[HTML][HTML] A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends
Artificial Intelligence (AI) uses systems and machines to simulate human intelligence and
solve common real-world problems. Machine learning and deep learning are Artificial …
solve common real-world problems. Machine learning and deep learning are Artificial …
Review of artificial intelligence and machine learning technologies: classification, restrictions, opportunities and challenges
Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of
applied issues. The core of AI is machine learning (ML)—a complex of algorithms and …
applied issues. The core of AI is machine learning (ML)—a complex of algorithms and …
Prediction of estuarine water quality using interpretable machine learning approach
S Wang, H Peng, S Liang - Journal of Hydrology, 2022 - Elsevier
Estuaries are principal sources of pollution in coastal areas. Estuarine water quality
prediction models can provide early warnings to prevent major disasters in coastal …
prediction models can provide early warnings to prevent major disasters in coastal …
Fastshap: Real-time shapley value estimation
Although Shapley values are theoretically appealing for explaining black-box models, they
are costly to calculate and thus impractical in settings that involve large, high-dimensional …
are costly to calculate and thus impractical in settings that involve large, high-dimensional …
Multi-objective robust optimization for enhanced safety in large-diameter tunnel construction with interactive and explainable AI
Robust optimization is an ideal solution for enhancing safety in tunnel construction in the
presence of unpredictable soil conditions, especially in large-diameter tunnel construction …
presence of unpredictable soil conditions, especially in large-diameter tunnel construction …
Leveraging explanations in interactive machine learning: An overview
Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …
(ML) communities in order to improve model transparency and allow users to form a mental …
Logic-based explainability in machine learning
J Marques-Silva - … Knowledge: 18th International Summer School 2022 …, 2023 - Springer
The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …
Explaining link prediction systems based on knowledge graph embeddings
Link Prediction (LP) aims at tackling Knowledge Graph incompleteness by inferring new,
missing facts from the already known ones. The rise of novel Machine Learning techniques …
missing facts from the already known ones. The rise of novel Machine Learning techniques …
Integrating prior knowledge to build transformer models
Abstract The big Artificial General Intelligence models inspire hot topics currently. The black
box problems of Artificial Intelligence (AI) models still exist and need to be solved urgently …
box problems of Artificial Intelligence (AI) models still exist and need to be solved urgently …
Additive mil: Intrinsically interpretable multiple instance learning for pathology
Abstract Multiple Instance Learning (MIL) has been widely applied in pathology towards
solving critical problems such as automating cancer diagnosis and grading, predicting …
solving critical problems such as automating cancer diagnosis and grading, predicting …