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Found 6 papers, 3 papers with code
A Transformer Model for Symbolic Regression towards Scientific Discovery
Once trained, we apply our best model to the SRSD datasets (Symbolic Regression for Scientific Discovery datasets) which yields state-of-the-art results using the normalized tree-based edit distance, at no extra computational cost.
Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density Approach
We compare our method with previous data imputation methods using artificial and real-world data with different data missing scenarios and various data missing rates, and show that our method can cope with complex original data structure, yields lower data imputation errors, and provides probabilistic estimates with higher likelihood than current methods.
Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
Understanding the long-term evolution of hierarchical triple systems is challenging due to its inherent chaotic nature, and it requires computationally expensive simulations.
Tabular Data Imputation: Choose KNN over Deep Learning
As databases are ubiquitous nowadays, missing values constitute a pervasive problem for data analysis.
On the dissection of degenerate cosmologies with machine learning
Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos.
Distinguishing standard and modified gravity cosmologies with machine learning
We present a convolutional neural network to identify distinct cosmological scenarios based on the weak-lensing maps they produce.
Cosmology and Nongalactic Astrophysics
