Yulin Wang
Computer Vision and Pattern Recognition · 2019–2021
A researcher in the Science corpus: 6 papers; trusts 940 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 420 citations into it; #14,366 of 21,575 by trust here, #8,722 by citations
- Artificial Intelligence — 316 citations into it; #28,534 of 35,345 by trust here, #16,771 by citations
- Model Architectures — 161 citations into it
- Efficiency & Compression — 136 citations into it; #1,040 of 1,264 by trust here, #944 by citations
- Optimization & Training — 49 citations into it
- Electrical and Electronic Engineering — 31 citations into it
- Natural Language Processing — 22 citations into it
- Representation & Self-Supervised Learning — 19 citations into it
Whom Yulin Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Yoshua Bengio
- Karen Simonyan
- Ross B. Girshick
- Ilya Sutskever
- Diederik P. Kingma
- Yann LeCun
- Gao Huang
Who trusts Yulin Wang overall
- Yizeng Han
- Shiji Song
- Gao Huang
- Yang Liu
- Zhuofan Xia
- Youwei Liang
- Bowen Pan
- Boyi Li
- Jin Chen
- Xuran Pan
Papers from Yulin Wang's perspective
Ordered by how strongly the researchers Yulin Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Dynamic Neural Networks: A Survey (2021) — 940 citations
- Implicit Semantic Data Augmentation for Deep Networks (2019) — 242 citations
- Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification (2020) — 172 citations
- Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition (2021) — 264 citations
- Adaptive Focus for Efficient Video Recognition (2021) — 118 citations
- Not All Images are Worth 16x16 Words: Dynamic Vision Transformers with Adaptive Sequence Length (2021) — 48 citations