Graph neural networks are inherently good generalizers: Insights by bridging gnns and mlps
Graph neural networks (GNNs), as the de-facto model class for representation learning on
graphs, are built upon the multi-layer perceptrons (MLP) architecture with additional …
graphs, are built upon the multi-layer perceptrons (MLP) architecture with additional …
Graph Machine Learning through the Lens of Bilevel Optimization
Bilevel optimization refers to scenarios whereby the optimal solution of a lower-level energy
function serves as input features to an upper-level objective of interest. These optimal …
function serves as input features to an upper-level objective of interest. These optimal …
BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
Bilevel optimization refers to scenarios whereby the optimal solution of a lower-level energy
function serves as input features to an upper-level objective of interest. These optimal …
function serves as input features to an upper-level objective of interest. These optimal …
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
Given the large volume of side information from different modalities, multimodal
recommender systems have become increasingly vital, as they exploit richer semantic …
recommender systems have become increasingly vital, as they exploit richer semantic …
Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization
Multi-View Representation Learning (MVRL) aims to learn a unified representation of an
object from multi-view data. Deep Canonical Correlation Analysis (DCCA) and its variants …
object from multi-view data. Deep Canonical Correlation Analysis (DCCA) and its variants …