WiMi Releases Resource-Efficient Quantum Convolutional Neural Network Based on QRAM, Accelerating the Practical Implementation of Large-Scale Image Classificatio…

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) (WiMi or the Company), a leading global Hologram Augmented Reality (AR) Technology ...
IEEE

Fast Weighted Transforms for Linear Convolution

Abstract: Convolution is fundamental in digital signal processing across many applications. Existing works enable N-point linear convolution via N-point right-angle circular convolution (RCC) based on ...
Network World

What are TPUs? Your guide to tensor processing units and AI acceleration

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...
Scientific Research Publishing

Dai, Z., Liu, H., Le, Q.V. and Tan, M. (2021) CoAtNet: Marrying Convolution and Attention for All Data Sizes.

ABSTRACT: Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers ...
IEEE

Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data

Abstract: High-dimensional and incomplete (HDI) data are frequently encountered in diverse real-world applications involving complex interactions among numerous nodes. Approaches based on latent ...
Embedded

Understanding convolutional neural networks

The world of artificial intelligence (AI) is rapidly evolving, and AI is increasingly enabling applications that were previously unattainable or very difficult to implement. A subsequent article, ...
Frontiers

A Method of Containment Leakage Rate Estimation Based on Convolution Neural Network

As the nuclear power plant containment is the third barrier to nuclear safety, real-time monitoring of containment leakage rate is very important in addition to the overall leakage test before an ...
Frontiers

SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction

Deep learning methods, which can predict the binding affinity of a drug–target protein interaction, reduce the time and cost of drug discovery. In this study, we propose a novel deep convolutional ...