Multimodal Large Language Models (MLLMs) have tremendous potential to improve the accuracy, availability, and cost-effectiveness of healthcare by providing automated solutions or serving as aids to medical professionals. Despite promising first steps in developing medical MLLMs in the past few years, their capabilities and limitations are not well-understood. MediConfusion is a challenging medical Visual Question … Continue reading MediConfusion
Transformer architectures have risen as the preferred choice among deep-learning models, finding applications in diverse fields such as natural language processing, computer vision, and time-series forecasting. Notably, it forms the core of large language models like ChatGPT. What sets Transformers apart from conventional models like fully connected networks (FCNs), convolutional neural networks (CNNs), and residual … Continue reading ICASSP 2024 Tutorial on Fundamentals of Transformers
Ever wonder what allows the AI you use to work so efficiently? Or how computers can process data and spit it back to you so quickly? In this age of digital and technological renaissance, machine learning models, AI technologies and quantum computing, humanity has transformed the way we interact with computational models. One of these … Continue reading Faculty affiliated with the AIF4S center receive $700,000 Air Force Grant for GPU Cluster
AIF4S director wins the NIH Director’s new innovator award. “Part of the High-Risk, High-Reward Research program, the award supports exceptionally creative early career investigators who propose innovative, high-impact projects in the biomedical, behavioral or social sciences within the NIH mission.” The $2.4 million grant will be used to develop new AI-powered algorithms that could lessen … Continue reading Mahdi Soltanolkotabi Receives NIH Director’s New Innovator Award for Reliable AI for MRI
