Decision trees are a popular type of machine learning model that are known for their simplicity and interpretability. They use a hierarchical structure of…
Browsing: Interpretability
This Special Issue focuses on recent theoretical advances and practical applications in deep learning, with a particular emphasis on neural network model architectures, explainability…
This Special Issue explores the advancements and applications of Transformer-based deep learning architectures in artificial intelligence, particularly in natural language processing. It discusses the…
Franz Inc. has announced the release of AllegroGraph Cloud, a hosted version of its Neuro-Symbolic AI platform that combines rule-based systems and machine learning…
Professor Ardhendu Behera will be giving a lecture on computer vision and artificial intelligence (AI) at one of the 2024 Inaugural Lectures series. He…
Ulrike Luxburg gave a talk at NeurIPS last week which articulated the fundamental limitations of attempts to make deep machine learning models interpretable or…
This Special Issue aims to collect state-of-the-art research findings on the latest developments, up-to-date issues, and challenges in the field of knowledge representation formalisms…
This Special Issue focuses on applications of Machine Learning (ML) models in a wide range of fields and problems. It covers a wide range…
A study by MIT Lincoln Laboratory suggests that formal specifications, despite their mathematical precision, are not necessarily interpretable to humans. Participants in the study…
Deep learning has achieved incredible successes in tasks such as image recognition, speech recognition, language translation, and autonomous driving. However, there are still many…