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..
Using Inherent Structures to design Lean 2-layer RBMs
Authors: Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on synthetic datasets to verify our claim. Our main goals are to experimentally verify Theorems 1, 2 and Corollaries 3 4. All experiments were run on CPU with 2 Xeon Quad-Core processors (2.60GHz 12MB L2 Cache) and 16GB memory running Ubuntu 16.02 7. To validate the claim made in Corollary 3 we considered training a DBM with two hidden layers on the MNIST dataset. |
| Researcher Affiliation | Collaboration | 1IBM Research 2Dept of CSA, IISc, Bengaluru, India. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 7The source code and instructions to run is available at http: //mllab.csa.iisc.ernet.in/publications. |
| Open Datasets | Yes | To validate the claim made in Corollary 3 we considered training a DBM with two hidden layers on the MNIST dataset. We initialized weights and biases of each RBM architecture randomly and then performed gibbs sampling for 5000 steps to generate a synthetic dataset of 60,000 points. |
| Dataset Splits | No | The paper mentions using 'test data' for evaluation but does not specify details for a separate validation split or cross-validation methodology. |
| Hardware Specification | Yes | All experiments were run on CPU with 2 Xeon Quad-Core processors (2.60GHz 12MB L2 Cache) and 16GB memory running Ubuntu 16.02 7. |
| Software Dependencies | No | The paper mentions 'Ubuntu 16.02' but does not specify any particular software libraries, frameworks, or their version numbers used for the experiments. |
| Experiment Setup | Yes | We initialized weights and biases of each RBM architecture randomly and then performed gibbs sampling for 5000 steps to generate a synthetic dataset of 60,000 points. To estimate the models partition function we used 20,000 Îēk spaced uniformly from 0 to 1.0. |