Lingyang Chu

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 28 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it
• Optimization & Training — 34 citations into it
• Federated & Distributed Learning — 33 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Model Architectures — 19 citations into it
• Scaling & Learning Theory — 19 citations into it
• Computational Mechanics — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Lingyang Chu trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Yoshua Bengio
4. Kaiming He
5. Geoffrey E. Hinton
6. Dimitri P. Bertsekas
7. Jimmy Ba
8. Yann LeCun
9. R. Tyrrell Rockafellar
10. Ilya Sutskever

Papers from Lingyang Chu's perspective

Ordered by how strongly the researchers Lingyang Chu 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
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

• Model complexity of deep learning: a survey (2021) — 399 citations
• Personalized Cross-Silo Federated Learning on Non-IID Data (2020) — 856 citations
• Personalized Federated Learning: An Attentive Collaboration Approach (2020) — 29 citations