RubiConv enables boundary-respecting convolutions on packed sequences using an efficient algorithm that outperforms both attention and standard FFT baselines in speed.
Fu, Hermann Kumbong, Eric Nguyen, and Christopher R´e
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Gated linear attention Transformers achieve competitive language modeling results with linear-time inference, superior length generalization, and higher training throughput than Mamba.
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RubiConv -- Efficient Boundary-Respecting Convolutions
RubiConv enables boundary-respecting convolutions on packed sequences using an efficient algorithm that outperforms both attention and standard FFT baselines in speed.
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Gated Linear Attention Transformers with Hardware-Efficient Training
Gated linear attention Transformers achieve competitive language modeling results with linear-time inference, superior length generalization, and higher training throughput than Mamba.