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Found 5 papers, 2 papers with code
Revisiting Random Weight Perturbation for Efficiently Improving Generalization
Improving the generalization ability of modern deep neural networks (DNNs) is a fundamental challenge in machine learning.
Kernel PCA for Out-of-Distribution Detection
Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs).
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels
To enhance kernel flexibility, this paper introduces the concept of Locally-Adaptive-Bandwidths (LAB) as trainable parameters to enhance the Radial Basis Function (RBF) kernel, giving rise to the LAB RBF kernel.
Random Fourier Features for Asymmetric Kernels
The theoretical foundation of RFFs is based on the Bochner theorem that relates symmetric, positive definite (PD) functions to probability measures.
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation
Asymmetric kernels naturally exist in real life, e. g., for conditional probability and directed graphs.
