time-series-1
Non-Adversarial Robustness
3260 papers • 126 benchmarks • 313 datasets
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Most implemented papers
Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering Tasks
Zihao Wang, P. S. Pravin, Zhen Wu•Sun Jan 14 2024
This work introduces a novel network architecture, termed Input Convex Lipschitz Recurrent Neural Networks (ICLRNNs), which seamlessly integrates the benefits of convexity and Lipschitz continuity, enabling fast and robust neural network-based modeling and optimization.
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