AIR amortizes 2D Gaussian splatting into a self-supervised feed-forward network via residual stages, explicit stage control, and Predict-Optimize-Distill training.
Principal component analysis,
4 Pith papers cite this work. Polarity classification is still indexing.
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Post-release defects concentrate in older, frequently modified high-churn components and require longer and more complex fixes than pre-release defects.
T-BiGAN integrates window-attention Transformers in a BiGAN to achieve ROC-AUC 0.95 and average precision 0.996 for unsupervised spatiotemporal anomaly detection in PMU data.
Supervised models using 83 metrics achieve 0.85-0.9 recall for post-release Python faults, outperforming LLMs, with process metrics and code size most predictive and metrics plus embeddings capturing complementary information.
citing papers explorer
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AIR: Amortized Image Reconstruction Framework for Self-Supervised Feed-Forward 2D Gaussian Splatting
AIR amortizes 2D Gaussian splatting into a self-supervised feed-forward network via residual stages, explicit stage control, and Predict-Optimize-Distill training.
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What Makes Software Bugs Escape Testing? Evidence from a Large-Scale Empirical Study
Post-release defects concentrate in older, frequently modified high-churn components and require longer and more complex fixes than pre-release defects.
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Unsupervised Detection of Spatiotemporal Anomalies in PMU Data Using Transformer-Based BiGAN
T-BiGAN integrates window-attention Transformers in a BiGAN to achieve ROC-AUC 0.95 and average precision 0.996 for unsupervised spatiotemporal anomaly detection in PMU data.
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Will It Break in Production? Metric-Driven Prediction of Residual Defects in Python Systems
Supervised models using 83 metrics achieve 0.85-0.9 recall for post-release Python faults, outperforming LLMs, with process metrics and code size most predictive and metrics plus embeddings capturing complementary information.