Hong Liang

Artificial Intelligence · with Sun · 2017

A researcher in the Science corpus: 1 paper; trusts 25 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it
• Natural Language Processing — 24 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Model Architectures — 13 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Hong Liang trusts overall

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

1. Yoshua Bengio
2. Yann LeCun
3. Paul Smolensky
4. Geoffrey E. Hinton
5. Yoon Kim
6. Kunihiko Fukushima
7. Susan T. Dumais
8. Alex Graves
9. Liam Paninski
10. Ilya Sutskever

Papers from Hong Liang's perspective

Ordered by how strongly the researchers Hong Liang 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Text feature extraction based on deep learning: a review (2017) — 333 citations