Wenwu Zhu

A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful …

[Research on Marketing Permission of Cross-border Transfer Production of Medical Devices].

2020 · PubMed

At present, there is a growing call for overseas registration applicants to transfer the products that have been approved for import registration to China's domestic production. It deserves our regulatory authorities to study how to …

Disentangled Self-Supervision in Sequential Recommenders

2020

To learn a sequential recommender, the existing methods typically adopt the sequence-to-item (seq2item) training strategy, which supervises a sequence model with a user's next behavior as the label and the user's past behaviors as the …

Enhancing Unsupervised Semantic Segmentation Through Context-Aware Clustering

2024 · IEEE Transactions on Multimedia

Despite the great progress of semantic segmentation with supervised learning, annotating large amounts of pixel-wise labels is, however, very expensive and time-consuming. To this end, Unsupervised Semantic Segmentation(USS) has been proposed to learn semantic segmentation, …

Causal-aware Graph Neural Architecture Search under Distribution Shifts

2024 · arXiv (Cornell University)

Graph NAS has emerged as a promising approach for autonomously designing GNN architectures by leveraging the correlations between graphs and architectures. Existing methods fail to generalize under distribution shifts that are ubiquitous in real-world graph …

Curriculum Learning for Multimedia in the Era of Large Language Models

2024

This tutorial focuses on curriculum learning (CL), an important topic in machine learning, which gains an increasing amount of attention in the research community. CL is a learning paradigm that enables machines to learn from …

Aligning Large Multimodal Model with Sequential Recommendation via Content-Behavior Guidance

2025

Large language models (LLMs) have significantly influenced advancements in sequential recommendation. Nevertheless, the integration and alignment of LLMs with sequence recommenders is often underexploited in current research. Existing LLM-based sequential recommenders mostly rely on textual …

Learning Disentangled Representations for Recommendation

2019 · arXiv (Cornell University)

User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users' decision making processes. The factors are highly entangled, and may range from high-level ones that govern …