14 citations · 15 across the 4 of their papers we have counts for
4 papers
Sensitivity-Aware Post-Training Quantization for Deep Neural Networks
Zekang Zheng, Haokun Li, Yaofo Chen +2
Model quantization reduces neural network parameter precision to achieve compression, but often compromises accuracy. Existing post-training quantization (PTQ) methods employ itera…
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…
Boost Test-Time Performance with Closed-Loop Inference
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang +6
Conventional deep models predict a test sample with a single forward propagation, which, however, may not be sufficient for predicting hard-classified samples. On the contrary, we…
Generative Low-bitwidth Data Free Quantization
Shoukai Xu, Haokun Li, Bohan Zhuang +4
Neural network quantization is an effective way to compress deep models and improve their execution latency and energy efficiency, so that they can be deployed on mobile or embedde…