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Research map / Probabilistic and scientific modeling / Variational and generative sampling

Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation

ICML 2020 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Regularizing VAE latent information · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add the dispersion term to the mixture VAE objective (ablation)GM-VAE on PTB · PTB testrPPL: 983.50 -> 257.68Helped
add the dispersion term to the mixture VAE objective (ablation)GM-VAE on PTB · PTB testBLEU: 2.34 -> 8.17Helped
add the dispersion term to the mixture VAE objective (ablation)GM-VAE on DD · DD unsupervisedBLEU: 2.03 -> 7.63Helped
add the mutual information term (ablation)DGM-VAE on PTB · PTB testrPPL: 257.68 -> 247.37Helped
add the mutual information term (ablation)DGM-VAE on PTB · PTB testNLL: ≤104.26 -> ≤105.73Hurt
add the mutual information term (ablation)DGM-VAE on DD · DD unsupervisedact homogeneity: 0.11 -> 0.23Helped
add the mutual information term (ablation)DGM-VAE on DD · DD unsupervisedBLEU: 7.63 -> 7.39Hurt
add the mutual information term (ablation)GM-VAE on PTB · PTB testrPPL: 983.50 -> 287.07Helped
add the mutual information term (ablation)GM-VAE on PTB · PTB testBLEU: 2.34 -> 6.26Helped
replace independent discrete and continuous latents with a mixture prior linking the latent component to continuous zDGM-VAE versus semi-VAE on PTB · PTB testBLEU: 2.71 -> 8.17Helped
use a dispersion penalty rather than reweighting the whole KL term with β (ablation)DGM-VAE versus β-GM-VAE on PTB · PTB testBLEU: 8.17 versus 5.88; 5.92; 1.78Helped
use a dispersion penalty rather than aggressive posterior updates (ablation)DGM-VAE versus GM-VAE + aggressive on PTB · PTB testBLEU: 8.17 versus 1.47Helped

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