Deep Narrow Boltzmann Machines are Universal Approximators
Authors: Guido Montufar
ICLR 2015 | Conference PDF | Archive PDF | Plain Text | 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 montufar@mis.mpg.de |
| 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. |