15 citations · 29 across the 14 of their papers we have counts for
14 papers
Communicability-Inspired Positional Encoding (CIPE)
Yipeng Zhang, Zhongtian Sun, Pietro Liò +1
Positional encodings (PEs) are essential for Transformers. Yet designing effective PEs for non-Euclidean graphs remains challenging. Such encodings should ideally induce an Attenti…
Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger
Li Sun, Ming Zhang, Wenxin Jin +5
Hypergraphs are the natural description of higher-order interactions among objects, widely applied in social network analysis, cross-modal retrieval, etc. Hypergraph Neural Network…
EWC-Guided Diffusion Replay for Exemplar-Free Continual Learning in Medical Imaging
Anoushka Harit, William Prew, Zhongtian Sun +1
Medical imaging foundation models must adapt over time, yet full retraining is often blocked by privacy constraints and cost. We present a continual learning framework that avoids…
RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial Graphs
Zhongtian Sun, Anoushka Harit
We propose RicciFlowRec, a geometric recommendation framework that performs root cause attribution via Ricci curvature and flow on dynamic financial graphs. By modelling evolving i…
ManifoldMind: Dynamic Hyperbolic Reasoning for Trustworthy Recommendations
Anoushka Harit, Zhongtian Sun, Suncica Hadzidedic
We introduce ManifoldMind, a probabilistic geometric recommender system for exploratory reasoning over semantic hierarchies in hyperbolic space. Unlike prior methods with fixed cur…
GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning
Zhongtian Sun, Anoushka Harit, Alexandra Cristea +2
Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data but often struggle on heterophilous graphs, where connected nodes differ i…
Causal Spherical Hypergraph Networks for Modelling Social Uncertainty
Anoushka Harit, Zhongtian Sun
Human social behaviour is governed by complex interactions shaped by uncertainty, causality, and group dynamics. We propose Causal Spherical Hypergraph Networks (Causal-SphHN), a p…
Actionable Interpretability via Causal Hypergraphs: Unravelling Batch Size Effects in Deep Learning
Zhongtian Sun, Anoushka Harit, Pietro Lio
While the impact of batch size on generalisation is well studied in vision tasks, its causal mechanisms remain underexplored in graph and text domains. We introduce a hypergraph-ba…
Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation
Jongmin Yu, Zhongtian Sun, Chen Bene Chi +2
Semantic segmentation requires extensive pixel-level annotation, motivating unsupervised domain adaptation (UDA) to transfer knowledge from labelled source domains to unlabelled or…
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs
Anoushka Harit, Zhongtian Sun, Jongmin Yu +1
In the fast-paced and volatile financial markets, accurately predicting stock movements based on financial news is critical for investors and analysts. Traditional models often str…
Language as a Latent Sequence: deep latent variable models for semi-supervised paraphrase generation
Jialin Yu, Alexandra I. Cristea, Anoushka Harit +4
This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequ…
INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations
Jialin Yu, Alexandra I. Cristea, Anoushka Harit +4
XAI with natural language processing aims to produce human-readable explanations as evidence for AI decision-making, which addresses explainability and transparency. However, from…
Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums
Jialin Yu, Laila Alrajhi, Anoushka Harit +3
Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. However, due to large numbers of learners and their diverse backg…
Is MOOC Learning Different for Dropouts? A Visually-Driven, Multi-granularity Explanatory ML Approach
Ahmed Alamri, Zhongtian Sun, Alexandra I. Cristea +3
Millions of people have enrolled and enrol (especially in the Covid-19 pandemic world) in MOOCs. However, the retention rate of learners is notoriously low. The majority of the res…