Adaptive Binning improves tabular SSL by coupling feature discretization to training via representation-aware curriculum learning and a heterogeneity-aware objective, yielding gains on medical datasets without per-dataset tuning.
arXiv preprint arXiv:2207.03208 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
DPOP is a new loss function that prevents DPO from lowering preferred response likelihoods and outperforms standard DPO on diverse datasets, MT-Bench, and enables Smaug-72B to exceed 80% on the Open LLM Leaderboard.
citing papers explorer
-
When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning
Adaptive Binning improves tabular SSL by coupling feature discretization to training via representation-aware curriculum learning and a heterogeneity-aware objective, yielding gains on medical datasets without per-dataset tuning.