Deep learning has been highly successful in recent years and has led to dramatic improvements in multiple domains. Deep-learning algorithms often generalize ... and there are certainly important ...

To solve this problem, a new transfer learning method, namely Wasserstein distance transfer learning algorithm based on matrix-norm regularization (MRWDTL), is proposed. To satisfy the Lipschitz ...

In modern Deep Learning approaches is data more important than algorithms? Well, again, yes and no. It is true that these approaches are very “data-hungry”. Without going into many details ...

An application using deep learning applied color ... computing required to run the matrix-based logic that characterizes machine-learning algorithms, Oppenheimer said. If an algorithm is open ...

Rice researchers created a cost-saving alternative to GPU, an algorithm ... said SLIDE is important because it shows there are other ways to implement deep learning. The whole message is, ‘Let’s not ...

Data analytics often rely on machine learning (ML) algorithms. Among ML algorithms, deep convolutional neural networks (DNNs) offer state-of-the-art accuracies for important image classification ...