In this letter we explore an alternative structural representation for Steinbuch-type binary associative memories. These networks offer very generous storage capacities (both asy...
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
A bayesian network is an appropriate tool for working with uncertainty and probability, that are typical of real-life applications. In literature we find different approaches for b...
Evelina Lamma, Fabrizio Riguzzi, Andrea Stambazzi,...
Due to the tremendous increase rate and the high change frequency of Web documents, maintaining an up-to-date index for searching purposes (search engines) is becoming a challenge...
Odysseas Papapetrou, Stavros Papastavrou, George S...
Sensornet lifespan and utility is limited by the energy resources of individual motes. Network designers seek to maximise energy efficiency while maintaining acceptable Quality o...