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1 code implementation • 27 Jun 2022 • Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré La Tour, Ghislain Durif, Cassio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoit Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter

Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.

no code implementations • 29 May 2022 • Michael E. Sander, Pierre Ablin, Gabriel Peyré

As a byproduct of our analysis, we consider the use of a memory-free discrete adjoint method to train a ResNet by recovering the activations on the fly through a backward pass of the network, and show that this method theoretically succeeds at large depth if the residual functions are Lipschitz with the input.

1 code implementation • 31 Jan 2022 • Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau

However, computing the gradient of the value function involves solving a linear system, which makes it difficult to derive unbiased stochastic estimates.

1 code implementation • NeurIPS 2021 • Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen

While ShICA-J is based on second-order statistics, we further propose to leverage non-Gaussianity of the components using a maximum-likelihood method, ShICA-ML, that is both more accurate and more costly.

1 code implementation • 22 Oct 2021 • Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

We show that the row-wise stochastic attention matrices in classical Transformers get close to doubly stochastic matrices as the number of epochs increases, justifying the use of Sinkhorn normalization as an informative prior.

1 code implementation • 20 May 2021 • Anna Korba, Pierre-Cyril Aubin-Frankowski, Szymon Majewski, Pierre Ablin

We investigate the properties of its Wasserstein gradient flow to approximate a target probability distribution $\pi$ on $\mathbb{R}^d$, known up to a normalization constant.

no code implementations • 22 Feb 2021 • Hugo Richard, Pierre Ablin, Aapo Hyvärinen, Alexandre Gramfort, Bertrand Thirion

By contrast, we propose Adaptive multiView ICA (AVICA), a noisy ICA model where each view is a linear mixture of shared independent sources with additive noise on the sources.

1 code implementation • 15 Feb 2021 • Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

We show on CIFAR and ImageNet that Momentum ResNets have the same accuracy as ResNets, while having a much smaller memory footprint, and show that pre-trained Momentum ResNets are promising for fine-tuning models.

Ranked #119 on Image Classification on CIFAR-10

1 code implementation • 15 Feb 2021 • Pierre Ablin, Gabriel Peyré

We consider the problem of minimizing a function over the manifold of orthogonal matrices.

no code implementations • 27 Nov 2020 • 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.

no code implementations • 21 Aug 2020 • Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

Signals are modelled as a linear mixing of independent sources corrupted by additive noise, where sources and the noise are stationary Gaussian time series.

1 code implementation • NeurIPS 2020 • Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin

Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization.

no code implementations • 25 May 2020 • Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein

As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have ballooned in recent years.

no code implementations • ICML 2020 • Pierre Ablin, Gabriel Peyré, Thomas Moreau

In most cases, the minimum has no closed-form, and an approximation is obtained via an iterative algorithm.

1 code implementation • NeurIPS 2019 • David Sabbagh, Pierre Ablin, Gael Varoquaux, Alexandre Gramfort, Denis A. Engemann

We show that Wasserstein and geometric distances allow perfect out-of-sample prediction on the generative models.

1 code implementation • NeurIPS 2019 • Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort

We demonstrate that for a large class of unfolded algorithms, if the algorithm converges to the solution of the Lasso, its last layers correspond to ISTA with learned step sizes.

1 code implementation • 28 Nov 2018 • Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

The approximate joint diagonalization of a set of matrices consists in finding a basis in which these matrices are as diagonal as possible.

1 code implementation • 6 Nov 2018 • Pierre Ablin, Dylan Fagot, Herwig Wendt, Alexandre Gramfort, Cédric Févotte

Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing.

no code implementations • 25 Jun 2018 • Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

We study optimization methods for solving the maximum likelihood formulation of independent component analysis (ICA).

1 code implementation • 25 May 2018 • Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis Bach

We derive an online algorithm for the streaming setting, and an incremental algorithm for the finite sum setting, with the following benefits.

1 code implementation • 29 Nov 2017 • Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences.

2 code implementations • 25 Jun 2017 • Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data that is widely used in observational sciences.

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