24 citations · 76 across the 10 of their papers we have counts for
12 papers
Adversarially-Trained Nonnegative Matrix Factorization
Ting Cai, Vincent Y. F. Tan, Cédric Févotte
We consider an adversarially-trained version of the nonnegative matrix factorization, a popular latent dimensionality reduction technique. In our formulation, an attacker adds an a…
Second-order step-size tuning of SGD for non-convex optimization
Camille Castera, Jérôme Bolte, Cédric Févotte +1
In view of a direct and simple improvement of vanilla SGD, this paper presents a fine-tuning of its step-sizes in the mini-batch case. For doing so, one estimates curvature, based…
Neural content-aware collaborative filtering for cold-start music recommendation
Paul Magron, Cédric Févotte
State-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from the available listening data.…
Expanding boundaries of Gap Safe screening
Cassio F. Dantas, Emmanuel Soubies, Cédric Févotte
Sparse optimization problems are ubiquitous in many fields such as statistics, signal/image processing and machine learning. This has led to the birth of many iterative algorithms…
Phase retrieval with Bregman divergences: Application to audio signal recovery
Pierre-Hugo Vial, Paul Magron, Thomas Oberlin +1
Phase retrieval aims to recover a signal from magnitude or power spectra measurements. It is often addressed by considering a minimization problem involving a quadratic cost functi…
Leveraging the structure of musical preference in content-aware music recommendation
Paul Magron, Cédric Févotte
State-of-the-art music recommendation systems are based on collaborative filtering, which predicts a user's interest from his listening habits and similarities with other users' pr…
Phase recovery with Bregman divergences for audio source separation
Paul Magron, Pierre-Hugo Vial, Thomas Oberlin +1
Time-frequency audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a phase recovery algori…
Phase retrieval with Bregman divergences and application to audio signal recovery
Pierre-Hugo Vial, Paul Magron, Thomas Oberlin +1
Phase retrieval (PR) aims to recover a signal from the magnitudes of a set of inner products. This problem arises in many audio signal processing applications which operate on a sh…
Positive Semidefinite Matrix Factorization: A Connection with Phase Retrieval and Affine Rank Minimization
Dana Lahat, Yanbin Lang, Vincent Y. F. Tan +1
Positive semidefinite matrix factorization (PSDMF) expresses each entry of a nonnegative matrix as the inner product of two positive semidefinite (psd) matrices. When all these psd…
Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing
Valentin Leplat, Nicolas Gillis, Cédric Févotte
Many datasets are obtained as a resolution trade-off between two adversarial dimensions; for example between the frequency and the temporal resolutions for the spectrogram of an au…
Ordinal Non-negative Matrix Factorization for Recommendation
Olivier Gouvert, Thomas Oberlin, Cédric Févotte
We introduce a new non-negative matrix factorization (NMF) method for ordinal data, called OrdNMF. Ordinal data are categorical data which exhibit a natural ordering between the ca…
Nonnegative Matrix Factorization with Transform Learning
Dylan Fagot, Cédric Févotte, Herwig Wendt
Traditional NMF-based signal decomposition relies on the factorization of spectral data, which is typically computed by means of short-time frequency transform. In this paper we pr…