The Burrows-Wheeler transformation is used for effective data compression, e.g., in the well known program bzip2. Compression and decompression are done in a block-wise fashion; la...
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...
In this paper we investigate the problem of partitioning an input string T in such a way that compressing individually its parts via a basecompressor C gets a compressed output th...
We propose a new machine learning paradigm called Graph Transformer Networks that extends the applicability of gradient-based learning algorithms to systems composed of modules th...
We develop energy-efficient, adaptive distributed transforms for data gathering in wireless sensor networks. In particular, we consider a class of unidirectional transforms that ...