Qing He

Artificial Intelligence · IEEE Transactions on Knowledge and Data Engineering · 2009–2021

A researcher in the Science corpus: 13 papers; trusts 355 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 275 citations into it; #32,479 of 35,345 by trust here, #6,517 by citations
• Computer Vision and Pattern Recognition — 146 citations into it
• Representation & Self-Supervised Learning — 121 citations into it
• Information Systems — 84 citations into it
• Natural Language Processing — 36 citations into it
• Recommender Systems — 31 citations into it
• Signal Processing — 25 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it

Whom Qing He trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Diederik P. Kingma
6. Ilya Sutskever
7. Ruslan Salakhutdinov
8. Alex Graves
9. Kaiming He
10. Radford M. Neal

Who trusts Qing He overall

1. Mohammad Rostami

Papers from Qing He's perspective

Ordered by how strongly the researchers Qing He 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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

• A Comprehensive Survey on Transfer Learning (2019) — 5,955 citations
• Mechanism-Aware Neural Machine for Dialogue Response Generation (2017) — 81 citations
• Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (2021) — 571 citations
• Deep Subdomain Adaptation Network for Image Classification (2020) — 1,091 citations
• A Survey on Knowledge Graph-Based Recommender Systems (2020) — 1,001 citations
• A General Cross-Domain Recommendation Framework via Bayesian Neural Network (2018) — 37 citations
• Attention-driven Factor Model for Explainable Personalized Recommendation (2018) — 57 citations
• Representation learning via Dual-Autoencoder for recommendation (2017) — 107 citations
• Supervised Representation Learning: Transfer Learning with Deep Autoencoders (2015) — 370 citations
• Collaborating between Local and Global Learning for Distributed Online Multiple Tasks (2015) — 22 citations
• Embedding with Autoencoder Regularization (2013) — 48 citations
• Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective (2010) — 69 citations
• Parallel K-Means Clustering Based on MapReduce (2009) — 706 citations