As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output, and hence, are ...
Association for the Advancement of Artificial Intelligence2025
Introduction Physical inactivity and sedentary behaviour are significant modifiable risk factors for chronic diseases, yet their prevalence remains high despite their well-established negative impact on health. This study evaluates regular moderate exercis ...
Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...
Artificial intelligence (AI) as a multi-purpose technology is gaining increased attention and is now widely used across all sectors of the economy. The growing complexity of planning and operating power systems makes AI extremely valuable for the power ind ...
Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instanc ...
Traffic forecasting problems of freeway networks are heavily tackled by deep learning methods because it requires learning highly complex correlations between variables both in time and space. Adopting a graph convolutional network (GCN) becomes a standard ...
This article proposes a new logic synthesis and verification paradigm based on circuit simulation. In this paradigm, high quality, expressive simulation patterns are pregenerated to be reused in multiple runs of optimization and verification algorithms, re ...
