429 citations · 450 across the 16 of their papers we have counts for
5 papers · 1 filter
R-GAP: Recursive Gradient Attack on Privacy
Junyi Zhu, Matthew Blaschko
Federated learning frameworks have been regarded as a promising approach to break the dilemma between demands on privacy and the promise of learning from large collections of distr…
Discriminative training of conditional random fields with probably submodular constraints
Maxim Berman, Matthew B. Blaschko
Problems of segmentation, denoising, registration and 3D reconstruction are often addressed with the graph cut algorithm. However, solving an unconstrained graph cut problem is NP-…
Scattering Networks for Hybrid Representation Learning
Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang +4
Scattering networks are a class of designed Convolutional Neural Networks (CNNs) with fixed weights. We argue they can serve as generic representations for modelling images. In par…
Yes, IoU loss is submodular - as a function of the mispredictions
Maxim Berman, Matthew B. Blaschko, Amal Rannen Triki +1
This note is a response to [7] in which it is claimed that [13, Proposition 11] is false. We demonstrate here that this assertion in [7] is false, and is based on a misreading of t…
A Note on k-support Norm Regularized Risk Minimization
Matthew Blaschko
The k-support norm has been recently introduced to perform correlated sparsity regularization. Although Argyriou et al. only reported experiments using squared loss, here we apply…