118 citations · 216 across the 30 of their papers we have counts for
5 papers · 1 filter
EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting
George Onoufriou, Marc Hanheide, Georgios Leontidis
We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE compatible neural networks wit…
Fully Homomorphically Encrypted Deep Learning as a Service
George Onoufriou, Paul Mayfield, Georgios Leontidis
Fully Homomorphic Encryption (FHE) is a relatively recent advancement in the field of privacy-preserving technologies. FHE allows for the arbitrary depth computation of both additi…
Hyperspherically Regularized Networks for Self-Supervision
Aiden Durrant, Georgios Leontidis
Bootstrap Your Own Latent (BYOL) introduced an approach to self-supervised learning avoiding the contrastive paradigm and subsequently removing the computational burden of negative…
The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector
Aiden Durrant, Milan Markovic, David Matthews +3
Data sharing remains a major hindering factor when it comes to adopting emerging AI technologies in general, but particularly in the agri-food sector. Protectiveness of data is nat…
Contrastive Domain Adaptation
Mamatha Thota, Georgios Leontidis
Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive…