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6 papers · 2 filters
Variational Graph Contrastive Learning
Shifeng Xie, Jhony H. Giraldo
Graph representation learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-supervised…
Activation Map Compression through Tensor Decomposition for Deep Learning
Le-Trung Nguyen, Aël Quélennec, Enzo Tartaglione +2
Internet of Things and Deep Learning are synergetically and exponentially growing industrial fields with a massive call for their unification into a common framework called Edge AI…
Long-time asymptotics of noisy SVGD outside the population limit
Victor Priser, Pascal Bianchi, Adil Salim
Stein Variational Gradient Descent (SVGD) is a widely used sampling algorithm that has been successfully applied in several areas of Machine Learning. SVGD operates by iteratively…
Winner-takes-all learners are geometry-aware conditional density estimators
Victor Letzelter, David Perera, Cédric Rommel +4
Winner-takes-all training is a simple learning paradigm, which handles ambiguous tasks by predicting a set of plausible hypotheses. Recently, a connection was established between W…
Gegenbauer Graph Neural Networks for Time-varying Signal Reconstruction
Jhon A. Castro-Correa, Jhony H. Giraldo, Mohsen Badiey +1
Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from mi…
Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
Paul Krzakala, Junjie Yang, Rémi Flamary +3
We propose Any2graph, a generic framework for end-to-end Supervised Graph Prediction (SGP) i.e. a deep learning model that predicts an entire graph for any kind of input. The frame…