Search Results for author:
Found 7 papers, 3 papers with code
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students).
Long-tail Augmented Graph Contrastive Learning for Recommendation
To make the data augmentation schema learnable, we design an auto drop module to generate pseudo-tail nodes from head nodes and a knowledge transfer module to reconstruct the head nodes from pseudo-tail nodes.
Generative Contrastive Graph Learning for Recommendation
Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.
Bandit Samplers for Training Graph Neural Networks
However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT).
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Belief dynamics extraction
Estimating the structure of these internal states is crucial for understanding the neural basis of behavior.
Inverse Rational Control: Inferring What You Think from How You Forage
Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning.
Store Location Selection via Mining Search Query Logs of Baidu Maps
In this paper, we propose a Demand Distribution Driven Store Placement (D3SP) framework for business store placement by mining search query data from Baidu Maps.
