Jennifer G. Dy

Artificial Intelligence · Journal of machine learning research · 2000–2017

A researcher in the Science corpus: 7 papers; trusts 26 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it; #20,843 of 35,345 by trust here, #31,361 by citations
• Evaluation & Benchmarks — 12 citations into it
• Statistics and Probability — 12 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #9,165 of 21,575 by trust here, #16,787 by citations
• Statistical Learning Theory — 9 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Jennifer G. Dy trusts overall

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

1. Geoffrey J. McLachlan
2. Ron Kohavi
3. Arthur P. Dempster
4. Hirotugu Akaike
5. Igor Kononenko
6. George H. John
7. Robert Tibshirani
8. Dan Pelleg
9. Anil K. Jain
10. Jerome H. Friedman

Who trusts Jennifer G. Dy overall

1. Zheng Zhao
2. Huan Liu
3. Feiping Nie
4. Salem Alelyani
5. Deng Cai
6. Jiliang Tang
7. Xuelian Deng
8. Manoranjan Dash
9. Jie Cai
10. A. Adam Ding

Papers from Jennifer G. Dy's perspective

Ordered by how strongly the researchers Jennifer G. Dy 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. The Nature of Statistical Learning Theory

Their papers

• Visualization and interactive feature selection for unsupervised data (2000) — 70 citations
• Monitoring Motor Fluctuations in Patients with Parkinson’s Disease Using Wearable Sensors (2009) — 606 citations
• Convex Principal Feature Selection (2010) — 55 citations
• A Robust-Equitable Measure for Feature Ranking and Selection (2017) — 49 citations
• Active Learning from Crowds (2011) — 354 citations
• Impact of imputation of missing values on classification error for discrete data (2008) — 375 citations
• Feature Selection for Unsupervised Learning (2004) — 1,094 citations