We derive categories directly from robot sensor data to address the symbol grounding problem. Unlike model-based approaches where human intuitive correspondences are sought betwee...
Daniel H. Grollman, Odest Chadwicke Jenkins, Frank...
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Numerous interaction techniques have been developed that make “virtual” pointing at targets in graphical user interfaces easier than analogous physical pointing tasks by invok...
We describe a novel max-margin parameter learning approach for structured prediction problems under certain non-decomposable performance measures. Structured prediction is a commo...
Abstract For many centuries scientists have wondered how the human brain represents thoughts in terms of the underlying biology of neural activity. Philosophers, linguists, cogniti...