PipeWeave predicts GPU kernel performance with 6.1% average error and end-to-end inference with 8.5% error by feeding analytical pipeline features into ML, cutting prior method errors by 4-7x across 11 GPUs.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
FAME achieves F1 of 98.16 on BGL and 99.95 on Thunderbird for message-level log anomaly detection using at most K=100 labels per template, reducing annotation effort by 76x while detecting anomalies from unseen EventIDs.
citing papers explorer
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PipeWeave: Synergizing Analytical and Learning Models for Unified GPU Performance Prediction
PipeWeave predicts GPU kernel performance with 6.1% average error and end-to-end inference with 8.5% error by feeding analytical pipeline features into ML, cutting prior method errors by 4-7x across 11 GPUs.
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FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection
FAME achieves F1 of 98.16 on BGL and 99.95 on Thunderbird for message-level log anomaly detection using at most K=100 labels per template, reducing annotation effort by 76x while detecting anomalies from unseen EventIDs.