71 citations · 294 across the 49 of their papers we have counts for
12 papers · 1 filter
Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling
Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin +3
This paper introduces von Mises-Fisher exploration (vMF-exp), a scalable method for exploring large action sets in reinforcement learning problems where hyperspherical embedding ve…
New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction
Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas +2
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP). Their performances are less impressive on community detecti…
Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction
Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas +2
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction. Their performances are less impressive on community detection pr…
Explainability in Music Recommender Systems
Darius Afchar, Alessandro B. Melchiorre, Markus Schedl +3
The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing thes…
Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders
Guillaume Salha-Galvan, Romain Hennequin, Benjamin Chapus +2
On an artist's profile page, music streaming services frequently recommend a ranked list of "similar artists" that fans also liked. However, implementing such a feature is challeng…
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
Darius Afchar, Romain Hennequin, Vincent Guigue
Feature attribution is often loosely presented as the process of selecting a subset of relevant features as a rationale of a prediction. Task-dependent by nature, precise definitio…
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…
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…
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…