Build and train Lipschitz constrained networks: TensorFlow implementation of k-Lipschitz layers
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Updated
Mar 14, 2025 - Python
Build and train Lipschitz constrained networks: TensorFlow implementation of k-Lipschitz layers
Constrained deep learning is an advanced approach to training deep neural networks by incorporating domain-specific constraints into the learning process.
Code for paper "ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks"
Quantum faireness verifying algorithm based on calculating Lipschitz constant, an implementaion of paper: https://arxiv.org/pdf/2207.11173.pdf💿
Reference implementation for fixed-threshold fairness auditing and ReLiF multi-task learning experiments
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