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
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Text as Data
TL;DR: An introduction to the use of text as an input to economic research is provided, the features that make text different from other forms of data are discussed, and a practical overview of relevant statistical methods is offered.
Journal ArticleDOI
Probabilistic movement modeling for intention inference in human-robot interaction
Zhikun Wang,Katharina Mülling,Marc Peter Deisenroth,Heni Ben Amor,David Vogt,Bernhard Schölkopf,Jan Peters +6 more
TL;DR: The Intention-Driven Dynamics Model is proposed to probabilistically model the generative process of movements that are directed by the intention and allows the intention to be inferred from observed movements using Bayes’ theorem.
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Variance Reduction in SGD by Distributed Importance Sampling
TL;DR: This work proposes a framework for distributing deep learning in which one set of workers search for the most informative examples in parallel while a single worker updates the model on examples selected by importance sampling, which leads the model to update using an unbiased estimate of the gradient.
Journal ArticleDOI
Robotic ICSI (Intracytoplasmic Sperm Injection)
TL;DR: The first report of robotic intracytoplasmic sperm injection is reported, and the system performs visual tracking of single sperm, robotic immobilization of sperm, aspiration of sperm with picoliter volume, and insertion of sperm into an oocyte with a high degree of reproducibility.
Journal ArticleDOI
Learning representations of microbe–metabolite interactions
James T. Morton,Alexander A. Aksenov,Alexander A. Aksenov,Louis-Félix Nothias,Louis-Félix Nothias,James R. Foulds,Robert A. Quinn,Michelle H. Badri,Tami L. Swenson,Marc W. Van Goethem,Trent R. Northen,Trent R. Northen,Yoshiki Vázquez-Baeza,Mingxun Wang,Mingxun Wang,Nicholas A. Bokulich,Aaron Watters,Se Jin Song,Richard Bonneau,Pieter C. Dorrestein,Pieter C. Dorrestein,Rob Knight +21 more
TL;DR: In this paper, the conditional probability that each molecule is present given the presence of a specific microorganism was estimated by using neural networks to infer the interactions between microbially produced metabolites and inflammatory bowel disease.