Ling Liang

Artificial Intelligence · with Deng · 2020

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it
• Graph Learning — 12 citations into it

Whom Ling Liang trusts overall

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

1. William L. Hamilton

Papers from Ling Liang's perspective

Ordered by how strongly the researchers Ling Liang 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. Semi-Supervised Classification with Graph Convolutional Networks (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. TensorFlow: A system for large-scale machine learning
10. PyTorch: An Imperative Style, High-Performance Deep Learning Library

Their papers

• Rethinking the performance comparison between SNNS and ANNS (2020) — 355 citations
• HyGCN: A GCN Accelerator with Hybrid Architecture (2020) — 351 citations