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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Narrow Boltzmann Machines are Universal Approximators
Authors: Guido Montufar
ICLR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we prove that deep narrow Boltzmann machines are universal approximators, provided they have sufficiently many hidden layers, each containing the same number of units as the visible layer. |
| Researcher Affiliation | Academia | Guido Mont ufar Max Planck Institute for Mathematics in the Sciences Inselstrasse 22, 04103 Leipzig, Germany EMAIL |
| Pseudocode | No | The paper focuses on theoretical proofs and mathematical derivations. It does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve training models or using datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation or dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe any hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or training settings. |