as a repository link, or an explicit statement about code release in supplementary materials. |
| Open Datasets | Yes | Finally, we experiment on a gene splice dataset [13]. ... [13] Dua Dheeru and EfiKarra Taniskidou. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | Yes | The hyperparameter d for Algorithm 3 is tuned by 3-fold cross validation on training data, and λ is set to 100 to ensure a succinct model (see Appendix E.1). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The hyperparameter d for Algorithm 3 is tuned by 3-fold cross validation on training data, and λ is set to 100 to ensure a succinct model (see Appendix E.1). |