He is also researching methods for setting regularisation parameters in conventional networks, based on weak assumptions about the distribution of correlation scales. Recognising that Bayesian methods alone are inadequate for handling parameterisation invariance issues, Dr. Rohwer is also involved in research which combines information geometry with Bayes theory. This work has recently yielded a practical yet rigourously quantifiable on-line learning method. He is also working on applications of this Bayesian information geometry theory to search algorithms, essentially by formulating search problems as problems of statistical estimation of imperfectly known optima. This perspective is also yielding promising new training methods, related to the adaptive critic methods of credit assignment in control problems.
Statistical rigour is merely a prerequisite for a neural approach to artificial intelligence.
Dr. Rohwer maintains a minor research interest in representation, which he currently expects to become the next major issue. Dr. Richard Rohwer obtained a BSc in Physics from Stanford University in 1978, and a PhD in Physics from the University of Texas at Austin in 1985, working in Quantum Cosmology. Following a one-year postdoctoral appointment at the University of Newcastle upon Tyne, he moved into neural networks and pattern recognition, particularly for speech recognition, in a series of further postdocs at Edinburgh University and Strathclyde University. He began lecturing at Aston University as the Neural Computing Research Group was forming in 1992.