Li Yu

On the Decision Boundary of Deep Neural Networks

2018 · arXiv (Cornell University)

While deep learning models and techniques have achieved great empirical success, our understanding of the source of success in many aspects remains very limited. In an attempt to bridge the gap, we investigate the decision …

Resources Sharing in 5G Networks: Learning-Enabled Incentives and Coalitional Games

2019 · IEEE Systems Journal

Smart systems are often battery-constrained, and compete for resources from remote clouds, which results in high delay. Collaboratively sharing resource among neighbors in proximity is promising to control such delay for time-sensitive applications. Rather few …

Co-Capsule Networks Based Knowledge Transfer for Cross-Domain Recommendation

2021

Cross-domain recommendation (CDR) technology is proved to be an effective way to tackle the difficulties encountered by traditional recommender technology (e.g. CF), such as data sparsity and cold-start. However, on account of the heterogeneity, it …

An Ensemble Strategy with Gradient Conflict for Multi-Domain Neural Machine Translation

2023 · ACM Transactions on Asian and Low-Resource Language Information Processing

Multi-domain neural machine translation aims to construct a unified neural machine translation model to translate sentences across various domains. Nevertheless, previous studies have one limitation is the incapacity to acquire both domain-general and domain-specific representations …

RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation

2025 · arXiv (Cornell University)

Cross-domain recommendation systems face the challenge of integrating fine-grained user and item relationships across various product domains. To address this, we introduce RankGraph, a scalable graph learning framework designed to serve as a core component …