Jiannan Wang

Artificial Intelligence · with Franklin · 2012–2018

A researcher in the Science corpus: 13 papers; trusts 60 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 132 citations into it; #6,148 of 35,345 by trust here, #6,266 by citations
• Management Science and Operations Research — 81 citations into it; #1,375 of 11,946 by trust here, #738 by citations
• Computer Science Applications — 54 citations into it; #362 of 1,855 by trust here, #381 by citations
• Computer Networks and Communications — 44 citations into it; #9,691 of 9,886 by trust here, #7,832 by citations
• Information Systems — 44 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Signal Processing — 21 citations into it; #2,461 of 7,572 by trust here, #2,743 by citations
• Evaluation & Benchmarks — 19 citations into it; #1,433 of 3,147 by trust here, #2,062 by citations

Whom Jiannan Wang trusts overall

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

1. Peter Christen
2. Sanjay Krishnan
3. M. Franklin
4. Mikhail Bilenko
5. Eugene Wu
6. Erhard Rahm
7. Adam Marcus
8. Mohamed Yakout
9. A. P. Dawid
10. Samuel Madden

Who trusts Jiannan Wang overall

1. Sanjay Krishnan
2. Ihab F. Ilyas
3. M. Franklin
4. Xu Chu
5. Eugene Wu
6. Ken Goldberg
7. Yeounoh Chung
8. Adam Marcus
9. Marc-André Zöller
10. Moria Bergman

Papers from Jiannan Wang's perspective

Ordered by how strongly the researchers Jiannan Wang 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Et al
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A sample-and-clean framework for fast and accurate query processing on dirty data (2014) — 127 citations
• Data Cleaning: Overview and Emerging Challenges (2016) — 559 citations
• ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016) — 282 citations
• CrowdER: Crowdsourcing Entity Resolution (2012) — 611 citations
• Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views (2015) — 28 citations
• Wisteria: Nurturing Scalable Data Cleaning Infrastructure (2015) — 43 citations
• ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016) — 29 citations
• Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (2014) — 84 citations
• ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning (2016) — 71 citations
• Cleaning Crowdsourced Labels Using Oracles For Statistical Classification (2018) — 39 citations
• PrivateClean: Data Cleaning and Differential Privacy (2016) — 38 citations
• Crowdsourced Data Management: A Survey (2016) — 303 citations
• SampleClean: Fast and Reliable Analytics on Dirty Data (2015) — 61 citations