26 citations · 71 across the 16 of their papers we have counts for
9 papers · 1 filter
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings
Yehya Farhat, Hamza ElMokhtar Shili, Fangshuo Liao +7
Mixture-of-Experts (MoEs) achieve scalability by dynamically activating subsets of their components. Yet, understanding how expertise emerges through joint training of gating mecha…
Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity
Kaize Ding, Elnaz Nouri, Guoqing Zheng +2
The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice. When only few labeled nodes…
ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels
Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee +2
Existing works on anomaly detection (AD) rely on clean labels from human annotators that are expensive to acquire in practice. In this work, we propose a method to leverage weak/no…
MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces
Srinagesh Sharma, Guoqing Zheng, Ahmed Hassan Awadallah
Albeit the universal representational power of pre-trained language models, adapting them onto a specific NLP task still requires a considerably large amount of labeled data. Effec…
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News
Kai Shu, Guoqing Zheng, Yichuan Li +4
Social media has greatly enabled people to participate in online activities at an unprecedented rate. However, this unrestricted access also exacerbates the spread of misinformatio…
Meta Label Correction for Noisy Label Learning
Guoqing Zheng, Ahmed Hassan Awadallah, Susan Dumais
Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance has further increased recently due t…