55 citations · 92 across the 9 of their papers we have counts for
9 papers
Structure As Search: Unsupervised Permutation Learning for Combinatorial Optimization
Yimeng Min, Carla P. Gomes
We propose a non-autoregressive framework for the Travelling Salesman Problem where solutions emerge directly from learned permutations, without requiring explicit search. By apply…
Unsupervised Ordering for Maximum Clique
Yimeng Min, Carla P. Gomes
We propose an unsupervised approach for learning vertex orderings for the maximum clique problem by framing it within a permutation-based framework. We transform the combinatorial…
On Size and Hardness Generalization in Unsupervised Learning for the Travelling Salesman Problem
Yimeng Min, Carla P. Gomes
We study the generalization capability of Unsupervised Learning in solving the Travelling Salesman Problem (TSP). We use a Graph Neural Network (GNN) trained with a surrogate loss…
Unsupervised Learning for Solving the Travelling Salesman Problem
Yimeng Min, Yiwei Bai, Carla P. Gomes
We propose UTSP, an unsupervised learning (UL) framework for solving the Travelling Salesman Problem (TSP). We train a Graph Neural Network (GNN) using a surrogate loss. The GNN ou…
Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?
Yimeng Min, Frederik Wenkel, Michael Perlmutter +1
We propose a geometric scattering-based graph neural network (GNN) for approximating solutions of the NP-hard maximum clique (MC) problem. We construct a loss function with two ter…
Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks
Frederik Wenkel, Yimeng Min, Matthew Hirn +2
Geometric deep learning has made great strides towards generalizing the design of structure-aware neural networks from traditional domains to non-Euclidean ones, giving rise to gra…
Geometric Scattering Attention Networks
Yimeng Min, Frederik Wenkel, Guy Wolf
Geometric scattering has recently gained recognition in graph representation learning, and recent work has shown that integrating scattering features in graph convolution networks…
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
Yimeng Min, Frederik Wenkel, Guy Wolf
Graph convolutional networks (GCNs) have shown promising results in processing graph data by extracting structure-aware features. This gave rise to extensive work in geometric deep…
Predicting ice flow using machine learning
Yimeng Min, S. Karthik Mukkavilli, Yoshua Bengio
Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and clima…