Hongyu Guo

Artificial Intelligence · with Liu · 2004–2021

A researcher in the Science corpus: 7 papers; trusts 421 researchers and is trusted by 71 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it; #5,024 of 35,345 by trust here, #9,170 by citations
• Computer Vision and Pattern Recognition — 70 citations into it; #9,839 of 21,575 by trust here, #12,419 by citations
• Natural Language Processing — 57 citations into it; #1,430 of 5,688 by trust here, #2,767 by citations
• Representation & Self-Supervised Learning — 49 citations into it
• Optimization & Training — 26 citations into it; #2,098 of 2,601 by trust here, #1,715 by citations
• Model Architectures — 25 citations into it; #546 of 2,278 by trust here, #1,285 by citations
• Generative Models — 13 citations into it
• Computational Chemistry & Drug Discovery — 10 citations into it

Whom Hongyu Guo trusts overall

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

1. Geoffrey E. Hinton
2. Yoon Kim
3. Diederik P. Kingma
4. Yann LeCun
5. Alex Krizhevsky
6. Yoshua Bengio
7. Ilya Sutskever
8. Alex Graves
9. J. Urgen Schmidhuber
10. Ruslan Salakhutdinov

Who trusts Hongyu Guo overall

1. Nitesh V. Chawla
2. Tal Schuster
3. Andrea Dal Pozzolo
4. Shuo Wang
5. Rukshan Batuwita
6. Benjamin X. Wang
7. Nathalie Japkowicz
8. Alberto Fernández
9. Jang-Hyun Kim
10. Yanmin Sun

Papers from Hongyu Guo's perspective

Ordered by how strongly the researchers Hongyu Guo 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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

• MixUp as Locally Linear Out-Of-Manifold Regularization (2018) — 353 citations
• A Deep Network with Visual Text Composition Behavior (2017) — 3 citations
• Nonlinear Mixup: Out-Of-Manifold Data Augmentation for Text Classification (2020) — 125 citations
• Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach (2004) — 659 citations
• Pre-training Molecular Graph Representation with 3D Geometry (2021) — 466 citations
• End-to-End Multi-View Networks for Text Classification (2017) — 12 citations
• Long Short-Term Memory Over Recursive Structures (2015) — 347 citations