1 citations · 1 across the 6 of their papers we have counts for
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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…
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…