3 articles tagged with #noise-resilience. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose Deep Active Re-Labeling (DARL), a framework addressing human annotation errors in deep active learning by allocating budget to re-annotate potentially mislabeled data. The method uses noise detection strategies to identify suspect instances, improving data quality and model performance under annotation noise.
Researchers introduce SpikeWFM, a hybrid neural architecture combining spiking neural networks with transformer-based models for wireless communications. The approach aims to improve noise resilience and energy efficiency in wireless foundation models while maintaining strong performance across diverse prediction tasks like channel estimation and positioning.
Researchers introduce a resilience framework for bi-criteria combinatorial optimization under noisy conditions, extending bandit feedback algorithms from single-objective to multi-objective settings. The framework achieves sublinear regret bounds without requiring structural assumptions like linearity or submodularity, with potential applications to constrained optimization problems in machine learning and algorithmic decision-making.
