This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms. All methods ...

With the growing importance of data privacy and regulatory compliance, machine unlearning has become a critical requirement in deep learning. However, existing approaches often require access to the ...

Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.

a.The architecture of the all-optical CNN for OAM-mediated machine learning, which can be applied to encode a data-specific image into OAM states. The photonic neural network comprises a trainable ...

Autoencoders are a class of unsupervised neural networks designed to learn efficient data representations by encoding inputs into a compact latent space and then reconstructing them. Their versatility ...