Search Results for author:
Found 6 papers, 4 papers with code
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.
Neural Networks Models for Analyzing Magic: the Gathering Cards
Historically, games of all kinds have often been the subject of study in scientific works of Computer Science, including the field of machine learning.
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.
Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate
We translate these sentences into English using the Google Translate API, and collect statistics about the frequency of female, male and gender-neutral pronouns in the translated output.
