5 papers
Detecting Differences Is Not Understanding Structure: Large Language Models Fail at Graph Isomorphism
Kumar Thushalika, Sukumar Kishanthan, Asela Hevapathige
Large language models (LLMs) have shown impressive performance on diverse reasoning tasks, yet their capacity for structural reasoning in graphs remains unclear. We investigate whe…
Large Language Models for Math Education in Low-Resource Languages: A Study in Sinhala and Tamil
Sukumar Kishanthan, Kumar Thushalika, Buddhi Jayasekara +1
Large language models (LLMs) have achieved strong results in mathematical reasoning, and are increasingly deployed as tutoring and learning support tools in educational settings. H…
Orthogonal Activation with Implicit Group-Aware Bias Learning for Class Imbalance
Sukumar Kishanthan, Asela Hevapathige
Class imbalance is a common challenge in machine learning and data mining, often leading to suboptimal performance in classifiers. While deep learning excels in feature extraction,…
AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification
Sukumar Kishanthan, Asela Hevapathige
Class imbalance in machine learning poses a significant challenge, as skewed datasets often hinder performance on minority classes. Traditional oversampling techniques, which are c…
Deep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification
Sukumar Kishanthan, Asela Hevapathige
Despite extensive research spanning several decades, class imbalance is still considered a profound difficulty for both machine learning and deep learning models. While data oversa…