Bowen Liu

Artificial Intelligence · with Pande · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 75 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 137 citations into it; #23,871 of 35,345 by trust here, #8,810 by citations
• Computational Theory and Mathematics — 104 citations into it; #6,355 of 12,182 by trust here, #898 by citations
• Graph Learning — 91 citations into it; #298 of 693 by trust here, #140 by citations
• Molecular Biology — 90 citations into it; #8,049 of 17,773 by trust here, #6,028 by citations
• Computational Chemistry & Drug Discovery — 79 citations into it
• Materials Chemistry — 67 citations into it; #2,283 of 11,513 by trust here, #2,723 by citations
• Representation & Self-Supervised Learning — 56 citations into it; #2,543 of 3,145 by trust here, #1,415 by citations
• Computer Vision and Pattern Recognition — 48 citations into it

Whom Bowen Liu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Sergey Ioffe
7. Oriol Vinyals
8. Thomas Kipf
9. William L. Hamilton
10. Max Welling

Who trusts Bowen Liu overall

1. Shitong Luo
2. Chence Shi
3. Wengong Jin
4. Weihua Hu
5. Vijay Prakash Dwivedi
6. William L. Hamilton
7. Brooke E. Husic
8. Yuning You
9. Kyle Swanson
10. Jiaxuan You

Papers from Bowen Liu's perspective

Ordered by how strongly the researchers Bowen Liu 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Strategies for Pre-training Graph Neural Networks (2019) — 1,844 citations
• Is Multitask Deep Learning Practical for Pharma? (2017) — 241 citations
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (2018) — 1,038 citations
• Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models (2017) — 486 citations
• Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020) — 3,715 citations
• Pre-training Graph Neural Networks (2019) — 52 citations