Researchers At The University Of Waterloo Introduce Orchid: Revolutionizing Deep Learning With Data-dependent Convolutions For Scalable Sequence Modeling

Orchid is a new sequence modeling architecture that integrates a data-dependent convolution mechanism to overcome the limitations of traditional attention-based models. It aims to reduce the computational burden of attention mechanisms while preserving their expressiveness, making it a promising solution for handling long-context tasks in deep learning.