Search Results for author:
Found 4 papers, 0 papers with code
Regularization of Persistent Homology Gradient Computation
Persistent homology is a method for computing the topological features present in a given data.
An End-to-End Graph Convolutional Kernel Support Vector Machine
The SVM feature space mapping consists of a sequence of graph convolutional layers, which generates a vector space representation for each vertex, followed by a pooling layer which generates a reproducing kernel Hilbert space (RKHS) representation for the graph.
Deep Q-Learning for Directed Acyclic Graph Generation
We present a method to generate directed acyclic graphs (DAGs) using deep reinforcement learning, specifically deep Q-learning.
Function Space Pooling For Graph Convolutional Networks
In this article we propose a novel pooling method which maps a set of vertex representations to a function space representation.
Ranked #63 on
Graph Classification
on MUTAG
