From the other hand, machine learning algorithms can benefit from the vast inverse problem literature and the existing contributions to the theory of inverse problems, and they can be used to simulate boundary value data when they are missing.Springer Nature is the publisher of the world’s most influential journals and a pioneer in the field of open research. Across our wide portfolio of journals we cover the full range of research disciplines – providing a home for all sound research and a platform for some of the most important discoveries of our generation.Īcross our three platforms we publish thousands of articles that help the research community to advance discovery for all of us. From one hand, in fact, machine learning algorithms can leverage large collections of training data to directly compute regularized reconstructions and estimate unknown parameters. Recent contributions in these areas aim at exploring potential synergies between their two different domains of research. A typical inverse problem seeks to find a mathematical model that admits given observational data as an approximate solution. Machine Learning is a subset of Artificial Intelligence focusing on computers’ ability to learn from data and to imitate intelligence human behaviour. This special issue aims at bringing together articles that discuss recent advances in machine learning and inverse problems. Auroux (Universite’ Cote d’Azur, France), V. We encourage submissions from researchers in this background to demonstrate the effectiveness of graph theory-based approaches on various benchmark datasets and real-world applications. Graph theory provides a framework for modeling neural networks as graphs provided with neurons as nodes and connections as edges. However, the increased complexity of these models comes with a trade-off. In Neural networks, especially deep learning models have demonstrated remarkable success in various tasks such as image recognition, natural language processing and speech synthesis. Exploiting graph theory principles can address challenges related to model complexity, training efficiency and generalization capabilities. As the field of artificial intelligence continues to advance, researchers and engineers look for innovative methods to design more efficient and effective neural networks. Graph theory has emerged as a powerful tool for optimizing neural network architectures. This special issue aims at bringing together articles that discuss recent advances in Graph Theory-based Approaches for Optimizing Neural Network Architectures. Mohammad Reza Farahani (Iran University of Science and Technology, Iran) Muhammad Javaid (University of Management and Technology, Pakistan), Dr. Jia-Bao Liu (Anhui Jianzhu University, China), Dr. How to publish with us, including Open Access Journal metrics 2.1 (2022) Impact factor 2.4 (2022) Five year impact factor 143,557 (2022) Downloads
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