Yike Guo

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 351 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 135 citations into it; #18,098 of 35,345 by trust here, #29,412 by citations
• Computer Vision and Pattern Recognition — 83 citations into it
• Optimization & Training — 44 citations into it
• Generative Models — 40 citations into it
• Privacy & Security — 34 citations into it
• Safety, Robustness & Interpretability — 27 citations into it
• Model Architectures — 22 citations into it
• Cognitive Neuroscience — 19 citations into it; #1,527 of 10,852 by trust here, #3,875 by citations

Whom Yike Guo trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yann LeCun
5. Alex Krizhevsky
6. Kaiming He
7. Ilya Sutskever
8. Ruslan Salakhutdinov
9. Sergey Ioffe
10. Christian Szegedy

Who trusts Yike Guo overall

1. Huy P Phan
2. Albert Vilamala
3. Stanislas Chambon
4. Sajad Mousavi
5. Akara Supratak
6. Fernando Andreotti
7. Ruizhe Zhao
8. Oliver Y. Chen
9. Emadeldeen Eldele

Papers from Yike Guo's perspective

Ordered by how strongly the researchers Yike 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Semantic Image Synthesis via Adversarial Learning (2017) — 278 citations
• Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks (2017) — 785 citations
• Privacy preservation in federated learning: An insightful survey from the GDPR perspective (2020) — 368 citations
• DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (2018) — 985 citations
• DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG (2017) — 1,220 citations
• Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks (2016) — 331 citations
• DropNeuron: Simplifying the Structure of Deep Neural Networks (2016) — 40 citations
• Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders (2015) — 330 citations