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Found 6 papers, 3 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.
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 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.
Typed Graph Networks
To illustrate the generality of the original model, we present a Graph Neural Network formalisation, which partitions the vertices of a graph into a number of types.
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network
We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded.
Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP
Our model is trained to function as an effective message-passing algorithm in which edges (embedded with their weights) communicate with vertices for a number of iterations after which the model is asked to decide whether a route with cost $<C$ exists.
