Latent Force Models

Mauricio A. ÁlvarezDavid LuengoNeil D. Lawrence
,  5:9-16, 2009.

Abstract

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 modeling 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 computational biology, motion capture and geostatistics.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-alvarez-lfm09, title = {Latent Force Models}, author = {Mauricio A. Álvarez and David Luengo and Neil D. Lawrence}, pages = {9--16}, year = {}, editor = {}, volume = {5}, address = {Clearwater Beach, FL}, url = {http://inverseprobability.com/publications/alvarez-lfm09.html}, abstract = {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 modeling 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 computational biology, motion capture and geostatistics.} }
Endnote
%0 Conference Paper %T Latent Force Models %A Mauricio A. Álvarez %A David Luengo %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-alvarez-lfm09 %I PMLR %J Proceedings of Machine Learning Research %P 9--16 %U http://inverseprobability.com %V %W PMLR %X 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 modeling 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 computational biology, motion capture and geostatistics.
RIS
TY - CPAPER TI - Latent Force Models AU - Mauricio A. Álvarez AU - David Luengo AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-alvarez-lfm09 PB - PMLR SP - 9 DP - PMLR EP - 16 L1 - UR - http://inverseprobability.com/publications/alvarez-lfm09.html AB - 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 modeling 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 computational biology, motion capture and geostatistics. ER -
APA
Álvarez, M.A., Luengo, D. & Lawrence, N.D.. (). Latent Force Models. , in PMLR :9-16

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