Tengfei Ma

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 69 researchers and is trusted by 65 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it; #3,940 of 35,345 by trust here, #7,310 by citations
• Graph Learning — 45 citations into it; #120 of 693 by trust here, #246 by citations
• Representation & Self-Supervised Learning — 27 citations into it
• Molecular Biology — 24 citations into it
• Statistical and Nonlinear Physics — 20 citations into it
• Computational Theory and Mathematics — 19 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Model Architectures — 14 citations into it

Whom Tengfei Ma trusts overall

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

1. Thomas Kipf
2. Max Welling
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Yann LeCun
7. Alex Krizhevsky
8. Ilya Sutskever
9. Aditya Grover
10. Bryan Perozzi

Who trusts Tengfei Ma overall

1. Rex Ying
2. Ziniu Hu
3. Hao Zhu
4. Enyan Dai
5. Kenta Oono
6. Christopher Morris
7. Junyuan Shang
8. Liang Yao
9. Yue Yu
10. Davide Bacciu

Papers from Tengfei Ma's perspective

Ordered by how strongly the researchers Tengfei Ma 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. 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling (2018) — 1,744 citations
• GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination (2018) — 365 citations
• Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders (2018) — 189 citations
• Pre-training of Graph Augmented Transformers for Medication Recommendation (2019) — 352 citations
• Conceptual design report for the LUXE experiment (2021) — 201 citations
• EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs (2019) — 1,524 citations