Flexible Inference

Flexible inference aims to develop methods for adapting and applying machine learning models to diverse and challenging scenarios, improving their efficiency and effectiveness across various tasks and hardware constraints. Current research focuses on enhancing the generalization capabilities of graph neural networks for relational reasoning, optimizing deep learning models for resource-limited devices (like MCUs) with intermittent power, and developing efficient inference techniques for complex data like spatio-temporal processes and irregular time series. These advancements are significant for improving the applicability of AI across domains, enabling more robust and adaptable systems for real-world applications ranging from robotics and healthcare to natural language processing.