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
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Proceedings Article
End-to-end text recognition with convolutional neural networks
TL;DR: This paper combines the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows them to use a common framework to train highly-accurate text detector and character recognizer modules.
Proceedings ArticleDOI
Dense visual SLAM for RGB-D cameras
TL;DR: This paper proposes a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels, and proposes an entropy-based similarity measure for keyframe selection and loop closure detection.
Proceedings Article
A note on the evaluation of generative models
TL;DR: This article reviews mostly known but often underappreciated properties relating to the evaluation and interpretation of generative models with a focus on image models and shows that three of the currently most commonly used criteria---average log-likelihood, Parzen window estimates, and visual fidelity of samples---are largely independent of each other when the data is high-dimensional.
Posted Content
A Conceptual Introduction to Hamiltonian Monte Carlo
TL;DR: This review provides a comprehensive conceptual account of these theoretical foundations of Hamiltonian Monte Carlo, focusing on developing a principled intuition behind the method and its optimal implementations rather of any exhaustive rigor.
Journal ArticleDOI
Visual Word Ambiguity
TL;DR: It is demonstrated that explicitly modeling visual word assignment ambiguity improves classification performance compared to the hard assignment of the traditional codebook model, and the proposed model performs consistently.