Recent work by Bengio et al. (2013) proposes a sampling procedure for denoising autoencoders which involves learning the transition operator of a Markov chain. The transition operator is typically unimodal, which limits its capacity to model complex data. In order to perform efficient sampling from conditional distributions, we extend this work, both theoretically and algorithmically, to gated ...

Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of...

This paper discusses the work of Herbert Raabe (1909–2004) and its significance in terms of sampling. Raabe’s thesis, published in 1939, represents a milestone in the development of sampling, as pointed out in the historical works of Lüke. Raabe built and analysed the first time-division multiplex system for telephony, a task that required of significance from the viewpoint of sampling, the gen...

The contemporary development of bootstrap methods, from the time of Efron’s early articles to the present day, is well documented and widely appreciated. Likewise, the relationship of bootstrap techniques to certain early work on permutation testing, the jackknife and cross-validation is well understood. Less known, however, are the connections of the bootstrap to research on survey sampling fo...

Using a mathematical procedure, we determine appropriate sampling rates for logging heart rate, at a variety of exercise intensities. The mathematical procedure involves correlating exercise and heart rate data to determine a dynamical mathematical model, from which the frequency response of the relationship between exercise intensity and heart rate can be determined. The sampling rate is then ...

Many stochastic optimization algorithms work by estimating the gradient of the cost function on the fly by sampling datapoints uniformly at random from a training set. However, the estimator might have a large variance, which inadvertantly slows down the convergence rate of the algorithms. One way to reduce this variance is to sample the datapoints from a carefully selected non-uniform distribu...

Abstract Objectives Respondent-Driven Sampling (RDS) is a variant of link-tracing, sampling technique for surveying hard-to-reach communities that takes advantage community members' social networks to reach potential participants. While the RDS mechanism and associated methods adjusting at analysis stage are well-documented in statistical sciences literature, methodological focus has largely be...

the main objective of this research was identifying an appropriate model for the work values in public services of iran within the framework of islamic value system. the basic in this study is: what are components and dimensions of the work values in public services of iran within the framework of islamic value system? in this basic research, data was gathered through filed and library data col...