49 citations · 136 across the 8 of their papers we have counts for
8 papers
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security
Youyang Qu, Ming Liu, Tianqing Zhu +3
Federated Learning (FL) offers a promising paradigm for training Large Language Models (LLMs) in a decentralized manner while preserving data privacy and minimizing communication o…
Learning to Learn for Few-shot Continual Active Learning
Stella Ho, Ming Liu, Shang Gao +1
Continual learning strives to ensure stability in solving previously seen tasks while demonstrating plasticity in a novel domain. Recent advances in continual learning are mostly c…
Prototype-Guided Memory Replay for Continual Learning
Stella Ho, Ming Liu, Lan Du +2
Continual learning (CL) refers to a machine learning paradigm that learns continuously without forgetting previously acquired knowledge. Thereby, major difficulty in CL is catastro…
Federated Learning Meets Natural Language Processing: A Survey
Ming Liu, Stella Ho, Mengqi Wang +3
Federated Learning aims to learn machine learning models from multiple decentralized edge devices (e.g. mobiles) or servers without sacrificing local data privacy. Recent Natural L…
SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline
Jiaxin Ju, Ming Liu, Longxiang Gao +1
The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task. It contains three shared tasks and we participat…
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
Jinming Zhao, Ming Liu, Longxiang Gao +5
Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains. In this pa…
Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
Yuan Jin, He Zhao, Ming Liu +5
Matrix factorization (MF) has been widely applied to collaborative filtering in recommendation systems. Its Bayesian variants can derive posterior distributions of user and item em…
Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data
Yuan Jin, Ming Liu, Yunfeng Li +4
Non-negative tensor factorization models enable predictive analysis on count data. Among them, Bayesian Poisson-Gamma models can derive full posterior distributions of latent facto…