Lei Tang

Statistical and Nonlinear Physics · with Liu · 2008–2013

A researcher in the Science corpus: 8 papers; trusts 102 researchers and is trusted by 101 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 99 citations into it; #175 of 10,127 by trust here, #609 by citations
• Artificial Intelligence — 73 citations into it; #2,280 of 35,345 by trust here, #5,424 by citations
• Graph Learning — 32 citations into it; #91 of 693 by trust here, #321 by citations
• Information Systems — 25 citations into it
• Computer Networks and Communications — 22 citations into it
• Representation & Self-Supervised Learning — 18 citations into it; #590 of 3,145 by trust here, #1,597 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #4,689 of 21,575 by trust here, #11,286 by citations
• Molecular Biology — 14 citations into it

Whom Lei Tang trusts overall

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

1. Mark Newman
2. Sofus A. Macskassy
3. Jennifer Neville
4. Ulrike von Luxburg
5. Mark S. Handcock
6. Xiaojin Zhu
7. B. Taskar
8. Foster J. Provost
9. Purnamrita Sarkar
10. Stuart Geman

Who trusts Lei Tang overall

1. Bryan Perozzi
2. Daokun Zhang
3. Shaosheng Cao
4. Charu C. Aggarwal
5. Juzheng Li
6. Huan Liu
7. Sharad Nandanwar
8. Aditya Grover
9. Jie Yin
10. Zubair Muhammad Fadlullah

Papers from Lei Tang's perspective

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

Their papers

• Leveraging social media networks for classification (2011) — 356 citations
• Relational learning via latent social dimensions (2009) — 834 citations
• Exploiting social relations for sentiment analysis in microblogging (2013) — 395 citations
• Scalable Learning of Collective Behavior (2012) — 62 citations
• Community Detection and Mining in Social Media (2010) — 414 citations
• Scalable learning of collective behavior based on sparse social dimensions (2009) — 255 citations
• A convex formulation for learning shared structures from multiple tasks (2009) — 210 citations
• Identifying the influential bloggers in a community (2008) — 567 citations