Interpretable machine learning: Fundamental principles and 10 grand challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
Explainable ai for text classification: Lessons from a comprehensive evaluation of post hoc methods
This paper addresses the notable gap in evaluating eXplainable Artificial Intelligence (XAI)
methods for text classification. While existing frameworks focus on assessing XAI in areas …
methods for text classification. While existing frameworks focus on assessing XAI in areas …
Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement Learning
Continuously learning new skills is essential for talents to gain a competitive advantage in
the labor market. Despite extensive efforts on relevance-or preference-based skill …
the labor market. Despite extensive efforts on relevance-or preference-based skill …
A Comprehensive Survey on Self-Interpretable Neural Networks
Prediction with Visual Evidence: Sketch Classification Explanation via Stroke-Level Attributions
Sketch classification models have been extensively investigated by designing a task-driven
deep neural network. Despite their successful performances, few works have attempted to …
deep neural network. Despite their successful performances, few works have attempted to …
Prominet: Prototype-based multi-view network for interpretable email response prediction
Email is a widely used tool for business communication, and email marketing has emerged
as a cost-effective strategy for enterprises. While previous studies have examined factors …
as a cost-effective strategy for enterprises. While previous studies have examined factors …
Prototype Learning for Medical Time Series Classification via Human–Machine Collaboration
Deep neural networks must address the dual challenge of delivering high-accuracy
predictions and providing user-friendly explanations. While deep models are widely used in …
predictions and providing user-friendly explanations. While deep models are widely used in …
Adaax: Explaining recurrent neural networks by learning automata with adaptive states
Recurrent neural networks (RNN) are widely used for handling sequence data. However,
their black-box nature makes it difficult for users to interpret the decision-making process …
their black-box nature makes it difficult for users to interpret the decision-making process …
GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Text Classification
Pretrained transformer-based Language Models (LMs) are well-known for their ability to
achieve significant improvement on text classification tasks with their powerful word …
achieve significant improvement on text classification tasks with their powerful word …