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pro vyhledávání: '"McDonald, Thomas"'
Deep Gaussian Processes (DGPs) leverage a compositional structure to model non-stationary processes. DGPs typically rely on local inducing point approximations across intermediate GP layers. Recent advances in DGP inference have shown that incorporat
Externí odkaz:
http://arxiv.org/abs/2407.01856
Modelling the behaviour of highly nonlinear dynamical systems with robust uncertainty quantification is a challenging task which typically requires approaches specifically designed to address the problem at hand. We introduce a domain-agnostic model
Externí odkaz:
http://arxiv.org/abs/2311.14828
Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a multi-armed bandit
Externí odkaz:
http://arxiv.org/abs/2307.09943
The processing, storage and transmission of large-scale point clouds is an ongoing challenge in the computer vision community which hinders progress in the application of 3D models to real-world settings, such as autonomous driving, virtual reality a
Externí odkaz:
http://arxiv.org/abs/2303.15225
A key challenge in the practical application of Gaussian processes (GPs) is selecting a proper covariance function. The moving average, or process convolutions, construction of GPs allows some additional flexibility, but still requires choosing a pro
Externí odkaz:
http://arxiv.org/abs/2206.08972
Autor:
McDonald, Thomas N.
Lean production prescribes training workers on all tasks within the cell to adapt to changes in customer demand. Multi-skilling of workers can be achieved by cross-training. Cross-training can be improved and reinforced by implementing job rotation.
Externí odkaz:
http://hdl.handle.net/10919/26386
http://scholar.lib.vt.edu/theses/available/etd-03082004-120627/
http://scholar.lib.vt.edu/theses/available/etd-03082004-120627/
Effectively modeling phenomena present in highly nonlinear dynamical systems whilst also accurately quantifying uncertainty is a challenging task, which often requires problem-specific techniques. We present a novel, domain-agnostic approach to tackl
Externí odkaz:
http://arxiv.org/abs/2106.05960
Publikováno v:
In Toxicology in Vitro February 2024 94