Beyond Graph Neural Networks with Lifted Relational Neural Networks

Explore a declarative differentiable programming framework based on Lifted Relational Neural Networks in this 15-minute conference talk. Delve into how small parameterized logic programs encode relational learning scenarios, covering topics such as symbolic vs. statistical AI, deep learning, relational representations, and statistical relational learning. Learn about declarative ANN encoding, multi-layer perceptrons, convolutional networks, and graph neural networks. Discover how this framework extends beyond traditional GNNs, offering insights into lossless model compression and increased expressiveness through atom rings. Access related resources, including the original paper, framework, and blog posts, to further expand your understanding of this cutting-edge approach in neuro-symbolic AI.