Search Results for author:
Found 3 papers, 0 papers with code
Neural Network-Based Rule Models With Truth Tables
We also compare the TT-rules framework to state-of-the-art rule-based methods.
TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture
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).
A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
