Tian Guo

Artificial Intelligence · with Antulov-Fantulin · 2019

A researcher in the Science corpus: 1 paper; trusts 37 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #28,114 of 35,345 by trust here, #29,412 by citations
• Model Architectures — 13 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Signal Processing — 10 citations into it; #6,015 of 7,572 by trust here, #6,083 by citations
• Management Science and Operations Research — 8 citations into it; #9,351 of 11,946 by trust here, #8,906 by citations
• Natural Language Processing — 6 citations into it
• Time-Series & Forecasting — 6 citations into it

Whom Tian Guo trusts overall

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

1. Diederik P. Kingma
2. Ilya Sutskever
3. Oriol Vinyals
4. Jerome H. Friedman
5. Yoshua Bengio
6. W. James Murdoch
7. Geoffrey E. Hinton
8. Alex Graves
9. Dzmitry Bahdanau
10. Jimmy Ba

Who trusts Tian Guo overall

1. B. Kann
2. Bryan Lim
3. Alex Tank
4. Ahmed Hosny
5. Sercan Ö. Arik
6. Ian Covert

Papers from Tian Guo's perspective

Ordered by how strongly the researchers Tian Guo 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Exploring Interpretable LSTM Neural Networks over Multi-Variable Data (2019) — 184 citations