Hanlin Goh

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 5 papers; trusts 283 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #16,733 of 21,575 by trust here, #14,151 by citations
• Artificial Intelligence — 74 citations into it
• Model Architectures — 38 citations into it
• Reinforcement Learning — 33 citations into it
• Management Science and Operations Research — 13 citations into it
• Aerospace Engineering — 12 citations into it
• Optimization & Training — 11 citations into it
• Computational Mechanics — 10 citations into it; #7,706 of 10,001 by trust here, #9,276 by citations

Whom Hanlin Goh trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Jimmy Ba
5. Yann LeCun
6. Yoshua Bengio
7. Kaiming He
8. Sara Sabour
9. Ilya Sutskever
10. David G. Lowe

Who trusts Hanlin Goh overall

1. Geoffrey E. Hinton
2. Weiwei Sun

Papers from Hanlin Goh's perspective

Ordered by how strongly the researchers Hanlin Goh 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. Gradient-based learning applied to document recognition
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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• DeepHash: Getting Regularization, Depth and Fine-Tuning Right (2015) — 35 citations
• Capsules with Inverted Dot-Product Attention Routing (2020) — 93 citations
• Geometric Capsule Autoencoders for 3D Point Clouds (2019) — 22 citations
• An Attention Free Transformer (2021) — 186 citations
• Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning (2021) — 230 citations