Shou-de Lin

Artificial Intelligence · 3 papers · 2009–2017

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it
• Information Systems — 14 citations into it
• Optimization & Training — 9 citations into it
• Recommender Systems — 9 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Management Science and Operations Research — 5 citations into it
• Privacy & Security — 5 citations into it

Whom Shou-de Lin trusts overall

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

1. Steffen Rendle
2. Yehuda Koren
3. Peter Filzmoser
4. Max Welling
5. Yoshua Bengio
6. Ruslan Salakhutdinov
7. Martin A. Riedmiller
8. John C. Duchi
9. Tamara G. Kolda
10. Geoffrey E. Hinton

Papers from Shou-de Lin's perspective

Ordered by how strongly the researchers Shou-de Lin 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Towards a More Reliable Privacy-preserving Recommender System (2017) — 39 citations
• Toward Implicit Sample Noise Modeling: Deviation-driven Matrix Factorization (2016)
• An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes (2009) — 17 citations