300 citations · 1.5k across the 38 of their papers we have counts for
6 papers · 1 filter
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
Kaiwen Yang, Yanchao Sun, Jiahao Su +5
Data augmentation is a critical contributing factor to the success of deep learning but heavily relies on prior domain knowledge which is not always available. Recent works on auto…
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
Yu Cao, Dianqi Li, Meng Fang +4
We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while main…
Phrase-level Textual Adversarial Attack with Label Preservation
Yibin Lei, Yu Cao, Dianqi Li +3
Generating high-quality textual adversarial examples is critical for investigating the pitfalls of natural language processing (NLP) models and further promoting their robustness.…
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels
Zhuowei Wang, Tianyi Zhou, Guodong Long +2
Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are…
Token Dropping for Efficient BERT Pretraining
Le Hou, Richard Yuanzhe Pang, Tianyi Zhou +4
Transformer-based models generally allocate the same amount of computation for each token in a given sequence. We develop a simple but effective "token dropping" method to accelera…
On the Convergence of Clustered Federated Learning
Jie Ma, Guodong Long, Tianyi Zhou +2
Knowledge sharing and model personalization are essential components to tackle the non-IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1)…