Michelle M. Li

Molecular Biology · 2021

A researcher in the Science corpus: 1 paper; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 56 citations into it
• Artificial Intelligence — 50 citations into it
• Computational Theory and Mathematics — 35 citations into it
• Graph Learning — 33 citations into it
• Materials Chemistry — 18 citations into it
• Representation & Self-Supervised Learning — 17 citations into it

Whom Michelle M. Li trusts overall

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

1. Jure Leskovec
2. Marinka Zitnik
3. William L. Hamilton
4. Aditya Grover
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Diederik P. Kingma
8. Feixiong Cheng
9. José Jiménez-Luna
10. Alex Krizhevsky

Papers from Michelle M. Li's perspective

Ordered by how strongly the researchers Michelle M. Li 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Graph Representation Learning in Biomedicine and Healthcare (2021) — 298 citations