Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....
Most existing work uses dual decomposition and subgradient methods to solve Network Utility Maximization (NUM) problems in a distributed manner, which suffer from slow rate of con...
Deep architectures are families of functions corresponding to deep circuits. Deep Learning algorithms are based on parametrizing such circuits and tuning their parameters so as to ...
This paper analyses the efficiency of different data structures for detecting overlap in digital documents. Most existing approaches use some hash function to reduce the space req...
Given the resurgent attractiveness of single-instruction-multiple-data (SIMD) processing, it is important for high-performance computing applications to be SIMD-capable. The Hartr...
Tirath Ramdas, Gregory K. Egan, David Abramson, Ki...