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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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2 results for “CIFAR-N”

Label Noise: Ignorance Is Blisscs.LG

Noise-ignorant empirical risk minimization achieves state-of-the-art performance on noisy data.

problem Learning with noisy labels in multi-class classification problems.

method Introducing relative signal strength (RSS) to quantify transferability and applying Noise Ignorant Empirical Risk Minimization (NI-ERM).

result NI-ERM achieves state-of-the-art performance on CIFAR-N data challenge.

label noise domain adaptation relative signal strength empirical risk minimization CIFAR-N

2024-10-31abs ↗pdf ↗

SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixingcs.LG

SelectMix improves deep learning robustness against noisy labels.

problem Deep neural networks memorize noisy labels, degrading performance.

method Confidence-guided targeted sample mixing with soft labels.

result SelectMix consistently outperforms baseline methods on noisy label datasets.

deep learning label noise robustness mixup confidence analysis

2025-09-14abs ↗pdf ↗