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20242026
most citedPriPHiT: Privacy-Preserving Hierarchical Training of Deep Neural Networks

1 citations · 1 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.CL2024

NMT-Obfuscator Attack: Ignore a sentence in translation with only one word

Sahar Sadrizadeh, César Descalzo, Ljiljana Dolamic +1

Neural Machine Translation systems are used in diverse applications due to their impressive performance. However, recent studies have shown that these systems are vulnerable to car…

cs.LG2024

Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data

William Cappelletti, Pascal Frossard

Representing and exploiting multivariate signals requires capturing relations between variables, which we can represent by graphs. Graph dictionaries allow to describe complex rela…

cs.LG2024

Deep End-to-End Survival Analysis with Temporal Consistency

Mariana Vargas Vieyra, Pascal Frossard

In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspiration from Reinforcement Learni…

cs.CV2024★ 1 cited

PriPHiT: Privacy-Preserving Hierarchical Training of Deep Neural Networks

Yamin Sepehri, Pedram Pad, Pascal Frossard +1

The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training d…

cs.IT2024

Subgraph Matching via Partial Optimal Transport

Wen-Xin Pan, Isabel Haasler, Pascal Frossard

In this work, we propose a novel approach for subgraph matching, the problem of finding a given query graph in a large source graph, based on the fused Gromov-Wasserstein distance.…