G. Régnier

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 6 citations into it
• Statistics and Probability — 5 citations into it

Whom G. Régnier trusts

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

1. G. De’ath (in Nature and Landscape Conservation)
2. S. Menard (in Economics and Econometrics)

Papers from G. Régnier's perspective

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

1. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
3. A working guide to boosted regression trees.
4. Three-dimensional transient finite element analysis of the selective laser sintering process
5. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
6. Absorptance of powder materials suitable for laser sintering
7. Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches (Applied Sciences)
8. Polymer Interface and Adhesion
9. Coefficients of Determination for Multiple Logistic Regression Analysis
10. Boosted trees for ecological modeling and prediction. (Ecology)

Their papers

• Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees (2019) — 79 citations
• Experimental and numerical analysis of the selective laser sintering (SLS) of PA12 and PEKK semi-crystalline polymers (2015) — 144 citations