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
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
Semi-supervised Deep Learning-Driven Anomaly Detection Schemes for Cyber-Attack Detection in Smart Grids
TL;DR: In this paper , two semi-supervised hybrid deep learning-based anomaly detection methods for intrusion detection in industrial control system (ICS) traffic of smart grid are presented. And the detection performance is demonstrated on IEC 60870-5-104 (aka IEC 104) control communication that is often utilized for substation control in smart grids.
Posted ContentDOI
MCIBox: A Toolkit for Single-molecule Multi-way Chromatin Interaction Visualization and Micro-Domains Identification
Simon Zhongyuan Tian,Guoliang Li,Duo Ning,Kai Jing,Yewen Xu,Yang Yang,Melissa J. Fullwood,Pengfei Yin,Guangyu Huang,Dariusz Plewczynski,Jixian Zhai,Ziwei Dai,Wei Chen,Meizhen Zheng +13 more
TL;DR: MCIBox is a toolkit for Multi-way Chromatin Interaction (MCI) analysis, including a visualization tool and a platform for identifying micro-domains with clustered single-molecule chromatin complexes, based on various clustering algorithms integrated with dimensionality reduction methods.
Journal ArticleDOI
Trust-aware denoising autoencoder with spatial-temporal activity for cross-domain personalized recommendations
TL;DR: In this article , the authors proposed a cross-domain recommendation system that not only takes into account the time at finer granularity levels (e.g., hours, days, weeks, etc.), but also considers a persons location, trust level, and sentiment analysis while computing recommendations.
Dissertation
Anomaly Detection in Video
TL;DR: Novel approaches for learning motion features and modelling normal spatio-temporal dynamics for anomaly detection using deep convolutional neural networks and a sequence-to-sequence encoder-decoder for prediction and reconstruction are presented.
Nuevas arquitecturas hardware de procesamiento de alto rendimiento para aprendizaje profundo
TL;DR: The need for this type of specialized hardware is introduced, describing its purpose, operation and current implementations, including those designed to accelerate work with deep neural networks.
References
More filters
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.