This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
: Tasks such as forecasting, diagnosis, and planning frequently require quantitative predictions . Typically, quantitative predictions are obtained by characterizing a system in te...
Memory-bounded techniques have shown great promise in solving complex multi-agent planning problems modeled as DEC-POMDPs. Much of the performance gains can be attributed to pruni...
The min-SHIFT DESIGN problem (MSD) is an important scheduling problem that needs to be solved in many industrial contexts. The issue is to find a minimum number of shifts and the...
Moving-Least-Squares (MLS) Surfaces undergoing large deformations need periodic regeneration of the point set (point-set resampling) so as to keep the point-set density quasi-unif...