154 citations · 359 across the 22 of their papers we have counts for
6 papers · 1 filter
FedYolo: Augmenting Federated Learning with Pretrained Transformers
Xuechen Zhang, Mingchen Li, Xiangyu Chang +4
The growth and diversity of machine learning applications motivate a rethinking of learning with mobile and edge devices. How can we address diverse client goals and learn with sca…
How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain
Mingchen Li, Rui Zhang
Recent advancements in language models (LMs) have led to the emergence of powerful models such as Small LMs (e.g., T5) and Large LMs (e.g., GPT-4). These models have demonstrated e…
Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation
Davoud Ataee Tarzanagh, Mingchen Li, Pranay Sharma +1
Stochastic approximation with multiple coupled sequences (MSA) has found broad applications in machine learning as it encompasses a rich class of problems including bilevel optimiz…
W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition
Mingchen Li, Yang Ye, Jeremy Yeung +3
Contrastive learning has become a popular solution for few-shot Name Entity Recognization (NER). The conventional configuration strives to reduce the distance between tokens with t…
Understand the Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking
Mingchen Li, Lifu Huang
Open domain entity state tracking aims to predict reasonable state changes of entities (i.e., [attribute] of [entity] was [before_state] and [after_state] afterwards) given the act…
TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling
Pan Tan, Mingchen Li, Liang Zhang +2
We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling. By assembling an ext…