Deep learning identifies voids in NGC 628 with low association to known star clusters, supporting an evolutionary sequence from molecular clouds via stellar feedback to detached voids.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Traditional ML models outperform a multi-task time series model for churn prediction across multiple datasets and labeling methods.
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Automated void identification by Blendmask: from hierarchical molecular gas to hierarchical voids in NGC 628
Deep learning identifies voids in NGC 628 with low association to known star clusters, supporting an evolutionary sequence from molecular clouds via stellar feedback to detached voids.
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ChurnNet: A Optimized Modern AI for Churn Prediction
Traditional ML models outperform a multi-task time series model for churn prediction across multiple datasets and labeling methods.