Interpretability research of deep learning: A literature survey
B Xua, G Yang - Information Fusion, 2024 - Elsevier
Deep learning (DL) has been widely used in various fields. However, its black-box nature
limits people's understanding and trust in its decision-making process. Therefore, it becomes …
limits people's understanding and trust in its decision-making process. Therefore, it becomes …
Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion
Gully erosion possess a serious hazard to critical resources such as soil, water, and
vegetation cover within watersheds. Therefore, spatial maps of gully erosion hazards can be …
vegetation cover within watersheds. Therefore, spatial maps of gully erosion hazards can be …
Explainable artificial intelligence (xai) for internet of things: a survey
Artificial intelligence (AI) and machine learning (ML) are widely employed to make the
solutions more accurate and autonomous in many smart and intelligent applications in the …
solutions more accurate and autonomous in many smart and intelligent applications in the …
[HTML][HTML] RuleXAI—A package for rule-based explanations of machine learning model
The ability to use eXplainable Artificial Intelligence (XAI) methods is very important for both
AI users and AI developers. This paper presents the RuleXAI library, which provides XAI …
AI users and AI developers. This paper presents the RuleXAI library, which provides XAI …
Beyond one-shot explanations: a systematic literature review of dialogue-based xAI approaches
In the last decade, there has been increasing interest in allowing users to understand how
the predictions of machine-learned models come about, thus increasing transparency and …
the predictions of machine-learned models come about, thus increasing transparency and …
Do not trust additive explanations
A Gosiewska, P Biecek - arXiv preprint arXiv:1903.11420, 2019 - arxiv.org
Explainable Artificial Intelligence (XAI) has received a great deal of attention recently.
Explainability is being presented as a remedy for the distrust of complex and opaque …
Explainability is being presented as a remedy for the distrust of complex and opaque …
What would you ask the machine learning model? Identification of user needs for model explanations based on human-model conversations
Recently we see a rising number of methods in the field of eXplainable Artificial Intelligence.
To our surprise, their development is driven by model developers rather than a study of …
To our surprise, their development is driven by model developers rather than a study of …
Benchmarking transfer learning strategies in time-series imaging: recommendations for analyzing raw sensor data
J Gross, R Buettner, H Baumgartl - IEEE Access, 2022 - ieeexplore.ieee.org
With the growing availability and complexity of time-series sequences, scalable and robust
machine learning approaches are required that overcome the sampling challenge of …
machine learning approaches are required that overcome the sampling challenge of …
Disease-causing variant recommendation system for clinical genome interpretation with adjusted scores for artefactual variants
Background In the process of finding the causative variant of rare diseases (RD), accurate
assessment and prioritization of genetic variants is essential. Although quality control (QC) …
assessment and prioritization of genetic variants is essential. Although quality control (QC) …
Credit Risk Modeling Using Interpreted XGBoost
M Hernes, J Adaszyński, P Tutak - European Management Studies, 2023 - ceeol.com
Purpose: The aim of the paper is to develop a credit risk assessment model usingb the
XGBoost classifier supported by interpretation issues. Design/methodology/approach: The …
XGBoost classifier supported by interpretation issues. Design/methodology/approach: The …