Weng-Keen Wong

Artificial Intelligence · with Burnett · 2009–2019

A researcher in the Science corpus: 12 papers; trusts 16 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #7,846 of 35,345 by trust here, #4,137 by citations
• Computer Vision and Pattern Recognition — 59 citations into it
• Information Systems and Management — 30 citations into it
• Human-Computer Interaction — 20 citations into it; #554 of 2,253 by trust here, #187 by citations
• Generative Models — 18 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Statistical Learning Theory — 10 citations into it
• Statistics and Probability — 8 citations into it

Whom Weng-Keen Wong trusts overall

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

1. Brian Y. Lim
2. Anind K. Dey
3. Walter J. Scheirer
4. Yiming Yang
5. Todd Kulesza
6. D. Norman
7. Eric R. Ziegel
8. Geoffrey E. Hinton
9. Yoshua Bengio
10. Diederik P. Kingma

Who trusts Weng-Keen Wong overall

1. Giulia Vilone
2. Todd Kulesza
3. Daniel S. Weld
4. Piyawat Lertvittayakumjorn
5. Sina Mohseni
6. Minsuk Kahng
7. Kacper Sokol
8. Longo Luca
9. Shane T. Mueller
10. Tim Miller

Papers from Weng-Keen Wong's perspective

Ordered by how strongly the researchers Weng-Keen Wong trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. GENERATIVE ADVERSARIAL NETS
6. Et al
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Interacting meaningfully with machine learning systems: Three experiments (2009) — 225 citations
• Principles of Explanatory Debugging to Personalize Interactive Machine Learning (2015) — 692 citations
• Open Set Learning with Counterfactual Images (2018) — 537 citations
• Too much, too little, or just right? Ways explanations impact end users' mental models (2013) — 427 citations
• The eBird enterprise: An integrated approach to development and application of citizen science (2014) — 1,001 citations
• Counterfactual States for Atari Agents via Generative Deep Learning (2019) — 28 citations
• A Meta-Analysis of the Anomaly Detection Problem (2015) — 96 citations
• You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems (2014) — 76 citations
• Data-intensive science applied to broad-scale citizen science. (2012) — 379 citations
• Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach (2011) — 21 citations
• Why-oriented end-user debugging of naive Bayes text classification (2011) — 99 citations
• Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs (2010) — 115 citations