What is devising validating and testing of algorithms
The project lead implements the algorithm in R, since our engine for doing MCMC runs natively there.This works well for our team, since everyone is comfortable in R, and code may be shared and reviewed easily.This is arguably the most fun part of the entire process.This involves anywhere from two to four people actually hitting the whiteboard to discuss ideas. We review theory and literature between these sessions, too.This means that the hyper-parameters selection algorithm should be generic and versatile enough to produce correct predictions on data coming from different customers, different technology processes and different measurement tools.The solutions are also designed to be robust to variations in the size of the training set.There is little (or no) documentation at this point.
The workflow I describe is the process, from idea inception to publication, of creating an automated procedure to improve the sampling efficiency of Markov chain Monte Carlo (MCMC) sampling.Once again, deep knowledge of our tools, allows us to characterize expected tool noise characteristics to an unprecedented level of accuracy.Since the training, validating, and testing are all done at the customer’s site, Nova’s algorithm needs to be fully automated.All these requirements are achieved while maintain the accuracy required by our customers.
My name is Daniel Turek, and I'm an Assistant Professor in the Department of Mathematics and Statistics at Williams College.
The process of learning is an optimization problem that minimizes error between predicted output and reference response.