"To address these issues of limited time and diagnostic variability, we are investigating how deep learning can be applied to digital pathology, by creating an automated detection algorithm that can naturally complement…Read More
Abstract:
We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledge-base. Knowledge is…Read More
Deep reinforcement learning methods attain super-human performance in a wide range of environments. Such methods are grossly inefficient, often taking orders of magnitudes more data than humans to achieve reasonable performance.…Read More
Current Deep Learning approaches have been very successful using convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers. Three limitations of this approach are: 1) they are…Read More
"We’re also seeing significant gains from something called “multitask deep learning,” in which a system trained simultaneously to detect lane markings, cars, and pedestrians does better than three…Read More
The paper presents he first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. It's a must read for AI scientists
