Research map / Probabilistic and scientific modeling / Variational and generative sampling
Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add variational dropout sparsity on latent code (sparse MCVAE) vs non-sparse MCVAE (ablation) | Multi-channel VAE · Synthetic multi-channel scenarios (Tab. 1) | testing MAE benchmark | Helped |
| Sparse latent code via variational dropout (sMCVAE) vs standard VAE | Multi-channel VAE · Synthetic benchmark (Tab. 2) | MAE difference vs VAE (95% bootstrap CI) | No clear effect |
| Sparsity via variational dropout prunes parameters | sMCVAE · Synthetic benchmark (Tab. 2) | average compression factor: 45% pruned vs 0% | Helped |
| Joint multi-channel model vs independent per-channel VAEs (IVAEs) | Multi-channel VAE · Synthetic multi-channel scenarios | testing MAE | Helped |
| Multi-channel joint model vs VAE on concatenated channels in complex many-channel cases | Multi-channel VAE · Synthetic 5- and 10-channel cases | testing MAE | Mixed |
| Sparse latent (sMCVAE, 1 layer) vs non-sparse MCVAE for ADNI clinical status classification | Multi-channel VAE · ADNI LDA classification (Tab. 3) | proportion correctly classified (Normal/MCI/Dementia) | Mixed |
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