71 citations · 294 across the 49 of their papers we have counts for
6 papers · 1 filter
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
Guillaume Salha, Romain Hennequin, Michalis Vazirgiannis
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link predic…
Improving Collaborative Metric Learning with Efficient Negative Sampling
Viet-Anh Tran, Romain Hennequin, Jimena Royo-Letelier +1
Distance metric learning based on triplet loss has been applied with success in a wide range of applications such as face recognition, image retrieval, speaker change detection and…
Leveraging Knowledge Bases And Parallel Annotations For Music Genre Translation
Elena V. Epure, Anis Khlif, Romain Hennequin
Prevalent efforts have been put in automatically inferring genres of musical items. Yet, the propose solutions often rely on simplifications and fail to address the diversity and s…
Singing voice separation: a study on training data
Laure Prétet, Romain Hennequin, Jimena Royo-Letelier +1
In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial fact…
Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Guillaume Salha, Stratis Limnios, Romain Hennequin +2
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackl…
A Degeneracy Framework for Scalable Graph Autoencoders
Guillaume Salha, Romain Hennequin, Viet Anh Tran +1
In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE). This framework leverages graph degeneracy concepts to train…