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What are the two proposed sampling strategies to train machine learning models in the lowest amounts of data?
What are the two proposed sampling strategies to train machine learning models in the lowest amounts of data?
The two proposed sampling strategies to train machine learning models in the lowest amounts of data are not explicitly mentioned in the text.
Why is the need for generating large datasets a roadblock to building machine learning models?
Why is the need for generating large datasets a roadblock to building machine learning models?
The need for generating large datasets is a roadblock to building machine learning models because it is prohibitively expensive and time-consuming.
Which machine learning model was used in this work and how did the proposed sampling strategies improve its performance?
Which machine learning model was used in this work and how did the proposed sampling strategies improve its performance?
Crystal Graph Convolutional Neural Network (CGCNN) was used in this work and the proposed sampling strategies improved its performance by allowing it to reach the benchmark performance in fewer data samples.
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"Unlocking the Potential of Machine Learning in Material Science: Test Your Knowledge on Overcoming Data Challenges!" Are you interested in learning how machine learning is revolutionizing material science? Take our quiz and test your knowledge on the latest advances in ML-based methodologies, and discover how researchers are overcoming the challenges of data collection and analysis to predict the physical properties of materials. From simulated data to experimental data, this quiz covers it all. Sharpen your understanding of the field and become an expert in the latest
