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Found 9 papers, 2 papers with code
$\mathbf{\mathbb{E}^{FWI}}$: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data.
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications.
Impossibility of Depth Reduction in Explainable Clustering
Formally, we show that there exists a data set X in the Euclidean plane, for which there is a decision tree of depth k-1 whose k-means/k-median cost matches the optimal clustering cost of X, but every decision tree of depth less than k-1 has unbounded cost w. r. t.
On the Robustness and Generalization of Deep Learning Driven Full Waveform Inversion
For robustness, we prove the upper bounds of the deviation between the predictions from clean and noisy data.
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion
The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community.
On the Global Self-attention Mechanism for Graph Convolutional Networks
Applying Global Self-attention (GSA) mechanism over features has achieved remarkable success on Convolutional Neural Networks (CNNs).
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations
Variational Autoencoders (VAEs) are powerful in data representation inference, but it cannot learn relations between features with its vanilla form and common variations.
Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification
In addition, the proposed method has other preferred properties such as special advantages in dealing with highly imbalanced data, and it pioneers the research on the regularization for dynamic ensemble methods.
Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost
The paper presents Imbalance-XGBoost, a Python package that combines the powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced classification tasks.
