23 citations · 39 across the 4 of their papers we have counts for
5 papers
SQuAT: Sharpness- and Quantization-Aware Training for BERT
Zheng Wang, Juncheng B Li, Shuhui Qu +2
Quantization is an effective technique to reduce memory footprint, inference latency, and power consumption of deep learning models. However, existing quantization methods suffer f…
On Adversarial Robustness of Large-scale Audio Visual Learning
Juncheng B Li, Shuhui Qu, Xinjian Li +2
As audio-visual systems are being deployed for safety-critical tasks such as surveillance and malicious content filtering, their robustness remains an under-studied area. Existing…
Audio-Visual Event Recognition through the lens of Adversary
Juncheng B Li, Kaixin Ma, Shuhui Qu +2
As audio/visual classification models are widely deployed for sensitive tasks like content filtering at scale, it is critical to understand their robustness along with improving th…
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System
Juncheng B. Li, Shuhui Qu, Xinjian Li +3
Voice Assistants (VAs) such as Amazon Alexa or Google Assistant rely on wake-word detection to respond to people's commands, which could potentially be vulnerable to audio adversar…
A Comparison of deep learning methods for environmental sound
Juncheng Li, Wei Dai, Florian Metze +2
Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically a…