Nature

Dimensionality reduction for images of IoT using machine learning

Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are becoming increasingly integrated into all aspects of our lives. They are capable of gathering enormous amounts of ...
Nature

Deep learning assisted XRF spectra classification

EDXRF spectrometry is a well-established and often-used analytical technique in examining materials from which cultural heritage objects are made. The analytical results are traditionally subjected to ...
EurekAlert!

Deep variational autoencoder for proteomics mass spectrometry data analysis

Jianwei Shuai's team and Jiahuai Han's team at Xiamen University have developed a deep autoencoder-based data-independent acquisition data analysis software for protein mass spectrometry, which ...
Visual Studio Magazine

Data Dimensionality Reduction Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...
VentureBeat

DeepMind makes big jump toward interpreting LLMs with sparse autoencoders

Large language models (LLMs) have made remarkable progress in recent years. But understanding how they work remains a challenge and scientists at artificial intelligence labs are trying to peer into ...
Medical Device and Diagnostic Industry (MD+DI)

The AI Era: How Deep Learning is Overcoming Data Scarcity in Medical Imaging

AI medical imaging market is projected to exceed $20B by 2035. Generative models address class imbalances in medical imaging ...