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..
Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness
Authors: Dazhong Shen, Chuan Qin, Chao Wang, Hengshu Zhu, Enhong Chen, Hui Xiong
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | extensive experiments on three benchmark datasets clearly show that our approach can outperform state-of-the-art baselines on both likelihood estimation and underlying classification tasks. |
| Researcher Affiliation | Collaboration | 1 School of Computer Science and Technology, University of Science and Technology of China 2Baidu Talent Intelligence Center 3Rutgers, The State University of New Jersey |
| Pseudocode | Yes | Algorithm 1 Training Procedure of DU-VAE |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code. |
| Open Datasets | Yes | we evaluated our method on two text benchmark datasets, i.e., Yahoo and Yelp corpora [Yang et al., 2017] and one image benchmark dataset, i.e., OMNIGLOT [Lake et al., 2015]. |
| Dataset Splits | No | The paper mentions using 'dynamically binarized images for training and the fixed binarization as test data' for OMNIGLOT, but does not specify a validation split or provide specific numeric splits for all datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions optimizers (SGD, Adam) and model architectures (LSTM, ResNet, Pixel CNN) but does not provide specific software names with version numbers for reproducibility (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | For text datasets, we utilized a single layer LSTM as both encoder and decoder networks... For images, a 3-layer Res Net [He et al., 2016] encoder and a 13-layer Gated Pixel CNN [Van den Oord et al., 2016] decoder are applied. We set the dimension of z as 32. and utilized SGD to optimize the ELBO objective for text and Adam [Kingma and Ba, 2015] for images. Following [Bowman et al., 2015a], we applied a linear annealing strategy to increasing the KL weight from 0 to 1 in the first 10 epochs if possible. |