Stanford University Researchers Introduce FlashFFTConv: A New Artificial Intelligence System for Optimizing FFT Convolutions for Long Sequences

Reasoning efficiently across extended sequences is a major difficulty in machine learning. Recently, convolutions have emerged as a critical primitive for sequence modeling, supporting state-of-the-art performance in language modeling, time-series analysis, computer vision, DNA modeling, and more. Despite these impressive quality findings and additional advantages, such as improved stability and better scalability as the sequence […]

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