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..
$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
Authors: Juno Kim, Jaehyuk Kwon, Mincheol Cho, Hyunjong Lee, Joong-Ho Won
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | t3VAE demonstrates superior generation of low-density regions when trained on heavytailed synthetic data. Furthermore, we show that t3VAE significantly outperforms other models on Celeb A and imbalanced CIFAR-100 datasets. |
| Researcher Affiliation | Academia | 1Department of Mathematical Informatics, The University of Tokyo 2Center for Advanced Intelligence Project, RIKEN 3Department of Statistics, Seoul National University |
| Pseudocode | Yes | A summary of our framework is provided in Algorithm 1 to assist with implementation. |
| Open Source Code | Yes | The code is available on Github. |
| Open Datasets | Yes | We now showcase the effectiveness of our model on high-dimensional data via both reconstruction and generation tasks in Celeb A (Liu et al., 2015; 2018)... we conduct reconstruction experiments with the CIFAR100-LT dataset (Cao et al., 2019), which is a long-tailed version of the original CIFAR-100 (Krizhevsky, 2009). |
| Dataset Splits | Yes | We first generate 200K train data, 200K validation data, and 500K test data from the heavy-tailed bimodal distribution (22). |
| Hardware Specification | Yes | All experiments are implemented via Python 3.8.10 with the Py Torch package (Paszke et al., 2019) version 1.13.1+cu117, and run on Linux Ubuntu 20.04 with Intel Xeon Silver 4114 @ 2.20GHz processors, an Nvidia Titan V GPU with 12GB memory, CUDA 11.3 and cu DNN 8.2. |
| Software Dependencies | Yes | All experiments are implemented via Python 3.8.10 with the Py Torch package (Paszke et al., 2019) version 1.13.1+cu117, and run on Linux Ubuntu 20.04 with Intel Xeon Silver 4114 @ 2.20GHz processors, an Nvidia Titan V GPU with 12GB memory, CUDA 11.3 and cu DNN 8.2. |
| Experiment Setup | Yes | In the training process, we use a batch size of 128 and employ the Adam optimizer (Kingma & Ba, 2014) with a learning rate of 1 10 3 and weight decay 1 10 4. Moreover, we adapt early stopping using validation data with patience 15 to prevent overfitting. All VAE models are trained for 50 epochs using a batch size of 128 and a latent variable dimension of 64 with the Adam optimizer. |