Fast, effective, and reliable models: these are the desiderata of every theorist and practitioner. Machine Learning (ML) algorithms, proposed in the last decades, proved to be effective and reliable in solving complex real-world problems, but they are usually designed without taking into account the underlying computing architecture. On the contrary, the effort of contemplating the exploited computing device is often motivated by application-specific and real-world requirements, such as the need to accelerate the learning process with dedicated/distributed hardware, or to foster energy-sparing requirements of applications based on mobile standalone devices. The ESANN 2014 Byte The Bullet: Learning on Real-World Computing Architectures special session has pooled a compilation of the most recent proposals in this area, by encouraging submissions related to the development and the application of fast, effective, reliable techniques, which consider possibilities, potentialities and constraints of real-world computing architectures as basic cornerstones and motivations.
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