שימושי למי? ערך דגימה מוגדר רק ביחס ללומד

תקציר מקורי באנגליתarXiv:2610.00221v1 Announce Type: new Abstract: What kind of data does a model need in order to learn? Coreset selection makes this question concrete: under a budget, keep the samples most useful for training. Easy-first and geometric coverage criteria can win in different budget regimes, separated by a crossover boundary. We ask whether this boundary is fixed by the data or changes with the target learner. Controlled experiments freeze the selected subsets and manipulate only the training learner. On low-resolution ImageNet-100, doubling ResNet-18's width moves the crossover from 57 to 85 samples per class: the learner changes the relative value of the same samples. A wider sweep reveals an interaction between input grid and capacity. Enlarging the grid while retaining the same image info