Journal ArticleDOI
Pattern Recognition and Machine Learning
Reads0
Chats0
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
More filters
Journal ArticleDOI
Semantics derived automatically from language corpora contain human-like biases
TL;DR: This article showed that applying machine learning to ordinary human language results in human-like semantic biases and replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web.
Journal ArticleDOI
Aggregating Local Image Descriptors into Compact Codes
TL;DR: This paper first presents and evaluates different ways of aggregating local image descriptors into a vector and shows that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension.
Proceedings ArticleDOI
Lost in quantization: Improving particular object retrieval in large scale image databases
TL;DR: In this paper, a weighted set of visual words is obtained by selecting words based on proximity in descriptor space, and this representation may be incorporated into a standard tf-idf architecture and how spatial verification is modified in the case of this soft-assignment.
Posted Content
Deeply-Recursive Convolutional Network for Image Super-Resolution
TL;DR: This work proposes an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN) with two extensions: recursive-supervision and skip-connection, which outperforms previous methods by a large margin.
Proceedings ArticleDOI
From learning models of natural image patches to whole image restoration
Daniel Zoran,Yair Weiss +1 more
TL;DR: A generic framework which allows for whole image restoration using any patch based prior for which a MAP (or approximate MAP) estimate can be calculated is proposed and a generic, surprisingly simple Gaussian Mixture prior is presented, learned from a set of natural images.