26 citations · 63 across the 8 of their papers we have counts for
8 papers
DNA Family: Boosting Weight-Sharing NAS with Block-Wise Supervisions
Guangrun Wang, Changlin Li, Liuchun Yuan +5
Neural Architecture Search (NAS), aiming at automatically designing neural architectures by machines, has been considered a key step toward automatic machine learning. One notable…
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…
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…
Blockwisely Supervised Neural Architecture Search with Knowledge Distillation
Changlin Li, Jiefeng Peng, Liuchun Yuan +4
Neural Architecture Search (NAS), aiming at automatically designing network architectures by machines, is hoped and expected to bring about a new revolution in machine learning. De…
Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview
Lili Huang, Jiefeng Peng, Ruimao Zhang +2
Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused wid…
Attentive Crowd Flow Machines
Lingbo Liu, Ruimao Zhang, Jiefeng Peng +3
Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow ch…
Batch Kalman Normalization: Towards Training Deep Neural Networks with Micro-Batches
Guangrun Wang, Jiefeng Peng, Ping Luo +2
As an indispensable component, Batch Normalization (BN) has successfully improved the training of deep neural networks (DNNs) with mini-batches, by normalizing the distribution of…