Search Results for author:
Found 5 papers, 4 papers with code
Are Graph Neural Networks Optimal Approximation Algorithms?
In this work we design graph neural network architectures that capture optimal approximation algorithms for a large class of combinatorial optimization problems, using powerful algorithmic tools from semidefinite programming (SDP).
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects.
Combinatorial Optimization
Vocal Bursts Intensity Prediction
Neural Extensions: Training Neural Networks with Set Functions
Our framework includes well-known extensions such as the Lovasz extension of submodular set functions and facilitates the design of novel continuous extensions based on problem-specific considerations, including constraints.
Partition and Code: learning how to compress graphs
Can we use machine learning to compress graph data?
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances.
