Journal ArticleDOI
Pattern Recognition and Machine Learning
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TLDR
This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
Citations
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Proceedings ArticleDOI
Physical-layer identification of UHF RFID tags
TL;DR: It is shown that, in controlled environments, UHF RFID tags can be uniquely identified based on their signal spectral features with an Equal Error Rate of 0% (within the authors' population); the application of those techniques to cloning detection in RFID-enabled supply chains is discussed.
Journal ArticleDOI
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
TL;DR: An overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids is provided.
Proceedings ArticleDOI
On Variational Message Passing on Factor Graphs
TL;DR: It is shown how (naive and structured) variational algorithms may be derived from a factor graph by mechanically applying generic message computation rules; in this way, one can bypass error-prone variational calculus.
Journal ArticleDOI
Normative evidence accumulation in unpredictable environments
TL;DR: A novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds is presented, which provides new insights into the expectation-driven dynamics of the underlying neural signals.
Posted Content
Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database.
TL;DR: This work proposes a comprehensive study of deep learning algorithms when applied in the context of side-channel analysis and addresses the question of the choice of the hyper-parameters for the class of multi-layer perceptron networks and convolutional neural networks.