Cyril Zhang
Artificial Intelligence · 2017–2025
A researcher in the Science corpus: 11 papers; trusts 45 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 146 citations into it; #28,502 of 35,345 by trust here, #9,760 by citations
- Computer Vision and Pattern Recognition — 50 citations into it
- Optimization & Training — 49 citations into it
- Natural Language Processing — 35 citations into it
- Management Science and Operations Research — 17 citations into it; #9,490 of 11,946 by trust here, #6,024 by citations
- Computational Mechanics — 12 citations into it; #8,082 of 10,001 by trust here, #5,157 by citations
- Computer Networks and Communications — 9 citations into it
- Statistical Learning Theory — 9 citations into it; #929 of 1,038 by trust here, #893 by citations
Whom Cyril Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Elad Hazan
- Martin A. Zinkevich
- Geoffrey E. Hinton
- Sam T. Roweis
- James Martens
- Yoshua Bengio
- John C. Duchi
- Alex Krizhevsky
- Diederik P. Kingma
- Noam Shazeer
Who trusts Cyril Zhang overall
Papers from Cyril Zhang's perspective
Ordered by how strongly the researchers Cyril Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Learning Linear Dynamical Systems via Spectral Filtering (2017) — 122 citations
- The Case for Full-Matrix Adaptive Regularization (2018) — 21 citations
- Spectral Filtering for General Linear Dynamical Systems (2018) — 109 citations
- Calibration, Entropy Rates, and Memory in Language Models (2019) — 50 citations
- gpt-oss-120b&gpt-oss-20b Model Card (2025) — 1,045 citations
- OpenAI GPT-5 System Card (2025) — 667 citations
- Phi-4 Technical Report (2024) — 716 citations
- Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone (2024) — 2,340 citations
- Extreme Tensoring for Low-Memory Preconditioning (2019) — 11 citations
- Revisiting the Generalization of Adaptive Gradient Methods (2019) — 5 citations
- Towards Provable Control for Unknown Linear Dynamical Systems (2018) — 39 citations