58 citations · 90 across the 19 of their papers we have counts for
4 papers · 1 filter
Robustifying Language Models with Test-Time Adaptation
Noah Thomas McDermott, Junfeng Yang, Chengzhi Mao
Large-scale language models achieved state-of-the-art performance over a number of language tasks. However, they fail on adversarial language examples, which are sentences optimize…
Interpreting and Controlling Vision Foundation Models via Text Explanations
Haozhe Chen, Junfeng Yang, Carl Vondrick +1
Large-scale pre-trained vision foundation models, such as CLIP, have become de facto backbones for various vision tasks. However, due to their black-box nature, understanding the u…
Monitoring and Adapting ML Models on Mobile Devices
Wei Hao, Zixi Wang, Lauren Hong +5
ML models are increasingly being pushed to mobile devices, for low-latency inference and offline operation. However, once the models are deployed, it is hard for ML operators to tr…
Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder
Yun-Yun Tsai, Ju-Chin Chao, Albert Wen +4
Training-time defenses, known as adversarial training, incur high training costs and do not generalize to unseen attacks. Test-time defenses solve these issues but most existing te…