CSD conference

Recordings of human brain suggest that concepts are represented through sparse sets of neurons that fire together when the concept is activated: we talk about neuronal assemblies. Neuroscientists have identified local learning rules to adjust synaptic weights, but to our knowledge there is no mathematical proof that such local rules enable to learn, nor that they create neuronal assemblies. In this purpose, we propose a spiking neural network named CHANI (Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration), whose neurons activity is modeled by Hawkes processes. Synaptic weights are updated thanks […]

Disentanglement, or identifying salient statistically independent factors of the data, is of interest in many areas of machine learning and statistics, with relevance to synthetic data generation with controlled properties, robust classification of features, parsimonious encoding, and a greater understanding of the generative process underlying the data. Disentanglement arises in several generative paradigms, including Variational Autoencoders (VAEs), Generative Adversarial Networks and diffusion models. Particular progress has recently been made in understanding disentanglement in VAEs, where the choice of diagonal posterior covariance matrices is shown to promote mutual orthogonality between columns […]

Title: Plug-and-Play image restoration with Stochastic deNOising REgularization.   Abstract: Plug-and-Play (PnP) algorithms are a class of iterative algorithms that address image inverse problems by combining a physical model and a deep neural network for regularization. Even if they produce impressive image restoration results, these algorithms rely on a non-standard use of a denoiser on images that are less and less noisy along the iterations, which contrasts with recent algorithms based on Diffusion Models (DM), where the denoiser is applied only on re-noised images. In this presentation, we propose a new PnP […]

Title: Optimal denoising of geometrically regular images with scattering coefficients   Abstract: Optimal suppression of additive Gaussian white noise has many image processing applications and is a key step to generate images with score diffusion algorithms. For images with edges that are piecewise regular, nearly optimal denoisers can be computed by thresholding a sparse representation in a dictionary of curvelets or bandlets. It requires to adapt the support of selected dictionary vectors to geometric image properties. In contrast, convolutional deep neural networks can implement optimal denoising algorithms with a cascade […]