These simulated signals are added to a pool of actual data until the autoencoder learns to pick them out reliably. Once it can do that, the AI is set loose on the real work.

As the autoencoder processed the data, it “learned” to identify salient features in the data. In a second step, these features were fed to an algorithm called a random forest classifier.

As the autoencoder processed the data, it ‘learned’ to identify salient features in the data. A radio technosignature search towards Proxima Centauri resulting in a signal of interest (Picture ...

As the autoencoder processed the data, it “learned” to identify salient features in the data. In a second step, these features were fed to an algorithm called a random forest classifier.

Transfer learning: Signal discrimination . Then, AI training is performed to either train a model that was built from scratch or, to train an established model (e.g., AlexNet, GoogleNet) ...

What sets their work apart from previous studies is that they have improved applicability to 1-D signals (e.g., human speech), and are testing against stronger noise sources than usually ...

Serial-autoencoder for personalized recommendation. Higher Education Press . Journal Frontiers of Computer Science DOI 10.1007/s11704-023-2441-1 ...