Sequence Model

Sequence models aim to effectively learn and predict patterns within sequential data, addressing challenges like long-range dependencies and computational efficiency. Current research focuses on improving model architectures such as state-space models (SSMs) and transformers, exploring techniques like selective gating, over-parameterization, and quantization to enhance performance and reduce computational costs. These advancements are impacting diverse fields, including time-series forecasting, natural language processing, and biological sequence analysis, by enabling more accurate and efficient modeling of complex sequential phenomena. The development of more tractable and interpretable sequence models remains a key area of ongoing investigation.