This AI Paper from USC Introduces FFTNet: An Adaptive Spectral Filtering Framework for Efficient and Scalable Sequence Modeling
Deep learning models have significantly advanced natural language processing and computer vision by enabling efficient data-driven learning. However, the computational burden of self-attention mechanisms remains a major obstacle, particularly for handling long sequences. Traditional transformers require pairwise comparisons that scale quadratically with sequence length, making them impractical for tasks involving extensive data. Researchers have been exploring alternative architectures that improve scalability without sacrificing expressivity, focusing on reducing computational complexity while preserving essential long-range dependencies. A primary issue in sequence modeling is the prohibitive cost of self-attention in long-context tasks. As sequences grow, the quadratic complexity of standard transformers becomes unsustainable, hindering their practical deployment. While effective for shorter sequences, these models struggle with excessive memory usage and slow inference times. Thi...