3 citations · 4 across the 4 of their papers we have counts for
5 papers
Latent-Condensed Transformer for Efficient Long Context Modeling
Zeng You, Yaofo Chen, Qiuwu Chen +5
Large language models (LLMs) face significant challenges in processing long contexts due to the linear growth of the key-value (KV) cache and quadratic complexity of self-attention…
Training-free Context-adaptive Attention for Efficient Long Context Modeling
Zeng You, Yaofo Chen, Shuhai Zhang +5
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. These capabilities stem primarily from the self-att…
HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models
Jizhihui Liu, Feiyi Du, Guangdao Zhu +5
Vision-Language Models (VLMs) encode images and videos into abundant tokens, which contain substantial redundancy and computation cost. While visual token pruning mitigates the iss…
Core Context Aware Transformers for Long Context Language Modeling
Yaofo Chen, Zeng You, Shuhai Zhang +4
Transformer-based Large Language Models (LLMs) have exhibited remarkable success in extensive tasks primarily attributed to self-attention mechanism, which requires a token to cons…
Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation
Yaofo Chen, Shuaicheng Niu, Yaowei Wang +3
The conventional deep learning paradigm often involves training a deep model on a server and then deploying the model or its distilled ones to resource-limited edge devices. Usuall…