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..
Regularization for Unsupervised Deep Neural Nets
Authors: Baiyang Wang, Diego Klabjan
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we compare the performance of these methods based on their likelihood and classification error rates for various pattern recognition data sets. |
| Researcher Affiliation | Academia | Baiyang Wang, Diego Klabjan Department of Industrial Engineering and Management Sciences, Northwestern University, 2145 Sheridan Road, C210 Evanston, Illinois 60208 |
| Pseudocode | Yes | Algorithm 1. (Partial Drop Connect) |
| Open Source Code | No | The paper does not provide a link to its source code or explicitly state that it is open-source. |
| Open Datasets | Yes | We compare the empirical performance of the aforementioned regularization methods on the following data sets: MNIST, NORB (image recognition); 20 Newsgroups, Reuters21578 (text classification); ISOLET (speech recognition). |
| Dataset Splits | Yes | The MNIST data set consists of 282 pixels of handwritten 0-9 digits. There are 50,000 training examples, 10,000 validation and 10,000 testing examples. |
| Hardware Specification | Yes | All results are obtained using Ge Force GTX TITAN X in Theano. |
| Software Dependencies | No | The paper mentions 'Theano' but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | The number of pretraining epochs is 100 per layer and the number of finetuning epochs is 300, with a finetuning learning rate of 0.1. For L2 + AL1, SNP, and INP which need re-calibration, we cut the 100 epochs into two halves (4 quarters for INP). For regularization parameters, we apply the following ranges: p = 0.8 0.9 for DO/DC/SNP/INP; λ = 10 5 10 4 for L2, similar to Hinton (2010); μ = 0.01 0.1 for L2 +AL1; p0 = 0.5, q = 0.7 0.9 or the reverse for PDO/PDC. |