Summary of Uncertainty Estimation Via Ensembles Of Deep Learning Models and Dropout Layers For Seismic Traces, by Giovanni Messuti et al.
Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces
by Giovanni Messuti, ortensia Amoroso, Ferdinando Napolitano, Mariarosaria Falanga, Paolo Capuano, Silvia Scarpetta
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle two significant challenges in deep learning: classifying seismic waveforms and estimating model uncertainty. To address these issues, they develop Convolutional Neural Networks (CNNs) to classify seismic waveforms based on first-motion polarity. They train multiple CNN models with different settings and construct ensembles of networks to estimate uncertainty. The results show that each training setting achieves satisfactory performances, but the ensemble method outperforms individual networks in uncertainty estimation. Furthermore, they find that enhancing the uncertainty estimation ability of ensembles using dropout layers improves robustness to mislabeled examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how deep learning models can be used to classify seismic waveforms and estimate model uncertainty. The researchers use Convolutional Neural Networks (CNNs) to classify seismic waveforms based on first-motion polarity. They train multiple CNN models with different settings and combine them to get a better result. This helps them figure out how certain the model is about its answers. They also find that using something called dropout layers can make their model more robust to mistakes in the data. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Dropout
