Songhe Wang

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 36 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it; #12,020 of 35,345 by trust here, #17,412 by citations
• Natural Language Processing — 19 citations into it; #2,309 of 5,688 by trust here, #3,529 by citations
• Evaluation & Benchmarks — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it; #8,340 of 21,575 by trust here, #11,721 by citations
• Information Systems — 5 citations into it

Whom Songhe Wang trusts overall

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

1. Bill MacCartney
2. Yorick Wilks
3. Alec Radford
4. James Thorne
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. Jacob Devlin
8. Samuel R. Bowman
9. Diederik P. Kingma
10. Yixin Nie

Who trusts Songhe Wang overall

1. Yuyu Zhang
2. Swering With Extra
3. Canasai Kruengkrai
4. Vidhisha Balachandran
5. Zhenghao Liu
6. Peng Qi
7. Ping Nie
8. Dirk Groeneveld
9. Akari Asai
10. Chenyan Xiong

Papers from Songhe Wang's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Revealing the Importance of Semantic Retrieval for Machine Reading at Scale (2019) — 147 citations