RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.
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ShuffleGate learns polarized importance gates by measuring model sensitivity to random component shuffling, unifying feature selection, dimension selection, and embedding compression with SOTA results on four recommendation benchmarks.
ProReFiCIA uses LLMs with tailored prompts to identify impacted requirements, achieving 85.7% recall on unseen industrial data while requiring review of only 3% of requirements, rising to 95.7% recall with RAG at 3.6% review cost.
citing papers explorer
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RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases
RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.
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ShuffleGate: A Unified Gating Mechanism for Feature Selection, Model Compression, and Importance Estimation
ShuffleGate learns polarized importance gates by measuring model sensitivity to random component shuffling, unifying feature selection, dimension selection, and embedding compression with SOTA results on four recommendation benchmarks.
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LLM-Driven Cost-Effective Requirements Change Impact Analysis
ProReFiCIA uses LLMs with tailored prompts to identify impacted requirements, achieving 85.7% recall on unseen industrial data while requiring review of only 3% of requirements, rising to 95.7% recall with RAG at 3.6% review cost.