Search Results for author:
Found 12 papers, 8 papers with code
Spectral Norm of Convolutional Layers with Circular and Zero Paddings
This paper leverages the use of \emph{Gram iteration} an efficient, deterministic, and differentiable method for computing spectral norm with an upper bound guarantee.
The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
The certified radius in this context is a crucial indicator of the robustness of models.
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
Since the control of the Lipschitz constant has a great impact on the training stability, generalization, and robustness of neural networks, the estimation of this value is nowadays a real scientific challenge.
End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities
In brain-computer interfaces (BCI), most of the approaches based on event-related potential (ERP) focus on the detection of P300, aiming for single trial classification for a speller task.
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning
Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI).
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
This paper addresses the structurally-constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries.
Online SSVEP-based BCI using Riemannian geometry
We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible.
From Euclidean to Riemannian Means: Information Geometry for SSVEP Classification
Brain Computer Interfaces (BCI) based on electroencephalog-raphy (EEG) rely on multichannel brain signal processing.
On the need for metrics in dictionary learning assessment
Dictionary-based approaches are the focus of a growing attention in the signal processing community, often achieving state of the art results in several application fields.
Subspace metrics for multivariate dictionaries and application to EEG
Overcomplete representations and dictionary learning algorithms are attracting a growing interest in the machine learning community.
Multi-dimensional sparse structured signal approximation using split Bregman iterations
An extensive empirical evaluation shows how the proposed approach compares to the state of the art depending on the signal features.
Metrics for Multivariate Dictionaries
Despite a recurrent need to rely on a distance for learning or assessing multivariate overcomplete representations, no metrics in their underlying spaces have yet been proposed.
