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..
Ornstein Auto-Encoders
Authors: Youngwon Choi, Joong-Ho Won
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that OAEs successfully separate individual sequences in the latent space, and can generate new variations of unknown, as well as known, identity. |
| Researcher Affiliation | Academia | Department of Statistics, Seoul National University, Republic of Korea |
| Pseudocode | Yes | Algorithm 1 Ornstein Auto-Encoder for Exchangeable Data |
| Open Source Code | No | Details of implementation are given in the Online Supplement available at https://tinyurl.com/y5x6ufuj. |
| Open Datasets | Yes | Consider the VGGFace2 dataset [Cao et al., 2018], an expansion of the famous VGGFace dataset [Parkhi et al., 2015]. and The images of the MNIST dataset show strong correlations within a digit. |
| Dataset Splits | No | The paper describes training and test sets for both VGGFace2 and MNIST, but does not explicitly mention a separate validation split or dataset. |
| Hardware Specification | No | No specific hardware details (like GPU or CPU models) are mentioned in the paper. |
| Software Dependencies | No | The paper mentions optimizers and normalization techniques but does not provide specific version numbers for any software dependencies or libraries (e.g., PyTorch, TensorFlow). |
| Experiment Setup | Yes | We chose dz = 128 as the latent space dimension, and used hyperparameters ยต0 = 0, ฯ2 0 = 1, ฯ 2 0 = 100. The encoder-decoder architecture had 13.6M parameters and the discriminator had 12.8M parameters. We set ฮป1 = 10, ฮป2 = 10 for OAE, and ฮป = 10 for WAE and c AAE. All models were trained for 100 epochs with a constant learning rate of 0.0005 for the encoder and decoder, and 0.001 for the discriminator. We used mini-batches of size 200. |