In the modern digital industry, web scraping has become critically necessary for developers. Companies must rely on the ...

This important work introduces an integrated open-source platform for behavioral acquisition and pose estimation that substantially improves the accessibility and speed of real-time animal tracking ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

There are many stories of how artificial intelligence came to take over the world, but one of the most important developments is the emergence in 2012 of AlexNet, a neural network that, for the first ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

This paper describes a method based on a deep neural network (DNN) for estimating the number of tillers on a plant. A tiller is a branch on a grass plant, and the number of tillers is one of the most ...

We present three possible strategies to effectively incorporate geological and/or geophysical constraints into deep neural networks (DNNs). They help address the main challenges of poor ...

Physical scientists and engineering research and development (R&D) teams are embracing neural networks in attempts to accelerate their simulations. From quantum mechanics to the prediction of blood ...

Please note that this is not an officially supported Google product. If you find this code useful in your research then please cite @inproceedings{jiang2018mentornet, title={MentorNet: Learning ...

Density estimation is among the most fundamental problems in statistics. It is notoriously difficult to estimate the density of high-dimensional data due to the “curse of dimensionality.” Here, we ...