Deep decarbonization faces deep challenges

Accelerating sociotechnical transitions for deep decarbonization

Deep Fisher Faces

Most current state-of-the-art methods for unconstrained face recognition use deep convolutional neural networks. Recently, it has been proposed to augment the typically used softmax cross-entropy loss by adding a center loss trying to minimize the distance between the face images and their class centers. In this work we further extend the center (intra-class) loss with an inter-class loss remin...

Power system balancing for deep decarbonization of the electricity sector

We explore the operations, balancing requirements, and costs of the Western Electricity Coordinating Council power system under a stringent greenhouse gas emission reduction target. We include sensitivities for technology costs and availability, fuel prices and emissions, and demand profile. Meeting an emissions target of 85% below 1990 levels is feasible across a range of assumptions, but the ...

Challenges in Deep Learning

In recent years, Deep Learning methods and architectures have reached impressive results, allowing quantum-leap improvements in performance in many difficult tasks, such as speech recognition, end-toend machine translation, image classification/understanding, just to name a few. After a brief introduction to some of the main achievements of Deep Learning, we discuss what we think are the genera...

Politikberatung kompakt Deep Decarbonization in Germany A Macro - Analysis of Economic and Political Challenges of the ' Energiewende ' ( Energy Transition ) 93

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