Zhengyan Zhang
Artificial Intelligence · 2018–2026
A researcher in the Science corpus: 14 papers; trusts 542 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 774 citations into it; #18,578 of 35,345 by trust here, #2,287 by citations
- Computer Vision and Pattern Recognition — 363 citations into it
- Natural Language Processing — 317 citations into it
- Large Language Models — 163 citations into it; #430 of 799 by trust here, #320 by citations
- Representation & Self-Supervised Learning — 101 citations into it
- Graph Learning — 99 citations into it
- Efficiency & Compression — 87 citations into it
- Model Architectures — 85 citations into it
Whom Zhengyan Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Kaiming He
- Diederik P. Kingma
- Alex Krizhevsky
- Ilya Sutskever
- Yann LeCun
- Richard Socher
- Alex Graves
- Karen Simonyan
Who trusts Zhengyan Zhang overall
- Yuxian Gu
- Xu Han
- Nina Poerner
- Joseph Fisher
- Katikapalli Subramanyam Kalyan
- Xiao Liu
- Chenguang Zhu
- Tianxiang Sun
- Junyi Li
- Michihiro Yasunaga
Papers from Zhengyan Zhang's perspective
Ordered by how strongly the researchers Zhengyan Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Graph Neural Networks: A Review of Methods and Applications (2018) — 7,152 citations
- CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models (2021) — 257 citations
- ERNIE: Enhanced Language Representation with Informative Entities (2019) — 1,575 citations
- Pre-Trained Models: Past, Present and Future (2021) — 1,095 citations
- CPM-2: Large-scale Cost-effective Pre-trained Language Models (2021) — 101 citations
- CPM: A Large-scale Generative Chinese Pre-trained Language Model (2020) — 132 citations
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence (2026) — 347 citations
- Red Alarm for Pre-trained Models: Universal Vulnerabilities by Neuron-Level Backdoor Attacks (2021) — 15 citations
- Knowledge Inheritance for Pre-trained Language Models (2021) — 65 citations
- Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger (2021) — 338 citations
- Know What You Don't Need: Single-Shot Meta-Pruning for Attention Heads (2020) — 36 citations
- Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning (2020) — 37 citations
- Train No Evil: Selective Masking for Task-guided Pre-training (2020) — 68 citations
- Adversarial Language Games for Advanced Natural Language Intelligence (2019) — 8 citations