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Learning to Generate with Memory

ICML 2016 · Architectures and efficiency › Attention and transformers · approach: Sparse and structured attention · 14 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add external memory to VAE (ablation)VAE · MNISTlog-likelihood: -85.67 -> -84.41Helped
add external memory to IWAE-50 (ablation)IWAE-50 · MNISTlog-likelihood: -83.67 -> -82.84Helped
add external memory to IWAE-5 (ablation)IWAE-5 · OCR-letterslog-likelihood: -28.69 -> -27.65Helped
add external memory to VAE (ablation)VAE · OCR-letterslog-likelihood: -30.09 -> -29.09Helped
enlarge VAE to 530-530-100 to match parameters instead of adding memory (ablation)VAE · MNISTlog-likelihood (1 sample): -85.67 -> -85.69No clear effect
remove local reconstruction error regularizer (ablation)MEM-VAE · MNISTlog-likelihood: -84.41 -> -84.44No clear effect
add local reconstruction regularizer to VAE without memory (ablation)VAE · MNISTlog-likelihood: -85.67 -> -85.68No clear effect
add memory to VAE (ablation)VAE · Frey faces batch 100log density: 1308 -> 1330Helped
add memory to VAE (ablation)VAE · Frey faces batch 10log density: 1055 -> 1240Helped
add memory to VAE (ablation)VAE · MNIST RECT-12 imputationMSE: 0.1403 -> 0.1362Helped
add memory to VAE (ablation)VAE · MNIST RAND-0.6 imputationMSE: 0.0194 -> 0.0187Helped
add memory to VAE (ablation)VAE · MNIST HALF imputationMSE: 0.0550 -> 0.0539Helped

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