Heng Luo

Artificial Intelligence · with Bengio · 2012–2017

A researcher in the Science corpus: 2 papers; trusts 254 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #14,183 of 35,345 by trust here, #15,866 by citations
• Natural Language Processing — 41 citations into it; #2,643 of 5,688 by trust here, #3,282 by citations
• Computer Vision and Pattern Recognition — 36 citations into it; #8,873 of 21,575 by trust here, #9,563 by citations
• Model Architectures — 13 citations into it
• Optimization & Training — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Heng Luo trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Radford M. Neal
5. Yann LeCun
6. Ilya Sutskever
7. Diederik P. Kingma
8. Marc'Aurelio Ranzato
9. Kaiming He
10. Tijmen Tieleman

Who trusts Heng Luo overall

1. Yoshua Bengio
2. Yujia Li
3. Reza Ghaeini
4. Yi Tay
5. Jason Baldridge
6. Zhiquan Ye
7. Kyunghyun Cho
8. Yixin Nie
9. Jie Zhou
10. Daniel Tarlow

Papers from Heng Luo's perspective

Ordered by how strongly the researchers Heng Luo 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. 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Natural Language Inference over Interaction Space (2017) — 279 citations
• Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions (2012) — 25 citations