Tian Gao

Artificial Intelligence · with Yu · 2019

A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it
• Natural Language Processing — 28 citations into it
• Information Systems — 21 citations into it
• Graph Learning — 13 citations into it; #296 of 693 by trust here, #519 by citations
• Probabilistic & Bayesian Methods — 11 citations into it
• Management Science and Operations Research — 8 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Tian Gao trusts overall

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

1. Diederik P. Kingma
2. David Maxwell Chickering
3. Tom L. Burr
4. Ioannis Tsamardinos
5. Max Welling
6. Jiji Zhang
7. James Cussens
8. Geoffrey E. Hinton
9. Yoshua Bengio
10. Thomas Kipf

Who trusts Tian Gao overall

1. Sébastien Lachapelle
2. Philippe Brouillard
3. Matthew James Vowels
4. Chao Shang
5. Chris Cundy
6. Ignavier Ng
7. Lars Lorch
8. Xun Zheng
9. Jonas Rothfuss
10. Mengyue Yang

Papers from Tian Gao's perspective

Ordered by how strongly the researchers Tian Gao trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering (2019) — 54 citations
• Do Multi-hop Readers Dream of Reasoning Chains? (2019) — 27 citations
• DAG-GNN: DAG Structure Learning with Graph Neural Networks (2019) — 689 citations