Zobrazeno 1 - 10
of 144
pro vyhledávání: '"Reece, Steven"'
Autor:
Reece, Steven, O'Donnell, Emma, Liu, Felicia, Wolstenholme, Joanna, Arriaga, Frida, Ascenzi, Giacomo, Pywell, Richard
There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial institutions is chal
Externí odkaz:
http://arxiv.org/abs/2404.17369
High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastructure status such as building damage, flooding, and obstructions to acce
Externí odkaz:
http://arxiv.org/abs/2111.03693
This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyse a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings
Externí odkaz:
http://arxiv.org/abs/2007.01076
Publikováno v:
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (2017), pp. 109-125, Springer, Cham
Unstructured data from diverse sources, such as social media and aerial imagery, can provide valuable up-to-date information for intelligent situation assessment. Mining these different information sources could bring major benefits to applications s
Externí odkaz:
http://arxiv.org/abs/1904.03063
Autor:
Isupova, Olga, Li, Yunpeng, Kuzin, Danil, Roberts, Stephen J, Willis, Katherine, Reece, Steven
Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements.
Externí odkaz:
http://arxiv.org/abs/1811.12258
This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and
Externí odkaz:
http://arxiv.org/abs/1507.00566
To date, the radial velocity (RV) method has been one of the most productive techniques for detecting and confirming extrasolar planetary candidates. Unfortunately, stellar activity can induce RV variations which can drown out or even mimic planetary
Externí odkaz:
http://arxiv.org/abs/1506.07304
Autor:
Reece, Steven Y., 1980
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemistry, 2007.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bib
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bib
Externí odkaz:
http://hdl.handle.net/1721.1/40867
We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters. More often than not, the critical tuning of ML algorithm parameters
Externí odkaz:
http://arxiv.org/abs/1407.7969
Latent force models (LFM) are principled approaches to incorporating solutions to differential equations within non-parametric inference methods. Unfortunately, the development and application of LFMs can be inhibited by their computational cost, esp
Externí odkaz:
http://arxiv.org/abs/1310.6319