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].
Discrete Restricted Boltzmann Machines
Authors: Guido Montúfar, Jason Morton
JMLR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We detail the inference functions and distributed representations arising in these models in terms of configurations of projected products of simplices and normal fans of products of simplices. We bound the number of hidden variables, depending on the cardinalities of their state spaces, for which these models can approximate any probability distribution on their visible states to any given accuracy. In addition, we use algebraic methods and coding theory to compute their dimension. In this paper we generalize some of these theoretical results to discrete RBMs. |
| Researcher Affiliation | Academia | Guido Mont ufar EMAIL Max Planck Institute for Mathematics in the Sciences Inselstrasse 22, 04103 Leipzig, Germany; Jason Morton EMAIL Department of Mathematics Pennsylvania State University University Park, PA 16802, USA |
| Pseudocode | No | The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor does it present structured, code-like steps for a procedure. The content is primarily theoretical and mathematical. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to code repositories or mention code in supplementary materials for the methodology described. |
| Open Datasets | No | This paper is theoretical and focuses on the mathematical properties of Discrete Restricted Boltzmann Machines. It does not conduct experiments on specific datasets and therefore does not provide access information for any open datasets. |
| Dataset Splits | No | This paper is theoretical and does not describe experiments that would involve splitting datasets into training, validation, or test sets. |
| Hardware Specification | No | The paper focuses on theoretical aspects of Restricted Boltzmann Machines and does not describe any experimental setup that would require specifying hardware details. |
| Software Dependencies | No | The paper is theoretical and does not describe any implementation details that would require listing specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical, presenting mathematical analysis, definitions, and theorems related to Discrete Restricted Boltzmann Machines. It does not describe any experimental setup, training configurations, or hyperparameter details. |