Wavelength-multiplexed diffractive optical storage enables massively parallel image retrieval

The explosive growth of data generated by artificial intelligence, cloud computing and modern digital infrastructure is ...
Tech Xplore

Programmable metasurface generates dozens of holograms at once

Over the past few decades, engineers have developed various devices that can create holograms, three-dimensional (3D) or ...
techtimes

Anthropic Hires Berkeley CS Chair Jelani Nelson, Signaling New Phase of AI Race

On Wednesday, Jelani Nelson, a professor of theoretical computer science and chair of UC Berkeley's electrical engineering and computer science division, announced he was taking a leave of absence to ...
IEEE

Accelerated Griffin-Lim Algorithm: A Fast and Provably Converging Numerical Method for Phase Retrieval

Abstract: The recovery of a signal from the magnitudes of its transformation, like the Fourier transform, is known as the phase retrieval problem and is of big relevance in various fields of ...
IEEE

Phase Retrieval With Learning Unfolded Expectation Consistent Signal Recovery Algorithm

Abstract: Phase retrieval algorithms are now an important component of many modern computational imaging systems. A recently proposed scheme called generalized expectation consistent signal recovery ...
leewayhertz

Advanced RAG: Architecture, techniques, applications and use cases and development

Retrieval-augmented generation (RAG) has emerged as a pivotal framework in AI, significantly enhancing the accuracy and relevance of responses generated by large language models (LLMs) leveraging ...
VentureBeat

How LinkedIn replaced five feed retrieval systems with one LLM model, at 1.3 billion-user scale

LinkedIn's feed reaches more than 1.3 billion members — and the architecture behind it hadn't kept pace. The system had accumulated five separate retrieval pipelines, each with its own infrastructure ...
GitHub

Ansal06/Iterative-Phase-Retrieval

This repository contains my implementation of the Hybrid Input-Output (HIO) algorithm for phase retrieval — recovering phase information from Fourier magnitude data, a key problem in computational ...
GitHub

SELFPHISH: Self-supervised, Physics-Informed Generative Networks for Phase Retrieval

SELFPHISH is a flexible data reconstruction framework that harnesses self-supervised, physics-informed generative networks. Unlike traditional methods that rely on complex algorithms, SELFPHISH ...
VentureBeat

Mayo Clinic's secret weapon against AI hallucinations: Reverse RAG in action

Even as large language models (LLMs) become ever more sophisticated and capable, they continue to suffer from hallucinations: Offering up inaccurate information, or, to put it more harshly, lying.
EurekAlert!

Deep learning for phase recovery

(a) Deep-learning-pre-processing for phase recovery. (b) Deep-learning-in-processing for phase recovery. (c) Deep-learning-post-processing for phase recovery. (d) Deep learning for phase processing.