A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines
David Charte,Francisco Charte,Salvador García,María José del Jesus,Francisco Herrera,Francisco Herrera +5 more
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TLDR
Autoencoders (AEs) as mentioned in this paper have emerged as an alternative to manifold learning for conducting nonlinear feature fusion, and they can be used to generate reduced feature sets through the fusion of the original ones.About:
This article is published in Information Fusion.The article was published on 2018-11-01 and is currently open access. It has received 209 citations till now. The article focuses on the topics: Isomap & Feature (computer vision).read more
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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: In this paper, a taxonomy of recent contributions related to explainability of different machine learning models, including those aimed at explaining Deep Learning methods, is presented, and a second dedicated taxonomy is built and examined in detail.
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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: Previous efforts to define explainability in Machine Learning are summarized, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought, and a taxonomy of recent contributions related to the explainability of different Machine Learning models are proposed.
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Artificial Intelligence Forecasting of Covid-19 in China
TL;DR: If the data are reliable and there are no second transmissions, the AI-inspired methods can accurately forecast the transmission dynamics of the Covid-19 across the provinces/cities in China, which is a powerful tool for helping public health planning and policymaking.
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A new divergence measure for belief functions in D–S evidence theory for multisensor data fusion
TL;DR: The proposed RB divergence is the first such measure to consider the correlations between both belief functions and subsets of the sets of belief functions, thus allowing it to provide a more convincing and effective solution for measuring the discrepancy between BBAs in D–S evidence theory.
References
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Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images
TL;DR: A Stacked Sparse Autoencoder, an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer and out-performed nine other state of the art nuclear detection strategies.
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TL;DR: This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process, and contains a comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature.
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Autoencoders, Unsupervised Learning, and Deep Architectures
TL;DR: In this article, the authors present a general mathematical framework for the study of both linear and non-linear autoencoders, including the Boolean autoencoder, which is equivalent to a clustering problem that can be solved in polynomial time when the number of clusters is small and becomes NP complete when the size of the clusters is large.
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Unsupervised Learning of Video Representations using LSTMs
TL;DR: This work uses Long Short Term Memory networks to learn representations of video sequences and evaluates the representations by finetuning them for a supervised learning problem - human action recognition on the UCF-101 and HMDB-51 datasets.
Journal ArticleDOI
Deep belief networks
TL;DR: This paper discusses three ideas based on greedily learning a hierarchy of features that can be repeated several times to learn a deep, hierarchical model in which each layer of features captures strong high-order correlations between the activities of features in the layer below.