Jianjun Hu

Materials Chemistry · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 17 citations into it

Whom Jianjun Hu trusts

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

1. Chris Wolverton (in Materials Chemistry)
2. Alok N. Choudhary (in Materials Chemistry)
3. Xavier Glorot (in Optimization & Training)

Papers from Jianjun Hu's perspective

Ordered by how strongly the researchers Jianjun Hu 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. Deep Learning
3. Understanding the difficulty of training deep feedforward neural networks
4. Fully convolutional networks for semantic segmentation
5. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
6. Prototypical Networks for Few-shot Learning
7. Siamese Neural Networks for One-Shot Image Recognition
8. One-shot learning of object categories
9. Scikit-learn: Machine Learning in Python
10. Self-Normalizing Neural Networks

Their papers

• Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation (2020) — 300 citations
• Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019) — 349 citations
• A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images (2018) — 125 citations