פיתוח תכנון נוירוסימבולי חדש: תכנון טנסוריאלי עם קונסטריינט דרגה ואימון עם ספורט תגובה מוגבל

תקציר מקורי באנגליתarXiv:2609.40131v1 Announce Type: new Abstract: Rank-constrained tensor neural networks reduce the parameterization of high-order inputs, but they do not explicitly constrain class geometry in the learned representation. This study investigates whether a differentiable prototype-rule can provide a complementary inductive bias for Rank-R tensor learning under limited supervision. The proposed framework augments the Rank-R objective with prototype-based regularization and optionally fuses prototype evidence with neural logits at inference. Four hyperspectral benchmarks are evaluated with four Rank-R configurations under both seven-fold stratification and spatially separated folds that mitigate leakage; a separate spatial study varies the class support budget from 2 to 20 samples. Under spati