Complementary Learning is an integral part of the Honors experience at RIT that supports student participation in activities that complement traditional academic work. Please see below events and ...

Abstract: Few-shot class-incremental learning (FSCIL) presents a significant challenge in machine learning, requiring models to integrate new classes from limited examples while preserving performance ...

Abstract: Quasi complementary sequence sets (QCSSs) are important in modern communication systems as they are capable of supporting more users, which is desired in applications like MC-CDMA nowadays.