Davide Chicco

Artificial Intelligence · 2014–2023

A researcher in the Science corpus: 10 papers; trusts 114 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #10,538 of 35,345 by trust here, #14,718 by citations
• Statistics and Probability — 61 citations into it
• Computer Vision and Pattern Recognition — 39 citations into it; #18,211 of 21,575 by trust here, #21,103 by citations
• Molecular Biology — 29 citations into it; #2,232 of 17,773 by trust here, #7,041 by citations
• Evaluation & Benchmarks — 26 citations into it
• Information Systems — 22 citations into it; #2,047 of 8,771 by trust here, #1,707 by citations
• Management Science and Operations Research — 18 citations into it
• Model Architectures — 12 citations into it

Whom Davide Chicco trusts overall

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

1. Anne-Laure Boulesteix
2. Eamonn J. Keogh
3. David Martin Ward Powers
4. Pedro M. Domingos
5. Giuseppe Jurman
6. C. Ferro
7. Leo Breiman
8. Matthijs J. Warrens
9. A. Jiménez‐Valverde
10. Nancy R. Cook

Who trusts Davide Chicco overall

1. Giuseppe Jurman
2. Oguzhan Gencoglu
3. Hui Yie Teh
4. Matthijs J. Warrens
5. Margherita Grandini
6. Carter J. Wilson
7. Manfredo Atzori
8. Niklas Tötsch
9. Jafar Tanha
10. Bingzhi Chen

Papers from Davide Chicco's perspective

Ordered by how strongly the researchers Davide Chicco 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Ten quick tips for machine learning in computational biology (2017) — 777 citations
• Bioconda: sustainable and comprehensive software distribution for the life sciences (2018) — 1,435 citations
• Deep autoencoder neural networks for gene ontology annotation predictions (2014) — 196 citations
• The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation (2021) — 793 citations
• The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification (2023) — 542 citations
• The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment (2021) — 331 citations
• Siamese Neural Networks: An Overview (2021) — 623 citations
• The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation (2021) — 4,647 citations
• The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation (2020) — 5,423 citations
• Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone (2020) — 532 citations