Jul 12, 2021 · We propose Combiner, which provides full attention capability in each attention head while maintaining low computation and memory complexity.

In this paper we propose Combiner, a drop-in replacement for the vanilla quadratic attention mech- anism with sub-quadratic computation and memory cost.

Jun 10, 2024 · Combiner is a drop-in replacement for attention layers in existing transformers and can be easily implemented in common frameworks. An ...

Oct 28, 2021 · Combiner achieves full attention with reduced cost without making explicit sparsity or low-rank assumptions over the attention matrix.

Combiner is a drop-in replacement for attention layers in existing transformers and can be easily implemented in common frameworks, ...

Sep 10, 2024 · Combiner is a drop-in replacement for attention layers in existing transformers and can be easily implemented in common frameworks. An ...

Jul 14, 2021 · Combiner: Full Attention Transformer with Sparse Computation Cost Proposes O(L log L) efficient attention Transformer that yields SotA ...

Jul 14, 2021 · We propose Combiner, which provides full attention capability in each attention head while maintaining low computation and memory complexity.

Explore all code implementations available for Combiner: Full Attention Transformer with Sparse Computation Cost.

Jul 14, 2021 · Combiner: Full Attention Transformer with Sparse Computation Cost pdf: https://t.co/lZ39kcGlLu achieves sota performance on both ...