Sequence Learning

Sequence learning, the ability of systems to process and understand ordered data, aims to build models that effectively capture temporal dependencies and make accurate predictions or classifications based on sequential information. Current research focuses on improving the efficiency and accuracy of various architectures, including recurrent neural networks (RNNs), transformers, and spiking neural networks (SNNs), often incorporating techniques like attention mechanisms and self-distillation. These advancements have significant implications for diverse applications, such as time-series forecasting, speech and sign language recognition, and even materials science, by enabling more accurate and efficient processing of sequential data in various domains.