EurekAlert!

New quantum encoding methods slash circuit complexity in machine learning

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 ...
Nature

Energy-efficient priority encoding strategies using machine learning based hybrid MAC protocol for wireless sensor networks

Efficient Medium Access Control (MAC) protocol is a primary requirement and plays a critical role in managing robust communication, energy consumption, and data transmission reliability in Wireless ...
Georgia Tech News Center

Machine Learning Encoder Improves Weather Forecasting and Tsunami Prediction

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.
Nature

Feature-indistinguishable machine unlearning via negative-hot label encoding and class weight masking

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 ...
EurekAlert!

Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

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 ...
inc42

What Is Encoder-Decoder Architecture? Here’s All You Need to Know

What Is An Encoder-Decoder Architecture? An encoder-decoder architecture is a powerful tool used in machine learning, specifically for tasks involving sequences like text or speech. It’s like a ...