Sequence Modeling

Sequence modeling aims to efficiently process and predict sequential data, a ubiquitous challenge across diverse fields. Current research focuses on improving the efficiency and accuracy of models like Transformers and State Space Models (SSMs), particularly for long sequences, by addressing computational complexities and exploring novel architectures such as Mamba and its variants. These advancements are impacting various applications, including natural language processing, time series forecasting, and reinforcement learning, by enabling more accurate and efficient predictions from complex sequential data. The development of robust and efficient sequence models is crucial for advancing these fields and unlocking new possibilities in data analysis and decision-making.