21 citations · 35 across the 9 of their papers we have counts for
7 papers · 1 filter
Kernel Stein Discrepancy Descent
Anna Korba, Pierre-Cyril Aubin-Frankowski, Szymon Majewski +1
Among dissimilarities between probability distributions, the Kernel Stein Discrepancy (KSD) has received much interest recently. We investigate the properties of its Wasserstein gr…
Deep orthogonal linear networks are shallow
Pierre Ablin
We consider the problem of training a deep orthogonal linear network, which consists of a product of orthogonal matrices, with no non-linearity in-between. We show that training th…
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA
Hugo Richard, Luigi Gresele, Aapo Hyvärinen +3
Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. However, the aggregation of data coming from multip…
Super-efficiency of automatic differentiation for functions defined as a minimum
Pierre Ablin, Gabriel Peyré, Thomas Moreau
In min-min optimization or max-min optimization, one has to compute the gradient of a function defined as a minimum. In most cases, the minimum has no closed-form, and an approxima…
Learning step sizes for unfolded sparse coding
Pierre Ablin, Thomas Moreau, Mathurin Massias +1
Sparse coding is typically solved by iterative optimization techniques, such as the Iterative Shrinkage-Thresholding Algorithm (ISTA). Unfolding and learning weights of ISTA using…
A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning
Pierre Ablin, Dylan Fagot, Herwig Wendt +2
Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing. While NMF is traditionally applied to off-the-shelf time-frequency representations based on…