Ruihao Gong

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 383 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 191 citations into it
• Artificial Intelligence — 161 citations into it
• Efficiency & Compression — 105 citations into it; #1,074 of 1,264 by trust here, #613 by citations
• Model Architectures — 31 citations into it
• Optimization & Training — 29 citations into it
• Statistical and Nonlinear Physics — 12 citations into it
• Neurology — 9 citations into it
• Media Technology — 7 citations into it

Whom Ruihao Gong trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ross B. Girshick
6. Shaoqing Ren
7. Diederik P. Kingma
8. Yann LeCun
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Ruihao Gong overall

1. Haotong Qin
2. Tim Dettmers
3. Jing Liu
4. Yuhang Li
5. Zechun Liu
6. Alexandre D'efossez
7. Qing Jin
8. Sein Park
9. Xianglong Liu
10. Jangho Kim

Papers from Ruihao Gong's perspective

Ordered by how strongly the researchers Ruihao Gong 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction (2021) — 675 citations
• Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models (2022) — 229 citations
• Binary Neural Networks: A Survey (2020) — 577 citations
• Towards Unified INT8 Training for Convolutional Neural Network (2019) — 179 citations
• Forward and Backward Information Retention for Accurate Binary Neural Networks (2019) — 399 citations
• Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks (2019) — 545 citations