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Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discovery
Xiaoxuan Li, Yao Liu, Ruoyu Wang +1
As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from obser…
Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs
Ruoyu Wang, Xiaoxuan Li, Lina Yao
Large Language Models (LLMs) have demonstrated remarkable efficiency in tackling various tasks based on human instructions, but studies reveal that they often struggle with tasks r…
Independence Constrained Disentangled Representation Learning from Epistemological Perspective
Ruoyu Wang, Lina Yao
Disentangled Representation Learning aims to improve the explainability of deep learning methods by training a data encoder that identifies semantically meaningful latent variables…
Causality-Aware Transformer Networks for Robotic Navigation
Ruoyu Wang, Yao Liu, Yuanjiang Cao +1
Current research in Visual Navigation reveals opportunities for improvement. First, the direct adoption of RNNs and Transformers often overlooks the specific differences between Em…