Teaching AI the laws of navigation: New physics guided detector catches GNSS spoofing even in never before seen attacks

While machine learning has improved detection, most models fail when confronted with attack scenarios they have never seen before, because they learn data patterns rather than the underlying physics ...
Tom's Hardware on MSN

SK hynix and TetraMem collaborate on experimental chip to bolster edge AI energy efficiency

SK hynix, TetraMem, and the University of Southern California built a memristor-based in-memory computing system-on-chip for ...
IEEE

Graph Convolutional Networks With Adaptive Neighborhood Awareness

Abstract: Graph convolutional networks (GCNs) can quickly and accurately learn graph representations and have shown powerful performance in many graph learning domains. Despite their effectiveness, ...
Agence France-Presse

TetraMem and SK hynix Showcase Successful Technology Collaboration Advancing Memory-Centric AI Computing

TetraMem Inc., a leader in Analog In-Memory Computing (A-IMC) technology, and SK hynix Inc., a global leader in AI memory and semiconductor technologies, today announced the successful completion of a ...
Technology Networks

Deep Learning for Variant Calling: How DeepVariant and Neural Networks Are Improving Genomic Accuracy

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...
The Chosun Ilbo on MSN

Universities bridge AI education gap for regional students

Recently, in a lecture room at the Daegu Startup Hub in Dong-gu, Daegu, Kim Soo-pil, a senior researcher at the Daegu ...

WiMi Develops Quantum Convolutional Neural Network Model for Classical Data Classification

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WIMI" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, has completed systematic benchmark testing on fully ...
IEEE

Modularized Graph Convolutional Network

Abstract: Dear Editor, This letter presents a novel graph neural network, namely modularized graph convolution network (MGCN), to address the underexplored issue in graph convolution networks (GCNs), ...
GitHub

Submanifold Sparse Convolutional Networks

This library brings Spatially-sparse convolutional networks to PyTorch. Moreover, it introduces Submanifold Sparse Convolutions, that can be used to build computationally efficient sparse ...
techxplore

Convolutional optical neural networks herald a new era for AI imaging

Convolutional neural networks (CNNs), with their exceptional image recognition capabilities, have performed outstandingly in the field of AI and notably within platforms like ChatGPT. Recently, a team ...
Frontiers

Dual-channel deep graph convolutional neural networks

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...