4 citations · 5 across the 7 of their papers we have counts for
7 papers
Sample-Guided Exact Top-K Selection for Long-Context Sparse Attention
Siran Liu, Yang Xue, Theo Tang +11
Sparse attention bounds downstream attention work by retaining a fixed-size subset of indexed tokens, but its standalone exact Top- stage must still process materialized score r…
Flash-DMD: Towards High-Fidelity Few-Step Image Generation with Efficient Distillation and Joint Reinforcement Learning
Guanjie Chen, Shirui Huang, Kai Liu +5
Diffusion Models have emerged as a leading class of generative models, yet their iterative sampling process remains computationally expensive. Timestep distillation is a promising…
SpecExit: Accelerating Large Reasoning Model via Speculative Exit
Rubing Yang, Huajun Bai, Song Liu +7
Despite their strong performance on reasoning tasks, large reasoning models (LRMs) often suffer from overthinking, producing unnecessarily long outputs and incurring high end-to-en…
Tequila: Trapping-free Ternary Quantization for Large Language Models
Hong Huang, Decheng Wu, Rui Cen +7
Quantization techniques are essential for the deployment of Large Language Models (LLMs) on edge devices. However, prevailing methods often rely on mixed-precision multiplication t…
PEARL: Parallel Speculative Decoding with Adaptive Draft Length
Tianyu Liu, Yun Li, Qitan Lv +4
Speculative decoding (SD), where an extra draft model is employed to provide multiple draft tokens first, and then the original target model verifies these tokens in parallel, has…
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs
Hanlin Tang, Yifu Sun, Decheng Wu +3
Large language models (LLMs) have proven to be very superior to conventional methods in various tasks. However, their expensive computations and high memory requirements are prohib…
E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity
Yun Li, Lin Niu, Xipeng Zhang +3
Traditional pruning methods are known to be challenging to work in Large Language Models (LLMs) for Generative AI because of their unaffordable training process and large computati…