Sequence Classifier

Sequence classifiers are machine learning models designed to assign labels to sequential data, such as text, audio, or biological sequences. Current research focuses on improving their accuracy and interpretability across diverse applications, employing architectures like transformers and perceivers, and exploring techniques like progressive inference for explanation and randomized smoothing for robustness against adversarial attacks. These advancements are impacting fields ranging from biomedical named entity recognition and malware detection to music informatics and clinical speech analysis, enabling more accurate and reliable classification in various domains.