Fangfang Yang

Automotive Engineering · 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

• Automotive Engineering — 43 citations into it
• Electrical and Electronic Engineering — 40 citations into it

Whom Fangfang Yang trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Xiaosong Hu
4. Jimmy Ba
5. J. C. Álvarez Antón

Papers from Fangfang Yang's perspective

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

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

Their papers

• SOH Estimation and SOC Recalibration of Lithium-Ion Battery with Incremental Capacity Analysis & Cubic Smoothing Spline (2020) — 52 citations
• A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries (2018) — 294 citations
• Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries (2019) — 299 citations