← Search

Benjamin Matthias Ruppik

2 accepted papers

2025

Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss Rescaling

AAAI 2025technical

Correct labels are indispensable for training effective machine learning models. However, creating high-quality labels is expensive, and even professionally labeled data contains errors and ambiguities. Filtering and denoising can be applied to curate labeled data prior to training, at the cost of a…

Cited by 0SourcePDFScholar
2025

Less is More: Local Intrinsic Dimensions of Contextual Language Models

NeurIPS 2025poster

Understanding the internal mechanisms of large language models (LLMs) remains a challenging and complex endeavor. Even fundamental questions, such as how fine-tuning affects model behavior, often require extensive empirical evaluation. In this paper, we introduce a novel perspective based on the g…

Cited by 0SourceScholar