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Found 5 papers, 1 papers with code
Multi-Prompt Fine-Tuning of Foundation Models for Enhanced Medical Image Segmentation
The Segment Anything Model (SAM) is a powerful foundation model that introduced revolutionary advancements in natural image segmentation.
Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label Smoothing
With the development of a series of Galaxy sky surveys in recent years, the observations increased rapidly, which makes the research of machine learning methods for galaxy image recognition a hot topic.
Estimation of stellar atmospheric parameters from LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30
Firstly, this scheme detected stellar atmospheric parameter-sensitive features from spectra by the Least Absolute Shrinkage and Selection Operator (LASSO), rejected ineffective data components and irrelevant data.
Pulsars Detection by Machine Learning with Very Few Features
In this work, two feature selection algorithms ----\textit{Grid Search} (GS) and \textit{Recursive Feature Elimination} (RFE)---- are proposed to improve the detection performance by removing the redundant and irrelevant features.
Linearly Supporting Feature Extraction For Automated Estimation Of Stellar Atmospheric Parameters
On real spectra, we extracted 23 features to estimate $T_{eff}$, 62 features for log$~g$, and 68 features for [Fe/H].
