Sparse autoencoders (SAEs) are an unsupervised learning technique designed to decompose a neural ... and resulted in hundreds of billions of sparse autoencoder parameters. The focus on JumpReLU SAEs ...

A technical paper titled “Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques” was ... for improving ML-based device modeling using variational ...

These methods belong to the field of machine learning, however there are also many ... we will apply three different autoencoders which are simple autoencoder, deep fully-connected autoencoder and ...

Moreover, to evaluate the quality of deep learning models, two distance-based metrics ... The dimension of the latent space is set to 2. The variational autoencoder with 4 hidden layers performed the ...

In this article, we propose a variational autoencoder-enhanced deep learning model (VAEDLM) for wafer defect imbalanced classification. It is light-weighted and effective in wafer defect pattern ...

In this article, we propose a variational autoencoder-enhanced deep learning model (VAEDLM) for wafer defect imbalanced classification. It is light-weighted and effective in wafer defect pattern ...