2 citations · 2 across the 5 of their papers we have counts for
5 papers
AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models
Adil Hasan, Thomas Peyrin
Significant investments have been made towards the commodification of diffusion models for generation of diverse media. Their mass-market adoption is however still hobbled by the i…
A New Interpretable Neural Network-Based Rule Model for Healthcare Decision Making
Adrien Benamira, Tristan Guerand, Thomas Peyrin
In healthcare applications, understanding how machine/deep learning models make decisions is crucial. In this study, we introduce a neural network framework, $\textit{Truth Table r…
Neural Network-Based Rule Models With Truth Tables
Adrien Benamira, Tristan Guérand, Thomas Peyrin +1
Understanding the decision-making process of a machine/deep learning model is crucial, particularly in security-sensitive applications. In this study, we introduce a neural network…
TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture
Adrien Benamira, Tristan Guérand, Thomas Peyrin +1
This paper presents TT-TFHE, a deep neural network Fully Homomorphic Encryption (FHE) framework that effectively scales Torus FHE (TFHE) usage to tabular and image datasets using a…
A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables
Adrien Benamira, Tristan Guérand, Thomas Peyrin +2
We propose ruth able net (net), a novel Convolutional Neural Network (CNN) architecture that addresses, by design, the open challenges of in…