A Scalable, Interpretable, Verifiable & Differentiable Logic Gate
Convolutional Neural Network Architecture From Truth Tables

A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables

18 August 2022

Adrien Benamira

Tristan Guérand

Bryan Hooi

Papers citing "A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables"

7 / 7 papers shown

Title
Logic-Based Explainability in Machine Learning João Marques-Silva LRM XAI 31 39 0 24 Oct 2022
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 139 684 0 31 Jan 2021
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 134 654 0 28 Dec 2020
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 217 674 0 19 Oct 2020
Exploiting Verified Neural Networks via Floating Point Numerical Error Kai Jia Martin Rinard AAML 27 34 0 06 Mar 2020
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 46 196 0 06 Apr 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 222 1,832 0 03 Feb 2017