Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families
Authors: Vaidotas Simkus, Michael U. Gutmann
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed methods for VAE estimation on synthetic and realistic data sets with missing data (section 6). |
| Researcher Affiliation | Academia | Vaidotas Simkus EMAIL Michael U. Gutmann EMAIL School of Informatics University of Edinburgh |
| Pseudocode | Yes | Algorithm 1 Shared computation of the De Miss VAE learning objectives |
| Open Source Code | Yes | The methods are summarised in table 1 and the code implementation is available at https://github.com/ vsimkus/demiss-vae. |
| Open Datasets | Yes | We here evaluate the proposed methods on real-world data sets from the UCI repository (Dua & Graff, 2017; Papamakarios et al., 2017). |
| Dataset Splits | No | The paper mentions evaluating on a 'complete test data set' and a '20K sample data set used to fit the VAEs' but does not provide specific split percentages or counts (e.g., train/validation/test splits). The missingness percentages (e.g., 20/50/80%) refer to data incompleteness, not dataset splits for training and evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models (e.g., NVIDIA A100), CPU models, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions the 'AMSGrad optimiser (Reddi et al., 2018)' and 'STL gradients (Roeder et al., 2017)' which are algorithms/techniques, but it does not specify software libraries with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | We then fitted a VAE model with 2-dimensional latent space using diagonal Gaussian encoder and decoder distributions, and a fixed standard Normal prior. For the decoder and encoder networks we used fullyconnected residual neural networks with 3 residual blocks, 200 hidden dimensions, and Re LU activations. To optimise the model parameters we have used AMSGrad optimiser (Reddi et al., 2018) with a learning rate of 10 3 for a total of 500 epochs. |