Gaussian processes for machine learning
Gaussian Processes for Machine Learning
... the machine learning com- looking ahead munity, Gaussian processes are receiving growing ...tor machine, which was taken up more quickly by ...by Gaussian processes in ...
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Gaussian Processes for Machine Learning (GPML) Toolbox
... Gaussian processes (GPs) (Rasmussen and Williams, 2006) have convenient properties for many modelling tasks in machine learning and ...reinforcement learning, spatial models, survival ...
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Digital Communication Receivers Using Gaussian Processes for Machine Learning
... propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication ...Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which ...
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Gaussian processes for machine learning
... account will be in red numbers only having access to a small sample of historical values. The main interest of being able to answer that question is that quite recently a leading bank company has been allocating a huge ...
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Gaussian Processes in Machine Learning
... 4 Conclusions and Future Directions We’ve seen how Gaussian processes can conveniently be used to specify very flex- ible non-linear regression. We only mentioned in passing one type of covariance function, ...
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Relational Learning with Gaussian Processes
... to machine learning ...for learning tasks, ...a learning algorithm can greatly benefit by taking into account the global network organization of such inter-relationships rather than relying on ...
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Efficient Reinforcement Learning using Gaussian Processes
... and machine learning, we proposed pilco, a general and fully Bayesian framework for efficient autonomous learning in Markov decision processes (see Chapter ...
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Learning Kernels over Strings using Gaussian Processes
... References Mart´ın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Cor- rado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, ...
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Gaussian Processes for Data-Efficient Learning in Robotics and Control
... [58] B. Wittenmark. Adaptive Dual Control Methods: An Overview. In In Proceedings of the IFAC Symposium on Adaptive Systems in Control and Signal Processing, 1995. Marc Peter Deisenroth conducted his Ph.D. re- search at ...
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Policy learning in Continuous-Time Markov Decision Processes using Gaussian Processes
... of machine learning, where much research has gone on defining good local search methods to learn effective randomised schedulers, for different criteria like time bounded reward and time unbounded ...
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Efficient modeling of latent information in supervised learning using Gaussian processes
... in machine learning, data are collected as a combination of multiple condi- tions, ...Output Gaussian Processes (LVMOGP) that allows to jointly model multiple conditions for regression and ...
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Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation
... Die automatische Berechnung eines Modells aus Messungen mit Hilfe von Regression erlaubt es, den Arbeitsaufwand deutlich zu verringern und ist daher eine sinnvolle Alternative zu klas- sischer, wissensbasierter ...
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Differentially Private Gaussian Processes
... Abstract A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the Differential ...
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Stein Variational Gaussian Processes
... References Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghe- mawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, ...
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Representations and regularity of Gaussian processes
... phenomena, Gaussian processes represent an important class of stochastic processes in probability ...random processes and functional analysis that comes closely connected to different ...
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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
... automatic estimation of modulation transfer functions In addition to Bayesian probabilistic inference, I have also carried out research in the field of computational photography. This work is not covered in this thesis. ...
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Machine learning methods based on diffusion processes
... (lin), Gaussian RBF (rbf), cosine (cos), parametrix (prx), and exact (ext) kernels in SVM to (1) classify WebKB-4-University web pages into four classes: student, faculty, course, and project; and (2) impute the ...
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Active learning of intuitive control knobs for synthesizers using gaussian processes
... As the user has accumulated a number of points in the box, the user can ask the machine to learn the concept and apply it to a starting sound in order to check how well the machine is understanding the ...
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Optimality of Poisson Processes Intensity Learning with Gaussian Processes
... The aim of this paper is to advance the theoretical understanding of the method of Adams et al. (2009), which they termed “Sigmoidal Gaussian Cox Process” (SGCP). It is by now well known both from theory and ...
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Asymmetric Transfer Learning with Deep Gaussian Processes
... transfer learning by deep sparse Gaussian pro- cesses The posterior density of the model given above for ATL-DGP is not tractable, hence approximate inference is ...
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