Marginalized Denoising Auto-encoders for Nonlinear Representations
Authors: Minmin Chen, Kilian Weinberger, Fei Sha, Yoshua Bengio
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In empirical evaluations we show that it attains 1-2 order-of-magnitude speedup in training time over other competing approaches. |
| Researcher Affiliation | Collaboration | Minmin Chen M.CHEN@CRITEO.COM Criteo Kilian Weinberger KILIAN@WUSTL.EDU Washington University in St. Louis Fei Sha FEISHA@USC.EDU University of Southern California Yoshua Bengio Universit e de Montr eal, Canadian Institute for Advanced Research |
| Pseudocode | No | The paper describes the algorithms and mathematical derivations but does not include pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | Our datasets consist of the original MNIST dataset (MNIST) for recognizing images of handwritten digits, for the sake of comparison with prior work a subsampled version (basic) and its several variants (Larochelle et al., 2007; Vincent et al., 2010; Rifai et al., 2011b). |
| Dataset Splits | Yes | Each dataset is split into three subsets: a training set for pre-training and fine-tuning the parameters, a validation set for choosing the hyper-parameters and a testing set on which the results are reported. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | These include the learning rate for pre-training and fine-tuning (candidate set [0.01, 0.05, 0.1, 0.2]), noise levels in m LDAE, DAE and our method m DAE (candidate set [0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.3])), and the regularization coefficient in CAE (candidate set [0.01, 0.05, 0.1, 0.3, 0.5, 0.7, 0.9]). |