1 code implementation • 26 Oct 2023 • Nina Wiedemann, Ourania Kounadi, Martin Raubal, Krzysztof Janowicz
Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data.
1 code implementation • 11 Aug 2023 • Alexander Timans, Nina Wiedemann, Nishant Kumar, Ye Hong, Martin Raubal
We compare two epistemic and two aleatoric UQ methods on both temporal and spatio-temporal transfer tasks, and find that meaningful uncertainty estimates can be recovered.
no code implementations • 25 Mar 2023 • Dominik J. Mühlematter, Nina Wiedemann, Yanan Xin, Martin Raubal
In particular, we compare the spatially-implicit Random Forest model with spatially-aware methods for predicting average monthly per-station demand.
1 code implementation • 17 Feb 2023 • Moritz Neun, Christian Eichenberger, Yanan Xin, Cheng Fu, Nina Wiedemann, Henry Martin, Martin Tomko, Lukas Ambühl, Luca Hermes, Michael Kopp
Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce.
no code implementations • 14 Oct 2022 • Lorenzo Nespoli, Nina Wiedemann, Esra Suel, Yanan Xin, Martin Raubal, Vasco Medici
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.
1 code implementation • 26 Sep 2022 • Nina Wiedemann, Valentin Wüest, Antonio Loquercio, Matthias Müller, Dario Floreano, Davide Scaramuzza
Conversely, learning-based offline optimization approaches, such as Reinforcement Learning (RL), allow fast and efficient execution on the robot but hardly match the accuracy of MPC in trajectory tracking tasks.
1 code implementation • 31 Mar 2022 • Christian Eichenberger, Moritz Neun, Henry Martin, Pedro Herruzo, Markus Spanring, Yichao Lu, Sungbin Choi, Vsevolod Konyakhin, Nina Lukashina, Aleksei Shpilman, Nina Wiedemann, Martin Raubal, Bo wang, Hai L. Vu, Reza Mohajerpoor, Chen Cai, Inhi Kim, Luca Hermes, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling, Alabi Bojesomo, Hasan Al Marzouqi, Panos Liatsis, Jay Santokhi, Dylan Hillier, Yiming Yang, Joned Sarwar, Anna Jordan, Emil Hewage, David Jonietz, Fei Tang, Aleksandra Gruca, Michael Kopp, David Kreil, Sepp Hochreiter
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 aggregated GPS probe data in time and space bins.
1 code implementation • 27 Oct 2021 • Nina Wiedemann, Martin Raubal
With the performance on the traffic4cast test data and further experiments on a validation set it is shown that patch-wise prediction indeed improves accuracy.
no code implementations • 29 Sep 2021 • Nina Wiedemann, Antonio Loquercio, Matthias Müller, Rene Ranftl, Davide Scaramuzza
We evaluate our approach on several complex systems and tasks, and experimentally analyze the advantages over model-free and model-based methods in terms of performance and sample efficiency.
1 code implementation • 13 Sep 2020 • Jannis Born, Nina Wiedemann, Gabriel Brändle, Charlotte Buhre, Bastian Rieck, Karsten Borgwardt
Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools.
5 code implementations • 25 Apr 2020 • Jannis Born, Gabriel Brändle, Manuel Cossio, Marion Disdier, Julie Goulet, Jérémie Roulin, Nina Wiedemann
For detecting COVID-19 in particular, the model performs with a sensitivity of 0. 96, a specificity of 0. 79 and F1-score of 0. 92 in a 5-fold cross validation.
no code implementations • 8 Mar 2020 • Nina Wiedemann, Carlos Dietrich, Claudio T. Silva
The baseball game is often seen as many contests that are performed between individuals.