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Found 12 papers, 8 papers with code
Where you go is who you are -- A study on machine learning based semantic privacy attacks
Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data.
Uncertainty Quantification for Image-based Traffic Prediction across Cities
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.
Spatially-Aware Car-Sharing Demand Prediction
In particular, we compare the spatially-implicit Random Forest model with spatially-aware methods for predicting average monthly per-station demand.
Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities
Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce.
National-scale bi-directional EV fleet control for ancillary service provision
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.
Training Efficient Controllers via Analytic Policy Gradient
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.
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
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.
Traffic Forecasting on Traffic Moving Snippets
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.
Jointly Learning Identification and Control for Few-Shot Policy Adaptation
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.
Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis
Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools.
POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
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.
A Tracking System For Baseball Game Reconstruction
The baseball game is often seen as many contests that are performed between individuals.
