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R.ROSETTA: an interpretable machine learning framework [PDF]

BMC Bioinformatics, 2021
Background Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz.
Mateusz Garbulowski, Klev Diamanti, Karolina Smolińska, Nicholas Baltzer, Patricia Stoll, Susanne Bornelöv, Aleksander Øhrn, Lars Feuk, Jan Komorowski   +8 more
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Review on Interpretable Machine Learning in Smart Grid

Energies, 2022
In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field.
Chongchong Xu, Zhicheng Liao, Chaojie Li, Xiaojun Zhou, Renyou Xie   +4 more
doaj   +4 more sources

Interpretable machine learning for building energy management: A state-of-the-art review

Advances in Applied Energy, 2023
Machine learning has been widely adopted for improving building energy efficiency and flexibility in the past decade owing to the ever-increasing availability of massive building operational data.
Zhe Chen, Fu Xiao, Fangzhou Guo, Jinyue Yan   +3 more
doaj   +3 more sources

Review Study of Interpretation Methods for Future Interpretable Machine Learning

IEEE Access, 2020
In recent years, black-box models have developed rapidly because of their high accuracy. Balancing the interpretability and accuracy is increasingly important.
Jian-Xun Mi, An-Di Li, Li-Fang Zhou
doaj   +3 more sources

Interpretable machine learning for shoreline forecasting [PDF]

Scientific Reports
Machine learning has revolutionized scientific modeling, providing breakthroughs in fields ranging from weather prediction to protein folding. However, its adoption in physics-based domains remains limited due to the lack of interpretability in ...
Mahmoud Al Najar, Dennis G. Wilson, Rafael Almar   +2 more
doaj   +2 more sources

Interpretable Machine Learning for Survival Analysis. [PDF]

Biom J
ABSTRACT With the spread and rapid advancement of black box machine learning (ML) models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade.
Langbein SH, Krzyziński M, Spytek M, Baniecki H, Biecek P, Wright MN.   +5 more
europepmc   +5 more sources

GraphAware: Interpretable machine learning on graphs

Discover Artificial Intelligence
Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in many applications from disease prediction to weather forecasting. However, each GNN layer introduces new trainable parameters that increase its complexity and limit its ...
Daniel Walke, Daniel Steinbach, Alexander Schönhuth, Gunter Saake, David Broneske, Robert Heyer   +5 more
doaj   +2 more sources

Editorial: Human-Interpretable Machine Learning

Frontiers in Big Data, 2022
Gabriele Tolomei, Fabio Pinelli, Fabrizio Silvestri   +2 more
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Interpretable machine learning [PDF]

Communications of the ACM, 2021
The emergence of machine learning as a society-changing technology in the past decade has triggered concerns about people's inability to understand the reasoning of increasingly complex models. The field of IML (interpretable machine learning) grew out of these concerns, with the goal of empowering various stakeholders to tackle use cases, such as ...
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar   +4 more
openaire   +2 more sources

Interpretable machine learning for genomics [PDF]

Human Genetics, 2021
AbstractHigh-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods.
openaire   +6 more sources