5 papers
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Weigao Sun, Jiaxi Hu, Yucheng Zhou +12
Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer m…
Comba: Improving Bilinear RNNs with Closed-loop Control
Jiaxi Hu, Yongqi Pan, Jusen Du +5
Recent efficient sequence modeling methods such as Gated DeltaNet, TTT, and RWKV-7 have achieved performance improvements by supervising the recurrent memory management through Del…
Multi-Order Wavelet Derivative Transform for Deep Time Series Forecasting
Ziyu Zhou, Jiaxi Hu, Qingsong Wen +2
In deep time series forecasting, the Fourier Transform (FT) is extensively employed for frequency representation learning. However, it often struggles in capturing multi-scale, tim…
Liger: Linearizing Large Language Models to Gated Recurrent Structures
Disen Lan, Weigao Sun, Jiaxi Hu +2
Transformers with linear recurrent modeling offer linear-time training and constant-memory inference. Despite their demonstrated efficiency and performance, pretraining such non-st…
MoM: Linear Sequence Modeling with Mixture-of-Memories
Jusen Du, Weigao Sun, Disen Lan +2
Linear sequence modeling methods, such as linear attention, state space modeling, and linear RNNs, offer significant efficiency improvements by reducing the complexity of training…