Variational Autoencoders with Jointly Optimized Latent Dependency Structure
Authors: Jiawei He, Yu Gong, Joseph Marino, Greg Mori, Andreas Lehrmann
ICLR 2019 | Venue 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 EMAIL Joseph Marino2 EMAIL Greg Mori1 EMAIL Andreas M. Lehrmann EMAIL 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. |