Learning From Crowdsourced Noisy Labels: A signal processing perspective

Keyphrases

• Crowdsourcing 100%
• Signal Processing 100%
• Noisy Labels 100%
• Learning Task 40%
• Annotator 40%
• Artificial Intelligence/machine Learning (AI/ML) 40%
• Statistical Model 20%
• Machine Learning 20%
• Machine Learning System 20%
• Deep Learning 20%
• Negative Impact 20%
• Classical Statistical 20%
• Edge AI 20%
• Signal Processing Theory 20%
• Inference Tasks 20%
• Curated Database 20%
• Non-negative Matrix Factorization 20%
• Unreliability 20%
• Signal Processing Techniques 20%
• Primary Catalyst 20%
• Label Noise 20%
• Multiple Annotators 20%
• Massive Datasets 20%
• Crowdsourcing Model 20%
• Insight Development 20%
• Fine-tuning Language Models 20%
• Annotation Process 20%
• Algorithmic Development 20%
• Analytical Insights 20%
• Matrix/tensor Factorization 20%
• Reinforcement Learning from Human Feedback 20%
• Direct Preference Optimization 20%

Computer Science

• Signal Processing 100%
• Machine Learning 66%
• Artificial Intelligence 66%
• Annotation 33%
• Reinforcement Learning 33%
• Inference Task 33%
• Massive Datasets 33%
• nonnegative matrix factorization 33%
• Negative Impact 33%
• Large Language Model 33%
• Learning System 33%
• Deep Learning Method 33%