Ming Jin

Artificial Intelligence · with Pan · 2021–2023

A researcher in the Science corpus: 2 papers; trusts 38 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 144 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it
• Graph Learning — 35 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Molecular Biology — 20 citations into it
• Generative Models — 6 citations into it

Whom Ming Jin trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Kaiming He
6. Sepp Hochreiter
7. Yann LeCun
8. Ilya Sutskever
9. Max Welling
10. J. Urgen Schmidhuber

Papers from Ming Jin's perspective

Ordered by how strongly the researchers Ming Jin 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023) — 498 citations
• Graph Self-Supervised Learning: A Survey (2021) — 758 citations