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A New Interpretable Neural Network-Based Rule Model for Healthcare Decision Making
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.
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.
Truth Table Deep Convolutional Neural Network, A New SAT-Encodable Architecture - Application To Complete Robustness
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
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