Qi Zhu

Artificial Intelligence · with Wang · 2017–2022

A researcher in the Science corpus: 5 papers; trusts 205 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 146 citations into it
• Computer Vision and Pattern Recognition — 75 citations into it
• Federated & Distributed Learning — 36 citations into it
• Optimization & Training — 25 citations into it
• Privacy & Security — 25 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Safety, Robustness & Interpretability — 16 citations into it
• Electrical and Electronic Engineering — 14 citations into it

Whom Qi Zhu trusts overall

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

1. Kaiming He
2. L. Eon Bottou
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yann LeCun
6. Yoshua Bengio
7. Ilya Sutskever
8. X. Zhang
9. Diederik P. Kingma
10. Ross B. Girshick

Who trusts Qi Zhu overall

1. Liang Yu
2. Jiahua Dong

Papers from Qi Zhu's perspective

Ordered by how strongly the researchers Qi Zhu 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. Gradient-based learning applied to document recognition
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep reinforcement learning for building HVAC control (2017) — 516 citations
• Federated Class-Incremental Learning (2022) — 299 citations
• Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization (2021) — 68 citations
• Addressing Class Imbalance in Federated Learning (2020) — 329 citations
• Eavesdrop the Composition Proportion of Training Labels in Federated Learning (2019) — 74 citations