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..
A Deep Learning Approach to Unsupervised Ensemble Learning
Authors: Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph Chang, Yuval Kluger
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on various simulated and real-world datasets demonstrate that our proposed DNN approach outperforms other state-of-the-art methods, in particular when the data violates the conditional independence assumption. |
| Researcher Affiliation | Academia | Uri Shaham EMAIL Xiuyuan Cheng EMAIL Omer Dror EMAIL Ariel Jaffe EMAIL Boaz Nadler EMAIL Joseph Chang EMAIL Yuval Kluger EMAIL |
| Pseudocode | No | The paper describes algorithms and procedures in text, but does not include formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | Our datasets, as well as the scripts used to obtain the reported results are publicly available at https: //github.com/ushaham/RBMpaper 1. |
| Open Datasets | Yes | Our datasets, as well as the scripts used to obtain the reported results are publicly available at https: //github.com/ushaham/RBMpaper 1. |
| Dataset Splits | No | The paper does not explicitly provide details about train/validation/test splits, only mentions sample size and evaluation on 'test dataset' without specifying percentages or absolute counts for each split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU, GPU models, memory, etc.). |
| Software Dependencies | No | The paper mentions using 'Weka machine learning software' and 'Geoffrey Hinton’s website' (which implies Matlab scripts), but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We remark that in our experiments, we observed that RBMs tend to be highly sensitive to hyper-parameter tuning (such as learning rate, momentum, regularization type and penalty), and these hyper-parameters need to be carefully tuned. To obtain a reasonable hyper-parameter setting we found it useful to apply the random configuration sampling procedure, proposed in (Bergstra & Bengio, 2012), and evaluate different models by average loglikelihood approximation, (see, for example, (Salakhutdinov & Murray, 2008) and the corresponding MATLAB scripts in (Salakhutdinov, 2010)). |