New research shows that smaller artificial intelligence models, enhanced with reasoning abilities, can match the performance of much larger systems in identifying critical risks to children.
A new study explores how well traditional machine learning techniques can identify sarcastic comments on Reddit, focusing solely on the text of replies.
A new deep learning model delivers precise black hole mass estimates for an unprecedented sample of quasars, offering a comprehensive view of these cosmic behemoths.
Researchers have developed a theoretical framework to optimize how large AI models distribute work, ensuring efficient use of specialized components.
New research reveals that the effectiveness of optimization algorithms hinges on the rank of neural network activations, explaining why some methods excel in certain scenarios.
A new deep learning approach leverages Bayesian neural networks to quantify uncertainty in displacement field measurements, enhancing the reliability of digital image correlation techniques.
Researchers have developed a new framework that automatically designs neural networks optimized for both accuracy and energy efficiency on edge computing platforms.
A new framework combines smartphone speech analysis with medical knowledge graphs to enable continuous, personalized monitoring of neurocognitive function.
A comprehensive analysis reveals how targeted interventions can control the behavior of large language models and significantly improve their safety.
New research reveals that automatically generated code from smaller AI models frequently introduces significant architectural flaws and incomplete implementations.