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Found 5 papers, 2 papers with code
On the Evolution of A.I. and Machine Learning: Towards a Meta-level Measuring and Understanding Impact, Influence, and Leadership at Premier A.I. Conferences
This work also contributes, to a certain extent, to shed new light on the history and evolution of AI by exploring the dynamics involved in the field's evolution by looking at papers published at the flagship AI and machine learning conferences since the first International Joint Conference on Artificial Intelligence (IJCAI) held in 1969.
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models
Computational learning theory states that many classes of boolean formulas are learnable in polynomial time.
Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases
The recent developments and growing interest in neural-symbolic models has shown that hybrid approaches can offer richer models for Artificial Intelligence.
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories.
Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems
Our results thus contribute to the standing challenge of integrating robust learning and symbolic reasoning in Deep Learning systems.
