Yimin Liu

Information Systems · with Klampfl · 2015–2017

A researcher in the Science corpus: 4 papers; trusts 19 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 40 citations into it; #8,352 of 8,771 by trust here, #4,518 by citations
• Artificial Intelligence — 37 citations into it; #19,611 of 35,345 by trust here, #13,760 by citations
• Computational Theory and Mathematics — 23 citations into it
• Statistical Learning Theory — 21 citations into it
• Algorithms & Complexity — 16 citations into it
• Economics and Econometrics — 12 citations into it
• Safety, Robustness & Interpretability — 11 citations into it; #1,441 of 2,251 by trust here, #1,110 by citations
• Signal Processing — 9 citations into it

Whom Yimin Liu trusts overall

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

1. Cynthia Rudin
2. Kenneth Train
3. Christian Borgelt
4. William W. Cohen
5. Benjamin Letham
6. Fulton Wang
7. Florian Heiss
8. Jerome H. Friedman
9. Daniel McFadden
10. Tyler H. McCormick

Who trusts Yimin Liu overall

1. Tong Wang
2. Nadia Burkart
3. Giulia Vilone
4. Cynthia Rudin
5. Christopher D. Higgins

Papers from Yimin Liu's perspective

Ordered by how strongly the researchers Yimin Liu 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. A theory of the learnable
8. The magical number seven plus or minus two: some limits on our capacity for processing information.
9. The CN2 Induction Algorithm
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015) — 41 citations
• Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China (2015) — 518 citations
• A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017) — 265 citations
• Bayesian Rule Sets for Interpretable Classification (2016) — 62 citations