Synthetic data augmentation helps channel-mixing time series models but degrades channel-independent ones, with reliable gains only from seasonal-trend generators and gradual schedules in low-resource settings.
arXiv preprint arXiv:2009.10897 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2representative citing papers
LogNEO applies PPO to GPT-Neo with a partial-credit exponentially decaying position-aware reward to reach F1 scores of 0.927/0.913/0.984 on HDFS/BGL/Thunderbird while running at production speeds.
citing papers explorer
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Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters
Synthetic data augmentation helps channel-mixing time series models but degrades channel-independent ones, with reliable gains only from seasonal-trend generators and gradual schedules in low-resource settings.
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LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection
LogNEO applies PPO to GPT-Neo with a partial-credit exponentially decaying position-aware reward to reach F1 scores of 0.927/0.913/0.984 on HDFS/BGL/Thunderbird while running at production speeds.