Variational Autoencoders with Jointly Optimized Latent Dependency Structure
Authors: Jiawei He, Yu Gong, Joseph Marino, Greg Mori, Andreas Lehrmann
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our framework in extensive experiments on MNIST, Omniglot, and CIFAR-10. Comparisons to state-of-the-art structured variational autoencoder baselines show improvements in terms of the expressiveness of the learned model. |
| Researcher Affiliation | Academia | Jiawei He1 & Yu Gong1 {jha203, gongyug}@sfu.ca Joseph Marino2 jmarino@caltech.edu Greg Mori1 mori@cs.sfu.ca Andreas M. Lehrmann andreas.lehrmann@gmail.com 1School of Computing Science, Simon Fraser University Burnaby, BC, V5B1Z1, Canada 2California Institute of Technology Pasadena, CA, 91125, USA |
| Pseudocode | Yes | Algorithm 1 Optimizing VAEs with Latent Dependency Structure |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate the proposed latent dependency learning approach on three benchmark datasets: MNIST (Lecun et al., 1998; Larochelle & Murray, 2011), Omniglot (Lake et al., 2013), and CIFAR10 (Krizhevsky, 2009). |
| Dataset Splits | No | The paper mentions using benchmark datasets and evaluating on a 'test set' but does not explicitly provide details about training, validation, and test dataset splits (e.g., percentages, sample counts, or predefined split citations for all three). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2017)' and 'Adam (Kingma & Ba, 2015)', but does not provide specific version numbers for these software components to ensure reproducibility. |
| Experiment Setup | Yes | All models were implemented with Py Torch (Paszke et al., 2017) and trained using the Adam (Kingma & Ba, 2015) optimizer with a mini-batch size of 64 and learning rate of 1e 3. Learning rate is decresed by 0.25 every 200 epochs. The Gumbel-softmax temperature was initialized at 1 and decreased to 0.99epoch at each epoch. MNIST and Omniglot took 2000 epochs to converge, and CIFAR took 3000 epochs to converge. |