Exact Inference for Integer Latent-Variable Models
Authors: Kevin Winner, Debora Sujono, Dan Sheldon
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our techniques are more scalable than existing approximate methods and enable new applications. |
| Researcher Affiliation | Academia | 1College of Information and Computer Sciences, University of Massachusetts Amherst 2Department of Computer Science, Mount Holyoke College. |
| Pseudocode | Yes | Algorithm 1 Ak(sk) and Algorithm 2 LAk(hsk, dskiq) GDUAL-FORWARD are provided in the paper. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the methodology described is publicly available. |
| Open Datasets | No | The paper uses simulated data for its experiments but does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses simulated data and discusses experimental results, but it does not specify explicit train/validation/test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names). |
| Experiment Setup | Yes | The paper describes aspects of the experimental setup for parameter estimation, including using the 'L-BFGS-B algorithm', '10 random restarts', generating '10 independent observation vectors for K = 7 time steps', and repeating experiments '50 times'. |