Tien-Thinh Le

Artificial Intelligence · 2019–2020

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 — 12 citations into it
• Statistics and Probability — 7 citations into it

Whom Tien-Thinh Le trusts overall

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

1. G. De’ath
2. S. Menard

Papers from Tien-Thinh Le's perspective

Ordered by how strongly the researchers Tien-Thinh Le 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. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
5. Artificial Neural Network Methods for the Solution of Second Order Boundary Value Problems (Computers Materials & Continua)
6. Coefficients of Determination for Multiple Logistic Regression Analysis
7. Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction (Expert systems with applications)
8. Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches (Applied Sciences)
9. Polymer Interface and Adhesion
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
• A spatially explicit deep learning neural network model for the prediction of landslide susceptibility (2020) — 294 citations