8 citations · 16 across the 4 of their papers we have counts for
5 papers
Improving Non-autoregressive Generation with Mixup Training
Ting Jiang, Shaohan Huang, Zihan Zhang +6
While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation t…
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…
Multi-Exit Vision Transformer for Dynamic Inference
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Deep neural networks can be converted to multi-exit architectures by inserting early exit branches after some of their intermediate layers. This allows their inference process to b…
Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Deploying deep learning services for time-sensitive and resource-constrained settings such as IoT using edge computing systems is a challenging task that requires dynamic adjustmen…
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
Yuhang Li, Ruihao Gong, Xu Tan +6
We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training…