Yanyan Shen

Artificial Intelligence · with Cheng · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it
• Information Systems — 50 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Recommender Systems — 20 citations into it
• Management Science and Operations Research — 15 citations into it
• Optimization & Training — 10 citations into it
• Model Architectures — 8 citations into it
• Efficiency & Compression — 5 citations into it

Whom Yanyan Shen trusts overall

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

1. Diederik P. Kingma
2. Yehuda Koren
3. Xiangnan He
4. Geoffrey E. Hinton
5. Barret Zoph
6. Yoshua Bengio
7. Jimmy Ba
8. James Martens
9. Steffen Rendle
10. Alex Krizhevsky

Who trusts Yanyan Shen overall

1. Raha Moraffah

Papers from Yanyan Shen's perspective

Ordered by how strongly the researchers Yanyan Shen 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. 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Differentiable Neural Input Search for Recommender Systems (2020) — 40 citations
• Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis (2018) — 55 citations
• DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation (2018) — 60 citations