Who Cited It

Gaussian Processes for Machine Learning

2005 · The MIT Press eBooks · 10,470 citations · 13 from inside this corpus

Carl Edward Rasmussen, Christopher K. I. Williams

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Gaussian Processes for Machine Learning (2005)Gaussian Processes for Machin…The Nature of Statistical Learning Theory (1995)The Nature of Statistical Lea…Statistical Learning Theory (1999)Statistical Learning TheoryNeural networks for pattern recognition (1994)Neural networks for pattern r…Term-weighting approaches in automatic text retrieval (1988)Term-weighting approaches in …Generalized Additive Models. (1991)Generalized Additive Models.Solutions of Ill-Posed Problems. (1978)Solutions of Ill-Posed Proble…Information theory, inference, and learning algorithms (2004)Information theory, inference…Pattern Recognition and Neural Networks (1996)Pattern Recognition and Neura…Bayesian Learning for Neural Networks (1996)Bayesian Learning for Neural …An Introduction to Support Vector Machines (2000)An Introduction to Support Ve…A Probabilistic Theory of Pattern Recognition (1996)A Probabilistic Theory of Pat…Learning with Kernels (2001)Learning with KernelsUsing the Nyström Method to Speed Up Kernel Machines (2000)Using the Nyström Method to S…GTM: The Generative Topographic Mapping (1998)GTM: The Generative Topograph…Practical Bayesian Optimization of Machine Learning Algorithms (2012)Practical Bayesian Optimizati…Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Lapl… (2009)Approximate Bayesian Inferenc…Algorithms for Hyper-Parameter Optimization (2011)Algorithms for Hyper-Paramete…Reinforcement learning in robotics: A survey (2013)Reinforcement learning in rob…A review of uncertainty quantification in deep learning: Techniques, applications and cha… (2021)A review of uncertainty quant…Automatic differentiation in machine learning: a survey (2017)Automatic differentiation in …Probabilistic machine learning and artificial intelligence (2015)Probabilistic machine learnin…Machine learning methods for solar radiation forecasting: A review (2017)Machine learning methods for …A Unifying View of Sparse Approximate Gaussian Process Regression (2005)A Unifying View of Sparse App…A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions (2018)A tutorial on Gaussian proces…Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f… (2013)Making a Science of Model Sea…Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021)Explaining Deep Neural Networ…Explaining nonlinear classification decisions with deep Taylor decomposition (2016)Explaining nonlinear classifi…
27 of 27 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
The Nature of Statistical Learning Theory199539,409
Statistical Learning Theory199926,957
Neural networks for pattern recognition199418,720
Term-weighting approaches in automatic text retrieval19889,714
Generalized Additive Models.19918,296
Solutions of Ill-Posed Problems.19788,221
Information theory, inference, and learning algorithms20046,580
Pattern Recognition and Neural Networks19966,514
Bayesian Learning for Neural Networks19964,397
An Introduction to Support Vector Machines20004,134
A Probabilistic Theory of Pattern Recognition19963,327
Learning with Kernels20012,541
Using the Nyström Method to Speed Up Kernel Machines20001,519
GTM: The Generative Topographic Mapping19981,395

What cites it, inside the corpus

PaperYearCited
Practical Bayesian Optimization of Machine Learning Algorithms20125,674
Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Lapl…20095,476
Algorithms for Hyper-Parameter Optimization20113,188
Reinforcement learning in robotics: A survey20133,154
A review of uncertainty quantification in deep learning: Techniques, applications and cha…20212,644
Automatic differentiation in machine learning: a survey20172,094
Probabilistic machine learning and artificial intelligence20152,031
Machine learning methods for solar radiation forecasting: A review20171,793
A Unifying View of Sparse Approximate Gaussian Process Regression20051,782
A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions20181,743
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…20131,645
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications20211,424
Explaining nonlinear classification decisions with deep Taylor decomposition20161,413

Links

DOI · OpenAlex record

Topics

Gaussian Processes and Bayesian InferenceComputer Science
Neural Networks and ApplicationsComputer Science
Scientific Research and DiscoveriesPhysics and Astronomy

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports2 author record(s) attached.
  • supports180 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:40+00:00.

sha256 7e3d99a592f7f61f…