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High Level High Performance Computing for Multitask Learning of Time-varying Models

Marco Signoretto, Emanuele Frandi, Zahra Karevan, Johan A. K. Suykens
ESAT-STADIUS, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven (BELGIUM)
Internal Report 14-127, ESAT-SISTA, KU Leuven (Leuven, Belgium), 2014
@article{signoretto2014high,

   title={High Level High Performance Computing for Multitask Learning of Time-varying Models},

   author={Signoretto, Marco and Frandi, Emanuele and Karevan, Zahra and Suykens, Johan AK},

   year={2014}

}

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We propose an approach suitable to learn multiple time-varying models jointly and discuss an application in data-driven weather forecasting. The methodology relies on spectral regularization and encodes the typical multi-task learning assumption that models lie near a common low dimensional subspace. The arising optimization problem amounts to estimating a matrix from noisy linear measurements within a trace norm ball. Depending on the problem, the matrix dimensions as well as the number of measurements can be large. We discuss an algorithm that can handle large-scale problems and is amenable to parallelization. We then compare high level high performance implementation strategies that rely on JIT decorators. The approach enables, in particular, to offload computations to a GPU without hard-coding computationally intensive operations via a low-level language. As such, it allows for fast prototyping and therefore it is of general interest for developing and testing novel computational models.
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