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https://papers.nips.cc/paper_files/paper/2018/hash/b865367fc4c0845c0682bd466e6ebf4c-Abstract.html
Infinite-Horizon Gaussian Processes
infinite horizongaussian processes
https://www.semanticscholar.org/search?q=Non-asymptotic+approximations+of+neural+networks+by+Gaussian+processes.
Non-asymptotic approximations of neural networks by Gaussian processes. | Semantic Scholar
An academic search engine that utilizes artificial intelligence methods to provide highly relevant results and novel tools to filter them with ease.
processes semantic scholarneural networksnonasymptoticapproximations
https://arxiv.org/abs/2112.10334
[2112.10334] Gaussian Processes for Finite Size Extrapolation of Many-Body Simulations
Abstract page for arXiv paper 2112.10334: Gaussian Processes for Finite Size Extrapolation of Many-Body Simulations
gaussian processesmany body2112finitesize
https://arxiv.org/abs/1705.08933
[1705.08933] Doubly Stochastic Variational Inference for Deep Gaussian Processes
Abstract page for arXiv paper 1705.08933: Doubly Stochastic Variational Inference for Deep Gaussian Processes
gaussian processes1705doublystochasticvariational
https://openreview.net/forum?id=oJp7uTL7ox-
Gray-Box Gaussian Processes for Automated Reinforcement Learning | OpenReview
Despite having achieved spectacular milestones in an array of important real-world applications, most Reinforcement Learning (RL) methods are very brittle...
gaussian processesreinforcement learninggrayboxautomated
https://deepgram.com/ai-glossary/gaussian-processes
Gaussian Processes
Ever pondered how machines learn to make sense of complex data, or how financial analysts forecast market trends with remarkable accuracy? In this article, we...
gaussian processes
https://www.deisenroth.cc/publication/souza-2025/
Infinite Neural Operators: Gaussian Processes on Functions | Marc Deisenroth
Dec 4, 2025 - A variety of infinitely wide neural architectures (e.g., dense NNs, CNNs, and transformers) induce Gaussian process (GP) priors over their outputs. These...
gaussian processesmarc deisenrothinfiniteneuraloperators
https://ieeexplore.ieee.org/document/735807/
Bayesian classification with Gaussian processes | IEEE Journals & Magazine | IEEE Xplore
We consider the problem of assigning an input vector to one of m classes by predicting P(c|x) for c=1,...,m. For a two-class problem, the probability of class o
ieee journals magazinegaussian processesbayesianclassificationxplore
https://scikit-learn.org/stable/modules/gaussian_process.html
1.7. Gaussian Processes — scikit-learn 1.8.0 documentation
Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems. The advantages of...
scikit learn 81 7gaussian processes0 documentation
https://www.sandia.gov/research/publications/details/bayesian-learning-with-gaussian-processes-for-low-dimensional-representatio-2025-05-01/
Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent...
bayesian learninggaussian processeslow dimensionaltime dependentrepresentations
https://cmjt.r-universe.dev/stelfi
stelfi: Hawkes and Log-Gaussian Cox Point Processes Using Template Model Builder
processes usingtemplate modelhawkesloggaussian