[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Fusion of probability density functions
G Koliander, Y El-Laham, PM Djurić… - Proceedings of the …, 2022 - ieeexplore.ieee.org
Fusing probabilistic information is a fundamental task in signal and data processing with
relevance to many fields of technology and science. In this work, we investigate the fusion of …
relevance to many fields of technology and science. In this work, we investigate the fusion of …
Gaussian process-based real-time learning for safety critical applications
The safe operation of physical systems typically relies on high-quality models. Since a
continuous stream of data is generated during run-time, such models are often obtained …
continuous stream of data is generated during run-time, such models are often obtained …
Ensemble Gaussian processes for online learning over graphs with adaptivity and scalability
In the past decade, semi-supervised learning (SSL) over graphs has gained popularity due
to its importance in a gamut of network science applications. While most of existing SSL …
to its importance in a gamut of network science applications. While most of existing SSL …
Incremental ensemble Gaussian processes
Q Lu, GV Karanikolas… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Belonging to the family of Bayesian nonparametrics, Gaussian process (GP) based
approaches have well-documented merits not only in learning over a rich class of nonlinear …
approaches have well-documented merits not only in learning over a rich class of nonlinear …
A Bayesian clustering ensemble Gaussian process model for network-wide traffic flow clustering and prediction
Traffic flow prediction is an essential component in intelligent transportation systems.
Recently, there has been a notable trend in applying machine learning models, especially …
Recently, there has been a notable trend in applying machine learning models, especially …
Injury to thalamocortical projections following traumatic brain injury results in attractor dynamics for cortical networks
S Mofakham, Y Liu, A Hensley, JR Saadon… - Progress in …, 2022 - Elsevier
Major theories of consciousness predict that complex electroencephalographic (EEG)
activity is required for consciousness, yet it is not clear how such activity arises in the …
activity is required for consciousness, yet it is not clear how such activity arises in the …
Sequential estimation of Gaussian process-based deep state-space models
We consider the problem of sequential estimation of the unknowns of state-space and deep
state-space models that include estimation of functions and latent processes of the models …
state-space models that include estimation of functions and latent processes of the models …
Surrogate modeling for Bayesian optimization beyond a single Gaussian process
Bayesian optimization (BO) has well-documented merits for optimizing black-box functions
with an expensive evaluation cost. Such functions emerge in applications as diverse as …
with an expensive evaluation cost. Such functions emerge in applications as diverse as …
Bayesian optimization with ensemble learning models and adaptive expected improvement
Optimizing a black-box function that is expensive to evaluate emerges in a gamut of
machine learning and artificial intelligence applications including drug discovery, policy …
machine learning and artificial intelligence applications including drug discovery, policy …