Yuan Jiang

Artificial Intelligence · with Zhou · 2002–2023

A researcher in the Science corpus: 11 papers; trusts 253 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it; #6,992 of 35,345 by trust here, #9,465 by citations
• Computer Vision and Pattern Recognition — 81 citations into it; #7,048 of 21,575 by trust here, #10,905 by citations
• Representation & Self-Supervised Learning — 62 citations into it
• Optimization & Training — 32 citations into it
• Signal Processing — 16 citations into it; #2,351 of 7,572 by trust here, #4,201 by citations
• Evaluation & Benchmarks — 13 citations into it
• Model Architectures — 13 citations into it
• Statistical Learning Theory — 12 citations into it

Whom Yuan Jiang trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ricardo Barandela
8. Marco Cuturi
9. Ross B. Girshick
10. Ilya Sutskever

Who trusts Yuan Jiang overall

1. Da-Wei Zhou
2. Jakramate Bootkrajang
3. Han-Jia Ye
4. Zhi-Hua Zhou
5. Yijie Lin
6. Nadia Burkart
7. Guido Bologna
8. Qiwen Wang
9. Menglei Hu
10. Nimrod Rappoport

Papers from Yuan Jiang's perspective

Ordered by how strongly the researchers Yuan Jiang 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Lung cancer cell identification based on artificial neural network ensembles (2002) — 394 citations
• Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (2019) — 112 citations
• Cost-Effective Incremental Deep Model: Matching Model Capacity With the Least Sampling (2023) — 57 citations
• Learning with Feature and Distribution Evolvable Streams (2020) — 49 citations
• Heterogeneous Few-Shot Model Rectification With Semantic Mapping (2020) — 36 citations
• Rectify Heterogeneous Models with Semantic Mapping (2018) — 34 citations
• Auxiliary Information Regularized Machine for Multiple Modality Feature Learning (2015) — 33 citations
• Partial Multi-View Clustering (2014) — 451 citations
• Exploiting Unlabeled Data in Content-Based Image Retrieval (2004) — 97 citations
• Editing Training Data for kNN Classifiers with Neural Network Ensemble (2004) — 135 citations
• Extracting symbolic rules from trained neural network ensembles (2003) — 216 citations