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Found 10 papers, 0 papers with code
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation
Subsequently, we design a precise personalized model distribution strategy to allow clients to obtain the most suitable model from the server side.
Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering
Here we propose a non-contrastive learning objective, named nCL, which explicitly mitigates dimensional collapse of representations in collaborative filtering.
Invariant Graph Transformer
Based on the self-attention module, our proposed invariant graph Transformer (IGT) can achieve fine-grained, more specifically, node-level and virtual node-level intervention.
Tackling Diverse Minorities in Imbalanced Classification
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers.
Adversarial Collaborative Filtering for Free
In this paper, we present Sharpness-aware Collaborative Filtering (SharpCF), a simple yet effective method that conducts adversarial training without extra computational cost over the base optimizer.
Sharpness-Aware Graph Collaborative Filtering
Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering.
Towards Generating Adversarial Examples on Mixed-type Data
The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues.
Few-shot graph link prediction with domain adaptation
In this work, we propose an adversarial training based modification to the current state-of-the-arts link prediction method to solve this problem.
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems.
motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks
Supervised machine learning tasks in networks such as node classification and link prediction require us to perform feature engineering that is known and agreed to be the key to success in applied machine learning.
