Ke Wang

Artificial Intelligence · 5 papers · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 63 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it
• Information Systems — 40 citations into it; #6,889 of 8,771 by trust here, #3,402 by citations
• Software — 29 citations into it; #955 of 1,227 by trust here, #394 by citations
• Natural Language Processing — 23 citations into it
• Reinforcement Learning — 20 citations into it
• Program Analysis & Verification — 18 citations into it; #221 of 301 by trust here, #151 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Graph Learning — 13 citations into it

Whom Ke Wang trusts overall

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

1. Richard Socher
2. Geoffrey E. Hinton
3. Lili Mou
4. Yoshua Bengio
5. Alex Krizhevsky
6. Kai Sheng Tai
7. Sepp Hochreiter
8. Diederik P. Kingma
9. Ilya Sutskever
10. Yann LeCun

Who trusts Ke Wang overall

1. Xinyun Chen
2. Baptiste Rozière
3. Paras Jain
4. Goutham Ramakrishnan
5. Richard Shin
6. Michihiro Yasunaga
7. Zimin Chen

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. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• COSET: A Benchmark for Evaluating Neural Program Embeddings (2019) — 41 citations
• Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation (2020) — 77 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