Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. Intuitively, discovering a good feature rep...
Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qi...
Cloud computing, social networking and information networks (for search, news feeds, etc) are driving interest in the deployment of large data centers. TCP is the dominant Layer 3...
Mohammad Alizadeh, Adel Javanmard, Balaji Prabhaka...
The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting...
In a multi-service network such as ATM, adaptive data services ( such as ABR) share the bandwidth left unused by higher priority services. The network indicates to the ABR sources ...
We propose an illumination invariant and rotation insensitive texture representation based on a Markovian textural model. A texture is aligned with its dominant orientation and te...