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Found 3 papers, 2 papers with code
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
While state-of-the-art (SoTA) compression methods boast impressive advancements in preserving benign task performance, the potential risks of compression in terms of safety and trustworthiness have been largely neglected.
Optimizing for ROC Curves on Class-Imbalanced Data by Training over a Family of Loss Functions
Although binary classification is a well-studied problem in computer vision, training reliable classifiers under severe class imbalance remains a challenging problem.
Neural Image Compression: Generalization, Robustness, and Spectral Biases
Our benchmarks, spectral inspection tools, and findings provide a crucial bridge to the real-world adoption of NIC.
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