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Autor Kůrková, Věra |
Documentos disponibles escritos por este autor (8)
Crear una solicitud de compra Refinar búsquedaArtificial Neural Networks and Machine Learning – ICANN 2018 / Kůrková, Věra ; Manolopoulos, Yannis ; Hammer, Barbara ; Iliadis, Lazaros ; Maglogiannis, Ilias
TÃtulo : Artificial Neural Networks and Machine Learning – ICANN 2018 : 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I Tipo de documento: documento electrónico Autores: Kůrková, VÄ›ra, ; Manolopoulos, Yannis, ; Hammer, Barbara, ; Iliadis, Lazaros, ; Maglogiannis, Ilias, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XLIII, 824 p. 311 ilustraciones ISBN/ISSN/DL: 978-3-030-01418-6 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Visión por computador IngenierÃa Informática Red de computadoras Protección de datos Algoritmos IngenierÃa Informática y Redes Redes de comunicación informática Seguridad de datos e información Clasificación: 006.3 Resumen: Este conjunto de tres volúmenes LNCS 11139-11141 constituye las actas arbitradas de la 27.ª Conferencia Internacional sobre Redes Neuronales Artificiales, ICANN 2018, celebrada en Rodas, Grecia, en octubre de 2018. Los artÃculos presentados en estos volúmenes fueron cuidadosamente revisados ​​y seleccionados de un total de 360 presentaciones. Están relacionados con los siguientes temas temáticos: IA y bioinformática, redes bayesianas y de estado de eco, computación inspirada en el cerebro, modelos complejos caóticos, clustering, minerÃa, análisis exploratorio, arquitecturas de codificación, patrones de activación complejos, redes neuronales convolucionales, aprendizaje profundo (DL) , DL en Sistemas de Tiempo Real, DL y Big Data Analytics, DL y Big Data, DL y Forense, DL y Ciberseguridad, DL y Redes Sociales, Sistemas en Evolución – Optimización, Máquinas de Aprendizaje Extremo, De las Neuronas al Neuromorfismo, De la Sensación a la Percepción, De neuronas individuales a redes, modelado difuso, ANN jerárquico, inferencia y reconocimiento, información y optimización, interacción con el cerebro, aprendizaje automático (ML), ML para sistemas biomédicos, ML y procesamiento de imágenes de vÃdeo, ML y ciencia forense, ML y Ciberseguridad, ML y redes sociales, ML en ingenierÃa, movimiento y detección de movimiento, perceptrones multicapa y redes de núcleo, lenguaje natural, reconocimiento de objetos y rostros, redes neuronales recurrentes y computación de reservorios, aprendizaje por refuerzo, computación de reservorios, mapas autoorganizados, dinámica de picos /Spiking ANN, máquinas de vectores de soporte, inteligencia de enjambre y toma de decisiones, minerÃa de textos, computación neuronal teórica, series temporales y pronósticos, capacitación y aprendizaje. Tipo de medio : Computadora Summary : This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Artificial Neural Networks and Machine Learning – ICANN 2018 : 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I [documento electrónico] / Kůrková, VÄ›ra, ; Manolopoulos, Yannis, ; Hammer, Barbara, ; Iliadis, Lazaros, ; Maglogiannis, Ilias, . - 1 ed. . - [s.l.] : Springer, 2018 . - XLIII, 824 p. 311 ilustraciones.
ISBN : 978-3-030-01418-6
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Visión por computador IngenierÃa Informática Red de computadoras Protección de datos Algoritmos IngenierÃa Informática y Redes Redes de comunicación informática Seguridad de datos e información Clasificación: 006.3 Resumen: Este conjunto de tres volúmenes LNCS 11139-11141 constituye las actas arbitradas de la 27.ª Conferencia Internacional sobre Redes Neuronales Artificiales, ICANN 2018, celebrada en Rodas, Grecia, en octubre de 2018. Los artÃculos presentados en estos volúmenes fueron cuidadosamente revisados ​​y seleccionados de un total de 360 presentaciones. Están relacionados con los siguientes temas temáticos: IA y bioinformática, redes bayesianas y de estado de eco, computación inspirada en el cerebro, modelos complejos caóticos, clustering, minerÃa, análisis exploratorio, arquitecturas de codificación, patrones de activación complejos, redes neuronales convolucionales, aprendizaje profundo (DL) , DL en Sistemas de Tiempo Real, DL y Big Data Analytics, DL y Big Data, DL y Forense, DL y Ciberseguridad, DL y Redes Sociales, Sistemas en Evolución – Optimización, Máquinas de Aprendizaje Extremo, De las Neuronas al Neuromorfismo, De la Sensación a la Percepción, De neuronas individuales a redes, modelado difuso, ANN jerárquico, inferencia y reconocimiento, información y optimización, interacción con el cerebro, aprendizaje automático (ML), ML para sistemas biomédicos, ML y procesamiento de imágenes de vÃdeo, ML y ciencia forense, ML y Ciberseguridad, ML y redes sociales, ML en ingenierÃa, movimiento y detección de movimiento, perceptrones multicapa y redes de núcleo, lenguaje natural, reconocimiento de objetos y rostros, redes neuronales recurrentes y computación de reservorios, aprendizaje por refuerzo, computación de reservorios, mapas autoorganizados, dinámica de picos /Spiking ANN, máquinas de vectores de soporte, inteligencia de enjambre y toma de decisiones, minerÃa de textos, computación neuronal teórica, series temporales y pronósticos, capacitación y aprendizaje. Tipo de medio : Computadora Summary : This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Artificial Neural Networks and Machine Learning – ICANN 2018 : 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part II Tipo de documento: documento electrónico Autores: Kůrková, VÄ›ra, ; Manolopoulos, Yannis, ; Hammer, Barbara, ; Iliadis, Lazaros, ; Maglogiannis, Ilias, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XXVIII, 632 p. 242 ilustraciones ISBN/ISSN/DL: 978-3-030-01421-6 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Visión por computador IngenierÃa Informática Red de computadoras Protección de datos Algoritmos IngenierÃa Informática y Redes Redes de comunicación informática Seguridad de datos e información Clasificación: 006.3 Resumen: Este conjunto de tres volúmenes LNCS 11139-11141 constituye las actas arbitradas de la 27.ª Conferencia Internacional sobre Redes Neuronales Artificiales, ICANN 2018, celebrada en Rodas, Grecia, en octubre de 2018. Los 139 artÃculos completos y 28 breves, asà como 41 artÃculos completos tipo póster y 41 artÃculos breves presentados en estos volúmenes fueron cuidadosamente revisados ​​y seleccionados de un total de 360 ​​presentaciones. Están relacionados con los siguientes temas temáticos: IA y bioinformática, redes bayesianas y de estado de eco, computación inspirada en el cerebro, modelos complejos caóticos, clustering, minerÃa, análisis exploratorio, arquitecturas de codificación, patrones de activación complejos, redes neuronales convolucionales, aprendizaje profundo (DL) , DL en Sistemas de Tiempo Real, DL y Big Data Analytics, DL y Big Data, DL y Forense, DL y Ciberseguridad, DL y Redes Sociales, Sistemas en Evolución – Optimización, Máquinas de Aprendizaje Extremo, De las Neuronas al Neuromorfismo, De la Sensación a la Percepción, De neuronas individuales a redes, modelado difuso, ANN jerárquico, inferencia y reconocimiento, información y optimización, interacción con el cerebro, aprendizaje automático (ML), ML para sistemas biomédicos, ML y procesamiento de imágenes de vÃdeo, ML y ciencia forense, ML y Ciberseguridad, ML y redes sociales, ML en ingenierÃa, movimiento y detección de movimiento, perceptrones multicapa y redes de núcleo, lenguaje natural, reconocimiento de objetos y rostros, redes neuronales recurrentes y computación de reservorios, aprendizaje por refuerzo, computación de reservorios, mapas autoorganizados, dinámica de picos /Spiking ANN, máquinas de vectores de soporte, inteligencia de enjambre y toma de decisiones, minerÃa de textos, computación neuronal teórica, series temporales y pronósticos, capacitación y aprendizaje. Tipo de medio : Computadora Summary : This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The 139 full and 28 short papers as well as 41 full poster papers and 41 short poster papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Artificial Neural Networks and Machine Learning – ICANN 2018 : 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part II [documento electrónico] / Kůrková, VÄ›ra, ; Manolopoulos, Yannis, ; Hammer, Barbara, ; Iliadis, Lazaros, ; Maglogiannis, Ilias, . - 1 ed. . - [s.l.] : Springer, 2018 . - XXVIII, 632 p. 242 ilustraciones.
ISBN : 978-3-030-01421-6
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Visión por computador IngenierÃa Informática Red de computadoras Protección de datos Algoritmos IngenierÃa Informática y Redes Redes de comunicación informática Seguridad de datos e información Clasificación: 006.3 Resumen: Este conjunto de tres volúmenes LNCS 11139-11141 constituye las actas arbitradas de la 27.ª Conferencia Internacional sobre Redes Neuronales Artificiales, ICANN 2018, celebrada en Rodas, Grecia, en octubre de 2018. Los 139 artÃculos completos y 28 breves, asà como 41 artÃculos completos tipo póster y 41 artÃculos breves presentados en estos volúmenes fueron cuidadosamente revisados ​​y seleccionados de un total de 360 ​​presentaciones. Están relacionados con los siguientes temas temáticos: IA y bioinformática, redes bayesianas y de estado de eco, computación inspirada en el cerebro, modelos complejos caóticos, clustering, minerÃa, análisis exploratorio, arquitecturas de codificación, patrones de activación complejos, redes neuronales convolucionales, aprendizaje profundo (DL) , DL en Sistemas de Tiempo Real, DL y Big Data Analytics, DL y Big Data, DL y Forense, DL y Ciberseguridad, DL y Redes Sociales, Sistemas en Evolución – Optimización, Máquinas de Aprendizaje Extremo, De las Neuronas al Neuromorfismo, De la Sensación a la Percepción, De neuronas individuales a redes, modelado difuso, ANN jerárquico, inferencia y reconocimiento, información y optimización, interacción con el cerebro, aprendizaje automático (ML), ML para sistemas biomédicos, ML y procesamiento de imágenes de vÃdeo, ML y ciencia forense, ML y Ciberseguridad, ML y redes sociales, ML en ingenierÃa, movimiento y detección de movimiento, perceptrones multicapa y redes de núcleo, lenguaje natural, reconocimiento de objetos y rostros, redes neuronales recurrentes y computación de reservorios, aprendizaje por refuerzo, computación de reservorios, mapas autoorganizados, dinámica de picos /Spiking ANN, máquinas de vectores de soporte, inteligencia de enjambre y toma de decisiones, minerÃa de textos, computación neuronal teórica, series temporales y pronósticos, capacitación y aprendizaje. Tipo de medio : Computadora Summary : This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The 139 full and 28 short papers as well as 41 full poster papers and 41 short poster papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Artificial Neural Networks and Machine Learning – ICANN 2018 : 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III Tipo de documento: documento electrónico Autores: Kůrková, VÄ›ra, ; Manolopoulos, Yannis, ; Hammer, Barbara, ; Iliadis, Lazaros, ; Maglogiannis, Ilias, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XXIX, 846 p. 311 ilustraciones ISBN/ISSN/DL: 978-3-030-01424-7 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Visión por computador IngenierÃa Informática Red de computadoras Protección de datos Algoritmos IngenierÃa Informática y Redes Redes de comunicación informática Seguridad de datos e información Clasificación: 006.3 Resumen: Este conjunto de tres volúmenes LNCS 11139-11141 constituye las actas arbitradas de la 27.ª Conferencia Internacional sobre Redes Neuronales Artificiales, ICANN 2018, celebrada en Rodas, Grecia, en octubre de 2018. Los artÃculos presentados en estos volúmenes fueron cuidadosamente revisados ​​y seleccionados de un total de 360 presentaciones. Están relacionados con los siguientes temas temáticos: IA y bioinformática, redes bayesianas y de estado de eco, computación inspirada en el cerebro, modelos complejos caóticos, clustering, minerÃa, análisis exploratorio, arquitecturas de codificación, patrones de activación complejos, redes neuronales convolucionales, aprendizaje profundo (DL) , DL en Sistemas de Tiempo Real, DL y Big Data Analytics, DL y Big Data, DL y Forense, DL y Ciberseguridad, DL y Redes Sociales, Sistemas en Evolución – Optimización, Máquinas de Aprendizaje Extremo, De las Neuronas al Neuromorfismo, De la Sensación a la Percepción, De neuronas individuales a redes, modelado difuso, ANN jerárquico, inferencia y reconocimiento, información y optimización, interacción con el cerebro, aprendizaje automático (ML), ML para sistemas biomédicos, ML y procesamiento de imágenes de vÃdeo, ML y ciencia forense, ML y Ciberseguridad, ML y redes sociales, ML en ingenierÃa, movimiento y detección de movimiento, perceptrones multicapa y redes de núcleo, lenguaje natural, reconocimiento de objetos y rostros, redes neuronales recurrentes y computación de reservorios, aprendizaje por refuerzo, computación de reservorios, mapas autoorganizados, dinámica de picos /Spiking ANN, máquinas de vectores de soporte, inteligencia de enjambre y toma de decisiones, minerÃa de textos, computación neuronal teórica, series temporales y pronósticos, capacitación y aprendizaje. Tipo de medio : Computadora Summary : This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Artificial Neural Networks and Machine Learning – ICANN 2018 : 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III [documento electrónico] / Kůrková, VÄ›ra, ; Manolopoulos, Yannis, ; Hammer, Barbara, ; Iliadis, Lazaros, ; Maglogiannis, Ilias, . - 1 ed. . - [s.l.] : Springer, 2018 . - XXIX, 846 p. 311 ilustraciones.
ISBN : 978-3-030-01424-7
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Visión por computador IngenierÃa Informática Red de computadoras Protección de datos Algoritmos IngenierÃa Informática y Redes Redes de comunicación informática Seguridad de datos e información Clasificación: 006.3 Resumen: Este conjunto de tres volúmenes LNCS 11139-11141 constituye las actas arbitradas de la 27.ª Conferencia Internacional sobre Redes Neuronales Artificiales, ICANN 2018, celebrada en Rodas, Grecia, en octubre de 2018. Los artÃculos presentados en estos volúmenes fueron cuidadosamente revisados ​​y seleccionados de un total de 360 presentaciones. Están relacionados con los siguientes temas temáticos: IA y bioinformática, redes bayesianas y de estado de eco, computación inspirada en el cerebro, modelos complejos caóticos, clustering, minerÃa, análisis exploratorio, arquitecturas de codificación, patrones de activación complejos, redes neuronales convolucionales, aprendizaje profundo (DL) , DL en Sistemas de Tiempo Real, DL y Big Data Analytics, DL y Big Data, DL y Forense, DL y Ciberseguridad, DL y Redes Sociales, Sistemas en Evolución – Optimización, Máquinas de Aprendizaje Extremo, De las Neuronas al Neuromorfismo, De la Sensación a la Percepción, De neuronas individuales a redes, modelado difuso, ANN jerárquico, inferencia y reconocimiento, información y optimización, interacción con el cerebro, aprendizaje automático (ML), ML para sistemas biomédicos, ML y procesamiento de imágenes de vÃdeo, ML y ciencia forense, ML y Ciberseguridad, ML y redes sociales, ML en ingenierÃa, movimiento y detección de movimiento, perceptrones multicapa y redes de núcleo, lenguaje natural, reconocimiento de objetos y rostros, redes neuronales recurrentes y computación de reservorios, aprendizaje por refuerzo, computación de reservorios, mapas autoorganizados, dinámica de picos /Spiking ANN, máquinas de vectores de soporte, inteligencia de enjambre y toma de decisiones, minerÃa de textos, computación neuronal teórica, series temporales y pronósticos, capacitación y aprendizaje. Tipo de medio : Computadora Summary : This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II / Tipo de documento: documento electrónico Autores: Tetko, Igor V., ; Kůrková, VÄ›ra, ; Karpov, Pavel, ; Theis, Fabian, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XXX, 807 p. 294 ilustraciones, 193 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-30484-3 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Visión por computador IngenierÃa Informática Red de computadoras Algoritmos Protección de datos IngenierÃa Informática y Redes Redes de comunicación informática Seguridad de datos e información Clasificación: 006.3 Resumen: Las actas establecidas LNCS 11727, 11728, 11729, 11730 y 11731 constituyen las actas de la 28.ª Conferencia Internacional sobre Redes Neuronales Artificiales, ICANN 2019, celebrada en Munich, Alemania, en septiembre de 2019. El total de 277 artÃculos completos y 43 artÃculos breves presentado en estas actas fue cuidadosamente revisado y seleccionado entre 494 presentaciones. Estaban organizados en 5 volúmenes centrados en la computación neuronal teórica; aprendizaje profundo; procesamiento de imágenes; texto y series temporales; y talleres y sesiones especiales. . Nota de contenido: Adaptive Graph Fusion for Unsupervised Feature Selection -- Unsupervised Feature Selection via Local Total-order Preservation -- Discrete Stochastic Search and its Application to Feature-Selection for Deep Relational Machines -- Joint Dictionary Learning for Unsupervised Feature Selection -- Comparison between Filter Criteria for Feature Selection in Regression -- CancelOut: A layer for feature selection in deep neural networks -- Adaptive-L2 Batch Neural Gas -- Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network -- Hierarchical Reinforcement Learning with Unlimited Recursive Subroutine Calls -- Automatic Augmentation by Hill Climbing -- Learning Camera-invariant Representation for Person Re-identification -- PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection -- Singular Value Decomposition and Neural Networks -- PCI: Principal Component Initialization for Deep Autoencoders -- Improving Weight Initialization of ReLU and Output Layers -- Post-synaptic potential regularization has potential -- A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training -- Sign Based Derivative Filtering for Stochastic Gradient Descent -- Architecture-aware Bayesian Optimization for Neural Network Tuning -- Non-Convergence and Limit Cycles in the Adam Optimizer -- Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network -- Using feature entropy to guide filter pruning for efficient convolutional networks -- Simultaneously Learning Architectures and Features of Deep Neural Networks -- Learning Sparse Hidden States in Long Short-Term Memory -- Multi-objective Pruning for CNNs using Genetic Algorithm -- Dynamically Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence -- Light-Weight Edge Enhanced Network for On-orbit Semantic Segmentation -- Local Normalization Based BN Layer Pruning -- On Practical Approach to Uniform Quantizationof Non-redundant Neural Networks -- Residual learning for FC kernels of convolutional network -- A Novel Neural Network-based Symbolic Regression Method: Neuro-Encoded Expression Programming -- Compute-efficient neural network architecture optimization by a genetic algorithm -- Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures -- Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization -- Sparse Recurrent Mixture Density Networks for Forecasting High Variability Time Series with Confidence Estimates -- A multitask learning neural network for short-term traffic speed prediction and confidence estimation -- Central-diffused Instance Generation Method in Class Incremental Learning -- Marginal Replay vs Conditional Replay for Continual Learning -- Simplified computation and interpretation of Fisher matrices in incremental learning with deep neural networks -- Active Learning for Image Recognition using a Visualization-Based User Interface -- Basic Evaluation Scenarios for Incrementally Trained Classifiers -- Embedding Complexity of Learned Representations in Neural Networks -- Joint Metric Learning on Riemannian Manifold of Global Gaussian Distributions -- Multi-Task Sparse Regression Metric Learning for Heterogeneous Classification -- Fast Approximate Geodesics for Deep Generative Models -- Spatial Attention Network for Few-Shot Learning -- Routine Modeling with Time Series Metric Learning -- Leveraging Domain Knowledge for Reinforcement Learning using MMC Architectures -- Conditions for Unnecessary Logical Constraints in Kernel Machines -- HiSeqGAN: Hierarchical Sequence Synthesis and Prediction -- DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting -- Transferable Adversarial Cycle Alignment for Domain Adaption -- Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets -- Named Entity Recognition for Chinese Social Media with Domain Adversarial Training and Language Modeling -- Deep Domain Knowledge Distillation for Person Re-identification -- A study on catastrophic forgetting in deep LSTM networks -- A Label-specific Attention-based Network with Regularized Loss for Multi-label Classification -- An Empirical Study of Multi-domain and Multi-task Learning in Chinese Named Entity Recognition -- Filter Method Ensemble with Neural Networks -- Dynamic Centroid Insertion and Adjustment for Data Sets with Multiple Imbalanced Classes -- Increasing the Generalisaton Capacity of Conditional VAEs -- Playing the Large Margin Preference Game. Tipo de medio : Computadora Summary : The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II / [documento electrónico] / Tetko, Igor V., ; Kůrková, VÄ›ra, ; Karpov, Pavel, ; Theis, Fabian, . - 1 ed. . - [s.l.] : Springer, 2019 . - XXX, 807 p. 294 ilustraciones, 193 ilustraciones en color.
ISBN : 978-3-030-30484-3
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Visión por computador IngenierÃa Informática Red de computadoras Algoritmos Protección de datos IngenierÃa Informática y Redes Redes de comunicación informática Seguridad de datos e información Clasificación: 006.3 Resumen: Las actas establecidas LNCS 11727, 11728, 11729, 11730 y 11731 constituyen las actas de la 28.ª Conferencia Internacional sobre Redes Neuronales Artificiales, ICANN 2019, celebrada en Munich, Alemania, en septiembre de 2019. El total de 277 artÃculos completos y 43 artÃculos breves presentado en estas actas fue cuidadosamente revisado y seleccionado entre 494 presentaciones. Estaban organizados en 5 volúmenes centrados en la computación neuronal teórica; aprendizaje profundo; procesamiento de imágenes; texto y series temporales; y talleres y sesiones especiales. . Nota de contenido: Adaptive Graph Fusion for Unsupervised Feature Selection -- Unsupervised Feature Selection via Local Total-order Preservation -- Discrete Stochastic Search and its Application to Feature-Selection for Deep Relational Machines -- Joint Dictionary Learning for Unsupervised Feature Selection -- Comparison between Filter Criteria for Feature Selection in Regression -- CancelOut: A layer for feature selection in deep neural networks -- Adaptive-L2 Batch Neural Gas -- Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network -- Hierarchical Reinforcement Learning with Unlimited Recursive Subroutine Calls -- Automatic Augmentation by Hill Climbing -- Learning Camera-invariant Representation for Person Re-identification -- PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection -- Singular Value Decomposition and Neural Networks -- PCI: Principal Component Initialization for Deep Autoencoders -- Improving Weight Initialization of ReLU and Output Layers -- Post-synaptic potential regularization has potential -- A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training -- Sign Based Derivative Filtering for Stochastic Gradient Descent -- Architecture-aware Bayesian Optimization for Neural Network Tuning -- Non-Convergence and Limit Cycles in the Adam Optimizer -- Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network -- Using feature entropy to guide filter pruning for efficient convolutional networks -- Simultaneously Learning Architectures and Features of Deep Neural Networks -- Learning Sparse Hidden States in Long Short-Term Memory -- Multi-objective Pruning for CNNs using Genetic Algorithm -- Dynamically Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence -- Light-Weight Edge Enhanced Network for On-orbit Semantic Segmentation -- Local Normalization Based BN Layer Pruning -- On Practical Approach to Uniform Quantizationof Non-redundant Neural Networks -- Residual learning for FC kernels of convolutional network -- A Novel Neural Network-based Symbolic Regression Method: Neuro-Encoded Expression Programming -- Compute-efficient neural network architecture optimization by a genetic algorithm -- Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures -- Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization -- Sparse Recurrent Mixture Density Networks for Forecasting High Variability Time Series with Confidence Estimates -- A multitask learning neural network for short-term traffic speed prediction and confidence estimation -- Central-diffused Instance Generation Method in Class Incremental Learning -- Marginal Replay vs Conditional Replay for Continual Learning -- Simplified computation and interpretation of Fisher matrices in incremental learning with deep neural networks -- Active Learning for Image Recognition using a Visualization-Based User Interface -- Basic Evaluation Scenarios for Incrementally Trained Classifiers -- Embedding Complexity of Learned Representations in Neural Networks -- Joint Metric Learning on Riemannian Manifold of Global Gaussian Distributions -- Multi-Task Sparse Regression Metric Learning for Heterogeneous Classification -- Fast Approximate Geodesics for Deep Generative Models -- Spatial Attention Network for Few-Shot Learning -- Routine Modeling with Time Series Metric Learning -- Leveraging Domain Knowledge for Reinforcement Learning using MMC Architectures -- Conditions for Unnecessary Logical Constraints in Kernel Machines -- HiSeqGAN: Hierarchical Sequence Synthesis and Prediction -- DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting -- Transferable Adversarial Cycle Alignment for Domain Adaption -- Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets -- Named Entity Recognition for Chinese Social Media with Domain Adversarial Training and Language Modeling -- Deep Domain Knowledge Distillation for Person Re-identification -- A study on catastrophic forgetting in deep LSTM networks -- A Label-specific Attention-based Network with Regularized Loss for Multi-label Classification -- An Empirical Study of Multi-domain and Multi-task Learning in Chinese Named Entity Recognition -- Filter Method Ensemble with Neural Networks -- Dynamic Centroid Insertion and Adjustment for Data Sets with Multiple Imbalanced Classes -- Increasing the Generalisaton Capacity of Conditional VAEs -- Playing the Large Margin Preference Game. Tipo de medio : Computadora Summary : The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III / Tipo de documento: documento electrónico Autores: Tetko, Igor V., ; Kůrková, VÄ›ra, ; Karpov, Pavel, ; Theis, Fabian, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XXX, 733 p. 417 ilustraciones, 273 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-30508-6 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Visión por computador IngenierÃa Informática Red de computadoras Algoritmos Protección de datos IngenierÃa Informática y Redes Redes de comunicación informática Seguridad de datos e información Clasificación: 006.3 Resumen: Las actas establecidas LNCS 11727, 11728, 11729, 11730 y 11731 constituyen las actas de la 28.ª Conferencia Internacional sobre Redes Neuronales Artificiales, ICANN 2019, celebrada en Munich, Alemania, en septiembre de 2019. El total de 277 artÃculos completos y 43 artÃculos breves presentado en estas actas fue cuidadosamente revisado y seleccionado entre 494 presentaciones. Estaban organizados en 5 volúmenes centrados en la computación neuronal teórica; aprendizaje profundo; procesamiento de imágenes; texto y series temporales; y talleres y sesiones especiales. . Nota de contenido: Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification -- Distortion Estimation Through Explicit Modeling of the Refractive Surface -- Eye Movement-based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation -- IBDNet: Lightweight Network for On-orbit Image Blind Denoising -- Aggregating Rich Deep Semantic Features for Fine-Grained Place Classification -- Improving Reliability of Object Detection for Lunar Craters using Monte Carlo Dropout -- An improved convolutional neural network for steganalysis in the scenario of reuse of the stego-key -- A New Learning-based One Shot Detection Framework For Natural Images -- Dense Receptive Field Network: A Backbone Network for Object Detection -- Referring Expression Comprehension via Co-attention and Visual Context -- Comparison between U-Net and U-ReNet models in OCR tasks -- Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentation -- Action Recognition Based on Divide-and-conquer -- An Adaptive Feature Channel Weighting Scheme for Correlation Tracking -- In-silico staining from bright-field and fluorescent images using deep learning -- A lightweight neural network for hard exudate segmentation of fundus image -- Attentional Residual Dense Factorized Network for Real-time Semantic Segmentation -- Random drop loss for tiny object segmentation: Application to lesion segmentation in fundus images -- Flow2Seg: Motion-Aided Semantic Segmentation -- COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation -- Learning Deep Structured Multi-Scale Features for crisp and occlusion edge detection -- Graph-Boosted Attentive Network for Semantic Body Parsing -- A Global-Local Architecture Constrained by Multiple Attributes for Person Re-identification -- Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders -- Learning Relational-Structural Networks for Robust Face Alignment -- An Efficient 3D-NAS Method for Video-based Gesture Recognition -- Robustness of deep LSTM networks in freehand gesture recognition -- Delving into the Impact of Saliency Detector: A GeminiNet for Accurate Saliency Detection -- FCN Salient Object Detection Using Region Cropping -- Object-Level Salience Detection By Progressively Enhanced Network -- Action unit assisted Facial Expression Recognition -- Discriminative Feature Learning using Two-stage Training Strategy for Facial Expression Recognition -- Action Units Classification using ClusWiSARD -- Automatic Estimation of Dog Age: The DogAge Dataset and Challenge -- Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality -- Variational Deep Embedding with Regularized Student-t Mixture Model -- A mixture-of-experts model for vehicle prediction using an online learning approach -- An Application of Convolutional Neural Networks for Analyzing Dogs' Sleep Patterns -- On the Inability of Markov Models to Capture Criticality in Human Mobility -- LSTM with Uniqueness Attention for Human Activity Recognition -- Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training -- Generative Creativity: Adversarial Learning for Bionic Design -- Self-attention StarGAN for Multi-domain Image-to-image Translation -- Generative Adversarial Networks for Operational Scenario Planing of Renewable Energy Farms: A Study on Wind and Photovoltaic -- Constraint-Based Visual Generation -- Text to Image Synthesis based on Multiple Discrimination -- Disentangling Latent Factors of Variational Auto-Encoder with Whitening -- Training Discriminative Models to Evaluate Generative Ones -- Scene Graph Generation via Convolutional Message Passing and Class-aware Memory Embeddings -- Change Detection in Satellite Images using Reconstruction Errors of Joint Autoencoders -- Physical Adversarial Attacks by Projecting Perturbations -- Improved Forward-backward Propagation to Generate Adversarial Examples -- Incremental Learning of GAN for Detecting Multiple Adversarial Attacks -- Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples -- DCT:Differential Combination Testing of Deep Learning Systems -- Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection -- HLR: Generating Adversarial Examples by High-Level Representations. Tipo de medio : Computadora Summary : The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III / [documento electrónico] / Tetko, Igor V., ; Kůrková, VÄ›ra, ; Karpov, Pavel, ; Theis, Fabian, . - 1 ed. . - [s.l.] : Springer, 2019 . - XXX, 733 p. 417 ilustraciones, 273 ilustraciones en color.
ISBN : 978-3-030-30508-6
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Visión por computador IngenierÃa Informática Red de computadoras Algoritmos Protección de datos IngenierÃa Informática y Redes Redes de comunicación informática Seguridad de datos e información Clasificación: 006.3 Resumen: Las actas establecidas LNCS 11727, 11728, 11729, 11730 y 11731 constituyen las actas de la 28.ª Conferencia Internacional sobre Redes Neuronales Artificiales, ICANN 2019, celebrada en Munich, Alemania, en septiembre de 2019. El total de 277 artÃculos completos y 43 artÃculos breves presentado en estas actas fue cuidadosamente revisado y seleccionado entre 494 presentaciones. Estaban organizados en 5 volúmenes centrados en la computación neuronal teórica; aprendizaje profundo; procesamiento de imágenes; texto y series temporales; y talleres y sesiones especiales. . Nota de contenido: Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification -- Distortion Estimation Through Explicit Modeling of the Refractive Surface -- Eye Movement-based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation -- IBDNet: Lightweight Network for On-orbit Image Blind Denoising -- Aggregating Rich Deep Semantic Features for Fine-Grained Place Classification -- Improving Reliability of Object Detection for Lunar Craters using Monte Carlo Dropout -- An improved convolutional neural network for steganalysis in the scenario of reuse of the stego-key -- A New Learning-based One Shot Detection Framework For Natural Images -- Dense Receptive Field Network: A Backbone Network for Object Detection -- Referring Expression Comprehension via Co-attention and Visual Context -- Comparison between U-Net and U-ReNet models in OCR tasks -- Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentation -- Action Recognition Based on Divide-and-conquer -- An Adaptive Feature Channel Weighting Scheme for Correlation Tracking -- In-silico staining from bright-field and fluorescent images using deep learning -- A lightweight neural network for hard exudate segmentation of fundus image -- Attentional Residual Dense Factorized Network for Real-time Semantic Segmentation -- Random drop loss for tiny object segmentation: Application to lesion segmentation in fundus images -- Flow2Seg: Motion-Aided Semantic Segmentation -- COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation -- Learning Deep Structured Multi-Scale Features for crisp and occlusion edge detection -- Graph-Boosted Attentive Network for Semantic Body Parsing -- A Global-Local Architecture Constrained by Multiple Attributes for Person Re-identification -- Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders -- Learning Relational-Structural Networks for Robust Face Alignment -- An Efficient 3D-NAS Method for Video-based Gesture Recognition -- Robustness of deep LSTM networks in freehand gesture recognition -- Delving into the Impact of Saliency Detector: A GeminiNet for Accurate Saliency Detection -- FCN Salient Object Detection Using Region Cropping -- Object-Level Salience Detection By Progressively Enhanced Network -- Action unit assisted Facial Expression Recognition -- Discriminative Feature Learning using Two-stage Training Strategy for Facial Expression Recognition -- Action Units Classification using ClusWiSARD -- Automatic Estimation of Dog Age: The DogAge Dataset and Challenge -- Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality -- Variational Deep Embedding with Regularized Student-t Mixture Model -- A mixture-of-experts model for vehicle prediction using an online learning approach -- An Application of Convolutional Neural Networks for Analyzing Dogs' Sleep Patterns -- On the Inability of Markov Models to Capture Criticality in Human Mobility -- LSTM with Uniqueness Attention for Human Activity Recognition -- Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training -- Generative Creativity: Adversarial Learning for Bionic Design -- Self-attention StarGAN for Multi-domain Image-to-image Translation -- Generative Adversarial Networks for Operational Scenario Planing of Renewable Energy Farms: A Study on Wind and Photovoltaic -- Constraint-Based Visual Generation -- Text to Image Synthesis based on Multiple Discrimination -- Disentangling Latent Factors of Variational Auto-Encoder with Whitening -- Training Discriminative Models to Evaluate Generative Ones -- Scene Graph Generation via Convolutional Message Passing and Class-aware Memory Embeddings -- Change Detection in Satellite Images using Reconstruction Errors of Joint Autoencoders -- Physical Adversarial Attacks by Projecting Perturbations -- Improved Forward-backward Propagation to Generate Adversarial Examples -- Incremental Learning of GAN for Detecting Multiple Adversarial Attacks -- Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples -- DCT:Differential Combination Testing of Deep Learning Systems -- Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection -- HLR: Generating Adversarial Examples by High-Level Representations. Tipo de medio : Computadora Summary : The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
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