Jun 7, 2018 � We present methods based on importance weighting that can estimate the loss with respect to a target distribution, even if we cannot access that�...
Apr 30, 2020 � We demonstrate that importance weighted estimators allow deep generative models to match target distributions for common and challenging cases�...
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We present methods to accommodate this difference via importance weighting, which allow us to estimate a loss function with respect to a target distribution�...
Jan 14, 2020 � Our technique consistently improves sample quality metrics for state-of-the-art generative models while also benefiting downstream use cases of�...
This paper proposes an importance weighted adversar- ial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where�...
May 14, 2024 � Importance weighting is a fundamental procedure in statistics and machine learning that weights the objective function or probability�...
A learned generative model often produces biased statistics relative to the under- lying data distribution. A standard technique to correct this bias is�...
A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true�...
Jun 12, 2019 � We propose a search method for neural network architectures that can already perform a task without any explicit weight training.
Jan 25, 2024 � For a good generative model and an optimal classifier, the weight distribution will typically peak near one, with tails to the left (w ≪ 1) and�...