Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on th...
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...
This paper investigates “Schelling points” on 3D meshes, feature points selected by people in a pure coordination game due to their salience. To collect data for this investig...
Xiaobai Chen, Abulhair Saparov, Bill Pang, Thomas ...
Recent research has suggested that a large class of software bugs fall into the category of inconsistencies, or cases where two pieces of program code make incompatible assumption...
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...