3 papers
cs.CL2025
Selection of LLM Fine-Tuning Data based on Orthogonal Rules
Xiaomin Li, Mingye Gao, Zhiwei Zhang +2
High-quality training data is critical to the performance of large language models (LLMs). Recent work has explored using LLMs to rate and select data based on a small set of human…
cs.LG2025
Learning Interpretable Differentiable Logic Networks for Time-Series Classification
Chang Yue, Niraj K. Jha
Differentiable logic networks (DLNs) have shown promising results in tabular domains by combining accuracy, interpretability, and computational efficiency. In this work, we apply D…
cs.LG2025
Learning Interpretable Differentiable Logic Networks for Tabular Regression
Chang Yue, Niraj K. Jha
Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be computationally expensive in re…