Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
Most existing methods of semi-supervised clustering introduce supervision from outside, e.g., manually label some data samples or introduce constrains into clustering results. Thi...
Despite their simplicity, scalar threshold operators effectively remove additive white Gaussian noise from wavelet detail coefficients of many practical signals. This paper explor...
Alyson K. Fletcher, Vivek K. Goyal, Kannan Ramchan...
Sensors capable of sensing phenomena at high data rates on the order of tens to hundreds of thousands of samples per second are now widely deployed in many industrial, civil engine...
Lewis Girod, Yuan Mei, Ryan Newton, Stanislav Rost...
We introduce Pulse, a framework for processing continuous queries over models of continuous-time data, which can compactly and accurately represent many real-world activities and p...