Zhiyuan Xu

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 9 papers; trusts 141 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 147 citations into it; #28,572 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 81 citations into it; #17,553 of 21,575 by trust here, #14,151 by citations
• Reinforcement Learning — 56 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Electrical and Electronic Engineering — 20 citations into it
• Model Architectures — 20 citations into it
• Optimization & Training — 17 citations into it; #2,264 of 2,601 by trust here, #2,313 by citations
• Statistics and Probability — 17 citations into it

Whom Zhiyuan Xu trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Richard S. Sutton
4. Volodymyr Mnih
5. Alex Krizhevsky
6. Alex Graves
7. Diederik P. Kingma
8. David Silver
9. Yann LeCun
10. Ilya Sutskever

Who trusts Zhiyuan Xu overall

1. Chaoyun Zhang
2. Qian Mao
3. Paul Patras
4. Nguyen Cong Luong
5. Changyang She
6. Hamed Haddadi
7. Fei Hu
8. Micah Goldblum

Papers from Zhiyuan Xu's perspective

Ordered by how strongly the researchers Zhiyuan Xu trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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

• TinyVLA: Toward Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation (2024) — 387 citations
• Adversarial Meta-Learning (2018) — 46 citations
• A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs (2017) — 243 citations
• Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017) — 117 citations
• Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control (2020) — 56 citations
• Hierarchical Graph Attention Network for Few-shot Visual-Semantic Learning (2021) — 13 citations
• Experience-driven Networking: A Deep Reinforcement Learning based Approach (2018) — 414 citations
• Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach (2017) — 353 citations
• A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning (2017) — 279 citations