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20022026
most citedBeyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

1.8k citations

Showing 2024 · cs.LGShow all

6 papers · 2 filters

cs.LG20243 cited

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…

cs.LG202411 cited

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…

cs.LG2024

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…

cs.LG202426 cited

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…

cs.LG202418 cited

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…

cs.LG2024

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…