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Found 10 papers, 1 papers with code
Solving the Kidney-Exchange Problem via Graph Neural Networks with No Supervision
The proposed technique consists of two main steps: the first is a Graph Neural Network (GNN) trained without supervision; the second is a deterministic non-learned search heuristic that uses the output of the GNN to find paths and cycles.
A Neural Lambda Calculus: Neurosymbolic AI meets the foundations of computing and functional programming
To study the capabilities of neural networks in the symbolic AI domain, researchers have explored the ability of deep neural networks to learn mathematical constructions, such as addition and multiplication, logic inference, such as theorem provers, and even the execution of computer programs.
Graph-based Neural Modules to Inspect Attention-based Architectures: A Position Paper
Encoder-decoder architectures are prominent building blocks of state-of-the-art solutions for tasks across multiple fields where deep learning (DL) or foundation models play a key role.
Neurosymbolic AI: The 3rd Wave
Current advances in Artificial Intelligence (AI) and Machine Learning (ML) have achieved unprecedented impact across research communities and industry.
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.
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.
Discrete and Continuous Deep Residual Learning Over Graphs
In this paper we propose the use of continuous residual modules for graph kernels in Graph Neural Networks.
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems.
Machine Learning in Network Centrality Measures: Tutorial and Outlook
In this tutorial, we explain how the use of neural network learning algorithms can render the application of the metrics in complex networks of arbitrary size.
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation.
