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 | Conference PDF | Archive PDF | Plain Text | 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 URI.SHAHAM@YALE.EDU Xiuyuan Cheng XIUYUAN.CHENG@YALE.EDU Omer Dror OMERDR@GMAIL.COM Ariel Jaffe ARIEL.JY@GMAIL.COM Boaz Nadler BOAZ.NADLER@WEIZMANN.AC.IL Joseph Chang JOSEPH.CHANG@YALE.EDU Yuval Kluger YUVAL.KLUGER@YALE.EDU |
| 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)). |