69 citations · 426 across the 38 of their papers we have counts for
4 papers · 1 filter
How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study
Alexander Erben, Ruben Mayer, Hans-Arno Jacobsen
This paper aims to answer the question: Can deep learning models be cost-efficiently trained on a global market of spot VMs spanning different data centers and cloud providers? To…
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines
Alexander Isenko, Ruben Mayer, Jeffrey Jedele +1
Preprocessing pipelines in deep learning aim to provide sufficient data throughput to keep the training processes busy. Maximizing resource utilization is becoming more challenging…
Real-time Load Prediction with High Velocity Smart Home Data Stream
Christoph Doblander, Martin Strohbach, Holger Ziekow +1
This paper addresses the use of smart-home sensor streams for continuous prediction of energy loads of individual households which participate as an agent in local markets. We intr…
Household Electricity Demand Forecasting -- Benchmarking State-of-the-Art Methods
Andreas Veit, Christoph Goebel, Rohit Tidke +2
The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible lo…