Liping Li

Artificial Intelligence · with Chen · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it
• Federated & Distributed Learning — 26 citations into it
• Computational Mechanics — 19 citations into it
• Optimization & Training — 16 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Liping Li trusts overall

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

1. Peter Richtárik
2. Saeed Ghadimi
3. Yang Liu
4. Tat-seng Chua
5. H. Brendan McMahan

Papers from Liping Li's perspective

Ordered by how strongly the researchers Liping Li 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. Large Scale Distributed Deep Networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features (2022) — 139 citations
• A Communication Efficient Vertical Federated Learning Framework (2019) — 79 citations
• A Communication Efficient Collaborative Learning Framework for Distributed Features (2019) — 62 citations