记忆相关差分学习
Memory-Associated Differential Learning
摘要：
传统的监督学习方法关注于从输入特征到输出标签的映射。在训练之后，学习的模型单独地适应于测试特征以孤立地预测测试标签，训练数据被浪费并且它们的关联被忽略。为了充分利用大量的训练数据及其关联，我们提出了一种新的学习范式，称为记忆相关差分（MAD）学习。我们首先引入一个名为Memory的附加组件，用于存储所有训练数据。然后，我们学习的标签的差异，以及在一个微分方程和一些抽样方法的组合的特征的关联。最后，在评估阶段，我们预测未知的标签，从记忆的事实加上学习的差异和协会在几何意义上的方式进行推断。我们轻轻地建立这个理论在一元的情况下，并将其应用于图像识别，然后将其扩展到链接预测作为一个二进制的情况下，其中我们的方法优于强大的国家的最先进的基线ogbl-ddi数据集。
英文摘要：
Conventional Supervised Learning approaches focus on the mapping from input features to output labels. After training, the learnt models alone are adapted onto testing features to predict testing labels in isolation, with training data wasted and their associations ignored. To take full advantage of the vast number of training data and their associations, we propose a novel learning paradigm called Memory-Associated Differential (MAD) Learning. We first introduce an additional component called Memory to memorize all the training data. Then we learn the differences of labels as well as the associations of features in the combination of a differential equation and some sampling methods. Finally, in the evaluating phase, we predict unknown labels by inferencing from the memorized facts plus the learnt differences and associations in a geometrically meaningful manner. We gently build this theory in unary situations and apply it on Image Recognition, then extend it into Link Prediction as a binary situation, in which our method outperforms strong state-of-the-art baselines on ogbl-ddi dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
