Resampled Priors for Variational Autoencoders

We propose Learned Accept/Reject Sampling (LARS), a method for constructing richer priors using rejection sampling with a learned acceptance function. This work is motivated by recent analyses of the VAE objective, which pointed out that commonly used simple priors can lead to underfitting. As the distribution induced by LARS involves an intractable normalizing constant, we show how to estimate it and its gradients efficiently. We demonstrate that LARS priors improve VAE performance on several standard datasets both when they are learned jointly with the rest of the model and when they are fitted to a pretrained model. Finally, we show that LARS can be combined with existing methods for defining flexible priors for an additional boost in performance

External Eff ects of Education: Human Capital Spillovers in Regions and Firms

Using a matched employer-employee panel dataset for Germany, we analyze the external eff ects of education on individual wages. Following the basic framework of Moretti (2004), we allow spillover eff ects to occur both within a specifi c fi rm and a specifi c region rather than analyzing spillover eff ects only on a regional level. Controlling for individual- and fi rm-specifi c fi xed eff ects and using an instrumental variable strategy, our results confi rm the existence of positive but small external eff ects of human capital. Positive spillover eff ects within fi rms occur only for the group of high-skilled workers.External eff ects; human capital; employer-employee matched data

Eating words: some notes on a metaphor and its use in Much ado about nothing

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The language of dogs: mythos and logos in Emily Dickinson

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Little Dorrit: Dickens and the language of things

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