Ke Wang

Artificial Intelligence · 4 papers · 2017–2022

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it
• Information Systems — 34 citations into it; #6,890 of 8,771 by trust here, #4,012 by citations
• Natural Language Processing — 32 citations into it
• Software — 25 citations into it; #956 of 1,227 by trust here, #489 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Program Analysis & Verification — 15 citations into it; #220 of 301 by trust here, #151 by citations
• Graph Learning — 12 citations into it
• Signal Processing — 12 citations into it

Whom Ke Wang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Richard Socher
4. Diederik P. Kingma
5. Jacob Devlin
6. Scott E. Reed
7. Ilya Sutskever
8. Alex Krizhevsky
9. Sahil Bhatia
10. Sepp Hochreiter

Who trusts Ke Wang overall

1. Xinyun Chen
2. Baptiste Rozière
3. Richard Shin
4. Michihiro Yasunaga
5. Zimin Chen
6. Thibaud Lutellier
7. Marie-Anne Lachaux
8. Dawn Song
9. Chang Liu
10. Illia Polosukhin

Papers from Ke Wang's perspective

Ordered by how strongly the researchers Ke Wang 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space (2022) — 595 citations
• Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs (2020) — 215 citations
• Learning semantic program embeddings with graph interval neural network (2020) — 69 citations
• Dynamic Neural Program Embedding for Program Repair (2017) — 144 citations