Ningning Ma
Computer Vision and Pattern Recognition · 2018–2021
A researcher in the Science corpus: 4 papers; trusts 1,050 researchers and is trusted by 607 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 139 citations into it; #1,753 of 21,575 by trust here, #2,304 by citations
- Artificial Intelligence — 103 citations into it; #2,236 of 35,345 by trust here, #3,091 by citations
- Model Architectures — 77 citations into it; #1,803 of 2,278 by trust here, #2,192 by citations
- Efficiency & Compression — 36 citations into it; #76 of 1,264 by trust here, #176 by citations
- Optimization & Training — 25 citations into it
- Evaluation & Benchmarks — 12 citations into it
- Media Technology — 5 citations into it
- Natural Language Processing — 5 citations into it
Whom Ningning Ma trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Karen Simonyan
- Christian Szegedy
- Ross B. Girshick
- Yoshua Bengio
- X. Zhang
- Ilya Sutskever
- Andrew Zisserman
Who trusts Ningning Ma overall
- Jiahui Yu
- Mingxing Tan
- Yuhui Yuan
- Han Cai
- X. Zhang
- Sachin Mehta
- Yinpeng Chen
- Chao Li
- Xiaoliang Dai
- Chien-Yao Wang
Papers from Ningning Ma's perspective
Ordered by how strongly the researchers Ningning Ma 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
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design (2018) — 6,472 citations
- RepVGG: Making VGG-style ConvNets Great Again (2021) — 2,415 citations
- WeightNet: Revisiting the Design Space of Weight Networks (2020) — 124 citations
- Funnel Activation for Visual Recognition (2020) — 100 citations