Search Results for author: Guillaume Salha

Found 10 papers, 8 papers with code

Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation

1 code implementation16 Sep 2020 Elena V. Epure, Guillaume Salha, Romain Hennequin

However, without a parallel corpus, previous solutions could not handle tag systems in other languages, being limited to the English-language only.

Information Retrieval Music Recommendation +4

Carousel Personalization in Music Streaming Apps with Contextual Bandits

1 code implementation14 Sep 2020 Walid Bendada, Guillaume Salha, Théo Bontempelli

Media services providers, such as music streaming platforms, frequently leverage swipeable carousels to recommend personalized content to their users.

Multi-Armed Bandits

Muzeeglot : annotation multilingue et multi-sources d'entit\'es musicales \`a partir de repr\'esentations de genres musicaux (Muzeeglot : cross-lingual multi-source music item annotation from music genre embeddings)

no code implementations JEPTALNRECITAL 2020 Elena V. Epure, Guillaume Salha, F{\'e}lix Voituret, Marion Baranes, Romain Hennequin

Au sein de cette d{\'e}monstration, nous pr{\'e}sentons Muzeeglot, une interface web permettant de visualiser des espaces de repr{\'e}sentations de genres musicaux provenant de sources vari{\'e}es et de langues diff{\'e}rentes.

FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding

2 code implementations5 Feb 2020 Guillaume Salha, Romain Hennequin, Jean-Baptiste Remy, Manuel Moussallam, Michalis Vazirgiannis

Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues.

Simple and Effective Graph Autoencoders with One-Hop Linear Models

1 code implementation21 Jan 2020 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 such as link prediction and node clustering.

Link Prediction Node Clustering

Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks

1 code implementation2 Oct 2019 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 prediction and node clustering.

Link Prediction Node Clustering

Gravity-Inspired Graph Autoencoders for Directed Link Prediction

3 code implementations23 May 2019 Guillaume Salha, Stratis Limnios, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods.

Link Prediction

A Degeneracy Framework for Scalable Graph Autoencoders

1 code implementation23 Feb 2019 Guillaume Salha, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis

In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE).

Adaptive Submodular Influence Maximization with Myopic Feedback

no code implementations23 Apr 2017 Guillaume Salha, Nikolaos Tziortziotis, Michalis Vazirgiannis

This paper examines the problem of adaptive influence maximization in social networks.

Social and Information Networks

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