Markov Latent Feature Models
Authors: Aonan Zhang, John Paisley
ICML 2016 | Conference PDF | Archive PDF | Plain Text | 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 AZ2385@COLUMBIA.EDU John Paisley JPAISLEY@COLUMBIA.EDU 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. |