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20232026
most citedCRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection

3 citations · 5 across the 13 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CL2024

Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework

Qiao Sun, Jiexin Xie, Nanyang Ye +2

This paper explores the application of large language models (LLMs) in nursing and elderly care, focusing on AI-driven patient monitoring and interaction. We introduce a novel Chin…

cs.LG2024

Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing

Nanyang Ye, Qiao Sun, Yifei Wang +10

Analog computing using non-volatile memristors has emerged as a promising solution for energy-efficient deep learning. New materials, like perovskites-based memristors are recently…

cs.CL2024

MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction

Qiao Sun, Liujia Yang, Minghao Ma +2

Aspect Sentiment Triplet Extraction (ASTE) aims to co-extract the sentiment triplets in a given corpus. Existing approaches within the pretraining-finetuning paradigm tend to eithe…

cs.CV2024★ 3 cited

CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection

Lin Zhu, Yifeng Yang, Qinying Gu +3

Recent vision-language pre-trained models (VL-PTMs) have shown remarkable success in open-vocabulary tasks. However, downstream use cases often involve further fine-tuning of VL-PT…

cs.CL2024★ 1 cited

Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning

Qiao Sun, Liujia Yang, Minghao Ma +2

Aspect Sentiment Triplet Extraction (ASTE) is a burgeoning subtask of fine-grained sentiment analysis, aiming to extract structured sentiment triplets from unstructured textual dat…