Arctic Wolf Networks acquires Sevco Security to expand exposure assessment capabilities

Cybersecurity company Arctic Wolf Networks Inc. today announced that it has acquired exposure assessment startup Sevco ...
eLife

Inverted encoding of neural responses to audiovisual stimuli reveals super-additive multisensory enhancement

A multivariate analysis of electroencephalography activity reveals super-additive enhancements to the neural encoding of audiovisual stimuli, providing new insights into how the brain integrates ...
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
Security Boulevard

NDSS 2025 – PBP: Post-Training Backdoor Purification For Malware Classifiers

Dung Thuy Nguyen (Vanderbilt University), Ngoc N. Tran (Vanderbilt University), Taylor T. Johnson (Vanderbilt University), Kevin Leach (Vanderbilt University) PAPER PBP: Post-Training Backdoor ...

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...
GitHub

batch-normalization

Machine Learning Practical - Coursework 2: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during ...
IEEE

Unit-Centric Regularization for Efficient Deep Neural Networks

Abstract: Deep neural networks excel by learning hierarchical representations, often requiring architectural enhancements like increased width, normalization layers, or skip connections, each adding ...
Phys.org

Common feature between forest fires and neural networks reveals universal framework

Researchers from the University of Tokyo in collaboration with Aisin Corporation have demonstrated that universal scaling laws, which describe how the properties of a system change with size and scale ...
GitHub

Integrating physics and topology in neural networks for learning rigid body dynamics

This repository contains the code used to develop HOPNet, part of the publication titled "Integrating physics and topology in neural networks for learning rigid body dynamics". This work was published ...
Frontiers

Quantized convolutional neural networks: a hardware perspective

With the rapid development of machine learning, Deep Neural Network (DNN) exhibits superior performance in solving complex problems like computer vision and natural language processing compared with ...