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..
On the mapping between Hopfield networks and Restricted Boltzmann Machines
Authors: Matthew Smart, Anton Zilman
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we conduct experiments on the MNIST dataset which suggest the mapping provides a useful initialization to the RBM weights. and 3 EXPERIMENTS ON MNIST DATASET |
| Researcher Affiliation | Academia | Matthew Smart Department of Physics University of Toronto EMAIL Anton Zilman Department of Physics and Institute for Biomedical Engingeering University of Toronto EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement about releasing code or links to a source code repository for the described methodology were found. |
| Open Datasets | Yes | We consider the popular MNIST dataset of handwritten digits (Le Cun et al., 1998) |
| Dataset Splits | No | The paper mentions training and testing images but does not explicitly provide details about a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'scikit-learn (Pedregosa et al., 2011)' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | In our experiments we train for 50 epochs with mini-batches of size 100 (3 105 weight updates) (...) The learning rate is η0 = 10 4 except the first 25 epochs of the randomly initialized weights in (b), where we used η = 5η0 due to slow training. (...) Training parameters: β = 2, and CD-20. |