BerLU constructs a C1-differentiable activation with Lipschitz constant 1 via Bernstein polynomial approximation, showing better performance and efficiency than baselines on image classification with ViTs and CNNs.
Lipschitz constant estimation of neural networks via sparse polynomial optimization
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Zubov-Net aligns prescribed regions of attraction defined by learnable Lyapunov functions with true regions in Neural ODEs via a differentiable Zubov consistency loss, claiming to reconcile accuracy and certified robustness.
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Universal Smoothness via Bernstein Polynomials: A Constructive Approximation Approach for Activation Functions
BerLU constructs a C1-differentiable activation with Lipschitz constant 1 via Bernstein polynomial approximation, showing better performance and efficiency than baselines on image classification with ViTs and CNNs.
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Learning Aligned Stability in Neural ODEs Reconciling Accuracy with Robustness
Zubov-Net aligns prescribed regions of attraction defined by learnable Lyapunov functions with true regions in Neural ODEs via a differentiable Zubov consistency loss, claiming to reconcile accuracy and certified robustness.