Haibo Wang

Artificial Intelligence · International Conference on Information and Knowledge Management · 2010–2021

A researcher in the Science corpus: 9 papers; trusts 271 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 170 citations into it; #34,623 of 35,345 by trust here, #12,761 by citations
• Computer Vision and Pattern Recognition — 147 citations into it
• Information Systems — 81 citations into it; #8,540 of 8,771 by trust here, #2,912 by citations
• Representation & Self-Supervised Learning — 36 citations into it
• Management Science and Operations Research — 22 citations into it
• Optimization & Training — 21 citations into it
• Model Architectures — 20 citations into it
• Computer Networks and Communications — 18 citations into it

Whom Haibo Wang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Qing-Hua Qin
6. Yoshua Bengio
7. Ilya Sutskever
8. Karen Simonyan
9. Yann LeCun
10. Jimmy Ba

Who trusts Haibo Wang overall

1. Hui Wang
2. Qing-Hua Qin
3. Junliang Yu
4. Yupeng Hou
5. Da-Wei Zhou
6. Xiuyi Chen

Papers from Haibo Wang's perspective

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

Their papers

• Memory-Efficient Class-Incremental Learning for Image Classification (2020) — 108 citations
• What and Where: Learn to Plug Adapters via NAS for Multidomain Learning (2020) — 21 citations
• Zanubrutinib, rituximab and lenalidomide induces deep and durable remission in TP53-mutated B-cell prolymphocytic leukemia: a case report and literature review (2021) — 768 citations
• Complementary Incremental Hashing With Query-Adaptive Re-Ranking for Image Retrieval (2020) — 23 citations
• S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization (2020) — 1,154 citations
• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020) — 601 citations
• Iterative Context-Aware Graph Inference for Visual Dialog (2020) — 54 citations
• Lyapunov-Guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks (2020) — 301 citations
• Fundamental-solution-based finite element model for plane orthotropic elastic bodies (2010) — 75 citations