Hyeonwoo Noh

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 8 papers; trusts 1,204 researchers and is trusted by 3,426 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 164 citations into it; #597 of 21,575 by trust here, #1,065 by citations
• Artificial Intelligence — 151 citations into it; #3,248 of 35,345 by trust here, #4,797 by citations
• Reinforcement Learning — 57 citations into it
• Model Architectures — 42 citations into it
• Natural Language Processing — 32 citations into it
• Multimodal Learning — 23 citations into it; #235 of 1,211 by trust here, #569 by citations
• Optimization & Training — 19 citations into it
• Aerospace Engineering — 15 citations into it

Whom Hyeonwoo Noh trusts overall

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

1. Kaiming He
2. Karen Simonyan
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Ilya Sutskever
7. Yoshua Bengio
8. Andrew Zisserman
9. Ross B. Girshick
10. Yann LeCun

Who trusts Hyeonwoo Noh overall

1. Kushal Kafle
2. Liang-Chieh Chen
3. Chunhua Shen
4. Seunghoon Hong
5. Christopher Kanan
6. Trevor Darrell
7. Xiaogang Wang
8. Golnaz Ghiasi
9. Bohyung Han
10. Andrea Vedaldi

Papers from Hyeonwoo Noh's perspective

Ordered by how strongly the researchers Hyeonwoo Noh 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Learning Deconvolution Network for Semantic Segmentation (2015) — 4,362 citations
• Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction (2015) — 337 citations
• Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation (2015) — 345 citations
• Large-Scale Image Retrieval with Attentive Deep Local Features (2016) — 860 citations
• Neural Program Synthesis from Diverse Demonstration Videos (2018) — 81 citations
• Training Recurrent Answering Units with Joint Loss Minimization for VQA (2016) — 73 citations
• Exploiting Hierarchy for Learning and Transfer in KL-regularized RL (2019) — 45 citations
• Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization (2017) — 233 citations