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Found 4 papers, 3 papers with code
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning
Machine-Generated Text (MGT) detection, a task that discriminates MGT from Human-Written Text (HWT), plays a crucial role in preventing misuse of text generative models, which excel in mimicking human writing style recently.
Unify Local and Global Information for Top-$N$ Recommendation
To tackle this research gap, we propose a novel duet representation learning framework named \sysname to fuse local information (user-item interaction data) and global information (external knowledge graph) for the top-$N$ recommendation, which is composed of two separate sub-models.
Machine learning formation enthalpies of intermetallics
The model uses easily accessible elemental properties as descriptors and has a mean absolute error (MAE) of 0. 025 eV/atom in predicting the formation enthalpy of stable binary intermetallics reported in the Materials Project database.
Materials Science
Does DetectGPT Fully Utilize Perturbation? Bridge Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better
Hence, we propose a novel fine-tuned detector, Pecola, bridging metric-based and fine-tuned detectors by contrastive learning on selective perturbation.
