3 papers
cs.LG2025
Parallelizing Linear Transformers with the Delta Rule over Sequence Length
Songlin Yang, Bailin Wang, Yu Zhang +2
Transformers with linear attention (i.e., linear transformers) and state-space models have recently been suggested as a viable linear-time alternative to transformers with softmax…
cs.LG2024
Gated Linear Attention Transformers with Hardware-Efficient Training
Songlin Yang, Bailin Wang, Yikang Shen +2
Transformers with linear attention allow for efficient parallel training but can simultaneously be formulated as an RNN with 2D (matrix-valued) hidden states, thus enjoying linear-…
cs.CL2024
HGRN2: Gated Linear RNNs with State Expansion
Zhen Qin, Songlin Yang, Weixuan Sun +4
Hierarchically gated linear RNN (HGRN, \citealt{HGRN}) has demonstrated competitive training speed and performance in language modeling while offering efficient inference. However,…