中国科学院自动化研究所机构知识库

Convolutional neural networks (CNNs) provide a dramatically powerful class of models, but are subject to traditional
convolution that can merely aggregate permutation-ordered and dimension-equal local inputs. It causes that CNNs are allowed to only
manage signals on Euclidean or grid-like domains (e.g., images), not ones on non-Euclidean or graph domains (e.g., traffic networks). To
eliminate this limitation, we develop a local-aggregation function, a sharable nonlinear operation, to aggregate permutation-unordered
and dimension-unequal local inputs on non-Euclidean domains. In the context of the function approximation theory, the local-aggregation
function is parameterized with a group of orthonormal polynomials in an effective and efficient manner. By replacing the traditional
convolution in CNNs with the parameterized local-aggregation function, Local-Aggregation Graph Networks (LAGNs) are readily
established, which enable to fit nonlinear functions without activation functions and can be expediently trained with the standard
back-propagation. Extensive experiments on various datasets strongly demonstrate the effectiveness and efficiency of LAGNs,
leading to superior performance on numerous pattern recognition and machine learning tasks, including text categorization,
molecular activity detection, taxi flow prediction, and image classification.