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Tucker Bottleneck Attention for Multi-Dimensional Sequence Modeling

Solgi, Ryan et al.
arXiv (All) · Papers
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machine-learning
The quadratic cost of self-attention limits scalability to long sequences from multidimensional data. We introduce Tucker bottleneck attention (TuBA), which exploits low-rank tensor structure for efficient global token mixing. TuBA projects hidden tensors into compact Tucker cores, performs multi-he
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