no code implementations • 20 Sep 2023 • Adrien Benamira, Tristan Guerand, Thomas Peyrin
In this study, we introduce a neural network framework, $\textit{Truth Table rules}$ (TT-rules), that combines the global and exact interpretability properties of rule-based models with the high performance of deep neural networks.
no code implementations • 18 Sep 2023 • Adrien Benamira, Tristan Guérand, Thomas Peyrin, Hans Soegeng
We also compare the TT-rules framework to state-of-the-art rule-based methods.
no code implementations • 3 Feb 2023 • Adrien Benamira, Tristan Guérand, Thomas Peyrin, Sayandeep Saha
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 recent family of convolutional neural networks called Truth-Table Neural Networks (TTnet).
no code implementations • 18 Aug 2022 • Adrien Benamira, Tristan Guérand, Thomas Peyrin, Trevor Yap, Bryan Hooi
We propose $\mathcal{T}$ruth $\mathcal{T}$able net ($\mathcal{TT}$net), a novel Convolutional Neural Network (CNN) architecture that addresses, by design, the open challenges of interpretability, formal verification, and logic gate conversion.
no code implementations • 29 Sep 2021 • Adrien Benamira, Thomas Peyrin, Bryan Hooi
Moreover, the corresponding SAT conversion method intrinsically leads to formulas with a large number of variables and clauses, impeding interpretability as well as formal verification scalability.
Explainable Artificial Intelligence (XAI) Explanation Generation +1