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20242026
most citedData Science Students Perspectives on Learning Analytics: An Application of Human-Led and LLM Content Analysis

2 citations · 4 across the 13 of their papers we have counts for

collaborators

13 papers

cs.LG2026

Hyperbolic Manifold Constrained Tabular Neural Network

Tian Li, Lucy Robinson, Varun Ojha +1

Tabular prediction is central to a wide range of real-world applications. Tabular data typically contain heterogeneous features as well as rich and complex relational information t…

cs.LG2026

Systematic Characterization of Minimal Deep Learning Architectures: A Unified Analysis of Convergence, Pruning, and Quantization

Ziwei Zheng, Huizhi Liang, Vaclav Snasel +4

Deep learning networks excel at classification, yet identifying minimal architectures that reliably solve a task remains challenging. We present a computational methodology for sys…

cs.CV2025

Deep Learning-Assisted Detection of Sarcopenia in Cross-Sectional Computed Tomography Imaging

Manish Bhardwaj, Huizhi Liang, Ashwin Sivaharan +4

Sarcopenia is a progressive loss of muscle mass and function linked to poor surgical outcomes such as prolonged hospital stays, impaired mobility, and increased mortality. Although…

cs.CV2025

AdaGAT: Adaptive Guidance Adversarial Training for the Robustness of Deep Neural Networks

Zhenyu Liu, Huizhi Liang, Xinrun Li +2

Adversarial distillation (AD) is a knowledge distillation technique that facilitates the transfer of robustness from teacher deep neural network (DNN) models to lightweight target…

cs.CV2025

D2R: dual regularization loss with collaborative adversarial generation for model robustness

Zhenyu Liu, Huizhi Liang, Rajiv Ranjan +3

The robustness of Deep Neural Network models is crucial for defending models against adversarial attacks. Recent defense methods have employed collaborative learning frameworks to…

cs.CL2025

NCL-UoR at SemEval-2025 Task 3: Detecting Multilingual Hallucination and Related Observable Overgeneration Text Spans with Modified RefChecker and Modified SeflCheckGPT

Jiaying Hong, Thanet Markchom, Jianfei Xu +2

SemEval-2025 Task 3 (Mu-SHROOM) focuses on detecting hallucinations in content generated by various large language models (LLMs) across multiple languages. This task involves not o…

cs.CL2025

UoR-NCL at SemEval-2025 Task 1: Using Generative LLMs and CLIP Models for Multilingual Multimodal Idiomaticity Representation

Thanet Markchom, Tong Wu, Liting Huang +1

SemEval-2025 Task 1 focuses on ranking images based on their alignment with a given nominal compound that may carry idiomatic meaning in both English and Brazilian Portuguese. To a…

cs.CY2025★ 2 cited

Data Science Students Perspectives on Learning Analytics: An Application of Human-Led and LLM Content Analysis

Raghda Zahran, Jianfei Xu, Huizhi Liang +1

Objective This study is part of a series of initiatives at a UK university designed to cultivate a deep understanding of students' perspectives on analytics that resonate with thei…

cs.CV2024

CRRG-CLIP: Automatic Generation of Chest Radiology Reports and Classification of Chest Radiographs

Jianfei Xu, Thanet Markchom, Huizhi Liang

The complexity of stacked imaging and the massive number of radiographs make writing radiology reports complex and inefficient. Even highly experienced radiologists struggle to mai…

cs.LG2024

Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks

Zhenyu Liu, Haoran Duan, Huizhi Liang +5

Adversarial training is one of the most effective methods for enhancing model robustness. Recent approaches incorporate adversarial distillation in adversarial training architectur…

cs.LG2024

On Learnable Parameters of Optimal and Suboptimal Deep Learning Models

Ziwei Zheng, Huizhi Liang, Vaclav Snasel +4

We scrutinize the structural and operational aspects of deep learning models, particularly focusing on the nuances of learnable parameters (weight) statistics, distribution, node i…

cs.IR2024

Review of Explainable Graph-Based Recommender Systems

Thanet Markchom, Huizhi Liang, James Ferryman

Explainability of recommender systems has become essential to ensure users' trust and satisfaction. Various types of explainable recommender systems have been proposed including ex…

cs.CL2024★ 2 cited

Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection

Feng Xiong, Thanet Markchom, Ziwei Zheng +3

SemEval-2024 Task 8 introduces the challenge of identifying machine-generated texts from diverse Large Language Models (LLMs) in various languages and domains. The task comprises t…