13 citations · 13 across the 2 of their papers we have counts for
6 papers
Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models
Xiao-Wen Yang, Jie-Jing Shao, Lan-Zhe Guo +5
Large Language Models (LLMs) have shown promising results across various tasks, yet their reasoning capabilities remain a fundamental challenge. Developing AI systems with strong r…
Curriculum Abductive Learning
Wen-Chao Hu, Qi-Jie Li, Lin-Han Jia +4
Abductive Learning (ABL) integrates machine learning with logical reasoning in a loop: a learning model predicts symbolic concept labels from raw inputs, which are revised through…
Detecting Scarce and Sparse Anomalous: Solving Dual Imbalance in Multi-Instance Learning
Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou +3
In real-world applications, it is highly challenging to detect anomalous samples with extremely sparse anomalies, as they are highly similar to and thus easily confused with normal…
Verification Learning: Make Unsupervised Neuro-Symbolic System Feasible
Lin-Han Jia, Wen-Chao Hu, Jie-Jing Shao +2
The current Neuro-Symbolic (NeSy) Learning paradigm suffers from an over-reliance on labeled data, so if we completely disregard labels, it leads to less symbol information, a larg…
A Smooth Transition Between Induction and Deduction: Fast Abductive Learning Based on Probabilistic Symbol Perception
Lin-Han Jia, Si-Yu Han, Lan-Zhe Guo +4
Abductive learning (ABL) that integrates strengths of machine learning and logical reasoning to improve the learning generalization, has been recently shown effective. However, its…
Robust Semi-Supervised Learning in Open Environments
Lan-Zhe Guo, Lin-Han Jia, Jie-Jing Shao +1
Semi-supervised learning (SSL) aims to improve performance by exploiting unlabeled data when labels are scarce. Conventional SSL studies typically assume close environments where i…