Search

Your search keyword '"Girolami, Mark"' showing total 974 results
Start Over Author "Girolami, Mark"
974 results on '"Girolami, Mark"'

151. Inferring Meta-covariates in Classification

Author

Harris, Keith, McMillan, Lisa, Girolami, Mark, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Kadirkamanathan, Visakan, editor, Sanguinetti, Guido, editor, Girolami, Mark, editor, Niranjan, Mahesan, editor, and Noirel, Josselin, editor

Published
2009
Full Text
View/download PDF

157. On the Use of Diagonal and Class-Dependent Weighted Distances for the Probabilistic k-Nearest Neighbor

Author

Paredes, Roberto, Girolami, Mark, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Vitrià, Jordi, editor, Sanches, João Miguel, editor, and Hernández, Mario, editor

Published
2011
Full Text
View/download PDF

170. Disease Classification from Capillary Electrophoresis: Mass Spectrometry

Author

Rogers, Simon, Girolami, Mark, Krebs, Ronald, Mischak, Harald, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Singh, Sameer, editor, Singh, Maneesha, editor, Apte, Chid, editor, and Perner, Petra, editor

Published
2005
Full Text
View/download PDF

172. An Assessment of Feature Relevance in Predicting Protein Function from Sequence

Author

Al-Shahib, Ali, He, Chao, Tan, Aik Choon, Girolami, Mark, Gilbert, David, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Zheng Rong, editor, Yin, Hujun, editor, and Everson, Richard M., editor

Published
2004
Full Text
View/download PDF

176. Population-Level Modelling for Truck Fleet Survival Analysis

Author

Bull, Lawrence, Dhada, Maharshi, Steinert, Olof, Lindgren, Tony, Parlikad, Ajith Kumar, and Girolami, Mark

Abstract

Population-level modelling is used to address issues of data sparsity in the survival analysis of a simulated truck fleet. Specifically, hierarchical Bayes with mixed effects improves the predictive capability of hazard models. A set of correlated functions is learnt over the vehicle population – in a combined model – to approximate fleet predictors. Model uncertainty is reduced when sub-fleets of vehicles are allowed to share correlated information. In turn, vehicle groups with incomplete data (automatically) borrow statistical strength from data-rich groups.

Published
2022
Full Text
View/download PDF

184. ITFoM – The IT Future of Medicine

Author

Lehrach, Hans, Subrak, Ralf, Boyle, Peter, Pasterk, Markus, Zatloukal, Kurt, Müller, Heimo, Hubbard, Tim, Brand, Angela, Girolami, Mark, Jameson, Daniel, Bruggeman, Frank J., and Westerhoff, Hans V.

Published
2011
Full Text
View/download PDF

185. Multi-fidelity approach to Bayesian parameter estimation in subsurface heat and fluid transport models

Author

Menberg, Kathrin, Bidarmaghz, Asal, Gregory, Alastair, Choudhary, Ruchi, Girolami, Mark, Girolami, Mark [0000-0003-3008-253X], and Apollo - University of Cambridge Repository

Subjects
Numerical modelling, Bayesian inference, Large-scale hydro-thermal modelling, Parameter estimation, Subsurface temperature
Abstract

The increased use of the urban subsurface for competing purposes, such as anthropogenic infrastructures and geothermal energy applications, leads to an urgent need for large-scale sophisticated modelling approaches for coupled mass and heat transfer. However, such models are subject to large uncertainties in model parameters, the physical model itself and in available measured data, which is often rare. Thus, the robustness and reliability of the computer model and its outcomes largely depend on successful parameter estimation and model calibration, which are hampered by the computational burden of large-scale coupled models. To tackle this problem, we develop a novel Bayesian approach for parameter estimation, which allows us to account for different sources of uncertainty, is capable of dealing with sparse field data and makes optimal use of the output data from expensive numerical model runs. This is achieved by combining output data from different models that represent the same physical problem, but at different levels of fidelity, e.g. reflected by different spatial resolution. By applying this new approach to a 1D analytical heat transfer model and a large-scale semi-3D numerical model while using synthetic data, we show that the accuracy and precision of parameter estimation by this multi-fidelity framework by far exceeds the standard single-fidelity results. The consideration of different error terms in the Bayesian framework also allows assessment of the model bias and the discrepancy between the different fidelity levels. These are emulated by Gaussian Process models, which facilitate re-iteration of the parameter estimation without additional model runs.

Published
2020

186. Building Information Modelling, Artificial Intelligence and Construction Tech

Author

Brilakis, Ioannis, Girolami, Mark, Sacks, Rafael, Brilakis, Ioannis [0000-0003-1829-2083], Girolami, Mark [0000-0003-3008-253X], Sacks, Rafael [0000-0001-9427-5053], and Apollo - University of Cambridge Repository

Subjects
Artificial intelligence, Building information modelling, Construction technology, Digital construction, Innovation, Digital twins
Abstract

Development and adoption of digital information tools in the construction sector provides fertile ground for the birth and growth of companies that specialize in applications of technologies to design and construction. While some of the technologies are themselves new, the majority are based on ideas that have proliferated in construction research for decades but could not be implemented without a sound digital building information foundation. Building Information Modelling (BIM) itself can be traced to a landmark paper from 1975; ideas for artificially intelligent design and code checking tools date from the mid-1980s; and construction robots have laboured in research labs for decades. Yet it is only within the past five years that venture capital has actively sought startup companies in the ‘Construction Tech’ sector. We follow a set of digital construction innovations through their known past and their uncertain present, and we review their increasingly optimistic future, all through the lens of their dependence on digital information. The review identifies new challenges, yielding a set of research topics with the potential to unlock a range of future applications that make extensive use of artificial intelligence., Centre for Digital Built Britain

Published
2020

193. Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights.

Author

Malekloo, Arman, Ozer, Ekin, AlHamaydeh, Mohammad, and Girolami, Mark

Subjects
STRUCTURAL health monitoring, TECHNOLOGICAL innovations, SMART cities, INFRASTRUCTURE (Economics), ELECTRONIC data processing, MACHINE learning
Abstract

Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past's applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

196. The Silicon Trypanosome

Author

Achcar, Fiona, primary, Fadda, Abeer, additional, Haanstra, Jurgen R., additional, Kerkhoven, Eduard J., additional, Kim, Dong-Hyun, additional, Leroux, Alejandro E., additional, Papamarkou, Theodore, additional, Rojas, Federico, additional, Bakker, Barbara M., additional, Barrett, Michael P., additional, Clayton, Christine, additional, Girolami, Mark, additional, Krauth-Siegel, R. Luise, additional, Matthews, Keith R., additional, and Breitling, Rainer, additional

Published
2014
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results