noisy environments
Noisy environments are conditions where data is distorted by random variations or irrelevant information. AI models must be robust to noise to maintain performance and accuracy, particularly in real-world applications where perfect data collection is often unachievable.
- Continuous Simplicial Neural Networks
- Decoupled Entropy Minimization
- Fair Continuous Resource Allocation with Equality of Impact
- Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
- Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals