Journal ArticleDOI
Pattern Recognition and Machine Learning
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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Book ChapterDOI
Who wrote this code? identifying the authors of program binaries
TL;DR: Casting authorship attribution as a machine learning problem, this work presents a novel program representation and techniques that automatically detect the stylistic features of binary code and provides strong evidence that programmer style is preserved in program binaries.
Journal ArticleDOI
Joint Antenna Selection and Hybrid Beamformer Design Using Unquantized and Quantized Deep Learning Networks
TL;DR: In this paper, the authors proposed a deep learning-based approach for antenna selection and hybrid beamforming design in millimeter-wave MIMO systems, where the CNN accepts the channel matrix as input and outputs a subarray with optimal spectral efficiency.
Journal ArticleDOI
Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties
TL;DR: It is suggested that QSARs exploration of NP-cell association data, considering the role of both NP protein corona and physicochemical properties, can support the planning and interpretation of toxicity studies and guide the design of NPs for biomedical applications.
Proceedings ArticleDOI
Predicting bounce rates in sponsored search advertisements
TL;DR: In this article, the authors explore an important and relatively unstudied quality measure of a sponsored search advertisement: bounce rate, which is defined as the fraction of users who click on the ad but almost immediately move on to other tasks.
Journal ArticleDOI
Adaptive decoding for brain-machine interfaces through bayesian parameter updates
TL;DR: The proposed Bayesian regression self-training method for updating the parameters of an unscented Kalman filter decoder uses the decoder's output to periodically update its neuronal tuning model in a Bayesian linear regression, and significantly improved the accuracy of offline reconstructions.