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07/25/2023
How to Scale Your EMA
Preserving training dynamics across batch sizes is an important tool for...
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06/20/2023
Learning Costs for Structured Monge Displacements
Optimal transport theory has provided machine learning with several tool...
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03/29/2023
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
Orthogonality constraints naturally appear in many machine learning prob...
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02/17/2023
A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
Bilevel optimization problems, which are problems where two optimization...
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02/08/2023
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps
Optimal transport (OT) theory focuses, among all maps T:ℝ^d→ℝ^d that can...
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05/29/2022
Do Residual Neural Networks discretize Neural Ordinary Differential Equations?
Neural Ordinary Differential Equations (Neural ODEs) are the continuous ...
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01/31/2022
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
Bilevel optimization, the problem of minimizing a value function which i...
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10/22/2021
Sinkformers: Transformers with Doubly Stochastic Attention
Attention based models such as Transformers involve pairwise interaction...
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05/20/2021
Kernel Stein Discrepancy Descent
Among dissimilarities between probability distributions, the Kernel Stei...
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02/15/2021
Momentum Residual Neural Networks
The training of deep residual neural networks (ResNets) with backpropaga...
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02/15/2021
Fast and accurate optimization on the orthogonal manifold without retraction
We consider the problem of minimizing a function over the manifold of or...
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11/27/2020
Deep orthogonal linear networks are shallow
We consider the problem of training a deep orthogonal linear network, wh...
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08/21/2020
Spectral independent component analysis with noise modeling for M/EEG source separation
Background: Independent Component Analysis (ICA) is a widespread tool fo...
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02/10/2020
Super-efficiency of automatic differentiation for functions defined as a minimum
In min-min optimization or max-min optimization, one has to compute the ...
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06/04/2019
Manifold-regression to predict from MEG/EEG brain signals without source modeling
Magnetoencephalography and electroencephalography (M/EEG) can reveal neu...
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05/27/2019
Learning step sizes for unfolded sparse coding
Sparse coding is typically solved by iterative optimization techniques, ...
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11/28/2018
Beyond Pham's algorithm for joint diagonalization
The approximate joint diagonalization of a set of matrices consists in f...
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11/06/2018
A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning
Nonnegative matrix factorization (NMF) is a popular method for audio spe...
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06/25/2018
Accelerating likelihood optimization for ICA on real signals
We study optimization methods for solving the maximum likelihood formula...
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05/25/2018
EM algorithms for ICA
Independent component analysis (ICA) is a widely spread data exploration...
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11/29/2017
Faster ICA under orthogonal constraint
Independent Component Analysis (ICA) is a technique for unsupervised exp...
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06/25/2017
