Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Markov Latent Feature Models
Authors: Aonan Zhang, John Paisley
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirical results on a genome analysis task and an image denoising task. |
| Researcher Affiliation | Academia | Aonan Zhang EMAIL John Paisley EMAIL Department of Electrical Engineering & Data Science Institute Columbia University, New York, NY, USA |
| Pseudocode | Yes | Algorithm 1 Sparse coding with greedy search |
| Open Source Code | No | The paper does not include an unambiguous statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | For this small-scale experiment, we use a subset of 266 individuals across 11 countries from the HGDP-CEPH Human Genome Diversity Cell Line Panel (Rosenberg et al., 2002) |
| Dataset Splits | Yes | We split this data into a set of 54 individuals for testing, and use the rest for training. |
| 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. |
| Experiment Setup | Yes | We set hyper-parameters to be η = 1, λ = 1, σ = 0.8. [...] We set η = 1/2552, λ = 1/10, α = 1, γ = 1, K = 256, and online parameters |Ct| = 1000, t0 = 10, κ = 0.75. |