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통찰 - Innovative Sequence Modeling Architecture
Orchid: Data-Dependent Convolution for Sequence Modeling
The author introduces Orchid, a novel architecture that utilizes data-dependent convolution to address the limitations of traditional attention mechanisms, offering high expressivity and scalability for long sequences. The main thesis is that Orchid outperforms traditional attention-based architectures like BERT and Vision Transformers with smaller model sizes while extending feasible sequence lengths beyond dense attention layers.
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling
Orchid introduces a novel data-dependent convolution mechanism to address the limitations of traditional attention mechanisms, offering high expressivity and scalability for sequence modeling.
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling
Orchid introduces a data-dependent convolution mechanism to enhance sequence modeling efficiency and scalability.
