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 ...

Unsupervised domain adaptation has provoked vast amount of attention and research in past decades. Among all the deep-based methods, the autoencoder-based approach have achieved sound performance for ...

Analog compute-in-memory combines compute and storage using crossbar arrays of non-volatile memory, thus promising to reduce the energy demand for artificial intelligence workloads. Yet, significant ...