Results 21 to 30 of about 424,586 (329)
Frontiers in Robotics and AI, 2022 Recent advances in deep learning have bolstered our ability to forecast the evolution of dynamical systems, but common neural networks do not adhere to physical laws, critical information that could lead to sounder state predictions.
Frances Zhu +3 more
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Toward Practical Usage of the Attention Mechanism as a Tool for Interpretability
IEEE Access, 2022 Natural language processing (NLP) has been one of the subfields of artificial intelligence much affected by the recent neural revolution. Architectures such as recurrent neural networks (RNNs) and attention-based transformers helped propel the state of ...
Martin Tutek, Jan Snajder
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Frontiers in Medicine, 2022 PurposeTo develop handcrafted radiomics (HCR) and deep learning (DL) based automated diagnostic tools that can differentiate between idiopathic pulmonary fibrosis (IPF) and non-IPF interstitial lung diseases (ILDs) in patients using high-resolution ...
Turkey Refaee +14 more
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Frontiers in Artificial Intelligence, 2022 The lack of transparency is one of the artificial intelligence (AI)'s fundamental challenges, but the concept of transparency might be even more opaque than AI itself.
Anastasiya Kiseleva +3 more
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A Knowledge Query Network Model Based on Rasch Model Embedding for Personalized Online Learning
Frontiers in Psychology, 2022 The vigorous development of online education has produced massive amounts of education data. How to mine and analyze education big data has become an urgent problem in the field of education and big data knowledge engineering. As for the dynamic learning
Yan Cheng +5 more
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Investigating the Interpretability of ML-Guided Radiological Source Searches
IEEE Access, 2022 The coupling of reinforcement learning (RL) and deep neural networks (DNN) has demonstrated promising results in many task-oriented scenarios, including radiological source localization. However, these black box approaches present an issue from the user&#
Gregory R. Romanchek, Shiva Abbaszadeh
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Toward Interpretable Machine Learning Models for Materials Discovery
Advanced Intelligent Systems, 2019 Machine learning (ML) and artificial intelligence (AI) methods for modeling useful materials properties are now important technologies for rational design and optimization of bespoke functional materials.
Paulius Mikulskis +2 more
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A Novel Hyperparameter-Free Approach to Decision Tree Construction That Avoids Overfitting by Design
IEEE Access, 2019 Decision trees are an extremely popular machine learning technique. Unfortunately, overfitting in decision trees still remains an open issue that sometimes prevents achieving good performance.
Rafael Garcia Leiva +3 more
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Hierarchical Architectures of Fuzzy Models: From Type-1 fuzzy sets to Information Granules of Higher Type [PDF]
International Journal of Computational Intelligence Systems, 2010 Complex phenomena are perceived from different perspectives, diversified conceptual points of view and at various levels of granularity. Symbolic and sub-symbolic processing becomes an inherently visible computing practice.
Witold Pedrycz
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Topic Modeling for Interpretable Text Classification From EHRs
Frontiers in Big Data, 2022 The clinical notes in electronic health records have many possibilities for predictive tasks in text classification. The interpretability of these classification models for the clinical domain is critical for decision making.
Emil Rijcken +9 more
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