31 citations · 116 across the 14 of their papers we have counts for
15 papers
Spiking Convolutional Neural Networks for Text Classification
Changze Lv, Jianhan Xu, Xiaoqing Zheng
Spiking neural networks (SNNs) offer a promising pathway to implement deep neural networks (DNNs) in a more energy-efficient manner since their neurons are sparsely activated and i…
Watermarking Pre-trained Language Models with Backdooring
Chenxi Gu, Chengsong Huang, Xiaoqing Zheng +2
Large pre-trained language models (PLMs) have proven to be a crucial component of modern natural language processing systems. PLMs typically need to be fine-tuned on task-specific…
Improving the Adversarial Robustness of NLP Models by Information Bottleneck
Cenyuan Zhang, Xiang Zhou, Yixin Wan +3
Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manip…
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
Zongyi Li, Jianhan Xu, Jiehang Zeng +5
Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversari…
Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
Chong Li, Cenyuan Zhang, Xiaoqing Zheng +1
A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spe…
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
Jiehang Zeng, Xiaoqing Zheng, Jianhan Xu +3
Recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions. However, all existing ce…