W. Zhang

Artificial Intelligence · with Ma · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 493 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 230 citations into it
• Natural Language Processing — 151 citations into it
• Computer Vision and Pattern Recognition — 89 citations into it
• Safety, Robustness & Interpretability — 56 citations into it
• Signal Processing — 29 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Multimodal Learning — 25 citations into it
• Model Architectures — 23 citations into it

Whom W. Zhang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Yann LeCun
7. Ian J. Goodfellow
8. Oriol Vinyals
9. Kaiming He
10. Christian Szegedy

Who trusts W. Zhang overall

1. Viktor Schlegel
2. Ben Hutchinson
3. Tianhao Shen
4. Tuan Dinh
5. Goran Nenadic
6. Negar Rostamzadeh

Papers from W. Zhang's perspective

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

Their papers

• Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering (2020) — 56 citations
• Multi-document Summarization via Deep Learning Techniques: A Survey (2020) — 162 citations
• Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey (2019) — 601 citations