A new algorithm that fast forwards simulations could bring greater use ability to current and near-term quantum computers, opening the way for applications to run past strict time limits that hamper ...
本文提出基于原型学习的Forward-Forward（PLFF）算法，通过将卷积核分组为类原型，利用二进制交叉熵损失分别优化正负样本特征，同时最大化原型间余弦距离，有效提升长尾场景下的分类性能，并在多个数据集上验证优于现有FF方法。 与反向传播算法相比，Hinton [1 ...
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