Neural architecture search promises to speed up the process of finding neural network architectures that will yield good models for a given dataset. Neural architecture search is the task of ...

Deep neural networks have a huge advantage: They replace “feature engineering”—a difficult and arduous part of the classic machine learning cycle—with an end-to-end process that automatically learns ...

A technical paper titled “HyDe: A Hybrid PCM/FeFET/SRAM Device-search for Optimizing Area and Energy-efficiencies in Analog IMC Platforms” was published by researchers at Yale University. “Today, ...

Today's enterprises need effective retrieval-augmented generation that extends existing data architectures without replacing current investments. As organizations face challenges in scaling RAG ...

Vector embeddings are the backbone of modern enterprise AI, powering everything from retrieval-augmented generation (RAG) to semantic search. But a new study from Google DeepMind reveals a fundamental ...