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03/10/2023
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
When dealing with electro or magnetoencephalography records, many superv...
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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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10/10/2022
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels
Temporal point processes (TPP) are a natural tool for modeling event-bas...
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06/29/2022
Data augmentation for learning predictive models on EEG: a systematic comparison
The use of deep learning for electroencephalography (EEG) classification...
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06/10/2022
PAVI: Plate-Amortized Variational Inference
Given some observed data and a probabilistic generative model, Bayesian ...
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02/04/2022
Deep invariant networks with differentiable augmentation layers
Designing learning systems which are invariant to certain data transform...
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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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12/08/2021
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals
The quantitative analysis of non-invasive electrophysiology signals from...
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06/25/2021
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals
Data augmentation is a key element of deep learning pipelines, as it inf...
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06/11/2021
Dictionary and prior learning with unrolled algorithms for unsupervised inverse problems
Inverse problems consist in recovering a signal given noisy observations...
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06/01/2021
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models
In recent years, implicit deep learning has emerged as a method to incre...
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02/12/2021
Leveraging Global Parameters for Flow-based Neural Posterior Estimation
Inferring the parameters of a stochastic model based on experimental obs...
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10/19/2020
Learning to solve TV regularized problems with unrolled algorithms
Total Variation (TV) is a popular regularization strategy that promotes ...
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07/03/2020
Neumann networks: differential programming for supervised learning with missing values
The presence of missing values makes supervised learning much more chall...
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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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05/27/2019
Learning step sizes for unfolded sparse coding
Sparse coding is typically solved by iterative optimization techniques, ...
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01/26/2019
Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals
Convolutional dictionary learning (CDL) estimates shift invariant basis ...
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05/24/2018
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
Frequency-specific patterns of neural activity are traditionally interpr...
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06/02/2017
Understanding the Learned Iterative Soft Thresholding Algorithm with matrix factorization
Sparse coding is a core building block in many data analysis and machine...
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05/29/2017
Distributed Convolutional Sparse Coding
We consider the problem of building shift-invariant representations for ...
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11/14/2016
Post Training in Deep Learning with Last Kernel
One of the main challenges of deep learning methods is the choice of an ...
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09/01/2016