Results 51 to 60 of about 5,652,238 (291)
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
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CoRR, 2016 Gaussian process models are flexible, Bayesian non-parametric approaches to regression. Properties of multivariate Gaussians mean that they can be combined linearly in the manner of additive models and via a link function (like in generalized linear models) to handle non-Gaussian data. However, the link function formalism is restrictive, link functions
Alan D. Saul +3 more
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Gaussian processes on graphs [PDF]
In the ever-growing field of machine learning research, the use of graphs has recently gathered significant interest for modelling on data with relational structures.
Zhi, Yin-Cong
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Ligand‐dependent transcriptional heterogeneity in cell cycle gene expression delays G1/S entry
FEBS Letters, EarlyView.EGF and HRG induce distinct G1/S progression programs in ErbB2‐amplified BT474 breast cancer cells. Despite activating the potent ErbB2–ErbB3 heterodimer, HRG does not accelerate cell‐cycle entry. Instead, EGF promotes earlier restriction‐point passage via ERK–FOS signaling, whereas HRG activates the AKT–MYC axis, driving transcriptional heterogeneity ...
Ririn Rahmala Febri +5 more
wiley +1 more source
Classification of protein interaction sentences via gaussian processes [PDF]
, 2009 The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions.
Polajnar, T. +5 more
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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
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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
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Emerging experimental and computational methods for studying redox‐regulated structural transitions
FEBS Letters, EarlyView.Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass +2 more
wiley +1 more source
Entropy, 2019 In 2000, Kennedy and O’Hagan proposed a model for uncertainty quantification that combines data of several levels of sophistication, fidelity, quality, or accuracy, e.g., a coarse and a fine mesh in finite-element simulations.
Sascha Ranftl +5 more
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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
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