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..
Auxiliary Deep Generative Models
Authors: Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show state-of-the-art performance within semi-supervised learning on MNIST, SVHN and NORB datasets. |
| Researcher Affiliation | Academia | 1Department of Applied Mathematics and Computer Science, Technical University of Denmark 2Bioinformatics Centre, Department of Biology, University of Copenhagen |
| Pseudocode | No | The paper describes models and training procedures mathematically and textually, but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementation is available in a repository named auxiliary-deep-generative-models on github.com. |
| Open Datasets | Yes | For the benchmark datasets we show (cf. Sec. 4.4): (iii) State-of-the-art results on several semi-supervised classification tasks. We demonstrate the benefits of the increased flexibility by achieving state-of-the-art performance in the semi-supervised setting for the MNIST (Le Cun et al., 1998), SVHN (Netzer et al., 2011) and NORB (Le Cun et al., 2004) datasets. |
| Dataset Splits | Yes | For the MNIST dataset we have combined the training set of 50000 examples with the validation set of 10000 examples. The test set remained as is. |
| Hardware Specification | Yes | This research was supported by the Novo Nordisk Foundation, Danish Innovation Foundation and the NVIDIA Corporation with the donation of TITAN X and Tesla K40 GPUs. |
| Software Dependencies | No | The models are implemented in Python using Theano (Bastien et al., 2012), Lasagne (Dieleman et al., 2015) and Parmesan libraries |
| Experiment Setup | Yes | For training, we have used the Adam (Kingma and Ba, 2014) optimization framework with a learning rate of 3e4, exponential decay rate for the 1st and 2nd moment at 0.9 and 0.999, respectively. The β constant was between 0.1 and 2 throughout the experiments. |