[PDF] Top 20 Gaussian Processes in Machine Learning
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Gaussian Processes in Machine Learning
... a Gaussian distribution are indexed by their position in the ...the Gaussian process it is the argument x (of the random function f (x)) which plays the role of index set: for every input x there is an ... See full document
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Gaussian Processes for Machine Learning
... munity, Gaussian processes are receiving growing ...tor machine, which was taken up more quickly by ...by Gaussian processes in machine ... See full document
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Digital Communication Receivers Using Gaussian Processes for Machine Learning
... Gaussian processes can be extended for solving classi- fication ...A Gaussian distribution is typically used to approximate the GPC’s posterior, either using Laplace [26] or expectation propagation ... See full document
12
Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for prediction of skin temperature in lower limb prostheses
... This study addresses the challenges of non-invasively measur- ing the in-socket residual limb temperature by comparing two dif- ferent modeling techniques, namely ANFIS and Gaussian processes. The ... See full document
7
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 ... See full document
5
Semi-described and semi-supervised learning with Gaussian processes
... GP learning involved the cluster assumption [Lawrence and Jordan, 2005] for clas- ...In machine learning, Ghahra- mani and Jordan [1994] learn the joint density of the in- put and output data and ... See full document
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Efficient modeling of latent information in supervised learning using Gaussian processes
... Machine learning has been very successful in providing tools for learning a function mapping from an input to an output, which is typically referred to as supervised ...and machine translation ... See full document
9
How Deep Are Deep Gaussian Processes?
... Gaussian processes have proved remarkably successful as a tool for various statistical infer- ence and machine learning tasks (Rasmussen and Williams, 2006; Kennedy and O’Hagan, 2001; Higdon ... See full document
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Detecting periodicities with Gaussian processes
... and machine learning (Rasmussen & Williams, ...and machine learning framework, the solution of the interpolation problem corresponds to the expectation of a Gaussian process, Z , ... See full document
19
Gaussian Processes for Blazar Variability Studies
... Considering the full length of available multi-frequency light curves, GP regression was applied to the data at each frequency separately. A variant of the software by [25] was used, which was adapted to our specific ... See full document
7
Learning Kernels over Strings using Gaussian Processes
... 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, Michael ... See full document
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A practical design and implementation of a low cost platform for remote monitoring of lower limb health of amputees in the developing world
... However, the financial costs associated with it are sub- stantially high as around 75% of those affected by diabetes live in middle or low income countries [2]. Many healthcare technologies and products presume that ... See full document
12
Forecasting modeling with kernel function integration in gaussian processes
... In machine learning, it can be used and performed efficiently and well-done, although, it has scare training data and provides better Convergence rate than SARIMA, ANN and support vector machine for ... See full document
6
Embarrassingly Parallel Inference for Gaussian Processes
... for learning the posterior so it is more efficient from a computational and memory perspective to perform Bayesian inference on a subset of the ...a Gaussian process context, as mentioned in Section ... See full document
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Modelling Annotator Bias with Multi task Gaussian Processes: An Application to Machine Translation Quality Estimation
... WPTP12: This dataset was distributed by Ko- ponen et al. (2012). It contains 299 English sen- tences translated into Spanish using two or more of eight MT systems randomly selected from all system submissions for WMT11 ... See full document
11
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems
... On first thought, one may straightaway consider endowing each agent with an existing online/stochastic variant of a GP or sparse GP model (Bui, Nguyen, and Turner 2017; Cheng and Boots 2016; Csat´o and Opper 2002; ... See full document
8
String and Membrane Gaussian Processes
... As previously discussed the prediction scheme operated by the BCM is Kolmogorov-inconsistent in that the resulting predictive distributions are not consistent by marginalization. 15 Moreover, jointly predicting all ... See full document
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Active learning of intuitive control knobs for synthesizers using gaussian processes
... Figure 2. User interface for learning and applying high-level knobs to sounds. (1) Sliders for adjusting the low-level control-parameters on the synthe- sizer directly. There are a total of 64, many are cropped in ... See full document
11
Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review
... In this section, the machine learning methods have been classified into the following popular subsections, i.e., tree-based, support vector-based, neural net- work-based, and hybrids and ensembles. Further, ... See full document
24
Gaussian Processes for Ordinal Regression
... supervised learning problem with properties of both metric re- gression and ...for Gaussian processes can be adapted directly to tackle relatively large data ... See full document
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