Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling
Authors: Vaidotas Simkus, Michael U. Gutmann
TMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we systematically outline the pitfalls in the context of VAEs, propose two original methods that address these pitfalls, and demonstrate an improved performance of the proposed methods on a set of sampling tasks... Evaluate the samplers on a set of conditional sampling tasks: (semi-)synthetic, where sampling from the ground truth conditional distributions is computationally tractable, and real-world missing data imputation tasks, where the ground truth distribution is not available. |
| Researcher Affiliation | Academia | Vaidotas Simkus EMAIL Michael U. Gutmann EMAIL School of Informatics University of Edinburgh |
| Pseudocode | Yes | Algorithm 1 Adaptive collapsed-Metropolis-within-Gibbs... Algorithm 2 Latent-adaptive importance resampling |
| Open Source Code | Yes | Detailed evaluation of the proposed methods is provided in section 5 and the code to reproduce the experiments is available at https://github.com/vsimkus/vae-conditional-sampling. |
| Open Datasets | Yes | 5.1 Mixture-of-Gaussians MNIST... 5.2 Real-world UCI data sets... 5.3 Omniglot data set... C.2 Mixture-of-Gaussians MNIST... C.3 UCI data sets... C.4 Handwritten character Omniglot data set |
| Dataset Splits | Yes | We now evaluate the proposed methods on real-world data sets from the UCI repository... on incomplete test data with 50% missingness... We then evaluate the existing and proposed methods for conditional imputation of test set images that miss 1, 2, and 3 random quadrants. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for experiments, only mentioning architectures like "Conv Res Net". |
| Software Dependencies | No | The paper mentions "Adam optimiser" and deep learning frameworks implicitly through architectures like "Res Net", but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | To optimise the VAE model we have used the sticking-the-landing gradients (Roeder et al., 2017) and fit the model using batch size of 200 for 6000 epochs using Adam optimiser (Kingma & Ba, 2014) with a learning rate of 10 4... For all models, the variational and the generator (decoder) distributions were fitted to be in the diagonal Gaussian family... Adam optimiser (Kingma & Ba, 2014) with learning rate of 10 3 for a total of 200k stochastic gradient ascent steps... using batch size of 512... while using 8 Monte Carlo samples in each iteration... Adam optimiser (Kingma & Ba, 2014) with a learning rate of 10 4 and a cosine annealing schedule, for a total of 3k stochastic gradient ascent steps using a batch size of 200. |