May D. Wang

Molecular Biology · 2003–2021

A researcher in the Science corpus: 7 papers; trusts 103 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 75 citations into it
• Artificial Intelligence — 69 citations into it; #24,490 of 35,345 by trust here, #27,551 by citations
• Computer Vision and Pattern Recognition — 48 citations into it
• Radiology, Nuclear Medicine and Imaging — 29 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Health Information Management — 8 citations into it
• Medical & Health AI — 8 citations into it
• Model Architectures — 8 citations into it

Whom May D. Wang trusts overall

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

1. Yoshua Bengio
2. Kyunghyun Cho
3. Geoffrey E. Hinton
4. Dzmitry Bahdanau
5. Sepp Hochreiter
6. Alex Krizhevsky
7. Tomas Mikolov
8. Ilya Sutskever
9. Andrej Karpathy
10. Diederik P. Kingma

Who trusts May D. Wang overall

1. Tian Bai

Papers from May D. Wang's perspective

Ordered by how strongly the researchers May D. Wang 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. 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

• A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium (2014) — 938 citations
• GoMiner: a resource for biological interpretation of genomic and proteomic data (2003) — 1,268 citations
• In vivo tumor targeting and spectroscopic detection with surface-enhanced Raman nanoparticle tags (2008) — 1,210 citations
• The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models (2010) — 549 citations
• Multimodal deep learning models for early detection of Alzheimer’s disease stage (2021) — 653 citations
• Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation (2020) — 79 citations
• Interpretable Predictions of Clinical Outcomes with An Attention-based Recurrent Neural Network (2017) — 103 citations