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 [1].
Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences
Authors: Takashi Takenouchi, Takafumi Kanamori
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments showed that the proposed estimator comparably performs to the maximum likelihood estimator but with drastically lower computational cost. Keywords: unnormalized model, homogeneous divergence, empirical localization, discrete model |
| Researcher Affiliation | Academia | Takashi Takenouchi EMAIL Future University Hakodate RIKEN Center for Advanced Intelligence Project 116-2 Kamedanakano, Hakodate, Hokkaido, 040-8655, Japan Takafumi Kanamori EMAIL Department of Computing and Software Systems, Nagoya University RIKEN Center for Advanced Intelligence Project Furocho, Chikusaku, Nagoya 464-8603, Japan |
| Pseudocode | No | The paper describes methods and theoretical properties but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to a code repository or supplementary materials containing code. |
| Open Datasets | Yes | In numerical experiments, training samples were generated from the Poisson distribution having the probability function pθ(x) = exθ / e^eθ for x = 0, 1, 2, . . . , where θ is the natural parameter. ... The dimension d of input was set to 10 and the synthetic dataset was randomly generated from the second order Boltzmann machine (Example 3) with a parameter θ N(0, I/d). |
| Dataset Splits | Yes | For each dimension d = 2, 3, . . . , 21, we generated a dataset containing n = 50 2k(k = 1, . . . , 9) examples from the fully visible Boltzmann machine... Figure 8 (b) shows median of averaged log-likelihoods of each method for test dataset consists of 10000 examples, over 50 trials. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | All methods were optimized with the optim function in R language (R Core Team, 2015). While R language is mentioned, a specific version number for R itself or for the 'optim' function is not provided. |
| Experiment Setup | Yes | In numerical experiments, the parameters of Sα,α was set to α = 1.1, α = 0.1. ... we compared the proposed estimator with parameter settings (α, α ) = (1.01, 0.01), (1.01, 0.01), (2, 1)... To overcome the degrade of performance of the proposed estimator caused by lack of example patterns, we consider a regularized version of the proposed estimator as, Sα,α ( p, qθ) + λ/2n||θ||2. ... and λ = 0, 10-2, 10-4). ... An initial value of the parameter was set by N(0, I) and commonly used by all methods. |