LoRM is a self-supervised framework that models multi-modal rotating machinery signals as token sequences for prediction with fine-tuned language models, using prediction errors to monitor machine health in real time.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A new reliability score computed from the IoU difference between class-specific and class-agnostic heatmaps, boosted by adversarial enhancement, detects false negatives in binary industrial defect detectors with up to 100% recall.
D-Legion proposes a scalable architecture of Legions containing adaptive-precision systolic array cores that accelerates quantized LLM matrix multiplications, delivering up to 8.2x lower latency and 3.8x higher memory savings versus prior designs.
GenPOI is a generative POI retrieval system that unifies heterogeneous contexts via LLMs, uses geo-semantic tokenization, and applies proximity constraints to achieve superior performance on large-scale map search data.
citing papers explorer
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LoRM: Learning the Language of Rotating Machinery for Self-Supervised Condition Monitoring
LoRM is a self-supervised framework that models multi-modal rotating machinery signals as token sequences for prediction with fine-tuned language models, using prediction errors to monitor machine health in real time.
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When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide
A new reliability score computed from the IoU difference between class-specific and class-agnostic heatmaps, boosted by adversarial enhancement, detects false negatives in binary industrial defect detectors with up to 100% recall.
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D-Legion: A Scalable Many-Core Architecture for Accelerating Matrix Multiplication in Quantized LLMs
D-Legion proposes a scalable architecture of Legions containing adaptive-precision systolic array cores that accelerates quantized LLM matrix multiplications, delivering up to 8.2x lower latency and 3.8x higher memory savings versus prior designs.
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Revisiting General Map Search via Generative Point-of-Interest Retrieval
GenPOI is a generative POI retrieval system that unifies heterogeneous contexts via LLMs, uses geo-semantic tokenization, and applies proximity constraints to achieve superior performance on large-scale map search data.