
The Transformer Network for the Traveling Salesman Problem
The Traveling Salesman Problem (TSP) is the most popular and most studie...
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An Experimental Study of the Transferability of Spectral Graph Networks
Spectral graph convolutional networks are generalizations of standard co...
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A Generalization of Transformer Networks to Graphs
We propose a generalization of transformer neural network architecture f...
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Learning TSP Requires Rethinking Generalization
Endtoend training of neural network solvers for combinatorial problems...
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Benchmarking Graph Neural Networks
Graph neural networks (GNNs) have become the standard toolkit for analyz...
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MultiGraph Transformer for FreeHand Sketch Recognition
Learning meaningful representations of freehand sketches remains a chal...
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On Learning Paradigms for the Travelling Salesman Problem
We explore the impact of learning paradigms on training deep neural netw...
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A TwoStep Graph Convolutional Decoder for Molecule Generation
We propose a simple autoencoder framework for molecule generation. The ...
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An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
This paper introduces a new learningbased approach for approximately so...
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GraphTSNE: A Visualization Technique for GraphStructured Data
We present GraphTSNE, a novel visualization technique for graphstructur...
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Residual Gated Graph ConvNets
Graphstructured data such as functional brain networks, social networks...
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Geometric Matrix Completion with Recurrent MultiGraph Neural Networks
Matrix completion models are among the most common formulations of recom...
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Structured Sequence Modeling with Graph Convolutional Recurrent Networks
This paper introduces Graph Convolutional Recurrent Network (GCRN), a de...
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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
In this work, we are interested in generalizing convolutional neural net...
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Song Recommendation with NonNegative Matrix Factorization and Graph Total Variation
This work formulates a novel song recommender system as a matrix complet...
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Enhanced Lasso Recovery on Graph
This work aims at recovering signals that are sparse on graphs. Compress...
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Robust Principal Component Analysis on Graphs
Principal Component Analysis (PCA) is the most widely used tool for line...
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Functional correspondence by matrix completion
In this paper, we consider the problem of finding dense intrinsic corres...
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Consistency of Cheeger and Ratio Graph Cuts
This paper establishes the consistency of a family of graphcutbased al...
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Matrix Completion on Graphs
The problem of finding the missing values of a matrix given a few of its...
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Multiclass Total Variation Clustering
Ideas from the image processing literature have recently motivated a new...
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Enhanced Compressed Sensing Recovery with Level Set Normals
We propose a compressive sensing algorithm that exploits geometric prope...
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Xavier Bresson
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Computer scientist, interested in AI, deep learning, and data science. Teach deep learning and standard machine learning techniques at University and for companies.