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Found 3 papers, 1 papers with code
Empowering Biomedical Discovery with AI Agents
We envision 'AI scientists' as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate machine learning tools with experimental platforms.
Neural correlates of cognitive ability and visuo-motor speed: validation of IDoCT on UK Biobank Data
Previously, we developed a novel modelling approach that leverages continuous performance recordings from large-cohort studies to achieve an iterative decomposition of cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive abilities, and device and visuomotor latencies, whilst recalibrating trial-difficulty scales.
Towards Training GNNs using Explanation Directed Message Passing
With the increasing use of Graph Neural Networks (GNNs) in critical real-world applications, several post hoc explanation methods have been proposed to understand their predictions.
