12 citations · 17 across the 5 of their papers we have counts for
5 papers
SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack
Han Liu, Zhi Xu, Xiaotong Zhang +5
Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especially transferable attacks that…
SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
Han Liu, Haotian Gao, Xiaotong Zhang +5
Large language models (LLMs) have shown remarkable performance in various domains, but they are constrained by massive computational and storage costs. Quantization, an effective t…
RUQuant: Towards Refining Uniform Quantization for Large Language Models
Han Liu, Haotian Gao, Changya Li +4
The increasing size and complexity of large language models (LLMs) have raised significant challenges in deployment efficiency, particularly under resource constraints. Post-traini…
SEVGGNet-LSTM: a fused deep learning model for ECG classification
Tongyue He, Yiming Chen, Junxin Chen +2
This paper presents a fused deep learning algorithm for ECG classification. It takes advantages of the combined convolutional and recurrent neural network for ECG classification, a…
Emotion Recognition From Gait Analyses: Current Research and Future Directions
Shihao Xu, Jing Fang, Xiping Hu +4
Human gait refers to a daily motion that represents not only mobility, but it can also be used to identify the walker by either human observers or computers. Recent studies reveal…