Ming Dong

Artificial Intelligence · IEEE transactions on multimedia · 2006–2018

A researcher in the Science corpus: 4 papers; trusts 119 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #16,619 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Signal Processing — 18 citations into it; #4,107 of 7,572 by trust here, #3,428 by citations
• Representation & Self-Supervised Learning — 16 citations into it
• Experimental and Cognitive Psychology — 12 citations into it; #4,210 of 9,731 by trust here, #4,576 by citations
• Computer Networks and Communications — 7 citations into it; #7,655 of 9,886 by trust here, #9,214 by citations
• Model Architectures — 7 citations into it
• Electrical and Electronic Engineering — 5 citations into it

Whom Ming Dong trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Kaiming He
6. Ilya Sutskever
7. Honglak Lee
8. David E. Rumelhart
9. Andrew Y. Ng
10. Diederik P. Kingma

Who trusts Ming Dong overall

1. Shiqing Zhang
2. Mustaqeem
3. Yan Wang
4. Jinran Chen
5. .. Mustaqeem
6. Aharon Satt
7. Wootaek Lim
8. Soonil Kwon
9. Shiliang Zhang
10. Mingyi Chen

Papers from Ming Dong's perspective

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

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

Their papers

• Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks (2014) — 538 citations
• Coupled End-to-End Transfer Learning with Generalized Fisher Information (2018) — 30 citations
• Speech Emotion Recognition Using CNN (2014) — 378 citations
• On distributed fault-tolerant detection in wireless sensor networks (2006) — 363 citations