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 ...

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 ...

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". 🌟 ...

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 ...

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 ...

parser.add_argument("--task", type=str, default="Isaac-Velocity-Flat-Forward-Unitree-Go2-v0") parser.add_argument("--dataset-path", type=str, default='imitation_data ...

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...