A survey on neural network interpretability
Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …
their black-box nature. The interpretability issue affects people's trust on deep learning …
From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …
black boxes raised the question of how to evaluate explanations of machine learning (ML) …
Going beyond xai: A systematic survey for explanation-guided learning
As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing
DNNs become more complex and diverse, ranging from improving a conventional model …
DNNs become more complex and diverse, ranging from improving a conventional model …
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 …
On explaining decision trees
Decision trees (DTs) epitomize what have become to be known as interpretable machine
learning (ML) models. This is informally motivated by paths in DTs being often much smaller …
learning (ML) models. This is informally motivated by paths in DTs being often much smaller …
The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms
In recent years, the machine learning research community has benefited tremendously from
the availability of openly accessible benchmark datasets. Clinical data are usually not …
the availability of openly accessible benchmark datasets. Clinical data are usually not …
Interpretable neural networks: principles and applications
In recent years, with the rapid development of deep learning technology, great progress has
been made in computer vision, image recognition, pattern recognition, and speech signal …
been made in computer vision, image recognition, pattern recognition, and speech signal …
Factors influencing secondary school students' reading literacy: An analysis based on XGBoost and SHAP methods
H Liu, X Chen, X Liu - Frontiers in Psychology, 2022 - frontiersin.org
This paper constructs a predictive model of student reading literacy based on data from
students who participated in the Program for International Student Assessment (PISA 2018) …
students who participated in the Program for International Student Assessment (PISA 2018) …
Designing an interpretability-based model to explain the artificial intelligence algorithms in healthcare
The lack of interpretability in artificial intelligence models (ie, deep learning, machine
learning, and rules-based) is an obstacle to their widespread adoption in the healthcare …
learning, and rules-based) is an obstacle to their widespread adoption in the healthcare …
A Survey of Neural Trees: Co-Evolving Neural Networks and Decision Trees
Neural networks (NNs) and decision trees (DTs) are both popular models of machine
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …