Yu Rong

Artificial Intelligence · International Conference on Learning Representations · 2018–2021

A researcher in the Science corpus: 11 papers; trusts 656 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 235 citations into it; #12,268 of 35,345 by trust here, #5,749 by citations
• Computer Vision and Pattern Recognition — 180 citations into it; #20,344 of 21,575 by trust here, #6,281 by citations
• Representation & Self-Supervised Learning — 88 citations into it
• Graph Learning — 72 citations into it
• Molecular Biology — 45 citations into it
• Computational Theory and Mathematics — 43 citations into it
• Optimization & Training — 43 citations into it
• Model Architectures — 29 citations into it

Whom Yu Rong trusts overall

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

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

Who trusts Yu Rong overall

1. Shaked Brody
2. Guohao Li
3. Uri Alon
4. Dongsheng Luo
5. Ruosong Ye
6. Yixin Liu
7. Ming Chen
8. Yanqiao Zhu
9. Wenzheng Feng
10. Yaochen Xie

Papers from Yu Rong's perspective

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

Their papers

• Progressive Feature Alignment for Unsupervised Domain Adaptation (2018) — 481 citations
• DropEdge: Towards Deep Graph Convolutional Networks on Node Classification (2019) — 1,663 citations
• Graph Convolutional Networks for Temporal Action Localization (2019) — 542 citations
• Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation (2021) — 35 citations
• Deep Multimodal Fusion by Channel Exchanging (2020) — 310 citations
• Graph Representation Learning via Graphical Mutual Information Maximization (2020) — 693 citations
• Adaptive Sampling Towards Fast Graph Representation Learning (2018) — 553 citations
• Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (2020) — 816 citations
• Frustratingly Easy Transferability Estimation (2021) — 75 citations
• Self-Supervised Graph Transformer on Large-Scale Molecular Data (2020) — 1,044 citations
• On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches (2018) — 7 citations