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..
Supervised autoencoders: Improving generalization performance with unsupervised regularizers
Authors: Lei Le, Andrew Patterson, Martha White
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We theoretically and empirically analyze one such model, called a supervised auto-encoder: a neural network that jointly predicts targets and inputs (reconstruction). We then demonstrate empirically that, across an array of architectures with a different number of hidden units and activation functions, the supervised auto-encoder compared to the corresponding standard neural network never harms performance and can improve generalization. |
| Researcher Affiliation | Academia | Lei Le Department of Computer Science Indiana University Bloomington, IN EMAIL Andrew Patterson and Martha White Department of Computing Science University of Alberta Edmonton, AB T6G 2E8, Canada EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | SUSY is a high-energy particle physics dataset [37]. Deterding is a vowel dataset [38] containing 11 steady-state vowels of British English spoken by 15 speakers. CIFAR-10 is an image dataset [39] with 10 classes and 60000 32x32 color images. MNIST is a dataset [40] of 70000 examples of 28x28 images of handwritten digits from 0 to 9. |
| Dataset Splits | Yes | We used 10-fold cross-validation to choose the best metaparameters for each algorithm on each dataset. For each of the training-test splits, we used a random subset of 50,000 images for training and 10,000 images for testing. |
| Hardware Specification | No | No specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper does not specify version numbers for any ancillary software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We used 10-fold cross-validation to choose the best metaparameters for each algorithm on each dataset. We use a network with two convolutional layers of sizes {32, 64} and 4 dense layers of sizes {2048, 512, 128, 32} with Re Lu activation. We focus on the impact of using reconstruction error, and compare SAE and NN with a variety of nonlinear structures, including sigmoid (SAE-Sigmoid and NN-Sigmoid), Re Lu (SAE-Re Lu and NN-Re Lu) and Gaussian kernel (SAE-Kernel and NN-Kernel). |