Abstract Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and
regression tasks in Big Data. Researchers should be familiar with the strengths and …

The family of gradient boosting algorithms has been recently extended with several
interesting proposals (ie XGBoost, LightGBM and CatBoost) that focus on both speed and …

Strong gravitational lenses provide unique laboratories for cosmological and astrophysical
investigations, but they must first be discovered–a task that can be met with significant …

In-time perception of changing geological conditions is crucial for safe and efficient TBM
tunneling. Precisely detecting or predicting the rock mass qualities ahead of the tunnel face …

Based on features extracted from Raman spectra, regularization algorithms, SVR, DT, RF,
LightGBM, CatBoost, and XGBoost were used to develop prediction models for lignin …

As part of an on-going effort to identify, understand and correct for astrophysics biases in the
standardization of Type Ia supernovae (SN Ia) for cosmology, we have statistically classified …

We report new high-quality galaxy-scale strong lens candidates found in the Kilo-Degree
Survey data release 4 using machine learning. We have developed a new convolutional …

We present 97 new high-quality strong lensing candidates found in the final∼ 350 deg 2
that complete the full∼ 1350 deg 2 area of the Kilo-Degree Survey (KiDS). Together with our …

We present the first results from a new survey for high-redshift (z≳ 5) gravitationally lensed
quasars and close quasar pairs. We carry out candidate selection based on the colors and …

Quasars experiencing strong lensing offer unique viewpoints on subjects related to the
cosmic expansion rate, the dark matter profile within the foreground deflectors, and the …