Gary M. Weiss

Computer Vision and Pattern Recognition · 2001–2019

A researcher in the Science corpus: 6 papers; trusts 24 researchers and is trusted by 157 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 20 citations into it; #4,537 of 21,575 by trust here, #7,092 by citations
• Artificial Intelligence — 19 citations into it; #1,868 of 35,345 by trust here, #4,923 by citations
• Biomedical Engineering — 14 citations into it; #448 of 10,734 by trust here, #3,200 by citations
• Statistical Learning Theory — 9 citations into it
• Information Systems — 7 citations into it; #165 of 8,771 by trust here, #786 by citations
• Electrical and Electronic Engineering — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Gary M. Weiss trusts overall

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

1. Charles Peter Elkan
2. Andrew P. Bradley
3. Foster J. Provost
4. William W. Cohen
5. J. Ross Quinlan
6. Nitesh V. Chawla
7. Nathalie Japkowicz
8. Robert E. Schapire
9. Philip Ka-Fai Chan
10. C. Drummond

Who trusts Gary M. Weiss overall

1. Kush R. Varshney
2. Alberto Fernández
3. Taghi M. Khoshgoftaar
4. Salvador Garcia
5. Nitesh V. Chawla
6. Jonathan Burez
7. Zhi-Hua Zhou
8. Andrea Dal Pozzolo
9. Francisco Herrera
10. Rukshan Batuwita

Papers from Gary M. Weiss's perspective

Ordered by how strongly the researchers Gary M. Weiss trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Et al
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. C4.5: Programs for Machine Learning

Their papers

• Mining with rarity: a unifying framework (2004) — 1,509 citations
• Activity recognition using cell phone accelerometers (2011) — 2,977 citations
• Cell phone-based biometric identification (2010) — 303 citations
• Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living (2019) — 298 citations
• Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction (2003) — 1,022 citations
• The effect of class distribution on classifier learning: an empirical study (2001) — 531 citations