Peng Cui

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 …

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 …

Sliding Selector Network with Dynamic Memory for Extractive Summarization of Long Documents

2021

Neural-based summarization models suffer from the length limitation of text encoder. Long documents have to been truncated before they are sent to the model, which results in huge loss of summary-relevant contents. To address this …

ListReader: Extracting List-form Answers for Opinion Questions

2021 · arXiv (Cornell University)

Question answering (QA) is a high-level ability of natural language processing. Most extractive ma-chine reading comprehension models focus on factoid questions (e.g., who, when, where) and restrict the output answer as a short and continuous …

Research on Intelligent Operational Assisted Decision-making of Naval Battlefield Based on Deep Reinforcement Learning

2021

research-article Share on Research on Intelligent Operational Assisted Decision-making of Naval Battlefield Based on Deep Reinforcement Learning Authors: Xin-ye Zhao Dalian Naval Academy, China Dalian Naval Academy, ChinaView Profile , Mei Yang Dalian Naval Academy, …

A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization

2021 · arXiv (Cornell University)

Covariate-shift generalization, a typical case in out-of-distribution (OOD) generalization, requires a good performance on the unknown test distribution, which varies from the accessible training distribution in the form of covariate shift. Recently, independence-driven importance weighting …

Graph Neural Networks: Foundation, Frontiers and Applications

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning, have …

Improving Accuracy and Calibration via Differentiated Deep Mutual Learning

2025

Deep Neural Networks (DNNs) have achieved remarkable success in a variety of tasks, particularly in terms of prediction accuracy. However, in real-world scenarios, especially in safety-critical applications, accuracy alone is insufficient; reliable uncertainty estimates are …

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 …

Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks

2020

Text summarization aims to compress a textual document to a short summary while keeping salient information. Extractive approaches are widely used in text summarization because of their fluency and efficiency. However, most of existing extractive …

Interpreting and Unifying Graph Neural Networks with An Optimization Framework

2021

Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism which has been demonstrated effective is the most fundamental part of GNNs. Although …