Название: Artificial Neural Networks and Machine Learning – ICANN 2018. Part I Автор: Vera Kurkova, Yannis Manolopoulos, Barbara Hammer
Издательство: Springer
ISBN: 3030014177
Год: 2018
Страниц: 854
Язык: английский
Формат: pdf (true)
Размер: 72.1 MB
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.
Technological advances in artificial intelligence (AI) are leading the rapidly changing world of the twenty-first century. We have already passed from machine learning to deep learning with numerous applications. The contribution of AI so far to the improvement of our quality of life is profound. Major challenges but also risks and threats are here. Brain-inspired computing explores, simulates, and imitates the structure and the function of the human brain, achieving high-performance modeling plusvisualization capabilities.
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.
Contents:
Fast CNN Pruning via Redundancy-Aware Training
Two-Stream Convolutional Neural Network for Multimodal Matching
Kernel Graph Convolutional Neural Networks
Detection of Fingerprint Alterations Using Deep Convolutional Neural Networks
Balanced Cortical Microcircuitry-Based Network for Working Memory
Spiking Neural Networks Evolved to Perform Multiplicative Operations
Machine Learning to Predict Toxicity of Compounds
Discovering Thermoelectric Materials Using Machine Learning: Insights and Challenges
Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge
...
Fuzzy Implications Generating from Fuzzy Negations
Improving Ensemble Learning Performance with Complementary Neural Networks for Facial Expression Recognition
Automatic Beautification for Group-Photo Facial Expressions Using Novel Bayesian GANs
Fast and Accurate Affect Prediction Using a Hierarchy of Random Forests
Gender-Aware CNN-BLSTM for Speech Emotion Recognition
Semi-supervised Model for Emotion Recognition in Speech
Real-Time Embedded Intelligence System: Emotion Recognition on Raspberry Pi with Intel NCS
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