gpflow 2.1.3 on PyPI -

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The structure of GPflowOpt — GPflowOpt 0.1.1 documentation

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  • GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end.1 The distinguishing features of GPflow are that it uses variational inference as
  • Gaussian Process Molecule Property Prediction with FlowMO
  • GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with a particular emphasis on software testing and is able to exploit GPU hardware.
  • GPflow: A Gaussian process library using TensorFlow |

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GitHub - GPflow/GPflow: Gaussian processes in TensorFlow

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1D Example -

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  • 10708 Probabilistic Graphical Models
  • Graph kernels have emerged as an e ective tool for tackling the graph similarity problem. A graph kernel is a function that corresponds to an inner-product in a Hilbert space, and can be thought of as a similarity measure de ned directly on graphs. The main advantage of graph kernels is that they allow a large family of machine learning algorithms,
  • 2 exp/big(-/frac{1}{2}(x_p - x_q)
  • Spectral Mixture Kernels with Time and Phase Delay

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Kernel expectations by markvdw · Pull Request #195

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Getting basic periodic model to fit-CSDN问答

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  • Defining new acquisition functions — GPflowOpt 0.1.1
  • Neural Non-Stationary Spectral Kernel | DeepAI
  • Nov 17, 2020
  • Bayesian Multiobjective Optimization — GPflowOpt 0.1.1

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Periodic kernel — gaussian_processes 1.0.5 documentation

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Kernel Design — GPflow 2.1.3 documentation

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  • Improve gpflow2 code coverage by adding tests-CSDN问答
  • WARNING:sities:Shape of x must be 2D at computation. INFO:tensorflow:Optimization terminated with: Message: bCONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH Objective function value: -1834.949908 Number of iterations: 17 Number of functions evaluations: 27
  • While single-channel kernels implemented in GPflow have input data of shape N × P (with N ∈ N the total number of data points and P ∈ N the number of input dimensions), the MOGPTK base kernel has input data of shape N × (1 + P), where the first column contains integers denoting the channel index to which the remaining columns correspond
  • machine learning - Feature map for the Gaussian kernel

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