Xiao Huang

Artificial Intelligence · with Hu · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 268 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 177 citations into it; #8,089 of 35,345 by trust here, #9,968 by citations
• Computer Vision and Pattern Recognition — 68 citations into it
• Graph Learning — 65 citations into it; #466 of 693 by trust here, #606 by citations
• Representation & Self-Supervised Learning — 45 citations into it
• Statistical and Nonlinear Physics — 44 citations into it; #1,933 of 10,127 by trust here, #2,437 by citations
• Information Systems — 23 citations into it; #4,522 of 8,771 by trust here, #3,198 by citations
• Natural Language Processing — 22 citations into it
• Model Architectures — 21 citations into it

Whom Xiao Huang trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Antoine Bordes
7. Tomas Mikolov
8. Kaiming He
9. Robert Tibshirani
10. Yann LeCun

Who trusts Xiao Huang overall

1. Peng Cui
2. Xia Hu
3. Qingquan Song
4. Daokun Zhang
5. Lizi Liao
6. Palash Goyal
7. Benedek Rozemberczki
8. Jundong Li
9. Xiao Wang
10. Qiaoyu Tan

Papers from Xiao Huang's perspective

Ordered by how strongly the researchers Xiao Huang 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Towards Deeper Graph Neural Networks with Differentiable Group Normalization (2020) — 236 citations
• Accelerated Attributed Network Embedding (2017) — 309 citations
• Auto-GNN: Neural architecture search of graph neural networks (2019) — 217 citations
• Knowledge Graph Embedding Based Question Answering (2019) — 637 citations
• Large-Scale Heterogeneous Feature Embedding (2019) — 30 citations
• Multi-label Adversarial Perturbations (2018) — 43 citations
• Label Informed Attributed Network Embedding (2017) — 497 citations