We present a new approach to multiple instance learning (MIL) that is particularly effective when the positive bags are sparse (i.e. contain few positive instances). Unlike other ...
Previous approaches to RAID scaling either require a very large amount of data to be migrated, or cannot tolerate multiple disk additions without resulting in disk imbalance. In t...
There is a growing demand for network devices capable of examining the content of data packets in order to improve network security and provide application-specific services. Most...
We propose a new approach to adaptive system identification when the system model is sparse. The approach applies the ℓ1 relaxation, common in compressive sensing, to improve t...
We propose a new learning method which exploits temporal consistency to successfully learn a complex appearance model from a sparsely labeled training video. Our approach consists...