Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Adversarial Symmetric Variational Autoencoder
Authors: Yuchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,238 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,740 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive set of experiments is performed, in which we demonstrate state-of-the-art data reconstruction and generation on several image benchmark datasets. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering, Duke University EMAIL |
| Pseudocode | No | The paper describes algorithms and formulations mathematically but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | Yes | We evaluate our model on three datasets: MNIST, CIFAR-10 and Image Net. |
| Dataset Splits | No | Early stopping is employed based on average reconstruction loss of x and z on validation sets. The paper mentions using validation sets but does not specify the split percentages, sample counts, or the exact methodology for creating these splits. |
| Hardware Specification | Yes | while our model only requires less than 2 days (4 hours per epoch) for training and 0.01 seconds/image for generating on a single TITAN X GPU. |
| Software Dependencies | No | The paper mentions optimizers like Adam and initialization methods like Xavier, but does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | All parameters were initialized with Xavier [36], and optimized via Adam [37] with learning rate 0.0001. We do not perform any dataset-specific tuning or regularization other than dropout [38]. Early stopping is employed based on average reconstruction loss of x and z on validation sets. |