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Z-Forcing: Training Stochastic Recurrent Networks

NeurIPS 2017 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Variational methods for sequence models · 31 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the auxiliary cost to Ours (ablation)Z-Forcing · Blizzard testaverage log-likelihood per sequence: ≥ 14435 -> ≥ 15430Helped
Add the auxiliary cost to Ours (ablation)Z-Forcing · TIMIT testaverage log-likelihood per sequence: ≥ 68132 -> ≥ 69530Helped
Add KL annealing to Ours (ablation)Z-Forcing · Blizzard testaverage log-likelihood per sequence: ≥ 14435 -> ≥ 14226Hurt
Add KL annealing to Ours (ablation)Z-Forcing · TIMIT testaverage log-likelihood per sequence: ≥ 68132 -> ≥ 68903Helped
Add KL annealing alongside the auxiliary cost (ablation)Z-Forcing · TIMIT testaverage log-likelihood per sequence: ≥ 69530 -> ≥ 70469Helped
Add the auxiliary cost (ablation)Z-Forcing · Sequential MNIST testnegative log-likelihood: ≤ 80.60 -> ≤ 80.09Helped
Use KL annealing (ablation)Z-Forcing · Sequential MNISTnegative log-likelihoodHurt
Add the auxiliary cost with α=0.0025 (ablation)Z-Forcing · IMDB validword perplexity (ELBO): 53.93 -> 55.71Hurt
Add the auxiliary cost with α=0.0025 (ablation)Z-Forcing · IMDB testword perplexity (ELBO): 54.67 -> 56.57Hurt
Increase auxiliary-cost hyperparameter α from 0.0025 to 0.005 (ablation)Z-Forcing with auxiliary cost · IMDB validword perplexity (ELBO): 55.71 -> 65.03Hurt
Increase auxiliary-cost hyperparameter α from 0.0025 to 0.005 (ablation)Z-Forcing with auxiliary cost · IMDB testword perplexity (ELBO): 56.57 -> 65.84Hurt
Train with the IWAE bound instead of the standard ELBO (ablation)Z-Forcing · IMDB validationperplexityNo clear effect

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