15 citations · 27 across the 10 of their papers we have counts for
11 papers
Procedural Refinement by LLM-driven Algorithmic Debugging for ARC-AGI-2
Yu-Ning Qiu, Lin-Feng Zou, Jiong-Da Wang +2
In high-complexity abstract reasoning, a system must infer a latent rule from a few examples or structured observations and apply it to unseen instances. LLMs can express such rule…
ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection
Runzhi Deng, Yundi Hu, Xinshuang Zhang +5
Few-shot multi-class industrial anomaly detection identifies diverse defects across multiple categories using a single unified model and limited normal samples. Although vision-lan…
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…
Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection
Wen-Chao Hu, Wang-Zhou Dai, Yuan Jiang +1
Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 with sym…
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks
Lun Ai, Shi-Shun Liang, Wang-Zhou Dai +3
An important application of Synthetic Biology is the engineering of the host cell system to yield useful products. However, an increase in the scale of the host system leads to hug…
Deciphering Raw Data in Neuro-Symbolic Learning with Provable Guarantees
Lue Tao, Yu-Xuan Huang, Wang-Zhou Dai +1
Neuro-symbolic hybrid systems are promising for integrating machine learning and symbolic reasoning, where perception models are facilitated with information inferred from a symbol…
Reduced Implication-bias Logic Loss for Neuro-Symbolic Learning
Haoyuan He, Wangzhou Dai, Ming Li
Integrating logical reasoning and machine learning by approximating logical inference with differentiable operators is a widely used technique in Neuro-Symbolic systems. However, s…
Automated Biodesign Engineering by Abductive Meta-Interpretive Learning
Wang-Zhou Dai, Liam Hallett, Stephen H. Muggleton +1
The application of Artificial Intelligence (AI) to synthetic biology will provide the foundation for the creation of a high throughput automated platform for genetic design, in whi…
Abductive Knowledge Induction From Raw Data
Wang-Zhou Dai, Stephen H. Muggleton
For many reasoning-heavy tasks involving raw inputs, it is challenging to design an appropriate end-to-end learning pipeline. Neuro-Symbolic Learning, divide the process into sub-s…
Tunneling Neural Perception and Logic Reasoning through Abductive Learning
Wang-Zhou Dai, Qiu-Ling Xu, Yang Yu +1
Perception and reasoning are basic human abilities that are seamlessly connected as part of human intelligence. However, in current machine learning systems, the perception and rea…
Inductive Logic Boosting
Wang-Zhou Dai, Zhi-Hua Zhou
Recent years have seen a surge of interest in Probabilistic Logic Programming (PLP) and Statistical Relational Learning (SRL) models that combine logic with probabilities. Structur…