Gongshen Liu

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 26 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Natural Language Processing — 17 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 5 citations into it

Whom Gongshen Liu trusts overall

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

1. Jessica J. Fridrich
2. Yoshua Bengio
3. Kaiming He
4. Tomás Pevný
5. Diederik P. Kingma
6. Karen Simonyan
7. Belhassen Bayar
8. Geoffrey E. Hinton
9. Sergey Ioffe
10. Christian Szegedy

Papers from Gongshen Liu's perspective

Ordered by how strongly the researchers Gongshen Liu 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Going deeper with convolutions
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Modeling Multi-turn Conversation with Deep Utterance Aggregation (2018) — 256 citations
• Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis (2020) — 358 citations