Recent algorithmic and theoretical advances in reinforcement learning (RL) have attracted widespread interest. RL algorithmshave appeared that approximatedynamic programming on an ...
Statistical debugging uses lightweight instrumentation and statistical models to identify program behaviors that are strongly predictive of failure. However, most software is most...
Currently, a large amount of data can be best represented as graphs, e.g., social networks, protein interaction networks, etc. The analysis of these networks is an urgent research ...
Given a set of pins and a set of obstacles on routing layers, a multilayer obstacle-avoiding rectilinear Steiner minimal tree (ML-OARSMT) connects these pins by rectilinear edges w...
Aggregating statistical representations of classes is an important task for current trends in scaling up learning and recognition, or for addressing them in distributed infrastruc...