With the emergence of a large amount of malicious code data, the efficiency of traditional machine learning algorithms is getting worse and worse. In this paper, a workflow based on deep learning is ...

Although deep learning (DL ... 4.3. To incorporate our DL models into the neuroscientists' workflow, we implemented a GUI application that integrates the best networks and the postprocessing ...

However, most of algorithms assume that cloud resources ... which is formulated as a Markov Decision Process. And, a dynamic workflow scheduling approach based on deep reinforcement learning (RLWS) is ...

Right: volume-rendered 3D reconstruction image. The aneurysm was missed in the initial report but successfully detected with the deep-learning algorithm. (Courtesy: Radiological Society of North ...

Deep learning algorithms date back to the late 1980s ... Cloud companies like Google and AWS (Amazon Web Services) use deep learning for their workflow and load balancing, and they use hardware for ...

Hinton points out that ANNs can be trained using reinforcement learning ... FF algorithm can be much more memory efficient than the classical backprop, with up to 45% memory savings for deep ...

A deep reinforcement learning algorithm can solve the Rubik's Cube puzzle in a fraction of a second. The work is a step toward making AI systems that can think, reason, plan and make decisions.