71 citations · 294 across the 49 of their papers we have counts for
5 papers · 1 filter
Modeling the Music Genre Perception across Language-Bound Cultures
Elena V. Epure, Guillaume Salha, Manuel Moussallam +1
The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures. These variations cannot be modeled as mere…
Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation
Elena V. Epure, Guillaume Salha, Romain Hennequin
Annotating music items with music genres is crucial for music recommendation and information retrieval, yet challenging given that music genres are subjective concepts. Recently, i…
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
Darius Afchar, Romain Hennequin
Explaining recommendations enables users to understand whether recommended items are relevant to their needs and has been shown to increase their trust in the system. More generall…
FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
Guillaume Salha, Romain Hennequin, Jean-Baptiste Remy +2
Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general f…
Simple and Effective Graph Autoencoders with One-Hop Linear Models
Guillaume Salha, Romain Hennequin, Michalis Vazirgiannis
Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks suc…