Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Variational Inference for Sparse and Undirected Models
Authors: John Ingraham, Debora Marks
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,813 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,734 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We ο¬nd 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. |