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- W2063202388 abstract "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 Just-in-Time (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." @default.
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- W2063202388 date "2014-12-01" @default.
- W2063202388 modified "2023-09-25" @default.
- W2063202388 title "High level high performance computing for multitask learning of time-varying models" @default.
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- W2063202388 doi "https://doi.org/10.1109/cibd.2014.7011522" @default.
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