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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Journal ArticleDOI
Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data
TL;DR: A modular framework is presented which allows to recover the temporal behaviour from single-cell snapshot data and reverse engineer the dynamics of gene expression to reconstruct gene expression dynamics during differentiation pathways and infer the structure of a key gene regulatory network.
Journal ArticleDOI
Deep Learning in Image Cytometry: A Review
Anindya Gupta,Philip J. Harrison,Håkan Wieslander,Nicolas Pielawski,Kimmo Kartasalo,Kimmo Kartasalo,Gabriele Partel,Leslie Solorzano,Amit Suveer,Anna H. Klemm,Ola Spjuth,Ida-Maria Sintorn,Carolina Wählby +12 more
TL;DR: This review focuses on deep learning and how it is applied to microscopy image data of cells and tissue samples, and gives the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data.
Journal ArticleDOI
Data-driven modeling and learning in science and engineering
TL;DR: This paper reviews the application of data-driven modeling and model learning procedures to different fields in science and engineering and finds the traditional approach seemed to be highly satisfactory.
Journal ArticleDOI
A Dry EEG-System for Scientific Research and Brain–Computer Interfaces
Thorsten O. Zander,Moritz Lehne,Klas Ihme,Sabine Jatzev,Joao Correia,Christian Kothe,Bernd Picht,Femke Nijboer +7 more
TL;DR: The tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications and easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.
Proceedings ArticleDOI
Asynchronous stochastic gradient descent for DNN training
TL;DR: This paper describes an effective approach to achieve an approximation of BP - asynchronous stochastic gradient descent (ASGD), which is used to parallelize computing on multi-GPU, which achieves a 3.2 times speed-up on 4 GPUs than the single one, without any recognition performance loss.