A survey of explainable AI terminology
Abstract The field of Explainable Artificial Intelligence attempts to solve the problem of
algorithmic opacity. Many terms and notions have been introduced recently to define …
algorithmic opacity. Many terms and notions have been introduced recently to define …
Understanding user intent modeling for conversational recommender systems: a systematic literature review
User intent modeling in natural language processing deciphers user requests to allow for
personalized responses. The substantial volume of research (exceeding 13,000 …
personalized responses. The substantial volume of research (exceeding 13,000 …
Advancing cardiac diagnostics: Exceptional accuracy in abnormal ECG signal classification with cascading deep learning and explainability analysis
Arrhythmias, cardiac rhythm disorders, demand precise diagnosis for effective treatment
planning, emphasizing the crucial role of electrocardiogram (ECG) signal interpretation …
planning, emphasizing the crucial role of electrocardiogram (ECG) signal interpretation …
Artificial agents' explainability to support trust: considerations on timing and context
Strategies for improving the explainability of artificial agents are a key approach to support
the understandability of artificial agents' decision-making processes and their …
the understandability of artificial agents' decision-making processes and their …
[BUCH][B] Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
A significant robustness gap exists between machine intelligence and human perception
despite recent advances in deep learning. Deep learning is not provably secure. A critical …
despite recent advances in deep learning. Deep learning is not provably secure. A critical …
Recurrence-aware long-term cognitive network for explainable pattern classification
Machine-learning solutions for pattern classification problems are nowadays widely
deployed in society and industry. However, the lack of transparency and accountability of …
deployed in society and industry. However, the lack of transparency and accountability of …
ML-based performance modeling in SDN-enabled data center networks
Traffic optimization and smart buffering are fundamental to achieve both great application
performance and resource efficiency in data centers with heterogeneous workloads …
performance and resource efficiency in data centers with heterogeneous workloads …
Counterfactual explanation generation with minimal feature boundary
D You, S Niu, S Dong, H Yan, Z Chen, D Wu, L Shen… - Information …, 2023 - Elsevier
The complex and opaque decision-making process of machine learning models restrains
the interpretability of predictions and makes them cannot mine results outside of learning …
the interpretability of predictions and makes them cannot mine results outside of learning …
Learning hybrid interpretable models: Theory, taxonomy, and methods
A hybrid model involves the cooperation of an interpretable model and a complex black box.
At inference, any input of the hybrid model is assigned to either its interpretable or complex …
At inference, any input of the hybrid model is assigned to either its interpretable or complex …
Integration of machine learning and statistical models for crash frequency modeling
Crash frequency modeling has been an active research topic in traffic safety, for which
various techniques have been proposed that can be loosely classified as either statistical …
various techniques have been proposed that can be loosely classified as either statistical …