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- KTH Royal Institute of TechnologySE30 papers
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6 papers · 2 filters
Predicting Pedestrian Crossing Behavior in Germany and Japan: Insights into Model Transferability
Chi Zhang, Janis Sprenger, Zhongjun Ni +1
Predicting pedestrian crossing behavior is important for intelligent traffic systems to avoid pedestrian-vehicle collisions. Most existing pedestrian crossing behavior models are t…
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
Federated learning (FL) has received significant attention in recent years for its advantages in efficient training of machine learning models across distributed clients without di…
Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation
Boqi Chen, Kristóf Marussy, Oszkár Semeráth +2
Graph convolutional neural networks (GCNs) are powerful tools for learning graph-based knowledge representations from training data. However, they are vulnerable to small perturbat…
Uncertainty Quantification Metrics for Deep Regression
Simon Kristoffersson Lind, Ziliang Xiong, Per-Erik Forssén +1
When deploying deep neural networks on robots or other physical systems, the learned model should reliably quantify predictive uncertainty. A reliable uncertainty allows downstream…
Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks
Resmi Ramachandranpillai, Md Fahim Sikder, David Bergström +1
Synthetic data generation offers a promising solution to enhance the usefulness of Electronic Healthcare Records (EHR) by generating realistic de-identified data. However, the exis…
Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings
Chi Zhang, Janis Sprenger, Zhongjun Ni +1
Understanding and predicting pedestrian crossing behavior is essential for enhancing automated driving and improving driving safety. Predicting gap selection behavior and the use o…