Ting Chen

Molecular Biology · with Chen · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 22 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 27 citations into it
• Artificial Intelligence — 23 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Optimization & Training — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Ting Chen trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Tijmen Tieleman
4. Yoshua Bengio
5. Alex Krizhevsky
6. Max Welling
7. Ruslan Salakhutdinov
8. Karen Simonyan
9. Yann LeCun
10. Ilya Sutskever

Papers from Ting Chen's perspective

Ordered by how strongly the researchers Ting Chen trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• DeepEnhancer: Predicting enhancers by convolutional neural networks (2016) — 40 citations
• Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach (2015) — 220 citations
• Patient outcome prediction via convolutional neural networks based on multi-granularity medical concept embedding (2017) — 25 citations