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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Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques
TL;DR: In this paper , a comparative study of two supervised and two unsupervised data normalization algorithms was conducted to eliminate environmental variability from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges.
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A hybrid deep learning framework for privacy preservation in edge computing
TL;DR: In this paper , a genetic algorithm is coupled with a deep learning network based on adversarial training to build a utility-privacy balanced, low computation solution, which aims to prevent inference of implicit privacy labels present in the data while maintaining data utility.
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Stacked supervised Poisson autoencoders-based soft-sensor for defects prediction in steelmaking process
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Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study
Mahdi Mahdavi,Hadi Choubdar,Zahra Rostami,Behnaz Niroomand,Alexandra T. Levine,Alireza Fatemi,Ehsan Bolhasani,Abdol-Hossein Vahabie,Stephen G. Lomber,Yaser Merrikhi +9 more
TL;DR: In this article , the predictive power of latent representations obtained from a hybrid autoencoder (HAE) framework combining VAE characteristics with mean squared error (MSE) and triplet loss was investigated for forecasting COVID-19 patients with high mortality risk in a retrospective framework.
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A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation
TL;DR: In this article , a discriminant information theoretic learning (DITL) framework is proposed to address the challenges of joint utilization of discriminatory representations and complementary representations from multi-modal features.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.