Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. We show how different, physically-inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology and geostatistics.
Abstract-H.264/AVC is a new international video coding standard that provides higher coding efficiency with respect to previous standards at the expense of a higher computational complexity. The complexity is even higher when H.264/AVC is used in applications with high bandwidth and high quality like high definition (HD) video decoding. In this paper, we analyze the computational requirements of H.264 decoder with a special emphasis in HD video and we compare it with previous standards and lower resolutions. The analysis was done with a SIMD optimized decoder using hardware performance monitoring. The main objective is to identify the application bottlenecks and to suggest the necessary support in the architecture for processing HD video efficiently. We have found that H.264/AVC decoding of HD video perform many more operations per frame that MPEG-4 and MPEG-2, has new kernels with more demanding memory access patterns and has a lot data dependent branches that are difficult to predict. In order to improve the H.264/AVC decoding process at HD it is necessary to explore a better support in media instructions, specialized prefetching techniques and possibly, the use of some kind of multiprocessor architecture.
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