Qian Li

Triple-Memory Networks: A Brain-Inspired Method for Continual Learning

2021 · IEEE Transactions on Neural Networks and Learning Systems

Continual acquisition of novel experience without interfering with previously learned knowledge, i.e., continual learning, is critical for artificial neural networks, while limited by catastrophic forgetting. A neural network adjusts its parameters when learning a new …

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

2021 · arXiv (Cornell University)

Event extraction is a fundamental task for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doing so for real-life event descriptions is challenging because an …

Be Causal: De-biasing Social Network Confounding in Recommendation

2021 · arXiv (Cornell University)

In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called …

Contrastive Learning with Generated Representations for Inductive Knowledge Graph Embedding

2023

With the evolution of Knowledge Graphs (KGs), new entities emerge which are not seen before. Representation learning of KGs in such an inductive setting aims to capture and transfer the structural patterns from existing entities …

A hybrid algorithm for quadratically constrained quadratic optimization problems

2023 · arXiv (Cornell University)

Quadratically Constrained Quadratic Programs (QCQPs) are an important class of optimization problems with diverse real-world applications. In this work, we propose a variational quantum algorithm for general QCQPs. By encoding the variables on the amplitude …

Exploiting the Adversarial Example Vulnerability of Transfer Learning of Source Code

2024 · IEEE Transactions on Information Forensics and Security

State-of-the-art source code classification models exhibit excellent task transferability, in which the source code encoders are first pre-trained on a source domain dataset in a self-supervised manner and then fine-tuned on a supervised downstream dataset. …

A Cross-Domain Recommendation Model Based on Asymmetric Vertical Federated Learning and Heterogeneous Representation

2025 · IEEE Transactions on Emerging Topics in Computational Intelligence

Cross-domain recommendation meets the personalized needs of users by integrating user preference features from different fields. However, the current cross-domain recommendation algorithm needs to be further strengthened in terms of privacy protection. This paper proposes …