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].
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. |