Researchers at the University of Waterloo Introduce Orchid: Revolutionizing Deep Learning with Data-Dependent Convolutions for Scalable Sequence Modeling

In deep learning, especially in NLP, image analysis, and biology, there is an increasing focus on developing models that offer both computational efficiency and robust expressiveness. Attention mechanisms have been revolutionary, allowing for better handling of sequence modeling tasks. However, the computational complexity associated with these mechanisms scales quadratically with sequence length, which becomes a […]

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