Wei Zhang

Artificial Intelligence · with Wang · 2015–2019

A researcher in the Science corpus: 7 papers; trusts 113 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 143 citations into it; #10,029 of 35,345 by trust here, #6,664 by citations
• Natural Language Processing — 95 citations into it
• Computer Vision and Pattern Recognition — 83 citations into it; #7,313 of 21,575 by trust here, #5,287 by citations
• Optimization & Training — 47 citations into it
• Model Architectures — 22 citations into it
• Computational Mechanics — 8 citations into it
• Management Science and Operations Research — 8 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Wei Zhang trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yann LeCun
6. J. Urgen Schmidhuber
7. Ilya Sutskever
8. Kaiming He
9. Alex Graves
10. John C. Duchi

Who trusts Wei Zhang overall

1. Umair Mohammad
2. Corentin Hardy
3. Alham Fikri Aji
4. Jianyu Wang
5. Shuohang Wang
6. Xiangru Lian
7. Bharadwaj Pudipeddi
8. Jianmin Chen
9. Sameh Sorour
10. Tal Ben-Nun

Papers from Wei Zhang's perspective

Ordered by how strongly the researchers Wei Zhang 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering (2017) — 166 citations
• End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension (2016) — 77 citations
• Staleness-Aware Async-SGD for Distributed Deep Learning (2015) — 282 citations
• GaDei: On Scale-Up Training as a Service for Deep Learning (2016) — 11 citations
• Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks (2019) — 255 citations
• R3: Reinforced Reader-Ranker for Open-Domain Question Answering (2017) — 64 citations
• Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study (2015) — 179 citations