Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
Problems that can be sampled randomly are a good source of test suites for comparing quality of constraint satisfaction techniques. Quasigroup problems are representatives of struc...
This paper addresses the interaction between randomization, with restart strategies, and learning, an often crucial technique for proving unsatisfiability. We use instances of SAT ...
Stochastic simulations and other scientific applications that depend on random numbers are increasingly implemented in a parallelized manner in programmable logic. High-quality ps...
Evaluation of incomplete algorithms that solve SAT requires to generate hard satisfiable instances. For that purpose, the kSAT uniform random generation is not usable. The other g...