Sample Objectives
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Improved generator objectives for GANs
We present a framework to understand GAN training as alternating density ratio estimation, and approximate divergence minimization. This provides an interpretation for the mismatched GAN generator and discriminator objectives often used in practice, and explains the problem of poor sample diversity. Further, we derive a family of generator objectives that target arbitrary f -divergences without...
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Effective determinations of sample size require interaction between statisticians and their research collaborators who wish to initiate studies. The majority of grant applications, clinical trial protocols, and IRB submissions will not be approved unless there is some statistical justification for the planned sample size that is appropriate for the study’s primary objective [1]. However, obtain...
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In an effort to understand the value of Internet commerce, Keeney interviewed over 100 individuals and proposed two sets of variables: means and fundamental objectives for Internet shopping. The first help businesses deliver what is important for customers so that the customers’ objectives are achieved. Fundamental objectives represent ultimate values that customers care about and will influenc...
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Recent progress in deep latent variable models has largely been driven by the development of flexible and scalable variational inference methods. Variational training of this type involves maximizing a lower bound on the log-likelihood, using samples from the variational posterior to compute the required gradients. Recently, Burda et al. (2015) have derived a tighter lower bound using a multi-s...
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The present chapter reviews basic issues concerning the design of household survey questionnaires for use in developing countries. It begins with the first step of questionnaire design, which is to formulate the objectives of the survey and then modify those objectives to take into account the underlying constraints. After these broad issues are discussed, more detailed advice is given on many ...
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