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Found 3 papers, 1 papers with code
DynLLM: When Large Language Models Meet Dynamic Graph Recommendation
To bridge this gap, in this paper, we propose a novel framework, called DynLLM, to deal with the dynamic graph recommendation task with LLMs.
Faithful Abstractive Summarization via Fact-aware Consistency-constrained Transformer
To deal with this challenge, in this paper, we propose a novel fact-aware abstractive summarization model, named Entity-Relation Pointer Generator Network (ERPGN).
A Machine Learning-enhanced Robust P-Phase Picker for Real-time Seismic Monitoring
To this end, in this paper, we present a machine learning-enhanced framework based on ensemble learning strategy, EL-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms.
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