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Singing Language Identification using a Deep Phonotactic Approach


May 31, 2021
Lenny Renault, Andrea Vaglio, Romain Hennequin

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* ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 271-275 
* 5 pages, 1 figure, ICASSP 2021 

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Towards Rigorous Interpretations: a Formalisation of Feature Attribution


Apr 26, 2021
Darius Afchar, Romain Hennequin, Vincent Guigue

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Modeling the Music Genre Perception across Language-Bound Cultures


Oct 13, 2020
Elena V. Epure, Guillaume Salha, Manuel Moussallam, Romain Hennequin

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* 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020) 

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


Sep 16, 2020
Elena V. Epure, Guillaume Salha, Romain Hennequin

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* 21st International Society for Music Information Retrieval Conference (ISMIR 2020) 

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Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction


Aug 26, 2020
Darius Afchar, Romain Hennequin

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* 14th ACM Conference on Recommender Systems (RecSys '20) 

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FastGAE: Fast, Scalable and Effective Graph Autoencoders with Stochastic Subgraph Decoding


Feb 20, 2020
Guillaume Salha, Romain Hennequin, Jean-Baptiste Remy, Manuel Moussallam, Michalis Vazirgiannis

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Simple and Effective Graph Autoencoders with One-Hop Linear Models


Jan 21, 2020
Guillaume Salha, Romain Hennequin, Michalis Vazirgiannis

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* Under review. A preliminary version of this work has previously been presented at a NeurIPS 2019 workshop without proceedings: arXiv:1910.00942 

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Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks


Oct 02, 2019
Guillaume Salha, Romain Hennequin, Michalis Vazirgiannis

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* NeurIPS 2019 Graph Representation Learning Workshop 

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Improving Collaborative Metric Learning with Efficient Negative Sampling


Sep 24, 2019
Viet-Anh Tran, Romain Hennequin, Jimena Royo-Letelier, Manuel Moussallam

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* SIGIR 2019 

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