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Advanced Intelligent Discovery, EarlyView.Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
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Solving Data Overlapping Problem Using A Class‐Separable Extreme Learning Machine Auto‐Encoder
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.The overlapping and imbalanced data in classification present key challenges. Class‐separable extreme learning machine auto‐encoding (CS‐ELM‐AE) is proposed, which is an enhancement of ELM‐AE that better handles overlapping data by clustering points from the same class together. Applying oversampling addresses imbalanced data.
Ekkarat Boonchieng, Wanchaloem Nadda
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Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.OxSpred, an eXtreme‐Gradient‐Boosting‐‐based supervised learning model, accurately annotates oxidative stress in innate immune cells at the single‐cell level, providing interpretable embeddings with significant biological relevance. This innovative tool revolutionizes the understanding of innate immune cell functions during inflammation and enhances ...
Po‐Yuan Chen, Tai‐Ming Ko
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Quantum machine learning [PDF]
Nature, 2017 Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical computers
Nicola Pancotti +2 more
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Quantum Driven Machine Learning
International Journal of Theoretical Physics, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shivani Saini +3 more
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Quantum enhanced machine learning: An overview [PDF]
, 2021 Machine learning is now widely used almost everywhere, primarily for forecasting. The main idea of the work is to identify the possibility of achieving a quantum advantage when solving machine learning problems on a quantum computer.
Zahorodko, Pavlo V. +4 more
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Quantum Algebraic Machine Learning
2020 IEEE 10th International Conference on Intelligent Systems (IS), 2020Quantum information processing is a rapidly growing industry with promising results in the near future. Its implementtation has serious impact on almost any areas of our lives: cyber-security, communication, AI, etc. Any computations can be considered as quantum ones and in this context classical computations are a subset of quantum computations.
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Machine learning for quantum physics
Science, 2017An artificial neural network can discover the ground state of a quantum many-body ...
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Generative Quantum Machine Learning
2021The goal of generative machine learning is to model the probability distribution underlying a given data set. This probability distribution helps to characterize the generation process of the data samples. While classical generative machine learning is solely based on classical resources, generative quantum machine learning can also employ quantum ...
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