Variational Inference for Sparse and Undirected Models
Authors: John Ingraham, Debora Marks
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We find that, together, these methods for variational inference substantially improve learning of sparse undirected graphical models in simulated and real problems from physics and biology. |
| Researcher Affiliation | Academia | John Ingraham 1 Debora Marks 1 1Harvard Medical School, Boston, Massachusetts. |
| Pseudocode | Yes | Algorithm 1 Computing ELBO for Fadeout |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the availability of its own source code for the described methodology. |
| Open Datasets | No | We generated synthetic couplings for two kinds of Ising systems... We sampled synthetic data for each system with the Swendsen-Wang algorithm (Appendix) (Swendsen & Wang, 1987). and We applied the hierarchical Bayesian model from the protein simulation to model across-species amino acid covariation in the SH3 domain family (Figure 6). The paper does not provide concrete access information for the datasets used. |
| Dataset Splits | No | No specific details on train/validation/test splits, such as percentages or sample counts for each partition, were provided for full reproducibility, although a test set is mentioned. |
| Hardware Specification | No | Portions of this work were conducted on the Orchestra HPC Cluster at Harvard Medical School. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | The paper does not provide specific details on hyperparameters, training configurations, or system-level settings for the experiments. |