End-to-end training of neural network solvers for combinatorial problems such as the Travelling Salesman Problem is intractable and inefficient beyond a few hundreds of nodes. While state-of-the-art Machine Learning approaches perform closely to …

Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes critical to identify key architectures and validate new ideas that generalize to larger, more complex …

We explore the impact of learning paradigms on training deep neural networks for the Travelling Salesman Problem. We design controlled experiments to train supervised learning (SL) and reinforcement learning (RL) models on fixed graph sizes up to 100 …

The most famous NP-hard combinatorial problem today, the Travelling Salesman Problem, is intractable to solve optimally at large scale. In practice, existing techniques such as Concorde can efficiently solve TSP up to thousands of nodes. This talk …

Scalable deep learning systems for practical NP-Hard combinatorial problems such as the TSP.

In this talk, I will discuss how to apply graph convolutional neural networks to quantum chemistry and operational research. The same high-level paradigm can be applied to generate new molecules with optimized chemical properties and to solve the …

This paper introduces a new learning-based approach for approximately solving the Travelling Salesman Problem on 2D Euclidean graphs. We use deep Graph Convolutional Networks to build efficient TSP graph representations and output tours in a …

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