We introduce DeepProbLog, a probabilistic logic programming language that incorporates
deep learning by means of neural predicates. We show how existing inference and learning …

Integrating logical reasoning within deep learning architectures has been a major goal of
modern AI systems. In this paper, we propose a new direction toward this goal by …

We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both
inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as …

In the history of artificial intelligence research, machine learning and logical reasoning have
almost been separately developed. It is often argued that advanced intelligent technologies …

Reinforcement learning is a major tool to realize intelligent agents that can be autonomously
adaptive to the environment. With deep models, reinforcement learning has shown great …

Large language models (LLMs) such as ChatGPT have recently demonstrated significant
potential in mathematical abilities, providing valuable reasoning paradigm consistent with …

Selecting proper descriptors, also known as features, is one of the key problems in modeling
for materials properties using machine learning models. Redundant features reduce …

The ability to conduct logical reasoning is a fundamental aspect of intelligent human
behavior, and thus an important problem along the way to human-level artificial intelligence …

Deep Learning architectures can develop feature representations and classification models
in an integrated way during training. This joint learning process requires large networks with …

Numerous neuro-symbolic approaches have recently been proposed typically with the goal
of adding symbolic knowledge to the output layer of a neural network. Ideally, such losses …