This AI Paper Introduces Rational Transfer Function: Advancing Sequence Modeling with FFT Techniques

This AI Paper Introduces Rational Transfer Function: Advancing Sequence Modeling with FFT Techniques

State-space models (SSMs) in Deep Learning

Challenges in Traditional SSMs

State-space models (SSMs) are crucial in deep learning for sequence modeling, but existing SSMs face inefficiency issues related to memory and computational costs. This limits their scalability and performance in large-scale applications.

Advancements in SSMs

Recent research has introduced practical solutions to address the inefficiency of traditional SSMs. Frameworks like S4, S4D, Hyena, and Liquid-S4, along with FFT-based methods and transformers, have significantly enhanced the efficiency and capability of sequence modeling.

Rational Transfer Function (RTF) Approach

Researchers have introduced the Rational Transfer Function (RTF) approach, leveraging transfer functions for efficient sequence modeling. This method stands out due to its state-free design, significantly improving computational speed and scalability in parallel inference using FFT.

Performance and Benefits

The RTF model has demonstrated significant improvements in training speed and accuracy across various benchmarks, including Long Range Arena and synthetic tasks. It offers an efficient solution for scalable and effective sequence modeling, addressing the challenges of traditional SSMs.

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