Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset.
Continue ReadingThe Master Algorithm
A comprehensive overview of the entire field of Machine Learning that is better than most of the book on the topic. Author also explores an idea, related to his scientific research, of a master algorithm which could explain everything given enough data.
Continue ReadingWhy Are There Still So Many Jobs? The History and Future of Workplace Automation
The research of the reasons why automation has not wiped out a majority of jobs over the decades and centuries. How recent and future advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth
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