
TaylorShift introduces a novel approach to self-attention, enabling full token-to-token interactions in linear time and space. The method enhances memory efficiency and accelerates inference for long sequences, without compromising accuracy.


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TaylorShift: Revolutionizing Self-Attention Efficiency with TaylorSoftmax


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Introducing Softmax-free Transformers for efficient visual recognition tasks.


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Softmax-free Linear Transformers: A Novel Approach to Efficient Visual Recognition



Self-attention complexity shifted to linear efficiency with TaylorShift.


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TaylorShift: Shifting Self-Attention Complexity to Linear Efficiency



TaylorShift introduces a novel reformulation of the Taylor softmax, enabling full token-to-token interactions in linear time and space, enhancing memory efficiency and accelerating inference for long sequences.
