74 citations · 99 across the 11 of their papers we have counts for
12 papers
Co2In: a Bi-level Memory Incremental Learning Framework with Knowledge Encoding, Consolidation, and Integration
Wenju Sun, Qingyong Li, Boyang Li +4
Incremental learning (IL) aims to continually acquire new knowledge (plasticity) while retaining previously learned information (stability). However, striking a balance between pla…
Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors
Yuze Qin, Qingyong Li, Zhiqing Guo +3
Precipitation nowcasting is critical for disaster mitigation and aviation safety. However, radar-only models frequently suffer from a lack of large-scale atmospheric context, leadi…
CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging
Wenju Sun, Qingyong Li, Yangli-ao Geng +1
Multi-task model merging offers a promising paradigm for integrating multiple expert models into a unified model without additional training. Existing state-of-the-art techniques,…
Towards Minimizing Feature Drift in Model Merging: Layer-wise Task Vector Fusion for Adaptive Knowledge Integration
Wenju Sun, Qingyong Li, Wen Wang +3
Multi-task model merging aims to consolidate knowledge from multiple fine-tuned task-specific experts into a unified model while minimizing performance degradation. Existing method…
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning
Yu Feng, Yangli-ao Geng, Yifan Zhu +7
Federated learning (FL) has gained widespread attention for its privacy-preserving and collaborative learning capabilities. Due to significant statistical heterogeneity, traditiona…
Task Arithmetic in Trust Region: A Training-Free Model Merging Approach to Navigate Knowledge Conflicts
Wenju Sun, Qingyong Li, Wen Wang +2
Multi-task model merging offers an efficient solution for integrating knowledge from multiple fine-tuned models, mitigating the significant computational and storage demands associ…
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein
Bo Chen, Xingyi Cheng, Pan Li +12
Protein language models have shown remarkable success in learning biological information from protein sequences. However, most existing models are limited by either autoencoding or…
Towards Plastic and Stable Exemplar-Free Incremental Learning: A Dual-Learner Framework with Cumulative Parameter Averaging
Wenju Sun, Qingyong Li, Wen Wang +1
The dilemma between plasticity and stability presents a significant challenge in Incremental Learning (IL), especially in the exemplar-free scenario where accessing old-task sample…
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
Zhen Yang, Tinglin Huang, Ming Ding +5
In-Batch contrastive learning is a state-of-the-art self-supervised method that brings semantically-similar instances close while pushing dissimilar instances apart within a mini-b…
Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers
Wenju Sun, Qingyong Li, Jing Zhang +3
The exemplar-free class incremental learning requires classification models to learn new class knowledge incrementally without retaining any old samples. Recently, the framework ba…
MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors
Haomin Yu, Yangli-ao Geng, Yingjun Zhang +2
The advances of sensor technology enable people to monitor air quality through widely distributed low-cost sensors. However, measurements from these sensors usually encounter high…
RECOME: a New Density-Based Clustering Algorithm Using Relative KNN Kernel Density
Yangli-ao Geng, Qingyong Li, Rong Zheng +3
Discovering clusters from a dataset with different shapes, densities, and scales is a known challenging problem in data clustering. In this paper, we propose the RElative COre MErg…