1 citations · 1 across the 6 of their papers we have counts for
6 papers
Random features for Grassmannian kernel approximation with bounded rank-one projections
Rémi Delogne, Laurent Jacques
We propose a family of random feature maps for scalable kernel machines on low-dimensional subspaces, ie on the Grassmannian manifold. Such representations are useful when data cla…
Random Wavelet Features for Graph Kernel Machines
Valentin de Bassompierre, Jean-Charles Delvenne, Laurent Jacques
Node embeddings map graph vertices into low-dimensional Euclidean spaces while preserving structural information. They are central to tasks such as node classification, link predic…
Compressive Spectral Imaging in View of Earth Observation Applications
Clément Thomas, Laurent Jacques, Marc Georges
Earth observation from space is an important scientific and industrial activity that has applications in many sectors. The instruments employed are often large, complex, and expens…
Random Features for Grassmannian Kernels
Rémi Delogne, Laurent Jacques
The Grassmannian manifold G(k, n) serves as a fundamental tool in signal processing, computer vision, and machine learning, where problems often involve classifying, clustering, or…
An Alternating Minimization Algorithm with Trajectory for Direct Exoplanet Detection -- The AMAT Algorithm
Hazan Daglayan, Simon Vary, Olivier Absil +6
Effective image post-processing algorithms are vital for the successful direct imaging of exoplanets. Standard PSF subtraction methods use techniques based on a low-rank approximat…
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
Alexander Stollenwerk, Laurent Jacques
We propose a novel algorithm for distributed stochastic gradient descent (SGD) with compressed gradient communication in the parameter-server framework. Our gradient compression te…