10 citations · 10 across the 1 of their papers we have counts for
8 papers
GeOT: A spatially explicit framework for evaluating spatio-temporal predictions
Nina Wiedemann, Théo Uscidda, Martin Raubal
When predicting observations across space and time, the spatial layout of errors impacts a model's real-world utility. For instance, in bike sharing demand prediction, error patter…
National-scale bi-directional EV fleet control for ancillary service provision
Lorenzo Nespoli, Nina Wiedemann, Esra Suel +3
Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases. In this pap…
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
Christian Eichenberger, Moritz Neun, Henry Martin +34
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggrega…
Traffic Forecasting on Traffic Moving Snippets
Nina Wiedemann, Martin Raubal
Advances in traffic forecasting technology can greatly impact urban mobility. In the traffic4cast competition, the task of short-term traffic prediction is tackled in unprecedented…
An Optimization Framework for Power Infrastructure Planning
Nina Wiedemann, David Adjiashvili
The ubiquitous expansion and transformation of the energy supply system involves large-scale power infrastructure construction projects. In the view of investments of more than a m…
Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis
Jannis Born, Nina Wiedemann, Gabriel Brändle +3
Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools. Ultrasound, in contrast to CT or X-Ray, has many practical…