Giovanna Menardi

Artificial Intelligence · 2012–2016

A researcher in the Science corpus: 3 papers; trusts 55 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #10,334 of 35,345 by trust here, #24,417 by citations
• Statistics and Probability — 18 citations into it; #10,475 of 12,932 by trust here, #12,603 by citations
• Information Systems — 10 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Accounting — 6 citations into it

Whom Giovanna Menardi trusts overall

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

1. Tarn Duong
2. Leo Breiman
3. Nitesh V. Chawla
4. Dorin Comaniciu
5. Adrian W. Bowman
6. Bernard Walter Silverman
7. Martin L. Hazelton
8. Chris Fraley
9. Jose Emmanuel Chacón
10. Surajit Ray

Who trusts Giovanna Menardi overall

1. Chakkrit Kla Tantithamthavorn
2. Paula Branco
3. Nicola Lunardon
4. Tarid Wongvorachan
5. Alberto Fernández
6. Ahmed E. Hassan
7. Luís Torgo
8. Surina He
9. Ken-ichi Matsumoto
10. Salvador Garcia

Papers from Giovanna Menardi's perspective

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

1. SMOTE: Synthetic Minority Over-sampling Technique
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet classification with deep convolutional neural networks
6. Density Estimation for Statistics and Data Analysis
7. Bagging Predictors
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Long Short-Term Memory
10. Going deeper with convolutions

Their papers

• A Review on Modal Clustering (2016) — 47 citations
• ROSE: a Package for Binary Imbalanced Learning (2014) — 471 citations
• Training and assessing classification rules with imbalanced data (2012) — 738 citations