Results 51 to 60 of about 196,185 (268)

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, Abhishek Srivastav, Asok Ray   +2 more
doaj   +1 more source

Peripheral lysosomes recruit PLEKHG3 to focal adhesions and restrain protrusion dynamics

FEBS Letters, EarlyView.
Proximity‐dependent labeling at the LAMTOR complex revealed the Rho GEF PLEKHG3 as a lysosome‐proximal protein directing the study toward the influence of lysosome positioning on actin dynamics and cell motility. We show that PLEKHG3 colocalizes with lysosomes at focal adhesion sites and observe that forced peripheral dispersion of lysosomes hinders ...
Rainer Ettelt, Ana‐Maria Sandru, Georg Vucak, Sebastian Didusch, Biljana Riemelmoser, Karin Ehrenreiter, Markus Hartl, Lukas A. Huber, Manuela Baccarini   +8 more
wiley   +1 more source

Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection

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, Gian Marco Melito, Vahid Badeli, Alice Reinbacher-Köstinger, Katrin Ellermann, Wolfgang von der Linden   +5 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, Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder   +4 more
doaj   +1 more source

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

Manifold Gaussian Processes for regression [PDF]

2016 International Joint Conference on Neural Networks (IJCNN), 2016
26.03.14 KB.
Roberto Calandra, Jan Peters 0001, Carl Edward Rasmussen, Marc Peter Deisenroth   +3 more
openaire   +5 more sources

Polarization‐resolved femtosecond Vis/IR spectroscopy tailored for resolving weak signals in biological samples using minimal sample volume

FEBS Open Bio, EarlyView.
Unique biological samples, such as site‐specific mutant proteins, are available only in limited quantities. Here, we present a polarization‐resolved transient infrared spectroscopy setup with referencing to improve signal‐to‐noise tailored towards tracing small signals. We provide an overview of characterizing the excitation conditions for polarization‐
Clark Zahn, Karsten Heyne
wiley   +1 more source

Distributed Gaussian Processes With Uncertain Inputs

IEEE Access
Gaussian Process regression is a powerful non-parametric approach that facilitates probabilistic uncertainty quantification in machine learning. Distributed Gaussian Process (DGP) methods offer scalable solutions by dividing data among multiple GP models
Peter L. Green
doaj   +1 more source

WLAN monopole antenna design by Siamese convolutional neural network and KNN exploiting Gaussian process [PDF]

MATEC Web of Conferences
In the process of antenna design, surrogate models can generally be used, but modeling requires a large number of samples. Although full wave electromagnetic simulation software can handle this task, obtaining a large number of samples is time-consuming,
Tian Yubo, Meng Fei
doaj   +1 more source

Deep Convolutional Gaussian Processes [PDF]

, 2020
We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared to current Gaussian process ...
Heinonen Markus, Kaski Samuel, Blomqvist Kenneth   +2 more
openaire   +2 more sources