Gaussian Processes for Machine Learning

Citation edge evidence

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• Practical Bayesian Optimization of Machine Learning Algorithms→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Lapl…→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Algorithms for Hyper-Parameter Optimization→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Reinforcement learning in robotics: A survey→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Automatic differentiation in machine learning: a survey→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Probabilistic machine learning and artificial intelligence→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Machine learning methods for solar radiation forecasting: A review→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• A Unifying View of Sparse Approximate Gaussian Process Regression→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Explaining nonlinear classification decisions with deep Taylor decomposition→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Statistical Learning TheoryLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Neural networks for pattern recognitionLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Term-weighting approaches in automatic text retrievalLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Generalized Additive Models.Legacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Solutions of Ill-Posed Problems.Legacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Information theory, inference, and learning algorithmsLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→An Introduction to Support Vector MachinesLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Learning with KernelsLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Using the Nyström Method to Speed Up Kernel MachinesLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→GTM: The Generative Topographic MappingLegacy edge — source detail unavailable
• Probabilistic machine learning and artificial intelligence→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Probabilistic machine learning and artificial intelligence→Information theory, inference, and learning algorithmsLegacy edge — source detail unavailable
• A Unifying View of Sparse Approximate Gaussian Process Regression→Using the Nyström Method to Speed Up Kernel MachinesLegacy edge — source detail unavailable
• A Unifying View of Sparse Approximate Gaussian Process Regression→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Algorithms for Hyper-Parameter Optimization→Neural networks for pattern recognitionLegacy edge — source detail unavailable
• GTM: The Generative Topographic Mapping→Neural networks for pattern recognitionLegacy edge — source detail unavailable
• Using the Nyström Method to Speed Up Kernel Machines→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…→Algorithms for Hyper-Parameter OptimizationLegacy edge — source detail unavailable
• Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
• Pattern Recognition and Neural Networks→Neural networks for pattern recognitionLegacy edge — source detail unavailable
• Pattern Recognition and Neural Networks→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Practical Bayesian Optimization of Machine Learning Algorithms→Algorithms for Hyper-Parameter OptimizationLegacy edge — source detail unavailable
• Statistical Learning Theory→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Explaining nonlinear classification decisions with deep Taylor decomposition→Neural networks for pattern recognitionLegacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Information theory, inference, and learning algorithmsLegacy edge — source detail unavailable
• Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications→Neural networks for pattern recognitionLegacy edge — source detail unavailable
• Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications→Explaining nonlinear classification decisions with deep Taylor decompositionLegacy edge — source detail unavailable