Lixu Wang

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 172 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it
• Computer Vision and Pattern Recognition — 72 citations into it
• Federated & Distributed Learning — 36 citations into it
• Privacy & Security — 25 citations into it
• Optimization & Training — 24 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 Lixu Wang trusts overall

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

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

Who trusts Lixu Wang overall

1. Jiahua Dong
2. Haftay Gebreslasie Abreha
3. Da-Wei Zhou
4. Shichao Xu
5. Yuchao Gu
6. Zhen Fang
7. Mohammad R. Hayajneh
8. Xiao Wang
9. Gan Sun
10. Qinbin Li

Papers from Lixu Wang's perspective

Ordered by how strongly the researchers Lixu Wang 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• 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