Ming Zhang

Artificial Intelligence · International Conference on Learning Representations · 2008–2020

A researcher in the Science corpus: 10 papers; trusts 91 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 126 citations into it; #5,360 of 35,345 by trust here, #2,863 by citations
• Computer Vision and Pattern Recognition — 56 citations into it; #11,730 of 21,575 by trust here, #9,563 by citations
• Information Systems — 54 citations into it
• Natural Language Processing — 44 citations into it
• Graph Learning — 30 citations into it; #144 of 693 by trust here, #82 by citations
• Statistical and Nonlinear Physics — 30 citations into it; #944 of 10,127 by trust here, #256 by citations
• Representation & Self-Supervised Learning — 27 citations into it; #668 of 3,145 by trust here, #413 by citations
• Computational Theory and Mathematics — 23 citations into it

Whom Ming Zhang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Tomas Mikolov
7. Ruslan Salakhutdinov
8. Yann LeCun
9. Jimmy Ba
10. Kaiming He

Who trusts Ming Zhang overall

1. Ehsan Amid
2. Bryan Perozzi
3. William L. Hamilton
4. Aleksandar Bojchevski
5. Jian Tang
6. Peng Cui
7. Xiao Huang
8. Shirui Pan
9. Yuxiao Dong
10. Vincent Wenchen Zheng

Papers from Ming Zhang's perspective

Ordered by how strongly the researchers Ming Zhang 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• LINE: Large-scale Information Network Embedding (2015) — 5,660 citations
• Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems (2016) — 114 citations
• AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (2018) — 1,089 citations
• GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation (2020) — 553 citations
• Pfp: parallel fp-growth for query recommendation (2008) — 532 citations
• Session-Based Social Recommendation via Dynamic Graph Attention Networks (2019) — 468 citations
• Visualizing Large-scale and High-dimensional Data (2016) — 426 citations
• Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis (2014) — 283 citations
• We know what @you #tag: does the dual role affect hashtag adoption? (2012) — 263 citations
• Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach (2011) — 481 citations