Graph Neural Networks

In this talk, I will discuss a graph convolutional neural network architecture for the molecule generation task. The proposed approach consists of two steps. First, a graph ConvNet is used to auto-encode molecules in one-shot. Second, beam search is …

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

Chemical synthesis, structure and property prediction using deep neural networks.

Graph Neural Network architectures for inductive representation learning on arbitrary graphs.

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 …

We present GraphTSNE, a novel visualization technique for graph-structured data based on t-SNE. The growing interest in graph-structured data increases the importance of gaining human insight into such datasets by means of visualization. However, …

Convolutional neural networks have greatly improved state-of-the-art performances in computer vision and speech analysis tasks, due to its high ability to extract multiple levels of representations of data. In this talk, we are interested in …

Text classification is the task of labeling text data from a predetermined set of thematic labels. It has become of increasing importance in recent years as we generate large volumes of data and require the ability to search through these vast …

Graph-structured data such as social networks, functional brain networks, chemical molecules have brought the interest in generalizing deep learning techniques to graph domains. In this work, we propose an empirical study of neural networks for …