2 papers
cs.LG2025
Maximizing Prefix-Confidence at Test-Time Efficiently Improves Mathematical Reasoning
Matthias Otth, Jonas Hübotter, Ido Hakimi +1
Recent work has shown that language models can self-improve by maximizing their own confidence in their predictions, without relying on external verifiers or reward signals. In thi…
cs.LG2024
Beyond Pairwise Correlations: Higher-Order Redundancies in Self-Supervised Representation Learning
David Zollikofer, Béni Egressy, Frederik Benzing +2
Several self-supervised learning (SSL) approaches have shown that redundancy reduction in the feature embedding space is an effective tool for representation learning. However, the…