Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets
Authors: Justin Domke
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 2: Ising Model Example. Left: The difference of the current test log-likelihood from the optimal log-likelihood on 5 random runs. |
| Researcher Affiliation | Collaboration | Justin Domke NICTA, Australian National University |
| Pseudocode | Yes | Figure 1: Left: Algorithm 1, approximate gradient descent with gradients approximated via MCMC, analyzed in this paper. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes using '5 random vectors as training data' for an Ising model example, but no concrete access information (link, DOI, repository, formal citation) for a publicly available dataset is provided. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or 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 | For a fast-mixing set, constrain |θij| .2 for all pairs. Since the maximum degree is 4, τ(ϵ) N log(N/ϵ) / (1 - 4 tanh(.2)). Fix λ = 1, ϵθ = .2 and δ = 0.1. Though the theory above suggests the Lipschitz constant L = 4R2 + λ = 97, a lower value of L = 10 is used, which converged faster in practice (with exact or approximate gradients). |