26 citations · 155 across the 33 of their papers we have counts for
8 papers · 1 filter
DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers
Changlin Li, Guangrun Wang, Bing Wang +3
Dynamic networks have shown their promising capability in reducing theoretical computation complexity by adapting their architectures to the input during inference. However, their…
EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation
Chenhe Dong, Guangrun Wang, Hang Xu +3
Pre-trained language models have shown remarkable results on various NLP tasks. Nevertheless, due to their bulky size and slow inference speed, it is hard to deploy them on edge de…
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift
Jiefeng Peng, Jiqi Zhang, Changlin Li +3
Recently proposed neural architecture search (NAS) methods co-train billions of architectures in a supernet and estimate their potential accuracy using the network weights detached…
Reward-Adaptive Reinforcement Learning: Dynamic Policy Gradient Optimization for Bipedal Locomotion
Changxin Huang, Guangrun Wang, Zhibo Zhou +2
Controlling a non-statically bipedal robot is challenging due to the complex dynamics and multi-criterion optimization involved. Recent works have demonstrated the effectiveness of…
Solving Inefficiency of Self-supervised Representation Learning
Guangrun Wang, Keze Wang, Guangcong Wang +2
Self-supervised learning (especially contrastive learning) has attracted great interest due to its huge potential in learning discriminative representations in an unsupervised mann…
Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition
Guangrun Wang, Liang Lin, Rongcong Chen +2
Current state-of-the-art visual recognition systems usually rely on the following pipeline: (a) pretraining a neural network on a large-scale dataset (e.g., ImageNet) and (b) finet…
Dynamic Slimmable Network
Changlin Li, Guangrun Wang, Bing Wang +3
Current dynamic networks and dynamic pruning methods have shown their promising capability in reducing theoretical computation complexity. However, dynamic sparse patterns on convo…
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
Changlin Li, Tao Tang, Guangrun Wang +4
A myriad of recent breakthroughs in hand-crafted neural architectures for visual recognition have highlighted the urgent need to explore hybrid architectures consisting of diversif…