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arXiv · Machine Learning Theory· Bahareh Nikpour, Hossein Rajabzadeh, Maryam Dialameh, Vahid Partovi Nia, Parsa Omidi·· 3 hr agoAI score37

Kronecker Product Attention:用结构化分解实现内存高效的 KV 压缩

Kronecker Product Attention: Memory-Efficient KV Compression via Structured Factorization

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研究者提出 Kronecker Product Attention(KPA),将 key 和 value 激活表示为紧凑的、依赖 token 的矩阵因子的 Kronecker 乘积之和,严格泛化了 Tensor Product Attention(TPA)。

Source: arXiv · Machine Learning Theory · arxiv.org