Results 41 to 50 of about 851,390 (284)
Sparse On-Line Gaussian Processes [PDF]
Neural Computation, 2002 We develop an approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian on-line algorithm, together with a sequential construction of a relevant subsample of the data that fully ...
Csato, Lehel, Opper, Manfred
openaire +4 more sources
Multiresolution Gaussian Processes
, 2012 We propose a multiresolution Gaussian process to capture long-range, non-Markovian dependencies while allowing for abrupt changes. The multiresolution GP hierarchically couples a collection of smooth GPs, each defined over an element of a random nested partition. Long-range dependencies are captured by the top-level GP while the partition points define
Emily B. Fox, David B. Dunson
openaire +3 more sources
GPstruct: Bayesian structured prediction using Gaussian processes [PDF]
, 2014 We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum ...
Bratieres, Sebastien +2 more
core +1 more source
CoRR, 2013 Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)
Nir Friedman, Iftach Nachman
openaire +3 more sources
Temporal Learning in Video Data Using Deep Learning and Gaussian Processes
International Journal of Prognostics and Health Management, 2016 This paper presents an approach for data-driven modeling of hidden, stationary temporal dynamics in sequential images or videos using deep learning and Bayesian non-parametric techniques.
Devesh K. Jha +2 more
doaj +1 more source
GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language
Journal of Statistical Software, 2022 Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources.
Jamie Fairbrother +4 more
doaj +1 more source
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs
, 2019 This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and ...
AS Stordal +17 more
core +1 more source
Manifold Gaussian Processes for regression [PDF]
2016 International Joint Conference on Neural Networks (IJCNN), 2016 26.03.14 KB.
Roberto Calandra +3 more
openaire +5 more sources
Thin and deep Gaussian processes
Advances in Neural Information Processing Systems 36, 2023 Accepted at the Conference on Neural Information Processing Systems (NeurIPS ...
de Souza, Daniel Augusto +8 more
openaire +4 more sources
FEBS Letters, EarlyView.This study reveals a unique active site enriched in methionine residues and demonstrates that these residues play a critical role by stabilizing carbocation intermediates through novel sulfur–cation interactions. Structure‐guided mutagenesis further revealed variants with significantly altered product profiles, enhancing pseudopterosin formation. These
Marion Ringel +13 more
wiley +1 more source