Minh Tang

Statistical and Nonlinear Physics · 2011–2017

A researcher in the Science corpus: 5 papers; trusts 23 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 49 citations into it; #6,480 of 10,127 by trust here, #6,421 by citations
• Statistics and Probability — 21 citations into it; #12,736 of 12,932 by trust here, #6,853 by citations
• Artificial Intelligence — 16 citations into it; #34,696 of 35,345 by trust here, #20,836 by citations
• Graph Learning — 15 citations into it
• Geometry and Topology — 14 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Discrete Mathematics and Combinatorics — 7 citations into it

Whom Minh Tang trusts overall

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

1. Ulrike von Luxburg
2. Brian Karrer
3. Mikhail Belkin
4. Jianbo Shi
5. Frank McSherry
6. Mark Newman
7. Tom A. B. Snijders
8. Peter J. Bickel
9. Karl Rohe
10. Paul Holland

Who trusts Minh Tang overall

1. Avanti Athreya
2. Carey E. Priebe
3. Donniell E. Fishkind
4. Renaud Lambiotte
5. Seyed Mehran Kazemi

Papers from Minh Tang's perspective

Ordered by how strongly the researchers Minh Tang 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. Gradient-based learning applied to document recognition
5. Normalized cuts and image segmentation
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Long Short-Term Memory
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Limit theorems for eigenvectors of the normalized Laplacian for random graphs (2016) — 109 citations
• A Limit Theorem for Scaled Eigenvectors of Random Dot Product Graphs (2013) — 125 citations
• Statistical Inference on Random Dot Product Graphs: a Survey (2017) — 290 citations
• A central limit theorem for an omnibus embedding of multiple random graphs and implications for multiscale network inference (2017)
• A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs (2011) — 318 citations