Jing Huo

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 159 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Optimization & Training — 9 citations into it

Whom Jing Huo trusts overall

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

1. Sebastian Thrun
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Alex Krizhevsky
7. Oriol Vinyals
8. Yann LeCun
9. Gregory R. Koch
10. Jimmy Ba

Papers from Jing Huo's perspective

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

Their papers

• Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning (2019) — 591 citations
• Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning (2019) — 249 citations
• Introduction of manganese based lithium-ion Sieve-A review (2020) — 200 citations
• Online Deep Metric Learning (2018) — 11 citations