We describe three applications in computational learning theory of techniques and ideas recently introduced in the study of parameterized computational complexity. (1) Using param...
Rodney G. Downey, Patricia A. Evans, Michael R. Fe...
A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that ar...
Michael J. Black, Yaser Yacoob, Allan D. Jepson, D...
Abstract— Many robotic control tasks involve complex dynamics that are hard to model. Hand-specifying trajectories that satisfy a system’s dynamics can be very time-consuming a...
Jie Tang, Arjun Singh, Nimbus Goehausen, Pieter Ab...
Although many of the software engineering activities can now be model-supported, the model is often missing in software development. We are interested in retrieving statemachine m...
We consider reinforcement learning in the parameterized setup, where the model is known to belong to a parameterized family of Markov Decision Processes (MDPs). We further impose ...