2 citations · 4 across the 4 of their papers we have counts for
7 papers
Does Unlearning Survive Collaboration? Robust Unlearning for Shared-Model LLM Teams
Hengsong Liu, Liangyu Teng, Jingnan Cai +5
LLM unlearning is essential for removing sensitive or unwanted knowledge while preserving useful capabilities. As LLM teams are increasingly used to solve complex problems, forgett…
Marginal Response Surface Elicitation for Zero-Label Tabular Learning
Liangyu Teng, Yicheng Ding, Jing Liu +5
Tabular learning uses structured data to predict target outcomes. Traditionally, this process has relied on labeled data. However, large language models (LLMs) can be used to elici…
Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection
Liangyu Teng, Hengsong Liu, Juncen Guo +4
The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictive differences. Although heter…
Adaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning
Juncen Guo, Xiaoguang Zhu, Liangyu Teng +4
Class-incremental Learning (CIL) enables the model to incrementally absorb knowledge from new classes and build a generic classifier across all previously encountered classes. When…
CalFuse: Multi-Modal Continual Learning via Feature Calibration and Parameter Fusion
Juncen Guo, Siao Liu, Xiaoguang Zhu +8
With the proliferation of multi-modal data in large-scale visual recognition systems, enabling models to continuously acquire knowledge from evolving data streams while preserving…
Privacy-Preserving Video Anomaly Detection: A Survey
Yang Liu, Siao Liu, Xiaoguang Zhu +7
Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm…
End-Cloud Collaboration Framework for Advanced AI Customer Service in E-commerce
Liangyu Teng, Yang Liu, Jing Liu +1
In recent years, the e-commerce industry has seen a rapid increase in the demand for advanced AI-driven customer service solutions. Traditional cloud-based models face limitations…