GitHub

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem

🚀 Update: If you are interested in this work, you may be interested in our latest paper and up-to-date codebase bringing together several architectures and learning paradigms for learning-driven TSP ...
IEEE

LDGM Codes-Based Near-Optimal Coding for Adaptive Steganography

Abstract: Steganographic coding is an essential part of adaptive steganography. There are only two practical near-optimal codes in the context of adaptive steganography so far: Syndrome-Trellis Codes ...
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, ...
IEEE

A Lightweight Road Detection Algorithm Based on Multiscale Convolutional Attention Network and Coupled Decoder Head

Abstract: Automatic road detection from remote sensing images has always been a significant research topic. It is of great value to many practical applications. However, there are still some problems ...
Frontiers

Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning

Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we ...
Frontiers

Multi-Dimensional Enhanced Seizure Prediction Framework Based on Graph Convolutional Network

In terms of seizure prediction, how to fully mine relational data information among multiple channels of epileptic EEG? This is a scientific research subject worthy of further exploration. Recently, ...
Nature

Multi-resolution convolutional neural networks for inverse problems

Most physical theories allow us to make predictions: given a complete description of the state of a physical system, we can predict or simulate some measurements. Recovering parameters that describe ...
Nature

Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks

Recently, the use of deep neural networks in solving scientific problems has increased, such as for finding exotic particles in high-energy physics 1 and predicting the sequence specificities of DNA ...
Harvard Business Review

To Solve a Problem, Stop Thinking About It

When you’re trying to come up with a creative solution to a problem, you might be tempted to buckle down and focus until you solve it. But recent research shows that taking breaks at regular intervals ...
acm.org

ImageNet Classification with Deep Convolutional Neural Networks

Five ILSVRC-2010 test images in the first column. Remaining columns show the training images that produce feature vectors in the last hidden layer with the smallest Euclidean distance from the feature ...