Abstract. Model selection is an important problem in statistics, machine learning, and data mining. In this paper, we investigate the problem of enabling multiple parties to perfor...
Although they work with two non-humanoid robots located several million miles away, the distributed team that operates the Mars Exploration Rovers demonstrates an uncanny sympathy...
Recent advances in statistical inference and machine learning close the divide between simulation and classical optimization, thereby enabling more rigorous and robust microarchit...
This paper focuses on generating efficient software pipelined schedules for in-order machines, which we call Converged Trace Schedules. For a candidate loop, we form a string of t...
Learning models for recognizing objects with few or no training examples is important, due to the intrinsic longtailed distribution of objects in the real world. In this paper, we...