Prototype-Rule Neurosymbolic Regularization for Rank-Constrained Tensor Neural Networks under Label Scarcity
Paper recorded by Signals 4 on 2026-09-30 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-30 on arXiv · recorded by Signals 4 on 2026-10-01
Category: cs.CV · 计算机视觉 · first seen 2026-10-01
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 prototyp
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Cite this page: Prototype-Rule Neurosymbolic Regularization for Rank-Constrained Tensor Neural Networks under Label Scarcity: the #23 most recent of 380 cs.CV papers we have recorded (as of 2026-09-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/prototype-rule-neurosymbolic-regularization-for-rank-constrained-tensor-neural-n.html
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