Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …

Abstract Abstract Visual Reasoning (AVR) problems are commonly used to approximate
human intelligence. They test the ability of applying previously gained knowledge …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

Large language models (LLMs) have revolutionized many areas (eg natural language
processing, software engineering, etc.) by achieving state-of-the-art performance on …

Deep neural network-based methods have recently enjoyed great popularity due to their
effectiveness in solving difficult tasks. Requiring minimal human effort, they have turned into …

Recently, research has increasingly focused on developing efficient neural network
architectures. In this work, we explore logic gate networks for machine learning tasks by …

Abstract We propose Neuro-Symbolic Hierarchical Rule Induction, an efficient interpretable
neuro-symbolic model, to solve Inductive Logic Programming (ILP) problems. In this model …

Rule-based models, eg, decision trees, are widely used in scenarios demanding high model
interpretability for their transparent inner structures and good model expressivity. However …

Neural-symbolic learning, aiming to combine the perceiving power of neural perception and
the reasoning power of symbolic logic together, has drawn increasing research attention …

The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …