Ken Gu

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it
• Graph Learning — 48 citations into it
• Computer Vision and Pattern Recognition — 37 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Statistical and Nonlinear Physics — 24 citations into it
• Computational Theory and Mathematics — 16 citations into it
• Molecular Biology — 16 citations into it
• Computer Networks and Communications — 8 citations into it

Whom Ken Gu trusts overall

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

1. Kaspar Riesen
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Thomas Kipf
5. Horst Bunke
6. Yoshua Bengio
7. Max Welling
8. L. V. D. van der Maaten
9. Alex Krizhevsky
10. Ilya Sutskever

Papers from Ken Gu's perspective

Ordered by how strongly the researchers Ken Gu 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. Attention is All you Need (Neural Information Processing Systems)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
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

• Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity (2019) — 10 citations
• Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (2019) — 86 citations