Neural Kernel

Neural kernels represent a burgeoning field bridging deep learning and kernel methods, aiming to leverage the strengths of both for improved model performance and generalization. Current research focuses on developing novel kernel architectures, such as those based on neural tangent kernels (NTKs) and their variants, for various applications including image processing, 3D reconstruction, and graph convolutional networks. This approach offers advantages in scalability, robustness to noise, and efficient learning, particularly for large datasets and complex data modalities, impacting both theoretical understanding of neural networks and practical applications across diverse domains.