15 citations · 22 across the 6 of their papers we have counts for
6 papers
CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation
Yuchen Liu, Zongzhen Yang, Binhang Qi +2
Model merging has recently attracted significant attention as a promising paradigm for constructing unified multi-task models without requiring additional retraining. However, para…
Harnessing Multiple Large Language Models: A Survey on LLM Ensemble
Zhijun Chen, Xiaodong Lu, Jingzheng Li +12
LLM Ensemble -- which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from thei…
Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
Zhijun Chen, Hailong Sun, Wanhao Zhang +3
We propose a neuralized undirected graphical model called Neural-Hidden-CRF to solve the weakly-supervised sequence labeling problem. Under the umbrella of probabilistic undirected…
Modularizing while Training: A New Paradigm for Modularizing DNN Models
Binhang Qi, Hailong Sun, Hongyu Zhang +2
Deep neural network (DNN) models have become increasingly crucial components in intelligent software systems. However, training a DNN model is typically expensive in terms of both…
Learning from Noisy Crowd Labels with Logics
Zhijun Chen, Hailong Sun, Haoqian He +1
This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels. We introduce Logic-guided Learning from Noisy Crowd…
Patching Weak Convolutional Neural Network Models through Modularization and Composition
Binhang Qi, Hailong Sun, Xiang Gao +1
Despite great success in many applications, deep neural networks are not always robust in practice. For instance, a convolutional neuron network (CNN) model for classification task…