Yuanzhong Xu

Artificial Intelligence · with Wang · 2019–2025

A researcher in the Science corpus: 14 papers; trusts 249 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 574 citations into it; #20,934 of 35,345 by trust here, #1,154 by citations
• Computer Vision and Pattern Recognition — 324 citations into it; #10,878 of 21,575 by trust here, #1,278 by citations
• Natural Language Processing — 251 citations into it
• Model Architectures — 65 citations into it
• Optimization & Training — 59 citations into it
• Efficiency & Compression — 56 citations into it; #881 of 1,264 by trust here, #131 by citations
• Hardware and Architecture — 44 citations into it
• ML Systems & Hardware — 22 citations into it

Whom Yuanzhong Xu trusts overall

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

1. Noam Shazeer
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Kaiming He
6. Ilya Sutskever
7. Yoshua Bengio
8. Jimmy Ba
9. Ashish Vaswani
10. Yann LeCun

Who trusts Yuanzhong Xu overall

1. Jiahui Yu
2. Hieu Pham
3. Yi Tay
4. Yu Zhang
5. Jing Yu Koh
6. Barret Zoph
7. Zihang Dai
8. Lijun Yu
9. Yanping Huang
10. Gaurav Menghani

Papers from Yuanzhong Xu's perspective

Ordered by how strongly the researchers Yuanzhong Xu 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (2022) — 1,537 citations
• LaMDA: Language Models for Dialog Applications (2022) — 1,891 citations
• GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding (2020) — 2,182 citations
• GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021) — 1,261 citations
• Vector-quantized Image Modeling with Improved VQGAN (2021) — 809 citations
• PaLM 2 Technical Report (2023) — 1,524 citations
• GSPMD: General and Scalable Parallelization for ML Computation Graphs (2021) — 188 citations
• PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023) — 301 citations
• Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models (2022) — 117 citations
• BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2021) — 208 citations
• Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training (2020) — 52 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Scale MLPerf-0.6 models on Google TPU-v3 Pods (2019) — 43 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations