2 citations · 2 across the 5 of their papers we have counts for
6 papers
Differentially private federated learning for localized control of infectious disease dynamics
Raouf Kerkouche, Henrik Zunker, Mario Fritz +1
In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and…
DocMIA: Document-Level Membership Inference Attacks against DocVQA Models
Khanh Nguyen, Raouf Kerkouche, Mario Fritz +1
Document Visual Question Answering (DocVQA) has introduced a new paradigm for end-to-end document understanding, and quickly became one of the standard benchmarks for multimodal LL…
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito +24
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a fede…
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
Derui Zhu, Dingfan Chen, Qing Li +4
Despite tremendous advancements in large language models (LLMs) over recent years, a notably urgent challenge for their practical deployment is the phenomenon of hallucination, whe…
Towards Biologically Plausible and Private Gene Expression Data Generation
Dingfan Chen, Marie Oestreich, Tejumade Afonja +3
Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, how…
Privacy-Aware Document Visual Question Answering
Rubèn Tito, Khanh Nguyen, Marlon Tobaben +11
Document Visual Question Answering (DocVQA) has quickly grown into a central task of document understanding. But despite the fact that documents contain sensitive or copyrighted in…