AHDM has a dual classifier framework. It trains first neural network based on the encoding features obtained from the autoencoder feature enhancement algorithm to detect small-sample malicious traffic ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...

This paper proposes Auto-KPCA, a two-step hybrid feature extraction technique that leverages (i) the fast computational capability of kernel PCA (KPCA) and (ii) the performance of a deep generalized ...