23 citations · 62 across the 10 of their papers we have counts for
11 papers
Error-aware Quantization through Noise Tempering
Zheng Wang, Juncheng B Li, Shuhui Qu +2
Quantization has become a predominant approach for model compression, enabling deployment of large models trained on GPUs onto smaller form-factor devices for inference. Quantizati…
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…
Robustness of Neural Architectures for Audio Event Detection
Juncheng B Li, Zheng Wang, Shuhui Qu +1
Traditionally, in Audio Recognition pipeline, noise is suppressed by the "frontend", relying on preprocessing techniques such as speech enhancement. However, it is not guaranteed t…
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…
AudioTagging Done Right: 2nd comparison of deep learning methods for environmental sound classification
Juncheng B Li, Shuhui Qu, Po-Yao Huang +1
After its sweeping success in vision and language tasks, pure attention-based neural architectures (e.g. DeiT) are emerging to the top of audio tagging (AT) leaderboards, which see…
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…
Learning Filter Banks Using Deep Learning For Acoustic Signals
Shuhui Qu, Juncheng Li, Wei Dai +1
Designing appropriate features for acoustic event recognition tasks is an active field of research. Expressive features should both improve the performance of the tasks and also be…
Understanding Audio Pattern Using Convolutional Neural Network From Raw Waveforms
Shuhui Qu, Juncheng Li, Wei Dai +1
One key step in audio signal processing is to transform the raw signal into representations that are efficient for encoding the original information. Traditionally, people transfor…
Very Deep Convolutional Neural Networks for Raw Waveforms
Wei Dai, Chia Dai, Shuhui Qu +2
Learning acoustic models directly from the raw waveform data with minimal processing is challenging. Current waveform-based models have generally used very few (~2) convolutional l…