Mingjie Sun

Artificial Intelligence · with Li · 2018–2023

A researcher in the Science corpus: 3 papers; trusts 306 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #17,149 of 35,345 by trust here, #6,229 by citations
• Computer Vision and Pattern Recognition — 84 citations into it; #10,403 of 21,575 by trust here, #5,013 by citations
• Efficiency & Compression — 73 citations into it; #621 of 1,264 by trust here, #352 by citations
• Model Architectures — 24 citations into it
• Large Language Models — 19 citations into it
• Natural Language Processing — 13 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Electrical and Electronic Engineering — 8 citations into it

Whom Mingjie Sun trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Kaiming He
3. Yann LeCun
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yoshua Bengio
7. Ilya Sutskever
8. Christian Szegedy
9. Ross B. Girshick
10. Karen Simonyan

Who trusts Mingjie Sun overall

1. Jonathan Frankle
2. Binghui Wang
3. Mengzhou Xia
4. Chaoqi Wang
5. Saleh Ashkboos
6. Yu Sun
7. Angela Fan
8. Michael Carbin
9. Hongrong Cheng
10. Eugene Lee

Papers from Mingjie Sun's perspective

Ordered by how strongly the researchers Mingjie Sun trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Rethinking the Value of Network Pruning (2018) — 1,668 citations
• A Simple and Effective Pruning Approach for Large Language Models (2023) — 914 citations
• Data Poisoning Attack against Unsupervised Node Embedding Methods (2018) — 70 citations