Single-step adversarial training (SSAT) has demonstrated the potential to achieve both efficiency and robustness. However, SSAT suffers from catastrophic overfitting (CO), a phenomenon that leads to a ...
Abstract: In this work, we propose a high-order regularization method to solve the ill-conditioned problems in robot localization. Numerical solutions to robot localization problems are often unstable ...
Regularization in Action: Real-World Examples and Success Stories Introduction Regularization is a technique used in machine learning and statistical modeling to prevent overfitting and improve the ...
Abstract: Adversarial examples are augmented data points generated by imperceptible perturbation of input samples. They have recently drawn much attention with the machine learning and data mining ...
In this respository, we document the numerical examples from our paper Learned Regularization for Inverse Problems: Insights from a Spectral Model. Note If you spot a mistake, encounter any problems, ...
Regularization in Action: Real-World Examples and Success Stories 📢 Exciting news! Check out our latest blog post on "Regularization in Action: Real-World Examples and Success Stories". 🌟 ...
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