DFKI
We present HyperCore, a lightweight adaptive coreset selection framework designed for noisy environments. HyperCore utilizes per class hypersphere models and adaptively selects pruning thresholds.
We fix Forward-Forward's slow inference by replacing per-class passes with a single forward pass through hyperspherical prototype matching. Thus, we achieve 40× faster inference with competitive accuracy.
We introduce PRISM, a framework that disentangles architectural priors for dataset distillation, outperforming single-teacher setups.
We introduce SubZeroCore, a novel, training-free coreset selection method that integrates submodular coverage and density into a single, unified objective.