Que.com on MSN

Enhancing Deep Neural Networks with Architectural Optimization Techniques

In the ever-evolving world of artificial intelligence, deep neural networks (DNNs) have revolutionized data processing, offering unparalleled accuracy across various ...
Deep Learning with Yacine on MSN

Network in Network (NiN) Explained – Deep Neural Network Tutorial with PyTorch

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...
IEEE

UADiff: A Deep Underwater Image Enhancement Network Using Generative Diffusion Prior and Uncertainty-Aware Learning

Abstract: Diffusion models have provided the state-of-the-art performances for different computer vision tasks, including the task of underwater image enhancement. One of the challenges in the task of ...
Forbes

The Surprising Idea That Generative AI Might Be Better Off Using Visual Images Of Text Rather Than Pure Text As Tokens

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant.
C&EN

Neural Network Prediction of Micrometer-Scale Equivalent Contact Angle Mapping: From Microforce Measurements to Local Wettability Characterization

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China ...
C&EN

SlaKoNet: A Unified Slater-Koster Tight-Binding Framework Using Neural Network Infrastructure for the Periodic Table

Department of Materials Science and Engineering, Whiting School of Engineering, The Johns Hopkins University, Baltimore, Maryland 21218, United States Department of Electrical and Computer Engineering ...
IEEE

CryptDNN: A Fast Privacy-Preserving Deep Neural Network Inference Architecture Based on Cloud-Edge-Client Collaboration

Abstract: Deep neural networks (DNNs) with fully homomorphic encryption (FHE) are gaining traction in Internet of Things (IoT) applications requiring robust protection of data inference and model ...
Frontiers

The nature of quantum parallel processing and its implications for coding in brain neural networks: a novel computational mechanism

Conventionally it is assumed that the nerve impulse is an electrical process based upon the observation that electrical stimuli produce an action potential as defined by Hodgkin Huxley (1952) (HH).