285 citations · 685 across the 12 of their papers we have counts for
13 papers
Neural Networks beyond explainability: Selective inference for sequence motifs
Antoine Villié, Philippe Veber, Yohann de Castro +1
Over the past decade, neural networks have been successful at making predictions from biological sequences, especially in the context of regulatory genomics. As in other fields of…
Convolutional Kernel Networks for Graph-Structured Data
Dexiong Chen, Laurent Jacob, Julien Mairal
We introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods. Our approach generalizes convolutional ker…
Recurrent Kernel Networks
Dexiong Chen, Laurent Jacob, Julien Mairal
Substring kernels are classical tools for representing biological sequences or text. However, when large amounts of annotated data are available, models that allow end-to-end train…
Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed
Laurent Jacob, Johann Gagnon-Bartsch, Terence P. Speed
When dealing with large scale gene expression studies, observations are commonly contaminated by unwanted variation factors such as platforms or batches. Not taking this unwanted v…
More power via graph-structured tests for differential expression of gene networks
Laurent Jacob, Pierre Neuvial, Sandrine Dudoit
We consider multivariate two-sample tests of means, where the location shift between the two populations is expected to be related to a known graph structure. An important applicat…
Group Lasso with Overlaps: the Latent Group Lasso approach
Guillaume Obozinski, Laurent Jacob, Jean-Philippe Vert
We study a norm for structured sparsity which leads to sparse linear predictors whose supports are unions of prede ned overlapping groups of variables. We call the obtained formula…