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..
Marginalized Denoising Auto-encoders for Nonlinear Representations
Authors: Minmin Chen, Kilian Weinberger, Fei Sha, Yoshua Bengio
ICML 2014 | Venue PDF | 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 EMAIL Criteo Kilian Weinberger EMAIL Washington University in St. Louis Fei Sha EMAIL 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]). |