9 citations · 11 across the 8 of their papers we have counts for
9 papers
How Reliable Are Large Language Models for Soil Organic Carbon Assessment? An Australian Benchmark
Yingxuan Cheng, Tong Li, Yayong Li +5
Soil organic carbon (SOC) is an indicator of agricultural soil condition and carbon storage, but numerical outputs from large language models (LLMs) require validation before use i…
Semi-Supervised Classification with Informative Missing Labels in Weibull Mixture Models
Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan
We consider semi-supervised classification from a partially classified sample arising from a two-component Weibull mixture. The feature is observed for all data, whereas some class…
Deep Skew-t Mixture Models
Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan
High-dimensional clustering is challenging when component distributions are both heavy-tailed and directionally asymmetric. We propose a deep skew- mixture model (DStMM), a hier…
Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification
You-Gan Wang, Jinran Wu, Geoffrey J. McLachlan
Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of label missingness depends on t…
Informative missingness and its implications in semi-supervised learning
Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan
Semi-supervised learning (SSL) constructs classifiers using both labelled and unlabelled data. It leverages information from labelled samples, whose acquisition is often costly or…
A Differential Index Measuring Rater's Capability in Educational Assessment
Y. -G. Wang, J. Wu, X. Qiu
A rater's ability to assign accurate scores can significantly impact the outcomes of educational assessments. However, common indices for evaluating rater characteristics typically…
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
Yilong Hou, Xi'an Li, Jinran Wu +1
Bayesian Physics Informed Neural Networks (BPINN) have attracted considerable attention for inferring the system states and physical parameters of differential equations according…
Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study
Jin Yuan, Xuelan Qiu, Jinran Wu +3
The interest in predicting online learning performance using ML algorithms has been steadily increasing. We first conducted a scientometric analysis to provide a systematic review…
Augmented physics informed extreme learning machine to solve the biharmonic equations via Fourier expansions
Xi'an Li, Jinran Wu, Yujia Huang +4
To address the sensitivity of parameters and limited precision for physics-informed extreme learning machines (PIELM) with common activation functions, such as sigmoid, tangent, an…