48 citations · 129 across the 10 of their papers we have counts for
10 papers
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
Xiuying Wei, Yunchen Zhang, Xiangguo Zhang +5
Transformer architecture has become the fundamental element of the widespread natural language processing~(NLP) models. With the trends of large NLP models, the increasing memory a…
Neural Color Operators for Sequential Image Retouching
Yili Wang, Xin Li, Kun Xu +4
We propose a novel image retouching method by modeling the retouching process as performing a sequence of newly introduced trainable neural color operators. The neural color operat…
PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation
Jing He, Yiyi Zhou, Qi Zhang +5
Pixel synthesis is a promising research paradigm for image generation, which can well exploit pixel-wise prior knowledge for generation. However, existing methods still suffer from…
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
Yuhang Li, Mingzhu Shen, Jian Ma +6
Model quantization has emerged as an indispensable technique to accelerate deep learning inference. While researchers continue to push the frontier of quantization algorithms, exis…
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…
Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Deploying deep learning models in time-critical applications with limited computational resources, for instance in edge computing systems and IoT networks, is a challenging task th…
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…