1 citations · 1 across the 7 of their papers we have counts for
7 papers
Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification
Yongcheng Li, Lingcong Cai, Ying Lu +8
Accurate classification of blood cells is of vital significance in the diagnosis of hematological disorders. However, in real-world scenarios, domain shifts caused by the variabili…
Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model
Yongcheng Li, Lingcong Cai, Ying Lu +7
Accurate classification of blood cells plays a vital role in hematological analysis as it aids physicians in diagnosing various medical conditions. In this study, we present a nove…
Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models
Jie Chen, Yupeng Zhang, Bingning Wang +3
Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs). Studies have shown that synthetic…
Full-ECE: A Metric For Token-level Calibration on Large Language Models
Han Liu, Yupeng Zhang, Bingning Wang +2
Deep Neural Networks (DNNs) excel in various domains but face challenges in providing accurate uncertainty estimates, which are crucial for high-stakes applications. Large Language…
Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA
Guanting Dong, Rumei Li, Sirui Wang +3
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and relations in KBs. However, traditional Pre-trained…
Numerical methods for computing the discrete and continuous Laplace transforms
Yupeng Zhang, Yueyang Shen, Rongqian Zhang +4
We propose a numerical method to spline-interpolate discrete signals and then apply the integral transforms to the corresponding analytical spline functions. This represents a robu…