Two NeurIPS Papers Explain Why Pretraining Beats Infeasible Incremental Learning
Two papers accepted to NeurIPS 2026 show that with Mahalanobis margin conditions on pretrained representations, task confusion decays exponentially. Building on the author's PhD result that optimal class-incremental learning is infeasible, the work explains why pretrained models can bypass this limitation.
2026-09-25 ~ 2026-09-25 · 2 related posts
- Two NeurIPS 2026 papers: margin conditions tame sequential task confusion — khademinori · 2026-09-25
- Two papers show how pretrained models bypass the proven infeasibility of optimal class-incremental learning — khademinori · 2026-09-25