In order to minimize inference time and computational energy, a convolutional autoencoder is used for learning a generalized representation of the images. Three scenarios are analyzed: transferring ...

Scientists at Massachusetts Institute of Technology have devised a way for large language models to keep learning on the fly—a step toward building AI that continually improves itself.

Article citations More>> Tschannen, M., Bachem, O. and Lucic, M. (2018) Recent Advances in Autoencoder-based Representation Learning. arXiv: 1812.05069. has been cited by the following article: TITLE: ...

To solve the above problems, an evidential clustering algorithm based on transfer learning and deep autoencoder (TDEC) is proposed. The TDEC utilizes deep autoencoder to obtain evidential ...

Machine learning (ML) accelerates the exploration of material properties and their links to the structure of the underlying molecules. In previous work [Shi et al. ACS Applied Materials & Interfaces ...

Firstly, we enhance the source domain with a convolutional autoencoder to improve the generalization ability of the model. Then, we introduce an adversarial learning-based boundary discrimination ...

University of Belgrade, Faculty of Civil Engineering, Belgrade, Serbia Soil organic carbon (SOC) is a vital component for sustainable agricultural production. This research investigates the transfer ...

If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder”. Autoencoders are one of the primary ways that unsupervised learning models are developed.