We present a methodology that brings simplicity to large and comt labs by using abstraction. The networking community has appreciated the value of large scale test labs to explore...
Simon Knight, Askar Jaboldinov, Olaf Maennel, Iain...
Often when modeling structured domains, it is desirable to leverage information that is not naturally expressed as simply a label. Examples include knowledge about the evaluation ...
Large collections of 3D models from the same object class (e.g., chairs, cars, animals) are now commonly available via many public repositories, but exploring the range of shape v...
Vladimir G. Kim, Wilmot Li, Niloy J. Mitra, Stephe...
As ever-larger training sets for learning to rank are created, scalability of learning has become increasingly important to achieving continuing improvements in ranking accuracy [...
We propose an energy-based framework for approximating surfaces from a cloud of point measurements corrupted by noise and outliers. Our energy assigns a tangent plane to each (noi...