Exclusive: US commanders bypassed warnings about outdated intelligence ahead of strike that hit school in Iran, sources say

Senior US military commanders bypassed warnings in critical databases that intelligence about potential targets in Iran was severely out of date and approved some strikes — including one that hit a ...
Hacker

Deep Learning CNN’s in Tensorflow with GPUs

In my last tutorial, you created a complex convolutional neural network from a pre-trained inception v3 model. In this tutorial, you’ll learn the architecture of a convolutional neural network (CNN), ...
IEEE

DCBT-Net: Training Deep Convolutional Neural Networks With Extremely Noisy Labels

Abstract: Obtaining data with correct labels is crucial to attain the state-of-the-art performance of Convolutional Neural Network (CNN) models. However, labeling datasets is significantly ...
GitHub

An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector Machine (SVM) for Image Classification

This project was inspired by Y. Tang's Deep Learning using Linear Support Vector Machines (2013). The full paper on this project may be read at arXiv.org. The experiments were conducted on a laptop ...
Frontiers

A resource-efficient quantum convolutional neural network

Quantum Convolutional Neural Network (QCNN) has achieved significant success in solving various complex problems, such as quantum many-body physics and image recognition. In comparison to the ...
GitHub

The MNIST-1D Dataset

Most machine learning models get around the same ~99% test accuracy on MNIST. Our dataset, MNIST-1D, is 100x smaller (default sample size: 4000+1000; dimensionality: 40) and does a better job of ...
IEEE

Energy-Efficient Precision-Scaled CNN Implementation With Dynamic Partial Reconfiguration

Abstract: A convolutional neural network (CNN) classifies images with high accuracy. However, CNN operation requires a large number of computations which consume a significant amount of power when ...