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Artificial Neural Networks and Machine Learning – ICANN 2022
The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications.
Fuzzy Logic and Soft Computing Applications
This book constitutes the proceedings of the 11th International Workshop on Fuzzy Logic and Applications, WILF 2016, held in Naples, Italy, in December 2016. The 22 revised full papers presented together with 2 invited lectures were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on fuzzy measures and transforms; granularity and multi-logics, clustering and learning; knowledge systems; and soft computing and applications.
Advances in Cognitive Neurodynamics (VII)
This book contains original articles submitted to the Seventh International Conference on Cognitive Neurodynamics (ICCN 2019). The brain is an endless case study of a complex system characterized by multiple levels of integration, multiple time scales of activity, and multiple coding and decoding properties. The contribution of several disciplines, mathematics, physics, computer science, neurobiology, pharmacology, physiology, and behavioral and clinical sciences, is necessary in order to cope with such seemingly unattainable complexity that transforms the experimental information into a tricky puzzle which hides the correspondence with model predictions. This conference gathered active participants to discuss ideas and pose new questions from different viewpoints, ranging from single neurons and neural networks to animal/human behavior in theoretical and experimental studies. The conference is organized with plenary lectures, mini-symposia, interdisciplinary round tables, and oral and poster sessions.
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation
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.
Computational Topology for Data Analysis
This book provides a computational and algorithmic foundation for techniques in topological data analysis, with examples and exercises.
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning
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.
Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions
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.
Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series
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.
Artificial Neural Networks and Machine Learning – ICANN 2018
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 Cybers...
Energy Efficiency and Robustness of Advanced Machine Learning Architectures
Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex m...
