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.
New research reveals that the combination of misleading text and AI-generated images on Reddit creates exceptionally viral content, rapidly spreading misinformation.
Researchers have developed a framework that leverages the power of large language models and semantic knowledge to significantly improve the accuracy of time series forecasting.
New research reveals that even sophisticated AI systems can be fooled by subtle manipulations of images, highlighting a critical vulnerability in multimodal perception.
New research highlights potential dangers to patient care stemming from inaccuracies and omissions in clinical documentation generated by emerging AI scribe technologies.
A decade-long study of top conferences reveals diverging trajectories in size and influence, challenging established hierarchies in the field.